def wrf_file_data(file, quiet=False):
'''
WRF data for ROMS
'''
out = {}
# time:
if not quiet: print(' --> get time')
time = read_time(file)
out['time'] = time
# lon,lat:
if not quiet: print(' --> reading x,y')
x = netcdf.use(file, 'XLONG', Time=0) #**{'0': 0})
y = netcdf.use(file, 'XLAT', Time=0) #**{'0': 0})
# tair [K-->C]
if not quiet: print(' --> T air')
tair = netcdf.use(file, 'T2') - 273.15
out['tair'] = Data(x, y, tair, 'Celsius')
# R humidity [kg/kg --> 0--1]
if not quiet: print(' --> R humidity from QV at 2m')
wv = netcdf.use(file, 'Q2') # water vapor mixing ratio at 2m
rhum = wv / air_sea.qsat(tair)
rhum[rhum > 1] = 1
out['rhum'] = Data(x, y, rhum, '0--1')
# surface pressure [Pa]
if not quiet: print(' --> Surface pressure')
pres = netcdf.use(file, 'PSFC')
out['pres'] = Data(x, y, pres, 'Pa')
# P rate [mm --> cm day-1]
if not quiet: print(' --> P rate (rainc+rainnc)')
rainc = netcdf.use(file, 'RAINC')
rainnc = netcdf.use(file, 'RAINNC')
prate = rainc + rainnc
if not quiet: print(' accum2avg...')
prate = accum2avg(prate, dt=time[1] - time[0]) # mm s-1
conv = 0.1 * 86400 # from mm s-1 --> cm day-1
prate = prate * conv # cm day-1
prate[prate < 0] = 0 # interpolation errors may result in negative rain!
out['prate'] = Data(x, y, prate, 'cm day-1')
# LW, SW, latent, sensible signs:
# positive (downward flux, heating) or negative (upward flux, cooling)
#https://www.myroms.org/forum/viewtopic.php?f=1&t=2621
# Net shortwave flux [W m-2]
if not quiet: print(' --> Net shortwave flux')
sw_down = netcdf.use(file, 'SWDOWN')
albedo = netcdf.use(file, 'ALBEDO')
sw_net = sw_down * (1 - albedo)
out['radsw'] = Data(x, y, sw_net, 'W m-2', info='positive downward')
# Net longwave flux [W m-2]
if not quiet: print(' --> Net longwave flux')
lw_down = netcdf.use(file, 'GLW') # positive
# sst needed:
if not quiet: print(' --> SST for LW up')
sst = netcdf.use(file, 'SST') # K
lw_net = air_sea.lwhf(sst, lw_down) # positive down
# here vars have roms-agrif signs --> radlw is positive upward!
#conversion to ROMS is done in surface.py
out['radlw'] = Data(x, y, -lw_net, 'W m-2', info='positive upward')
out['dlwrf'] = Data(x, y, -lw_down, 'W m-2', info='positive upward')
# U and V wind speed 10m
if not quiet: print(' --> U and V wind')
uwnd = netcdf.use(file, 'U10')
vwnd = netcdf.use(file, 'V10')
if not quiet: print(' --> calc wind speed and stress')
speed = np.sqrt(uwnd**2 + vwnd**2)
taux, tauy = air_sea.wind_stress(uwnd, vwnd)
out['wspd'] = Data(x, y, speed, 'm s-1')
out['uwnd'] = Data(x, y, uwnd, 'm s-1')
out['vwnd'] = Data(x, y, vwnd, 'm s-1')
out['sustr'] = Data(x, y, taux, 'Pa')
out['svstr'] = Data(x, y, tauy, 'Pa')
# Cloud cover [0--1]:
if not quiet:
print(' --> Cloud cover for LONGWAVE. Use LONGWAVE_OUT instead...')
