def main(year, dx=100000, snowType='SRLD', extraStr='v11'):
xptsG, yptsG, latG, lonG, proj = cF.create_grid(dxRes=dx)
print(xptsG)
print(yptsG)
dxStr = str(int(dx / 1000)) + 'km'
print(dxStr)
#region_mask, xptsI, yptsI = cF.get_region_mask_pyproj(anc_data_path, proj, xypts_return=1)
#region_maskG = griddata((xptsI.flatten(), yptsI.flatten()), region_mask.flatten(), (xptsG, yptsG), method='nearest')
#xptsDays, yptsDays, oibdates, snowDays= cF.read_icebridge_snowdepths(proj, oib_data_path, year)
xptsDays, yptsDays, _, _, snowDays, oibdates = cF.getSTOSIWIGyear_proj(
proj, oib_data_path + '/stosiwig/', snowType, year)
for x in range(len(oibdates)):
# Loop through dates of each flight. I want to keep separate to compre to the daily NESOSIM data.
oib_dayG = bin_oib(dx, xptsDays[x], yptsDays[x], xptsG, yptsG,
snowDays[x])
cF.plot_gridded_cartopy(
lonG,
latG,
oib_dayG,
proj=ccrs.NorthPolarStereo(central_longitude=-45),
out=figure_path + '/OIB/' + oibdates[x] + dxStr + snowType +
extraStr,
date_string=oibdates[x],
month_string='',
varStr='OIB snow depth ',
units_lab=r'm',
minval=0,
maxval=0.6,
cmap_1=plt.cm.viridis)
oib_dayG.dump(forcing_save_path + dxStr + '/OIB/' + str(year) + '/' +
oibdates[x] + dxStr + snowType + extraStr)
arr = np.hstack(xptsDays)
oib_daysG = cF.bin_oib(dx, np.hstack(xptsDays), np.hstack(yptsDays), xptsG,
yptsG, np.hstack(snowDays))
cF.plot_gridded_cartopy(lonG,
latG,
oib_daysG,
proj=ccrs.NorthPolarStereo(central_longitude=-45),
out=figure_path + '/OIB/' + str(year) + dxStr +
snowType + extraStr,
date_string=str(year),
month_string='',
varStr='OIB snow depth ',
units_lab=r'm',
minval=0,
maxval=0.6,
cmap_1=plt.cm.viridis)
def main(year, dx=100000, extraStr='v11'):
xptsG, yptsG, latG, lonG, proj = cF.create_grid(dxRes=dx)
#print(xptsG)
#print(yptsG)
dxStr = str(int(dx / 1000)) + 'km'
#print(dxStr)
files = glob(forcing_save_path + dxStr + '/OIB/' + str(year) + '/' + '*' +
dxStr + extraStr + '')
print(files)
oibdates = [file[-16:-8] for file in files]
print(oibdates)
for x in range(len(oibdates)):
# Loop through dates of each flight. I want to keep separate to compre to the daily NESOSIM data.
try:
oib_dayG1 = np.load(forcing_save_path + dxStr + '/OIB/' +
str(year) + '/' + oibdates[x] + dxStr +
'GSFC' + extraStr,
allow_pickle=True)
oib_dayG2 = np.load(forcing_save_path + dxStr + '/OIB/' +
str(year) + '/' + oibdates[x] + dxStr +
'SRLD' + extraStr,
allow_pickle=True)
oib_dayG3 = np.load(forcing_save_path + dxStr + '/OIB/' +
str(year) + '/' + oibdates[x] + dxStr + 'JPL' +
extraStr,
allow_pickle=True)
oib_dayGC = np.median((oib_dayG1, oib_dayG2, oib_dayG3), axis=0)
cF.plot_gridded_cartopy(
lonG,
latG,
oib_dayGC,
proj=ccrs.NorthPolarStereo(central_longitude=-45),
out=figure_path + '/OIB/' + oibdates[x] + dxStr + 'MEDIAN' +
extraStr,
date_string=str(year),
month_string='',
varStr='OIB snow depth MEDIAN',
units_lab=r'm',
minval=0,
maxval=0.6,
cmap_1=plt.cm.viridis)
oib_dayGC.dump(forcing_save_path + dxStr + '/OIB/' + str(year) +
'/' + oibdates[x] + dxStr + 'MEDIAN' + extraStr)
except:
print('All thre algs dont exist for this date:', oibdates[x])
#region_maskG = griddata((xptsI.flatten(), yptsI.flatten()), region_mask.flatten(), (xptsG, yptsG), method='nearest')
xptsDays, yptsDays, oibdates, snowDays = cF.read_icebridge_snowdepths(
proj, oib_data_path, year)
for x in range(len(oibdates)):
