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DAS_Driver.py
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DAS_Driver.py
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# -*- coding: utf-8 -*-
'''
Copyright of DasPy:
Author - Xujun Han (Forschungszentrum Jülich, Germany)
DasPy was funded by:
1. Forschungszentrum Jülich, Agrosphere (IBG 3), Jülich, Germany
2. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, PR China
3. Centre for High-Performance Scientific Computing in Terrestrial Systems: HPSC TerrSys, Geoverbund ABC/J, Jülich, Germany
Please include the following references related to DasPy:
1. Han, X., Li, X., He, G., Kumbhar, P., Montzka, C., Kollet, S., Miyoshi, T., Rosolem, R., Zhang, Y., Vereecken, H., and Franssen, H. J. H.:
DasPy 1.0 : the Open Source Multivariate Land Data Assimilation Framework in combination with the Community Land Model 4.5, Geosci. Model Dev. Discuss., 8, 7395-7444, 2015.
2. Han, X., Franssen, H. J. H., Rosolem, R., Jin, R., Li, X., and Vereecken, H.:
Correction of systematic model forcing bias of CLM using assimilation of cosmic-ray Neutrons and land surface temperature: a study in the Heihe Catchment, China, Hydrology and Earth System Sciences, 19, 615-629, 2015a.
3. Han, X., Franssen, H. J. H., Montzka, C., and Vereecken, H.:
Soil moisture and soil properties estimation in the Community Land Model with synthetic brightness temperature observations, Water Resour Res, 50, 6081-6105, 2014a.
4. Han, X., Franssen, H. J. H., Li, X., Zhang, Y. L., Montzka, C., and Vereecken, H.:
Joint Assimilation of Surface Temperature and L-Band Microwave Brightness Temperature in Land Data Assimilation, Vadose Zone J, 12, 0, 2013.
'''
from mpi4py import MPI
import multiprocessing, shutil
from DAS_Assim_Common import *
from DAS_Assim import *
from DAS_Misc import *
from DAS_Driver_Common import *
from DAS_Utilities import *
# Data Assimilation, Parameter Estimation, Bias Estimation
def DAS_Driver(mpi4py_comm, mpi4py_null, mpi4py_rank, mpi4py_size, mpi4py_name, Model_Driver,Do_DA_Flag, Def_Par_Sensitivity, Def_Par_Correlation, Def_Par_Optimized, Dim_Soil_Par, Dim_Veg_Par, Dim_PFT_Par, Dim_Hard_Par, Soil_Texture_Layer_Opt_Num, Observation_Box, LAI_Year_String, MODIS_LAI_Data_ID,\
Num_of_Days_Monthly, Start_Year, Start_Month, Start_Day, Start_Hour, Start_Minute, End_Year, End_Month, End_Day, End_Hour, End_Minute, Datetime_Start, Datetime_Start_Init, \
Datetime_End, Datetime_End_Init, Datetime_Initial, UTC_Zone, CLM_NA, NAvalue, Assim_Algorithm_Name, Station_XY, Station_XY_Index, dtime,\
NSLOTS, Feedback_Assim, Parameter_Optimization, Parameter_Regularization, Def_CDF_Matching, Bias_Estimation_Option_Model, Bias_Estimation_Option_Obs, Post_Inflation_Alpha, Def_Snow_Effects, N0, nlyr,\
Sub_Block_Ratio_Row, Sub_Block_Ratio_Col, Sub_Block_Index_Row_Mat_Vector, Sub_Block_Index_Col_Mat_Vector, Row_Offset, Col_Offset,
Row_Numbers_SubBlock_Array, Col_Numbers_SubBlock_Array, Sub_Block_Row_Start_Array, Sub_Block_Row_End_Array, Sub_Block_Col_Start_Array, Sub_Block_Col_End_Array,
Observation_Time_File_Path, Def_CESM_Multi_Instance, Constant_File_Name_Header, finidat_initial_CLM, finidat_initial_PFCLM, Def_PP, DAS_Fortran_Lib, Normal_Score_Trans, PDAF_Assim_Framework, PDAF_Filter_Type, \
Def_ParFor, Def_Region, Def_Initial, Irrig_Scheduling, Irrigation_Hours, Def_SpinUp, Def_First_Run, Def_Print, CLM_Flag, Def_ReBEL, Def_Localization, \
Num_Local_Obs_State, Num_Local_Obs_Par, Num_Local_Obs_Bias, eps, msw_infl, Def_Multiresolution, Def_Write_Initial, Ensemble_Number, Ensemble_Number_Predict, Call_Gstat_Flag, Write_DA_File_Flag, Use_Mask_Flag, Def_Figure_Output,\
Forcing_File_Path_Home, Soil_Layer_Num, Snow_Layer_Num, ParFlow_Layer_Num, maxpft, numrad, Density_of_liquid_water, Density_of_ice, Freezing_temperature_of_fresh_water, Plot_Analysis, Def_Debug, Initial_Perturbation, \
Weather_Forecast_Days, PicHeight, PicWidth, RegionName, Row_Numbers, Col_Numbers, Row_Numbers_String, Col_Numbers_String, Grid_Resolution_CEA_String, xllcenter, yllcenter, MODEL_X_Left, MODEL_X_Right, MODEL_Y_Lower, MODEL_Y_Upper,MODEL_CEA_X, MODEL_CEA_Y, Z_Resolution, Proj_String, \
Grid_Resolution_CEA, Grid_Resolution_GEO, mksrf_edgee, mksrf_edgew, mksrf_edges, mksrf_edgen, ntasks_CLM, rootpe_CLM, nthreads_CLM, omp_get_num_procs_ParFor, Low_Ratio_Par, High_Ratio_Par, Low_Ratio_Par_Uniform, High_Ratio_Par_Uniform, \
Soil_Par_Sens_Array, Veg_Par_Sens_Array, PFT_Par_Sens_Array, Hard_Par_Sens_Array, Region_Name, Run_Dir_Home, Model_Path, Hydraulic_File_Name, Mask_File, Observation_Path, DAS_Data_Path, DasPy_Path, DAS_Output_Path, DAS_Depends_Path,
octave, r, plt, cm, colors, inset_axes, fm, legend):
#print DasPy_Path+"ObsModel/COSMOS/COSMIC_Py.py"
#print DasPy_Path+"ObsModel/COSMOS/COSMIC_Py.py"
COSMIC_Py = imp.load_source("COSMIC_Py",DasPy_Path+"ObsModel/COSMOS/COSMIC_Py.py")
memory_profiler = []
COSMIC = imp.load_dynamic("COSMIC",DasPy_Path+"ObsModel/COSMOS/COSMIC.so")
num_processors = multiprocessing.cpu_count()
start = time.time()
UTM_Zone = int(round(mksrf_edgee/15.0)) # The Difference Between the UTM Time and the Local Time
diskless_flag = True
persist_flag = True
############################################ PFCLM ####################################################
COUP_OAS_PFL = False # whether to run coupled ParFlow or not
if Model_Driver == "PFCLM":
COUP_OAS_PFL = True
CESM_Init_Flag = 1 # whether is the first time to run ccsm_driver
############################################# PFCLM ###################################################
gelmna_threshold = 1.0 # Threshold to Stop Parameter Optimization (No Stop)
#gelmna_threshold = 1.08 # Threshold to Stop Parameter Optimization
if mpi4py_rank == 0:
if Def_Region == -1:
forcing_file_name = Forcing_File_Path_Home +"_Ens1/"+ Start_Year + '-' + Start_Month + '-' + Start_Day + '.nc'
if not os.path.exists(forcing_file_name):
print "Forcing file",forcing_file_name,"does not exists!!!!!!!!"
print "Please Change the Start Date and Time."
os.abort()
else:
forcing_file_name = Forcing_File_Path_Home +"/"+ Start_Year + '-' + Start_Month + '-' + Start_Day + '.nc'
if not os.path.exists(forcing_file_name):
print "Forcing file",forcing_file_name,"does not exists!!!!!!!!"
print "Please Change the Start Date and Time."
