[1675] | 1 | # Tools for the compute of diagnostics |
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| 2 | # L. Fita, CIMA. CONICET-UBA, CNRS UMI-IFAECI, Buenos Aires, Argentina |
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| 3 | |
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| 4 | # Available general pupose diagnostics (model independent) providing (varv1, varv2, ..., dimns, dimvns) |
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| 5 | # compute_accum: Function to compute the accumulation of a variable |
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| 6 | # compute_cllmh: Function to compute cllmh: low/medium/hight cloud fraction following |
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| 7 | # newmicro.F90 from LMDZ compute_clt(cldfra, pres, dimns, dimvns) |
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| 8 | # compute_clt: Function to compute the total cloud fraction following 'newmicro.F90' from LMDZ |
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| 9 | # compute_clivi: Function to compute cloud-ice water path (clivi) |
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| 10 | # compute_clwvl: Function to compute condensed water path (clwvl) |
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| 11 | # compute_deaccum: Function to compute the deaccumulation of a variable |
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| 12 | # compute_mslp: Function to compute mslp: mean sea level pressure following p_interp.F90 from WRF |
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| 13 | # compute_OMEGAw: Function to transform OMEGA [Pas-1] to velocities [ms-1] |
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| 14 | # compute_prw: Function to compute water vapour path (prw) |
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| 15 | # compute_rh: Function to compute relative humidity following 'Tetens' equation (T,P) ...' |
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| 16 | # compute_td: Function to compute the dew point temperature |
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| 17 | # compute_turbulence: Function to compute the rubulence term of the Taylor's decomposition ...' |
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[1687] | 18 | # C_diagnostic: Class to compute generic variables |
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[1980] | 19 | # compute_wd: Function to compute the wind direction 3D |
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[1675] | 20 | # compute_wds: Function to compute the wind direction |
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| 21 | # compute_wss: Function to compute the wind speed |
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[1710] | 22 | # compute_WRFhur: Function to compute WRF relative humidity following Teten's equation |
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[1675] | 23 | # compute_WRFta: Function to compute WRF air temperature |
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| 24 | # compute_WRFtd: Function to compute WRF dew-point air temperature |
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[1687] | 25 | # compute_WRFua: Function to compute geographical rotated WRF x-wind |
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| 26 | # compute_WRFva: Function to compute geographical rotated WRF y-wind |
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[1675] | 27 | # compute_WRFuava: Function to compute geographical rotated WRF 3D winds |
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[1687] | 28 | # compute_WRFuas: Function to compute geographical rotated WRF 2-meter x-wind |
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| 29 | # compute_WRFvas: Function to compute geographical rotated WRF 2-meter y-wind |
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[1675] | 30 | # compute_WRFuasvas: Fucntion to compute geographical rotated WRF 2-meter winds |
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| 31 | # derivate_centered: Function to compute the centered derivate of a given field |
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[2274] | 32 | # Forcompute_cellbnds: Function to compute cellboundaries using wind-staggered lon, |
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| 33 | # lats as intersection of their related parallels and meridians |
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[2277] | 34 | # Forcompute_cellbndsreg: Function to compute cellboundaries using lon, lat from a |
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| 35 | # reglar lon/lat projection as intersection of their related parallels and meridians |
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[1804] | 36 | # Forcompute_cllmh: Function to compute cllmh: low/medium/hight cloud fraction |
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| 37 | # following newmicro.F90 from LMDZ via Fortran subroutine |
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| 38 | # Forcompute_clt: Function to compute the total cloud fraction following |
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| 39 | # 'newmicro.F90' from LMDZ via a Fortran module |
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[1908] | 40 | # Forcompute_fog_K84: Computation of fog and visibility following Kunkel, (1984) |
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| 41 | # Forcompute_fog_RUC: Computation of fog and visibility following RUC method Smirnova, (2000) |
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[1909] | 42 | # Forcompute_fog_FRAML50: fog and visibility following Gultepe and Milbrandt, (2010) |
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[1804] | 43 | # Forcompute_potevap_orPM: Function to compute potential evapotranspiration following |
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| 44 | # Penman-Monteith formulation implemented in ORCHIDEE |
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| 45 | # Forcompute_psl_ptarget: Function to compute the sea-level pressure following |
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| 46 | # target_pressure value found in `p_interp.F' |
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[2209] | 47 | # Forcompute_range_faces: Function to compute faces [uphill, valley, downhill] of sections of a |
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| 48 | # mountain rage, along a given face |
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[1804] | 49 | # Forcompute_zmla_gen: Function to compute the boundary layer height following a |
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| 50 | # generic method with Fortran |
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| 51 | # Forcompute_zwind: Function to compute the wind at a given height following the |
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| 52 | # power law method |
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| 53 | # Forcompute_zwind_log: Function to compute the wind at a given height following the |
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| 54 | # logarithmic law method |
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| 55 | # Forcompute_zwindMO: Function to compute the wind at a given height following the |
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| 56 | # Monin-Obukhov theory |
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[1687] | 57 | # W_diagnostic: Class to compute WRF diagnostics variables |
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[1675] | 58 | |
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| 59 | # Others just providing variable values |
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| 60 | # var_cllmh: Fcuntion to compute cllmh on a 1D column |
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[1804] | 61 | # var_clt: Function to compute the total cloud fraction following 'newmicro.F90' from |
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| 62 | # LMDZ using 1D vertical column values |
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[2100] | 63 | # var_convini: Function returns convective initialization (pr(t) > 0.0001) in time units |
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[1804] | 64 | # var_hur: Function to compute relative humidity following 'August - Roche - Magnus' |
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| 65 | # formula |
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| 66 | # var_hur_Uhus: Function to compute relative humidity following 'August-Roche-Magnus' |
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| 67 | # formula using hus |
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[2391] | 68 | # var_hur_tas_tds: Function to compute hur relative humidity from tas and tds |
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[1675] | 69 | # var_mslp: Fcuntion to compute mean sea-level pressure |
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[1804] | 70 | # var_td: Function to compute dew-point air temperature from temperature and pressure |
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| 71 | # values |
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| 72 | # var_td_Uhus: Function to compute dew-point air temperature from temperature and |
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| 73 | # pressure values using hus |
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[2387] | 74 | # var_tws_S11: Function to compute Wet Bulb temperature after Stull, 2011 |
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[2140] | 75 | # var_timemax: This function returns the time at which variable reaches its maximum in time |
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| 76 | # units |
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[2138] | 77 | # var_timeoverthres: This function returns the time at which (varv(t) > thres) in time units |
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[1675] | 78 | # var_virtualTemp: This function returns virtual temperature in K, |
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| 79 | # var_WRFtime: Function to copmute CFtimes from WRFtime variable |
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[1687] | 80 | # var_wd: Function to compute the wind direction |
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| 81 | # var_wd: Function to compute the wind speed |
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[1675] | 82 | # rotational_z: z-component of the rotatinoal of horizontal vectorial field |
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[1804] | 83 | # turbulence_var: Function to compute the Taylor's decomposition turbulence term from |
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| 84 | # a a given variable |
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[1675] | 85 | |
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| 86 | import numpy as np |
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| 87 | from netCDF4 import Dataset as NetCDFFile |
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| 88 | import os |
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| 89 | import re |
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| 90 | import nc_var_tools as ncvar |
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| 91 | import generic_tools as gen |
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| 92 | import datetime as dtime |
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| 93 | import module_ForDiag as fdin |
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| 94 | import module_ForDef as fdef |
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| 95 | |
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| 96 | main = 'diag_tools.py' |
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| 97 | errormsg = 'ERROR -- error -- ERROR -- error' |
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| 98 | warnmsg = 'WARNING -- warning -- WARNING -- warning' |
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| 99 | |
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| 100 | # Constants |
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| 101 | grav = fdef.module_definitions.grav |
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| 102 | |
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[1687] | 103 | # Available WRFiag |
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[1713] | 104 | Wavailablediags = ['hur', 'p', 'ta', 'td', 'ua', 'va', 'uas', 'vas', 'wd', 'ws', 'zg'] |
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[1687] | 105 | |
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| 106 | # Available General diagnostics |
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[1724] | 107 | Cavailablediags = ['hur', 'hur_Uhus', 'td', 'td_Uhus', 'wd', 'ws'] |
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[1687] | 108 | |
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[1675] | 109 | # Gneral information |
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| 110 | ## |
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| 111 | def reduce_spaces(string): |
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| 112 | """ Function to give words of a line of text removing any extra space |
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| 113 | """ |
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| 114 | values = string.replace('\n','').split(' ') |
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| 115 | vals = [] |
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| 116 | for val in values: |
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| 117 | if len(val) > 0: |
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| 118 | vals.append(val) |
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| 119 | |
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| 120 | return vals |
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| 121 | |
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| 122 | def variable_combo(varn,combofile): |
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| 123 | """ Function to provide variables combination from a given variable name |
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| 124 | varn= name of the variable |
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| 125 | combofile= ASCII file with the combination of variables |
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| 126 | [varn] [combo] |
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| 127 | [combo]: '@' separated list of variables to use to generate [varn] |
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| 128 | [WRFdt] to get WRF time-step (from general attributes) |
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| 129 | >>> variable_combo('WRFprls','/home/lluis/PY/diagnostics.inf') |
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| 130 | deaccum@RAINNC@XTIME@prnc |
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| 131 | """ |
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| 132 | fname = 'variable_combo' |
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| 133 | |
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| 134 | if varn == 'h': |
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| 135 | print fname + '_____________________________________________________________' |
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| 136 | print variable_combo.__doc__ |
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| 137 | quit() |
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| 138 | |
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| 139 | if not os.path.isfile(combofile): |
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| 140 | print errormsg |
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| 141 | print ' ' + fname + ": file with combinations '" + combofile + \ |
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| 142 | "' does not exist!!" |
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| 143 | quit(-1) |
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| 144 | |
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| 145 | objf = open(combofile, 'r') |
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| 146 | |
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| 147 | found = False |
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| 148 | for line in objf: |
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| 149 | linevals = reduce_spaces(line) |
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| 150 | varnf = linevals[0] |
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| 151 | combo = linevals[1].replace('\n','') |
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| 152 | if varn == varnf: |
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| 153 | found = True |
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| 154 | break |
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| 155 | |
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| 156 | if not found: |
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| 157 | print errormsg |
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| 158 | print ' ' + fname + ": variable '" + varn + "' not found in '" + combofile +\ |
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| 159 | "' !!" |
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| 160 | combo='ERROR' |
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| 161 | |
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| 162 | objf.close() |
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| 163 | |
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| 164 | return combo |
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| 165 | |
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| 166 | # Mathematical operators |
