def narr_file_data(fname,xlim=False,ylim=False,quiet=False):
'''
Returns bulk data from one NARR file
'''
out={}
# loading grid:
if 0:
if not quiet: print(' reading lon,lat from file %s' % grd)
nc=netcdf.ncopen(grd)
x=nc.vars['East_longitude_0-360'][0,...]-360.
y=nc.vars['Latitude_-90_to_+90'][0,...] # time always 1 !!
nc.close()
else:
if not quiet: print(' reading lon,lat from file %s' % grdTxt)
x,y=load_grid()
#x=x-360.
x=-x
ny,nx=x.shape
if (xlim,ylim)==(False,False):i0,i1,j0,j1=0,nx,0,ny
else:
i0,i1,j0,j1=calc.ij_limits(x, y, xlim, ylim, margin=0)
x=x[j0:j1,i0:i1]
y=y[j0:j1,i0:i1]
try:
nc=netcdf.ncopen(fname)
except:
return {}
xx=str(i0)+':'+str(i1)
yy=str(j0)+':'+str(j1)
tdim=netcdf.fdim(nc,'time1')
if tdim!=1: print('WARNING: tdim !=1 !!!!!!')
# T surface [K->C]
if not quiet: print(' --> T air')
tair=netcdf.use(nc,'Temperature_surface',time1=0,x=xx,y=yy)
tair=tair-273.15
out['tair']=cb.Data(x,y,tair,'C')
# R humidity [% -> 0--1]
if not quiet: print(' --> R humidity')
rhum=netcdf.use(nc,'Relative_humidity',time1=0,x=xx,y=yy)
out['rhum']=cb.Data(x,y,rhum/100.,'0--1')
# surface pressure [Pa]
if not quiet: print(' --> Surface pressure')
pres=netcdf.use(nc,'Pressure_surface',time1=0,x=xx,y=yy)
out['pres']=cb.Data(x,y,pres,'Pa')
# P rate [kg m-2 s-1 -> cm/d]
if not quiet: print(' --> P rate')
prate=netcdf.use(nc,'Precipitation_rate',time1=0,x=xx,y=yy)
prate=prate*86400*100/1000.
out['prate']=cb.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(nc,'Downward_shortwave_radiation_flux',time1=0,x=xx,y=yy)
if not quiet: print(' SW up')
sw_up=netcdf.use(nc,'Upward_short_wave_radiation_flux_surface',time1=0,x=xx,y=yy)
sw_net=sw_down-sw_up
out['radsw']=cb.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(nc,'Downward_longwave_radiation_flux',time1=0,x=xx,y=yy)
if not quiet: print(' LW up')
lw_up=netcdf.use(nc,'Upward_long_wave_radiation_flux_surface',time1=0,x=xx,y=yy)
lw_net=lw_down-lw_up
out['radlw']=cb.Data(x,y,-lw_net,'W m-2',info='positive upward')
# downward lw:
out['dlwrf']=cb.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')
# vertical dim is height_above_ground1: 10 and 30 m
uwnd=netcdf.use(nc,'u_wind_height_above_ground',height_above_ground1=0,time1=0,x=xx,y=yy)
vwnd=netcdf.use(nc,'v_wind_height_above_ground',height_above_ground1=0,time1=0,x=xx,y=yy)
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']=cb.Data(x,y,speed,'m s-1')
out['uwnd']=cb.Data(x,y,uwnd,'m s-1')
out['vwnd']=cb.Data(x,y,vwnd,'m s-1')
out['sustr']=cb.Data(x,y,taux,'Pa')
out['svstr']=cb.Data(x,y,tauy,'Pa')
# Cloud cover [0--100 --> 0--1]:
if not quiet: print(' --> Cloud cover')
clouds=netcdf.use(nc,'Total_cloud_cover',time1=0,x=xx,y=yy)
out['cloud']=cb.Data(x,y,clouds/100.,'fraction (0--1)')
nc.close()
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 interim_file_data(files,quiet=False):
'''
ECMWF ERA INTERIM data for ROMS
To be used with data obtained from the new server:
http://apps.ecmwf.int/datasets/
and not the old one (http://data-portal.ecmwf.int/data/d/interim_daily/)
To deal with data from old server use module interim_past
=> forecast vars:
time=00h, 12h
step=3,6,9,12 --> n forec steps=4
needed (suggestion):
- Surface net solar radiation (ssr)
- Surface thermal radiation (str)
- Total precipitation (tp)
others:
- Surface thermal radiation downwards (strd)
- Evaporation (e)
- ...
=>analysis+forec vars: (interim analysis starts every 6h; forecast starts every 12h !!)
time=00h,6h,12h,18h
step=0,3,9 (3 and 9 are for the forecast 00h and 12h)
needed (suggestion):
- Surface pressure (sp)
- Total cloud cover (tcc)
- 10 metre U wind component (v10u or u10)
- 10 metre V wind component (v10v or v10)
- 2 metre temperature (v2t or t2m)
- 2 metre dewpoint temperature (v2d or d2m)
Accumulated vars (rad SW, LW and precipitation are converted to averages
by acum2avg
'''
# some variables may have different names!
Vars={}
Vars['v10u']='v10u','u10'
Vars['v10v']='v10v','v10'
Vars['v2t']='v2t','t2m'
Vars['v2d']='v2d','d2m'
def find_v(name):
if name in Vars.keys():
for v in Vars[name]:
if varfile(v): return v
else: return name
def varfile(var):
for f in files:
if var in netcdf.varnames(f): return f
def check_var_type(var):
# new interim dataserver provides forec+analysis vars with extra dim
# 'type', 0 or 1
if var.ndim==4:
if not quiet: print(' dealing with var type... '),
v=np.zeros(var.shape[1:],var.dtype)
v[::2]=var[0,::2,...]
v[1::2]=var[1,1::2,...]
var=v
if not quiet: print('done.')
return var
out={}
# time:
# all times from analysis file, except last ind which will be
# the last time of forecast file
aFile=varfile(find_v('v2t')) # air temp, for instance
fFile=varfile(find_v('ssr')) # sw rad, for instance
if not quiet: print(' reading "analysis" time from file %s' % aFile)
aTime=netcdf.nctime(aFile,'time')
aTime.sort() # analysis+forecast files may not have time sorted!!
if not quiet: print(' reading "forecast" time from file %s' % fFile)
fTime=netcdf.nctime(fFile,'time')
fTime.sort() # this one should be sorted...
time=np.append(aTime,fTime[-1])
out['time']=time
# calc number of forecast steps stored,nforec (used by accum2avg)
if [fTime[i].hour for i in range(8)]==range(3,22,3)+[0]: nforec=4
elif [fTime[i].hour for i in range(4)]==range(6,19,6)+[0]: nforec=2
else:
if not quiet: print('INTERIM WRONG TIME: cannot n forec steps')
return
if not quiet: print(' ==> n forecast steps = %d' % nforec)
# x,y:
if not quiet: print(' reading x,y from file %s' % files[0])
x=netcdf.use(files[0],'longitude')
y=netcdf.use(files[0],'latitude')
x[x>180]=x[x>180]-360
if x.ndim==1 and y.ndim==1:
x,y=np.meshgrid(x,y)
# tair [K-->C]
if not quiet: print(' --> T air')
vname=find_v('v2t')
f=varfile(vname)
# time may not be monotonically increasing !!
# when using mix of analysis and forecast variables and steps
sortInds=np.argsort(netcdf.use(f,'time'))
tair=netcdf.use(f,vname,time=sortInds)-273.15
tair=check_var_type(tair)
if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
if not quiet: print(' fill_tend...')
tair=fill_tend(tair)
out['tair']=Data(x,y,tair,'Celsius')
# R humidity [0--1]
if not quiet: print(' --> R humidity (from T dew)')
vname=find_v('v2d')
f=varfile(vname)
sortInds=np.argsort(netcdf.use(f,'time'))
Td=netcdf.use(f,vname,time=sortInds)-273.15
Td=check_var_type(Td)
if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
if not quiet: print(' fill_tend... (T dew)')
Td=fill_tend(Td)
T=tair
rhum=air_sea.relative_humidity(T,Td)
## rhum=((112-0.1*T+Td)/(112+0.9*T))**8
rhum[rhum>1]=1
out['rhum']=Data(x,y,rhum,'0--1')
# surface pressure [Pa]
if not quiet: print(' --> Surface pressure')
vname=find_v('sp')
f=varfile(vname)
sortInds=np.argsort(netcdf.use(f,'time'))
pres=netcdf.use(f,vname,time=sortInds)
pres=check_var_type(pres)
if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
if not quiet: print(' fill_tend...')