if 0:
pass
# next code is wrong! If cloud cover is really needed, it needs to be calculated using wrfpost.
# See http://www2.mmm.ucar.edu/wrf/users/docs/user_guide/users_guide_chap8.html#_ARWpost_1
#
# if 'CLDFRA' in netcdf.varnames(file):
# clouds=netcdf.use(file,'CLDFRA').sum(-3)
# clouds=np.where(clouds>1,1,clouds)
else:
if not quiet:
print('CLDFRA not found!! Using SST and air_sea.cloud_fraction')
sst = netcdf.use(file, 'SST') - 273.15
clouds = air_sea.cloud_fraction(lw_net, sst, tair, rhum, Wtype='net')
clouds[clouds < 0] = 0
clouds[clouds > 1] = 1
out['cloud'] = Data(x, y, clouds, 'fraction (0--1)')
return out
def cordex_file_data(f,lims=False,quiet=False):
'''
CORDEX data for ROMS
No accumulated variables are considered
'''
out={}
# time, x, y:
if not quiet: print(' reading time,x,y')
out['time']=netcdf.nctime(f,'time')
x=netcdf.use(f,'lon')
y=netcdf.use(f,'lat')
x[x>180]=x[x>180]-360
if x.ndim==1 and y.ndim==1:
x,y=np.meshgrid(x,y)
if np.ma.isMA(x): x=x.data
if np.ma.isMA(y): y=y.data
if lims:
from okean import calc
xlim,ylim=lims
i1,i2,j1,j2=calc.ij_limits(x,y,xlim,ylim,margin=3)
else:
i0=0
j0=0
j1,i1=x.shape
I=range(i1,i2)
J=range(j1,j2)
x=x[j1:j2,i1:i2]
y=y[j1:j2,i1:i2]
# tair [K-->C]
if not quiet: print(' --> T air')
vname='tair'
tair=netcdf.use(f,vname,lon=I,lat=J)-273.15
out['tair']=Data(x,y,tair,'Celsius')
# R humidity [0--1]
if not quiet: print(' --> R humidity (from specific humidity)')
vname='humid'
q=netcdf.use(f,vname,lon=I,lat=J) # specific humidity
rhum=q/air_sea.qsat(tair)
rhum[rhum>1]=1
out['rhum']=Data(x,y,rhum,'0--1')
# surface pressure [Pa]
if not quiet: print(' --> Surface pressure')
vname='press'
pres=netcdf.use(f,vname,lon=I,lat=J)
out['pres']=Data(x,y,pres,'Pa')
# P rate [kg m-2 s-1 -> cm/d]
if not quiet: print(' --> P rate')
vname='rain'
prate=netcdf.use(f,vname,lon=I,lat=J)
prate=prate*86400*100/1000.
prate[prate<0]=0
out['prate']=Data(x,y,prate,'cm/d')
# Net shortwave flux [W m-2]
if not quiet: print(' --> Net shortwave flux')
if not quiet: print(' SW down')
sw_down=netcdf.use(f,'sw_down',lon=I,lat=J)
if not quiet: print(' SW up')
sw_up=netcdf.use(f,'sw_up',lon=I,lat=J)
sw_net=sw_down-sw_up
out['radsw']=Data(x,y,sw_net,'W m-2',info='positive downward')
# Net longwave flux [W m-2]
if not quiet: print(' --> Net longwave flux')
if not quiet: print(' LW down')
lw_down=netcdf.use(f,'lw_down',lon=I,lat=J)
if not quiet: print(' LW up')
lw_up=netcdf.use(f,'lw_up',lon=I,lat=J)
lw_net=lw_down-lw_up
out['radlw']=Data(x,y,-lw_net,'W m-2',info='positive upward')
# downward lw:
out['dlwrf']=Data(x,y,-lw_down,'W m-2',info='negative... downward')
# signs convention is better explained in wrf.py
# U and V wind speed 10m
if not quiet: print(' --> U and V wind')
uwnd=netcdf.use(f,'u',lon=I,lat=J)
vwnd=netcdf.use(f,'v',lon=I,lat=J)
if not quiet: print(' --> calc wind speed and stress')
speed = np.sqrt(uwnd**2+vwnd**2)
taux,tauy=air_sea.wind_stress(uwnd,vwnd)
out['wspd']=Data(x,y,speed,'m s-1')
out['uwnd']=Data(x,y,uwnd,'m s-1')
out['vwnd']=Data(x,y,vwnd,'m s-1')
out['sustr']=Data(x,y,taux,'Pa')
out['svstr']=Data(x,y,tauy,'Pa')
# Cloud cover [0--100 --> 0--1]:
if not quiet: print(' --> Cloud cover')
if 'clouds' in netcdf.varnames(f):
clouds=netcdf.use(f,'clouds',lon=I,lat=J)/100.