# Loop through dates of each flight. I want to keep separate to compre to the daily NESOSIM data.
oib_dayG = cF.bin_oib(xptsDays[x], yptsDays[x], xptsG, yptsG, snowDays[x])
cF.plot_gridded_cartopy(lonG,
latG,
oib_dayG,
proj=ccrs.NorthPolarStereo(central_longitude=-45),
out=figure_path + '/OIB/' + oibdates[x] + dxStr +
extraStr,
date_string=oibdates[x],
month_string='',
varStr='OIB snow depth ',
units_lab=r'm',
minval=0,
maxval=0.6,
cmap_1=plt.cm.viridis)
oib_dayG.dump(forcing_save_path + dxStr + '/OIB/' + str(year) + '/' +
oibdates[x] + dxStr + extraStr)
arr = np.hstack(xptsDays)
oib_daysG = bin_oib(np.hstack(xptsDays), np.hstack(yptsDays), xptsG, yptsG,
np.hstack(snowDays))
def main(yearT,
extraStr='v11_1',
dx=100000,
data_path=reanalysis_raw_path + 'ERA5/',
out_path=forcing_save_path + 'Temp/ERA5/',
fig_path=figure_path + 'Temp/ERA5/',
anc_data_path='../../anc_data/'):
xptsG, yptsG, latG, lonG, proj = cF.create_grid(dxRes=dx)
print(xptsG)
print(yptsG)
dxStr = str(int(dx / 1000)) + 'km'
print(dxStr)
if not os.path.exists(out_path):
os.makedirs(out_path)
if not os.path.exists(fig_path):
os.makedirs(fig_path)
region_mask, xptsI, yptsI, _, _ = cF.get_region_mask_pyproj(anc_data_path,
proj,
xypts_return=1)
region_maskG = griddata((xptsI.flatten(), yptsI.flatten()),
region_mask.flatten(), (xptsG, yptsG),
method='nearest')
varStr = 't2m'
xptsM, yptsM, temp2mYear = get_ERA5_temps(proj, data_path, yearT)
hotdays_duration = xr.apply_ufunc(get_cumulative_hotdays,
temp2mYear['t2m'].compute(),
input_core_dims=[["time"]],
vectorize=True)
print(xptsM.flatten().shape)
print(yptsM.flatten().shape)
print(temp2mYear)
print(hotdays_duration.values.flatten().shape)
t2mdurG = griddata((xptsM.flatten(), yptsM.flatten()),
hotdays_duration.values.flatten(), (xptsG, yptsG),
method='linear')
#t2mdurG[where(region_maskG>10)]=0.