pres=fill_tend(pres)
out['pres']=Data(x,y,pres,'Pa')
# P rate [m --> cm day-1]
if not quiet: print(' --> P rate')
vname=find_v('tp')
f=varfile(vname)
sortInds=np.argsort(netcdf.use(f,'time'))
prate=netcdf.use(f,vname,time=sortInds)
prate=check_var_type(prate)
if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
if not quiet: print(' accum2avg...')
prate=accum2avg(prate,nforec)
conv= 100*86400 # from m s-1 --> cm day-1
#conv= 100*86400/1000. # from kg m-2 s-1 --> cm day-1
prate=prate*conv # cm day-1
if not quiet: print(' fill_t0...')
prate=fill_t0(prate)
prate[prate<0]=0
out['prate']=Data(x,y,prate,'cm day-1')
# Net shortwave flux [W m-2 s+1 --> W m-2]
if not quiet: print(' --> Net shortwave flux')
vname=find_v('ssr')
f=varfile(vname)
sortInds=np.argsort(netcdf.use(f,'time'))
sw_net=netcdf.use(f,vname,time=sortInds)
sw_net=check_var_type(sw_net)
if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
if not quiet: print(' accum2avg...')
sw_net=accum2avg(sw_net,nforec)
if not quiet: print(' fill_t0...')
sw_net=fill_t0(sw_net)
out['radsw']=Data(x,y,sw_net,'W m-2',info='positive downward')
# Net longwave flux [W m-2 s+1 --> W m-2]
if not quiet: print(' --> Net longwave flux')
vname=find_v('str')
f=varfile(vname)
sortInds=np.argsort(netcdf.use(f,'time'))
lw_net=netcdf.use(f,vname,time=sortInds)*-1 # let us consider positive upward (*-1)
lw_net=check_var_type(lw_net)
if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
if not quiet: print(' accum2avg...')
lw_net=accum2avg(lw_net,nforec)
if not quiet: print(' fill_t0...')
lw_net=fill_t0(lw_net)
out['radlw']=Data(x,y,lw_net,'W m-2',info='positive upward')
# longwave down:
# can be obtained from clouds!!
if not quiet: print(' --> Down longwave flux')
vname=find_v('strd')
f=varfile(vname)
if f:
sortInds=np.argsort(netcdf.use(f,'time'))
lw_down=netcdf.use(f,vname,time=sortInds)*-1 # let us consider positive upward (*-1)
lw_down=check_var_type(lw_down)
if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
if not quiet: print(' accum2avg...')
lw_down=accum2avg(lw_down,nforec)
if not quiet: print(' fill_t0...')
lw_down=fill_t0(lw_down)
out['dlwrf']=Data(x,y,lw_down,'W m-2',info='negative... downward')
else: print('down long wave CANNOT BE USED')
# U and V wind speed 10m
if not quiet: print(' --> U and V wind')
vname=find_v('v10u')
f=varfile(vname)
sortInds=np.argsort(netcdf.use(f,'time'))
uwnd=netcdf.use(f,vname,time=sortInds)
uwnd=check_var_type(uwnd)
if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
if not quiet: print(' fill_tend...')
uwnd=fill_tend(uwnd)
vname=find_v('v10v')
f=varfile(vname)
sortInds=np.argsort(netcdf.use(f,'time'))
vwnd=netcdf.use(f,vname,time=sortInds)
vwnd=check_var_type(vwnd)
if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
if not quiet: print(' fill_tend...')
vwnd=fill_tend(vwnd)
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')
vname=find_v('tcc')
f=varfile(vname)
sortInds=np.argsort(netcdf.use(f,'time'))
clouds=netcdf.use(f,vname,time=sortInds)
clouds=check_var_type(clouds)
if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
if not quiet: print(' fill_tend...')
clouds=fill_tend(clouds)
out['cloud']=Data(x,y,clouds,'fraction (0--1)')
return out
def interim_file_data(files, quiet=False):
'''
ECMWF ERA INTERIM data for ROMS
To be used with data obtained from the new server:
http://apps.ecmwf.int/datasets/
and not the old one (http://data-portal.ecmwf.int/data/d/interim_daily/)
To deal with data from old server use module interim_past
=> forecast vars:
time=00h, 12h
step=3,6,9,12 --> n forec steps=4
needed (suggestion):
- Surface net solar radiation (ssr)
- Surface thermal radiation (str)
- Total precipitation (tp)
others:
- Surface thermal radiation downwards (strd)
- Evaporation (e)
- ...
=>analysis+forec vars: (interim analysis starts every 6h; forecast starts every 12h !!)
time=00h,6h,12h,18h
step=0,3,9 (3 and 9 are for the forecast 00h and 12h)
needed (suggestion):
- Surface pressure (sp)
- Total cloud cover (tcc)
- 10 metre U wind component (v10u or u10)
- 10 metre V wind component (v10v or v10)
- 2 metre temperature (v2t or t2m)
- 2 metre dewpoint temperature (v2d or d2m)
Accumulated vars (rad SW, LW and precipitation are converted to averages
by acum2avg
'''
# some variables may have different names!
Vars = {}
Vars['v10u'] = 'v10u', 'u10'
Vars['v10v'] = 'v10v', 'v10'
Vars['v2t'] = 'v2t', 't2m'
Vars['v2d'] = 'v2d', 'd2m'
def find_v(name):
if name in Vars.keys():
for v in Vars[name]:
if varfile(v): return v
else: return name
def varfile(var):
for f in files:
if var in netcdf.varnames(f): return f
def check_var_type(var):
# new interim dataserver provides forec+analysis vars with extra dim
# 'type', 0 or 1
if var.ndim == 4:
if not quiet: print(' dealing with var type... '),
v = np.zeros(var.shape[1:], var.dtype)
v[::2] = var[0, ::2, ...]
v[1::2] = var[1, 1::2, ...]
var = v
if not quiet: print('done.')
return var
out = {}
# time:
# all times from analysis file, except last ind which will be
# the last time of forecast file
aFile = varfile(find_v('v2t')) # air temp, for instance
fFile = varfile(find_v('ssr')) # sw rad, for instance
if not quiet: print(' reading "analysis" time from file %s' % aFile)
aTime = netcdf.nctime(aFile, 'time')
aTime.sort() # analysis+forecast files may not have time sorted!!
if not quiet: print(' reading "forecast" time from file %s' % fFile)
fTime = netcdf.nctime(fFile, 'time')
fTime.sort() # this one should be sorted...
time = np.append(aTime, fTime[-1])
out['time'] = time
# calc number of forecast steps stored,nforec (used by accum2avg)
if [fTime[i].hour for i in range(8)] == range(3, 22, 3) + [0]: nforec = 4
elif [fTime[i].hour for i in range(4)] == range(6, 19, 6) + [0]: nforec = 2
else:
if not quiet: print('INTERIM WRONG TIME: cannot n forec steps')
return
if not quiet: print(' ==> n forecast steps = %d' % nforec)
# x,y:
if not quiet: print(' reading x,y from file %s' % files[0])
x = netcdf.use(files[0], 'longitude')
y = netcdf.use(files[0], 'latitude')
x[x > 180] = x[x > 180] - 360
if x.ndim == 1 and y.ndim == 1:
x, y = np.meshgrid(x, y)
# tair [K-->C]
if not quiet: print(' --> T air')
vname = find_v('v2t')
f = varfile(vname)
# time may not be monotonically increasing !!
# when using mix of analysis and forecast variables and steps
sortInds = np.argsort(netcdf.use(f, 'time'))
tair = netcdf.use(f, vname, time=sortInds) - 273.15
tair = check_var_type(tair)
if not quiet and np.any(sortInds != range(len(sortInds))):
print(' sort DONE')
if not quiet: print(' fill_tend...')