out['cloud']=Data(x,y,clouds,'fraction (0--1)')
else:
print('==> clouds not present!')
return fill_extremes(out,quiet)
def gfs_file_data(fname, xlim=False, ylim=False, quiet=False):
'''
Returns bulk data from one GFS file
'''
# the way to ectract differes if using pygrib or grib2 ! so check first:
try:
import pygrib
isPygrib = True
except:
isPygrib = False
out = {}
# T air 2m [K->C]
if not quiet: print(' --> T air')
if isPygrib:
#x,y,tair=gribu.getvar(fname,'temperature',tags=(':2 metre',),lons=xlim,lats=ylim)
#newest gribu:
x, y, tair = gribu.getvar(fname, '2t', lons=xlim, lats=ylim)
else:
x, y, tair = gribu.getvar(fname,
'temperature',
tags=(':2 m', 'TMP'),
lons=xlim,
lats=ylim)
tair = tair - 273.15
out['tair'] = Data(x, y, tair, 'C')
# R humidity 2m [%-->0--1]
if not quiet: print(' --> R humidity')
if 0:
# kg/kg
x, y, rhum = gribu.getvar(fname,
'humidity',
tags=(':2 m', 'kg'),
lons=xlim,
lats=ylim)
rhum = rhum / air_sea.qsat(tair)
rhum = np.where(rhum > 1.0, 1.0, rhum)
else:
# %
#x,y,rhum=gribu.getvar(fname,'humidity',tags=('2 m','%'),lons=xlim,lats=ylim)
x, y, rhum = gribu.getvar(fname, '2r', lons=xlim, lats=ylim)
rhum = rhum / 100.
out['rhum'] = Data(x, y, rhum, '0--1')
# surface pressure [Pa]
if not quiet: print(' --> Surface pressure')
#x,y,pres=gribu.getvar(fname,'pressure',tags='surface',lons=xlim,lats=ylim)
x, y, pres = gribu.getvar(fname, 'sp', lons=xlim, lats=ylim)
out['pres'] = Data(x, y, pres, 'Pa')
# P rate [kg m-2 s-1 -> cm/d]
if not quiet: print(' --> P rate')
#x,y,prate=gribu.getvar(fname,'precipitation rate',lons=xlim,lats=ylim)
x, y, prate = gribu.getvar(fname, 'prate', lons=xlim, lats=ylim)
# Conversion kg m^-2 s^-1 to cm/day
prate = prate * 86400 * 100 / 1000.
prate = np.where(abs(prate) < 1.e-4, 0, prate)
out['prate'] = Data(x, y, prate, 'cm/d')
# Net shortwave flux [W/m^2]
if not quiet: print(' --> Net shortwave flux')
if not quiet: print(' SW down')
if isPygrib:
#x,y,sw_down = gribu.getvar(fname,'',tags='Downward short-wave radiation flux',lons=xlim,lats=ylim)
x, y, sw_down = gribu.getvar(fname, 'dswrf', lons=xlim, lats=ylim)
else:
x, y, sw_down = gribu.getvar(fname,
'downward short-wave',
lons=xlim,
lats=ylim)
if not quiet: print(' SW up')
#x,y,sw_up = gribu.getvar(fname,'',tags='Upward short-wave radiation flux',lons=xlim,lats=ylim)
x, y, sw_up = gribu.getvar(fname, 'uswrf', lons=xlim, lats=ylim)
if sw_up is False:
if not quiet: print(' SW up not found: using albedo')
#x,y,albedo = gribu.getvar(fname,'albedo',lons=xlim,lats=ylim)
x, y, albedo = gribu.getvar(fname, 'al', lons=xlim, lats=ylim)
albedo = albedo / 100.