if Def_SpinUp != 1: # If we do multi years spinup, we could use one year foring to simulate multi years
os.abort()
PP_Port = 23335 + int(numpy.random.uniform(1000*Def_Region,2000*Def_Region))
active_nodes_server = []
else:
forcing_file_name = None
PP_Port = None
active_nodes_server = None
if Def_PP == 2:
mpi4py_comm.barrier()
mpi4py_comm.Barrier()
PP_Port = mpi4py_comm.bcast(PP_Port)
active_nodes_server = mpi4py_comm.bcast(active_nodes_server)
forcing_file_name = mpi4py_comm.bcast(forcing_file_name)
if mpi4py_rank == 0:
print "mpi4py_rank",mpi4py_rank,"PP_Port",PP_Port
job_server_node_array, active_nodes_server, PROCS_PER_NODE, PP_Port, PP_Servers_Per_Node = Start_ppserver(mpi4py_comm, mpi4py_rank, mpi4py_name, DAS_Output_Path, Ensemble_Number, DAS_Depends_Path, active_nodes_server, Def_Region, NSLOTS, Def_Print, DasPy_Path, Def_PP, Def_CESM_Multi_Instance, PP_Port)
if mpi4py_rank == 0:
restart_pp_server = (len(job_server_node_array) < 1)
print "-------------- restart_pp_server",restart_pp_server
else:
restart_pp_server = None
if Def_PP == 2:
mpi4py_comm.barrier()
mpi4py_comm.Barrier()
restart_pp_server = mpi4py_comm.bcast(restart_pp_server)
while Def_PP and restart_pp_server and Ensemble_Number > 1:
job_server_node_array = Stop_ppserver(mpi4py_rank, Def_PP, DAS_Depends_Path, job_server_node_array, NSLOTS, DasPy_Path, active_nodes_server, PP_Servers_Per_Node)
job_server_node_array, active_nodes_server, PROCS_PER_NODE, PP_Port, PP_Servers_Per_Node = Start_ppserver(mpi4py_comm, mpi4py_rank, mpi4py_name, DAS_Output_Path, Ensemble_Number, DAS_Depends_Path, active_nodes_server, Def_Region, NSLOTS, Def_Print, DasPy_Path, Def_PP, Def_CESM_Multi_Instance, PP_Port)
if Def_PP == 2:
mpi4py_comm.barrier()
mpi4py_comm.Barrier()
if mpi4py_rank == 0:
print "job_server_node_array",job_server_node_array
print "active_nodes_server",active_nodes_server
if mpi4py_rank == 0:
print "mpi4py_comm,mpi4py_rank,mpi4py_size,mpi4py_name",mpi4py_comm,mpi4py_rank,mpi4py_size,mpi4py_name
# MPI Split into Ensemble_Number Groups
mpi4py_comm_split = []
mpipy_comm_decomposition = 1
if Def_PP == 2:
if mpi4py_rank == 0:
mpipy_comm_decomposition = mpi4py_size/Ensemble_Number
else:
mpipy_comm_decomposition = None
mpipy_comm_decomposition = mpi4py_comm.bcast(mpipy_comm_decomposition)
if Ensemble_Number > 1:
color = mpi4py_rank/mpipy_comm_decomposition
key = mpi4py_rank
else:
color = 0
key = 0
mpi4py_comm_split = mpi4py_comm.Split(color, key)
mpi4py_comm.barrier()
mpi4py_comm.Barrier()
if mpi4py_rank == 0:
print "mpipy_comm_decomposition,mpi4py_comm_split",mpipy_comm_decomposition,mpi4py_comm_split
if Def_PP == 2:
print "mpi4py_comm_split.py2f()",mpi4py_comm_split.py2f(),"mpi4py_comm_split.Get_size()",mpi4py_comm_split.Get_size()
if Def_PP == 2:
ntasks_CLM[:] = mpi4py_comm_split.Get_size()
###################################################################
if Def_Region >= 9:
Grid_Resolution_GEO_Global = 360.0/43200.0
Resolution_Name = "1KM"
else:
Grid_Resolution_GEO_Global = 360.0/8640.0
Resolution_Name = "5KM"
# Path for DA
if mpi4py_rank == 0:
# Decrease the float bit
MODEL_CEA_X = MODEL_CEA_X - MODEL_X_Left
MODEL_CEA_Y = MODEL_CEA_Y - MODEL_Y_Lower
MODEL_X_Left = numpy.min(MODEL_CEA_X)
MODEL_X_Right = numpy.max(MODEL_CEA_X)
MODEL_Y_Lower = numpy.min(MODEL_CEA_Y)
MODEL_Y_Upper = numpy.max(MODEL_CEA_Y)
MODEL_X_Right = MODEL_X_Right - MODEL_X_Left
MODEL_X_Left = MODEL_X_Left - MODEL_X_Left
MODEL_Y_Upper = MODEL_Y_Upper - MODEL_Y_Lower
MODEL_Y_Lower = MODEL_Y_Lower - MODEL_Y_Lower
r.assign('MODEL_X_Left', MODEL_X_Left)
r.assign('MODEL_X_Right', MODEL_X_Right)
r.assign('MODEL_Y_Lower', MODEL_Y_Lower)
r.assign('MODEL_Y_Upper', MODEL_Y_Upper)
print "MODEL_X_Left,MODEL_X_Right,MODEL_Y_Lower,MODEL_Y_Upper"
print MODEL_X_Left,MODEL_X_Right,MODEL_Y_Lower,MODEL_Y_Upper
Run_Dir_Multi_Instance = []
Run_Dir_Array = []
Forcing_File_Path_Array = []
Forcing_File_Path_Array_Par = []
if Ensemble_Number == 1:
if not os.path.exists(Run_Dir_Home):
os.makedirs(Run_Dir_Home)
Run_Dir_Multi_Instance = Run_Dir_Home+"/"
Run_Dir_Array.append(Run_Dir_Home+"/")
Forcing_File_Path_Array.append(Forcing_File_Path_Home)
Forcing_File_Path_Array_Par.append(Forcing_File_Path_Home)
else:
Run_Dir_Multi_Instance = Run_Dir_Home+"_Ens/"
for Ens_Index in range(Ensemble_Number):
Run_Dir_Temp = Run_Dir_Home+"_Ens"+str(Ens_Index+1)+"/"
if Def_CESM_Multi_Instance == 1:
Run_Dir_Temp = Run_Dir_Multi_Instance
Forcing_File_Path_Temp = Forcing_File_Path_Home+"_Ens"+str(Ens_Index+1)+"/"
if not os.path.exists(Run_Dir_Temp):
os.makedirs(Run_Dir_Temp)
Run_Dir_Array.append(Run_Dir_Temp)
Forcing_File_Path_Array.append(Forcing_File_Path_Temp)
Forcing_File_Path_Array_Par.append(Forcing_File_Path_Home)
if not os.path.exists(Run_Dir_Multi_Instance):
os.makedirs(Run_Dir_Multi_Instance)
else:
Run_Dir_Multi_Instance = None
Run_Dir_Array = None
Forcing_File_Path_Array = None
Forcing_File_Path_Array_Par = None
if Def_PP == 2:
Run_Dir_Multi_Instance = mpi4py_comm.bcast(Run_Dir_Multi_Instance)
Run_Dir_Array = mpi4py_comm.bcast(Run_Dir_Array)
Forcing_File_Path_Array = mpi4py_comm.bcast(Forcing_File_Path_Array)
Forcing_File_Path_Array_Par = mpi4py_comm.bcast(Forcing_File_Path_Array_Par)
######################################################### NC Files
NC_FileName_Assimilation_2_Constant = DAS_Output_Path+"Analysis/"+Region_Name+"/Assimilation_2_Constant.nc"
NC_FileName_Assimilation_2_Diagnostic = DAS_Output_Path+"Analysis/"+Region_Name+"/Assimilation_2_Diagnostic.nc"
NC_FileName_Assimilation_2_Initial = DAS_Output_Path+"Analysis/"+Region_Name+"/Assimilation_2_Initial.nc"
NC_FileName_Assimilation_2_Initial_Copy = DAS_Output_Path+"Analysis/"+Region_Name+"/Assimilation_2_Initial_Copy.nc"
NC_FileName_Assimilation_2_Bias = DAS_Output_Path+"Analysis/"+Region_Name+"/Assimilation_2_Bias.nc"
NC_FileName_Assimilation_2_Bias_Copy = DAS_Output_Path+"Analysis/"+Region_Name+"/Assimilation_2_Bias_Copy.nc"
NC_FileName_Assimilation_2_Bias_Monthly = DAS_Output_Path+"Analysis/"+Region_Name+"/Assimilation_2_Bias_Monthly.nc"
NC_FileName_Assimilation_2_Bias_Monthly_Copy = DAS_Output_Path+"Analysis/"+Region_Name+"/Assimilation_2_Bias_Monthly_Copy.nc"
NC_FileName_Estimated_Bias = DAS_Output_Path+"Analysis/"+Region_Name+"/Estimated_Bias.nc"
NC_FileName_Assimilation_2_Parameter = DAS_Output_Path+"Analysis/"+Region_Name+"/Assimilation_2_Parameter.nc"
NC_FileName_Assimilation_2_Parameter_Copy = DAS_Output_Path+"Analysis/"+Region_Name+"/Assimilation_2_Parameter_Copy.nc"
NC_FileName_Assimilation_2_Parameter_Obs_Dim = DAS_Output_Path+"Analysis/"+Region_Name+"/Assimilation_2_Parameter_Obs_Dim.nc"
NC_FileName_Assimilation_2_Parameter_Monthly = DAS_Output_Path+"Analysis/"+Region_Name+"/Assimilation_2_Parameter_Monthly.nc"
NC_FileName_Assimilation_2_Parameter_Monthly_Copy = DAS_Output_Path+"Analysis/"+Region_Name+"/Assimilation_2_Parameter_Monthly_Copy.nc"
NC_FileName_Optimized_Parameter = DAS_Output_Path+"Analysis/"+Region_Name+"/Optimized_Parameter.nc"
NC_FileName_Soil_Moisture_Difference = DAS_Output_Path+"Analysis/"+Region_Name+"/Soil_Moisture_Difference.nc"
NC_FileName_Parameter_Space_Single = DAS_Output_Path+"Analysis/"+Region_Name+"/Parameter_Space_Single.nc"
DAS_File_Name_List = [NC_FileName_Assimilation_2_Constant, NC_FileName_Assimilation_2_Diagnostic,
NC_FileName_Assimilation_2_Initial, NC_FileName_Assimilation_2_Initial_Copy,
NC_FileName_Assimilation_2_Bias, NC_FileName_Assimilation_2_Bias_Copy,
NC_FileName_Assimilation_2_Bias_Monthly, NC_FileName_Assimilation_2_Bias_Monthly_Copy,
NC_FileName_Estimated_Bias,
NC_FileName_Assimilation_2_Parameter, NC_FileName_Assimilation_2_Parameter_Copy, NC_FileName_Assimilation_2_Parameter_Obs_Dim,