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| 167 | ## |
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| 168 | def compute_accum(varv, dimns, dimvns): |
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| 169 | """ Function to compute the accumulation of a variable |
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| 170 | compute_accum(varv, dimnames, dimvns) |
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| 171 | [varv]= values to accum (assuming [t,]) |
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| 172 | [dimns]= list of the name of the dimensions of the [varv] |
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| 173 | [dimvns]= list of the name of the variables with the values of the |
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| 174 | dimensions of [varv] |
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| 175 | """ |
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| 176 | fname = 'compute_accum' |
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| 177 | |
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| 178 | deacdims = dimns[:] |
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| 179 | deacvdims = dimvns[:] |
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| 180 | |
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| 181 | slicei = [] |
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| 182 | slicee = [] |
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| 183 | |
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| 184 | Ndims = len(varv.shape) |
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| 185 | for iid in range(0,Ndims): |
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| 186 | slicei.append(slice(0,varv.shape[iid])) |
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| 187 | slicee.append(slice(0,varv.shape[iid])) |
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| 188 | |
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| 189 | slicee[0] = np.arange(varv.shape[0]) |
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| 190 | slicei[0] = np.arange(varv.shape[0]) |
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| 191 | slicei[0][1:varv.shape[0]] = np.arange(varv.shape[0]-1) |
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| 192 | |
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| 193 | vari = varv[tuple(slicei)] |
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| 194 | vare = varv[tuple(slicee)] |
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| 195 | |
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| 196 | ac = vari*0. |
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| 197 | for it in range(1,varv.shape[0]): |
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| 198 | ac[it,] = ac[it-1,] + vare[it,] |
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| 199 | |
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| 200 | return ac, deacdims, deacvdims |
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| 201 | |
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| 202 | def compute_deaccum(varv, dimns, dimvns): |
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| 203 | """ Function to compute the deaccumulation of a variable |
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| 204 | compute_deaccum(varv, dimnames, dimvns) |
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| 205 | [varv]= values to deaccum (assuming [t,]) |
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| 206 | [dimns]= list of the name of the dimensions of the [varv] |
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| 207 | [dimvns]= list of the name of the variables with the values of the |
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| 208 | dimensions of [varv] |
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| 209 | """ |
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| 210 | fname = 'compute_deaccum' |
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| 211 | |
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| 212 | deacdims = dimns[:] |
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| 213 | deacvdims = dimvns[:] |
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| 214 | |
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| 215 | slicei = [] |
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| 216 | slicee = [] |
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| 217 | |
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| 218 | Ndims = len(varv.shape) |
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| 219 | for iid in range(0,Ndims): |
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| 220 | slicei.append(slice(0,varv.shape[iid])) |
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| 221 | slicee.append(slice(0,varv.shape[iid])) |
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| 222 | |
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| 223 | slicee[0] = np.arange(varv.shape[0]) |
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| 224 | slicei[0] = np.arange(varv.shape[0]) |
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| 225 | slicei[0][1:varv.shape[0]] = np.arange(varv.shape[0]-1) |
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| 226 | |
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| 227 | vari = varv[tuple(slicei)] |
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| 228 | vare = varv[tuple(slicee)] |
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| 229 | |
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| 230 | deac = vare - vari |
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| 231 | |
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| 232 | return deac, deacdims, deacvdims |
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| 233 | |
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| 234 | def derivate_centered(var,dim,dimv): |
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| 235 | """ Function to compute the centered derivate of a given field |
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| 236 | centered derivate(n) = (var(n-1) + var(n+1))/(2*dn). |
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| 237 | [var]= variable |
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| 238 | [dim]= which dimension to compute the derivate |
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| 239 | [dimv]= dimension values (can be of different dimension of [var]) |
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| 240 | >>> derivate_centered(np.arange(16).reshape(4,4)*1.,1,1.) |
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| 241 | [[ 0. 1. 2. 0.] |
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| 242 | [ 0. 5. 6. 0.] |
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| 243 | [ 0. 9. 10. 0.] |
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| 244 | [ 0. 13. 14. 0.]] |
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| 245 | """ |
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| 246 | |
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| 247 | fname = 'derivate_centered' |
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| 248 | |
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| 249 | vark = var.dtype |
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| 250 | |
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| 251 | if hasattr(dimv, "__len__"): |
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| 252 | # Assuming that the last dimensions of var [..., N, M] are the same of dimv [N, M] |
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| 253 | if len(var.shape) != len(dimv.shape): |
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| 254 | dimvals = np.zeros((var.shape), dtype=vark) |
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| 255 | if len(var.shape) - len(dimv.shape) == 1: |
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| 256 | for iz in range(var.shape[0]): |
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| 257 | dimvals[iz,] = dimv |
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| 258 | elif len(var.shape) - len(dimv.shape) == 2: |
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| 259 | for it in range(var.shape[0]): |
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| 260 | for iz in range(var.shape[1]): |
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| 261 | dimvals[it,iz,] = dimv |
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| 262 | else: |
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| 263 | print errormsg |
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| 264 | print ' ' + fname + ': dimension difference between variable', \ |
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| 265 | var.shape,'and variable with dimension values',dimv.shape, \ |
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| 266 | ' not ready !!!' |
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| 267 | quit(-1) |
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| 268 | else: |
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| 269 | dimvals = dimv |
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| 270 | else: |
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| 271 | # dimension values are identical everywhere! |
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| 272 | # from: http://stackoverflow.com/questions/16807011/python-how-to-identify-if-a-variable-is-an-array-or-a-scalar |
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| 273 | dimvals = np.ones((var.shape), dtype=vark)*dimv |
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| 274 | |
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| 275 | derivate = np.zeros((var.shape), dtype=vark) |
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| 276 | if dim > len(var.shape) - 1: |
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| 277 | print errormsg |
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| 278 | print ' ' + fname + ': dimension',dim,' too big for given variable of ' + \ |
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| 279 | 'shape:', var.shape,'!!!' |
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| 280 | quit(-1) |
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| 281 | |
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| 282 | slicebef = [] |
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| 283 | sliceaft = [] |
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| 284 | sliceder = [] |
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| 285 | |
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| 286 | for id in range(len(var.shape)): |
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| 287 | if id == dim: |
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| 288 | slicebef.append(slice(0,var.shape[id]-2)) |
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| 289 | sliceaft.append(slice(2,var.shape[id])) |
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| 290 | sliceder.append(slice(1,var.shape[id]-1)) |
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| 291 | else: |
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| 292 | slicebef.append(slice(0,var.shape[id])) |
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| 293 | sliceaft.append(slice(0,var.shape[id])) |
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| 294 | sliceder.append(slice(0,var.shape[id])) |
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| 295 | |
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| 296 | if hasattr(dimv, "__len__"): |
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| 297 | derivate[tuple(sliceder)] = (var[tuple(slicebef)] + var[tuple(sliceaft)])/ \ |
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| 298 | ((dimvals[tuple(sliceaft)] - dimvals[tuple(slicebef)])) |
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| 299 | print (dimvals[tuple(sliceaft)] - dimvals[tuple(slicebef)]) |
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| 300 | else: |
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| 301 | derivate[tuple(sliceder)] = (var[tuple(slicebef)] + var[tuple(sliceaft)])/ \ |
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| 302 | (2.*dimv) |
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| 303 | |
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| 304 | # print 'before________' |
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| 305 | # print var[tuple(slicebef)] |
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| 306 | |
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| 307 | # print 'after________' |
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| 308 | # print var[tuple(sliceaft)] |
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| 309 | |
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| 310 | return derivate |
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| 311 | |
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| 312 | def rotational_z(Vx,Vy,pos): |
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| 313 | """ z-component of the rotatinoal of horizontal vectorial field |
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| 314 | \/ x (Vx,Vy,Vz) = \/xVy - \/yVx |
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| 315 | [Vx]= Variable component x |
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| 316 | [Vy]= Variable component y |
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| 317 | [pos]= poisition of the grid points |
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| 318 | >>> rotational_z(np.arange(16).reshape(4,4)*1., np.arange(16).reshape(4,4)*1., 1.) |
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| 319 | [[ 0. 1. 2. 0.] |
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| 320 | [ -4. 0. 0. -7.] |
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| 321 | [ -8. 0. 0. -11.] |
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| 322 | [ 0. 13. 14. 0.]] |
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| 323 | """ |
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| 324 | |
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| 325 | fname = 'rotational_z' |
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| 326 | |
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| 327 | ndims = len(Vx.shape) |
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| 328 | rot1 = derivate_centered(Vy,ndims-1,pos) |
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| 329 | rot2 = derivate_centered(Vx,ndims-2,pos) |
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| 330 | |
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| 331 | rot = rot1 - rot2 |
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| 332 | |
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| 333 | return rot |
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| 334 | |
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| 335 | # Diagnostics |
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| 336 | ## |
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[1759] | 337 | def Forcompute_cape_afwa(ta, hur, pa, zg, hgt, parcelm, dimns, dimvns): |
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| 338 | """ Function to compute the CAPE, CIN, ZLFC, PLFC, LI following WRF |
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| 339 | 'phys/module_diaf_afwa.F' methodology |
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| 340 | Forcompute_cape_afwa(ta, hur, pa, hgt, zsfc, parcelm, dimns, dimvns) |
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| 341 | [ta]= air-temperature values (assuming [[t],z,y,x]) [K] |
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| 342 | [pa]= pressure values (assuming [[t],z,y,x]) [Pa] |
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| 343 | [zg]= gopotential height (assuming [[t],z,y,x]) [gpm] |
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| 344 | [hgt]= topographical height (assuming [m] |
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| 345 | [parcelm]= method of air-parcel to use |
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| 346 | [dimns]= list of the name of the dimensions of [pa] |
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| 347 | [dimvns]= list of the name of the variables with the values of the |
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| 348 | dimensions of [pa] |
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| 349 | """ |
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| 350 | fname = 'Forcompute_cape_afwa' |
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[1675] | 351 | |
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[1759] | 352 | psldims = dimns[:] |
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| 353 | pslvdims = dimvns[:] |
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| 354 | |
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| 355 | if len(pa.shape) == 4: |
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| 356 | cape = np.zeros((pa.shape[0],pa.shape[2],pa.shape[3]), dtype=np.float) |
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| 357 | cin = np.zeros((pa.shape[0],pa.shape[2],pa.shape[3]), dtype=np.float) |
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| 358 | zlfc = np.zeros((pa.shape[0],pa.shape[2],pa.shape[3]), dtype=np.float) |
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| 359 | plfc = np.zeros((pa.shape[0],pa.shape[2],pa.shape[3]), dtype=np.float) |
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| 360 | li = np.zeros((pa.shape[0],pa.shape[2],pa.shape[3]), dtype=np.float) |
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| 361 | |
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| 362 | dx = pa.shape[3] |
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| 363 | dy = pa.shape[2] |
