elif cthresh == '10mm':
ithresh = 4
cthresh_title = '$\geq$ 10 mm'
elif cthresh == '25mm':
ithresh = 5
cthresh_title = '$\geq$ 25 mm'
elif cthresh == '50mm':
ithresh = 6
cthresh_title = '$\geq$ 50 mm'
else:
print 'Invalid threshold', cthresh
print 'Please use POP, 1mm, 2p5mm, 5mm, 10mm, 25mm, 50mm'
print 'Exiting.'
sys.exit()
yyyy, mm, dd, hh = splitdate(cyyyymmddhh)
cyyyy = str(yyyy)
cdd = str(dd)
chh = str(hh)
cmonths = [
'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct',
'Nov', 'Dec'
]
cmonth = cmonths[mm - 1]
iyyyymmddhh = int(cyyyymmddhh)
# ---- read in precipitation analysis
filename = data_directory + 'precip_analyses_ccpa_v1_2002010100_to_2016123100.nc'
print 'reading ', filename
nc = Dataset(filename)
elif cthresh == '10mm':
ithresh = 4
cthresh_title = '$\geq$ 10 mm'
elif cthresh == '25mm':
ithresh = 5
cthresh_title = '$\geq$ 25 mm'
elif cthresh == '50mm':
ithresh = 6
cthresh_title = '$\geq$ 50 mm'
else:
print 'Invalid threshold', cthresh
print 'Please use POP, 1mm, 2p5mm, 5mm, 10mm, 25mm, 50mm'
print 'Exiting.'
sys.exit()
yyyy,mm,dd,hh = splitdate(cyyyymmddhh)
cyyyy = str(yyyy)
cdd = str(dd)
chh = str(hh)
cmonths = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
cmonth = cmonths[mm-1]
iyyyymmddhh = int(cyyyymmddhh)
yyyy_verif,mm_verif,dd_verif,hh_verif = splitdate(date_verif)
cyyyy_verif = str(yyyy_verif)
cdd_verif = str(dd_verif)
chh_verif = str(hh_verif)
cmonth_verif = cmonths[mm_verif-1]
# ---- read in precipitation analysis
acglall = []
bias = None
ntime = None
for date in dates:
datev = dateutils.dateshift(date, fhour)
# read analysis
filea = os.path.join(analpath, 'pgbanl.ecm.%s' % datev)
grbs = pygrib.open(filea)
grb = grbs.select(shortName=vargrb, level=level)[0]
verif_data = grb.values[::-1, :]
grbs.close()
#print verif_data.shape, verif_data.min(), verif_data.max()
# read climo
grbsclimo = pygrib.open(
os.path.join(climopath, 'cmean_1d.1959%s' % datev[4:8]))
yyyy, mm, dd, hh = dateutils.splitdate(datev)
grbclimo = grbsclimo.select(shortName=vargrb,
level=level,
dataTime=100 * hh)[0]
climo_data = grbclimo.values[::-1, :]
grbsclimo.close()
#print climo_data.shape, climo_data.min(), climo_data.max()
# read forecast data from tiled history files.
cube_data = np.zeros((6, res, res), np.float32)
for ntile in range(1, 7, 1):
datafile = '%s/%s/longfcst/fv3_history.tile%s.nc' % (datapath, date,
ntile)
nc = Dataset(datafile)
if ntime is None:
times = nc['time'][:].tolist()
ntime = times.index(
date = sys.argv[1]
ifhr1 = int(sys.argv[2])
ifhr2 = int(sys.argv[3])
ifhrinc = int(sys.argv[4])
if len(sys.argv) > 5:
run = sys.argv[5]
else:
run = 'FIMY'
expname = os.getenv('EXPT')
pngdir = os.getenv('PNGDIR')
if expname is None:
expname = 'gfsenkf_t574'
pngdir = '/lfs1/projects/gfsenkf/hurrplots/%s' % date
hr = date[8:10]
yyyy, mm, dd, hh = splitdate(date)
julday = dayofyear(yyyy, mm, dd) + 1
datapath = '/lfs1/projects/gfsenkf/tcvitals'
globstring = datapath + '/reftrk*%s*' % date
print globstring
reftrks = glob.glob(globstring)
print reftrks
reftrks.insert(0, 'WPAC')
reftrks.insert(0, 'EPAC')
reftrks.insert(0, 'ATL')
# just use the three basins (no domains centered on storms)
#reftrks = ['ATL','EPAC','WPAC']