NC_FileName_Assimilation_2_Parameter_Monthly, NC_FileName_Assimilation_2_Parameter_Monthly_Copy,
NC_FileName_Optimized_Parameter, NC_FileName_Soil_Moisture_Difference, NC_FileName_Parameter_Space_Single]
########################################################################################
Soil_Thickness = numpy.asarray([0.01751282, 0.02757897, 0.04547003, 0.07496741, 0.1236004, 0.2037826, 0.3359806, 0.5539384, 0.91329, 1.50576, 2.48258, 4.093082, 6.748351, 11.12615, 13.85115])
Variable_List = ["Soil_Moisture","Surface_Temperature","Vegetation_Temperature","Canopy_Water","Albedo_BSA_Band_vis","Albedo_BSA_Band_nir","Albedo_WSA_Band_vis",
"Albedo_WSA_Band_nir","Emissivity","Snow_Depth","Snow_Cover_Fraction","Snow_Water_Equivalent","LAI","Sensible_Heat","Irrigation_Scheduling"]
Dim_CLM_State = len(Variable_List)
Variable_Assimilation_Flag = numpy.zeros(Dim_CLM_State,dtype=numpy.float32)
Initial_Perturbation_SM_Flag = numpy.array([0 for i in range(15)]) # whether to perturb the initial data
Initial_Perturbation_ST_Flag = numpy.array([0 for i in range(15)]) # whether to perturb the initial data
################################################################################ CLM Input File Names
finidat_initial_CLM_Copy = finidat_initial_CLM
fndepdat_name = "fndep_clm_hist_simyr1849-2006_1.9x2.5_c100428.nc"
fatmgrid_name = "griddata_"+Row_Numbers_String+"x"+Col_Numbers_String+".nc"
fatmlndfrc_name = "domain.lnd_"+Row_Numbers_String+"x"+Col_Numbers_String+"_"+Region_Name+".nc"
#fatmlndfrc_name = "fracdata_"+Row_Numbers_String+"x"+Col_Numbers_String+"_"+Region_Name+".nc"
fsurdat_name = "surfdata_"+Row_Numbers_String+"x"+Col_Numbers_String+"_"+Region_Name+".nc"
fglcmask_name = "glcmaskdata_"+Row_Numbers_String+"x"+Col_Numbers_String+"_Gland20km.nc"
flndtopo_name = "topodata_"+Row_Numbers_String+"x"+Col_Numbers_String+"_"+Region_Name+".nc"
fsnowoptics_name = "snicar_optics_5bnd_c090915.nc"
fsnowaging_name = "snicar_drdt_bst_fit_60_c070416.nc"
fpftcon_name = "pft-physiology.c130503.nc"
domain_name = "domain.lnd_"+Row_Numbers_String+"x"+Col_Numbers_String+"_"+Region_Name+".nc"
rdirc_name = "rdirc_0.5x0.5_simyr2000_c101124.nc"
popd_streams_name = "clmforc.Li_2012_hdm_0.5x0.5_AVHRR_simyr1850-2010_c130401.nc"
light_streams_name = "clmforc.Li_2012_climo1995-2011.T62.lnfm_c130327.nc"
CLM_File_Name_List = [fndepdat_name, fatmgrid_name, fatmlndfrc_name, fsurdat_name, fglcmask_name, flndtopo_name,
fsnowoptics_name, fsnowaging_name, fpftcon_name, domain_name, rdirc_name, popd_streams_name, light_streams_name]
##################DA Parameters
Dim_Observation_Quantity = 4
# Irrigation Parameters
irrig_nsteps_per_day = 1
PFT_Num = 1
PFT_Type_Index = 4
irrig_nsteps_per_day = 3600.0 / dtime * Irrigation_Hours * PFT_Num
N_Steps = []
SensorType = []
SensorVariable = []
SensorQuantity = []
Variable_ID = []
QC_ID = []
SensorResolution = []
Observation_File_Name = []
Soil_Layer_Index_DA = 0
Def_First_Run_RTM = 1 # whether is RTM called at the first time
if mpi4py_rank == 0:
##### Some Index Variables
column_len = []
pft_len = []
finidat_name_string = []
if Do_DA_Flag:
finidat_name_string = Run_Dir_Home+"_Ens" + str(1) +"/"+ finidat_initial_CLM
print '============================= Open the Model Initial File and Read the Index Data ==========================================='
#------------------------------------------- Read the CLM Initial File
print "Open Initial File:", finidat_name_string
try:
CLM_Initial_File = netCDF4.Dataset(finidat_name_string, 'r')
column_len = len(CLM_Initial_File.dimensions['column'])
pft_len = len(CLM_Initial_File.dimensions['pft'])
CLM_Initial_File.close()
except:
print finidat_name_string,"not exists!!!!!!!!!!!!!!!!!!!!!"
os.abort()
else:
finidat_name_string = None
column_len = None
pft_len = None
if Def_PP == 2:
mpi4py_comm.barrier()
mpi4py_comm.Barrier()
finidat_name_string = mpi4py_comm.bcast(finidat_name_string)
column_len = mpi4py_comm.bcast(column_len)
pft_len = mpi4py_comm.bcast(pft_len)
if mpi4py_rank == 0:
##### Some Index Variables
#############################################################
if not os.path.exists(DasPy_Path+"Analysis/DAS_Temp/"+Region_Name):
os.makedirs(DasPy_Path+"Analysis/DAS_Temp/"+Region_Name)
####################################################################################
Land_Mask_Data = numpy.ones((Row_Numbers,Col_Numbers),dtype=numpy.float32)
Teta_Residual = numpy.ones((Soil_Layer_Num,Row_Numbers,Col_Numbers),dtype=numpy.float32)
Teta_Saturated = numpy.ones((Soil_Layer_Num,Row_Numbers,Col_Numbers),dtype=numpy.float32)
Teta_Field_Capacity = numpy.ones((Soil_Layer_Num,Row_Numbers,Col_Numbers),dtype=numpy.float32)
Teta_Wilting_Point = numpy.ones((Soil_Layer_Num,Row_Numbers,Col_Numbers),dtype=numpy.float32)
sucsat = numpy.ones((Soil_Layer_Num,Row_Numbers,Col_Numbers),dtype=numpy.float32)
bsw = numpy.ones((Soil_Layer_Num,Row_Numbers,Col_Numbers),dtype=numpy.float32)
watdry = numpy.ones((Soil_Layer_Num,Row_Numbers,Col_Numbers),dtype=numpy.float32)
watopt = numpy.ones((Soil_Layer_Num,Row_Numbers,Col_Numbers),dtype=numpy.float32)
watfc = numpy.ones((Soil_Layer_Num,Row_Numbers,Col_Numbers),dtype=numpy.float32)
smpso = []
smpsc = []
Sand_Top_Region = numpy.ones((Row_Numbers,Col_Numbers),dtype=numpy.float32)
Clay_Top_Region = numpy.ones((Row_Numbers,Col_Numbers),dtype=numpy.float32)
Organic_Top_Region = numpy.ones((Row_Numbers,Col_Numbers),dtype=numpy.float32)
Bulk_Density_Top_Region = numpy.ones((Row_Numbers,Col_Numbers),dtype=numpy.float32)
Sand_Sub_Region = numpy.ones((Row_Numbers,Col_Numbers),dtype=numpy.float32)
Clay_Sub_Region = numpy.ones((Row_Numbers,Col_Numbers),dtype=numpy.float32)
Organic_Sub_Region = numpy.ones((Row_Numbers,Col_Numbers),dtype=numpy.float32)
Bulk_Density_Sub_Region = numpy.ones((Row_Numbers,Col_Numbers),dtype=numpy.float32)
DEM_Data = numpy.ones((Row_Numbers,Col_Numbers),dtype=numpy.float32)
PCT_PFT = numpy.zeros((maxpft, Row_Numbers, Col_Numbers),dtype=numpy.float32)
STD_ELEV = numpy.ones((Row_Numbers,Col_Numbers),dtype=numpy.float32) # standard deviation of the elevation within a grid cell
topo_slope = numpy.ones((Row_Numbers,Col_Numbers),dtype=numpy.float32)
PCT_LAKE = numpy.ones((Row_Numbers,Col_Numbers),dtype=numpy.float32)
Irrigation_Grid_Flag = numpy.zeros((Row_Numbers,Col_Numbers),dtype=numpy.bool)
micro_sigma = numpy.zeros((Row_Numbers, Col_Numbers),dtype=numpy.float32)
# Calculate the Vegetation Fraction
PCT_Veg = numpy.zeros((Row_Numbers, Col_Numbers),dtype=numpy.float32)
PCT_PFT_High = numpy.zeros((Row_Numbers, Col_Numbers),dtype=numpy.float32)
PCT_PFT_Low = numpy.zeros((Row_Numbers, Col_Numbers),dtype=numpy.float32)
PCT_PFT_WATER = numpy.zeros((Row_Numbers, Col_Numbers),dtype=numpy.float32)
PFT_Dominant_Index = numpy.zeros((Row_Numbers, Col_Numbers),dtype=numpy.float32)
Crop_Sum = numpy.zeros((Row_Numbers, Col_Numbers),dtype=numpy.float32)
Bare_Grid_Index = numpy.zeros((Row_Numbers, Col_Numbers),dtype=numpy.float32)
print "Open Hydraulic_File_Name",Hydraulic_File_Name
try:
Hydraulic_File = netCDF4.Dataset(Hydraulic_File_Name, 'r')
Teta_Residual = Hydraulic_File.variables['RES'][:,:,:]
Teta_Saturated = Hydraulic_File.variables['SAT'][:,:,:]
Teta_Field_Capacity = Hydraulic_File.variables['FC'][:,:,:]
Teta_Wilting_Point = Hydraulic_File.variables['WP'][:,:,:]
sucsat = Hydraulic_File.variables["sucsat"][:,:,:]
bsw = Hydraulic_File.variables["bsw"][:,:,:]
watdry = Hydraulic_File.variables["watdry"][:,:,:]
watopt = Hydraulic_File.variables["watopt"][:,:,:]
watfc = Hydraulic_File.variables["watfc"][:,:,:]
Hydraulic_File.close()
except:
print "Open Hydraulic_File_Name",Hydraulic_File_Name,"Failed!!"
