def write_text():
with open("./experimetal_settings.log", "w") as f:
f.write("# Experimental Settings\n")
f.write("# " + pm.version() + "\n")
f.write("# " + pm.CaMa_ver() + "\n")
f.write("#======================================================\n")
# Experimental Settings
f.write("# Experiment Name: " + pm.experiment() + "\n")
f.write("# Experiment Mode: " + "%d" % (pm.mode()) + "\n")
# Runoff data
f.write("# Runoff Data: " + pm.input(pm.mode()) + "\n")
# Time domain for analysis
f.write("# Start Date: %04d-%02d-%02d\n" % (pm.starttime()))
f.write("# End Date: %04d-%02d-%02d\n" % (pm.endtime()))
# Calibration
f.write("# Model Calibration: " + calibration(pm.calibrate()) + "\n")
# Assimilation Settings
f.write("# Assimilation Mode: " + assimlation_mode(pm.conflag()) +
"\n")
f.write("# Assimilation Domain: \n")
f.write("# \tWest : %5.2f\n" % (pm.assimW()))
f.write("# \tSouth: %5.2f\n" % (pm.assimS()))
f.write("# \tEest : %5.2f\n" % (pm.assimE()))
f.write("# \tNorth: %5.2f\n" % (pm.assimN()))
f.write("# Assimilation Settings: \n")
f.write("# \tPatch Size : " + patch_character(pm.patch_size()) + "\n")
f.write("# \tPatch Id : " + pm.patch_name() + " " + pm.patch_id() +
"\n")
f.write("# \tInflation Method : " + inflation_para(pm.rho()) + "\n")
f.write("# Assimilation Statistics: \n")
f.write("# \tMean : " + stat_name(pm.conflag(), pm.calibrate())[0] +
"\n")
f.write("# \tStandrad Deviation : " +
stat_name(pm.conflag(), pm.calibrate())[1] + "\n")
return 0
def prepare_obs_old():
"""
Prepare observations as textfile
"""
# global lname, xlist, ylist, leledif, lEGM08, lEGM96, satellite
# if pm.obs_name() == "HydroWeb":
# lname, xlist, ylist, leledif, lEGM08, lEGM96, satellite = get_HydroWeb()
# print ("lname: ",len(lname))
# else:
# lname, xlist, ylist, leledif, lEGM08, lEGM96, satellite = get_HydroWeb()
# print (len(lname))
syear, smon, sday = pm.starttime()
eyear, emon, eday = pm.endtime()
start_dt = datetime.date(syear, smon, sday)
end_dt = datetime.date(eyear, emon, eday)
start = 0
last = (end_dt - start_dt).days
# print (start,last)
#-------
inputlist = []
for day in np.arange(start, last):
target_dt = start_dt + datetime.timedelta(days=day)
yyyy = '%04d' % (target_dt.year)
mm = '%02d' % (target_dt.month)
dd = '%02d' % (target_dt.day)
# print (yyyy,mm,dd) #,obs_dir
inputlist.append([yyyy, mm, dd])
# write text files parallel
p = Pool(pm.cpu_nums() * pm.para_nums())
p.map(write_txt, inputlist)
p.terminate()
# map(write_txt,inputlist)
return 0
rivwth = np.fromfile(rivwth, np.float32).reshape(ny, nx) rivhgt = np.fromfile(rivhgt, np.float32).reshape(ny, nx) rivlen = np.fromfile(rivlen, np.float32).reshape(ny, nx) elevtn = np.fromfile(elevtn, np.float32).reshape(ny, nx) lonlat = np.fromfile(lonlat, np.float32).reshape(2, ny, nx) uparea = np.fromfile(uparea, np.float32).reshape(ny, nx) # #higher resolution data # catmxy = pm.CaMa_dir()+"/map/"+pm.mapname()+"/1min/1min.catmxy.bin" # catmxy = np.fromfile(catmxy,np.int16).reshape(2,ny*60,nx*60) #---- rivnum = "../dat/rivnum_" + pm.mapname() + ".bin" rivnum = np.fromfile(rivnum, np.int32).reshape(ny, nx) rivermap = ((nextxy[0] > 0) * (rivnum == 1)) * 1.0 #---- syear, smonth, sdate = pm.starttime() #2004#1991 2004,1,1 # 2003,1,1 # eyear, emonth, edate = pm.endtime() #2005,1,1 # 2005,1,1 # #month=1 #date=1 start_dt = datetime.date(syear, smonth, sdate) end_dt = datetime.date(eyear, emonth, edate) size = 60 start = 0 last = (end_dt - start_dt).days N = int(last) green2 = "greenyellow" green = "green" #------- pname = [] xlist = []