sw_net = sw_down * (1 - albedo)
else:
sw_net = sw_down - sw_up
sw_net = np.where(sw_net < 1.e-10, 0, sw_net)
out['radsw'] = Data(x, y, sw_net, 'W m-2', info='positive downward')
# Net longwave flux [W/m^2]
if not quiet: print(' --> Net longwave flux')
if not quiet: print(' LW down')
if isPygrib:
#x,y,lw_down = gribu.getvar(fname,'',tags='Downward long-wave radiation flux',lons=xlim,lats=ylim)
x, y, lw_down = gribu.getvar(fname, 'dlwrf', lons=xlim, lats=ylim)
else:
x, y, lw_down = gribu.getvar(fname,
'downward long-wave',
lons=xlim,
lats=ylim)
if not quiet: print(' LW up')
#x,y,lw_up = gribu.getvar(fname,'',tags='Upward long-wave radiation flux',lons=xlim,lats=ylim)
x, y, lw_up = gribu.getvar(fname, 'ulwrf', lons=xlim, lats=ylim)
if lw_up is False:
if not quiet: print(' LW up not found: using sst')
if isPygrib:
#x,y,sst=gribu.getvar(fname,'temperature',tags='surface',lons=xlim,lats=ylim) # K
x, y, sst = gribu.getvar(fname, 't', lons=xlim, lats=ylim) # K
else:
x, y, sst = gribu.getvar(fname,
'temperature',
tags='water surface',
lons=xlim,
lats=ylim) # K
lw_net = air_sea.lwhf(sst, lw_down)
lw_up = lw_down - lw_net
else:
lw_net = lw_down - lw_up
# ROMS convention: positive upward
# GFS convention: positive downward --> * (-1)
lw_net = np.where(np.abs(lw_net) < 1.e-10, 0, lw_net)
out['radlw'] = Data(x, y, -lw_net, 'W m-2', info='positive upward')
# downward lw:
out['dlwrf'] = Data(x, y, -lw_down, 'W m-2', info='negative... downward')
# U and V wind speed 10m
if not quiet: print(' --> U and V wind')
#x,y,uwnd = gribu.getvar(fname,'u',tags=':10 m',lons=xlim,lats=ylim)
#x,y,vwnd = gribu.getvar(fname,'v',tags=':10 m',lons=xlim,lats=ylim)
x, y, uwnd = gribu.getvar(fname, '10u', lons=xlim, lats=ylim)
x, y, vwnd = gribu.getvar(fname, '10v', lons=xlim, lats=ylim)
if not quiet: print(' --> calc wind speed and stress')
speed = np.sqrt(uwnd**2 + vwnd**2)
taux, tauy = air_sea.wind_stress(uwnd, vwnd)
out['wspd'] = Data(x, y, speed, 'm s-1')
out['uwnd'] = Data(x, y, uwnd, 'm s-1')
out['vwnd'] = Data(x, y, vwnd, 'm s-1')
out['sustr'] = Data(x, y, taux, 'Pa')
out['svstr'] = Data(x, y, tauy, 'Pa')
# Cloud cover [0--100 --> 0--1]:
if not quiet: print(' --> Cloud cover')
#x,y,clouds = gribu.getvar(fname,'cloud cover',lons=xlim,lats=ylim)
x, y, clouds = gribu.getvar(fname, 'tcc', lons=xlim, lats=ylim)
if clouds is False:
if not quiet: print('CALC clouds from LW,TAIR,TSEA and RH')
# first get sst (maybe already done to calc lw_up)
try:
sst
except:
if not quiet: print(' get TSEA')
if isPygrib:
#x,y,sst=gribu.getvar(fname,'temperature',tags='surface',lons=xlim,lats=ylim) # K
x, y, sst = gribu.getvar(fname, 't', lons=xlim, lats=ylim) # K
else:
x, y, sst = gribu.getvar(fname,
'temperature',
tags='water surface',
lons=xlim,
lats=ylim) # K
clouds = air_sea.cloud(lw_net, sst - 273.15, tair, rhum, 'net')
else:
clouds = clouds / 100.