os.abort()
# Make sure the minimum soil moisture larger than 0.05, because we assume the maximum bias is 0.05
Teta_Residual[numpy.where(Teta_Residual < 0.05)] = 0.05
pft_physiology_file_name = DAS_Data_Path + "SysModel/CLM/inputdata/lnd/clm2/pftdata/"+fpftcon_name
pft_physiology_file = netCDF4.Dataset(pft_physiology_file_name, "r")
smpso = pft_physiology_file.variables["smpso"][:]
smpsc = pft_physiology_file.variables["smpsc"][:]
#smpso = numpy.zeros(len(pft_physiology_file.dimensions['pft']))
#smpso[:] = -50000.0
#print z0mr,PFT_Height_Top[1:17]
pft_physiology_file.close()
# Create Plot Folder
if not os.path.exists(DasPy_Path+"Analysis/DAS_Temp/"+Region_Name):
os.makedirs(DasPy_Path+"Analysis/DAS_Temp/"+Region_Name)
if Use_Mask_Flag:
# find the model grids which are need to be assiilated.
# Read the Mask Grid
print "Read the Land Water Mask Data"
mkdatadomain_NC_FileName_In = DAS_Data_Path+"SysModel/CLM/tools/" + fatmlndfrc_name
print "mkdatadomain_NC_FileName_In",mkdatadomain_NC_FileName_In
mkdatadomain_NC_File_In = netCDF4.Dataset(mkdatadomain_NC_FileName_In, 'r+')
Land_Mask_Data = numpy.flipud(mkdatadomain_NC_File_In.variables['mask'][:,:])
mkdatadomain_NC_File_In.close()
Land_Mask_Data[numpy.where(Land_Mask_Data == 0.0)] = NAvalue
Data = numpy.ma.masked_where(Land_Mask_Data == NAvalue, Land_Mask_Data)
fig1 = plt.figure(figsize=(15, 10), dpi=80)
ax = fig1.add_subplot(1, 1, 1)
im1 = ax.imshow(Data, cmap=cm.jet)
ax.set_title('Land_Mask_Data')
plt.grid(True)
plt.savefig(DasPy_Path+"Analysis/DAS_Temp/"+Region_Name+"/Land_Mask_Data.png")
plt.close('all')
del Data
####################################################################
Corner_Row_Index = 0
Corner_Col_Index = 0
if Def_Region == -2:
Grid_Resolution_GEO_Global = 360.0/432000.0*5.0
Resolution_Name = "500m"
elif Def_Region == -1:
Grid_Resolution_GEO_Global = 360.0/432000.0
Resolution_Name = "100m"
elif Def_Region <= 8:
Grid_Resolution_GEO_Global = 360.0/43200.0
Resolution_Name = "1KM"
elif Def_Region >= 9:
Grid_Resolution_GEO_Global = 360.0/8640.0
Resolution_Name = "5KM"
Sand_Top_Region, Clay_Top_Region, Organic_Top_Region, Bulk_Density_Top_Region, Sand_Sub_Region, Clay_Sub_Region, Organic_Sub_Region, Bulk_Density_Sub_Region \
= Read_Soil_Texture(Def_Region, DAS_Data_Path, Resolution_Name, Region_Name, Row_Numbers, Col_Numbers, Corner_Row_Index, Corner_Col_Index)
print "*******************************Read CLM mksurfdata"
mksurfdata_NC_FileName_In = DAS_Data_Path+"SysModel/CLM/tools/" + fsurdat_name
print "mksurfdata_NC_FileName_In",mksurfdata_NC_FileName_In
print "************************Open*******************",mksurfdata_NC_FileName_In
mksurfdata_NC_File_In = netCDF4.Dataset(mksurfdata_NC_FileName_In, 'r')
#print mksurfdata_NC_File_In.variables
STD_ELEV = numpy.flipud(mksurfdata_NC_File_In.variables['STD_ELEV'][::]) # standard deviation of the elevation within a grid cell
topo_slope = numpy.flipud(mksurfdata_NC_File_In.variables['SLOPE'][::]) # mean topographic slop
DEM_Data = numpy.flipud(mksurfdata_NC_File_In.variables['TOPO'][::]) # mean elevation on land
# check for near zero slopes, set minimum value
topo_slope[numpy.where(topo_slope < 0.2)] = 0.2
for Pft_index in range(maxpft):
#print numpy.shape(mksurfdata_NC_File_In.variables['PCT_PFT'])
PCT_PFT[Pft_index,::] = numpy.flipud(mksurfdata_NC_File_In.variables['PCT_PFT'][Pft_index,:,:])
PCT_LAKE = numpy.flipud(mksurfdata_NC_File_In.variables['PCT_LAKE'][::])
mksurfdata_NC_File_In.close()
#################################
###########################3
# microtopographic parameter, units are meters
minslope=0.05
slopemax=0.4
maxslope=(slopemax - minslope)/(slopemax)
# try smooth function of slope
slopebeta=3.0
slopemax=0.4
slope0=slopemax**(-1.0/slopebeta)
micro_sigma = (topo_slope + slope0)**(-slopebeta)
# Calculate the Vegetation Fraction
PCT_Veg = numpy.sum(PCT_PFT[1:maxpft,:,:],axis=0)
PCT_PFT_High = numpy.sum(PCT_PFT[1:9,:,:],axis=0) / 100.0
PCT_PFT_Low = numpy.sum(PCT_PFT[9:maxpft,:,:],axis=0) / 100.0
PCT_PFT_WATER = PCT_LAKE / 100.0
PFT_Dominant_Index = numpy.argmax(PCT_PFT,axis=0)
numpy.savetxt("PFT_Dominant_Index_"+Region_Name+".txt",PFT_Dominant_Index)
w,h = plt.figaspect(float(Row_Numbers)/Col_Numbers)
fig1 = plt.figure(figsize=(w,h))
ax1 = fig1.add_subplot(1,1,1)
im1 = ax1.imshow(PFT_Dominant_Index, cmap=cm.jet, interpolation='bilinear')
plt.colorbar(im1)
if Def_Figure_Output:
plt.savefig("DataBase/PFT_Dominant_Index_"+Region_Name+".png")
#plt.show()
Bare_Grid_Index = numpy.where(PFT_Dominant_Index == 0)
#Bare_Grid_Index = numpy.where(PCT_PFT[0,:,:] == 100)
print "numpy.size(Bare_Grid_Index)",numpy.size(Bare_Grid_Index)
for Soil_Layer_Index in range(Soil_Layer_Num):
watopt[Soil_Layer_Index,:,:][Bare_Grid_Index] = watfc[Soil_Layer_Index,:,:][Bare_Grid_Index]
watdry[Soil_Layer_Index,:,:][Bare_Grid_Index] = Teta_Residual[Soil_Layer_Index,:,:][Bare_Grid_Index]
#watopt[Soil_Layer_Index,:,:] = Teta_Saturated[Soil_Layer_Index,:,:]
#watdry[Soil_Layer_Index,:,:] = Teta_Residual[Soil_Layer_Index,:,:]
print "************************"
print "numpy.max(watopt),numpy.min(watopt),numpy.max(watdry),numpy.min(watdry)"
print numpy.max(watopt),numpy.min(watopt),numpy.max(watdry),numpy.min(watdry)
# w,h = plt.figaspect(float(Row_Numbers)/Col_Numbers)
# fig1 = plt.figure(figsize=(w,h))
# ax1 = fig1.add_subplot(1,1,1)
# im1 = ax1.imshow(MONTHLY_LAI[6,:,:], cmap=cm.jet, interpolation='bilinear')
# plt.colorbar(im1)
# if Def_Figure_Output:
# plt.savefig("SysModel/CLM/Surfdata_Figure/"+Region_Name+"_PFT/"+"MONTHLY_LAI.png")
# plt.show()
##############################################################################
# COSMOS Circle Mask
Mask_X_COSMOS = MODEL_CEA_X
Mask_Y_COSMOS = MODEL_CEA_Y
COSMOS_Circle_Plot = numpy.zeros((Row_Numbers,Col_Numbers),dtype=numpy.float32)
COSMOS_Circle_Array = []
COSMOS_Circle_Index_Array = []
COSMOS_Circle_Num_Array = []
#for Station_Index in range(numpy.size(Station_XY)/2):
for Station_Index in range(numpy.size(Station_XY)/2-12):
COSMOS_Circle = numpy.zeros((Row_Numbers,Col_Numbers),dtype=numpy.bool)
COSMOS_Circle[::] = False
print "Station_"+str(Station_Index+1),Station_XY[Station_Index][0],Station_XY[Station_Index][1]
r.assign('X_Coordiates',Station_XY[Station_Index][0])
r.assign('Y_Coordiates',Station_XY[Station_Index][1])
