def perc_compare(pc, winlen, season, obfile, modfile, intitle): import numpy as np from grid_tools import interp_togrid #Extract model and obs data odata, olat, olon = rcs_gpcp(winlen) mdata, mlat, mlon = rcs_gpcp(winlen, modfile) #Shift data to start of required season rlen = -1*(season - 1) odata = np.roll(odata, rlen, axis=0) odata = odata[0::12, :, :] mdata = np.roll(mdata, rlen, axis=0) mdata = mdata[0::12, :, :] #Calculate thresholds opc = np.percentile(odata, pc, axis=0) opc[opc < 0.0] = 0.0 #ensure percentile is not below zero, if below zero then set to 0 mpc = np.percentile(mdata, pc, axis=0) mpc[mpc < 0.0] = 0.0 #ensure percentile is not below zero, if below zero then set to 0 #Interpolate to the same grid spacing (coarsest) and compute difference as a percentage ob, mod, lon, lat = interp_togrid(opc, olat, olon, mpc, mlat, mlon) d = ((mod - ob)/ob)*100 plot = plot_perc_compare (d, lat, lon, pc, winlen, season, intitle) return d, lat, lon
def perc_compare_bsoblen(pc, winlen, season, obfile, modfile, intitle):
import numpy as np
from grid_tools import interp_togrid
#Extract model and obs data
odata, olat, olon = rcs_gpcp(winlen)
mdata, mlat, mlon = rcs_gpcp(winlen, modfile)
#Shift data to start of required season and trim
rlen = -1*(season - 1)
odata = np.roll(odata, rlen, axis=0)
odata = odata[0::12, :, :]
mdata = np.roll(mdata, rlen, axis=0)
mdata = mdata[0::12, :, :]
#Calculate thresholds
#Set the length of the bootstrapped data set
n = mdata.shape[0] #length is the length of the model data
print("Commencing bootstrap...")
opc, bspc = bootstrap_perc_obs(odata, n, pc)
print("Bootstrap completed...")
mpc = np.percentile(mdata, pc, axis=0)
mpc[mpc < 0.0] = 0.0 #ensure percentile is not below zero, if below zero then set to 0
#Interpolate to the same grid spacing (coarsest) and compute difference as a percentage
ob, mod, lon, lat = interp_togrid(opc, olat, olon, mpc, mlat, mlon)
d = ((mod - ob)/ob)*100
#Calculate significance
bslow = np.percentile(bspc, 2.5, axis=0)
bshi = np.percentile(bspc, 97.5, axis=0)
sig = np.zeros((lat.size, lon.size))-1
sig[np.logical_and(mod >= bslow, mod <= bshi)] = 1
plot = plot_perc_compare_bs (d, sig, lat, lon, pc, winlen, season, intitle)
return d, sig, lat, lon
def mmm_perc_compare(pc, winlen, season, obfile, thresh):
import numpy as np
from grid_tools import interp_togrid
#Extract model and obs data
odata, olat, olon = rcs_gpcp(winlen)
#Begin at correct time
rlen = -1*(season - 1)
odata = np.roll(odata, rlen, axis=0)
odata = odata[0::12, :, :]
#Calculate thresholds
opc = np.percentile(odata, pc, axis=0)
opc[opc < 0.0] = 0.0 #ensure percentile is not below zero, if below zero then set to 0
cmipfile = ['CMIP5/ACCESS1-0/r1i1p1/pr/pr_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc',\
'CMIP5/CanESM2/r1i1p1/pr/pr_Amon_CanESM2_historical_r1i1p1_185001-200512.nc',\
'CMIP5/GFDL-CM3/r1i1p1/pr/pr_Amon_GFDL-CM3_historical_r1i1p1_186001-200512.nc',\
'CMIP5/HadGEM2-CC/r1i1p1/pr/pr_Amon_HadGEM2-CC_historical_r1i1p1_185912-200511.nc',\
'CMIP5/MPI-ESM-P/r1i1p1/pr/pr_Amon_MPI-ESM-P_historical_r1i1p1_185001-200512.nc',\
'CMIP5/CCSM4/r1i1p1/pr/pr_Amon_CCSM4_historical_r1i1p1_185001-200512.nc',\
'CMIP5/FGOALS-s2/r1i1p1/pr/pr_Amon_FGOALS-s2_historical_r1i1p1_185001-200512.nc',\
'CMIP5/GISS-E2-R/r6i1p1/pr/pr_Amon_GISS-E2-R_historical_r6i1p1_185001-200512.nc',\
'CMIP5/NorESM1-M/r1i1p1/pr/pr_Amon_NorESM1-M_historical_r1i1p1_185001-200512.nc',\
'CMIP5/IPSL-CM5B-LR/r1i1p1/pr/pr_Amon_IPSL-CM5B-LR_historical_r1i1p1_185001-200512.nc']
amipfile = ['AMIP/FGOALS-s2/r1i1p1/pr/pr_Amon_FGOALS-s2_amip_r1i1p1_197901-200812.nc',\
'AMIP/GFDL-CM3/r1i1p1/pr/pr_Amon_GFDL-CM3_amip_r1i1p1_197901_200812.nc',\
'AMIP/HadGEM2-A/r1i1p1/pr/pr_Amon_HadGEM2-A_amip_r1i1p1_197809-200811.nc',\
'AMIP/NorESM1-M/r1i1p1/pr/pr_Amon_NorESM1-M_amip_r1i1p1_197901-200512.nc']
modpath = '/Users/ailieg/Data/drought_model_eval_data/data/'
modfile = cmipfile
d = np.zeros((len(modfile), olat.size, olon.size))
for i in range(0,len(modfile)):
#Extract model data
mdata, mlat, mlon = rcs_gpcp(winlen, modfile)
#Shift data to start of required season and trim
mdata = np.roll(mdata, rlen, axis=0)
mdata = mdata[0::12, :, :]
#Calculate thresholds
mpc = np.percentile(mdata, pc, axis=0)
mpc[mpc < 0.0] = 0.0 #ensure percentile is not below zero, if below zero then set to 0
#Interpolate to the same grid spacing (coarsest) and compute difference as a percentage
ob, mod, lon, lat = interp_togrid(opc, olat, olon, mpc, mlat, mlon)
d[i,:,:] = ((mod - ob)/ob)*100
print("Model "+str(i+1)+" of "+str(len(modfile))+" completed.")
#Compute multi-model mean
mmm = np.median(d, axis=0)
#Compute the agreement in the sign of the models
sign = d
sign[d < 0.0] = -1
sign[d > 0.0] = 1
sign = abs(np.sum(sign, axis=0))
plot = plot_perc_compare_mmm (mmm, sign, lat, lon, pc, winlen, season, thresh)
return mmm, sign, lat, lon