rivhgt = np.fromfile(rivhgt, np.float32).reshape(ny, nx) rivlen = np.fromfile(rivlen, np.float32).reshape(ny, nx) elevtn = np.fromfile(elevtn, np.float32).reshape(ny, nx) lonlat = np.fromfile(lonlat, np.float32).reshape(2, ny, nx) uparea = np.fromfile(uparea, np.float32).reshape(ny, nx) # #higher resolution data # catmxy = pm.CaMa_dir()+"/map/"+pm.mapname()+"/1min/1min.catmxy.bin" # catmxy = np.fromfile(catmxy,np.int16).reshape(2,ny*60,nx*60) #---- rivnum = "../dat/rivnum_" + pm.mapname() + ".bin" rivnum = np.fromfile(rivnum, np.int32).reshape(ny, nx) rivermap = ((nextxy[0] > 0) * (rivnum == 1)) * 1.0 # rivermap=rivermap*(uparea>1e12)*1.0 #---- syear, smonth, sdate = pm.starttime() #2004#1991 2004,1,1 # 2003,1,1 # eyear, emonth, edate = pm.endtime() # 2010,1,1 #2014,1,1 # 2004,1,1 # #month=1 #date=1 start_dt = datetime.date(syear, smonth, sdate) end_dt = datetime.date(eyear, emonth, edate) size = 60 start = 0 last = (end_dt - start_dt).days N = int(last) green2 = "greenyellow" green = "green" #------- staid = [] pname = []
#def sendslack(msg):
# slack=Slacker('xoxb-316041665447-hNXDYiTz9PZMITg0gfRGzyrC')
# message="` "+os.path.basename(__file__)+"` : "+msg
# slack.chat.post_message('#camasim',message);
# return 0
#---
#--
#slink("/hydro/covariance/CaMa_simulation/params.py", "params.py")
#slink("/hydro/SWOTDA/img_code/read_grdc.py","read_grdc.py")
import params as pm
import read_grdc as grdc
#--
mk_dir(pm.out_dir() + "/figures")
mk_dir(pm.out_dir() + "/figures/timeseries")
#--read outflow netCDF4--
tag = "%04d-%04d" % (pm.starttime()[0], pm.endtime()[0])
# sfcelv
fname = pm.out_dir() + "/CaMa_out/" + pm.input_name() + "/sfcelv" + tag + ".nc"
nc_sfcelv = xr.open_dataset(fname)
# removed trend line
fname = pm.out_dir() + "/CaMa_out/" + pm.input_name(
) + "/rmdtrnd" + tag + ".nc"
nc_rmdtrend = xr.open_dataset(fname)
# removed seasonality
fname = pm.out_dir() + "/CaMa_out/" + pm.input_name(
) + "/rmdsesn" + tag + ".nc"
nc_rmdseson = xr.open_dataset(fname)
# standardized
fname = pm.out_dir() + "/CaMa_out/" + pm.input_name(
) + "/standardized" + tag + ".nc"
nc_standz = xr.open_dataset(fname)
def prepare_input_old():
# spinup start_year
# simulation end_year
start_year=pm.spinup_end_year()
end_year,end_month,end_date=pm.endtime()
#start_year=pm.start_year()
#end_year=pm.end_year()
start_dt=datetime.date(start_year,1,1)
last_dt=datetime.date(end_year,end_month,end_date)
start=0
last=int((last_dt-start_dt).days)
#--------------
# E2O
if pm.input()=="E2O": # Earth2Observe
#print "E2O"
distopen=pm.distopen(1)
diststd=pm.diststd(1)
true_run=pm.true_run(1) # for true ensemble
runname=pm.runname(1) # get runoff name
# make courrpted/assimilated runoff
if(len(glob.glob(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_CORR/Roff*"))!=0):
#print "Roff_CORR available"
pass
# corrupted input file is not ready
# need preparation
# make directories
mkdir(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_CORR")
#print "E2O/Roff"