r('xy <- cbind(X_Coordiates,Y_Coordiates)')
print r['xy']
print "========================== GEO to CEA"
r('xy_cea <- project(xy,"+proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +a=6371228 +b=6371228 +units=m +ellps=WGS84 +no_defs")')
print 'x,y',r['xy_cea'][0][0],r['xy_cea'][0][1]
ii = r['xy_cea'][0][0]
jj = r['xy_cea'][0][1]
dist = numpy.sqrt(abs(ii - Mask_X_COSMOS) ** 2 + abs(jj - Mask_Y_COSMOS) ** 2)
COSMOS_Circle_Index = numpy.where(dist <= 300)
COSMOS_Circle[COSMOS_Circle_Index] = True
COSMOS_Circle_Array.append(COSMOS_Circle)
COSMOS_Circle_Index_Array.append(COSMOS_Circle_Index)
#print COSMOS_Circle_Index,numpy.size(COSMOS_Circle_Index)
COSMOS_Circle_Num = numpy.zeros_like(COSMOS_Circle_Index)
COSMOS_Circle_Num = numpy.floor(dist[COSMOS_Circle_Index] / Grid_Resolution_CEA)
#print COSMOS_Circle_Num
COSMOS_Circle_Num_Array.append(COSMOS_Circle_Num)
COSMOS_Circle_Plot[COSMOS_Circle] = 1.0
if Plot_Analysis:
fig1 = plt.figure(figsize=(15, 10), dpi=80)
ax = fig1.add_subplot(1, 1, 1)
im1 = ax.imshow(COSMOS_Circle_Plot, cmap=cm.jet)
ax.set_title('COSMOS_Circle_Plot')
plt.grid(True)
plt.savefig(DasPy_Path+"Analysis/DAS_Temp/"+Region_Name+"/COSMOS_Circle_Plot.png")
plt.close('all')
##########################################################
LONGXY_Mat = numpy.zeros((Row_Numbers, Col_Numbers), dtype=numpy.float32)
LATIXY_Mat = numpy.zeros((Row_Numbers, Col_Numbers), dtype=numpy.float32)
longitudes = numpy.linspace(mksrf_edgew+Grid_Resolution_GEO[0]/2.0,mksrf_edgee-Grid_Resolution_GEO[0]/2.0,Col_Numbers)
latitudes = numpy.linspace(mksrf_edges+Grid_Resolution_GEO[1]/2.0,mksrf_edgen-Grid_Resolution_GEO[1]/2.0,Row_Numbers)
LONGXY_Row = longitudes
LATIXY_Col = latitudes
for row in range(Row_Numbers):
LONGXY_Mat[row,:] = LONGXY_Row
for col in range(Col_Numbers):
LATIXY_Mat[:,col] = LATIXY_Col
#print LATIXY_Col
Mask_Index = numpy.zeros((Dim_CLM_State, Row_Numbers, Col_Numbers), dtype=numpy.bool)
Mask_Index[:,:,:] = False
#------------------------------------------- Data Assimilation Flags
print "Soil Moisture Products: SMAP(10km), AMSR-E(25km), SMOS(40km), ASCAT(12.5km,25km), MODIS(1km), ASAR(120m), PALSAR(60m)"
print "Soil Temperature Products: MODIS Terra and Aqua(1km)"
############################################################## For Bias Estimation #######################################
Bias_Remove_Start_Time_Array = ['' for i in range(Dim_CLM_State)]
# Flag to check wehter the Observation Bias of each observation type and each observation ensemble has been perturbed
Observation_Bias_Initialization_Flag = numpy.zeros((Dim_CLM_State,Dim_Observation_Quantity,Ensemble_Number),dtype=numpy.float32)
Model_Bias_Optimized = numpy.zeros((Ensemble_Number, Dim_CLM_State, numpy.size(Station_XY)/2), dtype=numpy.float32)
Observation_Bias_Optimized = numpy.zeros((Ensemble_Number, Dim_CLM_State, Dim_Observation_Quantity, numpy.size(Station_XY)/2), dtype=numpy.float32)
# Bias Estimation Range and Standard Deviation Defination
Model_Bias_Range = numpy.zeros((Dim_CLM_State,2),dtype=numpy.float32)
Observation_Bias_Range = numpy.zeros((Dim_CLM_State,Dim_Observation_Quantity,2),dtype=numpy.float32)
Model_Bias_Range_STD = numpy.zeros((Dim_CLM_State,2),dtype=numpy.float32)
Observation_Bias_Range_STD = numpy.zeros((Dim_CLM_State,Dim_Observation_Quantity,2),dtype=numpy.float32)
Model_Bias_STD = numpy.zeros(Dim_CLM_State,dtype=numpy.float32)
Observation_Bias_STD = numpy.zeros((Dim_CLM_State,Dim_Observation_Quantity),dtype=numpy.float32)
# Model State Ensemble Inflation STD
Model_State_Inflation_Range = numpy.zeros((Dim_CLM_State,2),dtype=numpy.float32)
Model_State_Inflation_Range_STD = numpy.zeros(Dim_CLM_State,dtype=numpy.float32)
########### Simulate Model Error
Additive_Noise_SM_Par = numpy.zeros((10,11),dtype=numpy.float32)
Additive_Noise_SM_Par[::] = numpy.array([[1.00E-3, 1.0, 0.7, 0.7, 0.6, 0.6, 0.6, 0.6, 0.4, 0.4, 0.4],
[7.00E-4, 0.7, 1.0, 0.7, 0.7, 0.6, 0.6, 0.6, 0.6, 0.4, 0.4],
[5.00E-4, 0.7, 0.7, 1.0, 0.7, 0.7, 0.6, 0.6, 0.6, 0.6, 0.4],
[3.00E-4, 0.6, 0.7, 0.7, 1.0, 0.7, 0.7, 0.6, 0.6, 0.6, 0.6],
[2.00E-5, 0.6, 0.6, 0.7, 0.7, 1.0, 0.7, 0.7, 0.6, 0.6, 0.6],
[2.00E-5, 0.6, 0.6, 0.6, 0.7, 0.7, 1.0, 0.7, 0.7, 0.6, 0.6],
[2.00E-5, 0.6, 0.6, 0.6, 0.6, 0.7, 0.7, 1.0, 0.7, 0.7, 0.6],
[1.50E-6, 0.4, 0.6, 0.6, 0.6, 0.6, 0.7, 0.7, 1.0, 0.7, 0.7],
[1.50E-6, 0.4, 0.4, 0.6, 0.6, 0.6, 0.6, 0.7, 0.7, 1.0, 0.7],
[5.00E-8, 0.4, 0.4, 0.4, 0.6, 0.6, 0.6, 0.6, 0.7, 0.7, 1.0]])
#print numpy.shape(Additive_Noise_SM_Par)
Additive_Noise_SM = numpy.zeros((Ensemble_Number,Soil_Layer_Num-5),dtype=numpy.float32)
Additive_Noise_ST = numpy.zeros((Ensemble_Number,2),dtype=numpy.float32)
Irrigation_Grid_Flag_Array = []
cols1d_ixy = numpy.zeros(column_len, dtype=numpy.integer)
cols1d_jxy = numpy.zeros(column_len, dtype=numpy.integer)
cols1d_ityplun = numpy.zeros(column_len, dtype=numpy.integer)
pfts1d_ixy = numpy.zeros(pft_len, dtype=numpy.integer)
pfts1d_jxy = numpy.zeros(pft_len, dtype=numpy.integer)
pfts1d_itypveg = numpy.zeros(pft_len, dtype=numpy.integer)
pfts1d_ci = numpy.zeros(pft_len, dtype=numpy.integer)
pfts1d_ityplun = numpy.zeros(pft_len, dtype=numpy.integer)
##### Some Index Variables
if Do_DA_Flag:
finidat_name_string = Run_Dir_Home+"_Ens" + str(1) +"/"+ finidat_initial_CLM
print '============================= Open the Model Initial File and Read the Index Data ==========================================='
#------------------------------------------- Read the CLM Initial File
print "Open Initial File:", finidat_name_string
try:
CLM_Initial_File = netCDF4.Dataset(finidat_name_string, 'r')
cols1d_ixy[:] = CLM_Initial_File.variables['cols1d_ixy'][:]
cols1d_jxy[:] = CLM_Initial_File.variables['cols1d_jxy'][:]
cols1d_ityplun[:] = CLM_Initial_File.variables['cols1d_ityplun'][:]
#numpy.savetxt('cols1d_ixy',cols1d_ixy)
#numpy.savetxt('cols1d_jxy',cols1d_jxy)
pfts1d_ixy[:] = CLM_Initial_File.variables['pfts1d_ixy'][:]
pfts1d_jxy[:] = CLM_Initial_File.variables['pfts1d_jxy'][:]
pfts1d_itypveg[:] = CLM_Initial_File.variables['pfts1d_itypveg'][:]
pfts1d_ci[:] = CLM_Initial_File.variables['pfts1d_ci'][:]
pfts1d_ityplun[:] = CLM_Initial_File.variables['pfts1d_ityplun'][:]
CLM_Initial_File.close()
except:
print finidat_name_string,"not exists!!!!!!!!!!!!!!!!!!!!!"