tair = fill_tend(tair)
out['tair'] = Data(x, y, tair, 'Celsius')
# R humidity [0--1]
if not quiet: print(' --> R humidity (from T dew)')
vname = find_v('v2d')
f = varfile(vname)
sortInds = np.argsort(netcdf.use(f, 'time'))
Td = netcdf.use(f, vname, time=sortInds) - 273.15
Td = check_var_type(Td)
if not quiet and np.any(sortInds != range(len(sortInds))):
print(' sort DONE')
if not quiet: print(' fill_tend... (T dew)')
Td = fill_tend(Td)
T = tair
rhum = relative_humidity(T, Td)
## rhum=((112-0.1*T+Td)/(112+0.9*T))**8
rhum[rhum > 1] = 1
out['rhum'] = Data(x, y, rhum, '0--1')
# surface pressure [Pa]
if not quiet: print(' --> Surface pressure')
vname = find_v('sp')
f = varfile(vname)
sortInds = np.argsort(netcdf.use(f, 'time'))
pres = netcdf.use(f, vname, time=sortInds)
pres = check_var_type(pres)
if not quiet and np.any(sortInds != range(len(sortInds))):
print(' sort DONE')
if not quiet: print(' fill_tend...')
pres = fill_tend(pres)
out['pres'] = Data(x, y, pres, 'Pa')
# P rate [m --> cm day-1]
if not quiet: print(' --> P rate')
vname = find_v('tp')
f = varfile(vname)
sortInds = np.argsort(netcdf.use(f, 'time'))
prate = netcdf.use(f, vname, time=sortInds)
prate = check_var_type(prate)
if not quiet and np.any(sortInds != range(len(sortInds))):
print(' sort DONE')
if not quiet: print(' accum2avg...')
prate = accum2avg(prate, nforec)
conv = 100 * 86400 # from m s-1 --> cm day-1
#conv= 100*86400/1000. # from kg m-2 s-1 --> cm day-1
prate = prate * conv # cm day-1
if not quiet: print(' fill_t0...')
prate = fill_t0(prate)
prate[prate < 0] = 0
out['prate'] = Data(x, y, prate, 'cm day-1')
# Net shortwave flux [W m-2 s+1 --> W m-2]
if not quiet: print(' --> Net shortwave flux')
vname = find_v('ssr')
f = varfile(vname)
sortInds = np.argsort(netcdf.use(f, 'time'))
sw_net = netcdf.use(f, vname, time=sortInds)
sw_net = check_var_type(sw_net)
if not quiet and np.any(sortInds != range(len(sortInds))):
print(' sort DONE')
if not quiet: print(' accum2avg...')
sw_net = accum2avg(sw_net, nforec)
if not quiet: print(' fill_t0...')
sw_net = fill_t0(sw_net)
out['radsw'] = Data(x, y, sw_net, 'W m-2', info='positive downward')
# Net longwave flux [W m-2 s+1 --> W m-2]
if not quiet: print(' --> Net longwave flux')
vname = find_v('str')
f = varfile(vname)
sortInds = np.argsort(netcdf.use(f, 'time'))
lw_net = netcdf.use(
f, vname, time=sortInds) * -1 # let us consider positive upward (*-1)
lw_net = check_var_type(lw_net)
if not quiet and np.any(sortInds != range(len(sortInds))):
print(' sort DONE')
if not quiet: print(' accum2avg...')
lw_net = accum2avg(lw_net, nforec)
if not quiet: print(' fill_t0...')
lw_net = fill_t0(lw_net)
out['radlw'] = Data(x, y, lw_net, 'W m-2', info='positive upward')
# longwave down:
# can be obtained from clouds!!
if not quiet: print(' --> Down longwave flux')
vname = find_v('strd')
f = varfile(vname)
if f:
sortInds = np.argsort(netcdf.use(f, 'time'))
lw_down = netcdf.use(
f, vname,
time=sortInds) * -1 # let us consider positive upward (*-1)
lw_down = check_var_type(lw_down)
if not quiet and np.any(sortInds != range(len(sortInds))):
print(' sort DONE')
if not quiet: print(' accum2avg...')
lw_down = accum2avg(lw_down, nforec)
if not quiet: print(' fill_t0...')
lw_down = fill_t0(lw_down)
out['dlwrf'] = Data(x,
y,
lw_down,
'W m-2',
info='negative... downward')
else:
print('down long wave CANNOT BE USED')
# U and V wind speed 10m
if not quiet: print(' --> U and V wind')
vname = find_v('v10u')
f = varfile(vname)
sortInds = np.argsort(netcdf.use(f, 'time'))
uwnd = netcdf.use(f, vname, time=sortInds)
uwnd = check_var_type(uwnd)
if not quiet and np.any(sortInds != range(len(sortInds))):
print(' sort DONE')
if not quiet: print(' fill_tend...')
uwnd = fill_tend(uwnd)
vname = find_v('v10v')
f = varfile(vname)
sortInds = np.argsort(netcdf.use(f, 'time'))
vwnd = netcdf.use(f, vname, time=sortInds)
vwnd = check_var_type(vwnd)
if not quiet and np.any(sortInds != range(len(sortInds))):
print(' sort DONE')
if not quiet: print(' fill_tend...')
vwnd = fill_tend(vwnd)
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')
vname = find_v('tcc')
f = varfile(vname)
sortInds = np.argsort(netcdf.use(f, 'time'))
clouds = netcdf.use(f, vname, time=sortInds)
clouds = check_var_type(clouds)
if not quiet and np.any(sortInds != range(len(sortInds))):
print(' sort DONE')
if not quiet: print(' fill_tend...')
clouds = fill_tend(clouds)
out['cloud'] = Data(x, y, clouds, 'fraction (0--1)')
return out
def cfsr_file_data(files, quiet=False):
'''
Returns bulk data from one CFRS files
'''
def load_time(f):
time = np.array((), datetime.datetime)
ff = glob.glob(f)
ff.sort()
for f in ff:
time = np.append(time, netcdf.nctime(f, 'time'))
return time
def load_time_main(f):
time = load_time(f)
# I want 0,6,12,... after 2006 results may be 3,9,15, ...
if time[0].hour in [3, 9, 15, 21]:
time = time + datetime.timedelta(hours=3)
# for 2011 1st time is not 0!
if time[0].hour == 6: time = np.hstack((time[0].replace(hour=0), time))
return time
def fix_time(t, var, t0, t1):
# convert 1h, 7h, ... to 0h, 6h, ...
if t[0].hour in [1, 7, 13,
19]: # not all! sp analysis starts at 0, 6,...!
print(' 1,7,... to 0,6,...')
var = (var[1:] * 5 + var[:-1] * 1) / 6.
t = t[1:] - datetime.timedelta(hours=1)
elif t[0].hour in [3, 9, 15, 21]:
print(' 3,9,... to 0,6,...')
var = (var[1:] * 3 + var[:-1] * 3) / 6.
t = t[1:] - datetime.timedelta(hours=3)
cond = (t >= t0) & (t <= t1)
t = t[cond]
var = var[cond]
if t[0] > t0:
dt = t[0] - t0
dt = dt.days * 24 + dt.seconds / 3600. # hours
print(
'missing data at start: %.2d h missing --> repeating 1st data'
% dt)
v = np.zeros((var.shape[0] + 1, ) + var.shape[1:], var.dtype)
v[1:] = var
v[0] = var[0]
var = v
t_ = np.zeros((t.shape[0] + 1, ) + t.shape[1:], t.dtype)
t_[1:] = t
t_[0] = t0
t = t_
if t[-1] < t1:
dt = t1 - t[-1]
dt = dt.days * 24 + dt.seconds / 3600. # hours
print(
'missing data at end: %.2d h missing --> repeating last data' %
dt)
v = np.zeros((var.shape[0] + 1, ) + var.shape[1:], var.dtype)
v[:-1] = var
v[-1] = var[-1]
var = v
t_ = np.zeros((t.shape[0] + 1, ) + t.shape[1:], t.dtype)
t_[:-1] = t
t_[-1] = t1
t = t_
return var, t
out = {}
# time:
if 0:
time = netcdf.nctime(files['cc'], 'time')
# files have diff units !! so, cannot load all times at once!
# these result will use only units of 1st file!!
else:
time = load_time_main(files['cc'])
out['time'] = time
# T air [K->C]
if not quiet: print(' --> T air')
f = files['st']
tair = netcdf.use(f, 'TMP_L103')
tair = tair - 273.15
x = netcdf.use(f, 'lon')
x[x > 180] = x[x > 180] - 360
y = netcdf.use(f, 'lat')
x, y = np.meshgrid(x, y)
# check time:
ttmp = load_time(f)
if ttmp.size == time.size and np.all(ttmp == time): print(' time ok')
else:
print(' time differs !!!!', )
tair, tfix = fix_time(ttmp, tair, time[0], time[-1])
if tfix.size == time.size and np.all(tfix == time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
out['tair'] = Data(x, y, tair, 'C')
# R humidity [%-->0--1]
if not quiet: print(' --> R humidity')
f = files['rh']
rhum = netcdf.use(f, 'R_H_L103')
rhum = rhum / 100.
x = netcdf.use(f, 'lon')
x[x > 180] = x[x > 180] - 360
y = netcdf.use(f, 'lat')
x, y = np.meshgrid(x, y)
# check time:
ttmp = load_time(f)
if ttmp.size == time.size and np.all(ttmp == time): print(' time ok')
else:
print(' time differs !!!!'
