def interpAlt(alt_in, Z, val_in, intrpOrder, logFlg):
if logFlg:
val_out = np.exp(np.flipud( intrpUniSpl( np.flipud(alt_in), np.log(np.flipud(val_in)), k=intrpOrder )( np.flipud(Z) ) ) )
else:
val_out = np.flipud( intrpUniSpl( np.flipud(alt_in), np.flipud(val_in), k=intrpOrder )( np.flipud(Z) ) )
return val_out
def main():
#----------------
# Initializations
#----------------
loc = 'fl0'
sondeDir = '/data1/ancillary_data/sondes/' + loc.lower() + '/'
verW = 'v77' # version 77 is used for individual retrievals at specific times
pltFlg = True
#-------------------
# Interpolation Oder
#-------------------
intrpOrder = 1
nSkip = 1 # Number of points to skip when merging WACCM and NCEP profiles
#-----------------------
# Date Range of interest
#-----------------------
iyear = 2010
imnth = 1
iday = 1
fyear = 2016
fmnth = 12
fday = 31
#----------------------
# Output Data Directory
#----------------------
dataDir = '/data1/' + loc.lower() + '/'
#-------------------------
# WACCM monthly means file
#-------------------------
WACCMfile = '/data/Campaign/' + loc.upper(
) + '/waccm/WACCM_pTW-meanV6.' + loc.upper()
#--------------------------------------------------------------------
# Read WACCM monthly mean data. WACCM monthly mean file is ascending,
# adjust so that it is descending. Also units are in km.
#--------------------------------------------------------------------
with open(WACCMfile, 'r') as fopen:
lines = fopen.readlines()
nlyrs = int(lines[0].strip().split()[0])
s_ind = 3
Z = np.flipud(
np.array([
float(row.strip().split()[0]) for row in lines[s_ind:nlyrs + s_ind]
]))
waccmT = np.flipud(
np.array([[float(x) for x in line.strip().split()[1:]]
for line in lines[s_ind:nlyrs + s_ind]]))
s_ind = 3 + nlyrs + 2
waccmP = np.flipud(
np.array([[float(x) for x in line.strip().split()[1:]]
for line in lines[s_ind:nlyrs + s_ind]]))
s_ind = 3 + nlyrs + 2 + nlyrs + 2
waccmW = np.flipud(
np.array([[float(x) for x in line.strip().split()[1:]]
for line in lines[s_ind:nlyrs + s_ind]]))
#----------------------------
# File and directory checking
#----------------------------
ckDir(sondeDir, exitFlg=True)
ckDir(dataDir, exitFlg=True)
s = rs.SondeClass(sondeDir,
loc,
iyear=iyear,
imnth=imnth,
iday=iday,
fyear=fyear,
fmnth=fmnth,
fday=fday,
fleoutFlg=False)
s.readfilessonde()
s.sondePrf()
sonde = s.sonde
sondedt = np.asarray(sonde['dt'])
sondedate = np.asarray(sonde['date'])
sondealt = np.asarray(sonde['Alt_km_a'])
sondeH2OPrf_a = np.asarray(sonde['H2Omr_ppmv_a']) * 1e-6
sondeH2OPrfsd_a = np.asarray(sonde['H2Osd_ppmv_a'])
sondealt_a = np.asarray(sonde['Alt_km_a'])
sondeairmass_a = np.asarray(sonde['airmass_a'])
#---------------------------
# Iterate through sonde Prfs
#---------------------------
for i, sngDay in enumerate(sondedt):
#----------------------------
# Get corresponding directory
#----------------------------
baseDir = dataDir + '{0:04d}{1:02d}{2:02d}/'.format(
sngDay.year, sngDay.month, sngDay.day)
if not os.path.exists(baseDir): continue
#--------------------------------------------------
# Find month index for monthly WACCM water profiles
#--------------------------------------------------
mnthInd = sngDay.month - 1 # -1 because January is in the 0th column
#---------------------------------
#
#---------------------------------
Z_day = sondealt[i]
Q_day = sondeH2OPrf_a[i]
sondetop = Z_day[-1]
topInd = np.argmin(
abs(Z - sondetop
)) # Where top of NCEP reanalysis fits in WACCM grid height
Zin = np.concatenate((Z[0:(topInd - nSkip)], np.flipud(Z_day)))
SHin = np.concatenate((waccmW[0:(topInd - nSkip),
mnthInd], np.flipud(Q_day)))
#-----------------------------------
# Interpolate retrieved profile onto
# sfit input grid
#-----------------------------------
H2Oout = interpolate.interp1d(Zin,
SHin,
axis=0,
fill_value=Q_day[0],
bounds_error=False,
kind='linear')(Z)
H2Oout2 = np.flipud(
intrpUniSpl(np.flipud(Zin), np.flipud(SHin), k=1)(np.flipud(Z)))
#------------------------
# remove v4 for 6-hourly files
#------------------------
waterFiles_test = glob.glob(baseDir + 'w-120*' + verW)
waterFiles_test = [
i for i in waterFiles_test if len(os.path.basename(i)) > 10
]
if len(waterFiles_test) >= 1:
for f in waterFiles_test:
os.remove(f)
#---------------------
# Write out water file
#---------------------
tstamp = '{0:04d}{1:02d}{2:02d}.{3:02d}{4:02d}{5:02d}'.format(
sngDay.year, sngDay.month, sngDay.day, sngDay.hour, sngDay.minute,
sngDay.second)
with open(baseDir + 'w-120.' + tstamp + '.' + verW, 'w') as fopen:
fopen.write(' 1 H2O from Sonde \n')
for row in segmnt(H2Oout, 5):
strformat = ','.join('{:>12.4E}' for i in row) + ', \n'
fopen.write(strformat.format(*row))
#--------------------
# Create plots to pdf
#--------------------
if pltFlg:
pdfsav = PdfPages(baseDir + 'Sonde_' + tstamp +
'_WaterProfile.pdf')
fig1, ax1 = plt.subplots()
ax1.plot(H2Oout, Z, 'rx-', label='Interpolated Water Profile')
ax1.plot(Q_day, Z_day, 'bx-', label='Original Sonde Water Profile')
ax1.grid(True, which='both')
ax1.legend(prop={'size': 9})
ax1.set_ylabel('Altitude [km]')
ax1.set_xlabel('VMR')
ax1.tick_params(axis='x', which='both', labelsize=8)
ax1.set_ylim((Z[-1], 60))
#ax1.set_xlim((0,np.max((waccmW[-1,mnthInd],dayShum[-1]))))
ax1.set_title(sngDay)
pdfsav.savefig(fig1, dpi=250)
pdfsav.close()
print 'Finished processing folder: {}'.format(sngDay)
def main():
#---------
# Location
#---------
loc = 'tab'
#loc = 'mlo'
#------------------------------------
# Version number to append water file
#------------------------------------
verW = 'v3'
#-----------------------------------------
# Interpolation flag for NCEP re-analysis:
# False = Nearest point. True = linear
#-----------------------------------------
interpFlg = True
#---------------------
# Interpolation things
#---------------------
nSkip = 3 # Number of points to skip when merging WACCM and NCEP profiles
intrpOrder = 1 # Order of interpolation
logFlg = True # Flag to do interpolation of log of water
#-----------------------
# Date Range of interest
#-----------------------
iyear = 2017
imnth = 9
iday = 1
fyear = 2017
fmnth = 12
fday = 31
#-------------------------------
# NCEP Reanalysis data directory
#-------------------------------
NCEPdirShum = '/data1/ancillary_data/NCEPdata/NCEP_Shum/'
NCEPdirHgt = '/data1/ancillary_data/NCEPdata/NCEP_hgt/'
#---------------
# Data Directory
#---------------
dataDir = '/data1/' + loc.lower() + '/'
#dataDir = '/data1/iortega/WYO/'
#-------------------------
# WACCM monthly means file
#-------------------------
WACCMfile = '/data/Campaign/' + loc.upper(
) + '/waccm/WACCM_pTW-meanV6.' + loc.upper()
#---------------------
# Establish date range
#---------------------
dRange = sc.DateRange(iyear, imnth, iday, fyear, fmnth, fday)
#---------------------------------------
# Altitude levels for different stations
#---------------------------------------
if loc.lower() == 'tab':
Z = np.array([
120.0000, 110.0000, 100.0000, 95.0000, 90.0000, 85.0000, 80.0000,
75.0000, 70.0000, 65.0000, 60.0000, 55.0000, 50.0000, 48.0000,
46.0000, 44.0000, 42.0000, 40.0000, 38.0000, 36.0000, 34.0000,
32.0000, 30.0000, 28.0000, 26.0000, 24.0000, 22.0000, 20.0000,
18.0000, 16.0000, 14.0000, 12.0000, 10.0000, 9.0000, 8.0000,
7.0000, 6.0000, 5.0000, 4.0000, 3.0000, 2.0000, 1.0000, 0.2250
])
sLat = 76.52
sLon = 291.23 # 68.77 W = (360.0 - 68.77) = 291.23 E
elif loc.lower() == 'mlo':
Z = np.array([
120.0000, 110.0000, 100.0000, 95.0000, 90.0000, 85.0000, 80.0000,
75.0000, 70.0000, 65.0000, 60.0000, 55.0000, 50.0000, 48.0000,
46.0000, 44.0000, 42.0000, 40.0000, 38.0000, 36.0000, 34.0000,
32.0000, 30.0000, 28.0000, 26.0000, 24.0000, 22.0000, 20.0000,
18.0000, 16.0000, 14.0000, 12.0000, 10.0000, 9.0000, 8.0000,
7.0000, 6.0000, 5.0000, 4.0000, 3.3960
])
sLat = 19.4
sLon = 204.43 # 155.57 W = (360 - 155.57) = 204.43 E
elif loc.lower() == 'fl0':
Z = np.array([
120.0000, 110.0000, 100.0000, 94.0000, 90.0000, 85.0000, 80.0000,
75.0000, 70.0000, 65.0000, 60.0000, 55.0000, 50.0000, 48.0000,
46.0000, 44.0000, 42.0000, 40.0000, 38.0000, 36.0000, 34.0000,
32.0000, 30.0000, 28.0000, 26.0000, 24.0000, 22.0000, 20.0000,
18.0000, 16.0000, 14.0000, 12.0000, 10.0000, 9.0000, 8.0000,
7.0000, 6.0000, 5.0000, 4.0000, 3.0000, 2.0000, 1.6120
])
sLat = 40.4
sLon = 254.76 # 105.24 W = (360 - 105.24) = 254.76 E
###sLat = 42.73
###sLon = 253.68 #WYOBA
#----------------------------
# File and directory checking
#----------------------------
ckDir(NCEPdirShum, exitFlg=True)
ckDir(NCEPdirHgt, exitFlg=True)
ckDir(dataDir, exitFlg=True)
ckFile(WACCMfile, exitFlg=True)