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| 364 | dz = pa.shape[1] |
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| 365 | dt = pa.shape[0] |
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| 366 | psldims.pop(1) |
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| 367 | pslvdims.pop(1) |
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| 368 | |
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| 369 | pcape,pcin,pzlfc,pplfc,pli= fdin.module_fordiagnostics.compute_cape_afwa4d( \ |
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| 370 | ta=ta[:].transpose(), hur=hur[:].transpose(), press=pa[:].transpose(), \ |
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| 371 | zg=zg[:].transpose(), hgt=hgt.transpose(), parcelmethod=parcelm, d1=dx, \ |
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| 372 | d2=dy, d3=dz, d4=dt) |
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| 373 | cape = pcape.transpose() |
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| 374 | cin = pcin.transpose() |
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| 375 | zlfc = pzlfc.transpose() |
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| 376 | plfc = pplfc.transpose() |
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| 377 | li = pli.transpose() |
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| 378 | else: |
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| 379 | print errormsg |
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| 380 | print ' ' + fname + ': rank', len(pa.shape), 'not ready !!' |
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| 381 | print ' it only computes 4D [t,z,y,x] rank values' |
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| 382 | quit(-1) |
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| 383 | |
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| 384 | return cape, cin, zlfc, plfc, li, psldims, pslvdims |
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| 385 | |
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[1773] | 386 | |
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| 387 | |
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[1675] | 388 | def var_clt(cfra): |
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| 389 | """ Function to compute the total cloud fraction following 'newmicro.F90' from |
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| 390 | LMDZ using 1D vertical column values |
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| 391 | [cldfra]= cloud fraction values (assuming [[t],z,y,x]) |
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| 392 | """ |
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| 393 | ZEPSEC=1.0E-12 |
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| 394 | |
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| 395 | fname = 'var_clt' |
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| 396 | |
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| 397 | zclear = 1. |
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| 398 | zcloud = 0. |
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| 399 | |
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| 400 | dz = cfra.shape[0] |
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| 401 | for iz in range(dz): |
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| 402 | zclear =zclear*(1.-np.max([cfra[iz],zcloud]))/(1.-np.min([zcloud,1.-ZEPSEC])) |
---|
| 403 | clt = 1. - zclear |
---|
| 404 | zcloud = cfra[iz] |
---|
| 405 | |
---|
| 406 | return clt |
---|
| 407 | |
---|
| 408 | def compute_clt(cldfra, dimns, dimvns): |
---|
| 409 | """ Function to compute the total cloud fraction following 'newmicro.F90' from |
---|
| 410 | LMDZ |
---|
| 411 | compute_clt(cldfra, dimnames) |
---|
| 412 | [cldfra]= cloud fraction values (assuming [[t],z,y,x]) |
---|
| 413 | [dimns]= list of the name of the dimensions of [cldfra] |
---|
| 414 | [dimvns]= list of the name of the variables with the values of the |
---|
| 415 | dimensions of [cldfra] |
---|
| 416 | """ |
---|
| 417 | fname = 'compute_clt' |
---|
| 418 | |
---|
| 419 | cltdims = dimns[:] |
---|
| 420 | cltvdims = dimvns[:] |
---|
| 421 | |
---|
| 422 | if len(cldfra.shape) == 4: |
---|
| 423 | clt = np.zeros((cldfra.shape[0],cldfra.shape[2],cldfra.shape[3]), \ |
---|
| 424 | dtype=np.float) |
---|
| 425 | dx = cldfra.shape[3] |
---|
| 426 | dy = cldfra.shape[2] |
---|
| 427 | dz = cldfra.shape[1] |
---|
| 428 | dt = cldfra.shape[0] |
---|
| 429 | cltdims.pop(1) |
---|
| 430 | cltvdims.pop(1) |
---|
| 431 | |
---|
| 432 | for it in range(dt): |
---|
| 433 | for ix in range(dx): |
---|
| 434 | for iy in range(dy): |
---|
| 435 | zclear = 1. |
---|
| 436 | zcloud = 0. |
---|
| 437 | gen.percendone(it*dx*dy + ix*dy + iy, dx*dy*dt, 5, 'diagnosted') |
---|
| 438 | clt[it,iy,ix] = var_clt(cldfra[it,:,iy,ix]) |
---|
| 439 | |
---|
| 440 | else: |
---|
| 441 | clt = np.zeros((cldfra.shape[1],cldfra.shape[2]), dtype=np.float) |
---|
| 442 | dx = cldfra.shape[2] |
---|
| 443 | dy = cldfra.shape[1] |
---|
| 444 | dy = cldfra.shape[0] |
---|
| 445 | cltdims.pop(0) |
---|
| 446 | cltvdims.pop(0) |
---|
| 447 | for ix in range(dx): |
---|
| 448 | for iy in range(dy): |
---|
| 449 | zclear = 1. |
---|
| 450 | zcloud = 0. |
---|
| 451 | gen.percendone(ix*dy + iy, dx*dy*dt, 5, 'diagnosted') |
---|
| 452 | clt[iy,ix] = var_clt(cldfra[:,iy,ix]) |
---|
| 453 | |
---|
| 454 | return clt, cltdims, cltvdims |
---|
| 455 | |
---|
| 456 | def Forcompute_clt(cldfra, dimns, dimvns): |
---|
| 457 | """ Function to compute the total cloud fraction following 'newmicro.F90' from |
---|
| 458 | LMDZ via a Fortran module |
---|
| 459 | compute_clt(cldfra, dimnames) |
---|
| 460 | [cldfra]= cloud fraction values (assuming [[t],z,y,x]) |
---|
| 461 | [dimns]= list of the name of the dimensions of [cldfra] |
---|
| 462 | [dimvns]= list of the name of the variables with the values of the |
---|
| 463 | dimensions of [cldfra] |
---|
| 464 | """ |
---|
| 465 | fname = 'Forcompute_clt' |
---|
| 466 | |
---|
| 467 | cltdims = dimns[:] |
---|
| 468 | cltvdims = dimvns[:] |
---|
| 469 | |
---|
| 470 | |
---|
| 471 | if len(cldfra.shape) == 4: |
---|
| 472 | clt = np.zeros((cldfra.shape[0],cldfra.shape[2],cldfra.shape[3]), \ |
---|
| 473 | dtype=np.float) |
---|
| 474 | dx = cldfra.shape[3] |
---|
| 475 | dy = cldfra.shape[2] |
---|
| 476 | dz = cldfra.shape[1] |
---|
| 477 | dt = cldfra.shape[0] |
---|
| 478 | cltdims.pop(1) |
---|
| 479 | cltvdims.pop(1) |
---|
| 480 | |
---|
| 481 | clt = fdin.module_fordiagnostics.compute_clt4d2(cldfra[:]) |
---|
| 482 | |
---|
| 483 | else: |
---|
| 484 | clt = np.zeros((cldfra.shape[1],cldfra.shape[2]), dtype=np.float) |
---|
| 485 | dx = cldfra.shape[2] |
---|
| 486 | dy = cldfra.shape[1] |
---|
| 487 | dy = cldfra.shape[0] |
---|
| 488 | cltdims.pop(0) |
---|
| 489 | cltvdims.pop(0) |
---|
| 490 | |
---|
| 491 | clt = fdin.module_fordiagnostics.compute_clt3d1(cldfra[:]) |
---|
| 492 | |
---|
| 493 | return clt, cltdims, cltvdims |
---|
| 494 | |
---|
| 495 | def var_cllmh(cfra, p): |
---|
| 496 | """ Fcuntion to compute cllmh on a 1D column |
---|
| 497 | """ |
---|
| 498 | |
---|
| 499 | fname = 'var_cllmh' |
---|
| 500 | |
---|
| 501 | ZEPSEC =1.0E-12 |
---|
| 502 | prmhc = 440.*100. |
---|
| 503 | prmlc = 680.*100. |
---|
| 504 | |
---|
| 505 | zclearl = 1. |
---|
| 506 | zcloudl = 0. |
---|
| 507 | zclearm = 1. |
---|
| 508 | zcloudm = 0. |
---|
| 509 | zclearh = 1. |
---|
| 510 | zcloudh = 0. |
---|
| 511 | |
---|
| 512 | dvz = cfra.shape[0] |
---|
| 513 | |
---|
| 514 | cllmh = np.ones((3), dtype=np.float) |
---|
| 515 | |
---|
| 516 | for iz in range(dvz): |
---|
| 517 | if p[iz] < prmhc: |
---|
| 518 | cllmh[2] = cllmh[2]*(1.-np.max([cfra[iz], zcloudh]))/(1.- \ |
---|
| 519 | np.min([zcloudh,1.-ZEPSEC])) |
---|
| 520 | zcloudh = cfra[iz] |
---|
| 521 | elif p[iz] >= prmhc and p[iz] < prmlc: |
---|
| 522 | cllmh[1] = cllmh[1]*(1.-np.max([cfra[iz], zcloudm]))/(1.- \ |
---|
| 523 | np.min([zcloudm,1.-ZEPSEC])) |
---|
| 524 | zcloudm = cfra[iz] |
---|
| 525 | elif p[iz] >= prmlc: |
---|
| 526 | cllmh[0] = cllmh[0]*(1.-np.max([cfra[iz], zcloudl]))/(1.- \ |
---|
| 527 | np.min([zcloudl,1.-ZEPSEC])) |
---|
| 528 | zcloudl = cfra[iz] |
---|
| 529 | |
---|
| 530 | cllmh = 1.- cllmh |
---|
| 531 | |
---|
| 532 | return cllmh |
---|
| 533 | |
---|
[2274] | 534 | def Forcompute_cellbnds(ulon, ulat, vlon, vlat, dimns, dimvns): |
---|
| 535 | """ Function to compute cellboundaries using wind-staggered lon, lats as |
---|
| 536 | intersection of their related parallels and meridians |
---|
| 537 | compute_cellbnds(ulon, ulat, vlon, vlat, dimns, dimvns) |
---|
| 538 | [ulon]= x-staggered longitudes (assuming [y,x+1]) |
---|
| 539 | [ulat]= x-staggered latitudes (assuming [y,x+1]) |
---|
| 540 | [vlon]= y-staggered longitudes (assuming [y+1,x]) |
---|
| 541 | [vlat]= y-staggered latitudes (assuming [y+1,x]) |
---|
| 542 | [dimns]= list of the name of the dimensions of [cldfra] |
---|
| 543 | [dimvns]= list of the name of the variables with the values of the |
---|
| 544 | dimensions of [ulon] |
---|
| 545 | """ |
---|
| 546 | fname = 'Forcompute_cellbnds' |
---|
| 547 | |
---|
| 548 | dims = dimns[:] |
---|
| 549 | vdims = dimvns[:] |
---|
| 550 | |
---|
| 551 | if len(ulon.shape) == 2: |
---|
| 552 | sdx = ulon.shape[1] |
---|
| 553 | dy = ulon.shape[0] |
---|
| 554 | dx = vlon.shape[1] |
---|
| 555 | sdy = vlon.shape[0] |
---|
| 556 | |
---|
| 557 | ulont = ulon.transpose() |
---|
| 558 | ulatt = ulat.transpose() |
---|
| 559 | vlont = vlon.transpose() |
---|
| 560 | vlatt = vlat.transpose() |
---|
| 561 | |
---|
| 562 | xbndst, ybndst = fdin.module_fordiagnostics.compute_cellbnds(ulon=ulont, \ |
---|
| 563 | ulat=ulatt, vlon=vlont, vlat=vlatt, dx=dx, dy=dy, sdx=sdx, sdy=sdy) |
---|
| 564 | else: |
---|
| 565 | print errormsg |
---|
| 566 | print ' ' + fname + ": wrong rank of variables !!" |
---|
| 567 | print ' 2D matrices are expected and its found instead' |
---|
| 568 | print ' ulon shape:', ulon.shape |
---|
| 569 | print ' ulat shape:', ulat.shape |
---|
| 570 | print ' vlon shape:', vlon.shape |
---|
| 571 | print ' vlat shape:', vlat.shape |
---|
| 572 | quit(-1) |
---|
| 573 | |
---|
| 574 | xbnds = xbndst.transpose() |
---|
| 575 | ybnds = ybndst.transpose() |
---|
| 576 | |
---|
| 577 | return xbnds, ybnds, dims, vdims |
---|
| 578 | |
---|
[2277] | 579 | |
---|
| 580 | def Forcompute_cellbndsreg(lon, lat, dimns, dimvns): |
---|
| 581 | """ Function to compute cellboundaries using lon, lat from a reglar lon/lat |
---|
| 582 | projection as intersection of their related parallels and meridians |
---|
| 583 | compute_cellbnds(ulon, ulat, vlon, vlat, dimns, dimvns) |
---|
| 584 | [ulon]= x-staggered longitudes (assuming [y,x+1]) |
---|
| 585 | [ulat]= x-staggered latitudes (assuming [y,x+1]) |
---|
| 586 | [vlon]= y-staggered longitudes (assuming [y+1,x]) |
---|
| 587 | [vlat]= y-staggered latitudes (assuming [y+1,x]) |
---|
| 588 | [dimns]= list of the name of the dimensions of [cldfra] |
---|
| 589 | [dimvns]= list of the name of the variables with the values of the |
---|
| 590 | dimensions of [ulon] |
---|
| 591 | """ |
---|
| 592 | fname = 'Forcompute_cellbndsreg' |
---|
| 593 | |
---|
| 594 | dims = dimns[:] |
---|
| 595 | vdims = dimvns[:] |
---|
| 596 | |
---|
| 597 | if len(lon.shape) == 2: |
---|
| 598 | dy = lon.shape[0] |
---|
| 599 | dx = lon.shape[1] |
---|
| 600 | |
---|
| 601 | lont = lon.transpose() |
---|
| 602 | latt = lat.transpose() |
---|
| 603 | |
---|
| 604 | xbndst, ybndst = fdin.module_fordiagnostics.compute_cellbndsreg(lon=lont, \ |
---|
| 605 | lat=latt, dx=dx, dy=dy) |
---|
| 606 | else: |
---|
| 607 | print errormsg |
---|
| 608 | print ' ' + fname + ": wrong rank of variables !!" |
---|
| 609 | print ' 2D matrices are expected and its found instead' |
---|
| 610 | print ' lon shape:', lon.shape |
---|
| 611 | print ' lat shape:', lat.shape |
---|
| 612 | quit(-1) |
---|
| 613 | |
---|
| 614 | xbnds = xbndst.transpose() |
---|
| 615 | ybnds = ybndst.transpose() |
---|
| 616 | |
---|
| 617 | return xbnds, ybnds, dims, vdims |
---|
| 618 | |
---|
[1675] | 619 | def Forcompute_cllmh(cldfra, pres, dimns, dimvns): |
---|
| 620 | """ Function to compute cllmh: low/medium/hight cloud fraction following newmicro.F90 from LMDZ via Fortran subroutine |
---|
| 621 | compute_clt(cldfra, pres, dimns, dimvns) |
---|
| 622 | [cldfra]= cloud fraction values (assuming [[t],z,y,x]) |
---|
| 623 | [pres] = pressure field |
---|
| 624 | [dimns]= list of the name of the dimensions of [cldfra] |
---|
| 625 | [dimvns]= list of the name of the variables with the values of the |
---|
| 626 | dimensions of [cldfra] |
---|
| 627 | """ |
---|
| 628 | fname = 'Forcompute_cllmh' |
---|
| 629 | |
---|
| 630 | cllmhdims = dimns[:] |
---|
| 631 | cllmhvdims = dimvns[:] |
---|
| 632 | |
---|
| 633 | if len(cldfra.shape) == 4: |
---|
| 634 | dx = cldfra.shape[3] |
---|
| 635 | dy = cldfra.shape[2] |
---|
| 636 | dz = cldfra.shape[1] |
---|
| 637 | dt = cldfra.shape[0] |
---|
| 638 | cllmhdims.pop(1) |
---|
| 639 | cllmhvdims.pop(1) |
---|
| 640 | |
---|
| 641 | cllmh = fdin.module_fordiagnostics.compute_cllmh4d2(cldfra[:], pres[:]) |
---|
| 642 | |
---|
| 643 | else: |
---|
| 644 | dx = cldfra.shape[2] |
---|
| 645 | dy = cldfra.shape[1] |
---|
| 646 | dz = cldfra.shape[0] |
---|
| 647 | cllmhdims.pop(0) |
---|
| 648 | cllmhvdims.pop(0) |
---|
| 649 | |
---|
| 650 | cllmh = fdin.module_fordiagnostics.compute_cllmh3d1(cldfra[:], pres[:]) |
---|
| 651 | |
---|
| 652 | return cllmh, cllmhdims, cllmhvdims |
---|
| 653 | |
---|
| 654 | def compute_cllmh(cldfra, pres, dimns, dimvns): |
---|
| 655 | """ Function to compute cllmh: low/medium/hight cloud fraction following newmicro.F90 from LMDZ |
---|
| 656 | compute_clt(cldfra, pres, dimns, dimvns) |
---|
| 657 | [cldfra]= cloud fraction values (assuming [[t],z,y,x]) |
---|
| 658 | [pres] = pressure field |
---|
| 659 | [dimns]= list of the name of the dimensions of [cldfra] |
---|
| 660 | [dimvns]= list of the name of the variables with the values of the |
---|
| 661 | dimensions of [cldfra] |
---|
| 662 | """ |
---|
| 663 | fname = 'compute_cllmh' |
---|
| 664 | |
---|
| 665 | cllmhdims = dimns[:] |
---|
| 666 | cllmhvdims = dimvns[:] |
---|
| 667 | |
---|
| 668 | if len(cldfra.shape) == 4: |
---|
| 669 | dx = cldfra.shape[3] |
---|
| 670 | dy = cldfra.shape[2] |
---|
| 671 | dz = cldfra.shape[1] |
---|
| 672 | dt = cldfra.shape[0] |
---|
| 673 | cllmhdims.pop(1) |
---|
| 674 | cllmhvdims.pop(1) |
---|
| 675 | |
---|
| 676 | cllmh = np.ones(tuple([3, dt, dy, dx]), dtype=np.float) |
---|
| 677 | |
---|
| 678 | for it in range(dt): |
---|
| 679 | for ix in range(dx): |
---|
| 680 | for iy in range(dy): |
---|
| 681 | gen.percendone(it*dx*dy + ix*dy + iy, dx*dy*dt, 5, 'diagnosted') |
---|
| 682 | cllmh[:,it,iy,ix] = var_cllmh(cldfra[it,:,iy,ix], pres[it,:,iy,ix]) |
---|
| 683 | |
---|
| 684 | else: |
---|
| 685 | dx = cldfra.shape[2] |
---|
| 686 | dy = cldfra.shape[1] |
---|
| 687 | dz = cldfra.shape[0] |
---|
| 688 | cllmhdims.pop(0) |
---|
| 689 | cllmhvdims.pop(0) |
---|
| 690 | |
---|
| 691 | cllmh = np.ones(tuple([3, dy, dx]), dtype=np.float) |
---|
| 692 | |
---|
| 693 | for ix in range(dx): |
---|
| 694 | for iy in range(dy): |
---|
| 695 | gen.percendone(ix*dy + iy,dx*dy, 5, 'diagnosted') |
---|
| 696 | cllmh[:,iy,ix] = var_cllmh(cldfra[:,iy,ix], pres[:,iy,ix]) |
---|
| 697 | |
---|
| 698 | return cllmh, cllmhdims, cllmhvdims |
---|
| 699 | |
---|
| 700 | def compute_clivi(dens, qtot, dimns, dimvns): |
---|
| 701 | """ Function to compute cloud-ice water path (clivi) |
---|
| 702 | [dens] = density [in kgkg-1] (assuming [t],z,y,x) |
---|
| 703 | [qtot] = added mixing ratio of all cloud-ice species in [kgkg-1] (assuming [t],z,y,x) |
---|
| 704 | [dimns]= list of the name of the dimensions of [q] |
---|
| 705 | [dimvns]= list of the name of the variables with the values of the |
---|
| 706 | dimensions of [q] |
---|
| 707 | """ |
---|
| 708 | fname = 'compute_clivi' |
---|
| 709 | |
---|
| 710 | clividims = dimns[:] |
---|
| 711 | clivivdims = dimvns[:] |
---|
| 712 | |
---|
| 713 | if len(qtot.shape) == 4: |
---|
| 714 | clividims.pop(1) |
---|
| 715 | clivivdims.pop(1) |
---|
| 716 | else: |
---|
| 717 | clividims.pop(0) |
---|
| 718 | clivivdims.pop(0) |
---|
| 719 | |
---|
| 720 | data1 = dens*qtot |
---|
| 721 | clivi = np.sum(data1, axis=1) |
---|
| 722 | |
---|
| 723 | return clivi, clividims, clivivdims |
---|
| 724 | |
---|
| 725 | |
---|
| 726 | def compute_clwvl(dens, qtot, dimns, dimvns): |
---|
| 727 | """ Function to compute condensed water path (clwvl) |
---|
| 728 | [dens] = density [in kgkg-1] (assuming [t],z,y,x) |
---|
| 729 | [qtot] = added mixing ratio of all cloud-water species in [kgkg-1] (assuming [t],z,y,x) |
---|
| 730 | [dimns]= list of the name of the dimensions of [q] |
---|
| 731 | [dimvns]= list of the name of the variables with the values of the |
---|
| 732 | dimensions of [q] |
---|
| 733 | """ |
---|
| 734 | fname = 'compute_clwvl' |
---|
| 735 | |
---|
| 736 | clwvldims = dimns[:] |
---|
| 737 | clwvlvdims = dimvns[:] |
---|
| 738 | |
---|
| 739 | if len(qtot.shape) == 4: |
---|
| 740 | clwvldims.pop(1) |
---|
| 741 | clwvlvdims.pop(1) |
---|
| 742 | else: |
---|
| 743 | clwvldims.pop(0) |
---|
| 744 | clwvlvdims.pop(0) |
---|
| 745 | |
---|
| 746 | data1 = dens*qtot |
---|
| 747 | clwvl = np.sum(data1, axis=1) |
---|
| 748 | |
---|
| 749 | return clwvl, clwvldims, clwvlvdims |
---|
| 750 | |
---|
| 751 | def var_virtualTemp (temp,rmix): |
---|
| 752 | """ This function returns virtual temperature in K, |
---|
| 753 | temp: temperature [K] |
---|
| 754 | rmix: mixing ratio in [kgkg-1] |
---|
| 755 | """ |
---|
| 756 | |
---|
| 757 | fname = 'var_virtualTemp' |
---|
| 758 | |
---|