), # should use end=' ' for python3 print continuation
rhum, tfix = fix_time(ttmp, rhum, time[0], time[-1])
if tfix.size == time.size and np.all(tfix == time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
out['rhum'] = Data(x, y, rhum, '0--1')
# surface pressure [Pa]
if not quiet: print(' --> Surface pressure')
f = files['sp']
pres = netcdf.use(f, 'PRES_L1')
x = netcdf.use(f, 'lon')
x[x > 180] = x[x > 180] - 360
y = netcdf.use(f, 'lat')
x, y = np.meshgrid(x, y)
# check time:
ttmp = load_time(f)
if ttmp.size == time.size and np.all(ttmp == time): print(' time ok')
else:
print(' time differs !!!!'),
pres, tfix = fix_time(ttmp, pres, time[0], time[-1])
if tfix.size == time.size and np.all(tfix == time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
out['pres'] = Data(x, y, pres, 'Pa')
# P rate [kg m-2 s-1 -> cm/d]
if not quiet: print(' --> P rate')
f = files['pr']
if 'PRATE_L1' in netcdf.varnames(f):
prate = netcdf.use(f, 'PRATE_L1')
else:
prate = netcdf.use(f, 'PRATE_L1_Avg_1')
x = netcdf.use(f, 'lon')
x[x > 180] = x[x > 180] - 360
y = netcdf.use(f, 'lat')
x, y = np.meshgrid(x, y)
# Conversion kg m^-2 s^-1 to cm/day
prate = prate * 86400 * 100 / 1000.
prate = np.where(abs(prate) < 1.e-4, 0, prate)
# check time:
ttmp = load_time(f)
if ttmp.size == time.size and np.all(ttmp == time): print(' time ok')
else:
print(' time differs !!!!'),
prate, tfix = fix_time(ttmp, prate, time[0], time[-1])
if tfix.size == time.size and np.all(tfix == time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
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')
f = files['rad']
sw_down = netcdf.use(f, 'DSWRF_L1_Avg_1')
x = netcdf.use(f, 'lon')
x[x > 180] = x[x > 180] - 360
y = netcdf.use(f, 'lat')
x, y = np.meshgrid(x, y)
if not quiet: print(' SW up')
sw_up = netcdf.use(f, 'USWRF_L1_Avg_1')
sw_net = sw_down - sw_up
sw_net = np.where(sw_net < 1.e-10, 0, sw_net)
# check time:
ttmp = load_time(f)
if ttmp.size == time.size and np.all(ttmp == time): print(' time ok')
else:
print(' time differs !!!!'),
sw_net, tfix = fix_time(ttmp, sw_net, time[0], time[-1])
if tfix.size == time.size and np.all(tfix == time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
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')
f = files['rad']
lw_down = netcdf.use(f, 'DLWRF_L1_Avg_1')
x = netcdf.use(f, 'lon')
x[x > 180] = x[x > 180] - 360
y = netcdf.use(f, 'lat')
x, y = np.meshgrid(x, y)
if not quiet: print(' LW up')
lw_up = netcdf.use(f, 'ULWRF_L1_Avg_1')
lw_net = lw_down - lw_up
lw_net = np.where(np.abs(lw_net) < 1.e-10, 0, lw_net)
# check time:
ttmp = load_time(f)
if ttmp.size == time.size and np.all(ttmp == time): print(' time ok')
else:
print(' time differs !!!!'),
lw_net, tfix1 = fix_time(ttmp, lw_net, time[0], time[-1])
lw_down, tfix2 = fix_time(ttmp, lw_down, time[0], time[-1])
if tfix1.size == tfix2.size == time.size and np.all((tfix1 == time)
& (tfix2 == time)):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
# ROMS (agrif, used to be!) convention: positive upward
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')
f = files['uv']
uwnd = netcdf.use(f, 'U_GRD_L103')
vwnd = netcdf.use(f, 'V_GRD_L103')
x = netcdf.use(f, 'lon')
x[x > 180] = x[x > 180] - 360
y = netcdf.use(f, 'lat')
x, y = np.meshgrid(x, y)
# check time:
ttmp = load_time(f)
if ttmp.size == time.size and np.all(ttmp == time): print(' time ok')
else:
print(' time differs !!!!'),
uwnd, tfix1 = fix_time(ttmp, uwnd, time[0], time[-1])
vwnd, tfix2 = fix_time(ttmp, vwnd, time[0], time[-1])
if tfix1.size == tfix2.size == time.size and np.all((tfix1 == time)
& (tfix2 == time)):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
#
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')
f = files['cc']
if 'T_CDC_L200' in netcdf.varnames(f):
clouds = netcdf.use(f, 'T_CDC_L200')
else:
clouds = netcdf.use(f, 'T_CDC_L200_Avg_1')
x = netcdf.use(f, 'lon')
x[x > 180] = x[x > 180] - 360
y = netcdf.use(f, 'lat')
x, y = np.meshgrid(x, y)
clouds = clouds / 100.
# check time:
ttmp = load_time(f)
if ttmp.size == time.size and np.all(ttmp == time): print(' time ok')
else:
print(' time differs !!!!'),
clouds, tfix = fix_time(ttmp, clouds, time[0], time[-1])
if tfix.size == time.size and np.all(tfix == time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
out['cloud'] = Data(x, y, clouds, 'fraction (0--1)')
# rhum has different resolution (0.5, just like dew point!)
# so, i can edit surface.py or just interpolate here rhum to
# other vars resolution:
if out['rhum'].data.shape != out['uwnd'].data.shape:
from okean import calc
print('rhum shape differs!! --> interp:')
nt, ny, nx = out['uwnd'].data.shape
x, y = out['uwnd'].x, out['uwnd'].y
rhum = np.zeros((nt, ny, nx), out['rhum'].data.dtype)
for it in range(nt):
if it % 100 == 0: print(' %d of %d' % (it, nt))
rhum[it] = calc.griddata(out['rhum'].x, out['rhum'].y,
out['rhum'].data[it], x, y)
out['rhum'] = Data(x, y, rhum, '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 update_wind(fname, data, new_wind_info, **kargs):
'''
new_wind_info will be added to fname as global attribute
(ex: 'new wind from database xxx')
'''
quiet = False
Margin = 4 # for griddata and for original data to keep
grd = False
keepOriginal = 2
for k in kargs.keys():
if k == 'quiet': quiet = kargs[k]
elif k == 'margin': Margin = kargs[k]
elif k == 'grid': grd = kargs[k]
elif k.lower().startswith('keepor'): keepOriginal = kargs[k]
if not grd: grd = netcdf.fatt(fname, 'grd_file')
g = roms.Grid(grd)
x0 = data['x']
y0 = data['y']
if x0.ndim == 1: x0, y0 = np.meshgrid(x0, y0)
dates = data.keys()[:]
dates.remove('x')
dates.remove('y')
dates = np.array(dates)
# interp in time:
time = netcdf.nctime(fname, 'time')
cond = False
tind = -1
fob = gennc.GenBlk(fname, grd)
for t in time:
newWind = {}
tind += 1
I, = np.where(dates == t)
if I.size:
I = I[0]
uv = data[dates[I]]
if not quiet: print(t, dates[I])
else:
i1, = np.where(dates > t)
i0, = np.where(dates < t)
if i0.size and i1.size:
i1 = i1[0]
i0 = i0[-1]
d0 = t - dates[i0]
d1 = dates[i1] - t
d0 = d0.days + d0.seconds / 86400.
d1 = d1.days + d1.seconds / 86400.