#--------------------------------------------------------------------
# Read WACCM monthly mean data. WACCM monthly mean file is ascending,
# adjust so that it is descending. Also units are in km.
#--------------------------------------------------------------------
with open(WACCMfile, 'r') as fopen:
lines = fopen.readlines()
nlyrs = int(lines[0].strip().split()[0])
s_ind = 3
Z = np.flipud(
np.array([
float(row.strip().split()[0]) for row in lines[s_ind:nlyrs + s_ind]
]))
waccmT = np.flipud(
np.array([[float(x) for x in line.strip().split()[1:]]
for line in lines[s_ind:nlyrs + s_ind]]))
s_ind = 3 + nlyrs + 2
waccmP = np.flipud(
np.array([[float(x) for x in line.strip().split()[1:]]
for line in lines[s_ind:nlyrs + s_ind]]))
s_ind = 3 + nlyrs + 2 + nlyrs + 2
waccmW = np.flipud(
np.array([[float(x) for x in line.strip().split()[1:]]
for line in lines[s_ind:nlyrs + s_ind]]))
#--------------------------------------------
# Walk through first level of directories in
# data directory and collect directory names
#--------------------------------------------
dirLst = []
for drs in os.walk(dataDir).next()[1]:
#-------------------------------------------
# Test directory to make sure it is a number
#-------------------------------------------
try:
int(drs[0:4])
except:
continue
if dRange.inRange(int(drs[0:4]), int(drs[4:6]), int(drs[6:8])):
dirLst.append(dataDir + drs + '/')
dirLst.sort()
#--------------------------------------------------------
# Loop through folders for individual years. This is done
# because NCEP NetCDF files are by year. Therefore only
# have to open and read yearly NCEP file once
#--------------------------------------------------------
yrList = dRange.yearList()
for year in yrList:
#-----------------------------
# Find all folders within year
#-----------------------------
dirListYr = np.array(
[d for d in dirLst if int(os.path.basename(d[:-1])[0:4]) == year])
#-------------------------------
# Open and read year NetCDF file
#-------------------------------
shumFile = NCEPdirShum + 'shum.' + str(year) + '.nc'
ghghtFile = NCEPdirHgt + 'hgt.' + str(year) + '.nc'
#-----------------------
# Specific humidity file
#-----------------------
#with netcdf.netcdf_file(shumFile,'r',mmap=False) as shumF: # Can only be done with scipy Ver > 0.12.0
#shumF = netcdf.netcdf_file(shumFile,'r',mmap=False)
shumObj = nc.Dataset(shumFile, 'r')
PlvlShum = shumObj.variables['level'] # Starts at the surface
timeShum = shumObj.variables['time'] # hours since 1-1-1 00:00:0.0
latShum = shumObj.variables['lat'] # degrees_north
lonShum = shumObj.variables['lon'] # degrees_east
shum = shumObj.variables[
'shum'] # Units: [kg/kg]. Dimensions: [time][vert][lat][lon] Only goes to 300mb!!
PlvlShum = PlvlShum[:]
timeShum = timeShum[:]
latShum = latShum[:]
lonShum = lonShum[:]
shum = shum[:]
shumObj.close()
#----------------------------------------
# Convert Specific humidity from kg/kg to
# molecules/molecules
#----------------------------------------
shum = shum * 1.608
#-----------------------------------------------
# If not interpolating point in NCEP re-analysis
# find closes lat lon indicies
#-----------------------------------------------
if not interpFlg:
latind = findCls(latShum, sLat)
lonind = findCls(lonShum, sLon)
#------------------------------------------------------
# Convert hours since 1-1-1 00:00:00 to datetime object
# NCEP reanalysis uses udunits for encoding times,
# meaning they don't follow standard leap year
# convention (whatever this means?). Must follow some
# leap year convention, but not clear. Therefore, take
# the first time in file as 1-1-YEAR.
#------------------------------------------------------
timeHrs = timeShum - timeShum[0]
timeAll = np.array([
dt.datetime(year, 1, 1) + dt.timedelta(hours=int(h))
for h in timeHrs
])
#-------------------------
# Geopotential Height file
#-------------------------
#with netcdf.netcdf_file(ghghtFile,'r',mmap=False) as gHghtF: # Can only be done with scipy Ver > 0.12.0
#gHghtF = netcdf.netcdf_file(ghghtFile,'r',mmap=False)
gHghtF = nc.Dataset(ghghtFile, 'r')
hgt = gHghtF.variables[
'hgt'] # Height in [meters]. Dimensions: [time][vert][lat][lon]
PlvlHght = gHghtF.variables['level']
hgt = hgt[:]
PlvlHght = PlvlHght[:]
gHghtF.close()
#------------------------------------------
# Loop through all folders in specific year
#------------------------------------------
for sngDir in dirListYr:
#----------------------------
# Get date in datetime format
#----------------------------
oneDay = dt.datetime(int(os.path.basename(sngDir[:-1])[0:4]),
int(os.path.basename(sngDir[:-1])[4:6]),
int(os.path.basename(sngDir[:-1])[6:8]))
#--------------------------------------------------
# Find month index for monthly WACCM water profiles
#--------------------------------------------------
mnthInd = oneDay.month - 1 # -1 because January is in the 0th column
#--------------------------------------
# Get hgt and specific humidity for day
#--------------------------------------
ind = np.where(timeAll == oneDay)[0]
dayHghtMat = np.squeeze(hgt[ind, :, :, :])
dayShumMat = np.squeeze(shum[ind, :, :, :])
#-----------------------------------------------------------
# 'Unpack' the data => (data[int])*scale_factor + add_offset
#-----------------------------------------------------------
#dayHghtMat = dayHghtMat * hgt.scale_factor + hgt.add_offset
#dayShumMat = dayShumMat * shum.scale_factor + shum.add_offset
#-----------------------------------------------------
# For each level interpolate hgt and specific humidity
# based on latitude and longitude of site
#-----------------------------------------------------
dayHgt = np.zeros(np.shape(dayShumMat)[0])
dayShum = np.zeros(np.shape(dayShumMat)[0])
for lvl in range(0, np.shape(dayShumMat)[0]):
dayHgtLvl = np.squeeze(dayHghtMat[lvl, :, :])
if interpFlg:
dayHgt[lvl] = interp2d(lonShum,
latShum,
dayHgtLvl,
kind='linear',
bounds_error=True)(sLon, sLat)
else:
dayHgt[lvl] = dayHgtLvl[latind, lonind]
dayShumLvl = np.squeeze(dayShumMat[lvl, :, :])
if interpFlg:
dayShum[lvl] = interp2d(lonShum,
latShum,
dayShumLvl,
kind='linear',
bounds_error=True)(sLon, sLat)
else:
dayShum[lvl] = dayShumLvl[latind, lonind]
dayHgt.astype(float)
dayShum.astype(float)
dayHgt = dayHgt / 1000.0 # Convert height units [m] => [km]