# # # dist std for ensembles
# # distopen_ranges={}
# # #open_list=np.setdiff1d(np.arange(1,7+1),[true_run])
# # open_list=np.arange(1,7+1)
# # random_runs=random.sample(open_list,k=2)
# # mkdir(pm.DA_dir()+"/out/"+pm.experiment()+"/assim_out/runoff_error")
# # f=open(pm.DA_dir()+"/out/"+pm.experiment()+"/assim_out/runoff_error/ensemble.txt","w")
# # for runens in open_list:
# # ens_char="%03d"%(runens)
# # diststd_num=3
# # distopen_range=rd.normal(1,diststd,diststd_num)
# # distopen_range=np.sort(distopen_range)
# # distopen_range=distopen_range.astype(np.float32)
# # distopen_ranges[ens_char]=distopen_range#[0.25,1.00,1.25]
# # line="%s %3.2f %3.2f %3.2f\n"%(ens_char,distopen_range[0],distopen_range[1],distopen_range[2])
# # f.write(line)
# # f.close()
# # #print "L1141"
# # inputlist=[]
# # for day in np.arange(start,last):
# # target_dt=start_dt+datetime.timedelta(days=day)
# # yyyy='%04d' % (target_dt.year)
# # mm='%02d' % (target_dt.month)
# # dd='%02d' % (target_dt.day)
# # ens_num=1
# # for runens in open_list: #np.arange(1,7+1):
# # #print runens
# # #if runens!=true_run:
# # run_num="%03d"%(runens)
# # iname=pm.DA_dir()+"/inp/"+runname+"/Roff/Roff__"+yyyy+mm+dd+run_num+".one"
# # distopens=distopen_ranges[run_num]
# # for dist in distopens:
# # ens_char="C%03d"%(ens_num)
# # oname=pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_CORR/Roff__"+yyyy+mm+dd+ens_char+".one"
# # inputlist.append([iname,oname,str(abs(dist))])
# # ens_num=ens_num+1
distopen_ranges={}
# fname="random_ensemble_E2O.txt"
fname="random_ensemble_E2O_49.txt"
with open(pm.DA_dir()+"/dat/"+fname,"r") as f:
lines=f.readlines()
for line in lines:
line = filter(None,re.split(" ", line))
# print line
runens = int(line[0])
rndnum = float(line[1].strip("\n"))
ens_char="C%03d"%(runens)
distopen_ranges[ens_char]=rndnum
#print "L1141"
inputlist=[]
open_list=np.arange(1,7+1)
for day in np.arange(start,last):
target_dt=start_dt+datetime.timedelta(days=day)
yyyy='%04d' % (target_dt.year)
mm='%02d' % (target_dt.month)
dd='%02d' % (target_dt.day)
ens_num=1
for runens in open_list: #np.arange(1,7+1):
#print runens
#if runens!=true_run:
run_num="%03d"%(runens)
iname=pm.DA_dir()+"/inp/"+runname+"/Roff/Roff__"+yyyy+mm+dd+run_num+".one"
# distopens=distopen_ranges[run_num]
# for dist in distopens:
for _ in np.arange(1,7+1):
ens_char="C%03d"%(ens_num)
dist=distopen_ranges[ens_char]
oname=pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_CORR/Roff__"+yyyy+mm+dd+ens_char+".one"
inputlist.append([iname,oname,str(abs(dist))])
ens_num=ens_num+1
# do parallel
p=Pool(pm.para_nums())
p.map(copy_runoff,inputlist)
p.terminate()
#---------
# ERA20CM
if pm.input()=="ERA20CM": #ECMWF ERA20CM
distopen=pm.distopen(2)
diststd=pm.diststd(2)
true_run=pm.true_run(2) # for true ensemble
runname=pm.runname(2) # get runoff name
# make courrpted runoff
if(len(glob.glob(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_CORR/Roff*"))!=0):
pass
# corrupted input file is not ready
# need preparation
# make directories
mkdir(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_CORR")
#--
# dist std for ensembles
distopen_ranges={}
#open_list=np.setdiff1d(np.arange(1,10+1),[true_run])
open_list=np.arange(1,10+1)
random_runs=random.sample(open_list,k=2)
for runens in open_list:
ens_char="%03d"%(runens)
diststd_num=2
distopen_range=rd.normal(1,diststd,diststd_num)