| 759 | virtual=temp*(0.622+rmix)/(0.622*(1.+rmix)) |
---|
| 760 | |
---|
| 761 | return virtual |
---|
| 762 | |
---|
[2100] | 763 | def var_convini(pr, time, dimns, dimvns): |
---|
| 764 | """ This function returns convective initialization (pr(t) > 0.0001) in time units |
---|
| 765 | pr: precipitation fux [kgm-2s-1] |
---|
| 766 | time: time in CF coordinates |
---|
| 767 | """ |
---|
| 768 | fname = 'var_convini' |
---|
| 769 | |
---|
| 770 | dt = pr.shape[0] |
---|
| 771 | dy = pr.shape[1] |
---|
| 772 | dx = pr.shape[2] |
---|
| 773 | |
---|
| 774 | vardims = dimns[:] |
---|
| 775 | varvdims = dimvns[:] |
---|
| 776 | |
---|
| 777 | vardims.pop(0) |
---|
| 778 | varvdims.pop(0) |
---|
| 779 | |
---|
| 780 | prmin = 0.0001 |
---|
| 781 | convini = np.ones((dy, dx), dtype=np.float)*gen.fillValueF |
---|
| 782 | for it in range(dt): |
---|
| 783 | # NOT working ? |
---|
| 784 | # convini = np.where(convini == gen.fillValueF and pr[it,:,:] >= prmin, \ |
---|
| 785 | # time[it], fillValueF) |
---|
| 786 | for j in range(dy): |
---|
| 787 | for i in range(dx): |
---|
| 788 | if convini[j,i] == gen.fillValueF and pr[it,j,i] >= prmin: |
---|
| 789 | convini[j,i] = time[it] |
---|
| 790 | break |
---|
| 791 | |
---|
| 792 | return convini, vardims, varvdims |
---|
| 793 | |
---|
[2140] | 794 | def var_timemax(varv, time, dimns, dimvns): |
---|
| 795 | """ This function returns the time at which variable reaches its maximum in time |
---|
| 796 | units |
---|
| 797 | varv: values of the variable to use |
---|
| 798 | time: time in CF coordinates |
---|
| 799 | """ |
---|
| 800 | fname = 'var_timemax' |
---|
| 801 | |
---|
| 802 | dt = varv.shape[0] |
---|
| 803 | dy = varv.shape[1] |
---|
| 804 | dx = varv.shape[2] |
---|
| 805 | |
---|
| 806 | vardims = dimns[:] |
---|
| 807 | varvdims = dimvns[:] |
---|
| 808 | |
---|
| 809 | vardims.pop(0) |
---|
| 810 | varvdims.pop(0) |
---|
| 811 | |
---|
| 812 | timemax = np.ones((dy, dx), dtype=np.float)*gen.fillValueF |
---|
| 813 | varmax = np.max(varv, axis=0) |
---|
| 814 | for j in range(dy): |
---|
| 815 | for i in range(dx): |
---|
| 816 | it = gen.index_vec(varv[:,j,i], varmax[j,i]) |
---|
| 817 | timemax[j,i] = time[it] |
---|
| 818 | |
---|
| 819 | return timemax, vardims, varvdims |
---|
| 820 | |
---|
[2138] | 821 | def var_timeoverthres(varv, time, thres, dimns, dimvns): |
---|
| 822 | """ This function returns the time at which (varv(t) > thres) in time units |
---|
| 823 | varv: values of the variable to use |
---|
| 824 | time: time in CF coordinates |
---|
| 825 | thres: threshold to overpass |
---|
| 826 | """ |
---|
| 827 | fname = 'var_timeoverthres' |
---|
| 828 | |
---|
| 829 | dt = varv.shape[0] |
---|
| 830 | dy = varv.shape[1] |
---|
| 831 | dx = varv.shape[2] |
---|
| 832 | |
---|
| 833 | vardims = dimns[:] |
---|
| 834 | varvdims = dimvns[:] |
---|
| 835 | |
---|
| 836 | vardims.pop(0) |
---|
| 837 | varvdims.pop(0) |
---|
| 838 | |
---|
| 839 | timeoverthres = np.ones((dy, dx), dtype=np.float)*gen.fillValueF |
---|
| 840 | for it in range(dt): |
---|
| 841 | for j in range(dy): |
---|
| 842 | for i in range(dx): |
---|
| 843 | if timeoverthres[j,i] == gen.fillValueF and varv[it,j,i] >= thres: |
---|
| 844 | timeoverthres[j,i] = time[it] |
---|
| 845 | break |
---|
| 846 | |
---|
| 847 | return timeoverthres, vardims, varvdims |
---|
| 848 | |
---|
[1762] | 849 | def Forcompute_zint(var, zinterlev, zweights, dimns, dimvns): |
---|
| 850 | """ Function to compute a vertical integration of volumetric quantities |
---|
| 851 | Forcompute_mrso(smois, dsoil, dimns, dimvns) |
---|
| 852 | [var]= values (assuming [[t],z,y,x]) [volumetric units] |
---|
| 853 | [zinterlev]= depth of each layer (assuming [z]) [same z units as var] |
---|
| 854 | [zweights]= weights to apply to each level (just in case...) |
---|
| 855 | [dimns]= list of the name of the dimensions of [smois] |
---|
| 856 | [dimvns]= list of the name of the variables with the values of the |
---|
| 857 | dimensions of [smois] |
---|
| 858 | """ |
---|
| 859 | fname = 'Forcompute_zint' |
---|
[1675] | 860 | |
---|
[1762] | 861 | vardims = dimns[:] |
---|
| 862 | varvdims = dimvns[:] |
---|
| 863 | |
---|
| 864 | if len(var.shape) == 4: |
---|
| 865 | zint = np.zeros((var.shape[0],var.shape[2],var.shape[3]), dtype=np.float) |
---|
| 866 | dx = var.shape[3] |
---|
| 867 | dy = var.shape[2] |
---|
| 868 | dz = var.shape[1] |
---|
| 869 | dt = var.shape[0] |
---|
| 870 | vardims.pop(1) |
---|
| 871 | varvdims.pop(1) |
---|
| 872 | |
---|
| 873 | zintvart=fdin.module_fordiagnostics.compute_zint4d(var4d=var[:].transpose(), \ |
---|
| 874 | dlev=zinterlev[:].transpose(), zweight=zweights[:].transpose(), d1=dx, \ |
---|
| 875 | d2=dy, d3=dz, d4=dt) |
---|
| 876 | zintvar = zintvart.transpose() |
---|
| 877 | else: |
---|
| 878 | print errormsg |
---|
| 879 | print ' ' + fname + ': rank', len(var.shape), 'not ready !!' |
---|
| 880 | print ' it only computes 4D [t,z,y,x] rank values' |
---|
| 881 | quit(-1) |
---|
| 882 | |
---|
| 883 | return zintvar, vardims, varvdims |
---|
| 884 | |
---|
[1675] | 885 | def var_mslp(pres, psfc, ter, tk, qv): |
---|
| 886 | """ Function to compute mslp on a 1D column |
---|
| 887 | """ |
---|
| 888 | |
---|
| 889 | fname = 'var_mslp' |
---|
| 890 | |
---|
| 891 | N = 1.0 |
---|
| 892 | expon=287.04*.0065/9.81 |
---|
| 893 | pref = 40000. |
---|
| 894 | |
---|
| 895 | # First find where about 400 hPa is located |
---|
| 896 | dz=len(pres) |
---|
| 897 | |
---|
| 898 | kref = -1 |
---|
| 899 | pinc = pres[0] - pres[dz-1] |
---|
| 900 | |
---|
| 901 | if pinc < 0.: |
---|
| 902 | for iz in range(1,dz): |
---|
| 903 | if pres[iz-1] >= pref and pres[iz] < pref: |
---|
| 904 | kref = iz |
---|
| 905 | break |
---|
| 906 | else: |
---|
| 907 | for iz in range(dz-1): |
---|
| 908 | if pres[iz] >= pref and pres[iz+1] < pref: |
---|
| 909 | kref = iz |
---|
| 910 | break |
---|
| 911 | |
---|
| 912 | if kref == -1: |
---|
| 913 | print errormsg |
---|
| 914 | print ' ' + fname + ': no reference pressure:',pref,'found!!' |
---|
| 915 | print ' values:',pres[:] |
---|
| 916 | quit(-1) |
---|
| 917 | |
---|
| 918 | mslp = 0. |
---|
| 919 | |
---|
| 920 | # We are below both the ground and the lowest data level. |
---|
| 921 | |
---|
| 922 | # First, find the model level that is closest to a "target" pressure |
---|
| 923 | # level, where the "target" pressure is delta-p less that the local |
---|
| 924 | # value of a horizontally smoothed surface pressure field. We use |
---|
| 925 | # delta-p = 150 hPa here. A standard lapse rate temperature profile |
---|
| 926 | # passing through the temperature at this model level will be used |
---|
| 927 | # to define the temperature profile below ground. This is similar |
---|
| 928 | # to the Benjamin and Miller (1990) method, using |
---|
| 929 | # 700 hPa everywhere for the "target" pressure. |
---|
| 930 | |
---|
| 931 | # ptarget = psfc - 15000. |
---|
| 932 | ptarget = 70000. |
---|
| 933 | dpmin=1.e4 |
---|
| 934 | kupper = 0 |
---|
| 935 | if pinc > 0.: |
---|
| 936 | for iz in range(dz-1,0,-1): |
---|
| 937 | kupper = iz |
---|
| 938 | dp=np.abs( pres[iz] - ptarget ) |
---|
| 939 | if dp < dpmin: exit |
---|
| 940 | dpmin = np.min([dpmin, dp]) |
---|
| 941 | else: |
---|
| 942 | for iz in range(dz): |
---|
| 943 | kupper = iz |
---|
| 944 | dp=np.abs( pres[iz] - ptarget ) |
---|
| 945 | if dp < dpmin: exit |
---|
| 946 | dpmin = np.min([dpmin, dp]) |
---|
| 947 | |
---|
| 948 | pbot=np.max([pres[0], psfc]) |
---|
| 949 | # zbot=0. |
---|
| 950 | |
---|
| 951 | # tbotextrap=tk(i,j,kupper,itt)*(pbot/pres_field(i,j,kupper,itt))**expon |
---|
| 952 | # tvbotextrap=virtual(tbotextrap,qv(i,j,1,itt)) |
---|
| 953 | |
---|
| 954 | # data_out(i,j,itt,1) = (zbot+tvbotextrap/.0065*(1.-(interp_levels(1)/pbot)**expon)) |
---|
| 955 | tbotextrap = tk[kupper]*(psfc/ptarget)**expon |
---|
| 956 | tvbotextrap = var_virtualTemp(tbotextrap, qv[kupper]) |
---|
| 957 | mslp = psfc*( (tvbotextrap+0.0065*ter)/tvbotextrap)**(1./expon) |
---|
| 958 | |
---|
| 959 | return mslp |
---|
| 960 | |
---|
| 961 | def compute_mslp(pressure, psurface, terrain, temperature, qvapor, dimns, dimvns): |
---|
| 962 | """ Function to compute mslp: mean sea level pressure following p_interp.F90 from WRF |
---|
| 963 | var_mslp(pres, ter, tk, qv, dimns, dimvns) |
---|
| 964 | [pressure]= pressure field [Pa] (assuming [[t],z,y,x]) |
---|
| 965 | [psurface]= surface pressure field [Pa] |
---|
| 966 | [terrain]= topography [m] |
---|
| 967 | [temperature]= temperature [K] |
---|
| 968 | [qvapor]= water vapour mixing ratio [kgkg-1] |
---|
| 969 | [dimns]= list of the name of the dimensions of [cldfra] |
---|
| 970 | [dimvns]= list of the name of the variables with the values of the |
---|
| 971 | dimensions of [pres] |
---|
| 972 | """ |
---|
| 973 | |
---|
| 974 | fname = 'compute_mslp' |
---|
| 975 | |
---|
| 976 | mslpdims = list(dimns[:]) |
---|
| 977 | mslpvdims = list(dimvns[:]) |
---|
| 978 | |
---|
| 979 | if len(pressure.shape) == 4: |
---|
| 980 | mslpdims.pop(1) |
---|
| 981 | mslpvdims.pop(1) |
---|
| 982 | else: |
---|
| 983 | mslpdims.pop(0) |
---|
| 984 | mslpvdims.pop(0) |
---|
| 985 | |
---|
| 986 | if len(pressure.shape) == 4: |
---|
| 987 | dx = pressure.shape[3] |
---|
| 988 | dy = pressure.shape[2] |
---|
| 989 | dz = pressure.shape[1] |
---|
| 990 | dt = pressure.shape[0] |
---|
| 991 | |
---|
| 992 | mslpv = np.zeros(tuple([dt, dy, dx]), dtype=np.float) |
---|
| 993 | |
---|
| 994 | # Terrain... to 2D ! |
---|
| 995 | terval = np.zeros(tuple([dy, dx]), dtype=np.float) |
---|
| 996 | if len(terrain.shape) == 3: |
---|
| 997 | terval = terrain[0,:,:] |
---|
| 998 | else: |
---|
| 999 | terval = terrain |
---|
| 1000 | |
---|
| 1001 | for ix in range(dx): |
---|
| 1002 | for iy in range(dy): |
---|
| 1003 | if terval[iy,ix] > 0.: |
---|
| 1004 | for it in range(dt): |
---|
| 1005 | mslpv[it,iy,ix] = var_mslp(pressure[it,:,iy,ix], \ |
---|
| 1006 | psurface[it,iy,ix], terval[iy,ix], temperature[it,:,iy,ix],\ |
---|
| 1007 | qvapor[it,:,iy,ix]) |
---|
| 1008 | |
---|
| 1009 | gen.percendone(it*dx*dy + ix*dy + iy, dx*dy*dt, 5, 'diagnosted') |
---|
| 1010 | else: |
---|
| 1011 | mslpv[:,iy,ix] = psurface[:,iy,ix] |
---|
| 1012 | |
---|
| 1013 | else: |
---|
| 1014 | dx = pressure.shape[2] |
---|
| 1015 | dy = pressure.shape[1] |
---|
| 1016 | dz = pressure.shape[0] |
---|
| 1017 | |
---|
| 1018 | mslpv = np.zeros(tuple([dy, dx]), dtype=np.float) |
---|
| 1019 | |
---|
| 1020 | # Terrain... to 2D ! |
---|
| 1021 | terval = np.zeros(tuple([dy, dx]), dtype=np.float) |
---|
| 1022 | if len(terrain.shape) == 3: |
---|
| 1023 | terval = terrain[0,:,:] |
---|
| 1024 | else: |
---|
| 1025 | terval = terrain |
---|
| 1026 | |
---|
| 1027 | for ix in range(dx): |
---|
| 1028 | for iy in range(dy): |
---|
| 1029 | gen.percendone(ix*dy + iy,dx*dy, 5, 'diagnosted') |
---|
| 1030 | if terval[iy,ix] > 0.: |
---|
[1758] | 1031 | mslpv[iy,ix] = var_mslp(pressure[:,iy,ix], psurface[iy,ix], \ |
---|
[1675] | 1032 | terval[iy,ix], temperature[:,iy,ix], qvapor[:,iy,ix]) |
---|
| 1033 | else: |
---|
| 1034 | mslpv[iy,ix] = psfc[iy,ix] |
---|
| 1035 | |
---|
| 1036 | return mslpv, mslpdims, mslpvdims |
---|
| 1037 | |
---|
[1795] | 1038 | def Forcompute_psl_ecmwf(ps, hgt, ta1, pa2, unpa1, dimns, dimvns): |
---|
| 1039 | """ Function to compute the sea-level pressure following Mats Hamrud and Philippe Courtier [Pa] |
---|
| 1040 | Forcompute_psl_ptarget(ps, hgt, ta1, pa2, unpa1, dimns, dimvns) |
---|
| 1041 | [ps]= surface pressure values (assuming [[t],y,x]) [Pa] |
---|
| 1042 | [hgt]= opography (assuming [y,x]) [m] |
---|
| 1043 | [ta1]= air-temperature values at first half-mass level (assuming [[t],y,x]) [K] |
---|
| 1044 | [pa2]= pressure values at second full-mass levels (assuming [[t],y,x]) [Pa] |
---|
| 1045 | [unpa1]= pressure values at first half-mass levels (assuming [[t],y,x]) [Pa] |
---|
| 1046 | [dimns]= list of the name of the dimensions of [pa] |
---|
| 1047 | [dimvns]= list of the name of the variables with the values of the |
---|
| 1048 | dimensions of [pa] |
---|
| 1049 | """ |
---|
| 1050 | fname = 'Forcompute_psl_ecmwf' |
---|
| 1051 | |
---|
| 1052 | vardims = dimns[:] |
---|
| 1053 | varvdims = dimvns[:] |
---|
| 1054 | |
---|
| 1055 | if len(pa2.shape) == 3: |
---|
| 1056 | psl = np.zeros((pa2.shape[0],pa2.shape[1],pa2.shape[2]), dtype=np.float) |
---|
| 1057 | dx = pa2.shape[2] |
---|
| 1058 | dy = pa2.shape[1] |
---|
| 1059 | dt = pa2.shape[0] |
---|
| 1060 | pslt= fdin.module_fordiagnostics.compute_psl_ecmwf( ps=ps[:].transpose(), \ |
---|
| 1061 | hgt=hgt[:].transpose(), t=ta1[:].transpose(), press=pa2[:].transpose(), \ |
---|
| 1062 | unpress=unpa1[:].transpose(), d1=dx, d2=dy, d4=dt) |
---|
| 1063 | psl = pslt.transpose() |
---|
| 1064 | else: |
---|
| 1065 | print errormsg |
---|
| 1066 | print ' ' + fname + ': rank', len(pa2.shape), 'not ready !!' |
---|
| 1067 | print ' it only computes 3D [t,y,x] rank values' |
---|
| 1068 | quit(-1) |
---|
| 1069 | |
---|
| 1070 | return psl, vardims, varvdims |
---|
| 1071 | |
---|
[1758] | 1072 | def Forcompute_psl_ptarget(pa, ps, ta, hgt, qv, target_pressure, dimns, dimvns): |
---|
| 1073 | """ Function to compute the sea-level pressure following target_pressure value |
---|
| 1074 | found in `p_interp.F' |
---|
| 1075 | Forcompute_psl_ptarget(pa, ps, ta, hgt, qv, dimns, dimvns) |
---|
| 1076 | [pa]= pressure values (assuming [[t],z,y,x]) [Pa] |
---|
| 1077 | [ps]= surface pressure values (assuming [[t],y,x]) [Pa] |
---|
| 1078 | [ta]= air-temperature values (assuming [[t],z,y,x]) [K] |
---|
| 1079 | [hgt]= opography (assuming [y,x]) [m] |
---|
| 1080 | [qv]= water vapour mixing ratio (assuming [[t],z,y,x]) [kgkg-1] |
---|
| 1081 | [dimns]= list of the name of the dimensions of [pa] |
---|
| 1082 | [dimvns]= list of the name of the variables with the values of the |
---|
| 1083 | dimensions of [pa] |
---|
| 1084 | """ |
---|
| 1085 | fname = 'Forcompute_psl_ptarget' |
---|
| 1086 | |
---|
| 1087 | psldims = dimns[:] |
---|
| 1088 | pslvdims = dimvns[:] |
---|
| 1089 | |
---|
| 1090 | if len(pa.shape) == 4: |
---|
| 1091 | psl = np.zeros((pa.shape[0],pa.shape[2],pa.shape[3]), dtype=np.float) |
---|
| 1092 | dx = pa.shape[3] |
---|
| 1093 | dy = pa.shape[2] |
---|
| 1094 | dz = pa.shape[1] |
---|
| 1095 | dt = pa.shape[0] |
---|
| 1096 | psldims.pop(1) |
---|
| 1097 | pslvdims.pop(1) |
---|
| 1098 | |
---|
| 1099 | pslt= fdin.module_fordiagnostics.compute_psl_ptarget4d2( \ |
---|
| 1100 | press=pa[:].transpose(), ps=ps[:].transpose(), hgt=hgt[:].transpose(), \ |
---|
| 1101 | ta=ta[:].transpose(), qv=qv[:].transpose(), ptarget=target_pressure, \ |
---|
| 1102 | d1=dx, d2=dy, d3=dz, d4=dt) |
---|
| 1103 | psl = pslt.transpose() |
---|
| 1104 | else: |
---|
| 1105 | print errormsg |
---|
| 1106 | print ' ' + fname + ': rank', len(pa.shape), 'not ready !!' |
---|
| 1107 | print ' it only computes 4D [t,z,y,x] rank values' |
---|
| 1108 | quit(-1) |
---|
| 1109 | |
---|
| 1110 | return psl, psldims, pslvdims |
---|
| 1111 | |
---|
[1773] | 1112 | def Forcompute_zmla_gen(theta, qratio, zpl, hgt, dimns, dimvns): |
---|
| 1113 | """ Function to compute the boundary layer height following a generic method |
---|
| 1114 | with Fortran |
---|
| 1115 | Forcompute_zmla_gen(theta, qratio, zpl, hgt, zmla, dimns, dimvns) |
---|
| 1116 | [theta]= potential air-temperature values (assuming [[t],z,y,x]) [K] |
---|
| 1117 | [qratio]= water mixing ratio (assuming [[t],z,y,x]) [kgkg-1] |
---|
| 1118 | [zpl]= height from sea level (assuming [[t],z,y,x]) [m] |
---|
| 1119 | [hgt]= topographical height (assuming [m] |
---|
| 1120 | [zmla]= boundary layer height [m] |
---|
| 1121 | [dimns]= list of the name of the dimensions of [theta] |
---|
| 1122 | [dimvns]= list of the name of the variables with the values of the |
---|
| 1123 | dimensions of [theta] |
---|
| 1124 | """ |
---|
| 1125 | fname = 'Forcompute_zmla_gen' |
---|
| 1126 | |
---|
| 1127 | zmladims = dimns[:] |
---|
| 1128 | zmlavdims = dimvns[:] |
---|
| 1129 | |
---|
| 1130 | if len(theta.shape) == 4: |
---|
| 1131 | zmla= np.zeros((theta.shape[0],theta.shape[2],theta.shape[3]), dtype=np.float) |
---|
| 1132 | |
---|
| 1133 | dx = theta.shape[3] |
---|
| 1134 | dy = theta.shape[2] |
---|
| 1135 | dz = theta.shape[1] |
---|
| 1136 | dt = theta.shape[0] |
---|
| 1137 | zmladims.pop(1) |
---|
| 1138 | zmlavdims.pop(1) |
---|
| 1139 | |
---|
| 1140 | pzmla= fdin.module_fordiagnostics.compute_zmla_generic4d( \ |
---|
| 1141 | tpot=theta[:].transpose(), qratio=qratio[:].transpose(), \ |
---|
| 1142 | z=zpl[:].transpose(), hgt=hgt.transpose(), d1=dx, d2=dy, d3=dz, d4=dt) |
---|
| 1143 | zmla = pzmla.transpose() |
---|
| 1144 | else: |
---|
| 1145 | print errormsg |
---|
| 1146 | print ' ' + fname + ': rank', len(theta.shape), 'not ready !!' |
---|
| 1147 | print ' it only computes 4D [t,z,y,x] rank values' |
---|
| 1148 | quit(-1) |
---|
| 1149 | |
---|
| 1150 | return zmla, zmladims, zmlavdims |
---|
| 1151 | |
---|
[1777] | 1152 | def Forcompute_zwind(ua, va, z, uas, vas, sina, cosa, zval, dimns, dimvns): |
---|
[1776] | 1153 | """ Function to compute the wind at a given height following the power law method |
---|
| 1154 | Forcompute_zwind(ua, va, zsl, uas, vas, hgt, sina, cosa, zval, dimns, dimvns) |
---|
| 1155 | [ua]= x-component of unstaggered 3D wind (assuming [[t],z,y,x]) [ms-1] |
---|
| 1156 | [va]= y-component of unstaggered 3D wind (assuming [[t],z,y,x]) [ms-1] |
---|
[1777] | 1157 | [z]= height above surface [m] |
---|
[1776] | 1158 | [uas]= x-component of unstaggered 10 m wind (assuming [[t],z,y,x]) [ms-1] |
---|
| 1159 | [vas]= y-component of unstaggered 10 m wind (assuming [[t],z,y,x]) [ms-1] |
---|
| 1160 | [sina]= local sine of map rotation [1.] |
---|
| 1161 | [cosa]= local cosine of map rotation [1.] |
---|
| 1162 | [zval]= desired height for winds [m] |
---|