uv = (data[dates[i0]] * d1 + data[dates[i1]] * d0) / (d0 + d1)
if not quiet: print(t, dates[i0], dates[i1], d0, d1)
elif not i1.size:
uv = data[dates[-1]]
if not quiet: print(t, dates[-1])
elif not i0.size:
uv = data[dates[0]]
if not quiet: print(t, dates[0])
# interp to grid:
if cond is False:
cond, inds = rt.grid_vicinity(grd,
x0,
y0,
margin=Margin,
rect=True,
retinds=True)
i1, i2, j1, j2 = inds
if not quiet: print(' --> inter uv %s' % t.isoformat(' '))
u = calc.griddata(x0[cond],
y0[cond],
uv.real[cond],
g.lon,
g.lat,
extrap=True)
v = calc.griddata(x0[cond],
y0[cond],
uv.imag[cond],
g.lon,
g.lat,
extrap=True)
# rotate wind, calc stress:
if not quiet: print(' --> rot U,V wind and U,V wind stress')
wspd = np.sqrt(u**2 + v**2)
sustr, svstr = air_sea.wind_stress(u, v)
angle = g.use('angle')
uwnd, vwnd = calc.rot2d(u, v, angle)
sustr, svstr = calc.rot2d(sustr, svstr, angle)
sustr = rt.rho2uvp(sustr, 'u')
svstr = rt.rho2uvp(svstr, 'v')
# update wind data:
newWind['date'] = t
newWind['uwnd'] = uwnd
newWind['vwnd'] = vwnd
newWind['sustr'] = sustr
newWind['svstr'] = svstr
newWind['wspd'] = wspd
# original xy:
if tind == 0:
newWind['attr'] = {'new_wind_info': new_wind_info}
if not quiet: print(' --> original xy')
if keepOriginal == 1:
newWind['x_wind'] = x0
newWind['y_wind'] = y0
elif keepOriginal == 2:
newWind['x_wind'] = x0[j1:j2, i1:i2]
newWind['y_wind'] = y0[j1:j2, i1:i2]
# add to file:
if not quiet: print(' --> adding to file')
fob.update_wind(newWind, quiet=quiet)
if not quiet: print('')
def update_wind(fname,data,new_wind_info,**kargs):
'''
new_wind_info will be added to fname as global attribute
(ex: 'new wind from database xxx')
'''
quiet = False
Margin = 4 # for griddata and for original data to keep
grd = False
keepOriginal = 2
for k in kargs.keys():
if k=='quiet': quiet=kargs[k]
elif k=='margin': Margin=kargs[k]
elif k=='grid': grd=kargs[k]
elif k.lower().startswith('keepor'): keepOriginal=kargs[k]
if not grd: grd=netcdf.fatt(fname,'grd_file')
g=roms.Grid(grd)
x0=data['x']
y0=data['y']
if x0.ndim==1: x0,y0=np.meshgrid(x0,y0)
dates=data.keys()[:]
dates.remove('x')
dates.remove('y')
dates=np.array(dates)
# interp in time:
time=netcdf.nctime(fname,'time')
cond=False
tind=-1
fob=gennc.GenBlk(fname,grd)
for t in time:
newWind={}
tind+=1
I,=np.where(dates==t)
if I.size:
I=I[0]
uv=data[dates[I]]
if not quiet: print(t,dates[I])
else:
i1,=np.where(dates>t)
i0,=np.where(dates<t)
if i0.size and i1.size:
i1=i1[0]
i0=i0[-1]
d0=t-dates[i0]
d1=dates[i1]-t
d0=d0.days+d0.seconds/86400.
d1=d1.days+d1.seconds/86400.
uv=(data[dates[i0]]*d1+data[dates[i1]]*d0)/(d0+d1)
if not quiet: print(t,dates[i0],dates[i1], d0,d1)
elif not i1.size:
uv=data[dates[-1]]
if not quiet: print(t,dates[-1])
elif not i0.size:
uv=data[dates[0]]
if not quiet: print(t,dates[0])
# interp to grid:
if cond is False:
cond,inds=rt.grid_vicinity(grd,x0,y0,margin=Margin,rect=True,retinds=True)
i1,i2,j1,j2=inds
if not quiet: print(' --> inter uv %s' % t.isoformat(' '))
u=calc.griddata(x0[cond],y0[cond],uv.real[cond],g.lon,g.lat,extrap=True)
v=calc.griddata(x0[cond],y0[cond],uv.imag[cond],g.lon,g.lat,extrap=True)
# rotate wind, calc stress:
if not quiet: print(' --> rot U,V wind and U,V wind stress')
wspd=np.sqrt(u**2+v**2)
sustr,svstr=air_sea.wind_stress(u,v)
angle=g.use('angle')
uwnd,vwnd=calc.rot2d(u,v,angle)
sustr,svstr=calc.rot2d(sustr,svstr,angle)
sustr=rt.rho2uvp(sustr,'u')
svstr=rt.rho2uvp(svstr,'v')
# update wind data:
newWind['date'] = t
newWind['uwnd'] = uwnd
newWind['vwnd'] = vwnd
newWind['sustr'] = sustr
newWind['svstr'] = svstr
newWind['wspd'] = wspd
# original xy:
if tind==0:
newWind['attr']={'new_wind_info':new_wind_info}
if not quiet: print(' --> original xy')
if keepOriginal==1:
newWind['x_wind']=x0
newWind['y_wind']=y0
elif keepOriginal==2:
newWind['x_wind']=x0[j1:j2,i1:i2]
newWind['y_wind']=y0[j1:j2,i1:i2]
# add to file:
if not quiet: print(' --> adding to file')
fob.update_wind(newWind,quiet=quiet)
if not quiet: print('')
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 update_wind(fname, data, new_wind_info, **kargs):
"""
new_wind_info will be added to fname as global attribute
(ex: 'new wind from database xxx')
"""
quiet = False
Margin = 4 # for griddata and for original data to keep
grd = False
keepOriginal = 2
for k in kargs.keys():
if k == "quiet":
quiet = kargs[k]
elif k == "margin":
Margin = kargs[k]
elif k == "grid":
grd = kargs[k]
elif k.lower().startswith("keepor"):
keepOriginal = kargs[k]
if not grd:
grd = netcdf.fatt(fname, "grd_file")
g = roms.Grid(grd)
x0 = data["x"]
y0 = data["y"]
if x0.ndim == 1:
x0, y0 = np.meshgrid(x0, y0)
dates = data.keys()[:]
dates.remove("x")
dates.remove("y")
dates = np.array(dates)
# interp in time:
time = netcdf.nctime(fname, "time")
cond = False
tind = -1
fob = gennc.GenBlk(fname, grd)
for t in time:
newWind = {}
tind += 1
I, = np.where(dates == t)
if I.size:
I = I[0]
uv = data[dates[I]]
if not quiet:
print t, dates[I]
else:
i1, = np.where(dates > t)
i0, = np.where(dates < t)
if i0.size and i1.size:
i1 = i1[0]
i0 = i0[-1]
d0 = t - dates[i0]
d1 = dates[i1] - t
d0 = d0.days + d0.seconds / 86400.0
d1 = d1.days + d1.seconds / 86400.0
uv = (data[dates[i0]] * d1 + data[dates[i1]] * d0) / (d0 + d1)
if not quiet:
print t, dates[i0], dates[i1], d0, d1
elif not i1.size:
uv = data[dates[-1]]
if not quiet:
print t, dates[-1]
elif not i0.size:
uv = data[dates[0]]
if not quiet:
print t, dates[0]
# interp to grid:
if cond is False:
cond, inds = rt.grid_vicinity(grd, x0, y0, margin=Margin, rect=True, retinds=True)
i1, i2, j1, j2 = inds
if not quiet:
print " --> inter uv %s" % t.isoformat(" ")
u = calc.griddata(x0[cond], y0[cond], uv.real[cond], g.lon, g.lat, extrap=True)
v = calc.griddata(x0[cond], y0[cond], uv.imag[cond], g.lon, g.lat, extrap=True)
# rotate wind, calc stress:
if not quiet:
print " --> rot U,V wind and U,V wind stress"
wspd = np.sqrt(u ** 2 + v ** 2)
sustr, svstr = air_sea.wind_stress(u, v)
angle = g.use("angle")
uwnd, vwnd = calc.rot2d(u, v, angle)
sustr, svstr = calc.rot2d(sustr, svstr, angle)
sustr = rt.rho2uvp(sustr, "u")
svstr = rt.rho2uvp(svstr, "v")
# update wind data:
newWind["date"] = t
newWind["uwnd"] = uwnd
newWind["vwnd"] = vwnd
newWind["sustr"] = sustr
newWind["svstr"] = svstr
newWind["wspd"] = wspd
# original xy:
if tind == 0:
newWind["attr"] = {"new_wind_info": new_wind_info}
if not quiet:
print " --> original xy"
if keepOriginal == 1:
newWind["x_wind"] = x0
newWind["y_wind"] = y0
elif keepOriginal == 2:
newWind["x_wind"] = x0[j1:j2, i1:i2]
newWind["y_wind"] = y0[j1:j2, i1:i2]
# add to file:
if not quiet:
print " --> adding to file"
fob.update_wind(newWind, quiet=quiet)
if not quiet:
print ""
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
def cfsr_file_data(files,quiet=False):
'''
Returns bulk data from one CFRS files
'''
def load_time(f):
time=np.array((),datetime.datetime)
ff=glob.glob(f)
ff.sort()
for f in ff: time=np.append(time,netcdf.nctime(f,'time'))
return time
def load_time_main(f):
time=load_time(f)