distopen_range=np.sort(distopen_range)
distopen_range=distopen_range.astype(np.float32)
distopen_ranges[ens_char]=distopen_range#[0.25,1.00,1.25]
#
inputlist=[]
for day in np.arange(start,last):
target_dt=start_dt+datetime.timedelta(days=day)
yyyy='%04d' % (target_dt.year)
mm='%02d' % (target_dt.month)
dd='%02d' % (target_dt.day)
ens_num=1
for runens in open_list: #np.arange(1,10+1):
run_num="%03d"%(runens)
iname=pm.DA_dir()+"/inp/"+runname+"/Roff/Roff__"+yyyy+mm+dd+run_num+".one"
distopens=distopen_ranges[run_num]
for dist in distopens:
ens_char="C%03d"%(ens_num)
oname=pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_CORR/Roff__"+yyyy+mm+dd+ens_char+".one"
inputlist.append([iname,oname,str(dist)])
ens_num=ens_num+1
# do parallel
p=Pool(pm.para_nums())
p.map(copy_runoff,inputlist)
p.terminate()
#---------
# VIC BC
if pm.input()=="VIC_BC": #VIC BC
nXX,nYY=1440,720
distopen=1.0 #pm.distopen(3)
diststd=0.25 #pm.diststd(3)
true_run=pm.true_run(3) # for true ensemble
runname=pm.runname(3) # get runoff name
# make courrpted runoff
if(len(glob.glob(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_CORR/Roff*"))!=0):
pass
# corrupted input file is not ready
# need preparation
# make directories
mkdir(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_CORR")
#--
# dist std for ensembles
#distopen_ranges={}
# for 12 months
distopen_range=np.zeros([12,pm.ens_mem(),nYY,nXX],np.float32)
# fname="../../dat/std_runoff_E2O_1980-2000.bin"
# #print fname
# std_runoff=np.fromfile(fname,np.float32).reshape(nYY,nXX)
# fname="../../dat/mean_runoff_E2O_1980-2000.bin"
# #print "L1481",fname
# mean_runoff=np.fromfile(fname,np.float32).reshape(nYY,nXX)
# #print mean_runoff
# std_runoff=ma.masked_where(std_runoff==-9999.0,std_runoff).filled(0.0)
# mean_runoff=ma.masked_where(mean_runoff==-9999.0,mean_runoff).filled(0.0)
for mon in range(1,12+1): # for 12 months
mm="%02d"%(mon)
fname=pm.DA_dir()+"/dat/std_month_runoff_E2O_"+mm+".bin"
#print fname
std_runoff=np.fromfile(fname,np.float32).reshape(nYY,nXX)
fname=pm.DA_dir()+"/dat/mean_month_runoff_E2O_"+mm+".bin"
#print "L1481",fname
mean_runoff=np.fromfile(fname,np.float32).reshape(nYY,nXX)
#print mean_runoff
std_runoff=ma.masked_where(std_runoff==-9999.0,std_runoff).filled(0.0)
mean_runoff=ma.masked_where(mean_runoff==-9999.0,mean_runoff).filled(0.0)
for iXX in range(nXX):
for iYY in range(nYY):
#distopen_range[:,iYY,iXX]=np.sort(rd.normal(distopen,std_runoff[iYY,iXX],pm.ens_mem()))
#Log-normal model
#sk=np.sort(rd.normal(distopen,diststd,pm.ens_mem()))
sk=np.sort(rd.normal(distopen,diststd,pm.ens_mem()))
beta=0.0
#E=std_runoff[iYY,iXX]/(mean_runoff[iYY,iXX]+1.0e-20)
E=diststd
#distopen_range[mon,:,iYY,iXX]=((1+beta)/math.sqrt(E**2+1))*np.exp(math.sqrt(math.log(E**2+1))*sk)
distopen_range[mon-1,:,iYY,iXX]=np.sort(rd.normal(distopen,E,pm.ens_mem()))
# distopen_range[mon-1,:,iYY,iXX]=rd.normal(distopen,E,pm.ens_mem())
#distopen_range[:,iYY,iXX]=np.sort(rd.normal(distopen,diststd,pm.ens_mem()))
#----------
for day in np.arange(start,last):
target_dt=start_dt+datetime.timedelta(days=day)
mon=int(target_dt.month)
yyyy='%04d' % (target_dt.year)
mm='%02d' % (target_dt.month)
dd='%02d' % (target_dt.day)
iname=pm.DA_dir()+"/inp/"+runname+"/Roff/Roff____"+yyyy+mm+dd+".one"