out['cloud'] = Data(x, y, clouds, 'fraction (0--1)')
return out
def gfs_file_data(fname,xlim=False,ylim=False,quiet=False):
'''
Returns bulk data from one GFS file
'''
# the way to ectract differes if using pygrib or grib2 ! so check first:
try:
import pygrib
isPygrib=True
except: isPygrib=False
out={}
# T air 2m [K->C]
if not quiet: print(' --> T air')
if isPygrib:
#x,y,tair=gribu.getvar(fname,'temperature',tags=(':2 metre',),lons=xlim,lats=ylim)
#newest gribu:
x,y,tair=gribu.getvar(fname,'2t',lons=xlim,lats=ylim)
else:
x,y,tair=gribu.getvar(fname,'temperature',tags=(':2 m','TMP'),lons=xlim,lats=ylim)
tair=tair-273.15
out['tair']=Data(x,y,tair,'C')
# R humidity 2m [%-->0--1]
if not quiet: print(' --> R humidity')
if 0:
# kg/kg
x,y,rhum=gribu.getvar(fname,'humidity',tags=(':2 m','kg'),lons=xlim,lats=ylim)
rhum=rhum/air_sea.qsat(tair)
rhum=np.where(rhum>1.0,1.0,rhum)
else:
# %
#x,y,rhum=gribu.getvar(fname,'humidity',tags=('2 m','%'),lons=xlim,lats=ylim)
x,y,rhum=gribu.getvar(fname,'2r',lons=xlim,lats=ylim)
rhum=rhum/100.
out['rhum']=Data(x,y,rhum,'0--1')
# surface pressure [Pa]
if not quiet: print(' --> Surface pressure')
#x,y,pres=gribu.getvar(fname,'pressure',tags='surface',lons=xlim,lats=ylim)
x,y,pres=gribu.getvar(fname,'sp',lons=xlim,lats=ylim)
out['pres']=Data(x,y,pres,'Pa')
# P rate [kg m-2 s-1 -> cm/d]
if not quiet: print(' --> P rate')
#x,y,prate=gribu.getvar(fname,'precipitation rate',lons=xlim,lats=ylim)
x,y,prate=gribu.getvar(fname,'prate',lons=xlim,lats=ylim)
# Conversion kg m^-2 s^-1 to cm/day
prate=prate*86400*100/1000.
prate=np.where(abs(prate)<1.e-4,0,prate)
out['prate']=Data(x,y,prate,'cm/d')
# Net shortwave flux [W/m^2]
if not quiet: print(' --> Net shortwave flux')
if not quiet: print(' SW down')
if isPygrib:
#x,y,sw_down = gribu.getvar(fname,'',tags='Downward short-wave radiation flux',lons=xlim,lats=ylim)
x,y,sw_down = gribu.getvar(fname,'dswrf',lons=xlim,lats=ylim)
else:
x,y,sw_down = gribu.getvar(fname,'downward short-wave',lons=xlim,lats=ylim)
if not quiet: print(' SW up')
#x,y,sw_up = gribu.getvar(fname,'',tags='Upward short-wave radiation flux',lons=xlim,lats=ylim)
x,y,sw_up = gribu.getvar(fname,'uswrf',lons=xlim,lats=ylim)
if sw_up is False:
if not quiet: print(' SW up not found: using albedo')
#x,y,albedo = gribu.getvar(fname,'albedo',lons=xlim,lats=ylim)
x,y,albedo = gribu.getvar(fname,'al',lons=xlim,lats=ylim)
albedo=albedo/100.