#---------------------------------------------------------
# Construct specific humidity and height profiles for
# interpolation on to the sfit input grid which is the
# same as the WACCM height in monthly profile file.
# NCEP reanalysis data only goes to 300mb therefore,
# merge with monthly averaged WACCM water profiles > 300mb
#---------------------------------------------------------
NCEPtop = dayHgt[-1]
topInd = np.argmin(
abs(Z - NCEPtop)
) # Where top of NCEP reanalysis fits in WACCM grid height
#Zin = np.concatenate( ( Z[0:(topInd-nSkip)] , np.flipud(dayHgt)) , axis=1 )
#SHin = np.concatenate( ( waccmW[0:(topInd-nSkip),mnthInd], np.flipud(dayShum)), axis=1 )
#Remove axis=1
Zin = np.concatenate((Z[0:(topInd - nSkip)], np.flipud(dayHgt)))
SHin = np.concatenate((waccmW[0:(topInd - nSkip),
mnthInd], np.flipud(dayShum)))
#--------------------------------------------------------------
# Interpolate to specific humidity on WACCM grid. X data must
# be increasing => flip dimensions and then flip back
#--------------------------------------------------------------
if logFlg:
SHout = np.exp(
np.flipud(
intrpUniSpl(np.flipud(Zin),
np.log(np.flipud(SHin)),
k=intrpOrder)(np.flipud(Z))))
else:
SHout = np.flipud(
intrpUniSpl(np.flipud(Zin), np.flipud(SHin),
k=intrpOrder)(np.flipud(Z)))
#---------------------
# Write out water file
#---------------------
with open(sngDir + 'w-120.' + verW, 'w') as fopen:
fopen.write(
' 1 H2O from NCEP reanalysis and WACCM V6 monthly mean \n'
)
for row in segmnt(SHout, 5):
strformat = ','.join('{:>12.4E}' for i in row) + ', \n'
fopen.write(strformat.format(*row))
#--------------------
# Create plots to pdf
#COMMENTED BELOW BY IVAN: CHECK MATPLOT PLT ERROR
#--------------------
pdfsav = PdfPages(sngDir + 'WaterProfile.pdf')
fig1, ax1 = plt.subplots()
ax1.plot(SHout, Z, 'rx-', label='Interpolated SH')
ax1.plot(waccmW[:, mnthInd], Z, 'bx-', label='WACCM V6 SH')
ax1.plot(dayShum, dayHgt, 'kx-', label='NCEP Reanalysis SH')
ax1.grid(True, which='both')
ax1.legend(prop={'size': 9})
ax1.set_ylabel('Altitude [km]')
ax1.set_xlabel('VMR [ppv]')
ax1.tick_params(axis='x', which='both', labelsize=8)
ax1.set_ylim((Z[-1], 60))
ax1.set_xlim((0, np.max((waccmW[-1, mnthInd], dayShum[-1]))))
ax1.set_title(oneDay)
pdfsav.savefig(fig1, dpi=250)
fig2, ax2 = plt.subplots()
ax2.plot(SHout, Z, 'rx-', label='Interpolated SH')
ax2.plot(waccmW[:, mnthInd], Z, 'bx-', label='WACCM V6 SH')
ax2.plot(dayShum, dayHgt, 'kx-', label='NCEP Reanalysis SH')
ax2.grid(True, which='both')
ax2.legend(prop={'size': 9})
ax2.set_ylabel('Altitude [km]')
ax2.set_xlabel('log VMR [ppv]')
ax2.tick_params(axis='x', which='both', labelsize=8)
ax2.set_xscale('log')
ax2.set_ylim((Z[-1], 60))
ax2.set_xlim((0, np.max((waccmW[-1, mnthInd], dayShum[-1]))))
ax2.set_title(oneDay)
pdfsav.savefig(fig2, dpi=250)
pdfsav.close()
print 'Finished processing folder: {}'.format(sngDir)
def main(argv):
#------------------
# Set default flags
#------------------
logFile = False
#---------------------------------
# Retrieve command line arguments
#---------------------------------
#------------------------------------------------------------------------------------------------------------#
try:
opts, args = getopt.getopt(sys.argv[1:], 'i:l:')
except getopt.GetoptError as err:
print str(err)
usage()
sys.exit()
#-----------------------------
# Parse command line arguments
#-----------------------------
for opt, arg in opts:
#-----------
# Input file
#-----------
if opt == '-i':
inputFile = arg
# Check if file exists
ckFile(inputFile,True)
# Option for Log File
elif opt == '-l':
if arg: logDir = arg
else: logDir = os.getcwd() +'/'
logFile = True
#------------------
# Unhandled options
#------------------
else:
print 'Unhandled option: ' + opt
usage()
sys.exit()
#------------------------------------------------------------------------------------------------------------#
#---------------------------------------
# Hard coded NCEP pressure values [mbar]
#---------------------------------------
nmcPress = np.array([0.4,1.0,2.0,5.0,10.0,30.0,50.0,70.0,100.0,150.0,200.0,250.0,300.0,400.0,500.0,700.0,850.0,1000.0])
#----------------
# Read input file
#----------------
inputs = {}
execfile(inputFile, inputs)
if '__builtins__' in inputs:
del inputs['__builtins__']
#-----------------------------------
# Check the existance of directories
# and files given in input file
#-----------------------------------
# Check directory of NCEP nmc data
if not ckDir(inputs['NCEPDir']):
print 'NCEP nmc directory does not exist: ' + inputs['NCEPDir']
sys.exit()
# check if '/' is included at end of path
if not( inputs['NCEPDir'].endswith('/') ):
inputs['NCEPDir'] = inputs['NCEPDir'] + '/'
# Check directory for output
if not ckDir(inputs['outBaseDir']):
print 'Output base directory does not exist: ' + inputs['outBaseDir']
sys.exit()
# check if '/' is included at end of path
if not( inputs['outBaseDir'].endswith('/') ):
inputs['outBaseDir'] = inputs['outBaseDir'] + '/'
if logFile:
# Check directory for log file
if not ckDir(logDir):
print 'Log File directory does not exist: ' + logDir
sys.exit()
# check if '/' is included at end of path
if not( logDir.endswith('/') ):
logDir = logDir + '/'
# Check for station layer file if doing interpolation
ckFile(inputs['WACCMfile'],True)
#--------------------
# Initialize log file
#--------------------
if logFile:
logFile = logging.getLogger('1')
logFile.setLevel(logging.INFO)
hdlr1 = logging.FileHandler(logDir+ 'MissingNMCzpt.log',mode='w')
fmt1 = logging.Formatter('%(asctime)s %(levelname)-8s %(message)s','%a, %d %b %Y %H:%M:%S')
hdlr1.setFormatter(fmt1)
logFile.addHandler(hdlr1)
logFile.info('**************** Starting Logging ***********************')
logFile.info('Start year, month, day: {:4}, {:02}, {:02}'.format(inputs['iyear'],inputs['imnth'],inputs['iday'] ) )
logFile.info('End year, month, day: {:4}, {:02}, {:02}'.format(inputs['fyear'],inputs['fmnth'],inputs['fday'] ) )
logFile.info('Station location: ' + inputs['loc'])
#-------------------
# Call to date class
#-------------------
DOI = dr.DateRange(inputs['iyear'],inputs['imnth'],inputs['iday'], # Create a dateRange instance object
inputs['fyear'],inputs['fmnth'],inputs['fday'])
daysList = DOI.dateList # Create a list of days within date range
#----------------------------------
# Initialize missing count based on
# how many years present
#----------------------------------
years = DOI.yearList()
misngCnt = {}
for y in years: misngCnt.setdefault(y,0)