#print iname
roff=np.fromfile(iname,np.float32).reshape(nYY,nXX)
#roff=np.ones([nYY,nXX],np.float32)*-9999.0
fname=pm.DA_dir()+"/dat/std_runoff_E2O_1980-2000.bin"
#print fname
std_runoff=np.fromfile(fname,np.float32).reshape(nYY,nXX)
fname=pm.DA_dir()+"/dat/mean_runoff_E2O_1980-2000.bin"
#print "L1481",fname
mean_runoff=np.fromfile(fname,np.float32).reshape(nYY,nXX)
#print mean_runoff
std_runoff=ma.masked_where(std_runoff==-9999.0,std_runoff).filled(0.0)
mean_runoff=ma.masked_where(mean_runoff==-9999.0,mean_runoff).filled(0.0)
for ens_num in np.arange(pm.ens_mem()):
ens_char="C%03d"%(ens_num+1)
roffc=roff+distopen_range[mon-1,ens_num,:,:]*mean_runoff #*std_runoff #
roffc=roff*distopen_range[mon-1,ens_num,:,:] #*mean_runoff
oname=pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_CORR/Roff__"+yyyy+mm+dd+ens_char+".one"
roffc.tofile(oname)
#--------------
# -25% biased runoff experiment
if pm.input()=="ELSE": # Kim 2009/E2O/ERA20CM
#print "-25% biased runoff experiment", pm.true_run(3), pm.runname(3)
distopen=pm.distopen(3)
diststd=pm.diststd(3)
true_run=pm.true_run(3) # for true ensemble
runname=pm.runname(3) # get runoff name
# copy for TRUE simulation
if(len(glob.glob(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_TRUE/Roff*"))!=0):
pass
# true input file is not ready
# need preparation
# make directories
mkdir(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_TRUE")
#print true_run
inputlist=[]
for day in np.arange(start,last):
target_dt=start_dt+datetime.timedelta(days=day)
yyyy='%04d' % (target_dt.year)
mm='%02d' % (target_dt.month)
dd='%02d' % (target_dt.day)
ens_char="T000"
true_char="%03d"%(true_run)
if runname=="ELSE_KIM2009":
nx,ny=360,180
prefix="Roff____"
suffix=".one"
if runname=="E2O":
nx,ny=1440,720
prefix="Roff__"
suffix="%03d.one"%(true_run)
if runname=="ERA20CM":
nx,ny=360,180
prefix="Roff__"
suffix="%03d.one"%(true_run)
iname=pm.DA_dir()+"/inp/"+runname+"/Roff/"+prefix+yyyy+mm+dd+suffix
oname=pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_TRUE/Roff__"+yyyy+mm+dd+ens_char+".one"
inputlist.append([iname,oname,"1.00"])
# do parallel
p=Pool(pm.para_nums())
p.map(copy_runoff,inputlist)
p.terminate()
# calculate mothly mean
cal_monthly_mean(start_year,end_year,24)
# calculate monthly total
cal_monthly_total(start_year,end_year,24)
# make courrpted runoff
if(len(glob.glob(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_CORR/Roff*"))!=0):
pass
# corrupted input file is not ready
# need preparation
# make directories
mkdir(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_CORR")
inputlist=[]
std=rd.normal(0,diststd,pm.ens_mem())
std=std.astype(np.float32)
for day in np.arange(start,last):
target_dt=start_dt+datetime.timedelta(days=day)
yyyy='%04d' % (target_dt.year)
mm='%02d' % (target_dt.month)
dd='%02d' % (target_dt.day)
# make random values
#std=np.fromfile("./CaMa_out/randlist.bin",np.float32)
#std=rd.normal(0,diststd,pm.ens_mem())
#std=std.astype(np.float32)
#print std
ifile=np.fromfile(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_TRUE/Roff__"+yyyy+mm+dd+"T000.one",np.float32).reshape(ny,nx)
roff_mean=np.fromfile(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/mean_month/mean_"+yyyy+mm+".bin",np.float32).reshape(ny,nx)