os.abort()
Analysis_Variable_Name = ['' for i in range(Dim_CLM_State)]
Soil_Sand_Clay_Sum = numpy.zeros((Soil_Texture_Layer_Opt_Num, Row_Numbers, Col_Numbers), dtype=numpy.float32)
print "################ Go to CLM"
Parameter_Soil_Optimized = numpy.zeros((Ensemble_Number, Dim_Soil_Par, numpy.size(Station_XY)/2), dtype=numpy.float32)
Parameter_PFT_Optimized = numpy.zeros((Ensemble_Number, Dim_PFT_Par, numpy.size(Station_XY)/2), dtype=numpy.float32)
Parameter_Hard_Optimized = numpy.zeros((Ensemble_Number, Dim_Hard_Par, numpy.size(Station_XY)/2), dtype=numpy.float32)
Parameter_Soil_PSRF = numpy.zeros((Dim_Soil_Par, numpy.size(Station_XY)/2), dtype=numpy.float32)
Parameter_PFT_PSRF = numpy.zeros((Dim_PFT_Par, numpy.size(Station_XY)/2), dtype=numpy.float32)
Parameter_Hard_PSRF = numpy.zeros((Dim_Hard_Par, numpy.size(Station_XY)/2), dtype=numpy.float32)
Parameter_Optimization_First_Flag = True
Mean_Index_Prop_Grid_Array_Sys = numpy.zeros((Dim_CLM_State, Row_Numbers, Col_Numbers),dtype=numpy.float32)
Model_Variance = numpy.zeros((Dim_CLM_State, Row_Numbers, Col_Numbers),dtype=numpy.float32)
Mask = numpy.zeros((Dim_CLM_State, 3, Row_Numbers, Col_Numbers),dtype=numpy.float32)
Mask_X = numpy.zeros((Row_Numbers, Col_Numbers),dtype=numpy.float32)
Mask_Y = numpy.zeros((Row_Numbers, Col_Numbers),dtype=numpy.float32)
Mask_X[::] = MODEL_CEA_X
Mask_Y[::] = MODEL_CEA_Y
###################################### CMEM Matrix
Clay_Fraction = []
Sand_Fraction = []
Soil_Density = numpy.zeros((Row_Numbers, Col_Numbers),dtype=numpy.float32)
CMEM_Work_Path_Array = []
Clay_Mat = []
Sand_Mat = []
ECOCVL_Mat = numpy.zeros((Row_Numbers, Col_Numbers), dtype=numpy.float32)
ECOCVH_Mat = numpy.zeros((Row_Numbers, Col_Numbers), dtype=numpy.float32)
ECOTVL_Mat = numpy.zeros((Row_Numbers, Col_Numbers), dtype=numpy.float32)
ECOTVH_Mat = numpy.zeros((Row_Numbers, Col_Numbers), dtype=numpy.float32)
ECOWAT_Mat = numpy.zeros((Row_Numbers, Col_Numbers), dtype=numpy.float32)
# Folder to Save Ensemble Mean
Mean_Dir = Run_Dir_Home+"_Ens_Mean"
if not os.path.exists(Mean_Dir):
os.makedirs(Mean_Dir)
if not os.path.exists(DAS_Output_Path+"Analysis/"+Region_Name):
os.makedirs(DAS_Output_Path+"Analysis/"+Region_Name)
for Block_Index in range(Sub_Block_Ratio_Row*Sub_Block_Ratio_Col):
if not os.path.exists(DAS_Output_Path+"Analysis/"+Region_Name+"/Block_"+str(Block_Index+1)):
os.makedirs(DAS_Output_Path+"Analysis/"+Region_Name+"/Block_"+str(Block_Index+1))
NC_File_In = netCDF4.Dataset(DAS_Data_Path + "SysModel/CLM/tools/"+fsurdat_name, 'r')
File_Format1 = NC_File_In.file_format
NC_File_In.close()
NC_File_In = netCDF4.Dataset(DAS_Data_Path + "SysModel/CLM/tools/"+fatmlndfrc_name, 'r')
File_Format2 = NC_File_In.file_format
NC_File_In.close()
NC_File_In = netCDF4.Dataset(DAS_Data_Path + "SysModel/CLM/inputdata/lnd/clm2/pftdata/"+fpftcon_name, 'r')
File_Format3 = NC_File_In.file_format
NC_File_In.close()
NC_File_In = netCDF4.Dataset(DAS_Data_Path + "SysModel/CLM/inputdata/lnd/clm2/rtmdata/"+rdirc_name, 'r')
File_Format4 = NC_File_In.file_format
NC_File_In.close()
NC_File_In = netCDF4.Dataset(DAS_Data_Path + "SysModel/CLM/inputdata/lnd/clm2/ndepdata/"+fndepdat_name, 'r')
File_Format5 = NC_File_In.file_format
NC_File_In.close()
if Def_First_Run == 1 and ((File_Format1 == 'NETCDF3_CLASSIC') or (File_Format1 == 'NETCDF3_64BIT') and \
(File_Format2 == 'NETCDF3_CLASSIC') or (File_Format2 == 'NETCDF3_64BIT') and \
(File_Format3 == 'NETCDF3_CLASSIC') or (File_Format3 == 'NETCDF3_64BIT') and \
(File_Format4 == 'NETCDF3_CLASSIC') or (File_Format4 == 'NETCDF3_64BIT') and \
(File_Format5 == 'NETCDF3_CLASSIC') or (File_Format5 == 'NETCDF3_64BIT') ):
print "Convert netCDF3 input to netCDF4 for CLM"
subprocess.call(DAS_Depends_Path+"bin/nccopy -k 3 "+DAS_Data_Path + "SysModel/CLM/tools/"+fatmlndfrc_name+" "+DAS_Data_Path + "SysModel/CLM/inputdata/"+fatmlndfrc_name,shell=True)
os.remove(DAS_Data_Path + "SysModel/CLM/tools/"+fatmlndfrc_name)
subprocess.call(DAS_Depends_Path+"bin/nccopy -d 4 "+DAS_Data_Path + "SysModel/CLM/inputdata/"+fatmlndfrc_name+" "+DAS_Data_Path + "SysModel/CLM/tools/"+fatmlndfrc_name,shell=True)
subprocess.call(DAS_Depends_Path+"bin/nccopy -k 3 "+DAS_Data_Path + "SysModel/CLM/tools/"+fsurdat_name+" "+DAS_Data_Path + "SysModel/CLM/inputdata/"+fsurdat_name,shell=True)
os.remove(DAS_Data_Path + "SysModel/CLM/tools/"+fsurdat_name)
subprocess.call(DAS_Depends_Path+"bin/nccopy -d 4 "+DAS_Data_Path + "SysModel/CLM/inputdata/"+fsurdat_name+" "+DAS_Data_Path + "SysModel/CLM/tools/"+fsurdat_name,shell=True)
subprocess.call(DAS_Depends_Path+"bin/nccopy -k 3 "+DAS_Data_Path + "SysModel/CLM/inputdata/lnd/clm2/pftdata/"+fpftcon_name+" "+DAS_Data_Path + "SysModel/CLM/inputdata/"+fpftcon_name,shell=True)
os.remove(DAS_Data_Path + "SysModel/CLM/inputdata/lnd/clm2/pftdata/"+fpftcon_name)
subprocess.call(DAS_Depends_Path+"bin/nccopy -d 4 "+DAS_Data_Path + "SysModel/CLM/inputdata/"+fpftcon_name+" "+DAS_Data_Path + "SysModel/CLM/inputdata/lnd/clm2/pftdata/"+fpftcon_name,shell=True)
subprocess.call(DAS_Depends_Path+"bin/nccopy -k 3 "+DAS_Data_Path + "SysModel/CLM/inputdata/lnd/clm2/rtmdata/"+rdirc_name+" "+DAS_Data_Path + "SysModel/CLM/inputdata/"+rdirc_name,shell=True)
os.remove(DAS_Data_Path + "SysModel/CLM/inputdata/lnd/clm2/rtmdata/"+rdirc_name)
subprocess.call(DAS_Depends_Path+"bin/nccopy -d 4 "+DAS_Data_Path + "SysModel/CLM/inputdata/"+rdirc_name+" "+DAS_Data_Path + "SysModel/CLM/inputdata/lnd/clm2/rtmdata/"+rdirc_name,shell=True)
subprocess.call(DAS_Depends_Path+"bin/nccopy -k 3 "+DAS_Data_Path + "SysModel/CLM/inputdata/lnd/clm2/ndepdata/"+fndepdat_name+" "+DAS_Data_Path + "SysModel/CLM/inputdata/"+fndepdat_name,shell=True)
os.remove(DAS_Data_Path + "SysModel/CLM/inputdata/lnd/clm2/ndepdata/"+fndepdat_name)
subprocess.call(DAS_Depends_Path+"bin/nccopy -d 4 "+DAS_Data_Path + "SysModel/CLM/inputdata/"+fndepdat_name+" "+DAS_Data_Path + "SysModel/CLM/inputdata/lnd/clm2/ndepdata/"+fndepdat_name,shell=True)
if (Def_First_Run == -1) and Ensemble_Number > 1:
copyLargeFile(NC_FileName_Assimilation_2_Initial_Copy, NC_FileName_Assimilation_2_Initial)
copyLargeFile(NC_FileName_Assimilation_2_Parameter_Copy, NC_FileName_Assimilation_2_Parameter)
copyLargeFile(NC_FileName_Assimilation_2_Parameter_Monthly_Copy, NC_FileName_Assimilation_2_Parameter_Monthly)
copyLargeFile(NC_FileName_Assimilation_2_Bias_Copy, NC_FileName_Assimilation_2_Bias)
copyLargeFile(NC_FileName_Assimilation_2_Bias_Monthly_Copy, NC_FileName_Assimilation_2_Bias_Monthly)
if Def_First_Run == 1:
print "**************** Prepare Initial netCDF file"
if os.path.exists(NC_FileName_Assimilation_2_Constant):
os.remove(NC_FileName_Assimilation_2_Constant)
print 'Write NetCDF File:',NC_FileName_Assimilation_2_Constant
NC_File_Out_Assimilation_2_Constant = netCDF4.Dataset(NC_FileName_Assimilation_2_Constant, 'w', diskless=True, persist=True, format='NETCDF4')
# Dim the dimensions of NetCDF
NC_File_Out_Assimilation_2_Constant.createDimension('lon', Col_Numbers)
NC_File_Out_Assimilation_2_Constant.createDimension('lat', Row_Numbers)
NC_File_Out_Assimilation_2_Constant.createDimension('Soil_Layer_Num', Soil_Layer_Num)
NC_File_Out_Assimilation_2_Constant.createDimension('ParFlow_Layer_Num', ParFlow_Layer_Num)
NC_File_Out_Assimilation_2_Constant.createDimension('Ensemble_Number', Ensemble_Number)
