sf = dict(dpi=150) # print properties
folder = '/home/me/Research/Dynamical Downscaling/figures/' # figure directory
if __name__ == '__main__':
# load WRF data
domains = [1]; ndom = len(domains) # WRF domain
way2, = openWRF(exp='2way-ctrl',years=[1983],months=[3], domains=domains)
grell3, = openWRF(exp='grell3-ctrl',years=[1981],months=[2], domains=domains)
ctrl1, = openWRF(exp='ctrl-1',years=[1985],months=[5], domains=domains)
ensA, = openWRF(exp='ens-A-ctrl',years=[1985],months=[5], domains=domains)
WRF = (ctrl1, grell3, )
# load CFSR data
CFSR = openCFSRclim()
# load NARR data
NARR = openNARRclim() # just load whole climatology
# print NARR
## compute data
data = []; lon = []; lat=[] # list of data and coordinate fields to be plotted
# compute average WRF precip
for exp in WRF:
nrec = exp.time.values[-1]+1
ndays = exp.xtime(time=nrec-1).get() /24/60 # xtime is in minutes, need days
dailyrain = exp.rain(time=nrec-1).get().squeeze() / ndays
data.append(dailyrain)
lon.append(exp.lon.get()); lat.append(exp.lat.get())
# compute annual/seasonal mean for NARR and CFSR
months = [31.,28.25,31.,30.,31.,30.,31.,31.,30.,31.,30.,31.]; days = 0.
# rng = [0,1,2,9,10,11]
rng = range(12)
exps = []; axtitles = []
for exp,prd in zip(explist,period):
ext = exp; axt = ''
if isinstance(exp,str):
if exp[0].isupper():
if exp == 'GPCC': ext = (openGPCCclim(resolution=resolution,period=prd),); axt = 'GPCC Observations' # ,period=prd
elif exp == 'CRU': ext = (openCRUclim(period=prd),); axt = 'CRU Observations'
elif exp[0:5] == 'PRISM': # all PRISM derivatives
if exp == 'PRISM': prismfile = 'prism_clim.nc'
elif exp == 'PRISM-10km': prismfile = 'prism_10km.nc'
if len(vars) ==1 and vars[0] == 'rain':
ext = (openGPCCclim(resolution='0.25'), openPRISMclim(filename=prismfile)); axt = 'PRISM (and GPCC)'
else: ext = (openCRUclim(period='1979-2009'), openPRISMclim(filename=prismfile)); axt = 'PRISM (and CRU)'
# ext = (openPRISMclim(),)
elif exp == 'CFSR': ext = (openCFSRclim(period=prd),); axt = 'CFSR Reanalysis'
elif exp == 'NARR': ext = (openNARRclim(),); axt = 'NARR Reanalysis'
else: # all other uppercase names are CESM runs
ext = (openCESMclim(exp=exp, period=prd),)
axt = CESMtitle.get(exp,exp)
else: # WRF runs are all in lower case
ext = openWRFclim(exp=exp, period=prd, domains=dom)
axt = WRFtitle.get(exp,exp)
exps.append(ext); axtitles.append(axt)
print exps[-1][-1]
# count experiment tuples (layers per panel)
nexps = []; nlen = len(exps)
for n in xrange(nlen):
if not isinstance(exps[n],(tuple,list)): # should not be necessary
exps[n] = (exps[n],)
nexps.append(len(exps[n])) # layer counter for each panel
years=[1981],
months=[2],
domains=domains)
ctrl1, = openWRF(exp='ctrl-1', years=[1985], months=[5], domains=domains)
ensA, = openWRF(exp='ens-A-ctrl',
years=[1985],
months=[5],
domains=domains)
WRF = (
ctrl1,
grell3,
)
# load CFSR data
CFSR = openCFSRclim()
# load NARR data
NARR = openNARRclim() # just load whole climatology
# print NARR
## compute data
data = []
lon = []
lat = [] # list of data and coordinate fields to be plotted
# compute average WRF precip
for exp in WRF:
nrec = exp.time.values[-1] + 1
ndays = exp.xtime(time=nrec -
1).get() / 24 / 60 # xtime is in minutes, need days
dailyrain = exp.rain(time=nrec - 1).get().squeeze() / ndays
data.append(dailyrain)
lon.append(exp.lon.get())
lat.append(exp.lat.get())
if exp == 'PRISM': prismfile = 'prism_clim.nc'
elif exp == 'PRISM-10km': prismfile = 'prism_10km.nc'
if len(vars) == 1 and vars[0] == 'rain':
ext = (openGPCCclim(resolution='0.25'),
openPRISMclim(filename=prismfile))
axt = 'PRISM (and GPCC)'
else:
ext = (openCRUclim(period='1979-2009'),
openPRISMclim(filename=prismfile))
axt = 'PRISM (and CRU)'
# ext = (openPRISMclim(),)
elif exp == 'CFSR':
ext = (openCFSRclim(period=prd), )
axt = 'CFSR Reanalysis'
elif exp == 'NARR':
ext = (openNARRclim(), )
axt = 'NARR Reanalysis'
else: # all other uppercase names are CESM runs
ext = (openCESMclim(exp=exp, period=prd), )
axt = CESMtitle.get(exp, exp)
else: # WRF runs are all in lower case
ext = openWRFclim(exp=exp, period=prd, domains=dom)
axt = WRFtitle.get(exp, exp)
exps.append(ext)
axtitles.append(axt)
print exps[-1][-1]
# count experiment tuples (layers per panel)
nexps = []
nlen = len(exps)
for n in xrange(nlen):
if not isinstance(exps[n], (tuple, list)): # should not be necessary