roff_total=np.fromfile(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/total_month/total_"+yyyy+mm+".bin",np.float32).reshape(ny,nx)
for ens in np.arange(1,pm.ens_mem()+1):
ens_char="C%03d"%(ens)
#print pm.distopen(),std[ens-1]
ofile=ifile*distopen + roff_mean*std[ens-1]#*10.0
#ofile=ifile*(distopen + std[ens-1])
#ofile=ifile*distopen + roff_total*std[ens-1]
#ofile=ifile*distopen + roff_total*std[ens-1]*0.75
ofile.astype(np.float32)
ofile.tofile(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_CORR/Roff__"+yyyy+mm+dd+ens_char+".one")
# do parallel
#p=Pool(pm.para_nums())
#p.map(copy_runoff,inputlist)
#p.terminate()
#--------------
# blind runoff experiment
if pm.input()=="ELSE_KIM2009": # differnt yeaer
#distopen=1.0 #0.75 #pm.distopen()
#diststd=0.5 #pm.diststd()
distopen=pm.distopen(4)
diststd=pm.diststd(4)
true_run=pm.true_run(4) # for true ensemble
runname=pm.runname(4) # get runoff name
# copy for TRUE simulation
if(len(glob.glob(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_TRUE/Roff*"))!=0):
pass
# true input file is not ready
# need preparation
# make directories
mkdir(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_TRUE")
inputlist=[]
for day in np.arange(start,last):
target_dt=start_dt+datetime.timedelta(days=day)
yyyy='%04d' % (target_dt.year)
mm='%02d' % (target_dt.month)
dd='%02d' % (target_dt.day)
ens_char="T000"
iname=pm.DA_dir()+"/inp/"+runname+"/Roff/Roff____"+yyyy+mm+dd+".one"
oname=pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_TRUE/Roff__"+yyyy+mm+dd+ens_char+".one"
inputlist.append([iname,oname,"1.00"])
# do parallel
p=Pool(pm.para_nums())
p.map(copy_runoff,inputlist)
p.terminate()
# calculate mothly mean
cal_monthly_mean(start_year,end_year,24)
# calculate monthly total
cal_monthly_total(start_year,end_year,24)
# make courrpted runoff
if(len(glob.glob(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_CORR/Roff*"))!=0):
pass
# corrupted input file is not ready
# need preparation
# make directories
mkdir(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_CORR")
inputlist=[]
for day in np.arange(start,last):
target_dt=start_dt+datetime.timedelta(days=day)
yyyy='%04d' % (target_dt.year)
mm='%02d' % (target_dt.month)
dd='%02d' % (target_dt.day)
# make random values
#std=np.fromfile("./CaMa_out/randlist.bin",np.float32)
std=rd.normal(0,diststd,pm.ens_mem())
std=std.astype(np.float32)
#print std
ifile=np.fromfile(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff/Roff____"+yyyy+mm+dd+".one",np.float32).reshape(ny,nx)
roff_mean=np.fromfile(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/mean_month/mean_"+yyyy+mm+".bin",np.float32).reshape(ny,nx)
roff_total=np.fromfile(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/total_month/total_"+yyyy+mm+".bin",np.float32).reshape(ny,nx)
for ens in np.arange(1,pm.ens_mem()+1):
ens_char="C%03d"%(ens)
#print pm.distopen(),std[ens-1]
ofile=ifile*distopen + roff_mean*std[ens-1]*10.0
#ofile=ifile*(distopen + std[ens-1])
#ofile=ifile*distopen + roff_total*std[ens-1]
#ofile=ifile*distopen + roff_total*std[ens-1]*0.75
ofile.astype(np.float32)
ofile.tofile(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_CORR/Roff__"+yyyy+mm+dd+ens_char+".one")
# do parallel
#p=Pool(pm.para_nums())
#p.map(copy_runoff,inputlist)
#p.terminate()
#---------