#t2mdur=t2mdur.astype('f2')
cF.plot_gridded_cartopy(lonG,
latG,
t2mdurG,
proj=ccrs.NorthPolarStereo(central_longitude=-45),
out=fig_path + '/duration' + str(yearT) + extraStr,
date_string=str(yearT),
extra=extraStr,
varStr='hot days',
units_lab=r'>0',
minval=0,
maxval=100,
cmap_1=plt.cm.viridis)
#monthStr='%02d' %(month+1)
t2mdurG.dump(out_path + '/ERA5_duration' + dxStr + '-' + str(yearT) +
extraStr)
def main(year,
startMonth=8,
endMonth=11,
dx=100000,
extraStr='v11_1',
data_path=reanalysis_raw_path + 'ERA5/',
out_path=forcing_save_path + 'Precip/ERA5/',
fig_path=figure_path + 'Precip/ERA5/',
anc_data_path='../../anc_data/'):
xptsG, yptsG, latG, lonG, proj = cF.create_grid(dxRes=dx)
print(xptsG)
print(yptsG)
dxStr = str(int(dx / 1000)) + 'km'
print(dxStr)
region_mask, xptsI, yptsI, _, _ = cF.get_region_mask_pyproj(anc_data_path,
proj,
xypts_return=1)
region_maskG = griddata((xptsI.flatten(), yptsI.flatten()),
region_mask.flatten(), (xptsG, yptsG),
method='nearest')
varStr = 'sf'
if not os.path.exists(fig_path):
os.makedirs(fig_path)
yearT = year
numDays = cF.getLeapYr(year)
if (numDays > 365):
monIndex = [0, 31, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 366]
else:
monIndex = [0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334, 365]
if not os.path.exists(out_path + '/' + str(year)):
os.makedirs(out_path + '/' + str(year))
startDay = monIndex[startMonth]
if (endMonth > 11):
endDay = monIndex[endMonth + 1 - 12] + monIndex[-1] - 1
else:
endDay = monIndex[endMonth + 1]
calc_weights = 1 # start as one to calculate weightings then gets set as zero for future files
for dayT in range(startDay, endDay):
dayStr = '%03d' % dayT
month = np.where(dayT - np.array(monIndex) >= 0)[0][-1]
monStr = '%02d' % (month + 1)
dayinmonth = dayT - monIndex[month]
print('Precip day:', dayT, dayinmonth)
#in kg/m2 per day
xptsM, yptsM, lonsM, latsM, Precip = cF.get_ERA5_precip_days_pyproj(
proj,
data_path,
str(yearT),
monStr,
dayinmonth,
lowerlatlim=30,
varStr=varStr)
# if it's the first day, calculate weights
if calc_weights == 1:
# calculate Delaunay triangulation interpolation weightings for first file of the year
print('calculating interpolation weightings')
ptM_arr = np.array([xptsM.flatten(), yptsM.flatten()]).T
tri = Delaunay(ptM_arr) # delaunay triangulation
calc_weights = 0
# grid using linearNDInterpolator with triangulation calculated above
# (faster than griddata but produces identical output)
interp = LinearNDInterpolator(tri, Precip.flatten())
PrecipG = interp((xptsG, yptsG))
cF.plot_gridded_cartopy(
lonG,
latG,
PrecipG,
proj=ccrs.NorthPolarStereo(central_longitude=-45),
out=fig_path + '/ERA5' + varStr + dxStr + '-' + str(yearT) + '_d' +
dayStr + extraStr,
date_string=str(yearT),
month_string=str(dayT),
extra=extraStr,
varStr='ERA5 snowfall ',
units_lab=r'kg/m2',
minval=0,
maxval=10,
cmap_1=plt.cm.viridis)
PrecipG.dump(out_path + str(yearT) + '/ERA5' + varStr + dxStr + '-' +
str(yearT) + '_d' + dayStr + extraStr)
def main(yearT,
extraStr='v11_1',
dx=100000,
data_path=reanalysis_raw_path + 'ERA5/',
out_path=forcing_save_path + 'InitialConditions/ERA5/',
fig_path=figure_path + 'InitialConditions/ERA5/',
anc_data_path='../../anc_data/'):
xptsG, yptsG, latG, lonG, proj = cF.create_grid(dxRes=dx)
print(xptsG)
print(yptsG)
dxStr = str(int(dx / 1000)) + 'km'
print(dxStr)
if not os.path.exists(out_path):
os.makedirs(out_path)
if not os.path.exists(fig_path):
os.makedirs(fig_path)
region_mask, xptsI, yptsI, _, _ = cF.get_region_mask_pyproj(anc_data_path,
proj,
xypts_return=1)
region_maskG = griddata((xptsI.flatten(), yptsI.flatten()),
region_mask.flatten(), (xptsG, yptsG),
method='nearest')
reanalysis = 'ERA5'
varStr = 't2m'
iceconc_path = forcing_save_path + '/IceConc/CDR/'
temp_path = forcing_save_path + '/Temp/ERA5/'
t2mdurGAll = []
for y in range(1980, 1991 + 1, 1):
if (y == 1987):
continue
t2mdurGT = np.load(temp_path + 'duration' + dxStr + '-' + str(y) +
extraStr,
allow_pickle=True)
t2mdurGAll.append(t2mdurGT)
t2mdurGclim = ma.mean(t2mdurGAll, axis=0)
print(t2mdurGclim.shape)
w99 = cF.getWarren(lonG, latG, 7)
for yearT in range(2021, 2021 + 1, 1):
if (yearT == 1987):
continue
t2mdurGT = np.load(temp_path + 'duration' + dxStr + '-' + str(yearT) +
extraStr,
allow_pickle=True)
print(t2mdurGT.shape)
W99yrT = w99 * (t2mdurGclim / t2mdurGT)
W99yrT[np.where(latG < 70)] = 0
W99yrT[np.where(region_maskG > 8.2)] = 0
W99yrT[np.where(region_maskG <= 7.8)] = 0
W99yrT[np.where(W99yrT < 0)] = 0
W99yrT[np.where(W99yrT > 10)] = 10
day = 226
dayStr = str(day) #226 is middle of August
iceConcDayG = np.load(iceconc_path + str(yearT) + '/iceConcG_CDR' +
dxStr + '-' + str(yearT) + '_d' + dayStr +
extraStr,
allow_pickle=True)
W99yrT[np.where(iceConcDayG < 0.15)] = 0
W99yrT = gaussian_filter(W99yrT, sigma=1)
# Convert to meters
W99yrT = W99yrT / 100.