# I want 0,6,12,... after 2006 results may be 3,9,15, ...
if time[0].hour in [3,9,15,21]: time=time+datetime.timedelta(hours=3)
# for 2011 1st time is not 0!
if time[0].hour==6: time=np.hstack((time[0].replace(hour=0),time))
return time
def fix_time(t,var,t0,t1):
# convert 1h, 7h, ... to 0h, 6h, ...
if t[0].hour in [1,7,13,19]: # not all! sp analysis starts at 0, 6,...!
print(' 1,7,... to 0,6,...')
var=(var[1:]*5+var[:-1]*1)/6.
t=t[1:]-datetime.timedelta(hours=1)
elif t[0].hour in [3,9,15,21]:
print(' 3,9,... to 0,6,...')
var=(var[1:]*3+var[:-1]*3)/6.
t=t[1:]-datetime.timedelta(hours=3)
cond=(t>=t0)&(t<=t1)
t=t[cond]
var=var[cond]
if t[0]>t0:
dt=t[0]-t0
dt=dt.days*24+dt.seconds/3600. # hours
print('missing data at start: %.2d h missing --> repeating 1st data'%dt)
v=np.zeros((var.shape[0]+1,)+var.shape[1:],var.dtype)
v[1:]=var
v[0]=var[0]
var=v
t_=np.zeros((t.shape[0]+1,)+t.shape[1:],t.dtype)
t_[1:]=t
t_[0]=t0
t=t_
if t[-1]<t1:
dt=t1-t[-1]
dt=dt.days*24+dt.seconds/3600. # hours
print('missing data at end: %.2d h missing --> repeating last data'%dt)
v=np.zeros((var.shape[0]+1,)+var.shape[1:],var.dtype)
v[:-1]=var
v[-1]=var[-1]
var=v
t_=np.zeros((t.shape[0]+1,)+t.shape[1:],t.dtype)
t_[:-1]=t
t_[-1]=t1
t=t_
return var,t
out={}
# time:
if 0:
time=netcdf.nctime(files['cc'],'time')
# files have diff units !! so, cannot load all times at once!
# these result will use only units of 1st file!!
else:
time=load_time_main(files['cc'])
out['time']=time
# T air [K->C]
if not quiet: print(' --> T air')
f=files['st']
tair=netcdf.use(f,'TMP_L103')
tair=tair-273.15
x=netcdf.use(f,'lon'); x[x>180]=x[x>180]-360
y=netcdf.use(f,'lat')
x,y=np.meshgrid(x,y)
# check time:
ttmp=load_time(f)
if ttmp.size==time.size and np.all(ttmp==time): print(' time ok')
else:
print(' time differs !!!!',)
tair,tfix=fix_time(ttmp,tair,time[0],time[-1])
if tfix.size==time.size and np.all(tfix==time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
out['tair']=Data(x,y,tair,'C')
# R humidity [%-->0--1]
if not quiet: print(' --> R humidity')
f=files['rh']
rhum=netcdf.use(f,'R_H_L103')
rhum=rhum/100.
x=netcdf.use(f,'lon'); x[x>180]=x[x>180]-360
y=netcdf.use(f,'lat')
x,y=np.meshgrid(x,y)
# check time:
ttmp=load_time(f)
if ttmp.size==time.size and np.all(ttmp==time): print(' time ok')
else:
print(' time differs !!!!'), # should use end=' ' for python3 print continuation
rhum,tfix=fix_time(ttmp,rhum,time[0],time[-1])
if tfix.size==time.size and np.all(tfix==time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
out['rhum']=Data(x,y,rhum,'0--1')
# surface pressure [Pa]
if not quiet: print(' --> Surface pressure')
f=files['sp']
pres=netcdf.use(f,'PRES_L1')
x=netcdf.use(f,'lon'); x[x>180]=x[x>180]-360
y=netcdf.use(f,'lat')
x,y=np.meshgrid(x,y)
# check time:
ttmp=load_time(f)
if ttmp.size==time.size and np.all(ttmp==time): print(' time ok')
else:
print(' time differs !!!!'),
pres,tfix=fix_time(ttmp,pres,time[0],time[-1])
if tfix.size==time.size and np.all(tfix==time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
out['pres']=Data(x,y,pres,'Pa')
# P rate [kg m-2 s-1 -> cm/d]
if not quiet: print(' --> P rate')
f=files['pr']
if 'PRATE_L1' in netcdf.varnames(f):
prate=netcdf.use(f,'PRATE_L1')
else:
prate=netcdf.use(f,'PRATE_L1_Avg_1')
x=netcdf.use(f,'lon'); x[x>180]=x[x>180]-360
y=netcdf.use(f,'lat')
x,y=np.meshgrid(x,y)
# Conversion kg m^-2 s^-1 to cm/day
prate=prate*86400*100/1000.
prate=np.where(abs(prate)<1.e-4,0,prate)
# check time:
ttmp=load_time(f)
if ttmp.size==time.size and np.all(ttmp==time): print(' time ok')
else:
print(' time differs !!!!'),
prate,tfix=fix_time(ttmp,prate,time[0],time[-1])
if tfix.size==time.size and np.all(tfix==time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
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')
f=files['rad']
sw_down=netcdf.use(f,'DSWRF_L1_Avg_1')
x=netcdf.use(f,'lon'); x[x>180]=x[x>180]-360
y=netcdf.use(f,'lat')
x,y=np.meshgrid(x,y)
if not quiet: print(' SW up')
sw_up=netcdf.use(f,'USWRF_L1_Avg_1')
sw_net=sw_down-sw_up
sw_net=np.where(sw_net<1.e-10,0,sw_net)
# check time:
ttmp=load_time(f)
if ttmp.size==time.size and np.all(ttmp==time): print(' time ok')
else:
print(' time differs !!!!'),
sw_net,tfix=fix_time(ttmp,sw_net,time[0],time[-1])
if tfix.size==time.size and np.all(tfix==time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
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')
f=files['rad']
lw_down=netcdf.use(f,'DLWRF_L1_Avg_1')
x=netcdf.use(f,'lon'); x[x>180]=x[x>180]-360
y=netcdf.use(f,'lat')
x,y=np.meshgrid(x,y)
if not quiet: print(' LW up')
lw_up=netcdf.use(f,'ULWRF_L1_Avg_1')
lw_net=lw_down-lw_up
lw_net=np.where(np.abs(lw_net)<1.e-10,0,lw_net)
# check time:
ttmp=load_time(f)
if ttmp.size==time.size and np.all(ttmp==time): print(' time ok')
else:
print(' time differs !!!!'),
lw_net,tfix1=fix_time(ttmp,lw_net,time[0],time[-1])
lw_down,tfix2=fix_time(ttmp,lw_down,time[0],time[-1])
if tfix1.size==tfix2.size==time.size and np.all((tfix1==time)&(tfix2==time)):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
# ROMS (agrif, used to be!) convention: positive upward
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')
f=files['uv']
uwnd=netcdf.use(f,'U_GRD_L103')
vwnd=netcdf.use(f,'V_GRD_L103')
x=netcdf.use(f,'lon'); x[x>180]=x[x>180]-360
y=netcdf.use(f,'lat')
x,y=np.meshgrid(x,y)
# check time:
ttmp=load_time(f)
if ttmp.size==time.size and np.all(ttmp==time): print(' time ok')
else:
print(' time differs !!!!'),
uwnd,tfix1=fix_time(ttmp,uwnd,time[0],time[-1])
vwnd,tfix2=fix_time(ttmp,vwnd,time[0],time[-1])
if tfix1.size==tfix2.size==time.size and np.all((tfix1==time)&(tfix2==time)):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
#
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')
f=files['cc']
if 'T_CDC_L200' in netcdf.varnames(f):
clouds=netcdf.use(f,'T_CDC_L200')
else:
clouds=netcdf.use(f,'T_CDC_L200_Avg_1')
x=netcdf.use(f,'lon'); x[x>180]=x[x>180]-360
y=netcdf.use(f,'lat')
x,y=np.meshgrid(x,y)
clouds=clouds/100.