#--------------------------------------------------------------------
# Read WACCM monthly mean data. WACCM monthly mean file is ascending,
# adjust so that it is descending. Also units are in km.
#--------------------------------------------------------------------
with open(inputs['WACCMfile'], 'r') as fopen:
lines = fopen.readlines()
nlyrs = int(lines[0].strip().split()[0])
s_ind = 3
Z = np.flipud( np.array( [ float(row.strip().split()[0]) for row in lines[s_ind:nlyrs+s_ind] ] ) )
waccmT = np.flipud( np.array( [ [float(x) for x in line.strip().split()[1:]] for line in lines[s_ind:nlyrs+s_ind] ] ) )
s_ind = 3 + nlyrs + 2
waccmP = np.flipud( np.array( [ [float(x) for x in line.strip().split()[1:]] for line in lines[s_ind:nlyrs+s_ind] ] ) )
s_ind = 3 + nlyrs + 2 + nlyrs + 2
waccmW = np.flipud( np.array( [ [float(x) for x in line.strip().split()[1:]] for line in lines[s_ind:nlyrs+s_ind] ] ) )
#----------------------
# Loop through day list
#----------------------
for i,snglDay in enumerate(daysList):
waccmFlg = False
#---------------------------
# Determine output directory
#---------------------------
mnthstr = "{0:02d}".format(snglDay.month)
yearstr = "{0:02d}".format(snglDay.year)
daystr = "{0:02d}".format(snglDay.day)
outDir = inputs['outBaseDir'] + yearstr+mnthstr+daystr + '/'
#------------------------------
# If input directory is missing
# skip to next day
#------------------------------
if not ckDir(outDir): continue
#------------------------------------
# Open and read yearly NCEP nmc files
#------------------------------------
if (not('nmcDate' in locals()) or (snglDay.year != lastyr) ):
NMChgtFname = inputs['NCEPDir'] + 'HgtNMC_' + inputs['loc'].lower() + '_' + str(snglDay.year) + '.dat'
NMCTempFname = inputs['NCEPDir'] + 'TempNMC_' + inputs['loc'].lower() + '_' + str(snglDay.year) + '.dat'
#-------------------------------------
# NCEP nmc height data along with date
# Convert [m] => [km]
#-------------------------------------
with open(NMChgtFname, 'r') as fopen:
lines = fopen.readlines()
nmcDate = np.array([dt.date(int(line.strip().split()[0][0:4]),
int(line.strip().split()[0][4:6]),
int(line.strip().split()[0][6:])) for line in lines if not('#' in line)])
nmcHgtData = np.array( [ [float(x) for x in line.strip().split()[1:]] for line in lines if not('#' in line) ] )
nmcHgtData[:] = nmcHgtData / 1000.0 # Convert [m] => [km]
#--------------------------------
# NCEP nmc Temperature data along
#--------------------------------
with open(NMCTempFname, 'r') as fopen:
lines = fopen.readlines()
nmcTempData = np.array( [ [float(x) for x in line.strip().split()[1:]] for line in lines if not('#' in line) ] )
#-----------------------------------
# Set year of last profile processed
#-----------------------------------
lastyr = snglDay.year
#--------------------------------------------------
# Merge Pressure, Temperatuer, and H2O data
# from WACCM and NCEP nmc.
# Note: NCEP nmc has some missing values (-999.999)
# Must provide one empty data point between WACCM
# and NCEP nmc for interpolation
#--------------------------------------------------
#--------------------------------
# Get NCEP nmc and WACCM profiles
# according to date
#--------------------------------
dateInd = np.where(nmcDate == snglDay)[0]
mnthInd = (snglDay.month - 1) # -1 because January is in 0th column
#-------------------------------------
# If date is entirely missing from nmc
# use WACCM
#-------------------------------------
if ( dateInd.size == 0 ):
misngCnt[snglDay.year] += 1
PressOut = waccmP[:,mnthInd]
TempOut = waccmT[:,mnthInd]
waccmFlg = True
#-----------------------------
# Date exists in NMC data base
#-----------------------------
else:
dateInd = dateInd[0]
# NCEP nmc
tmpNMCPres = nmcPress
heightNMCprf = nmcHgtData[dateInd,:]
tempNMCprf = nmcTempData[dateInd,:]
#-----------------------------------------
# Look for missing values in NCEP nmc data
# If only one missing value...remove,
# otherwise use WACCM data. Count number
# of times per year WACCM is used
#-----------------------------------------
misHgt = np.where(heightNMCprf < -900)[0]
misT = np.where(tempNMCprf < -900)[0]
misAll = np.union1d( misHgt, misT )
#---------------------------------------------
# If union of missing temperatures and heights
# is greater than 2 then use WACCM
#---------------------------------------------
if len( misAll ) > 2:
misngCnt[snglDay.year] += 1
PressOut = waccmP[:,mnthInd]
TempOut = waccmT[:,mnthInd]
waccmFlg = True
#-----------------------------------------------------------
# If only two or none values missing, remove and interpolate
#-----------------------------------------------------------
else:
tmpNMCPres[:] = np.delete(tmpNMCPres, misAll)
heightNMCprf[:] = np.delete(heightNMCprf,misAll)
#----------------------------------------------------
# Construct Height/Pressure profile for interpolation
#----------------------------------------------------
# Find top of NMC data
nmcTop = heightNMCprf[0]
# Where top of NMC fits in WACCM grid
topInd = np.argmin( abs( Z - heightNMCprf[0] ) )
#Take two points above top and concatonate
#heightIn = np.concatenate( (Z[0:(topInd-inputs['npntSkip'])] ,heightNMCprf), axis=1 )
#pressIn = np.concatenate( (waccmP[0:(topInd-inputs['npntSkip']),mnthInd],nmcPress) , axis=1 )
#tempIn = np.concatenate( (waccmT[0:(topInd-inputs['npntSkip']),mnthInd],tempNMCprf) , axis=1 )
#An error arise with the axis=1, so it was removed
heightIn = np.concatenate( (Z[0:(topInd-inputs['npntSkip'])] ,heightNMCprf))
pressIn = np.concatenate( (waccmP[0:(topInd-inputs['npntSkip']),mnthInd],nmcPress) )
tempIn = np.concatenate( (waccmT[0:(topInd-inputs['npntSkip']),mnthInd],tempNMCprf) )
#------------------------------------------------------------------
# Interpolate to grid. X data must be increasing => flip dimensions
# and then flip back
#------------------------------------------------------------------
PressOut = np.flipud( intrpUniSpl( np.flipud(heightIn), np.flipud(pressIn), k=inputs['Pintrp'])( np.flipud(Z) ) )
TempOut = np.flipud( intrpUniSpl( np.flipud(heightIn), np.flipud(tempIn), k=inputs['Tintrp'])( np.flipud(Z) ) )
#--------------------------------------
# Write to log if NCEP data is not used
#--------------------------------------
if waccmFlg and logFile: logFile.error('NCEP nmc data not complete, WACCM profiles used for date: ' + str(snglDay) )
#-------------------
# Write out ZPT file
#-------------------
with open(outDir+'ZPT.nmc.120', 'w') as fopen:
fopen.write("{0:>5}{1:>5} \n".format(1,nlyrs))
# Write altitude
fopen.write(" ALTITUDE [km] \n")
for row in segmnt(Z, 5):
strformat = ','.join('{:>12.4f}' for i in row) + ', \n'
fopen.write(strformat.format(*row))