# ERA5
if pm.input()=="ERA5": #ERA5-Land
nXX,nYY=3600,1800
distopen=pm.distopen(5)
diststd=pm.diststd(5)
true_run=pm.true_run(3) # for true ensemble
runname=pm.runname(3) # get runoff name
# make courrpted runoff
if(len(glob.glob(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_CORR/Roff*"))!=0):
pass
# corrupted input file is not ready
# need preparation
# make directories
mkdir(pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_CORR")
#--
# dist std for ensembles
#distopen_ranges={}
# for 12 months
distopen_range=np.zeros([12,pm.ens_mem(),nYY,nXX],np.float32)
# fname="../../dat/std_runoff_E2O_1980-2000.bin"
# #print fname
# std_runoff=np.fromfile(fname,np.float32).reshape(nYY,nXX)
# fname="../../dat/mean_runoff_E2O_1980-2000.bin"
# #print "L1481",fname
# mean_runoff=np.fromfile(fname,np.float32).reshape(nYY,nXX)
# #print mean_runoff
# std_runoff=ma.masked_where(std_runoff==-9999.0,std_runoff).filled(0.0)
# mean_runoff=ma.masked_where(mean_runoff==-9999.0,mean_runoff).filled(0.0)
for mon in range(1,12+1): # for 12 months
mm="%02d"%(mon)
fname=pm.DA_dir()+"/dat/std_month_runoff_E2O_"+mm+".bin"
#print fname
std_runoff=np.fromfile(fname,np.float32).reshape(nYY,nXX)
fname=pm.DA_dir()+"/dat/mean_month_runoff_E2O_"+mm+".bin"
#print "L1481",fname
mean_runoff=np.fromfile(fname,np.float32).reshape(nYY,nXX)
#print mean_runoff
std_runoff=ma.masked_where(std_runoff==-9999.0,std_runoff).filled(0.0)
mean_runoff=ma.masked_where(mean_runoff==-9999.0,mean_runoff).filled(0.0)
for iXX in range(nXX):
for iYY in range(nYY):
#distopen_range[:,iYY,iXX]=np.sort(rd.normal(distopen,std_runoff[iYY,iXX],pm.ens_mem()))
#Log-normal model
#sk=np.sort(rd.normal(distopen,diststd,pm.ens_mem()))
sk=np.sort(rd.normal(distopen,diststd,pm.ens_mem()))
beta=0.0
#E=std_runoff[iYY,iXX]/(mean_runoff[iYY,iXX]+1.0e-20)
E=diststd
#distopen_range[mon,:,iYY,iXX]=((1+beta)/math.sqrt(E**2+1))*np.exp(math.sqrt(math.log(E**2+1))*sk)
distopen_range[mon-1,:,iYY,iXX]=np.sort(rd.normal(distopen,E,pm.ens_mem()))
# distopen_range[mon-1,:,iYY,iXX]=rd.normal(distopen,E,pm.ens_mem())
#distopen_range[:,iYY,iXX]=np.sort(rd.normal(distopen,diststd,pm.ens_mem()))
#----------
for day in np.arange(start,last):
target_dt=start_dt+datetime.timedelta(days=day)
mon=int(target_dt.month)
yyyy='%04d' % (target_dt.year)
mm='%02d' % (target_dt.month)
dd='%02d' % (target_dt.day)
iname=pm.DA_dir()+"/inp/"+runname+"/Roff/Roff____"+yyyy+mm+dd+".sixmin"
#print iname
roff=np.fromfile(iname,np.float32).reshape(nYY,nXX)
#roff=np.ones([nYY,nXX],np.float32)*-9999.0
fname=pm.DA_dir()+"/dat/std_runoff_E2O_1980-2000.bin"
#print fname
std_runoff=np.fromfile(fname,np.float32).reshape(nYY,nXX)
fname=pm.DA_dir()+"/dat/mean_runoff_E2O_1980-2000.bin"
#print "L1481",fname
mean_runoff=np.fromfile(fname,np.float32).reshape(nYY,nXX)
#print mean_runoff
std_runoff=ma.masked_where(std_runoff==-9999.0,std_runoff).filled(0.0)
mean_runoff=ma.masked_where(mean_runoff==-9999.0,mean_runoff).filled(0.0)
for ens_num in np.arange(pm.ens_mem()):
ens_char="C%03d"%(ens_num+1)
roffc=roff+distopen_range[mon-1,ens_num,:,:]*mean_runoff #*std_runoff #
roffc=roff*distopen_range[mon-1,ens_num,:,:] #*mean_runoff
oname=pm.DA_dir()+"/out/"+pm.experiment()+"/CaMa_in/"+runname+"/Roff_CORR/Roff__"+yyyy+mm+dd+ens_char+".one"
roffc.tofile(oname)
return 0