sw_net=sw_down*(1-albedo)
else:
sw_net=sw_down-sw_up
sw_net=np.where(sw_net<1.e-10,0,sw_net)
out['radsw']=Data(x,y,sw_net,'W m-2',info='positive downward')
# Net longwave flux [W/m^2]
if not quiet: print(' --> Net longwave flux')
if not quiet: print(' LW down')
if isPygrib:
#x,y,lw_down = gribu.getvar(fname,'',tags='Downward long-wave radiation flux',lons=xlim,lats=ylim)
x,y,lw_down = gribu.getvar(fname,'dlwrf',lons=xlim,lats=ylim)
else:
x,y,lw_down = gribu.getvar(fname,'downward long-wave',lons=xlim,lats=ylim)
if not quiet: print(' LW up')
#x,y,lw_up = gribu.getvar(fname,'',tags='Upward long-wave radiation flux',lons=xlim,lats=ylim)
x,y,lw_up = gribu.getvar(fname,'ulwrf',lons=xlim,lats=ylim)
if lw_up is False:
if not quiet: print(' LW up not found: using sst')
if isPygrib:
#x,y,sst=gribu.getvar(fname,'temperature',tags='surface',lons=xlim,lats=ylim) # K
x,y,sst=gribu.getvar(fname,'t',lons=xlim,lats=ylim) # K
else:
x,y,sst=gribu.getvar(fname,'temperature',tags='water surface',lons=xlim,lats=ylim) # K
lw_net=air_sea.lwhf(sst,lw_down)
lw_up=lw_down-lw_net
else:
lw_net=lw_down-lw_up
# ROMS convention: positive upward
# GFS convention: positive downward --> * (-1)
lw_net=np.where(np.abs(lw_net)<1.e-10,0,lw_net)
out['radlw']=Data(x,y,-lw_net,'W m-2',info='positive upward')
# downward lw:
out['dlwrf']=Data(x,y,-lw_down,'W m-2',info='negative... downward')
# U and V wind speed 10m
if not quiet: print(' --> U and V wind')
#x,y,uwnd = gribu.getvar(fname,'u',tags=':10 m',lons=xlim,lats=ylim)
#x,y,vwnd = gribu.getvar(fname,'v',tags=':10 m',lons=xlim,lats=ylim)
x,y,uwnd = gribu.getvar(fname,'10u',lons=xlim,lats=ylim)
x,y,vwnd = gribu.getvar(fname,'10v',lons=xlim,lats=ylim)
if not quiet: print(' --> calc wind speed and stress')
speed = np.sqrt(uwnd**2+vwnd**2)
taux,tauy=air_sea.wind_stress(uwnd,vwnd)
out['wspd']=Data(x,y,speed,'m s-1')
out['uwnd']=Data(x,y,uwnd,'m s-1')
out['vwnd']=Data(x,y,vwnd,'m s-1')
out['sustr']=Data(x,y,taux,'Pa')
out['svstr']=Data(x,y,tauy,'Pa')
# Cloud cover [0--100 --> 0--1]:
if not quiet: print(' --> Cloud cover')
#x,y,clouds = gribu.getvar(fname,'cloud cover',lons=xlim,lats=ylim)
x,y,clouds = gribu.getvar(fname,'tcc',lons=xlim,lats=ylim)
if clouds is False:
if not quiet: print('CALC clouds from LW,TAIR,TSEA and RH')
# first get sst (maybe already done to calc lw_up)
try: sst
except:
if not quiet: print(' get TSEA')
if isPygrib:
#x,y,sst=gribu.getvar(fname,'temperature',tags='surface',lons=xlim,lats=ylim) # K
x,y,sst=gribu.getvar(fname,'t',lons=xlim,lats=ylim) # K
else:
x,y,sst=gribu.getvar(fname,'temperature',tags='water surface',lons=xlim,lats=ylim) # K
clouds=air_sea.cloud(lw_net,sst-273.15,tair,rhum,'net')
else: clouds=clouds/100.