| 1163 | [dimns]= list of the name of the dimensions of [ua] |
---|
| 1164 | [dimvns]= list of the name of the variables with the values of the |
---|
| 1165 | dimensions of [ua] |
---|
| 1166 | """ |
---|
| 1167 | fname = 'Forcompute_zwind' |
---|
| 1168 | |
---|
| 1169 | vardims = dimns[:] |
---|
| 1170 | varvdims = dimvns[:] |
---|
| 1171 | |
---|
| 1172 | if len(ua.shape) == 4: |
---|
| 1173 | var1= np.zeros((ua.shape[0],ua.shape[2],ua.shape[3]), dtype=np.float) |
---|
| 1174 | var2= np.zeros((ua.shape[0],ua.shape[2],ua.shape[3]), dtype=np.float) |
---|
| 1175 | |
---|
| 1176 | dx = ua.shape[3] |
---|
| 1177 | dy = ua.shape[2] |
---|
| 1178 | dz = ua.shape[1] |
---|
| 1179 | dt = ua.shape[0] |
---|
| 1180 | vardims.pop(1) |
---|
| 1181 | varvdims.pop(1) |
---|
| 1182 | |
---|
| 1183 | pvar1, pvar2= fdin.module_fordiagnostics.compute_zwind4d(ua=ua.transpose(), \ |
---|
[1777] | 1184 | va=va[:].transpose(), z=z[:].transpose(), uas=uas.transpose(), \ |
---|
| 1185 | vas=vas.transpose(), sina=sina.transpose(), cosa=cosa.transpose(), \ |
---|
| 1186 | zextrap=zval, d1=dx, d2=dy, d3=dz, d4=dt) |
---|
[1776] | 1187 | var1 = pvar1.transpose() |
---|
| 1188 | var2 = pvar2.transpose() |
---|
| 1189 | else: |
---|
| 1190 | print errormsg |
---|
| 1191 | print ' ' + fname + ': rank', len(ua.shape), 'not ready !!' |
---|
| 1192 | print ' it only computes 4D [t,z,y,x] rank values' |
---|
| 1193 | quit(-1) |
---|
| 1194 | |
---|
| 1195 | return var1, var2, vardims, varvdims |
---|
| 1196 | |
---|
[1784] | 1197 | def Forcompute_zwind_log(ua, va, z, uas, vas, sina, cosa, zval, dimns, dimvns): |
---|
| 1198 | """ Function to compute the wind at a given height following the logarithmic law method |
---|
| 1199 | Forcompute_zwind(ua, va, zsl, uas, vas, hgt, sina, cosa, zval, dimns, dimvns) |
---|
| 1200 | [ua]= x-component of unstaggered 3D wind (assuming [[t],z,y,x]) [ms-1] |
---|
| 1201 | [va]= y-component of unstaggered 3D wind (assuming [[t],z,y,x]) [ms-1] |
---|
| 1202 | [z]= height above surface [m] |
---|
| 1203 | [uas]= x-component of unstaggered 10 m wind (assuming [[t],z,y,x]) [ms-1] |
---|
| 1204 | [vas]= y-component of unstaggered 10 m wind (assuming [[t],z,y,x]) [ms-1] |
---|
| 1205 | [sina]= local sine of map rotation [1.] |
---|
| 1206 | [cosa]= local cosine of map rotation [1.] |
---|
| 1207 | [zval]= desired height for winds [m] |
---|
| 1208 | [dimns]= list of the name of the dimensions of [ua] |
---|
| 1209 | [dimvns]= list of the name of the variables with the values of the |
---|
| 1210 | dimensions of [ua] |
---|
| 1211 | """ |
---|
| 1212 | fname = 'Forcompute_zwind_log' |
---|
| 1213 | |
---|
| 1214 | vardims = dimns[:] |
---|
| 1215 | varvdims = dimvns[:] |
---|
| 1216 | |
---|
| 1217 | if len(ua.shape) == 4: |
---|
| 1218 | var1= np.zeros((ua.shape[0],ua.shape[2],ua.shape[3]), dtype=np.float) |
---|
| 1219 | var2= np.zeros((ua.shape[0],ua.shape[2],ua.shape[3]), dtype=np.float) |
---|
| 1220 | |
---|
| 1221 | dx = ua.shape[3] |
---|
| 1222 | dy = ua.shape[2] |
---|
| 1223 | dz = ua.shape[1] |
---|
| 1224 | dt = ua.shape[0] |
---|
| 1225 | vardims.pop(1) |
---|
| 1226 | varvdims.pop(1) |
---|
| 1227 | |
---|
| 1228 | pvar1, pvar2= fdin.module_fordiagnostics.compute_zwind_log4d( \ |
---|
| 1229 | ua=ua.transpose(), va=va[:].transpose(), z=z[:].transpose(), \ |
---|
| 1230 | uas=uas.transpose(), vas=vas.transpose(), sina=sina.transpose(), \ |
---|
| 1231 | cosa=cosa.transpose(), zextrap=zval, d1=dx, d2=dy, d3=dz, d4=dt) |
---|
| 1232 | var1 = pvar1.transpose() |
---|
| 1233 | var2 = pvar2.transpose() |
---|
| 1234 | else: |
---|
| 1235 | print errormsg |
---|
| 1236 | print ' ' + fname + ': rank', len(ua.shape), 'not ready !!' |
---|
| 1237 | print ' it only computes 4D [t,z,y,x] rank values' |
---|
| 1238 | quit(-1) |
---|
| 1239 | |
---|
| 1240 | return var1, var2, vardims, varvdims |
---|
| 1241 | |
---|
[1783] | 1242 | def Forcompute_zwindMO(ust, znt, rmol, uas, vas, sina, cosa, zval, dimns, dimvns): |
---|
| 1243 | """ Function to compute the wind at a given height following the Monin-Obukhov theory |
---|
| 1244 | Forcompute_zwind(ust, znt, rmol, uas, vas, sina, cosa, zval, dimns, dimvns) |
---|
| 1245 | [ust]: u* in similarity theory (assuming [[t],y,x]) [ms-1] |
---|
| 1246 | [znt]: thermal time-varying roughness length (assuming [[t],y,x]) [m] |
---|
| 1247 | [rmol]: inverse of Obukhov length (assuming [[t],y,x]) [m-1] |
---|
| 1248 | [uas]= x-component of unstaggered 10 m wind (assuming [[t],y,x]) [ms-1] |
---|
| 1249 | [vas]= y-component of unstaggered 10 m wind (assuming [[t],y,x]) [ms-1] |
---|
| 1250 | [sina]= local sine of map rotation [1.] |
---|
| 1251 | [cosa]= local cosine of map rotation [1.] |
---|
| 1252 | [zval]= desired height for winds [m] |
---|
| 1253 | [dimns]= list of the name of the dimensions of [uas] |
---|
| 1254 | [dimvns]= list of the name of the variables with the values of the |
---|
| 1255 | dimensions of [uas] |
---|
| 1256 | """ |
---|
| 1257 | fname = 'Forcompute_zwindMO' |
---|
| 1258 | |
---|
| 1259 | vardims = dimns[:] |
---|
| 1260 | varvdims = dimvns[:] |
---|
| 1261 | |
---|
| 1262 | if len(uas.shape) == 3: |
---|
| 1263 | var1= np.zeros((uas.shape[0],uas.shape[1],uas.shape[2]), dtype=np.float) |
---|
| 1264 | var2= np.zeros((uas.shape[0],uas.shape[1],uas.shape[2]), dtype=np.float) |
---|
| 1265 | |
---|
| 1266 | dx = uas.shape[2] |
---|
| 1267 | dy = uas.shape[1] |
---|
| 1268 | dt = uas.shape[0] |
---|
| 1269 | |
---|
| 1270 | pvar1, pvar2 = fdin.module_fordiagnostics.compute_zwindmo3d( \ |
---|
| 1271 | ust=ust.transpose(), znt=znt[:].transpose(), rmol=rmol[:].transpose(), \ |
---|
| 1272 | uas=uas.transpose(), vas=vas.transpose(), sina=sina.transpose(), \ |
---|
| 1273 | cosa=cosa.transpose(), newz=zval, d1=dx, d2=dy, d3=dt) |
---|
| 1274 | var1 = pvar1.transpose() |
---|
| 1275 | var2 = pvar2.transpose() |
---|
| 1276 | else: |
---|
| 1277 | print errormsg |
---|
| 1278 | print ' ' + fname + ': rank', len(uas.shape), 'not ready !!' |
---|
| 1279 | print ' it only computes 3D [t,y,x] rank values' |
---|
| 1280 | quit(-1) |
---|
| 1281 | |
---|
| 1282 | return var1, var2, vardims, varvdims |
---|
| 1283 | |
---|
[1804] | 1284 | def Forcompute_potevap_orPM(rho1, ust, uas, vas, tas, ps, qv1, dimns, dimvns): |
---|
| 1285 | """ Function to compute potential evapotranspiration following Penman-Monteith |
---|
[1833] | 1286 | formulation implemented in ORCHIDEE in src_sechiba/enerbil.f90 |
---|
[1804] | 1287 | Forcompute_potevap_orPM(rho1, uas, vas, tas, ps, qv2, qv1, dimns, dimvns) |
---|
| 1288 | [rho1]= air-density at the first layer (assuming [[t],y,m]) [kgm-3] |
---|
| 1289 | [ust]= u* in similarity theory (assuming [[t],y,x]) [ms-1] |
---|
| 1290 | [uas]= x-component of unstaggered 10 m wind (assuming [[t],y,x]) [ms-1] |
---|
| 1291 | [vas]= y-component of unstaggered 10 m wind (assuming [[t],y,x]) [ms-1] |
---|
| 1292 | [tas]= 2m air temperature [K] |
---|
| 1293 | [ps]= surface pressure [Pa] |
---|
| 1294 | [qv1]= mixing ratio at the first atmospheric layer [kgkg-1] |
---|
| 1295 | [dimns]= list of the name of the dimensions of [uas] |
---|
| 1296 | [dimvns]= list of the name of the variables with the values of the |
---|
| 1297 | dimensions of [uas] |
---|
| 1298 | """ |
---|
| 1299 | fname = 'Forcompute_potevap_orPM' |
---|
| 1300 | |
---|
| 1301 | vardims = dimns[:] |
---|
| 1302 | varvdims = dimvns[:] |
---|
| 1303 | |
---|
| 1304 | if len(uas.shape) == 3: |
---|
| 1305 | var1= np.zeros((uas.shape[0],uas.shape[1],uas.shape[2]), dtype=np.float) |
---|
| 1306 | var2= np.zeros((uas.shape[0],uas.shape[1],uas.shape[2]), dtype=np.float) |
---|
| 1307 | |
---|
| 1308 | dx = uas.shape[2] |
---|
| 1309 | dy = uas.shape[1] |
---|
| 1310 | dt = uas.shape[0] |
---|
| 1311 | |
---|
| 1312 | pvar = fdin.module_fordiagnostics.compute_potevap_orpm3d( \ |
---|
| 1313 | rho1=rho1.transpose(), ust=ust.transpose(), uas=uas.transpose(), \ |
---|
| 1314 | vas=vas.transpose(), tas=tas.transpose(), ps=ps.transpose(), \ |
---|
| 1315 | qv1=qv1.transpose(), d1=dx, d2=dy, d3=dt) |
---|
| 1316 | var = pvar.transpose() |
---|
| 1317 | else: |
---|
| 1318 | print errormsg |
---|
| 1319 | print ' ' + fname + ': rank', len(uas.shape), 'not ready !!' |
---|
| 1320 | print ' it only computes 3D [t,y,x] rank values' |
---|
| 1321 | quit(-1) |
---|
| 1322 | |
---|
| 1323 | return var, vardims, varvdims |
---|
| 1324 | |
---|
[1908] | 1325 | def Forcompute_fog_K84(qcloud, qice, dimns, dimvns): |
---|
| 1326 | """ Function to compute fog and visibility following Kunkel, (1984) |
---|
| 1327 | Forcompute_fog_K84(qcloud, qice, dimns, dimvns) |
---|
| 1328 | [qcloud]= cloud mixing ratio [kgk-1] |
---|
| 1329 | [qice]= ice mixing ratio [kgk-1] |
---|
| 1330 | [dimns]= list of the name of the dimensions of [uas] |
---|
| 1331 | [dimvns]= list of the name of the variables with the values of the |
---|
| 1332 | dimensions of [qcloud] |
---|
| 1333 | """ |
---|
| 1334 | fname = 'Forcompute_fog_K84' |
---|
| 1335 | |
---|
| 1336 | vardims = dimns[:] |
---|
| 1337 | varvdims = dimvns[:] |
---|
| 1338 | |
---|
| 1339 | if len(qcloud.shape) == 4: |
---|
| 1340 | var= np.zeros((qcloud.shape[0],qcloud.shape[2],qcloud.shape[3]), dtype=np.float) |
---|
| 1341 | |
---|
| 1342 | dx = qcloud.shape[3] |
---|
| 1343 | dy = qcloud.shape[2] |
---|
| 1344 | dz = qcloud.shape[1] |
---|
| 1345 | dt = qcloud.shape[0] |
---|
| 1346 | vardims.pop(1) |
---|
| 1347 | varvdims.pop(1) |
---|
| 1348 | |
---|
| 1349 | pvar1, pvar2 = fdin.module_fordiagnostics.compute_fog_k84( \ |
---|
| 1350 | qc=qcloud[:,0,:,:].transpose(), qi=qice[:,0,:,:].transpose(), d1=dx, d2=dy,\ |
---|
| 1351 | d3=dt) |
---|
| 1352 | var1 = pvar1.transpose() |
---|
| 1353 | var2 = pvar2.transpose() |
---|
| 1354 | else: |
---|
| 1355 | print errormsg |
---|
| 1356 | print ' ' + fname + ': rank', len(qcloud.shape), 'not ready !!' |
---|
| 1357 | print ' it only computes 4D [t,z,y,x] rank values' |
---|
| 1358 | quit(-1) |
---|
| 1359 | |
---|
| 1360 | return var1, var2, vardims, varvdims |
---|
| 1361 | |
---|
[1909] | 1362 | def Forcompute_fog_RUC(qvapor, temp, pres, dimns, dimvns): |
---|
[1908] | 1363 | """ Function to compute fog and visibility following RUC method Smirnova, (2000) |
---|
[1909] | 1364 | Forcompute_fog_RUC(qcloud, qice, dimns, dimvns) |
---|
| 1365 | [qvapor]= water vapor mixing ratio [kgk-1] |
---|
| 1366 | [temp]= temperature [K] |
---|
| 1367 | [pres]= pressure [Pa] |
---|
[1908] | 1368 | [dimns]= list of the name of the dimensions of [uas] |
---|
| 1369 | [dimvns]= list of the name of the variables with the values of the |
---|
| 1370 | dimensions of [qcloud] |
---|
| 1371 | """ |
---|
| 1372 | fname = 'Forcompute_fog_RUC' |
---|
| 1373 | |
---|
| 1374 | vardims = dimns[:] |
---|
| 1375 | varvdims = dimvns[:] |
---|
| 1376 | |
---|
[1909] | 1377 | if len(qvapor.shape) == 4: |
---|
| 1378 | var= np.zeros((qvapor.shape[0],qvapor.shape[2],qvapor.shape[3]), dtype=np.float) |
---|
[1908] | 1379 | |
---|
[1909] | 1380 | dx = qvapor.shape[3] |
---|
| 1381 | dy = qvapor.shape[2] |
---|
| 1382 | dz = qvapor.shape[1] |
---|
| 1383 | dt = qvapor.shape[0] |
---|
[1908] | 1384 | vardims.pop(1) |
---|
| 1385 | varvdims.pop(1) |
---|
| 1386 | |
---|
| 1387 | pvar1, pvar2 = fdin.module_fordiagnostics.compute_fog_ruc( \ |
---|
[1909] | 1388 | qv=qvapor[:,0,:,:].transpose(), ta=temp[:,0,:,:].transpose(), \ |
---|
| 1389 | pres=pres[:,0,:,:].transpose(), d1=dx, d2=dy, d3=dt) |
---|
[1908] | 1390 | var1 = pvar1.transpose() |
---|
| 1391 | var2 = pvar2.transpose() |
---|
[1909] | 1392 | elif len(qvapor.shape) == 3: |
---|
| 1393 | var= np.zeros((qvapor.shape[0],qvapor.shape[1],qvapor.shape[2]), dtype=np.float) |
---|
| 1394 | |
---|
| 1395 | dx = qvapor.shape[2] |
---|
| 1396 | dy = qvapor.shape[1] |
---|
| 1397 | dt = qvapor.shape[0] |
---|
| 1398 | |
---|
| 1399 | pvar1, pvar2 = fdin.module_fordiagnostics.compute_fog_ruc( \ |
---|
| 1400 | qv=qvapor[:].transpose(), ta=temp[:].transpose(), pres=pres[:].transpose(),\ |
---|
| 1401 | d1=dx, d2=dy, d3=dt) |
---|
| 1402 | var1 = pvar1.transpose() |
---|
| 1403 | var2 = pvar2.transpose() |
---|
[1908] | 1404 | else: |
---|
| 1405 | print errormsg |
---|
| 1406 | print ' ' + fname + ': rank', len(qcloud.shape), 'not ready !!' |
---|
[1909] | 1407 | print ' it only computes 4D [t,z,y,x] or 3D [t,z,y,x] rank values' |
---|
[1908] | 1408 | quit(-1) |
---|
| 1409 | |
---|
| 1410 | return var1, var2, vardims, varvdims |
---|
| 1411 | |
---|
[1909] | 1412 | def Forcompute_fog_FRAML50(qvapor, temp, pres, dimns, dimvns): |
---|
| 1413 | """ Function to compute fog (vis < 1km) and visibility following FRAM-L 50 % prob |
---|
| 1414 | Gultepe, and Milbrandt, (2010), J. Appl. Meteor. Climatol. |
---|
| 1415 | Forcompute_fog_FRAML50(qvapor, temp, pres, dimns, dimvns) |
---|
| 1416 | [qvapor]= vapor mixing ratio [kgk-1] |
---|
| 1417 | [temp]= temperature [K] |
---|
| 1418 | [pres]= pressure [Pa] |
---|
| 1419 | [dimns]= list of the name of the dimensions of [uas] |
---|
| 1420 | [dimvns]= list of the name of the variables with the values of the |
---|
| 1421 | dimensions of [qvapor] |
---|
| 1422 | """ |
---|
| 1423 | fname = 'Forcompute_fog_FRAML50' |
---|
| 1424 | |
---|
| 1425 | vardims = dimns[:] |
---|
| 1426 | varvdims = dimvns[:] |
---|
| 1427 | |
---|
| 1428 | if len(qvapor.shape) == 4: |
---|
| 1429 | var= np.zeros((qvapor.shape[0],qvapor.shape[2],qvapor.shape[3]), dtype=np.float) |
---|
| 1430 | |
---|
| 1431 | dx = qvapor.shape[3] |
---|
| 1432 | dy = qvapor.shape[2] |
---|
| 1433 | dz = qvapor.shape[1] |
---|
| 1434 | dt = qvapor.shape[0] |
---|
| 1435 | vardims.pop(1) |
---|
| 1436 | varvdims.pop(1) |
---|
| 1437 | |
---|
| 1438 | pvar1, pvar2 = fdin.module_fordiagnostics.compute_fog_framl50( \ |
---|
| 1439 | qv=qvapor[:,0,:,:].transpose(), ta=temp[:,0,:,:].transpose(), \ |
---|
| 1440 | pres=pres[:,0,:,:].transpose(), d1=dx, d2=dy, d3=dt) |
---|
| 1441 | var1 = pvar1.transpose() |
---|
| 1442 | var2 = pvar2.transpose() |
---|
| 1443 | elif len(qvapor.shape) == 3: |
---|
| 1444 | var= np.zeros((qvapor.shape[0],qvapor.shape[1],qvapor.shape[2]), dtype=np.float) |
---|
| 1445 | |
---|
| 1446 | dx = qvapor.shape[2] |
---|
| 1447 | dy = qvapor.shape[1] |
---|
| 1448 | dt = qvapor.shape[0] |
---|
| 1449 | |
---|
| 1450 | pvar1, pvar2 = fdin.module_fordiagnostics.compute_fog_framl50( \ |
---|
| 1451 | qv=qvapor[:].transpose(), ta=temp[:].transpose(), pres=pres[:].transpose(),\ |
---|
| 1452 | d1=dx, d2=dy, d3=dt) |
---|
| 1453 | var1 = pvar1.transpose() |
---|
| 1454 | var2 = pvar2.transpose() |
---|
| 1455 | else: |
---|
| 1456 | print errormsg |
---|
| 1457 | print ' ' + fname + ': rank', len(qvapor.shape), 'not ready !!' |
---|
| 1458 | print ' it only computes 4D [t,z,y,x] or 3D [t,y,x] rank values' |
---|
| 1459 | quit(-1) |
---|
| 1460 | |
---|
| 1461 | return var1, var2, vardims, varvdims |
---|
| 1462 | |
---|
[2260] | 1463 | def Forcompute_range_faces(lon, lat, hgt, dsx, dsy, ds, face, dsfilt, dsnewrng, \ |
---|
| 1464 | hvalleyrng, dimns, dimvns): |
---|
[2208] | 1465 | """ Function to compute faces [uphill, valley, downhill] of sections of a mountain |
---|
| 1466 | rage, along a given face |
---|
| 1467 | Forcompute_range_faces(lon, lat, hgt, face, dimns, dimvns) |
---|
| 1468 | [lon]= longitude values (assuming [y,x]) [degrees east] |
---|
| 1469 | [lat]= latitude values (assuming [y,x]) [degrees north] |
---|
| 1470 | [hgt]= height values (assuming [y,x]) [m] |
---|
[2260] | 1471 | [dsx]= distance between grid points along x-axis (assuming [y,x]) [m] |
---|
| 1472 | [dsy]= distance between grid points along y-axis (assuming [y,x]) [m] |
---|
[2215] | 1473 | [ds]= distance between grid points (assuming [y,x]) [m] |
---|
| 1474 | face= which face (axis along which produce slices) to use to compute the |
---|
| 1475 | faces: WE, SN |
---|
| 1476 | dsfilt= distance to filter orography smaller scale of it [m] |
---|
| 1477 | dsnewrng= distance to start a new mountain range [m] |
---|
| 1478 | hvalleyrng: maximum height of a valley to mark change of range [m] |
---|
[2208] | 1479 | [dimns]= list of the name of the dimensions of [smois] |
---|
| 1480 | [dimvns]= list of the name of the variables with the values of the |
---|
| 1481 | dimensions of [smois] |
---|
| 1482 | """ |
---|
| 1483 | fname = 'Forcompute_range_faces' |
---|
| 1484 | |
---|
| 1485 | vardims = dimns[:] |
---|
| 1486 | varvdims = dimvns[:] |
---|
| 1487 | |
---|
[2209] | 1488 | if len(hgt.shape) == 2: |
---|
| 1489 | faces = np.zeros(hgt.shape, dtype=np.float) |
---|
| 1490 | dx = hgt.shape[1] |
---|
| 1491 | dy = hgt.shape[0] |
---|
[2208] | 1492 | |
---|
[2223] | 1493 | hgtmaxt, pthgtmaxt, dhgtt, peakst, valleyst, ofacest, ffacest, rngt, \ |
---|
| 1494 | rnghgtmaxt, ptrnghgtmaxt = \ |
---|
[2212] | 1495 | fdin.module_fordiagnostics.compute_range_faces(lon=lon[:].transpose(), \ |
---|
[2260] | 1496 | lat=lat[:].transpose(), hgt=hgt[:].transpose(), xdist=ds[:].transpose(), \ |
---|
| 1497 | ydist=ds[:].transpose(), dist=ds[:].transpose(), face=face, dsfilt=dsfilt, \ |
---|
| 1498 | dsnewrange=dsnewrng, hvalrng=hvalleyrng, d1=dx, d2=dy) |
---|
[2338] | 1499 | |
---|
[2213] | 1500 | hgtmax = hgtmaxt.transpose() |
---|
| 1501 | pthgtmax = pthgtmaxt.transpose() |
---|
[2212] | 1502 | dhgt = dhgtt.transpose() |
---|
| 1503 | peaks = peakst.transpose() |
---|
| 1504 | valleys = valleyst.transpose() |
---|
| 1505 | origfaces = ofacest.transpose() |
---|
| 1506 | filtfaces = ffacest.transpose() |
---|
[2223] | 1507 | ranges = rngt.transpose() |
---|
[2214] | 1508 | rangeshgtmax = rnghgtmaxt.transpose() |
---|
| 1509 | ptrangeshgtmax = ptrnghgtmaxt.transpose() |
---|
[2338] | 1510 | |
---|
[2208] | 1511 | else: |
---|
| 1512 | print errormsg |
---|
| 1513 | print ' ' + fname + ': rank', len(var.shape), 'not ready !!' |
---|
| 1514 | print ' it only computes 2D [y,x] rank values' |
---|
| 1515 | quit(-1) |
---|
| 1516 | |