cF.plot_gridded_cartopy(
lonG,
latG,
W99yrT,
proj=ccrs.NorthPolarStereo(central_longitude=-45),
out=fig_path + '/initial_conditions' + str(yearT) + dxStr +
extraStr,
date_string=str(yearT),
extra=extraStr,
varStr='Snow depth ',
units_lab=r'm',
minval=0,
maxval=0.12,
cmap_1=plt.cm.viridis)
W99yrT.dump(out_path + 'ICsnow' + dxStr + '-' + str(yearT) + extraStr)
def main(year, startMonth=3, endMonth=3, extraStr='v11_1', dx=100000, data_path=cdr_raw_path, out_path=forcing_save_path, fig_path=figure_path+'IceConc/CDR/', anc_data_path='../../anc_data/'):
xptsG, yptsG, latG, lonG, proj = cF.create_grid(dxRes=dx)
print(xptsG)
print(yptsG)
dxStr=str(int(dx/1000))+'km'
print(dxStr)
region_mask, xptsI, yptsI, lonsI, latsI = cF.get_region_mask_pyproj(anc_data_path, proj, xypts_return=1)
region_maskG = griddata((xptsI.flatten(), yptsI.flatten()), region_mask.flatten(), (xptsG, yptsG), method='nearest')
product='CDR'
numDaysYr=cF.getLeapYr(year)
if (numDaysYr>365):
monIndex = [31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
else:
monIndex = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
if not os.path.exists(out_path+'/'+str(year)):
os.makedirs(out_path+'/'+str(year))
if not os.path.exists(fig_path):
os.makedirs(fig_path)
calc_weights = 1 # start as one to calculate weightings then gets set as zero for future files
for month in range(startMonth, endMonth+1):
print(month)
mstr='%02d' %(month+1)
# Get pole hole
pmask=cF.get_pmask(year, month)
numDays=monIndex[month]
# should return array with nans not masked as needed for regridding.
for x in range(numDays):
dayT=sum(monIndex[0:month])+x
daySumStr='%03d' %(dayT)
dayMonStr='%02d' %(x+1)
print('day month string', dayMonStr)
try:
# Try final first
fileT=glob(data_path+'/final/'+str(year)+'/'+mstr+'/*'+str(year)+mstr+dayMonStr+'*.nc')[0]
print(data_path+'/final/'+str(year)+'/'+mstr+'/*'+str(year)+mstr+dayMonStr+'*.nc')
print('final data')
except:
try:
# Try nrt
fileT=glob(data_path+'/nrt/'+str(year)+'/'+mstr+'/*'+str(year)+mstr+dayMonStr+'*.nc')[0]
print(data_path+'/nrt/'+str(year)+'/'+mstr+'/*'+str(year)+mstr+dayMonStr+'*.nc')
print('nrt data')
except:
try:
dayMonStr='%03d' %(dayT-1)
fileT=glob(data_path+'/final/'+str(year)+'/'+mstr+'/*'+str(year)+mstr+dayMonStr+'*.nc')[0]
print(data_path+'/final/'+str(year)+'/'+mstr+'/*'+str(year)+mstr+dayMonStr+'*.nc')
print('previous day final data')
except:
try:
dayMonStr='%03d' %(dayT+1)
fileT=glob(data_path+'/final/'+str(year)+'/'+mstr+'/*'+str(year)+mstr+dayMonStr+'*.nc')[0]
print(data_path+'/final/'+str(year)+'/'+mstr+'/*'+str(year)+mstr+dayMonStr+'*.nc')
print('following day final data')
except:
print('no conc')
# previously was pass but this meant it would just use the previous loop data below
continue
iceConcDay = cF.getCDRconcproj(fileT, mask=0, maxConc=1, lowerConc=1)
print(iceConcDay.shape)
# if it's the first day, calculate weights
if calc_weights == 1:
# calculate Delaunay triangulation interpolation weightings for first file of the year
print('calculating interpolation weightings')