NC_File_Out_Assimilation_2_Constant.createDimension('Dim_CLM_State', Dim_CLM_State)
NC_File_Out_Assimilation_2_Constant.createDimension('maxpft', maxpft)
NC_File_Out_Assimilation_2_Constant.createVariable('Land_Mask_Data','f4',('lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Constant.variables['Land_Mask_Data'][:,:] = Land_Mask_Data
NC_File_Out_Assimilation_2_Constant.createVariable('PCT_LAKE','f4',('lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Constant.variables['PCT_LAKE'][:,:] = PCT_LAKE
NC_File_Out_Assimilation_2_Constant.createVariable('PCT_Veg','f4',('lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Constant.variables['PCT_Veg'][:,:] = PCT_Veg
NC_File_Out_Assimilation_2_Constant.createVariable('PCT_PFT','f4',('maxpft','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Constant.variables['PCT_PFT'][:,:] = PCT_PFT
#print "numpy.mean(numpy.sum(PCT_PFT[:,:,:],axis=0))",numpy.mean(numpy.sum(PCT_PFT[:,:,:],axis=0))
NC_File_Out_Assimilation_2_Constant.createVariable('STD_ELEV','f4',('lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Constant.variables['STD_ELEV'][:,:] = STD_ELEV
NC_File_Out_Assimilation_2_Constant.createVariable('DEM_Data','f4',('lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Constant.variables['DEM_Data'][:,:] = DEM_Data
NC_File_Out_Assimilation_2_Constant.createVariable('Bulk_Density_Top_Region','f4',('lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Constant.variables['Bulk_Density_Top_Region'][:,:] = Bulk_Density_Top_Region
NC_File_Out_Assimilation_2_Constant.createVariable('Bulk_Density_Sub_Region','f4',('lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Constant.variables['Bulk_Density_Sub_Region'][:,:] = Bulk_Density_Sub_Region
NC_File_Out_Assimilation_2_Constant.createVariable('Teta_Residual','f4',('Soil_Layer_Num','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Constant.variables['Teta_Residual'][:,:,:] = Teta_Residual
NC_File_Out_Assimilation_2_Constant.createVariable('Teta_Saturated','f4',('Soil_Layer_Num','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Constant.variables['Teta_Saturated'][:,:,:] = Teta_Saturated
NC_File_Out_Assimilation_2_Constant.createVariable('Teta_Field_Capacity','f4',('Soil_Layer_Num','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Constant.variables['Teta_Field_Capacity'][:,:,:] = Teta_Field_Capacity
NC_File_Out_Assimilation_2_Constant.createVariable('Teta_Wilting_Point','f4',('Soil_Layer_Num','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Constant.variables['Teta_Wilting_Point'][:,:,:] = Teta_Wilting_Point
NC_File_Out_Assimilation_2_Constant.createVariable('watopt','f4',('Soil_Layer_Num','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Constant.variables['watopt'][:,:,:] = watopt
NC_File_Out_Assimilation_2_Constant.createVariable('watdry','f4',('Soil_Layer_Num','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Constant.variables['watdry'][:,:,:] = watdry
NC_File_Out_Assimilation_2_Constant.createVariable('watfc','f4',('Soil_Layer_Num','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Constant.variables['watfc'][:,:,:] = watfc
NC_File_Out_Assimilation_2_Constant.createVariable('PFT_Dominant_Index','f4',('lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Constant.variables['PFT_Dominant_Index'][:,:] = PFT_Dominant_Index
del Land_Mask_Data,PCT_LAKE,PCT_Veg,PCT_PFT,STD_ELEV,DEM_Data
del Bulk_Density_Top_Region,Bulk_Density_Sub_Region,Teta_Residual,Teta_Saturated,Teta_Field_Capacity,Teta_Wilting_Point,watopt,watdry,watfc
NC_File_Out_Assimilation_2_Constant.createVariable('CLM_Soil_Layer_Thickness','f4',('Soil_Layer_Num','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Constant.createVariable('CLM_Soil_Layer_Thickness_Cumsum','f4',('Soil_Layer_Num','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Constant.createVariable('Soil_Layer_Thickness_Ratio_Moisture','f4',('Soil_Layer_Num','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Constant.createVariable('Soil_Layer_Thickness_Ratio_Temperature','f4',('Soil_Layer_Num','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Constant.sync()
NC_File_Out_Assimilation_2_Constant.close()
NC_File_Out_Assimilation_2_Constant = netCDF4.Dataset(NC_FileName_Assimilation_2_Constant, 'r+', format='NETCDF4')
# Meters
NC_File_Out_Assimilation_2_Constant.variables['CLM_Soil_Layer_Thickness'][0, :, :] = Soil_Thickness[0]
NC_File_Out_Assimilation_2_Constant.variables['CLM_Soil_Layer_Thickness'][1, :, :] = Soil_Thickness[1]
NC_File_Out_Assimilation_2_Constant.variables['CLM_Soil_Layer_Thickness'][2, :, :] = Soil_Thickness[2]
NC_File_Out_Assimilation_2_Constant.variables['CLM_Soil_Layer_Thickness'][3, :, :] = Soil_Thickness[3]
NC_File_Out_Assimilation_2_Constant.variables['CLM_Soil_Layer_Thickness'][4, :, :] = Soil_Thickness[4]
NC_File_Out_Assimilation_2_Constant.variables['CLM_Soil_Layer_Thickness'][5, :, :] = Soil_Thickness[5]
NC_File_Out_Assimilation_2_Constant.variables['CLM_Soil_Layer_Thickness'][6, :, :] = Soil_Thickness[6]
NC_File_Out_Assimilation_2_Constant.variables['CLM_Soil_Layer_Thickness'][7, :, :] = Soil_Thickness[7]
NC_File_Out_Assimilation_2_Constant.variables['CLM_Soil_Layer_Thickness'][8, :, :] = Soil_Thickness[8]
NC_File_Out_Assimilation_2_Constant.variables['CLM_Soil_Layer_Thickness'][9, :, :] = Soil_Thickness[9]
NC_File_Out_Assimilation_2_Constant.variables['CLM_Soil_Layer_Thickness'][10, :, :] = Soil_Thickness[10]
NC_File_Out_Assimilation_2_Constant.variables['CLM_Soil_Layer_Thickness'][11, :, :] = Soil_Thickness[11]
NC_File_Out_Assimilation_2_Constant.variables['CLM_Soil_Layer_Thickness'][12, :, :] = Soil_Thickness[12]
NC_File_Out_Assimilation_2_Constant.variables['CLM_Soil_Layer_Thickness'][13, :, :] = Soil_Thickness[13]
NC_File_Out_Assimilation_2_Constant.variables['CLM_Soil_Layer_Thickness'][14, :, :] = Soil_Thickness[14]
NC_File_Out_Assimilation_2_Constant.variables['CLM_Soil_Layer_Thickness_Cumsum'][:,:,:] = numpy.cumsum(numpy.asarray(NC_File_Out_Assimilation_2_Constant.variables['CLM_Soil_Layer_Thickness'][:,:,:]), axis=0)
for Soil_Layer_Index in range(Soil_Layer_Num):
#NC_File_Out_Assimilation_2_Constant.variables['Soil_Layer_Thickness_Ratio_Moisture'][Soil_Layer_Index, :, :] = numpy.exp(-1.0*NC_File_Out_Assimilation_2_Constant.variables['CLM_Soil_Layer_Thickness_Cumsum'][Soil_Layer_Index,:,:])
NC_File_Out_Assimilation_2_Constant.variables['Soil_Layer_Thickness_Ratio_Moisture'][Soil_Layer_Index, :, :] = numpy.exp(-1.0*NC_File_Out_Assimilation_2_Constant.variables['CLM_Soil_Layer_Thickness'][Soil_Layer_Index,:,:])
for Soil_Layer_Index in range(Soil_Layer_Num):
#NC_File_Out_Assimilation_2_Constant.variables['Soil_Layer_Thickness_Ratio_Temperature'][Soil_Layer_Index, :, :] = numpy.exp(-1.0*numpy.log(NC_File_Out_Assimilation_2_Constant.variables['CLM_Soil_Layer_Thickness_Cumsum'][Soil_Layer_Index,:,:]))
NC_File_Out_Assimilation_2_Constant.variables['Soil_Layer_Thickness_Ratio_Temperature'][Soil_Layer_Index, :, :] = numpy.exp(-1.0*numpy.log(NC_File_Out_Assimilation_2_Constant.variables['CLM_Soil_Layer_Thickness'][Soil_Layer_Index,:,:]))
Ratio_Temp = NC_File_Out_Assimilation_2_Constant.variables['Soil_Layer_Thickness_Ratio_Temperature'][0, :, :]
for Soil_Layer_Index in range(Soil_Layer_Num):