# Write Pressure
if waccmFlg: fopen.write(" PRESSURE from WACCM V5 monthly mean [mbar] \n")
else: fopen.write(" PRESSURE from interpolated NCEP nmc (up to {:5.2f}km) and WACCM V5 monthly mean [mbar] \n".format(nmcTop))
for row in segmnt(PressOut,5):
strformat = ','.join('{:>12.4E}' for i in row) + ', \n'
fopen.write(strformat.format(*row))
# Write Temperature
if waccmFlg: fopen.write(" TEMPERATURE from WACCM V5 monthly mean [C] \n")
else: fopen.write(" TEMPERATURE from interpolated NCEP nmc (up to {:5.2f}km) and WACCM V5 monthly mean [C] \n".format(nmcTop))
for row in segmnt(TempOut,5):
strformat = ','.join('{:>12.4f}' for i in row) + ', \n'
fopen.write(strformat.format(*row))
#---------------------
# Write out water file
#---------------------
#-------------------------------------------------
# Determine if previous water files already exists
# If so rename the files add '.WACCM5'
#-------------------------------------------------
if inputs['mvOld']:
preWtrFiles = gb.glob(outDir + 'w-120*')
if len(preWtrFiles) > 0:
for indFile in preWtrFiles: os.rename(indFile,indFile+'.WACCM5')
with open(outDir+'w-120.v1','w') as fopen:
fopen.write(' 1 H2O from WACCM V5 monthly mean \n')
for row in segmnt(waccmW[:,mnthInd],5):
strformat = ','.join('{:>12.4E}' for i in row) + ', \n'
fopen.write(strformat.format(*row))
#--------------------
# Create plots to pdf
#--------------------
if not waccmFlg:
# Pressure
plt.plot(PressOut,Z,'rx-',label='Interpolated Pressure')
plt.plot(waccmP[:,mnthInd],Z,'bx-',label='WACCM V5 Pressure')
plt.plot(tmpNMCPres,heightNMCprf,'kx-',label='NCEP nmc Pressure')
plt.legend()
plt.xlabel('Pressure [mbar]')
plt.ylabel('log(Height) [km]')
plt.xscale('log')
plt.yscale('log')
plt.ylim( Z[-1], Z[0] )
ax = plt.gca()
ax.set_title('Pressure vs Height for Date:{}, K = {:2.0f}, nskips = {:2.0f}'.format(str(snglDay),inputs['Pintrp'],inputs['npntSkip']))
#ax.xaxis.set_major_locator(MultipleLocator(25))
#ax.xaxis.set_minor_locator(MultipleLocator(5))
plt.savefig(outDir+'PressureFigure.pdf',bbox_inches='tight')
plt.close()
## Temperature
plt.plot(TempOut,Z,'rx-', label='Interpolated Temperature')
plt.plot(waccmT[:,mnthInd],Z,'bx-',label='WACCM V5 Temperature')
plt.plot(tempNMCprf,heightNMCprf,'kx-',label='NCEP nmc Temperature')
plt.legend()
plt.xlabel('Temperature [C]')
plt.ylabel('Height [km]')
##plt.yscale('log')
plt.ylim( Z[-1], Z[0] )
ax = plt.gca()
#ax.xaxis.set_major_locator(MultipleLocator(25))
#ax.xaxis.set_minor_locator(MultipleLocator(5))
ax.set_title('Temperature vs Height for Date:{}, K = {:2.0f}, nskips = {:2.0f}'.format(str(snglDay),inputs['Tintrp'],inputs['npntSkip']))
plt.savefig(outDir+'TemperatureFigure.pdf',bbox_inches='tight')
plt.close()
#-------------------------------------
# Print out number of days where NCEP
# nmc data is incomplete => WACCM used
#-------------------------------------
if logFile:
for yrs in misngCnt:
logFile.info('Missing days for year ({:4}) = {:4}'.format(yrs,misngCnt[yrs]))
def main():
#----------------
# Initializations
#----------------
loc = 'tab' # Name of station location
gasName = 'h2o' # Name of gas
ver = 'Current_ERA' # Name of retrieval version to process
verW = 'v99' # version 99 is used for individual retrievals at specific times
#------
# Flags
#------
fltrFlg = True # Flag to filter the data
maxrms = 2 # Max Fit RMS to filter data. Data is filtered according to <= maxrms
pltFlg = True
logFlg = True # Flag to do interpolation of log of water
#-------------------
# Interpolation Oder
#-------------------
intrpOrder = 1 # Order of interpolation
#-----------------------
# Date Range of interest
#-----------------------
iyear = 2017
imnth = 8
iday = 1
fyear = 2017
fmnth = 8
fday = 31
#---------------------
# Input Data Directory
#---------------------
retDir = '/data1/ebaumer/' + loc.lower() + '/' + gasName.lower(
) + '/' + ver + '/' # Retrieval data directory
#----------------------
# Output Data Directory
#----------------------
dataDir = '/data1/' + loc.lower() + '/'
#------
# Files
#------
ctlFile = '/data1/ebaumer/' + loc.lower() + '/' + gasName.lower(
) + '/' + 'x.' + gasName.lower() + '/sfit4.ctl'
#---------------------------------------
# Altitude levels for different stations
#---------------------------------------
if loc.lower() == 'tab':
Z = np.array([
120.0000, 110.0000, 100.0000, 95.0000, 90.0000, 85.0000, 80.0000,
75.0000, 70.0000, 65.0000, 60.0000, 55.0000, 50.0000, 48.0000,
46.0000, 44.0000, 42.0000, 40.0000, 38.0000, 36.0000, 34.0000,
32.0000, 30.0000, 28.0000, 26.0000, 24.0000, 22.0000, 20.0000,
18.0000, 16.0000, 14.0000, 12.0000, 10.0000, 9.0000, 8.0000,
7.0000, 6.0000, 5.0000, 4.0000, 3.0000, 2.0000, 1.0000, 0.2250
])
sLat = 76.52
sLon = 291.23 # 68.77 W = (360.0 - 68.77) = 291.23 E
elif loc.lower() == 'mlo':
Z = np.array([
120.0000, 110.0000, 100.0000, 95.0000, 90.0000, 85.0000, 80.0000,
75.0000, 70.0000, 65.0000, 60.0000, 55.0000, 50.0000, 48.0000,
46.0000, 44.0000, 42.0000, 40.0000, 38.0000, 36.0000, 34.0000,
32.0000, 30.0000, 28.0000, 26.0000, 24.0000, 22.0000, 20.0000,
18.0000, 16.0000, 14.0000, 12.0000, 10.0000, 9.0000, 8.0000,
7.0000, 6.0000, 5.0000, 4.0000, 3.3960
])
sLat = 19.4
sLon = 204.43 # 155.57 W = (360 - 155.57) = 204.43 E
elif loc.lower() == 'fl0':
Z = np.array([
120.0000, 110.0000, 100.0000, 94.0000, 90.0000, 85.0000, 80.0000,
75.0000, 70.0000, 65.0000, 60.0000, 55.0000, 50.0000, 48.0000,
46.0000, 44.0000, 42.0000, 40.0000, 38.0000, 36.0000, 34.0000,
32.0000, 30.0000, 28.0000, 26.0000, 24.0000, 22.0000, 20.0000,
18.0000, 16.0000, 14.0000, 12.0000, 10.0000, 9.0000, 8.0000,
7.0000, 6.0000, 5.0000, 4.0000, 3.0000, 2.0000, 1.6120
])
sLat = 40.4
sLon = 254.76 # 105.24 W = (360 - 105.24) = 254.76 E
#----------------------------
# File and directory checking
#----------------------------
ckDir(retDir, exitFlg=True)
ckDir(dataDir, exitFlg=True)
ckFile(ctlFile, exitFlg=True)
#-------------------------------------
# Create instance of output data class
#-------------------------------------
statDataCl = dc.ReadOutputData(retDir, '', ctlFile, iyear, imnth, iday,
fyear, fmnth, fday)
#--------------
# Read profiles
#--------------
statDataCl.readprfs([statDataCl.PrimaryGas],
retapFlg=1) # Retrieved Profiles
#----------------------------------
# Read Summary data (For filtering)
#----------------------------------
statDataCl.readsummary()
#----------------------------
# Get retrieved water profile
#----------------------------
H2OrPrf = np.asarray(statDataCl.rprfs['H2O'])
dates = np.asarray(statDataCl.rprfs['date'])
alt = np.asarray(statDataCl.rprfs['ZBAR'][0, :])
#--------------------
# Call to filter data
#--------------------
if fltrFlg:
statDataCl.fltrData(statDataCl.PrimaryGas,
mxrms=maxrms,
rmsFlg=True,