# Use region mask which is on the same grid!
ptM_arr = np.array([xptsI.flatten(),yptsI.flatten()]).T
tri = Delaunay(ptM_arr) # delaunay triangulation
calc_weights = 0
iceConcDay[np.where(region_mask>10)]=np.nan
#iceConcDayG = griddata((xpts0.flatten(), ypts0.flatten()), iceConcDay.flatten(), (xptsG, yptsG), method='linear')
# grid using linearNDInterpolator with triangulation calculated above
# (faster than griddata but produces identical output)
interp = LinearNDInterpolator(tri,iceConcDay.flatten())
iceConcDayG = interp((xptsG,yptsG))
iceConcDayG[np.where(iceConcDayG<0.15)]=0
iceConcDayG[np.where(iceConcDayG>1)]=1
iceConcDayG[np.where(region_maskG>10)]=np.nan
cF.plot_gridded_cartopy(lonG, latG, iceConcDayG, proj=ccrs.NorthPolarStereo(central_longitude=-45), out=fig_path+'iceConcG_-'+str(year)+mstr+dayMonStr+dxStr+extraStr,
date_string=str(year), month_string=mstr+dayMonStr, extra=extraStr, varStr='CDR ice conc', units_lab='', minval=0, maxval=1, cmap_1=plt.cm.viridis)
iceConcDayG.dump(out_path+str(year)+'/iceConcG_CDR'+dxStr+'-'+str(year)+'_d'+daySumStr+extraStr)
def main(year1, month1, day1, year2, month2, day2, outPathT='.', forcingPathT='.', anc_data_pathT='../anc_data/', figPathT='../Figures/',
precipVar='ERA5', windVar='ERA5', driftVar='OSISAF', concVar='CDR', icVar='ERAI', densityTypeT='variable',
outStr='', extraStr='', IC=2, windPackFactorT=0.1, windPackThreshT=5., leadLossFactorT=0.1, atmLossFactorT=2.2e-8, dynamicsInc=1, leadlossInc=1,
windpackInc=1, atmlossInc=0, saveData=1, plotBudgets=1, plotdaily=1, saveFolder='', dx=50000,scaleCS=False):
"""
Main model function
Args:
The various model configuration parameters
"""
#------- Create map projection
xptsG, yptsG, latG, lonG, proj = cF.create_grid(dxRes=dx)
nx=xptsG.shape[0]
ny=xptsG.shape[1]
dxStr=str(int(dx/1000))+'km'
print(nx, ny, dxStr)
# Assign some global parameters
global dataPath, forcingPath, outPath, ancDataPath
outPath=outPathT+dxStr+'/'
forcingPath=forcingPathT+dxStr+'/'
ancDataPath=anc_data_pathT
print('OutPath:', outPath)
print('forcingPath:', forcingPath)
print('ancDataPath:', ancDataPath)
# Assign density of the two snow layers
global snowDensityFresh, snowDensityOld, minSnowD, minConc, leadLossFactor, atmLossFactor, windPackThresh, windPackFactor, deltaT
snowDensityFresh=200. # density of fresh snow layer
snowDensityOld=350. # density of old snow layer
minSnowD=0.02 # minimum snow depth for a density estimate
minConc=0.15 # mask budget values with a concentration below this value
deltaT=60.*60.*24. # time interval (seconds in a day)
region_mask, xptsI, yptsI = cF.get_region_mask_pyproj(anc_data_pathT, proj, xypts_return=1)
region_maskG = griddata((xptsI.flatten(), yptsI.flatten()), region_mask.flatten(), (xptsG, yptsG), method='nearest')
leadLossFactor=leadLossFactorT # Snow loss to leads coefficient
windPackThresh=windPackThreshT # Minimum winds needed for wind packing
windPackFactor=windPackFactorT # Fraction of snow packed into old snow layer