NC_File_Out_Assimilation_2_Constant.variables['Soil_Layer_Thickness_Ratio_Temperature'][Soil_Layer_Index, :, :] = \
numpy.asarray(NC_File_Out_Assimilation_2_Constant.variables['Soil_Layer_Thickness_Ratio_Temperature'][Soil_Layer_Index, :, :]) / Ratio_Temp
NC_File_Out_Assimilation_2_Constant.sync()
NC_File_Out_Assimilation_2_Constant.close()
print "**************** Prepare Initial netCDF file"
if os.path.exists(NC_FileName_Assimilation_2_Diagnostic):
os.remove(NC_FileName_Assimilation_2_Diagnostic)
print 'Write NetCDF File:',NC_FileName_Assimilation_2_Diagnostic
NC_File_Out_Assimilation_2_Diagnostic = netCDF4.Dataset(NC_FileName_Assimilation_2_Diagnostic, 'w', diskless=True, persist=True, format='NETCDF4')
# Dim the dimensions of NetCDF
NC_File_Out_Assimilation_2_Diagnostic.createDimension('lon', Col_Numbers)
NC_File_Out_Assimilation_2_Diagnostic.createDimension('lat', Row_Numbers)
NC_File_Out_Assimilation_2_Diagnostic.createDimension('Soil_Layer_Num', Soil_Layer_Num)
NC_File_Out_Assimilation_2_Diagnostic.createDimension('ParFlow_Layer_Num', ParFlow_Layer_Num)
NC_File_Out_Assimilation_2_Diagnostic.createDimension('Ensemble_Number', Ensemble_Number)
NC_File_Out_Assimilation_2_Diagnostic.createDimension('Dim_CLM_State', Dim_CLM_State)
NC_File_Out_Assimilation_2_Diagnostic.createVariable('Initial_SM_Noise','f4',('Ensemble_Number','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Diagnostic.variables['Initial_SM_Noise'][:,:,:] = 0.0
NC_File_Out_Assimilation_2_Diagnostic.createVariable('Initial_ST_Noise','f4',('Ensemble_Number','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Diagnostic.variables['Initial_ST_Noise'][:,:,:] = 0.0
NC_File_Out_Assimilation_2_Diagnostic.createVariable('Mask_Index','i4',('Dim_CLM_State','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Diagnostic.createVariable('CLM_Soil_Temperature_Ratio_Ensemble_Mat','f4',('Soil_Layer_Num','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Diagnostic.createVariable('CLM_Soil_Moisture_Ratio_Ensemble_Mat_MultiScale','f4',('Soil_Layer_Num','lat','lon','Ensemble_Number',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Diagnostic.createVariable('CLM_Soil_Moisture_Ratio_Ensemble_Mat','f4',('Soil_Layer_Num','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Diagnostic.variables['CLM_Soil_Moisture_Ratio_Ensemble_Mat'][:,:,:] = 1.0
NC_File_Out_Assimilation_2_Diagnostic.variables['CLM_Soil_Temperature_Ratio_Ensemble_Mat'][:,:,:] = 1.0
NC_File_Out_Assimilation_2_Diagnostic.variables['CLM_Soil_Moisture_Ratio_Ensemble_Mat_MultiScale'][:,:,:] = 1.0
NC_File_Out_Assimilation_2_Diagnostic.createVariable('Analysis_Grid_Array','f4',('Ensemble_Number','Dim_CLM_State','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Diagnostic.createVariable('Innovation_State','f4',('Ensemble_Number','Dim_CLM_State','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Diagnostic.createVariable('Increments_State','f4',('Ensemble_Number','Dim_CLM_State','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Diagnostic.createVariable('Observation','f4',('Dim_CLM_State','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Diagnostic.createVariable('CLM_2m_Air_Temperature_Ensemble_Mat','f4',('lat','lon','Ensemble_Number',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Diagnostic.createVariable('CLM_Air_Pressure_Ensemble_Mat','f4',('lat','lon','Ensemble_Number',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Diagnostic.sync()
NC_File_Out_Assimilation_2_Diagnostic.close()
print "**************** Prepare Initial netCDF file"
if os.path.exists(NC_FileName_Assimilation_2_Initial):
os.remove(NC_FileName_Assimilation_2_Initial)
if os.path.exists(NC_FileName_Assimilation_2_Initial_Copy):
os.remove(NC_FileName_Assimilation_2_Initial_Copy)
print 'Write NetCDF File:',NC_FileName_Assimilation_2_Initial
NC_File_Out_Assimilation_2_Initial = netCDF4.Dataset(NC_FileName_Assimilation_2_Initial, 'w', diskless=True, persist=True, format='NETCDF4')
# Dim the dimensions of NetCDF
NC_File_Out_Assimilation_2_Initial.createDimension('lon', Col_Numbers)
NC_File_Out_Assimilation_2_Initial.createDimension('lat', Row_Numbers)
NC_File_Out_Assimilation_2_Initial.createDimension('Soil_Layer_Num', Soil_Layer_Num)
NC_File_Out_Assimilation_2_Initial.createDimension('Ensemble_Number', Ensemble_Number)
NC_File_Out_Assimilation_2_Initial.createDimension('Dim_CLM_State', Dim_CLM_State)
NC_File_Out_Assimilation_2_Initial.createVariable('Prop_Grid_Array_Sys_Parallel','f4',('Dim_CLM_State','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Initial.createVariable('Prop_Grid_Array_H_Trans_Paralle','f4',('Dim_CLM_State','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Initial.createVariable('CLM_Soil_Moisture_Ensemble_Mat_Parallel','f4',('Soil_Layer_Num','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Initial.createVariable('CLM_Soil_Temperature_Ensemble_Mat_Parallel','f4',('Soil_Layer_Num','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Initial.createVariable('Prop_Grid_Array_Sys','f4',('Ensemble_Number','Dim_CLM_State','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Initial.createVariable('Prop_Grid_Array_H_Trans','f4',('Ensemble_Number','Dim_CLM_State','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Initial.createVariable('CLM_Soil_Moisture_Ensemble_Mat','f4',('Soil_Layer_Num','lat','lon','Ensemble_Number',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Initial.createVariable('CLM_Soil_Temperature_Ensemble_Mat','f4',('Soil_Layer_Num','lat','lon','Ensemble_Number',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Initial.createVariable('CLM_Vegetation_Temperature_Ensemble_Mat','f4',('lat','lon','Ensemble_Number',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Initial.createVariable('CLM_Ground_Temperature_Ensemble_Mat','f4',('lat','lon','Ensemble_Number',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Initial.createVariable('CLM_Snow_Depth_Ensemble_Mat','f4',('lat','lon','Ensemble_Number',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Initial.createVariable('CLM_Snow_Water_Ensemble_Mat','f4',('lat','lon','Ensemble_Number',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Initial.createVariable('CLM_INT_SNOW_Ensemble_Mat','f4',('lat','lon','Ensemble_Number',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Initial.createVariable('CLM_FH2OSFC_Ensemble_Mat','f4',('lat','lon','Ensemble_Number',),zlib=True,least_significant_digit=None)
#onset freezing degree days counters
NC_File_Out_Assimilation_2_Initial.createVariable('CLM_Onset_Freezing_Degree_Days_Counter_Ensemble_Mat','f4',('lat','lon','Ensemble_Number',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Initial.createVariable('Prop_Grid_Array_Sys_parm_infl','f4',('Dim_CLM_State','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Initial.createVariable('CLM_Soil_Moisture_parm_infl','f4',('Soil_Layer_Num','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Initial.createVariable('CLM_Soil_Temperature_parm_infl','f4',('Soil_Layer_Num','lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Initial.createVariable('CLM_Vegetation_Temperature_parm_infl','f4',('lat','lon',),zlib=True,least_significant_digit=None)
NC_File_Out_Assimilation_2_Initial.createVariable('CLM_Ground_Temperature_parm_infl','f4',('lat','lon',),zlib=True,least_significant_digit=None)