import sys ; sys.path.append('../../scripts') import norcpmTools as nt from netCDF4 import Dataset import numpy as np # prepare SST data field='sst' res=[5,5] nt.concatERSST([1950,2018]) nt.regrid2reg(field,'ERSSTv5',[1950,2018],res=res,ensave=True) for experiment in ['historical']: nt.writeEns(field,experiment,[1950,2029],[1,10]) nt.regrid2reg(field,experiment,[1950,2029],res=res,memRange=[1,10],ensave=False) for experiment in ['dcppA-assim-i1','dcppA-assim-i2']: nt.writeEns(field,experiment,[1950,2018],[1,10]) nt.regrid2reg(field,experiment,[1950,2018],res=res,memRange=[1,10],ensave=False) for experiment in ['dcppA-hindcast-i1','dcppA-hindcast-i2']: for syear in range(1960,2019): year1 = syear + 1 yearn = syear + 10 nt.writeEns(field,experiment,[year1,yearn],[1,10],syear=syear) nt.regrid2reg(field,experiment,[year1,yearn],res=res,memRange=[1,10],syear=syear,ensave=False,isMasked=False) # plot ACC fields = [['sst','sst']] resOptions=[[5,5]] leadRanges = [[0,0],[1,4],[5,8]] expOptions = [['HIN1',''],['HIN1','ANA1'],['HIN2','HIN1'],['HIN1','HIST'],['HIN1','PERS']] memRange = [1,10] doACC = True doMSSS = False product = 'hindcast' for field in fields: fieldMod = field[0] fieldObs = field[1] if fieldObs == 'sst': obsName = 'ERSSTv5' obsCoverage = [1950,2018] levRange = [0,0] landFill = True for leadRange in leadRanges: for res in resOptions: lon = np.arange(res[0]/2,360,res[0]) lat = np.arange(-90+res[1]/2,90,res[1]) lon2, lat2 = np.meshgrid(lon,lat) mskfdr = np.where(lat2 < 80, 1, 0) tagRes = '_{:d}x{:d}'.format(res[0],res[1]) tagField = '_' + fieldMod tagLead = '_LY{:d}'.format(leadRange[0]+1) if leadRange[0] == leadRange[1] else '_LY{:d}-{:d}'.format(leadRange[0]+1,leadRange[1]+1) # extract data for expOption in expOptions: if expOption[0][0:3] == 'ANA' or expOption[1][0:3] == 'ANA': modCoverage = [1950,2018] else: modCoverage = [1950,2029] if product == 'analysis': syear1 = np.max((modCoverage[0],obsCoverage[0])) else: syear1 = np.max((1960,obsCoverage[0])) if not expOption[1] == 'PERS' else np.max((1960,obsCoverage[0]+1+leadRange[1]-leadRange[0])) syearn = np.min((modCoverage[1],obsCoverage[1]))-leadRange[1]-1 syears = range(syear1,syearn+1) tagYears = '_s{:d}-{:d}'.format(syears[0],syears[-1]) print(tagYears) if obsName == 'GlobColour': obs = np.flip(nt.readHindcastLY(fieldObs,obsName,syears,leadRange,yearRange=obsCoverage,levRange=levRange,suffix=tagRes),axis=1) else: obs = nt.readHindcastLY(fieldObs,obsName,syears,leadRange,yearRange=obsCoverage,levRange=levRange,suffix=tagRes) if expOption[0] == 'HIST': fld1 = nt.readHindcastLY(fieldMod,'historical',syears,leadRange,yearRange=modCoverage,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False) elif expOption[0] == 'PERS': fld1 = nt.readHindcastLY(fieldObs,obsName,syears,leadRange,yearRange=obsCoverage,levRange=levRange,suffix=tagRes,persistence='mean',ensave=False) elif expOption[0] == 'HIN1': fld1 = nt.readHindcastLY(fieldMod,'dcppA-hindcast-i1',syears,leadRange,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False) elif expOption[0] == 'HIN2': fld1 = nt.readHindcastLY(fieldMod,'dcppA-hindcast-i2',syears,leadRange,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False) elif expOption[0] == 'ANA1': fld1 = np.squeeze(nt.readHindcastLY(fieldMod,'dcppA-assim-i1',syears,leadRange,yearRange=modCoverage,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False)) elif expOption[0] == 'ANA2': fld1 = nt.readHindcastLY(fieldMod,'dcppA-assim-i2',syears,leadRange,yearRange=modCoverage,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False) # if expOption[1] == 'HIST': fld2 = nt.readHindcastLY(fieldMod,'historical',syears,leadRange,yearRange=modCoverage,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False) elif expOption[1] == 'PERS': fld2 = nt.readHindcastLY(fieldObs,obsName,syears,leadRange,yearRange=obsCoverage,levRange=levRange,suffix=tagRes,persistence='mean',ensave=False) elif expOption[1] == 'HIN1': fld2 = nt.readHindcastLY(fieldMod,'dcppA-hindcast-i1',syears,leadRange,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False) elif expOption[1] == 'HIN2': fld2 = nt.readHindcastLY(fieldMod,'dcppA-hindcast-i2',syears,leadRange,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False) elif expOption[1] == 'ANA1': fld2 = np.squeeze(nt.readHindcastLY(fieldMod,'dcppA-assim-i1',syears,leadRange,yearRange=modCoverage,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False)) elif expOption[1] == 'ANA2': fld2 = nt.readHindcastLY(fieldMod,'dcppA-assim-i2',syears,leadRange,yearRange=modCoverage,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False) # tagExp = '_' + expOption[0] if expOption[1] == '' else '_{:s}-{:s}'.format(expOption[0],expOption[1]) rfilePrefix = 'ACC10mem' + tagField + tagExp + tagYears + tagLead + tagRes mfilePrefix = 'MSSS10mem' + tagField + tagExp + tagYears + tagLead + tagRes print(rfilePrefix + ' ' + mfilePrefix) # print(fld1.shape) if expOption[1] != '': print(fld2.shape) print(obs.shape) titleString = expOption[0] if expOption[1] == '' else expOption[0] + ' - ' + expOption[1] title2 = tagLead[1:] if product == 'hindcast' else '' if doACC: fld = nt.corrMultiArrayYeager(fld1,obs) if expOption[1] == '' else nt.corrMultiArrayDiffYeager(fld1,obs,fld2) nt.plotACC(lon=lon,lat=lat,fld=fld,filePrefix=rfilePrefix,lbLabelBarOn=False, title=titleString,title2=title2,landFill=landFill,plottype='ACC') if doMSSS: fld = nt.MSSSyeager(fld1,obs) if expOption[1] == '' else nt.MSSSyeager(fld1,obs,fld2) nt.plotACC(lon=lon,lat=lat,fld=fld,filePrefix=mfilePrefix,lbLabelBarOn=False, title=titleString,title2=title2,landFill=landFill,plottype='MSSS')