---|
[2213] | 1517 | return hgtmax, pthgtmax, dhgt, peaks, valleys, origfaces, filtfaces, vardims, \ |
---|
[2223] | 1518 | varvdims, ranges, rangeshgtmax, ptrangeshgtmax |
---|
[2208] | 1519 | |
---|
[1804] | 1520 | ####### ###### ##### #### ### ## # END Fortran diagnostics |
---|
| 1521 | |
---|
[1675] | 1522 | def compute_OMEGAw(omega, p, t, dimns, dimvns): |
---|
| 1523 | """ Function to transform OMEGA [Pas-1] to velocities [ms-1] |
---|
| 1524 | tacking: https://www.ncl.ucar.edu/Document/Functions/Contributed/omega_to_w.shtml |
---|
| 1525 | [omega] = vertical velocity [in ms-1] (assuming [t],z,y,x) |
---|
| 1526 | [p] = pressure in [Pa] (assuming [t],z,y,x) |
---|
| 1527 | [t] = temperature in [K] (assuming [t],z,y,x) |
---|
| 1528 | [dimns]= list of the name of the dimensions of [q] |
---|
| 1529 | [dimvns]= list of the name of the variables with the values of the |
---|
| 1530 | dimensions of [q] |
---|
| 1531 | """ |
---|
| 1532 | fname = 'compute_OMEGAw' |
---|
| 1533 | |
---|
| 1534 | rgas = 287.058 # J/(kg-K) => m2/(s2 K) |
---|
| 1535 | g = 9.80665 # m/s2 |
---|
| 1536 | |
---|
| 1537 | wdims = dimns[:] |
---|
| 1538 | wvdims = dimvns[:] |
---|
| 1539 | |
---|
| 1540 | rho = p/(rgas*t) # density => kg/m3 |
---|
| 1541 | w = -omega/(rho*g) |
---|
| 1542 | |
---|
| 1543 | return w, wdims, wvdims |
---|
| 1544 | |
---|
| 1545 | def compute_prw(dens, q, dimns, dimvns): |
---|
| 1546 | """ Function to compute water vapour path (prw) |
---|
| 1547 | [dens] = density [in kgkg-1] (assuming [t],z,y,x) |
---|
| 1548 | [q] = mixing ratio in [kgkg-1] (assuming [t],z,y,x) |
---|
| 1549 | [dimns]= list of the name of the dimensions of [q] |
---|
| 1550 | [dimvns]= list of the name of the variables with the values of the |
---|
| 1551 | dimensions of [q] |
---|
| 1552 | """ |
---|
| 1553 | fname = 'compute_prw' |
---|
| 1554 | |
---|
| 1555 | prwdims = dimns[:] |
---|
| 1556 | prwvdims = dimvns[:] |
---|
| 1557 | |
---|
| 1558 | if len(q.shape) == 4: |
---|
| 1559 | prwdims.pop(1) |
---|
| 1560 | prwvdims.pop(1) |
---|
| 1561 | else: |
---|
| 1562 | prwdims.pop(0) |
---|
| 1563 | prwvdims.pop(0) |
---|
| 1564 | |
---|
| 1565 | data1 = dens*q |
---|
| 1566 | prw = np.sum(data1, axis=1) |
---|
| 1567 | |
---|
| 1568 | return prw, prwdims, prwvdims |
---|
| 1569 | |
---|
| 1570 | def compute_rh(p, t, q, dimns, dimvns): |
---|
| 1571 | """ Function to compute relative humidity following 'Tetens' equation (T,P) ...' |
---|
| 1572 | [t]= temperature (assuming [[t],z,y,x] in [K]) |
---|
| 1573 | [p] = pressure field (assuming in [hPa]) |
---|
| 1574 | [q] = mixing ratio in [kgkg-1] |
---|
| 1575 | [dimns]= list of the name of the dimensions of [t] |
---|
| 1576 | [dimvns]= list of the name of the variables with the values of the |
---|
| 1577 | dimensions of [t] |
---|
| 1578 | """ |
---|
| 1579 | fname = 'compute_rh' |
---|
| 1580 | |
---|
| 1581 | rhdims = dimns[:] |
---|
| 1582 | rhvdims = dimvns[:] |
---|
| 1583 | |
---|
| 1584 | data1 = 10.*0.6112*np.exp(17.67*(t-273.16)/(t-29.65)) |
---|
| 1585 | data2 = 0.622*data1/(0.01*p-(1.-0.622)*data1) |
---|
| 1586 | |
---|
| 1587 | rh = q/data2 |
---|
| 1588 | |
---|
| 1589 | return rh, rhdims, rhvdims |
---|
| 1590 | |
---|
| 1591 | def compute_td(p, temp, qv, dimns, dimvns): |
---|
| 1592 | """ Function to compute the dew point temperature |
---|
| 1593 | [p]= pressure [Pa] |
---|
| 1594 | [temp]= temperature [C] |
---|
| 1595 | [qv]= mixing ratio [kgkg-1] |
---|
| 1596 | [dimns]= list of the name of the dimensions of [p] |
---|
| 1597 | [dimvns]= list of the name of the variables with the values of the |
---|
| 1598 | dimensions of [p] |
---|
| 1599 | """ |
---|
| 1600 | fname = 'compute_td' |
---|
| 1601 | |
---|
| 1602 | # print ' ' + fname + ': computing dew-point temperature from TS as t and Tetens...' |
---|
| 1603 | # tacking from: http://en.wikipedia.org/wiki/Dew_point |
---|
| 1604 | tk = temp |
---|
| 1605 | data1 = 10.*0.6112*np.exp(17.67*(tk-273.16)/(tk-29.65)) |
---|
| 1606 | data2 = 0.622*data1/(0.01*p-(1.-0.622)*data1) |
---|
| 1607 | |
---|
| 1608 | rh = qv/data2 |
---|
| 1609 | |
---|
| 1610 | pa = rh * data1 |
---|
| 1611 | td = 257.44*np.log(pa/6.1121)/(18.678-np.log(pa/6.1121)) |
---|
| 1612 | |
---|
| 1613 | tddims = dimns[:] |
---|
| 1614 | tdvdims = dimvns[:] |
---|
| 1615 | |
---|
| 1616 | return td, tddims, tdvdims |
---|
| 1617 | |
---|
| 1618 | def var_WRFtime(timewrfv, refdate='19491201000000', tunitsval='minutes'): |
---|
| 1619 | """ Function to copmute CFtimes from WRFtime variable |
---|
| 1620 | refdate= [YYYYMMDDMIHHSS] format of reference date |
---|
| 1621 | tunitsval= CF time units |
---|
| 1622 | timewrfv= matrix string values of WRF 'Times' variable |
---|
| 1623 | """ |
---|
| 1624 | fname = 'var_WRFtime' |
---|
| 1625 | |
---|
| 1626 | yrref=refdate[0:4] |
---|
| 1627 | monref=refdate[4:6] |
---|
| 1628 | dayref=refdate[6:8] |
---|
| 1629 | horref=refdate[8:10] |
---|
| 1630 | minref=refdate[10:12] |
---|
| 1631 | secref=refdate[12:14] |
---|
| 1632 | |
---|
| 1633 | refdateS = yrref + '-' + monref + '-' + dayref + ' ' + horref + ':' + minref + \ |
---|
| 1634 | ':' + secref |
---|
| 1635 | |
---|
| 1636 | dt = timewrfv.shape[0] |
---|
| 1637 | WRFtime = np.zeros((dt), dtype=np.float) |
---|
| 1638 | |
---|
| 1639 | for it in range(dt): |
---|
| 1640 | wrfdates = gen.datetimeStr_conversion(timewrfv[it,:],'WRFdatetime', 'matYmdHMS') |
---|
| 1641 | WRFtime[it] = gen.realdatetime1_CFcompilant(wrfdates, refdate, tunitsval) |
---|
| 1642 | |
---|
| 1643 | tunits = tunitsval + ' since ' + refdateS |
---|
| 1644 | |
---|
| 1645 | return WRFtime, tunits |
---|
| 1646 | |
---|
| 1647 | def turbulence_var(varv, dimvn, dimn): |
---|
| 1648 | """ Function to compute the Taylor's decomposition turbulence term from a a given variable |
---|
| 1649 | x*=<x^2>_t-(<X>_t)^2 |
---|
| 1650 | turbulence_var(varv,dimn) |
---|
| 1651 | varv= values of the variable |
---|
| 1652 | dimvn= names of the dimension of the variable |
---|
| 1653 | dimn= names of the dimensions (as a dictionary with 'X', 'Y', 'Z', 'T') |
---|
| 1654 | >>> turbulence_var(np.arange((27)).reshape(3,3,3),['time','y','x'],{'T':'time', 'Y':'y', 'X':'x'}) |
---|
| 1655 | [[ 54. 54. 54.] |
---|
| 1656 | [ 54. 54. 54.] |
---|
| 1657 | [ 54. 54. 54.]] |
---|
| 1658 | """ |
---|
| 1659 | fname = 'turbulence_varv' |
---|
| 1660 | |
---|
| 1661 | timedimid = dimvn.index(dimn['T']) |
---|
| 1662 | |
---|
| 1663 | varv2 = varv*varv |
---|
| 1664 | |
---|
| 1665 | vartmean = np.mean(varv, axis=timedimid) |
---|
| 1666 | var2tmean = np.mean(varv2, axis=timedimid) |
---|
| 1667 | |
---|
| 1668 | varvturb = var2tmean - (vartmean*vartmean) |
---|
| 1669 | |
---|
| 1670 | return varvturb |
---|
| 1671 | |
---|
| 1672 | def compute_turbulence(v, dimns, dimvns): |
---|
| 1673 | """ Function to compute the rubulence term of the Taylor's decomposition ...' |
---|
| 1674 | x*=<x^2>_t-(<X>_t)^2 |
---|
| 1675 | [v]= variable (assuming [[t],z,y,x]) |
---|
| 1676 | [dimns]= list of the name of the dimensions of [v] |
---|
| 1677 | [dimvns]= list of the name of the variables with the values of the |
---|
| 1678 | dimensions of [v] |
---|
| 1679 | """ |
---|
| 1680 | fname = 'compute_turbulence' |
---|
| 1681 | |
---|
| 1682 | turbdims = dimns[:] |
---|
| 1683 | turbvdims = dimvns[:] |
---|
| 1684 | |
---|
| 1685 | turbdims.pop(0) |
---|
| 1686 | turbvdims.pop(0) |
---|
| 1687 | |
---|
| 1688 | v2 = v*v |
---|
| 1689 | |
---|
| 1690 | vartmean = np.mean(v, axis=0) |
---|
| 1691 | var2tmean = np.mean(v2, axis=0) |
---|
| 1692 | |
---|
| 1693 | turb = var2tmean - (vartmean*vartmean) |
---|
| 1694 | |
---|
| 1695 | return turb, turbdims, turbvdims |
---|
| 1696 | |
---|
[1980] | 1697 | def compute_wd(u, v, dimns, dimvns): |
---|
| 1698 | """ Function to compute the wind direction |
---|
| 1699 | [u]= W-E wind direction [ms-1, knot, ...] |
---|
| 1700 | [v]= N-S wind direction [ms-1, knot, ...] |
---|
| 1701 | [dimns]= list of the name of the dimensions of [u] |
---|
| 1702 | [dimvns]= list of the name of the variables with the values of the |
---|
| 1703 | dimensions of [u] |
---|
| 1704 | """ |
---|
| 1705 | fname = 'compute_wds' |
---|
| 1706 | |
---|
| 1707 | # print ' ' + fname + ': computing wind direction as ATAN2(v,u) ...' |
---|
| 1708 | theta = np.arctan2(v,u) |
---|
| 1709 | theta = np.where(theta < 0., theta + 2.*np.pi, theta) |
---|
| 1710 | |
---|
| 1711 | var = 360.*theta/(2.*np.pi) |
---|
| 1712 | |
---|
| 1713 | vardims = dimns[:] |
---|
| 1714 | varvdims = dimvns[:] |
---|
| 1715 | |
---|
| 1716 | return var, vardims, varvdims |
---|
| 1717 | |
---|
[1675] | 1718 | def compute_wds(u, v, dimns, dimvns): |
---|
| 1719 | """ Function to compute the wind direction |
---|
| 1720 | [u]= W-E wind direction [ms-1, knot, ...] |
---|
| 1721 | [v]= N-S wind direction [ms-1, knot, ...] |
---|
| 1722 | [dimns]= list of the name of the dimensions of [u] |
---|
| 1723 | [dimvns]= list of the name of the variables with the values of the |
---|
| 1724 | dimensions of [u] |
---|
| 1725 | """ |
---|
| 1726 | fname = 'compute_wds' |
---|
| 1727 | |
---|
| 1728 | # print ' ' + fname + ': computing wind direction as ATAN2(v,u) ...' |
---|
| 1729 | theta = np.arctan2(v,u) |
---|
| 1730 | theta = np.where(theta < 0., theta + 2.*np.pi, theta) |
---|
| 1731 | |
---|
| 1732 | wds = 360.*theta/(2.*np.pi) |
---|
| 1733 | |
---|
| 1734 | wdsdims = dimns[:] |
---|
| 1735 | wdsvdims = dimvns[:] |
---|
| 1736 | |
---|
| 1737 | return wds, wdsdims, wdsvdims |
---|
| 1738 | |
---|
| 1739 | def compute_wss(u, v, dimns, dimvns): |
---|
| 1740 | """ Function to compute the wind speed |
---|
| 1741 | [u]= W-E wind direction [ms-1, knot, ...] |
---|
| 1742 | [v]= N-S wind direction [ms-1, knot, ...] |
---|
| 1743 | [dimns]= list of the name of the dimensions of [u] |
---|
| 1744 | [dimvns]= list of the name of the variables with the values of the |
---|
| 1745 | dimensions of [u] |
---|
| 1746 | """ |
---|
| 1747 | fname = 'compute_wss' |
---|
| 1748 | |
---|
| 1749 | # print ' ' + fname + ': computing wind speed as SQRT(v**2 + u**2) ...' |
---|
| 1750 | wss = np.sqrt(u*u + v*v) |
---|
| 1751 | |
---|
| 1752 | wssdims = dimns[:] |
---|
| 1753 | wssvdims = dimvns[:] |
---|
| 1754 | |
---|
| 1755 | return wss, wssdims, wssvdims |
---|
| 1756 | |
---|
| 1757 | def timeunits_seconds(dtu): |
---|
| 1758 | """ Function to transform a time units to seconds |
---|
| 1759 | timeunits_seconds(timeuv) |
---|
| 1760 | [dtu]= time units value to transform in seconds |
---|
| 1761 | """ |
---|
| 1762 | fname='timunits_seconds' |
---|
| 1763 | |
---|
| 1764 | if dtu == 'years': |
---|
| 1765 | times = 365.*24.*3600. |
---|
| 1766 | elif dtu == 'weeks': |
---|
| 1767 | times = 7.*24.*3600. |
---|
| 1768 | elif dtu == 'days': |
---|
| 1769 | times = 24.*3600. |
---|
| 1770 | elif dtu == 'hours': |
---|
| 1771 | times = 3600. |
---|
| 1772 | elif dtu == 'minutes': |
---|
| 1773 | times = 60. |
---|
| 1774 | elif dtu == 'seconds': |
---|
| 1775 | times = 1. |
---|
| 1776 | elif dtu == 'miliseconds': |
---|
| 1777 | times = 1./1000. |
---|
| 1778 | else: |
---|
| 1779 | print errormsg |
---|
| 1780 | print ' ' + fname + ": time units '" + dtu + "' not ready !!" |
---|
| 1781 | quit(-1) |
---|
| 1782 | |
---|
| 1783 | return times |
---|
| 1784 | |
---|
[1710] | 1785 | def compute_WRFhur(t, p, qv, dimns, dimvns): |
---|
| 1786 | """ Function to compute WRF relative humidity following Teten's equation |
---|
| 1787 | t= orginal WRF temperature |
---|
| 1788 | p= original WRF pressure (P + PB) |
---|
| 1789 | formula: |
---|
| 1790 | temp = theta*(p/p0)**(R/Cp) |
---|
| 1791 | |
---|
| 1792 | """ |
---|
| 1793 | fname = 'compute_WRFtd' |
---|
| 1794 | |
---|
| 1795 | tk = (t+300.)*(p/fdef.module_definitions.p0ref)**(fdef.module_definitions.rcp) |
---|
| 1796 | data1 = 10.*0.6112*np.exp(17.67*(tk-273.16)/(tk-29.65)) |
---|
| 1797 | data2 = 0.622*data1/(0.01*p-(1.-0.622)*data1) |
---|
| 1798 | |
---|
| 1799 | rh = qv/data2 |
---|
| 1800 | |
---|
| 1801 | dnamesvar = ['Time','bottom_top','south_north','west_east'] |
---|
| 1802 | dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dimns,dimvns) |
---|
| 1803 | |
---|
| 1804 | return rh, dnamesvar, dvnamesvar |
---|
| 1805 | |
---|
[1687] | 1806 | def compute_WRFua(u, v, sina, cosa, dimns, dimvns): |
---|
| 1807 | """ Function to compute geographical rotated WRF 3D winds |
---|
| 1808 | u= orginal WRF x-wind |
---|
| 1809 | v= orginal WRF y-wind |
---|
| 1810 | sina= original WRF local sinus of map rotation |
---|
| 1811 | cosa= original WRF local cosinus of map rotation |
---|
| 1812 | formula: |
---|
| 1813 | ua = u*cosa-va*sina |
---|
| 1814 | va = u*sina+va*cosa |
---|
| 1815 | """ |
---|
| 1816 | fname = 'compute_WRFua' |
---|
| 1817 | |
---|
| 1818 | var0 = u |
---|
| 1819 | var1 = v |
---|
| 1820 | var2 = sina |
---|
| 1821 | var3 = cosa |
---|
| 1822 | |
---|
| 1823 | # un-staggering variables |
---|
| 1824 | unstgdims = [var0.shape[0], var0.shape[1], var0.shape[2], var0.shape[3]-1] |
---|
| 1825 | ua = np.zeros(tuple(unstgdims), dtype=np.float) |
---|
| 1826 | unstgvar0 = np.zeros(tuple(unstgdims), dtype=np.float) |
---|
| 1827 | unstgvar1 = np.zeros(tuple(unstgdims), dtype=np.float) |
---|
| 1828 | unstgvar0 = 0.5*(var0[:,:,:,0:var0.shape[3]-1] + var0[:,:,:,1:var0.shape[3]]) |
---|
| 1829 | unstgvar1 = 0.5*(var1[:,:,0:var1.shape[2]-1,:] + var1[:,:,1:var1.shape[2],:]) |
---|
| 1830 | |
---|
| 1831 | for iz in range(var0.shape[1]): |
---|
| 1832 | ua[:,iz,:,:] = unstgvar0[:,iz,:,:]*var3 - unstgvar1[:,iz,:,:]*var2 |
---|
| 1833 | |
---|
| 1834 | dnamesvar = ['Time','bottom_top','south_north','west_east'] |
---|
| 1835 | dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dimns,dimvns) |
---|
| 1836 | |
---|
| 1837 | return ua, dnamesvar, dvnamesvar |
---|
| 1838 | |
---|
| 1839 | def compute_WRFva(u, v, sina, cosa, dimns, dimvns): |
---|
| 1840 | """ Function to compute geographical rotated WRF 3D winds |
---|
| 1841 | u= orginal WRF x-wind |
---|
| 1842 | v= orginal WRF y-wind |
---|
| 1843 | sina= original WRF local sinus of map rotation |
---|
| 1844 | cosa= original WRF local cosinus of map rotation |
---|
| 1845 | formula: |
---|
| 1846 | ua = u*cosa-va*sina |
---|
| 1847 | va = u*sina+va*cosa |
---|
| 1848 | """ |
---|
| 1849 | fname = 'compute_WRFva' |
---|
| 1850 | |
---|
| 1851 | var0 = u |
---|
| 1852 | var1 = v |
---|
| 1853 | var2 = sina |
---|
| 1854 | var3 = cosa |
---|
| 1855 | |
---|
| 1856 | # un-staggering variables |
---|
| 1857 | unstgdims = [var0.shape[0], var0.shape[1], var0.shape[2], var0.shape[3]-1] |
---|
| 1858 | va = np.zeros(tuple(unstgdims), dtype=np.float) |
---|
| 1859 | unstgvar0 = np.zeros(tuple(unstgdims), dtype=np.float) |
---|
| 1860 | unstgvar1 = np.zeros(tuple(unstgdims), dtype=np.float) |
---|
| 1861 | unstgvar0 = 0.5*(var0[:,:,:,0:var0.shape[3]-1] + var0[:,:,:,1:var0.shape[3]]) |
---|
| 1862 | unstgvar1 = 0.5*(var1[:,:,0:var1.shape[2]-1,:] + var1[:,:,1:var1.shape[2],:]) |
---|
| 1863 | |
---|
| 1864 | for iz in range(var0.shape[1]): |
---|
| 1865 | va[:,iz,:,:] = unstgvar0[:,iz,:,:]*var2 + unstgvar1[:,iz,:,:]*var3 |
---|
| 1866 | |
---|
| 1867 | dnamesvar = ['Time','bottom_top','south_north','west_east'] |
---|
| 1868 | dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dimns,dimvns) |
---|
| 1869 | |
---|
| 1870 | return va, dnamesvar, dvnamesvar |
---|
| 1871 | |
---|
[1675] | 1872 | def compute_WRFuava(u, v, sina, cosa, dimns, dimvns): |
---|
| 1873 | """ Function to compute geographical rotated WRF 3D winds |
---|
| 1874 | u= orginal WRF x-wind |
---|
| 1875 | v= orginal WRF y-wind |
---|
| 1876 | sina= original WRF local sinus of map rotation |
---|
| 1877 | cosa= original WRF local cosinus of map rotation |
---|
| 1878 | formula: |
---|
| 1879 | ua = u*cosa-va*sina |
---|
| 1880 | va = u*sina+va*cosa |
---|
| 1881 | """ |
---|
| 1882 | fname = 'compute_WRFuava' |
---|
| 1883 | |
---|
| 1884 | var0 = u |
---|
| 1885 | var1 = v |
---|
| 1886 | var2 = sina |
---|
| 1887 | var3 = cosa |
---|
| 1888 | |
---|
| 1889 | # un-staggering variables |
---|
| 1890 | unstgdims = [var0.shape[0], var0.shape[1], var0.shape[2], var0.shape[3]-1] |
---|
| 1891 | ua = np.zeros(tuple(unstgdims), dtype=np.float) |
---|
| 1892 | va = np.zeros(tuple(unstgdims), dtype=np.float) |
---|
| 1893 | unstgvar0 = np.zeros(tuple(unstgdims), dtype=np.float) |
---|
| 1894 | unstgvar1 = np.zeros(tuple(unstgdims), dtype=np.float) |
---|
| 1895 | unstgvar0 = 0.5*(var0[:,:,:,0:var0.shape[3]-1] + var0[:,:,:,1:var0.shape[3]]) |
---|
| 1896 | unstgvar1 = 0.5*(var1[:,:,0:var1.shape[2]-1,:] + var1[:,:,1:var1.shape[2],:]) |
---|
| 1897 | |
---|
| 1898 | for iz in range(var0.shape[1]): |
---|
| 1899 | ua[:,iz,:,:] = unstgvar0[:,iz,:,:]*var3 - unstgvar1[:,iz,:,:]*var2 |
---|
| 1900 | va[:,iz,:,:] = unstgvar0[:,iz,:,:]*var2 + unstgvar1[:,iz,:,:]*var3 |
---|
| 1901 | |
---|
| 1902 | dnamesvar = ['Time','bottom_top','south_north','west_east'] |
---|
| 1903 | dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dimns,dimvns) |
---|
| 1904 | |
---|
| 1905 | return ua, va, dnamesvar, dvnamesvar |
---|
| 1906 | |
---|
[1687] | 1907 | def compute_WRFuas(u10, v10, sina, cosa, dimns, dimvns): |
---|
| 1908 | """ Function to compute geographical rotated WRF 2-meter x-wind |
---|
| 1909 | u10= orginal WRF 10m x-wind |
---|
| 1910 | v10= orginal WRF 10m y-wind |
---|
| 1911 | sina= original WRF local sinus of map rotation |
---|
| 1912 | cosa= original WRF local cosinus of map rotation |
---|
| 1913 | formula: |
---|
| 1914 | uas = u10*cosa-va10*sina |
---|
| 1915 | vas = u10*sina+va10*cosa |
---|
| 1916 | """ |
---|
| 1917 | fname = 'compute_WRFuas' |
---|
| 1918 | |
---|
| 1919 | var0 = u10 |
---|
| 1920 | var1 = v10 |
---|
| 1921 | var2 = sina |
---|
| 1922 | var3 = cosa |
---|
| 1923 | |
---|
| 1924 | uas = np.zeros(var0.shape, dtype=np.float) |
---|
| 1925 | vas = np.zeros(var0.shape, dtype=np.float) |
