Example #1
0
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
Example #2
0
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)
Example #3
0
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
Example #4
0
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
Example #5
0
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
Example #6
0
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
Example #7
0
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('')
Example #8
0
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('')
Example #9
0
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
Example #10
0
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 ""
Example #11
0
File: wrf.py Project: jcmt/okean
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
Example #12
0
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
Example #13
0
    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')
Example #14
0
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
Example #15
0
  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)
Example #16
0
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
Example #17
0
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