atmLossFactor=atmLossFactorT # Snow loss to atmosphere coefficient
#---------- Current year
yearCurrent=year1
#--------- Get time period info
startDay, numDays, numDaysYear1, dateOut= cF.getDays(year1, month1, day1, year2, month2, day2)
print (startDay, numDays, numDaysYear1, dateOut)
# make this into a small function
dates=[]
for x in range(0, numDays):
#print x
date = datetime.datetime(year1, month1+1, day1+1) + datetime.timedelta(x)
#print (int(date.strftime('%Y%m%d')))
dates.append(int(date.strftime('%Y%m%d')))
#print(dates)
CSstr = ''
if scaleCS:
# load scaling factors; assumes scaling factors are in same directory as NESOSIM.py
monthlyScalingFactors = xr.open_dataset('{}scale_coeffs_{}_{}_v2.nc'.format(ancDataPath, precipVar, dxStr))['scale_factors']
CSstr = 'CSscaled'
#------ create output strings and file paths -----------
saveStr= precipVar+CSstr+'sf'+windVar+'winds'+driftVar+'drifts'+concVar+'sic'+'rho'+densityTypeT+'_IC'+str(IC)+'_DYN'+str(dynamicsInc)+'_WP'+str(windpackInc)+'_LL'+str(leadlossInc)+'_AL'+str(atmlossInc)+'_WPF'+str(windPackFactorT)+'_WPT'+str(windPackThreshT)+'_LLF'+str(leadLossFactorT)+'-'+dxStr+extraStr+outStr+'-'+dateOut
saveStrNoDate=precipVar+CSstr+'sf'+windVar+'winds'+driftVar+'drifts'+concVar+'sic'+'rho'+densityTypeT+'_IC'+str(IC)+'_DYN'+str(dynamicsInc)+'_WP'+str(windpackInc)+'_LL'+str(leadlossInc)+'_AL'+str(atmlossInc)+'_WPF'+str(windPackFactorT)+'_WPT'+str(windPackThreshT)+'_LLF'+str(leadLossFactorT)+'-'+dxStr+extraStr+outStr
print ('Saving to:', saveStr)
#'../../DataOutput/'
savePath=outPath+saveFolder+'/'+saveStrNoDate
# Declare empty arrays for compiling budgets
if not os.path.exists(savePath+'/budgets/'):
os.makedirs(savePath+'/budgets/')
if not os.path.exists(savePath+'/final/'):
os.makedirs(savePath+'/final/')
global figpath
figpath=figPathT+'/Diagnostic/'+dxStr+'/'+saveStrNoDate+'/'
if not os.path.exists(figpath):
os.makedirs(figpath)
if not os.path.exists(figpath+'/daily_snow_depths/'):
os.makedirs(figpath+'/daily_snow_depths/')
precipDays, iceConcDays, windDays, tempDays, snowDepths, density, snowDiv, snowAdv, snowAcc, snowOcean, snowWindPack, snowWindPackLoss, snowWindPackGain, snowLead, snowAtm = genEmptyArrays(numDays, nx, ny)
print('IC:', IC)
if (IC>0):
if (IC==1):
# August Warren climatology snow depths
ICSnowDepth = np.load(forcingPath+'InitialConditions/AugSnow'+dxStr, allow_pickle=True)
print('Initialize with August Warren climatology')
elif (IC==2):
# Petty initiail conditions
try:
ICSnowDepth = np.load(forcingPath+'InitialConditions/'+icVar+'/ICsnow'+dxStr+'-'+str(year1)+extraStr, allow_pickle=True)
print('Initialize with new v1.1 scaled initial conditions')
print(np.amax(ICSnowDepth))
except:
print('No initial conditions file available')
iceConcDayG, precipDayG, driftGdayG, windDayG, tempDayG =loadData(year1, startDay, precipVar, windVar, concVar, driftVar, dxStr, extraStr)
ICSnowDepth[np.where(iceConcDayG<minConc)]=0