---|
| 1926 | |
---|
| 1927 | uas = var0*var3 - var1*var2 |
---|
| 1928 | |
---|
| 1929 | dnamesvar = ['Time','south_north','west_east'] |
---|
| 1930 | dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dimns,dimvns) |
---|
| 1931 | |
---|
| 1932 | return uas, dnamesvar, dvnamesvar |
---|
| 1933 | |
---|
| 1934 | def compute_WRFvas(u10, v10, sina, cosa, dimns, dimvns): |
---|
| 1935 | """ Function to compute geographical rotated WRF 2-meter y-wind |
---|
| 1936 | u10= orginal WRF 10m x-wind |
---|
| 1937 | v10= orginal WRF 10m y-wind |
---|
| 1938 | sina= original WRF local sinus of map rotation |
---|
| 1939 | cosa= original WRF local cosinus of map rotation |
---|
| 1940 | formula: |
---|
| 1941 | uas = u10*cosa-va10*sina |
---|
| 1942 | vas = u10*sina+va10*cosa |
---|
| 1943 | """ |
---|
| 1944 | fname = 'compute_WRFvas' |
---|
| 1945 | |
---|
| 1946 | var0 = u10 |
---|
| 1947 | var1 = v10 |
---|
| 1948 | var2 = sina |
---|
| 1949 | var3 = cosa |
---|
| 1950 | |
---|
| 1951 | uas = np.zeros(var0.shape, dtype=np.float) |
---|
| 1952 | vas = np.zeros(var0.shape, dtype=np.float) |
---|
| 1953 | |
---|
| 1954 | vas = var0*var2 + var1*var3 |
---|
| 1955 | |
---|
| 1956 | dnamesvar = ['Time','south_north','west_east'] |
---|
| 1957 | dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dimns,dimvns) |
---|
| 1958 | |
---|
| 1959 | return vas, dnamesvar, dvnamesvar |
---|
| 1960 | |
---|
[1675] | 1961 | def compute_WRFuasvas(u10, v10, sina, cosa, dimns, dimvns): |
---|
| 1962 | """ Function to compute geographical rotated WRF 2-meter winds |
---|
| 1963 | u10= orginal WRF 10m x-wind |
---|
| 1964 | v10= orginal WRF 10m y-wind |
---|
| 1965 | sina= original WRF local sinus of map rotation |
---|
| 1966 | cosa= original WRF local cosinus of map rotation |
---|
| 1967 | formula: |
---|
| 1968 | uas = u10*cosa-va10*sina |
---|
| 1969 | vas = u10*sina+va10*cosa |
---|
| 1970 | """ |
---|
| 1971 | fname = 'compute_WRFuasvas' |
---|
| 1972 | |
---|
| 1973 | var0 = u10 |
---|
| 1974 | var1 = v10 |
---|
| 1975 | var2 = sina |
---|
| 1976 | var3 = cosa |
---|
| 1977 | |
---|
| 1978 | uas = np.zeros(var0.shape, dtype=np.float) |
---|
| 1979 | vas = np.zeros(var0.shape, dtype=np.float) |
---|
| 1980 | |
---|
| 1981 | uas = var0*var3 - var1*var2 |
---|
| 1982 | vas = var0*var2 + var1*var3 |
---|
| 1983 | |
---|
| 1984 | dnamesvar = ['Time','south_north','west_east'] |
---|
| 1985 | dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dimns,dimvns) |
---|
| 1986 | |
---|
| 1987 | return uas, vas, dnamesvar, dvnamesvar |
---|
| 1988 | |
---|
| 1989 | def compute_WRFta(t, p, dimns, dimvns): |
---|
| 1990 | """ Function to compute WRF air temperature |
---|
| 1991 | t= orginal WRF temperature |
---|
| 1992 | p= original WRF pressure (P + PB) |
---|
| 1993 | formula: |
---|
| 1994 | temp = theta*(p/p0)**(R/Cp) |
---|
| 1995 | |
---|
| 1996 | """ |
---|
| 1997 | fname = 'compute_WRFta' |
---|
| 1998 | |
---|
| 1999 | ta = (t+300.)*(p/fdef.module_definitions.p0ref)**(fdef.module_definitions.rcp) |
---|
| 2000 | |
---|
| 2001 | dnamesvar = ['Time','bottom_top','south_north','west_east'] |
---|
| 2002 | dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dimns,dimvns) |
---|
| 2003 | |
---|
| 2004 | return ta, dnamesvar, dvnamesvar |
---|
| 2005 | |
---|
| 2006 | def compute_WRFtd(t, p, qv, dimns, dimvns): |
---|
| 2007 | """ Function to compute WRF dew-point air temperature |
---|
| 2008 | t= orginal WRF temperature |
---|
| 2009 | p= original WRF pressure (P + PB) |
---|
| 2010 | formula: |
---|
| 2011 | temp = theta*(p/p0)**(R/Cp) |
---|
| 2012 | |
---|
| 2013 | """ |
---|
[1680] | 2014 | fname = 'compute_WRFtd' |
---|
[1675] | 2015 | |
---|
| 2016 | tk = (t+300.)*(p/fdef.module_definitions.p0ref)**(fdef.module_definitions.rcp) |
---|
| 2017 | data1 = 10.*0.6112*np.exp(17.67*(tk-273.16)/(tk-29.65)) |
---|
| 2018 | data2 = 0.622*data1/(0.01*p-(1.-0.622)*data1) |
---|
| 2019 | |
---|
| 2020 | rh = qv/data2 |
---|
| 2021 | |
---|
| 2022 | pa = rh * data1 |
---|
| 2023 | td = 257.44*np.log(pa/6.1121)/(18.678-np.log(pa/6.1121)) |
---|
| 2024 | |
---|
| 2025 | dnamesvar = ['Time','bottom_top','south_north','west_east'] |
---|
| 2026 | dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dimns,dimvns) |
---|
| 2027 | |
---|
| 2028 | return td, dnamesvar, dvnamesvar |
---|
[1685] | 2029 | |
---|
[1687] | 2030 | def compute_WRFwd(u, v, sina, cosa, dimns, dimvns): |
---|
| 2031 | """ Function to compute the wind direction |
---|
| 2032 | u= W-E wind direction [ms-1] |
---|
| 2033 | v= N-S wind direction [ms-1] |
---|
| 2034 | sina= original WRF local sinus of map rotation |
---|
| 2035 | cosa= original WRF local cosinus of map rotation |
---|
| 2036 | """ |
---|
| 2037 | fname = 'compute_WRFwd' |
---|
| 2038 | var0 = u |
---|
| 2039 | var1 = v |
---|
| 2040 | var2 = sina |
---|
| 2041 | var3 = cosa |
---|
| 2042 | |
---|
| 2043 | # un-staggering variables |
---|
| 2044 | unstgdims = [var0.shape[0], var0.shape[1], var0.shape[2], var0.shape[3]-1] |
---|
| 2045 | ua = np.zeros(tuple(unstgdims), dtype=np.float) |
---|
| 2046 | va = np.zeros(tuple(unstgdims), dtype=np.float) |
---|
| 2047 | unstgvar0 = np.zeros(tuple(unstgdims), dtype=np.float) |
---|
| 2048 | unstgvar1 = np.zeros(tuple(unstgdims), dtype=np.float) |
---|
| 2049 | unstgvar0 = 0.5*(var0[:,:,:,0:var0.shape[3]-1] + var0[:,:,:,1:var0.shape[3]]) |
---|
| 2050 | unstgvar1 = 0.5*(var1[:,:,0:var1.shape[2]-1,:] + var1[:,:,1:var1.shape[2],:]) |
---|
| 2051 | |
---|
| 2052 | for iz in range(var0.shape[1]): |
---|
| 2053 | ua[:,iz,:,:] = unstgvar0[:,iz,:,:]*var3 - unstgvar1[:,iz,:,:]*var2 |
---|
| 2054 | va[:,iz,:,:] = unstgvar0[:,iz,:,:]*var2 + unstgvar1[:,iz,:,:]*var3 |
---|
| 2055 | |
---|
| 2056 | theta = np.arctan2(va,ua) |
---|
| 2057 | theta = np.where(theta < 0., theta + 2.*np.pi, theta) |
---|
| 2058 | |
---|
| 2059 | wd = 360.*theta/(2.*np.pi) |
---|
| 2060 | |
---|
| 2061 | dnamesvar = ['Time','bottom_top','south_north','west_east'] |
---|
| 2062 | dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dimns,dimvns) |
---|
| 2063 | |
---|
| 2064 | return wd |
---|
| 2065 | |
---|
[1711] | 2066 | ####### Variables (as they arrive without dimensions staff) |
---|
| 2067 | |
---|
[1716] | 2068 | def var_hur(p, t, q): |
---|
| 2069 | """ Function to compute relative humidity following 'August - Roche - Magnus' formula |
---|
[1711] | 2070 | [t]= temperature (assuming [[t],z,y,x] in [K]) |
---|
[1714] | 2071 | [p] = pressure field (assuming in [Pa]) |
---|
[1711] | 2072 | [q] = mixing ratio in [kgkg-1] |
---|
[1715] | 2073 | ref.: M. G. Lawrence, BAMS, 2005, 225 |
---|
| 2074 | >>> print var_rh(101300., 290., 3.) |
---|
| 2075 | 0.250250256174 |
---|
[1711] | 2076 | """ |
---|
[1716] | 2077 | fname = 'var_hur' |
---|
[1711] | 2078 | |
---|
[1715] | 2079 | # Enthalpy of vaporization [Jkg-1] |
---|
| 2080 | L = 2.453*10.**6 |
---|
| 2081 | # Gas constant for water vapor [JK-1Kg-1] |
---|
| 2082 | Rv = 461.5 |
---|
[1711] | 2083 | |
---|
[1715] | 2084 | # Computing saturated pressure |
---|
| 2085 | #es = 10.*0.6112*np.exp(17.67*(t-273.16)/(t-29.65)) |
---|
| 2086 | #es = 6.11*np.exp(L/Rv*(1.-273./t)/273.) |
---|
[1711] | 2087 | |
---|
[1715] | 2088 | # August - Roche - Magnus formula |
---|
| 2089 | es = 6.1094*np.exp(17.625*(t-273.15)/((t-273.15)+243.04)) |
---|
| 2090 | |
---|
| 2091 | # Saturated mixing ratio [g/kg] |
---|
| 2092 | ws = 0.622*es/(0.01*p-es) |
---|
| 2093 | |
---|
[1716] | 2094 | hur = q/(ws*1000.) |
---|
[1715] | 2095 | |
---|
[1718] | 2096 | #print 'q:', q[5,5,5,5], 't:', t[5,5,5,5], 'p:', p[5,5,5,5] |
---|
| 2097 | #print 'es:', es[5,5,5,5], 'ws:', ws[5,5,5,5], 'hur:', hur[5,5,5,5] |
---|
[1717] | 2098 | |
---|
[1716] | 2099 | return hur |
---|
[1711] | 2100 | |
---|
[1718] | 2101 | def var_hur_Uhus(p, t, hus): |
---|
| 2102 | """ Function to compute relative humidity following 'August - Roche - Magnus' formula using hus |
---|
| 2103 | [t]= temperature (assuming [[t],z,y,x] in [K]) |
---|
| 2104 | [p] = pressure field (assuming in [Pa]) |
---|
| 2105 | [hus] = specific humidty [1] |
---|
| 2106 | ref.: M. G. Lawrence, BAMS, 2005, 225 |
---|
| 2107 | >>> print var_rh(101300., 290., 3.) |
---|
| 2108 | 0.250250256174 |
---|
| 2109 | """ |
---|
| 2110 | fname = 'var_hur_Uhus' |
---|
| 2111 | |
---|
| 2112 | # Enthalpy of vaporization [Jkg-1] |
---|
| 2113 | L = 2.453*10.**6 |
---|
| 2114 | # Gas constant for water vapor [JK-1Kg-1] |
---|
| 2115 | Rv = 461.5 |
---|
| 2116 | |
---|
| 2117 | # Computing saturated pressure |
---|
| 2118 | #es = 10.*0.6112*np.exp(17.67*(t-273.16)/(t-29.65)) |
---|
| 2119 | #es = 6.11*np.exp(L/Rv*(1.-273./t)/273.) |
---|
| 2120 | |
---|
| 2121 | # August - Roche - Magnus formula |
---|
| 2122 | es = 6.1094*np.exp(17.625*(t-273.15)/((t-273.15)+243.04)) |
---|
| 2123 | |
---|
| 2124 | # Saturated mixing ratio [g/kg] |
---|
| 2125 | ws = 0.622*es/(0.01*p-es) |
---|
| 2126 | |
---|
| 2127 | # Mixing ratio |
---|
| 2128 | q = hus/(1.+hus) |
---|
| 2129 | |
---|
[1726] | 2130 | hur = q/ws |
---|
[1718] | 2131 | |
---|
| 2132 | #print 'q:', q[5,5,5,5], 't:', t[5,5,5,5], 'p:', p[5,5,5,5] |
---|
| 2133 | #print 'es:', es[5,5,5,5], 'ws:', ws[5,5,5,5], 'hur:', hur[5,5,5,5] |
---|
| 2134 | |
---|
| 2135 | return hur |
---|
| 2136 | |
---|
[2390] | 2137 | def var_hur_tas_tds(tas, tds, dimns, dimvns): |
---|
| 2138 | """ Function to compute hur relative humidity from tas and tds |
---|
| 2139 | tas= surface temperature [K] |
---|
| 2140 | tds= dew point temperature [K] |
---|
| 2141 | Magnus formula with D. Bolton, 1980, Mon. Wea. Rev. values: |
---|
| 2142 | gamma = log(hur/100) + b*tas/(c+tas) |
---|
| 2143 | tdps = c*gamma/(b-gamma) |
---|
| 2144 | hur = 100*expr[b(tdps/(c+tdps)-tas/(c+tas))] |
---|
| 2145 | |
---|
| 2146 | """ |
---|
| 2147 | fname = 'compute_hur_tas_tds' |
---|
| 2148 | |
---|
| 2149 | dnamesvar = dimns |
---|
| 2150 | dvnamesvar = dimvns |
---|
| 2151 | |
---|
| 2152 | tasC = tas - fdef.module_definitions.svpt0 |
---|
| 2153 | tdsC = tds - fdef.module_definitions.svpt0 |
---|
| 2154 | |
---|
| 2155 | # Magnus formula with D. Bolton, 1980, Mon. Wea. Rev. values |
---|
| 2156 | b = 17.67 |
---|
| 2157 | c = 243.5 |
---|
| 2158 | |
---|
| 2159 | hur = np.exp(b*(tdsC/(c+tdsC)-tasC/(c+tasC))) |
---|
| 2160 | |
---|
| 2161 | return hur, dnamesvar, dvnamesvar |
---|
| 2162 | |
---|
[1685] | 2163 | def var_td(t, p, qv): |
---|
| 2164 | """ Function to compute dew-point air temperature from temperature and pressure values |
---|
| 2165 | t= temperature [K] |
---|
| 2166 | p= pressure (Pa) |
---|
| 2167 | formula: |
---|
| 2168 | temp = theta*(p/p0)**(R/Cp) |
---|
| 2169 | |
---|
| 2170 | """ |
---|
| 2171 | fname = 'compute_td' |
---|
| 2172 | |
---|
| 2173 | tk = (t)*(p/fdef.module_definitions.p0ref)**(fdef.module_definitions.rcp) |
---|
| 2174 | data1 = 10.*0.6112*np.exp(17.67*(tk-273.16)/(tk-29.65)) |
---|
| 2175 | data2 = 0.622*data1/(0.01*p-(1.-0.622)*data1) |
---|
| 2176 | |
---|
| 2177 | rh = qv/data2 |
---|
| 2178 | |
---|
| 2179 | pa = rh * data1 |
---|
| 2180 | td = 257.44*np.log(pa/6.1121)/(18.678-np.log(pa/6.1121)) |
---|
| 2181 | |
---|
| 2182 | return td |
---|
[1687] | 2183 | |
---|
[1720] | 2184 | def var_td_Uhus(t, p, hus): |
---|
| 2185 | """ Function to compute dew-point air temperature from temperature and pressure values using hus |
---|
| 2186 | t= temperature [K] |
---|
| 2187 | hus= specific humidity [1] |
---|
| 2188 | formula: |
---|
| 2189 | temp = theta*(p/p0)**(R/Cp) |
---|
| 2190 | |
---|
| 2191 | """ |
---|
| 2192 | fname = 'compute_td' |
---|
| 2193 | |
---|
| 2194 | tk = (t)*(p/fdef.module_definitions.p0ref)**(fdef.module_definitions.rcp) |
---|
| 2195 | data1 = 10.*0.6112*np.exp(17.67*(tk-273.16)/(tk-29.65)) |
---|
| 2196 | data2 = 0.622*data1/(0.01*p-(1.-0.622)*data1) |
---|
| 2197 | |
---|
| 2198 | qv = hus/(1.+hus) |
---|
| 2199 | |
---|
| 2200 | rh = qv/data2 |
---|
| 2201 | |
---|
| 2202 | pa = rh * data1 |
---|
| 2203 | td = 257.44*np.log(pa/6.1121)/(18.678-np.log(pa/6.1121)) |
---|
| 2204 | |
---|
| 2205 | return td |
---|
| 2206 | |
---|
[2387] | 2207 | def var_tws_S11(ta0, hur0): |
---|
| 2208 | """ Function to compute Wet Bulb temperature using equation after: |
---|
| 2209 | Stull, R. (2011), J. Appl. Meteor. Climatol. 50(11):2267-2269, |
---|
| 2210 | doi: 10.1175/JAMC-D-11-0143.1 |
---|
| 2211 | [ta]= temperature (assuming [[t],z,y,x] in [K]) |
---|
| 2212 | [hur] = relative humidity (assuming in [1]) |
---|
| 2213 | >>> var_rh_S11(293.15, 0.5) |
---|
| 2214 | 13.699341969 |
---|
| 2215 | """ |
---|
| 2216 | fname = 'var_tws_S11' |
---|
| 2217 | |
---|
| 2218 | ta = ta0 - 273.15 |
---|
| 2219 | hur = hur0*100. |
---|
| 2220 | |
---|
| 2221 | # Does it has any sense not in surface? |
---|
| 2222 | tws = ta*np.arctan(0.151977*np.sqrt(hur+8.313659)) + np.arctan(ta+hur) - \ |
---|
| 2223 | np.arctan(hur-1.676331) + 0.00391838*(hur)**(1.5)*np.arctan(0.023101*hur) - \ |
---|
| 2224 | 4.686035 |
---|
| 2225 | |
---|
| 2226 | return tws |
---|
| 2227 | |
---|
[1687] | 2228 | def var_wd(u, v): |
---|
| 2229 | """ Function to compute the wind direction |
---|
| 2230 | [u]= W-E wind direction [ms-1, knot, ...] |
---|
| 2231 | [v]= N-S wind direction [ms-1, knot, ...] |
---|
| 2232 | """ |
---|
| 2233 | fname = 'var_wd' |
---|
| 2234 | |
---|
| 2235 | theta = np.arctan2(v,u) |
---|
| 2236 | theta = np.where(theta < 0., theta + 2.*np.pi, theta) |
---|
| 2237 | |
---|
| 2238 | wd = 360.*theta/(2.*np.pi) |
---|
| 2239 | |
---|
| 2240 | return wd |
---|
| 2241 | |
---|
| 2242 | def var_ws(u, v): |
---|
| 2243 | """ Function to compute the wind speed |
---|
| 2244 | [u]= W-E wind direction [ms-1, knot, ...] |
---|
| 2245 | [v]= N-S wind direction [ms-1, knot, ...] |
---|
| 2246 | """ |
---|
| 2247 | fname = 'var_ws' |
---|
| 2248 | |
---|
| 2249 | ws = np.sqrt(u*u + v*v) |
---|
| 2250 | |
---|
| 2251 | return ws |
---|
| 2252 | |
---|
| 2253 | class C_diagnostic(object): |
---|
| 2254 | """ Class to compute generic variables |
---|
| 2255 | Cdiag: name of the diagnostic to compute |
---|
| 2256 | ncobj: netcdf object with data |
---|
| 2257 | sfcvars: dictionary with CF equivalencies of surface variables inside file |
---|
| 2258 | vars3D: dictionary with CF equivalencies of 3D variables inside file |
---|
| 2259 | dictdims: dictionary with CF equivalencies of dimensions inside file |
---|
| 2260 | self.values = Values of the diagnostic |
---|
| 2261 | self.dims = Dimensions of the diagnostic |
---|
| 2262 | self.units = units of the diagnostic |
---|
| 2263 | self.incvars = list of variables from the input netCDF object |
---|
| 2264 | """ |
---|
| 2265 | def __init__(self, Cdiag, ncobj, sfcvars, vars3D, dictdims): |
---|
| 2266 | fname = 'C_diagnostic' |
---|
| 2267 | self.values = None |
---|
| 2268 | self.dims = None |
---|
| 2269 | self.incvars = ncobj.variables |
---|
| 2270 | self.units = None |
---|
| 2271 | |
---|
[1711] | 2272 | if Cdiag == 'hur': |
---|
[1717] | 2273 | """ Computing relative humidity |
---|
[1687] | 2274 | """ |
---|
[1711] | 2275 | vn = 'hur' |
---|
[1718] | 2276 | CF3Dvars = ['ta', 'plev', 'q'] |
---|
| 2277 | for v3D in CF3Dvars: |
---|
| 2278 | if not vars3D.has_key(v3D): |
---|
| 2279 | print gen.errormsg |
---|
| 2280 | print ' ' + fname + ": missing variable '" + v3D + \ |
---|
| 2281 | "' attribution to compute '" + vn + "' !!" |
---|
| 2282 | print ' Equivalence of 3D variables provided _______' |
---|
| 2283 | gen.printing_dictionary(vars3D) |
---|
| 2284 | quit(-1) |
---|
| 2285 | if not self.incvars.has_key(vars3D[v3D]): |
---|
| 2286 | print gen.errormsg |
---|
| 2287 | print ' ' + fname + ": missing variable '" + vars3D[v3D] + \ |
---|
| 2288 | "' in input file to compute '" + vn + "' !!" |
---|
| 2289 | print ' available variables:', self.incvars.keys() |
---|
| 2290 | print ' looking for variables _______' |
---|
| 2291 | gen.printing_dictionary(vars3D) |
---|
| 2292 | quit(-1) |
---|
| 2293 | |
---|
| 2294 | ta = ncobj.variables[vars3D['ta']][:] |
---|
| 2295 | p = ncobj.variables[vars3D['plev']][:] |
---|
| 2296 | q = ncobj.variables[vars3D['q']][:] |
---|
| 2297 | |
---|
| 2298 | if len(ta.shape) != len(p.shape): |
---|
| 2299 | p = gen.fill_Narray(p, ta*0., filldim=[0,2,3]) |
---|
| 2300 | |
---|
| 2301 | self.values = var_hur(p, ta, q) |
---|
| 2302 | self.dims = [dictdims['time'], dictdims['plev'], dictdims['lat'], \ |
---|
| 2303 | dictdims['lon']] |
---|
| 2304 | self.units = '1' |
---|
| 2305 | |
---|
| 2306 | if Cdiag == 'hur_Uhus': |
---|
| 2307 | """ Computing relative humidity using hus |
---|
| 2308 | """ |
---|
| 2309 | vn = 'hur' |
---|
[1711] | 2310 | CF3Dvars = ['ta', 'plev', 'hus'] |
---|
| 2311 | for v3D in CF3Dvars: |
---|
| 2312 | if not vars3D.has_key(v3D): |
---|
| 2313 | print gen.errormsg |
---|
| 2314 | print ' ' + fname + ": missing variable '" + v3D + \ |
---|
| 2315 | "' attribution to compute '" + vn + "' !!" |
---|
| 2316 | print ' Equivalence of 3D variables provided _______' |
---|
| 2317 | gen.printing_dictionary(vars3D) |
---|
| 2318 | quit(-1) |
---|
| 2319 | if not self.incvars.has_key(vars3D[v3D]): |
---|
| 2320 | print gen.errormsg |
---|
| 2321 | print ' ' + fname + ": missing variable '" + vars3D[v3D] + \ |
---|
| 2322 | "' in input file to compute '" + vn + "' !!" |
---|
| 2323 | print ' available variables:', self.incvars.keys() |
---|
| 2324 | print ' looking for variables _______' |
---|
| 2325 | gen.printing_dictionary(vars3D) |
---|
| 2326 | quit(-1) |
---|
| 2327 | |
---|
| 2328 | ta = ncobj.variables[vars3D['ta']][:] |
---|
| 2329 | p = ncobj.variables[vars3D['plev']][:] |
---|
| 2330 | hus = ncobj.variables[vars3D['hus']][:] |
---|
| 2331 | |
---|
| 2332 | if len(ta.shape) != len(p.shape): |
---|
| 2333 | p = gen.fill_Narray(p, ta*0., filldim=[0,2,3]) |
---|
| 2334 | |
---|
[1718] | 2335 | self.values = var_hur_Uhus(p, ta, hus) |
---|