# check time:
ttmp=load_time(f)
if ttmp.size==time.size and np.all(ttmp==time): print(' time ok')
else:
print(' time differs !!!!'),
clouds,tfix=fix_time(ttmp,clouds,time[0],time[-1])
if tfix.size==time.size and np.all(tfix==time):
print(' ...fixed!')
else:
print('time is NOT OK. Please check !!')
return
out['cloud']=Data(x,y,clouds,'fraction (0--1)')
# rhum has different resolution (0.5, just like dew point!)
# so, i can edit surface.py or just interpolate here rhum to
# other vars resolution:
if out['rhum'].data.shape!=out['uwnd'].data.shape:
from okean import calc
print('rhum shape differs!! --> interp:')
nt,ny,nx=out['uwnd'].data.shape
x,y=out['uwnd'].x,out['uwnd'].y
rhum=np.zeros((nt,ny,nx), out['rhum'].data.dtype)
for it in range(nt):
if it%100==0: print(' %d of %d'%(it,nt))
rhum[it]=calc.griddata(out['rhum'].x,out['rhum'].y,out['rhum'].data[it],x,y)
out['rhum']=Data(x,y,rhum,'0--1')
return out
ttmp = load_time(f)
if ttmp.size == time.size and np.all(ttmp == time): print ' time ok'
else:
print ' time differs !!!!',
uwnd, tfix1 = fix_time(ttmp, uwnd, time[0], time[-1])
vwnd, tfix2 = fix_time(ttmp, vwnd, time[0], time[-1])
if tfix1.size == tfix2.size == time.size and np.all((tfix1 == time)
& (tfix2 == time)):
print ' ...fixed!'
else:
print 'time is NOT OK. Please check !!'
return
#
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'
f = files['cc']
if 'T_CDC_L200' in netcdf.varnames(f):
clouds = netcdf.use(f, 'T_CDC_L200')
else:
clouds = netcdf.use(f, 'T_CDC_L200_Avg_1')
x = netcdf.use(f, 'lon')
def narr_file_data(fname, xlim=False, ylim=False, quiet=False):
'''
Returns bulk data from one NARR file
'''
out = {}
# loading grid:
if 0:
if not quiet: print ' reading lon,lat from file %s' % grd
nc = netcdf.ncopen(grd)
x = nc.vars['East_longitude_0-360'][0, ...] - 360.
y = nc.vars['Latitude_-90_to_+90'][0, ...] # time always 1 !!
nc.close()
else:
if not quiet: print ' reading lon,lat from file %s' % grdTxt
x, y = load_grid()
#x=x-360.
x = -x
ny, nx = x.shape
if (xlim, ylim) == (False, False): i0, i1, j0, j1 = 0, nx, 0, ny
else:
i0, i1, j0, j1 = calc.ij_limits(x, y, xlim, ylim, margin=0)
x = x[j0:j1, i0:i1]
y = y[j0:j1, i0:i1]
try:
nc = netcdf.ncopen(fname)
except:
return {}
xx = str(i0) + ':' + str(i1)
yy = str(j0) + ':' + str(j1)
tdim = netcdf.fdim(nc, 'time1')
if tdim != 1: print 'WARNING: tdim !=1 !!!!!!'
# T surface [K->C]
if not quiet: print ' --> T air'
tair = netcdf.use(nc, 'Temperature_surface', time1=0, x=xx, y=yy)
tair = tair - 273.15
out['tair'] = cb.Data(x, y, tair, 'C')
# R humidity [% -> 0--1]
if not quiet: print ' --> R humidity'
rhum = netcdf.use(nc, 'Relative_humidity', time1=0, x=xx, y=yy)
out['rhum'] = cb.Data(x, y, rhum / 100., '0--1')
# surface pressure [Pa]
if not quiet: print ' --> Surface pressure'
pres = netcdf.use(nc, 'Pressure_surface', time1=0, x=xx, y=yy)
out['pres'] = cb.Data(x, y, pres, 'Pa')
# P rate [kg m-2 s-1 -> cm/d]
if not quiet: print ' --> P rate'
prate = netcdf.use(nc, 'Precipitation_rate', time1=0, x=xx, y=yy)
prate = prate * 86400 * 100 / 1000.
out['prate'] = cb.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(nc,
'Downward_shortwave_radiation_flux',
time1=0,
x=xx,
y=yy)
if not quiet: print ' SW up'
sw_up = netcdf.use(nc,
'Upward_short_wave_radiation_flux_surface',
time1=0,
x=xx,
y=yy)
sw_net = sw_down - sw_up
out['radsw'] = cb.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(nc,
'Downward_longwave_radiation_flux',
time1=0,
x=xx,
y=yy)
if not quiet: print ' LW up'
lw_up = netcdf.use(nc,
'Upward_long_wave_radiation_flux_surface',
time1=0,
x=xx,
y=yy)
lw_net = lw_down - lw_up
out['radlw'] = cb.Data(x, y, -lw_net, 'W m-2', info='positive upward')
# downward lw:
out['dlwrf'] = cb.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'
# vertical dim is height_above_ground1: 10 and 30 m
uwnd = netcdf.use(nc,
'u_wind_height_above_ground',
height_above_ground1=0,
time1=0,
x=xx,
y=yy)
vwnd = netcdf.use(nc,
'v_wind_height_above_ground',
height_above_ground1=0,
time1=0,
x=xx,
y=yy)
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'] = cb.Data(x, y, speed, 'm s-1')
out['uwnd'] = cb.Data(x, y, uwnd, 'm s-1')
out['vwnd'] = cb.Data(x, y, vwnd, 'm s-1')
out['sustr'] = cb.Data(x, y, taux, 'Pa')
out['svstr'] = cb.Data(x, y, tauy, 'Pa')
# Cloud cover [0--100 --> 0--1]:
if not quiet: print ' --> Cloud cover'
clouds = netcdf.use(nc, 'Total_cloud_cover', time1=0, x=xx, y=yy)
out['cloud'] = cb.Data(x, y, clouds / 100., 'fraction (0--1)')
nc.close()
return out
vwnd=netcdf.use(f,'V_GRD_L103')
x=netcdf.use(f,'lon'); x[x>180]=x[x>180]-360
y=netcdf.use(f,'lat')
x,y=np.meshgrid(x,y)
# check time:
ttmp=load_time(f)
if ttmp.size==time.size and np.all(ttmp==time): print ' time ok'
else:
print ' time differs !!!!',
uwnd=fix_time(ttmp,uwnd,time[0],time[-1])
vwnd=fix_time(ttmp,vwnd,time[0],time[-1])
print ' ...fixed!'