#--------Split the initial snow depth over both layers
snowDepths[0, 0]=ICSnowDepth*0.5
snowDepths[0, 1]=ICSnowDepth*0.5
#pF.plotSnow(m, xptsG, yptsG, densityT, date_string=str(startDay-1), out=figpath+'/Snow/2layer/densityD'+driftP+extraStr+reanalysisP+varStr+'_sy'+str(year1)+'d'+str(startDay)+outStr+'T0', units_lab=r'kg/m3', minval=180, maxval=360, base_mask=0, norm=0, cmap_1=cm.viridis)
# Loop over days
for x in range(numDays-1):
day = x+startDay
if (day>=numDaysYear1):
# If day goes beyond the number of days in initial year, jump to the next year
day=day-numDaysYear1
yearCurrent=year2
print ('Day of year:', day)
print ('Date:', dates[x])
#-------- Load daily data
iceConcDayG, precipDayG, driftGdayG, windDayG, tempDayG =loadData(yearCurrent, day, precipVar, windVar, concVar, driftVar, dxStr, extraStr)
#-------- Apply CloudSat scaling if used
if scaleCS:
currentMonth = doyToMonth(day, yearCurrent) # get current month
scalingFactor = monthlyScalingFactors.loc[currentMonth,:,:] # get scaling factor for current month
# apply scaling to current day's precipitation
precipDayG = applyScaling(precipDayG, scalingFactor,scaling_type='mul').values
#-------- Calculate snow budgets
calcBudget(xptsG, yptsG, snowDepths, iceConcDayG, precipDayG, driftGdayG, windDayG, tempDayG,
density, precipDays, iceConcDays, windDays, tempDays, snowAcc, snowOcean, snowAdv,
snowDiv, snowLead, snowAtm, snowWindPackLoss, snowWindPackGain, snowWindPack, region_maskG, dx, x, day,
densityType=densityTypeT, dynamicsInc=dynamicsInc, leadlossInc=leadlossInc, windpackInc=windpackInc, atmlossInc=atmlossInc)
if (plotdaily==1):
cF.plot_gridded_cartopy(lonG, latG, snowDepths[x+1, 0]+snowDepths[x+1, 1], proj=ccrs.NorthPolarStereo(central_longitude=-45), date_string='', out=figpath+'daily_snow_depths/snowTot_'+saveStrNoDate+str(x), units_lab='m', varStr='Snow depth', minval=0., maxval=0.6)
#------ Load last data
iceConcDayG, precipDayG, _, windDayG, tempDayG =loadData(yearCurrent, day+1, precipVar, windVar, concVar, driftVar, dxStr, extraStr)
precipDays[x+1]=precipDayG
iceConcDays[x+1]=iceConcDayG
windDays[x+1]=windDayG
tempDays[x+1]=tempDayG
if (saveData==1):
# Output snow budget terms to netcdf datafiles
cF.OutputSnowModelRaw(savePath, saveStr, snowDepths, density, precipDays, iceConcDays, windDays, snowAcc, snowOcean, snowAdv, snowDiv, snowLead, snowAtm, snowWindPack)
cF.OutputSnowModelFinal(savePath, 'NESOSIMv11_'+dateOut, lonG, latG, xptsG, yptsG, snowDepths[:, 0]+snowDepths[:, 1], (snowDepths[:, 0]+snowDepths[:, 1])/iceConcDays, density, iceConcDays, precipDays, windDays, tempDays, dates)
if (plotBudgets==1):
# Plot final snow budget terms
cF.plot_budgets_cartopy(lonG, latG, precipDayG, windDayG, snowDepths[x+1], snowOcean[x+1], snowAcc[x+1], snowDiv[x+1], \
snowAdv[x+1], snowLead[x+1], snowAtm[x+1], snowWindPack[x+1], snowWindPackLoss[x+1], snowWindPackGain[x+1], density[x+1], dates[-1], figpath, totalOutStr=saveStr)