tcFlg=True,
pcFlg=True,
cnvrgFlg=True)
else:
statDataCl.inds = np.array([])
#-----------------------------------
# Remove profiles based on filtering
#-----------------------------------
H2OrPrf = np.delete(H2OrPrf, statDataCl.inds, axis=0)
dates = np.delete(dates, statDataCl.inds)
#---------------------------
# Iterate through retrievals
#---------------------------
for i, sngDay in enumerate(dates):
#----------------------------
# Get corresponding directory
#----------------------------
baseDir = dataDir + '{0:04d}{1:02d}{2:02d}/'.format(
sngDay.year, sngDay.month, sngDay.day)
ckDir(baseDir, exitFlg=True)
#-----------------------------------
# Interpolate retrieved profile onto
# sfit input grid
#-----------------------------------
if logFlg:
H2Oout = np.exp(
np.flipud(
intrpUniSpl(np.flipud(alt),
np.log(np.flipud(H2OrPrf[i])),
k=intrpOrder)(np.flipud(Z))))
else:
H2Oout = np.flipud(
intrpUniSpl(np.flipud(alt),
np.flipud(H2OrPrf[i]),
k=intrpOrder)(np.flipud(Z)))
#---------------------
# Write out water file
#---------------------
tstamp = '{0:04d}{1:02d}{2:02d}.{3:02d}{4:02d}{5:02d}'.format(
sngDay.year, sngDay.month, sngDay.day, sngDay.hour, sngDay.minute,
sngDay.second)
with open(baseDir + 'w-120.' + tstamp + '.' + verW, 'w') as fopen:
fopen.write(' 1 H2O from sfit4 retrieval \n')
for row in segmnt(H2Oout, 5):
strformat = ','.join('{:>12.4E}' for i in row) + ', \n'
fopen.write(strformat.format(*row))
#--------------------
# Create plots to pdf
#--------------------
if pltFlg:
pdfsav = PdfPages(baseDir + 'Ret_' + tstamp + '_WaterProfile.pdf')
fig1, ax1 = plt.subplots()
ax1.plot(H2Oout, Z, 'rx-', label='Interpolated Water Profile')
ax1.plot(H2OrPrf[i],
alt,
'bx-',
label='Original Retrieved Water Profile')
ax1.grid(True, which='both')
ax1.legend(prop={'size': 9})
ax1.set_ylabel('Altitude [km]')
ax1.set_xlabel('VMR')
ax1.tick_params(axis='x', which='both', labelsize=8)
ax1.set_ylim((Z[-1], 60))
#ax1.set_xlim((0,np.max((waccmW[-1,mnthInd],dayShum[-1]))))
ax1.set_title(sngDay)
pdfsav.savefig(fig1, dpi=250)
pdfsav.close()
def main():
#---------
# Location
#---------
loc = 'tab'
#------------------------------------
# Version number to append water file
#------------------------------------
verW = 'v4' #version for daily Profile
verW_t = 'v66' #version for 6-hourly profiles
#-----------------------------------------
# Interpolation flag for NCEP re-analysis:
# False = Nearest point. True = linear
#-----------------------------------------
interpFlg = False
#---------------------
# Interpolation things
#---------------------
nSkip = 3 # Number of points to skip when merging WACCM and NCEP profiles
intrpOrder = 1 # Order of interpolation
logFlg = False # Flag to do interpolation of log of water
#-----------------------
# Date Range of interest
#-----------------------
iyear = 2017
imnth = 6
iday = 1
fyear = 2017
fmnth = 12
fday = 31
#------------------------------
# ERA Reanalysis data directory
#------------------------------
ERAdir = '/data1/ancillary_data/ERAdata/'
#---------------
# Data Directory
#---------------
dataDir = '/data1/' + loc.lower() + '/'
#-------------------------
# WACCM monthly means file
#-------------------------
WACCMfile = '/data/Campaign/' + loc.upper(
) + '/waccm/WACCM_pTW-meanV6.' + loc.upper()
#---------------------
# Establish date range
#---------------------
dRange = sc.DateRange(iyear, imnth, iday, fyear, fmnth, fday)
#---------------------------------------
# Altitude levels for different stations
#---------------------------------------
if loc.lower() == 'tab':
Z = np.array([
120.0000, 110.0000, 100.0000, 95.0000, 90.0000, 85.0000, 80.0000,
75.0000, 70.0000, 65.0000, 60.0000, 55.0000, 50.0000, 48.0000,
46.0000, 44.0000, 42.0000, 40.0000, 38.0000, 36.0000, 34.0000,
32.0000, 30.0000, 28.0000, 26.0000, 24.0000, 22.0000, 20.0000,
18.0000, 16.0000, 14.0000, 12.0000, 10.0000, 9.0000, 8.0000,
7.0000, 6.0000, 5.0000, 4.0000, 3.0000, 2.0000, 1.0000, 0.2250
])
sLat = 76.52
sLon = 291.23 # 68.77 W = (360.0 - 68.77) = 291.23 E
elif loc.lower() == 'mlo':
Z = np.array([
120.0000, 110.0000, 100.0000, 95.0000, 90.0000, 85.0000, 80.0000,
75.0000, 70.0000, 65.0000, 60.0000, 55.0000, 50.0000, 48.0000,
46.0000, 44.0000, 42.0000, 40.0000, 38.0000, 36.0000, 34.0000,
32.0000, 30.0000, 28.0000, 26.0000, 24.0000, 22.0000, 20.0000,
18.0000, 16.0000, 14.0000, 12.0000, 10.0000, 9.0000, 8.0000,
7.0000, 6.0000, 5.0000, 4.0000, 3.3960
])
sLat = 19.4
sLon = 204.43 # 155.57 W = (360 - 155.57) = 204.43 E
elif loc.lower() == 'fl0':
Z = np.array([
120.0000, 110.0000, 100.0000, 94.0000, 90.0000, 85.0000, 80.0000,
75.0000, 70.0000, 65.0000, 60.0000, 55.0000, 50.0000, 48.0000,
46.0000, 44.0000, 42.0000, 40.0000, 38.0000, 36.0000, 34.0000,
32.0000, 30.0000, 28.0000, 26.0000, 24.0000, 22.0000, 20.0000,
18.0000, 16.0000, 14.0000, 12.0000, 10.0000, 9.0000, 8.0000,
7.0000, 6.0000, 5.0000, 4.0000, 3.0000, 2.0000, 1.6120
])
sLat = 40.4
sLon = 254.76 # 105.24 W = (360 - 105.24) = 254.76 E
#----------------------------
# File and directory checking
#----------------------------
ckDir(ERAdir, exitFlg=True)
ckDir(dataDir, exitFlg=True)
ckFile(WACCMfile, exitFlg=True)
#--------------------------------------------------------------------
# Read WACCM monthly mean data. WACCM monthly mean file is ascending,
# adjust so that it is descending. Also units are in km.
#--------------------------------------------------------------------
with open(WACCMfile, 'r') as fopen:
lines = fopen.readlines()
nlyrs = int(lines[0].strip().split()[0])
s_ind = 3
Z = np.flipud(
np.array([
float(row.strip().split()[0]) for row in lines[s_ind:nlyrs + s_ind]
]))
waccmT = np.flipud(
np.array([[float(x) for x in line.strip().split()[1:]]
for line in lines[s_ind:nlyrs + s_ind]]))
s_ind = 3 + nlyrs + 2
waccmP = np.flipud(
np.array([[float(x) for x in line.strip().split()[1:]]
for line in lines[s_ind:nlyrs + s_ind]]))
s_ind = 3 + nlyrs + 2 + nlyrs + 2
waccmW = np.flipud(
np.array([[float(x) for x in line.strip().split()[1:]]
for line in lines[s_ind:nlyrs + s_ind]]))
#--------------------------------------------
# Walk through first level of directories in
# data directory and collect directory names
#--------------------------------------------
dirLst = []
for drs in os.walk(dataDir).next()[1]:
#-------------------------------------------
# Test directory to make sure it is a number
#-------------------------------------------
try:
int(drs[0:4])
except:
continue
if dRange.inRange(int(drs[0:4]), int(drs[4:6]), int(drs[6:8])):
dirLst.append(dataDir + drs + '/')
dirLst.sort()
#------------------------------------------
# Loop through all folders in specific year
#------------------------------------------
for sngDir in dirLst:
#----------------------------
# Get date in datetime format
#----------------------------
oneDay = dt.datetime(int(os.path.basename(sngDir[:-1])[0:4]),
int(os.path.basename(sngDir[:-1])[4:6]),
int(os.path.basename(sngDir[:-1])[6:8]))
#--------------------------------------------------
# Find month index for monthly WACCM water profiles
#--------------------------------------------------
mnthInd = oneDay.month - 1 # -1 because January is in the 0th column
#-------------------------------------------
# Open daily ERA interm files 00, 06, 12, 18
#-------------------------------------------
YYYY = "{0:04d}".format(oneDay.year)
MM = "{0:02d}".format(oneDay.month)
DD = "{0:02d}".format(oneDay.day)
ERA_F1 = ERAdir + YYYY + MM + '/' + 'ei.oper.an.pl.regn128sc.' + YYYY + MM + DD + '00.nc'
ERA_F2 = ERAdir + YYYY + MM + '/' + 'ei.oper.an.pl.regn128sc.' + YYYY + MM + DD + '06.nc'
ERA_F3 = ERAdir + YYYY + MM + '/' + 'ei.oper.an.pl.regn128sc.' + YYYY + MM + DD + '12.nc'
ERA_F4 = ERAdir + YYYY + MM + '/' + 'ei.oper.an.pl.regn128sc.' + YYYY + MM + DD + '18.nc'
f1 = netcdf.netcdf_file(ERA_F1, 'r', mmap=False)
f2 = netcdf.netcdf_file(ERA_F2, 'r', mmap=False)
f3 = netcdf.netcdf_file(ERA_F3, 'r', mmap=False)
f4 = netcdf.netcdf_file(ERA_F4, 'r', mmap=False)
#-----------------------------------
# Lat and lon should be the same for
# all files. Just grab once
#-----------------------------------
lat = f1.variables['g4_lat_1']
lon = f1.variables['g4_lon_2']
Plvl = f1.variables['lv_ISBL0']
nlvls = np.shape(Plvl[:])[0]
Q_00 = f1.variables['Q_GDS4_ISBL']
Q_06 = f2.variables['Q_GDS4_ISBL']
Q_12 = f3.variables['Q_GDS4_ISBL']
Q_18 = f4.variables['Q_GDS4_ISBL']
Z_00 = f1.variables['Z_GDS4_ISBL']
Z_06 = f2.variables['Z_GDS4_ISBL']
Z_12 = f3.variables['Z_GDS4_ISBL']
Z_18 = f4.variables['Z_GDS4_ISBL']
PV_00 = f1.variables['PV_GDS4_ISBL']
PV_06 = f2.variables['PV_GDS4_ISBL']
PV_12 = f3.variables['PV_GDS4_ISBL']
PV_18 = f4.variables['PV_GDS4_ISBL']
T_00 = f1.variables['T_GDS4_ISBL']
T_06 = f2.variables['T_GDS4_ISBL']
T_12 = f3.variables['T_GDS4_ISBL']
T_18 = f4.variables['T_GDS4_ISBL']
f1.close()
f2.close()
f3.close()
f4.close()
#-----------------------------------------------
# If not interpolating point in NCEP re-analysis
# find closes lat lon indicies
#-----------------------------------------------
if not interpFlg:
latind = findCls(lat[:], sLat)
lonind = findCls(lon[:], sLon)
#-----------------------------------------------------
# For each level interpolate hgt and specific humidity
# based on latitude and longitude of site
#-----------------------------------------------------
Hgt_00 = np.zeros(nlvls)
Hgt_06 = np.zeros(nlvls)
Hgt_12 = np.zeros(nlvls)
Hgt_18 = np.zeros(nlvls)
Qint_00 = np.zeros(nlvls)
Qint_06 = np.zeros(nlvls)
Qint_12 = np.zeros(nlvls)
Qint_18 = np.zeros(nlvls)
Tint_00 = np.zeros(nlvls)
Tint_06 = np.zeros(nlvls)
Tint_12 = np.zeros(nlvls)
Tint_18 = np.zeros(nlvls)
PVint_00 = np.zeros(nlvls)
PVint_06 = np.zeros(nlvls)
PVint_12 = np.zeros(nlvls)
PVint_18 = np.zeros(nlvls)
for lvl in range(0, nlvls):
HgtOneLvl_00 = np.squeeze(Z_00[lvl, :, :])
HgtOneLvl_06 = np.squeeze(Z_06[lvl, :, :])
HgtOneLvl_12 = np.squeeze(Z_12[lvl, :, :])
HgtOneLvl_18 = np.squeeze(Z_18[lvl, :, :])
Q_OneLvl_00 = np.squeeze(Q_00[lvl, :, :])
Q_OneLvl_06 = np.squeeze(Q_06[lvl, :, :])
Q_OneLvl_12 = np.squeeze(Q_12[lvl, :, :])
Q_OneLvl_18 = np.squeeze(Q_18[lvl, :, :])
T_OneLvl_00 = np.squeeze(T_00[lvl, :, :])
T_OneLvl_06 = np.squeeze(T_06[lvl, :, :])
T_OneLvl_12 = np.squeeze(T_12[lvl, :, :])
T_OneLvl_18 = np.squeeze(T_18[lvl, :, :])
PV_OneLvl_00 = np.squeeze(PV_00[lvl, :, :])
PV_OneLvl_06 = np.squeeze(PV_06[lvl, :, :])
PV_OneLvl_12 = np.squeeze(PV_12[lvl, :, :])
PV_OneLvl_18 = np.squeeze(PV_18[lvl, :, :])
if interpFlg:
Hgt_00[lvl] = interp2d(lon[:],
lat[:],
HgtOneLvl_00,
kind='linear',
bounds_error=True)(sLon, sLat)
Hgt_06[lvl] = interp2d(lon[:],
lat[:],
HgtOneLvl_06,
kind='linear',
bounds_error=True)(sLon, sLat)
Hgt_12[lvl] = interp2d(lon[:],
lat[:],
HgtOneLvl_12,
kind='linear',
bounds_error=True)(sLon, sLat)
Hgt_18[lvl] = interp2d(lon[:],
lat[:],
HgtOneLvl_18,
kind='linear',
bounds_error=True)(sLon, sLat)
Qint_00[lvl] = interp2d(lon[:],
lat[:],
Q_OneLvl_00,
kind='linear',
bounds_error=True)(sLon, sLat)
Qint_06[lvl] = interp2d(lon[:],
lat[:],
Q_OneLvl_06,
kind='linear',
bounds_error=True)(sLon, sLat)
Qint_12[lvl] = interp2d(lon[:],
lat[:],
Q_OneLvl_12,
kind='linear',
bounds_error=True)(sLon, sLat)
Qint_18[lvl] = interp2d(lon[:],
lat[:],
Q_OneLvl_18,
kind='linear',
bounds_error=True)(sLon, sLat)
Tint_00[lvl] = interp2d(lon[:],
lat[:],
T_OneLvl_00,
kind='linear',
bounds_error=True)(sLon, sLat)
Tint_06[lvl] = interp2d(lon[:],
lat[:],
T_OneLvl_06,
kind='linear',
bounds_error=True)(sLon, sLat)
Tint_12[lvl] = interp2d(lon[:],
lat[:],
T_OneLvl_12,
kind='linear',
bounds_error=True)(sLon, sLat)
Tint_18[lvl] = interp2d(lon[:],
lat[:],
T_OneLvl_18,
kind='linear',
bounds_error=True)(sLon, sLat)
PVint_00[lvl] = interp2d(lon[:],
lat[:],
PV_OneLvl_00,
kind='linear',
bounds_error=True)(sLon, sLat)
PVint_06[lvl] = interp2d(lon[:],
lat[:],
PV_OneLvl_06,
kind='linear',
bounds_error=True)(sLon, sLat)
PVint_12[lvl] = interp2d(lon[:],
lat[:],
PV_OneLvl_12,
kind='linear',
bounds_error=True)(sLon, sLat)
PVint_18[lvl] = interp2d(lon[:],
lat[:],
PV_OneLvl_18,
kind='linear',
bounds_error=True)(sLon, sLat)
else:
Hgt_00[lvl] = HgtOneLvl_00[latind, lonind]
Hgt_06[lvl] = HgtOneLvl_06[latind, lonind]
Hgt_12[lvl] = HgtOneLvl_12[latind, lonind]
Hgt_18[lvl] = HgtOneLvl_18[latind, lonind]
Qint_00[lvl] = Q_OneLvl_00[latind, lonind]
Qint_06[lvl] = Q_OneLvl_06[latind, lonind]
Qint_12[lvl] = Q_OneLvl_12[latind, lonind]
Qint_18[lvl] = Q_OneLvl_18[latind, lonind]
Tint_00[lvl] = T_OneLvl_00[latind, lonind]
Tint_06[lvl] = T_OneLvl_06[latind, lonind]
Tint_12[lvl] = T_OneLvl_12[latind, lonind]
Tint_18[lvl] = T_OneLvl_18[latind, lonind]
PVint_00[lvl] = PV_OneLvl_00[latind, lonind]
PVint_06[lvl] = PV_OneLvl_06[latind, lonind]
PVint_12[lvl] = PV_OneLvl_12[latind, lonind]
PVint_18[lvl] = PV_OneLvl_18[latind, lonind]
#----------------------------------------
# Convert Specific humidity from kg/kg to
# molecules/molecules
#----------------------------------------
Qint_00 = Qint_00 * 1.608
Qint_06 = Qint_06 * 1.608
Qint_12 = Qint_12 * 1.608
Qint_18 = Qint_18 * 1.608
#-------------------------------------
# Create daily averages of 00,06,12,18
#-------------------------------------
Q_day = np.mean(np.vstack((Qint_00, Qint_06, Qint_12, Qint_18)),
axis=0)
Z_day = np.mean(np.vstack((Hgt_00, Hgt_06, Hgt_12, Hgt_18)), axis=0)
T_day = np.mean(np.vstack((Tint_00, Tint_06, Tint_12, Tint_18)),
axis=0)
PV_day = np.mean(np.vstack((PVint_00, PVint_06, PVint_12, PVint_18)),
axis=0)