[1711] | 2336 | self.dims = [dictdims['time'], dictdims['plev'], dictdims['lat'], \ |
---|
| 2337 | dictdims['lon']] |
---|
| 2338 | self.units = '1' |
---|
| 2339 | |
---|
| 2340 | elif Cdiag == 'td': |
---|
| 2341 | """ Computing dew-point temperature |
---|
| 2342 | """ |
---|
[1687] | 2343 | vn = 'td' |
---|
[1720] | 2344 | CF3Dvars = ['ta', 'plev', 'q'] |
---|
| 2345 | for v3D in CF3Dvars: |
---|
| 2346 | if not vars3D.has_key(v3D): |
---|
| 2347 | print gen.errormsg |
---|
| 2348 | print ' ' + fname + ": missing variable '" + v3D + \ |
---|
| 2349 | "' attribution to compute '" + vn + "' !!" |
---|
| 2350 | print ' Equivalence of 3D variables provided _______' |
---|
| 2351 | gen.printing_dictionary(vars3D) |
---|
| 2352 | quit(-1) |
---|
| 2353 | if not self.incvars.has_key(vars3D[v3D]): |
---|
| 2354 | print gen.errormsg |
---|
| 2355 | print ' ' + fname + ": missing variable '" + vars3D[v3D] + \ |
---|
| 2356 | "' in input file to compute '" + vn + "' !!" |
---|
| 2357 | print ' available variables:', self.incvars.keys() |
---|
| 2358 | print ' looking for variables _______' |
---|
| 2359 | gen.printing_dictionary(vars3D) |
---|
| 2360 | quit(-1) |
---|
| 2361 | |
---|
| 2362 | ta = ncobj.variables[vars3D['ta']][:] |
---|
| 2363 | p = ncobj.variables[vars3D['plev']][:] |
---|
| 2364 | q = ncobj.variables[vars3D['q']][:] |
---|
| 2365 | |
---|
| 2366 | if len(ta.shape) != len(p.shape): |
---|
| 2367 | p = gen.fill_Narray(p, ta*0., filldim=[0,2,3]) |
---|
| 2368 | |
---|
| 2369 | self.values = var_td(ta, p, q) |
---|
| 2370 | self.dims = [dictdims['time'], dictdims['plev'], dictdims['lat'], \ |
---|
| 2371 | dictdims['lon']] |
---|
| 2372 | self.units = 'K' |
---|
| 2373 | |
---|
| 2374 | elif Cdiag == 'td_Uhus': |
---|
| 2375 | """ Computing dew-point temperature |
---|
| 2376 | """ |
---|
| 2377 | vn = 'td' |
---|
[1696] | 2378 | CF3Dvars = ['ta', 'plev', 'hus'] |
---|
[1687] | 2379 | for v3D in CF3Dvars: |
---|
| 2380 | if not vars3D.has_key(v3D): |
---|
| 2381 | print gen.errormsg |
---|
| 2382 | print ' ' + fname + ": missing variable '" + v3D + \ |
---|
| 2383 | "' attribution to compute '" + vn + "' !!" |
---|
| 2384 | print ' Equivalence of 3D variables provided _______' |
---|
| 2385 | gen.printing_dictionary(vars3D) |
---|
| 2386 | quit(-1) |
---|
| 2387 | if not self.incvars.has_key(vars3D[v3D]): |
---|
| 2388 | print gen.errormsg |
---|
| 2389 | print ' ' + fname + ": missing variable '" + vars3D[v3D] + \ |
---|
| 2390 | "' in input file to compute '" + vn + "' !!" |
---|
| 2391 | print ' available variables:', self.incvars.keys() |
---|
| 2392 | print ' looking for variables _______' |
---|
| 2393 | gen.printing_dictionary(vars3D) |
---|
| 2394 | quit(-1) |
---|
| 2395 | |
---|
| 2396 | ta = ncobj.variables[vars3D['ta']][:] |
---|
| 2397 | p = ncobj.variables[vars3D['plev']][:] |
---|
[1711] | 2398 | hus = ncobj.variables[vars3D['hus']][:] |
---|
[1687] | 2399 | |
---|
[1700] | 2400 | if len(ta.shape) != len(p.shape): |
---|
[1702] | 2401 | p = gen.fill_Narray(p, ta*0., filldim=[0,2,3]) |
---|
[1700] | 2402 | |
---|
[1720] | 2403 | self.values = var_td_Uhus(ta, p, hus) |
---|
[1687] | 2404 | self.dims = [dictdims['time'], dictdims['plev'], dictdims['lat'], \ |
---|
| 2405 | dictdims['lon']] |
---|
| 2406 | self.units = 'K' |
---|
| 2407 | |
---|
| 2408 | elif Cdiag == 'wd': |
---|
| 2409 | """ Computing wind direction |
---|
| 2410 | """ |
---|
| 2411 | vn = 'wd' |
---|
| 2412 | CF3Dvars = ['ua', 'va'] |
---|
| 2413 | for v3D in CF3Dvars: |
---|
| 2414 | if not vars3D.has_key(v3D): |
---|
| 2415 | print gen.errormsg |
---|
| 2416 | print ' ' + fname + ": missing variable '" + v3D + \ |
---|
| 2417 | "self.' attribution to compute '" + vn + "' !!" |
---|
| 2418 | print ' Equivalence of 3D variables provided _______' |
---|
| 2419 | gen.printing_dictionary(vars3D) |
---|
| 2420 | quit(-1) |
---|
| 2421 | if not self.incvars.has_key(vars3D[v3D]): |
---|
| 2422 | print gen.errormsg |
---|
| 2423 | print ' ' + fname + ": missing variable '" + vars3D[v3D] + \ |
---|
| 2424 | "' in input file to compute '" + vn + "' !!" |
---|
| 2425 | print ' available variables:', self.incvars.keys() |
---|
| 2426 | print ' looking for variables _______' |
---|
| 2427 | gen.printing_dictionary(vars3D) |
---|
| 2428 | quit(-1) |
---|
| 2429 | |
---|
| 2430 | ua = ncobj.variables[vars3D['ua']][:] |
---|
| 2431 | va = ncobj.variables[vars3D['va']][:] |
---|
| 2432 | |
---|
| 2433 | self.values = var_wd(ua, va) |
---|
| 2434 | self.dims = [dictdims['time'], dictdims['plev'], dictdims['lat'], \ |
---|
| 2435 | dictdims['lon']] |
---|
| 2436 | self.units = 'degree' |
---|
| 2437 | |
---|
| 2438 | elif Cdiag == 'ws': |
---|
| 2439 | """ Computing wind speed |
---|
| 2440 | """ |
---|
| 2441 | vn = 'ws' |
---|
| 2442 | CF3Dvars = ['ua', 'va'] |
---|
| 2443 | for v3D in CF3Dvars: |
---|
| 2444 | if not vars3D.has_key(v3D): |
---|
| 2445 | print gen.errormsg |
---|
| 2446 | print ' ' + fname + ": missing variable '" + v3D + \ |
---|
| 2447 | "' attribution to compute '" + vn + "' !!" |
---|
| 2448 | print ' Equivalence of 3D variables provided _______' |
---|
| 2449 | gen.printing_dictionary(vars3D) |
---|
| 2450 | quit(-1) |
---|
| 2451 | if not self.incvars.has_key(vars3D[v3D]): |
---|
| 2452 | print gen.errormsg |
---|
| 2453 | print ' ' + fname + ": missing variable '" + vars3D[v3D] + \ |
---|
| 2454 | "' in input file to compute '" + vn + "' !!" |
---|
| 2455 | print ' available variables:', self.incvars.keys() |
---|
| 2456 | print ' looking for variables _______' |
---|
| 2457 | gen.printing_dictionary(vars3D) |
---|
| 2458 | quit(-1) |
---|
| 2459 | |
---|
| 2460 | ua = ncobj.variables[vars3D['ua']][:] |
---|
| 2461 | va = ncobj.variables[vars3D['va']][:] |
---|
| 2462 | |
---|
| 2463 | self.values = var_ws(ua, va) |
---|
| 2464 | self.dims = [dictdims['time'], dictdims['plev'], dictdims['lat'], \ |
---|
| 2465 | dictdims['lon']] |
---|
| 2466 | self.units = ncobj.variables[vars3D['ua']].units |
---|
| 2467 | |
---|
| 2468 | else: |
---|
| 2469 | print gen.errormsg |
---|
| 2470 | print ' ' + fname + ": variable '" + Wdiag + "' not ready !!" |
---|
| 2471 | print ' available ones:', Cavailablediags |
---|
| 2472 | quit(-1) |
---|
| 2473 | |
---|
| 2474 | class W_diagnostic(object): |
---|
| 2475 | """ Class to compute WRF diagnostics variables |
---|
| 2476 | Wdiag: name of the diagnostic to compute |
---|
| 2477 | ncobj: netcdf object with data |
---|
| 2478 | sfcvars: dictionary with CF equivalencies of surface variables inside file |
---|
| 2479 | vars3D: dictionary with CF equivalencies of 3D variables inside file |
---|
| 2480 | indims: list of dimensions inside file |
---|
| 2481 | invardims: list of dimension-variables inside file |
---|
| 2482 | dictdims: dictionary with CF equivalencies of dimensions inside file |
---|
| 2483 | self.values = Values of the diagnostic |
---|
| 2484 | self.dims = Dimensions of the diagnostic |
---|
| 2485 | self.units = units of the diagnostic |
---|
| 2486 | self.incvars = list of variables from the input netCDF object |
---|
| 2487 | """ |
---|
| 2488 | def __init__(self, Wdiag, ncobj, sfcvars, vars3D, indims, invardims, dictdims): |
---|
| 2489 | fname = 'W_diagnostic' |
---|
| 2490 | |
---|
| 2491 | self.values = None |
---|
| 2492 | self.dims = None |
---|
| 2493 | self.incvars = ncobj.variables |
---|
| 2494 | self.units = None |
---|
| 2495 | |
---|
[1710] | 2496 | if Wdiag == 'hur': |
---|
| 2497 | """ Computing relative humidity |
---|
| 2498 | """ |
---|
| 2499 | vn = 'hur' |
---|
| 2500 | CF3Dvars = ['ta', 'hus'] |
---|
| 2501 | for v3D in CF3Dvars: |
---|
| 2502 | if not vars3D.has_key(v3D): |
---|
| 2503 | print gen.errormsg |
---|
| 2504 | print ' ' + fname + ": missing variable '" + v3D + \ |
---|
| 2505 | "' attribution to compute '" + vn + "' !!" |
---|
| 2506 | print ' Equivalence of 3D variables provided _______' |
---|
| 2507 | gen.printing_dictionary(vars3D) |
---|
| 2508 | quit(-1) |
---|
| 2509 | |
---|
| 2510 | ta = ncobj.variables['T'][:] |
---|
| 2511 | p = ncobj.variables['P'][:] + ncobj.variables['PB'][:] |
---|
| 2512 | hur = ncobj.variables['QVAPOR'][:] |
---|
| 2513 | |
---|
| 2514 | vals, dims, vdims = compute_WRFhur(ta, p, hur, indims, invardims) |
---|
| 2515 | self.values = vals |
---|
| 2516 | self.dims = [dictdims['time'], dictdims['plev'], dictdims['lat'], \ |
---|
| 2517 | dictdims['lon']] |
---|
| 2518 | self.units = '1' |
---|
| 2519 | |
---|
| 2520 | elif Wdiag == 'p': |
---|
[1687] | 2521 | """ Computing air pressure |
---|
| 2522 | """ |
---|
| 2523 | vn = 'p' |
---|
| 2524 | |
---|
| 2525 | self.values = ncobj.variables['PB'][:] + ncobj.variables['P'][:] |
---|
| 2526 | self.dims = [dictdims['time'], dictdims['plev'], dictdims['lat'], \ |
---|
| 2527 | dictdims['lon']] |
---|
| 2528 | self.units = ncobj.variables['PB'].units |
---|
| 2529 | |
---|
| 2530 | elif Wdiag == 'ta': |
---|
| 2531 | """ Computing air temperature |
---|
| 2532 | """ |
---|
| 2533 | vn = 'ta' |
---|
| 2534 | CF3Dvars = ['ta'] |
---|
| 2535 | for v3D in CF3Dvars: |
---|
| 2536 | if not vars3D.has_key(v3D): |
---|
| 2537 | print gen.errormsg |
---|
| 2538 | print ' ' + fname + ": missing variable '" + v3D + \ |
---|
| 2539 | "' attribution to compute '" + vn + "' !!" |
---|
| 2540 | print ' Equivalence of 3D variables provided _______' |
---|
| 2541 | gen.printing_dictionary(vars3D) |
---|
| 2542 | quit(-1) |
---|
| 2543 | |
---|
| 2544 | ta = ncobj.variables['T'][:] |
---|
| 2545 | p = ncobj.variables['P'][:] + ncobj.variables['PB'][:] |
---|
| 2546 | |
---|
| 2547 | vals, dims, vdims = compute_WRFta(ta, p, indims, invardims) |
---|
| 2548 | self.values = vals |
---|
| 2549 | self.dims = [dictdims['time'], dictdims['plev'], dictdims['lat'], \ |
---|
| 2550 | dictdims['lon']] |
---|
| 2551 | self.units = 'K' |
---|
| 2552 | |
---|
| 2553 | elif Wdiag == 'td': |
---|
| 2554 | """ Computing dew-point temperature |
---|
| 2555 | """ |
---|
| 2556 | vn = 'td' |
---|
| 2557 | CF3Dvars = ['ta', 'hus'] |
---|
| 2558 | for v3D in CF3Dvars: |
---|
| 2559 | if not vars3D.has_key(v3D): |
---|
| 2560 | print gen.errormsg |
---|
| 2561 | print ' ' + fname + ": missing variable '" + v3D + \ |
---|
| 2562 | "' attribution to compute '" + vn + "' !!" |
---|
| 2563 | print ' Equivalence of 3D variables provided _______' |
---|
| 2564 | gen.printing_dictionary(vars3D) |
---|
| 2565 | quit(-1) |
---|
| 2566 | |
---|
| 2567 | ta = ncobj.variables['T'][:] |
---|
| 2568 | p = ncobj.variables['P'][:] + ncobj.variables['PB'][:] |
---|
[1710] | 2569 | hus = ncobj.variables['QVAPOR'][:] |
---|
[1687] | 2570 | |
---|
[1710] | 2571 | vals, dims, vdims = compute_WRFtd(ta, p, hus, indims, invardims) |
---|
[1687] | 2572 | self.values = vals |
---|
| 2573 | self.dims = [dictdims['time'], dictdims['plev'], dictdims['lat'], \ |
---|
| 2574 | dictdims['lon']] |
---|
| 2575 | self.units = 'K' |
---|
| 2576 | |
---|
| 2577 | elif Wdiag == 'ua': |
---|
| 2578 | """ Computing x-wind |
---|
| 2579 | """ |
---|
| 2580 | vn = 'ua' |
---|
| 2581 | CF3Dvars = ['ua', 'va'] |
---|
| 2582 | for v3D in CF3Dvars: |
---|
| 2583 | if not vars3D.has_key(v3D): |
---|
| 2584 | print gen.errormsg |
---|
| 2585 | print ' ' + fname + ": missing variable '" + v3D + \ |
---|
| 2586 | "' attribution to compute '" + vn + "' !!" |
---|
| 2587 | print ' Equivalence of 3D variables provided _______' |
---|
| 2588 | gen.printing_dictionary(vars3D) |
---|
| 2589 | quit(-1) |
---|
| 2590 | |
---|
| 2591 | ua = ncobj.variables['U'][:] |
---|
| 2592 | va = ncobj.variables['V'][:] |
---|
| 2593 | sina = ncobj.variables['SINALPHA'][:] |
---|
| 2594 | cosa = ncobj.variables['COSALPHA'][:] |
---|
| 2595 | |
---|
| 2596 | vals, dims, vdims = compute_WRFua(ua, va, sina, cosa, indims, invardims) |
---|
| 2597 | self.values = vals |
---|
| 2598 | self.dims = [dictdims['time'], dictdims['plev'], dictdims['lat'], \ |
---|
| 2599 | dictdims['lon']] |
---|
| 2600 | self.units = ncobj.variables['U'].units |
---|
| 2601 | |
---|
| 2602 | elif Wdiag == 'uas': |
---|
| 2603 | """ Computing 10m x-wind |
---|
| 2604 | """ |
---|
| 2605 | vn = 'uas' |
---|
| 2606 | CFsfcvars = ['uas', 'vas'] |
---|
| 2607 | for vsf in CFsfcvars: |
---|
| 2608 | if not sfcvars.has_key(vsf): |
---|
| 2609 | print gen.errormsg |
---|
| 2610 | print ' ' + fname + ": missing variable '" + vsf + \ |
---|
| 2611 | "' attribution to compute '" + vn + "' !!" |
---|
| 2612 | print ' Equivalence of sfc variables provided _______' |
---|
| 2613 | gen.printing_dictionary(sfcvars) |
---|
| 2614 | quit(-1) |
---|
| 2615 | |
---|
| 2616 | uas = ncobj.variables['U10'][:] |
---|
| 2617 | vas = ncobj.variables['V10'][:] |
---|
| 2618 | sina = ncobj.variables['SINALPHA'][:] |
---|
| 2619 | cosa = ncobj.variables['COSALPHA'][:] |
---|
| 2620 | |
---|
| 2621 | vals,dims,vdims = compute_WRFuas(uas, vas, sina, cosa, indims, invardims) |
---|
| 2622 | self.values = vals |
---|
| 2623 | self.dims = [dictdims['time'], dictdims['plev'], dictdims['lat'], \ |
---|
| 2624 | dictdims['lon']] |
---|
| 2625 | self.units = ncobj.variables['U10'].units |
---|
| 2626 | |
---|
| 2627 | elif Wdiag == 'va': |
---|
| 2628 | """ Computing y-wind |
---|
| 2629 | """ |
---|
| 2630 | vn = 'ua' |
---|
| 2631 | CF3Dvars = ['ua', 'va'] |
---|
| 2632 | for v3D in CF3Dvars: |
---|
| 2633 | if not vars3D.has_key(v3D): |
---|
| 2634 | print gen.errormsg |
---|
| 2635 | print ' ' + fname + ": missing variable '" + v3D + \ |
---|
| 2636 | "' attribution to compute '" + vn + "' !!" |
---|
| 2637 | print ' Equivalence of 3D variables provided _______' |
---|
| 2638 | gen.printing_dictionary(vars3D) |
---|
| 2639 | quit(-1) |
---|
| 2640 | |
---|
| 2641 | ua = ncobj.variables['U'][:] |
---|
| 2642 | va = ncobj.variables['V'][:] |
---|
| 2643 | sina = ncobj.variables['SINALPHA'][:] |
---|
| 2644 | cosa = ncobj.variables['COSALPHA'][:] |
---|
| 2645 | |
---|
| 2646 | vals, dims, vdims = compute_WRFva(ua, va, sina, cosa, indims, invardims) |
---|
| 2647 | self.values = vals |
---|
| 2648 | self.dims = [dictdims['time'], dictdims['plev'], dictdims['lat'], \ |
---|
| 2649 | dictdims['lon']] |
---|
| 2650 | self.units = ncobj.variables['U'].units |
---|
| 2651 | |
---|
| 2652 | elif Wdiag == 'vas': |
---|
| 2653 | """ Computing 10m y-wind |
---|
| 2654 | """ |
---|
| 2655 | vn = 'uas' |
---|
| 2656 | CFsfcvars = ['uas', 'vas'] |
---|
| 2657 | for vsf in CFsfcvars: |
---|
| 2658 | if not sfcvars.has_key(vsf): |
---|
| 2659 | print gen.errormsg |
---|
| 2660 | print ' ' + fname + ": missing variable '" + vsf + \ |
---|
| 2661 | "' attribution to compute '" + vn + "' !!" |
---|
| 2662 | print ' Equivalence of sfc variables provided _______' |
---|
| 2663 | gen.printing_dictionary(sfcvars) |
---|
| 2664 | quit(-1) |
---|
| 2665 | |
---|
| 2666 | uas = ncobj.variables['U10'][:] |
---|
| 2667 | vas = ncobj.variables['V10'][:] |
---|
| 2668 | sina = ncobj.variables['SINALPHA'][:] |
---|
| 2669 | cosa = ncobj.variables['COSALPHA'][:] |
---|
| 2670 | |
---|
| 2671 | vals,dims,vdims = compute_WRFvas(uas, vas, sina, cosa, indims, invardims) |
---|
| 2672 | self.values = vals |
---|
| 2673 | self.dims = [dictdims['time'], dictdims['plev'], dictdims['lat'], \ |
---|
| 2674 | dictdims['lon']] |
---|
| 2675 | self.units = ncobj.variables['U10'].units |
---|
| 2676 | |
---|
| 2677 | elif Wdiag == 'wd': |
---|
| 2678 | """ Computing wind direction |
---|
| 2679 | """ |
---|
| 2680 | vn = 'wd' |
---|
| 2681 | CF3Dvars = ['ua', 'va'] |
---|
| 2682 | for v3D in CF3Dvars: |
---|
| 2683 | if not vars3D.has_key(v3D): |
---|
| 2684 | print gen.errormsg |
---|
| 2685 | print ' ' + fname + ": missing variable '" + v3D + \ |
---|
| 2686 | "' attribution to compute '" + vn + "' !!" |
---|
| 2687 | print ' Equivalence of 3D variables provided _______' |
---|
| 2688 | gen.printing_dictionary(vars3D) |
---|
| 2689 | quit(-1) |
---|
| 2690 | |
---|
| 2691 | ua = ncobj.variables['U10'][:] |
---|
| 2692 | va = ncobj.variables['V10'][:] |
---|
| 2693 | sina = ncobj.variables['SINALPHA'][:] |
---|
| 2694 | cosa = ncobj.variables['COSALPHA'][:] |
---|
| 2695 | |
---|
| 2696 | vals, dims, vdims = compute_WRFwd(ua, va, sina, cosa, indims, invardims) |
---|
| 2697 | self.values = vals |
---|
| 2698 | self.dims = [dictdims['time'], dictdims['plev'], dictdims['lat'], \ |
---|
| 2699 | dictdims['lon']] |
---|
| 2700 | self.units = 'degree' |
---|
| 2701 | |
---|
| 2702 | elif Wdiag == 'ws': |
---|
| 2703 | """ Computing wind speed |
---|
| 2704 | """ |
---|
| 2705 | vn = 'ws' |
---|
| 2706 | CF3Dvars = ['ua', 'va'] |
---|
| 2707 | for v3D in CF3Dvars: |
---|
| 2708 | if not vars3D.has_key(v3D): |
---|
| 2709 | print gen.errormsg |
---|
| 2710 | print ' ' + fname + ": missing variable '" + v3D + \ |
---|
| 2711 | "' attribution to compute '" + vn + "' !!" |
---|
| 2712 | print ' Equivalence of 3D variables provided _______' |
---|
| 2713 | gen.printing_dictionary(vars3D) |
---|
| 2714 | quit(-1) |
---|
| 2715 | |
---|
| 2716 | ua = ncobj.variables['U10'][:] |
---|
| 2717 | va = ncobj.variables['V10'][:] |
---|
| 2718 | |
---|
| 2719 | self.values = var_ws(ua, va) |
---|
| 2720 | self.dims = [dictdims['time'], dictdims['plev'], dictdims['lat'], \ |
---|
| 2721 | dictdims['lon']] |
---|
| 2722 | self.units = ncobj.variables['U10'].units |
---|
| 2723 | |
---|
| 2724 | elif Wdiag == 'zg': |
---|
| 2725 | """ Computing geopotential |
---|
| 2726 | """ |
---|
| 2727 | vn = 'zg' |
---|
| 2728 | |
---|
| 2729 | self.values = ncobj.variables['PHB'][:] + ncobj.variables['PH'][:] |
---|
| 2730 | self.dims = [dictdims['time'], dictdims['plev'], dictdims['lat'], \ |
---|
| 2731 | dictdims['lon']] |
---|
| 2732 | self.units = ncobj.variables['PHB'].units |
---|
| 2733 | |
---|
| 2734 | else: |
---|
| 2735 | print gen.errormsg |
---|
| 2736 | print ' ' + fname + ": variable '" + Wdiag + "' not ready !!" |
---|
| 2737 | print ' available ones:', Wavailablediags |
---|
| 2738 | quit(-1) |
---|