out['cloud']=Data(x,y,clouds,'fraction (0--1)')
return out
def wrf_file_data(file,quiet=False):
'''
WRF data for ROMS
'''
out={}
# time:
if not quiet: print ' --> get time'
time=read_time(file)
out['time']=time
# lon,lat:
if not quiet: print ' --> reading x,y'
x=netcdf.use(file,'XLONG',**{'0': 0})
y=netcdf.use(file,'XLAT',**{'0': 0})
# tair [K-->C]
if not quiet: print ' --> T air'
tair=netcdf.use(file,'T2')-273.15
out['tair']=Data(x,y,tair,'Celsius')
# R humidity [kg/kg --> 0--1]
if not quiet: print ' --> R humidity from QV at 2m'
wv=netcdf.use(file,'Q2') # water vapor mixing ratio at 2m
rhum=wv/air_sea.qsat(tair)
rhum[rhum>1]=1
out['rhum']=Data(x,y,rhum,'0--1')
# surface pressure [Pa]
if not quiet: print ' --> Surface pressure'
pres=netcdf.use(file,'PSFC')
out['pres']=Data(x,y,pres,'Pa')
# P rate [mm --> cm day-1]
if not quiet: print ' --> P rate (rainc+rainnc)'
rainc = netcdf.use(file,'RAINC')
rainnc = netcdf.use(file,'RAINNC')
prate=rainc+rainnc
if not quiet: print ' accum2avg...'
prate=accum2avg(prate,dt=time[1]-time[0]) # mm s-1
conv= 0.1*86400 # from mm s-1 --> cm day-1
prate=prate*conv # cm day-1
prate[prate<0]=0 # interpolation errors may result in negative rain!
out['prate']=Data(x,y,prate,'cm day-1')
# LW, SW, latent, sensible signs:
# positive (downward flux, heating) or negative (upward flux, cooling)
#https://www.myroms.org/forum/viewtopic.php?f=1&t=2621
# Net shortwave flux [W m-2]
if not quiet: print ' --> Net shortwave flux'
sw_down=netcdf.use(file,'SWDOWN')
albedo=netcdf.use(file,'ALBEDO')
sw_net=sw_down*(1-albedo)
out['radsw']=Data(x,y,sw_net,'W m-2',info='positive downward')
# Net longwave flux [W m-2]
if not quiet: print ' --> Net longwave flux'
lw_down=netcdf.use(file,'GLW') # positive
# sst needed:
if not quiet: print ' --> SST for LW up'
sst=netcdf.use(file,'SST') # K
lw_net = air_sea.lwhf(sst,lw_down) # positive down
# here vars have roms-agrif signs --> radlw is positive upward!
#conversion to ROMS is done in surface.py
out['radlw']=Data(x,y,-lw_net,'W m-2',info='positive upward')
out['dlwrf']=Data(x,y,-lw_down,'W m-2',info='positive upward')
# U and V wind speed 10m
if not quiet: print ' --> U and V wind'
uwnd=netcdf.use(file,'U10')
vwnd=netcdf.use(file,'V10')
if not quiet: print ' --> calc wind speed and stress'
speed = np.sqrt(uwnd**2+vwnd**2)
taux,tauy=air_sea.wind_stress(uwnd,vwnd)
out['wspd']=Data(x,y,speed,'m s-1')
out['uwnd']=Data(x,y,uwnd,'m s-1')
out['vwnd']=Data(x,y,vwnd,'m s-1')
out['sustr']=Data(x,y,taux,'Pa')
out['svstr']=Data(x,y,tauy,'Pa')
# Cloud cover [0--1]:
if not quiet: print ' --> Cloud cover for LONGWAVE. Use LONGWAVE_OUT instead...'
if 'CLDFRA' in netcdf.varnames(file):
clouds=netcdf.use(file,'CLDFRA').sum(-3)
clouds=np.where(clouds>1,1,clouds)
else:
if not quiet: print 'CLDFRA not found!! Using SST and air_sea.clouds'
sst=netcdf.use(f,'SST')
clouds=air_sea.clouds(lw_net,sst,tair,rhum,Wtype='net')
out['cloud']=Data(x,y,clouds,'fraction (0--1)')
return out