#
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'
f=files['cc']
clouds=netcdf.use(f,'T_CDC_L200')
x=netcdf.use(f,'lon'); x[x>180]=x[x>180]-360
y=netcdf.use(f,'lat')
x,y=np.meshgrid(x,y)
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 era5_file_data(files, quiet=False):
'''
ECMWF ERA5 data for ROMS
variables:
# radiation:
msdwlwrf - mean_surface_downward_long_wave_radiation_flux
#msdwswrf - mean_surface_downward_short_wave_radiation_flux -- not needed
msnlwrf - mean_surface_net_long_wave_radiation_flux
msnswrf - mean_surface_net_short_wave_radiation_flux
# rain:
mtpr - mean_total_precipitation_rate
# wind:
u10 - 10m_u_component_of_wind
v10 - 10m_v_component_of_wind
# temp:
t2m - 2m_temperature
d2m - 2m_dewpoint_temperature (for relative humidity)
# pres:
sp - surface_pressure
# clouds:
tcc - 'total_cloud_cover
'''
### # some variables may have different names!
### Vars={}
### Vars['v10u']='v10u','u10'
### Vars['v10v']='v10v','v10'
### Vars['v2t']='v2t','t2m'
### Vars['v2d']='v2d','d2m'
###
#### def find_v(name):
### if name in Vars.keys():
### for v in Vars[name]:
### if varfile(v): return v
### else: return name
def varfile(var):
for f in files:
if var in netcdf.varnames(f): return f
##3 def check_var_type(var):
### # new interim dataserver provides forec+analysis vars with extra dim
### # 'type', 0 or 1
### if var.ndim==4:
### if not quiet: print(' dealing with var type... '),
### v=np.zeros(var.shape[1:],var.dtype)
### v[::2]=var[0,::2,...]
### v[1::2]=var[1,1::2,...]
### var=v
### if not quiet: print('done.')
###
### return var
out = {}
# time:
File = varfile('t2m') # air temp, for instance
if not quiet: print(' reading time from file %s' % File)
time = netcdf.nctime(File, 'time')
# check if last time at 00h:
if time[-1].hour != 0:
dateEnd = datetime.datetime(time[-1].year, time[-1].month,
time[-1].day) + datetime.timedelta(days=1)
time = np.append(time, dateEnd)
else:
dateEnd = 0
out['time'] = time
### # all times from analysis file, except last ind which will be
###### # the last time of forecast file
### aFile=varfile(find_v('v2t')) # air temp, for instance
###### fFile=varfile(find_v('ssr')) # sw rad, for instance
### if not quiet: print(' reading "analysis" time from file %s' % aFile)
### aTime=netcdf.nctime(aFile,'time')
### aTime.sort() # analysis+forecast files may not have time sorted!!
### if not quiet: print(' reading "forecast" time from file %s' % fFile)
### fTime=netcdf.nctime(fFile,'time')
### fTime.sort() # this one should be sorted...
### time=np.append(aTime,fTime[-1])
### out['time']=time
##
# # calc number of forecast steps stored,nforec (used by accum2avg)
# if [fTime[i].hour for i in range(8)]==range(3,22,3)+[0]: nforec=4
# elif [fTime[i].hour for i in range(4)]==range(6,19,6)+[0]: nforec=2
# else:
# if not quiet: print('INTERIM WRONG TIME: cannot n forec steps')
# return
#
### if not quiet: print(' ==> n forecast steps = %d' % nforec)
# x,y:
if not quiet: print(' reading x,y from file %s' % File)
x = netcdf.use(File, 'longitude')
y = netcdf.use(File, 'latitude')
x[x > 180] = x[x > 180] - 360
if x.ndim == 1 and y.ndim == 1:
x, y = np.meshgrid(x, y)
# tair [K-->C]
if not quiet: print(' --> T air')
vname = 't2m'
f = varfile(vname)
tair = netcdf.use(f, vname) - 273.15
if dateEnd:
if not quiet: print(' fill_tend...')
tair = fill_tend(tair)
out['tair'] = Data(x, y, tair, 'Celsius')
# R humidity [0--1]
if not quiet: print(' --> R humidity (from T dew)')
vname = 'd2m'
f = varfile(vname)
Td = netcdf.use(f, vname) - 273.15
if dateEnd:
if not quiet: print(' fill_tend... (T dew)')
Td = fill_tend(Td)
T = tair
rhum = air_sea.relative_humidity(T, Td)
rhum[rhum > 1] = 1
out['rhum'] = Data(x, y, rhum, '0--1')
# surface pressure [Pa]
if not quiet: print(' --> Surface pressure')
vname = 'sp'
f = varfile(vname)
pres = netcdf.use(f, vname)
if dateEnd:
if not quiet: print(' fill_tend...')
pres = fill_tend(pres)
out['pres'] = Data(x, y, pres, 'Pa')
def avg_fix_time(v, DT):
'''Fix data to right time in avg rate fields (ie, prev half hour to now)
See:
https://confluence.ecmwf.int/display/CKB/ERA5+data+documentation#ERA5datadocumentation-Meanratesandaccumulations
'''
DTstep = 1
a = DTstep / 2.
b = DT - a
u = np.zeros_like(v)
u[:-1] = (v[:-1] * b + v[1:] * a) / (a + b)
# last one lost, use prev value (from 30 min before)
u[-1] = v[-1]
return u
DT = (time[1] - time[0]).total_seconds() / 3600. # hours
# P rate [kg m-2 s-1 --> cm day-1]
if not quiet: print(' --> P rate')
vname = 'mtpr'
f = varfile(vname)
prate = netcdf.use(f, vname)
if not quiet: print(' avg_fix_time - DTstep=1h - DT=%.2f h' % DT)
prate = avg_fix_time(prate, DT)
if dateEnd:
if not quiet: print(' fill_tend...')
prate = fill_tend(prate)
conv = 100 * 86400 / 1000. # from kg m-2 s-1 --> cm day-1
prate = prate * conv # cm day-1
prate[prate < 0] = 0
out['prate'] = Data(x, y, prate, 'cm day-1')
# dt=(TIMe[1]-time[0]).total_seconds()/3600.
# if not quiet: print(' accum to avg at correct time - DTstep=1h - DT=%.2f h'%DT)
# prate2=accum2avg(prate,DT)
# prate2=prate2*1000
# return prate,prate2
# prate=check_var_type(prate)
# if not quiet and np.any(sortInds!=range(len(sortInds))): print(' sort DONE')
# if not quiet: print(' accum2avg...')
# prate=accum2avg(prate,nforec)
# conv= 100*86400 # from m s-1 --> cm day-1
# #conv= 100*86400/1000. # from kg m-2 s-1 --> cm day-1
# prate=prate*conv # cm day-1
# if not quiet: print(' fill_t0...')
# prate=fill_t0(prate)
# prate[prate<0]=0
# out['prate']=Data(x,y,prate,'cm day-1')
#
# return out
# Net shortwave flux [W m-2 --> W m-2]
if not quiet: print(' --> Net shortwave flux')
vname = 'msnswrf'
f = varfile(vname)
sw_net = netcdf.use(f, vname)
if not quiet: print(' avg_fix_time - DTstep=1h - DT=%.2f h' % DT)
sw_net = avg_fix_time(sw_net, DT)
if dateEnd:
if not quiet: print(' fill_tend...')
sw_net = fill_tend(sw_net)
out['radsw'] = Data(x, y, sw_net, 'W m-2', info='positive downward')
# Net longwave flux [W m-2 --> W m-2]
if not quiet: print(' --> Net longwave flux')
vname = 'msnlwrf'
f = varfile(vname)
lw_net = netcdf.use(f, vname) * -1 # positive upward (*-1)
# here vars have roms-agrif signs --> radlw is positive upward!
# conversion to ROMS is done in surface.py
if not quiet: print(' avg_fix_time - DTstep=1h - DT=%.2f h' % DT)
lw_net = avg_fix_time(lw_net, DT)
if dateEnd:
if not quiet: print(' fill_tend...')
lw_net = fill_tend(lw_net)
out['radlw'] = Data(x, y, lw_net, 'W m-2', info='positive upward')
# longwave down:
if not quiet: print(' --> Down longwave flux')
vname = 'msdwlwrf'
f = varfile(vname)
lw_down = netcdf.use(f, vname) * -1 # positive upward (*-1)
if not quiet: print(' avg_fix_time - DTstep=1h - DT=%.2f h' % DT)
lw_down = avg_fix_time(lw_down, DT)
if dateEnd:
if not quiet: print(' fill_tend...')
lw_down = fill_tend(lw_down)
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')
vname = 'u10'
f = varfile(vname)
uwnd = netcdf.use(f, vname)
vname = 'v10'
f = varfile(vname)
vwnd = netcdf.use(f, vname)
if dateEnd:
if not quiet: print(' fill_tend...')
uwnd = fill_tend(uwnd)
vwnd = fill_tend(vwnd)
out['uwnd'] = Data(x, y, uwnd, 'm s-1')
out['vwnd'] = Data(x, y, vwnd, 'm s-1')
# speed and stress:
if 0:
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['sustr'] = Data(x, y, taux, 'Pa')
out['svstr'] = Data(x, y, tauy, 'Pa')
# Cloud cover [0--1]:
if not quiet: print(' --> Cloud cover')
vname = 'tcc'
f = varfile(vname)
clouds = netcdf.use(f, vname)
if dateEnd:
if not quiet: print(' fill_tend...')
clouds = fill_tend(clouds)
out['cloud'] = Data(x, y, clouds, 'fraction (0--1)')
return out