#--------------------------------
# Convert Height [m^2 s^-2] => km
#--------------------------------
Z_day = Z_day / 9.81 / 1000.0
#---------------------------------------------------------
# Construct specific humidity and height profiles for
# interpolation on to the sfit input grid which is the
# same as the WACCM height in monthly profile file.
# NCEP reanalysis data only goes to 300mb therefore,
# merge with monthly averaged WACCM water profiles > 300mb
#---------------------------------------------------------
ERAtop = Z_day[0]
topInd = np.argmin(abs(
Z -
ERAtop)) # Where top of NCEP reanalysis fits in WACCM grid height
#Zin = np.concatenate( ( Z[0:(topInd-nSkip)] , Z_day), axis=1 )
#SHin = np.concatenate( ( waccmW[0:(topInd-nSkip),mnthInd], Q_day), axis=1 )
#Remove axis=0
Zin = np.concatenate((Z[0:(topInd - nSkip)], Z_day))
SHin = np.concatenate((waccmW[0:(topInd - nSkip), mnthInd], Q_day))
SHin_00 = np.concatenate((waccmW[0:(topInd - nSkip),
mnthInd], Qint_00))
SHin_06 = np.concatenate((waccmW[0:(topInd - nSkip),
mnthInd], Qint_06))
SHin_12 = np.concatenate((waccmW[0:(topInd - nSkip),
mnthInd], Qint_12))
SHin_18 = np.concatenate((waccmW[0:(topInd - nSkip),
mnthInd], Qint_18))
#--------------------------------------------------------------
# Interpolate to specific humidity on WACCM grid. X data must
# be increasing => flip dimensions and then flip back
#--------------------------------------------------------------
if logFlg:
SHout = np.exp(
np.flipud(
intrpUniSpl(np.flipud(Zin),
np.log(np.flipud(SHin)),
k=intrpOrder)(np.flipud(Z))))
SHout_00 = np.exp(
np.flipud(
intrpUniSpl(np.flipud(Zin),
np.log(np.flipud(SHin_00)),
k=intrpOrder)(np.flipud(Z))))
SHout_06 = np.exp(
np.flipud(
intrpUniSpl(np.flipud(Zin),
np.log(np.flipud(SHin_06)),
k=intrpOrder)(np.flipud(Z))))
SHout_12 = np.exp(
np.flipud(
intrpUniSpl(np.flipud(Zin),
np.log(np.flipud(SHin_12)),
k=intrpOrder)(np.flipud(Z))))
SHout_18 = np.exp(
np.flipud(
intrpUniSpl(np.flipud(Zin),
np.log(np.flipud(SHin_18)),
k=intrpOrder)(np.flipud(Z))))
else:
SHout = np.flipud(
intrpUniSpl(np.flipud(Zin), np.flipud(SHin),
k=intrpOrder)(np.flipud(Z)))
SHout_00 = np.flipud(
intrpUniSpl(np.flipud(Zin), np.flipud(SHin_00),
k=intrpOrder)(np.flipud(Z)))
SHout_06 = np.flipud(
intrpUniSpl(np.flipud(Zin), np.flipud(SHin_06),
k=intrpOrder)(np.flipud(Z)))
SHout_12 = np.flipud(
intrpUniSpl(np.flipud(Zin), np.flipud(SHin_12),
k=intrpOrder)(np.flipud(Z)))
SHout_18 = np.flipud(
intrpUniSpl(np.flipud(Zin), np.flipud(SHin_18),
k=intrpOrder)(np.flipud(Z)))
#------------------------
# remove v4 for 6-hourly files
#------------------------
waterFiles_test = glob.glob(sngDir + 'w-120*' + verW)
waterFiles_test = [
i for i in waterFiles_test if len(os.path.basename(i)) > 10
]
if len(waterFiles_test) >= 1:
for f in waterFiles_test:
os.remove(f)
#---------------------
# Write out water file at 00
#---------------------
with open(sngDir + 'w-120.' + YYYY + MM + DD + '.000000.' + verW_t,
'w') as fopen:
fopen.write(
' 1 H2O from ERA reanalysis and WACCM V6 monthly mean \n'
)
for row in segmnt(SHout_00, 5):
strformat = ','.join('{:>12.4E}' for i in row) + ', \n'
fopen.write(strformat.format(*row))
#---------------------
# Write out water file at 06
#---------------------
with open(sngDir + 'w-120.' + YYYY + MM + DD + '.060000.' + verW_t,
'w') as fopen:
fopen.write(
' 1 H2O from ERA reanalysis and WACCM V6 monthly mean \n'
)
for row in segmnt(SHout_06, 5):
strformat = ','.join('{:>12.4E}' for i in row) + ', \n'
fopen.write(strformat.format(*row))
#---------------------
# Write out water file at 12
#---------------------
with open(sngDir + 'w-120.' + YYYY + MM + DD + '.120000.' + verW_t,
'w') as fopen:
fopen.write(
' 1 H2O from ERA reanalysis and WACCM V6 monthly mean \n'
)
for row in segmnt(SHout_12, 5):
strformat = ','.join('{:>12.4E}' for i in row) + ', \n'
fopen.write(strformat.format(*row))
#---------------------
# Write out water file at 18
#---------------------
with open(sngDir + 'w-120.' + YYYY + MM + DD + '.180000.' + verW_t,
'w') as fopen:
fopen.write(
' 1 H2O from ERA reanalysis and WACCM V6 monthly mean \n'
)
for row in segmnt(SHout_18, 5):
strformat = ','.join('{:>12.4E}' for i in row) + ', \n'
fopen.write(strformat.format(*row))
#---------------------
# Write out water file Daily
#---------------------
with open(sngDir + 'w-120.' + verW, 'w') as fopen:
fopen.write(
' 1 H2O from ERA reanalysis and WACCM V6 monthly mean \n'
)
for row in segmnt(SHout, 5):
strformat = ','.join('{:>12.4E}' for i in row) + ', \n'
fopen.write(strformat.format(*row))
#------------------------------
# Write out Temperature, water,
# and PV every 6 hours
#------------------------------
with open(sngDir + 'ERA_Interm_PV.' + YYYY + MM + DD, 'w') as fopen:
fopen.write(
'Daily Averaged and 6 hourly PV from ERA Interim [K m^2 kg^-1 s^-1] \n'
)
fopen.write(
'{0:>20s}{1:>20s}{2:>20s}{3:>20s}{4:>20s}{5:>20s}\n'.format(
'Height [km]', 'PV[K m^2 kg^-1 s^-1]', 'PV at 00',
'PV at 06', 'PV at 12', 'PV at 18'))
for row in zip(*(Z_day, PV_day, PVint_00, PVint_06, PVint_12,
PVint_18)):
strformat = ','.join('{:>20.7E}' for i in row) + ', \n'
fopen.write(strformat.format(*row))
with open(sngDir + 'ERA_Interm_T.' + YYYY + MM + DD, 'w') as fopen:
fopen.write(
'Daily averaged and 6 Hourly Temperature from ERA Interim [K] \n'
)
fopen.write(
'{0:>15s}{1:>15s}{2:>15s}{3:>15s}{4:>15s}{5:>15s}\n'.format(
'Height [km]', 'T [K]', 'T at 00', 'T at 06', 'T at 12',
'T at 18'))
for row in zip(*(Z_day, T_day, Tint_00, Tint_06, Tint_12,
Tint_18)):
strformat = ','.join('{:>15.7E}' for i in row) + ', \n'
fopen.write(strformat.format(*row))
with open(sngDir + 'ERA_Interm_Q.' + YYYY + MM + DD, 'w') as fopen:
fopen.write(
'Daily averaged and 6 hourly specific humidity Q from ERA Interim [VMR] \n'
)
fopen.write(
'{0:>15s}{1:>15s}{2:>15s}{3:>15s}{4:>15s}{5:>15s}\n'.format(
'Height [km]', 'Q [VMR]', 'Q at 00', 'Q at 06', 'Q at 12',
'Q at 18'))
for row in zip(*(Z_day, Q_day, Qint_00, Qint_06, Qint_12,
Qint_18)):
strformat = ','.join('{:>15.7E}' for i in row) + ', \n'
fopen.write(strformat.format(*row))
#--------------------
# Create plots to pdf
#--------------------
pdfsav = PdfPages(sngDir + 'WaterProfile_ERA.pdf')
fig1, ax1 = plt.subplots()
ax1.plot(SHout_00, Z, color='green', label='Interpolated SH-00')
ax1.plot(SHout_06, Z, color='gold', label='Interpolated SH-06')
ax1.plot(SHout_12, Z, color='cyan', label='Interpolated SH-12')
ax1.plot(SHout_18, Z, color='gray', label='Interpolated SH-18')
ax1.plot(SHout, Z, 'rx-', label='Interpolated SH-Mean')
ax1.plot(Q_day, Z_day, 'kx-', label='ERA Reanalysis SH-Mean')
ax1.plot(waccmW[:, mnthInd], Z, 'bx-', label='WACCM V6 SH')
ax1.grid(True, which='both')
ax1.legend(prop={'size': 9})
ax1.set_ylabel('Altitude [km]')
ax1.set_xlabel('VMR [ppv]')
ax1.tick_params(axis='x', which='both', labelsize=8)
ax1.set_ylim((Z[-1], 80))
#ax1.set_xlim((0,np.max((waccmW[-1,mnthInd],Q_day[-1]))))
ax1.set_title(YYYY + '-' + MM + '-' + DD)
pdfsav.savefig(fig1, dpi=250)
fig2, ax2 = plt.subplots()
ax2.plot(SHout_00, Z, color='green', label='Interpolated SH-00')
ax2.plot(SHout_06, Z, color='gold', label='Interpolated SH-06')
ax2.plot(SHout_12, Z, color='cyan', label='Interpolated SH-12')
ax2.plot(SHout_18, Z, color='gray', label='Interpolated SH-18')
ax2.plot(SHout, Z, 'rx-', label='Interpolated SH-Mean')
ax2.plot(Q_day, Z_day, 'kx-', label='ERA Reanalysis SH-Mean')
ax2.plot(waccmW[:, mnthInd], Z, 'bx-', label='WACCM V6 SH')
ax2.grid(True, which='both')
ax2.legend(prop={'size': 9})
ax2.set_ylabel('Altitude [km]')
ax2.set_xlabel('log VMR [ppv]')
ax2.tick_params(axis='x', which='both', labelsize=8)
ax2.set_xscale('log')
ax2.set_ylim((Z[-1], 80))
#ax2.set_xlim((0,np.max((waccmW[-1,mnthInd],Q_day[-1]))))
ax2.set_title(YYYY + '-' + MM + '-' + DD)
pdfsav.savefig(fig2, dpi=250)
pdfsav.close()
print 'Finished processing folder: {}'.format(sngDir)