{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import sys ; sys.path.remove('/mnt/bcpu-ns9039k/ingo/jupyter/Modules'); sys.path.append('../../scripts')\n", "import norcpmTools as nt\n", "from netCDF4 import Dataset\n", "import numpy as np\n", "\n", "# prepare PP data\n", "field='ppint'\n", "res=[5,5]\n", "for experiment in ['historical']:\n", " nt.writeEns(field,experiment,[1950,2029],[1,10])\n", " nt.regrid2reg(field,experiment,[1950,2029],res=res,memRange=[1,10],ensave=False)\n", "for experiment in ['dcppA-assim-i1','dcppA-assim-i2']:\n", " nt.writeEns(field,experiment,[1950,2018],[1,10])\n", " nt.regrid2reg(field,experiment,[1950,2018],res=res,memRange=[1,10],ensave=False)\n", "for experiment in ['dcppA-hindcast-i1','dcppA-hindcast-i2']:\n", " for syear in range(1960,2019):\n", " year1 = syear + 1\n", " yearn = syear + 10\n", " nt.writeEns(field,experiment,[year1,yearn],[1,10],syear=syear)\n", " nt.regrid2reg(field,experiment,[year1,yearn],res=res,memRange=[1,10],syear=syear,ensave=False,isMasked=False)\n", "\n", "# plot ACC \n", "fields = [['ppint','PP'],['ppintPerfect','ppint']]\n", "resOptions=[[5,5]]\n", "leadRanges = [[0,0],[1,4],[5,8]]\n", "leadRanges = [[0,0]]\n", "expOptions = [['HIN1',''],['HIN1','ANA1'],['HIN2','HIN1'],['HIN1','HIST'],['HIN1','PERS']]\n", "memRange = [1,10] \n", "doACC = True\n", "doMSSS = False\n", "product = 'hindcast'\n", "for field in fields:\n", " fieldMod = field[0]\n", " fieldObs = field[1]\n", " if fieldMod == 'ppint':\n", " fieldObs = 'PP'\n", " obsName = 'GlobColour'\n", " obsCoverage = [1998,2019]\n", " obsFactor = 1/(12*1000*24*3600) # mod = mol C m-2 s-1, obs = mg m-2 day-1\n", " obsOffset = 0.\n", " levRange = [0,0]\n", " landFill = True \n", " elif fieldMod == 'ppintPerfect':\n", " fieldMod = 'ppint'\n", " fieldObs = 'ppint'\n", " obsName = 'dcppA-assim-i1'\n", " obsCoverage = [1950,2018]\n", " obsFactor = 1. \n", " obsOffset = 0.\n", " levRange = [0,0]\n", " landFill = True \n", " for leadRange in leadRanges: \n", " for res in resOptions:\n", " lon = np.arange(res[0]/2,360,res[0])\n", " lat = np.arange(-90+res[1]/2,90,res[1])\n", " lon2, lat2 = np.meshgrid(lon,lat)\n", " mskfdr = np.where(lat2 < 80, 1, 0)\n", " tagRes = '_{:d}x{:d}'.format(res[0],res[1])\n", " tagField = '_' + fieldMod if obsName[0:11] != 'dcppA-assim' else '_' + fieldMod + 'Perfect'\n", " tagLead = '_LY{:d}'.format(leadRange[0]+1) if leadRange[0] == leadRange[1] else '_LY{:d}-{:d}'.format(leadRange[0]+1,leadRange[1]+1)\n", " # extract data\n", " for expOption in expOptions:\n", " if expOption[0][0:3] == 'ANA' or expOption[1][0:3] == 'ANA':\n", " modCoverage = [1950,2018]\n", " else:\n", " modCoverage = [1950,2029]\n", " if product == 'analysis': \n", " syear1 = np.max((modCoverage[0],obsCoverage[0]))\n", " else:\n", " syear1 = np.max((1960,obsCoverage[0])) if not expOption[1] == 'PERS' else np.max((1960,obsCoverage[0]+1+leadRange[1]-leadRange[0]))\n", " syearn = np.min((modCoverage[1],obsCoverage[1]))-leadRange[1]-1\n", " syears = range(syear1,syearn+1)\n", " tagYears = '_s{:d}-{:d}'.format(syears[0],syears[-1])\n", " print(tagYears)\n", " tagYears = '_s{:d}-{:d}'.format(syears[0],syears[-1])\n", " if obsName == 'GlobColour':\n", " obs = np.flip(nt.readHindcastLY(fieldObs,obsName,syears,leadRange,yearRange=obsCoverage,levRange=levRange,suffix=tagRes),axis=1) \n", " elif obsName[0:11] == 'dcppA-assim': \n", " obs = np.mean(nt.readHindcastLY(fieldObs,obsName,syears,leadRange,yearRange=obsCoverage,memRange=[1,10],levRange=levRange,suffix=tagRes,ensave=False),axis=1)\n", " else:\n", " obs = nt.readHindcastLY(fieldObs,obsName,syears,leadRange,yearRange=obsCoverage,levRange=levRange,suffix=tagRes) \n", " if fieldObs == 'fgco2':\n", " obs = -obs\n", " if expOption[0] == 'HIST':\n", " fld1 = nt.readHindcastLY(fieldMod,'historical',syears,leadRange,yearRange=modCoverage,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False)\n", " elif expOption[0] == 'PERS':\n", " if obsName[0:11] == 'dcppA-assim':\n", " fld1 = readHindcastLY(fieldObs,obsName,syears,leadRange,yearRange=obsCoverage,memRange=[1,10],\n", " levRange=levRange,suffix=tagRes,persistence='mean',ensave=False) \n", " elif obsName == 'GlobColour':\n", " fld1 = np.flip(readHindcastLY(fieldObs,obsName,syears,leadRange,yearRange=obsCoverage,\n", " levRange=levRange,suffix=tagRes,persistence='mean',ensave=False),axis=1)\n", " else:\n", " fld1 = readHindcastLY(fieldObs,obsName,syears,leadRange,yearRange=obsCoverage,\n", " levRange=levRange,suffix=tagRes,persistence='mean',ensave=False)\n", " elif expOption[0] == 'HIN1':\n", " fld1 = nt.readHindcastLY(fieldMod,'dcppA-hindcast-i1',syears,leadRange,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False)\n", " elif expOption[0] == 'HIN2':\n", " fld1 = nt.readHindcastLY(fieldMod,'dcppA-hindcast-i2',syears,leadRange,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False)\n", " elif expOption[0] == 'ANA1':\n", " fld1 = np.squeeze(nt.readHindcastLY(fieldMod,'dcppA-assim-i1',syears,leadRange,yearRange=modCoverage,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False))\n", " elif expOption[0] == 'ANA2':\n", " fld1 = nt.readHindcastLY(fieldMod,'dcppA-assim-i2',syears,leadRange,yearRange=modCoverage,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False) \n", " #\n", " if expOption[1] == 'HIST':\n", " fld2 = nt.readHindcastLY(fieldMod,'historical',syears,leadRange,yearRange=modCoverage,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False)\n", " elif expOption[1] == 'PERS':\n", " fld2 = nt.readHindcastLY(fieldObs,obsName,syears,leadRange,yearRange=obsCoverage,levRange=levRange,suffix=tagRes,persistence='mean',ensave=False)\n", " elif expOption[1] == 'HIN1':\n", " fld2 = nt.readHindcastLY(fieldMod,'dcppA-hindcast-i1',syears,leadRange,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False)\n", " elif expOption[1] == 'HIN2':\n", " fld2 = nt.readHindcastLY(fieldMod,'dcppA-hindcast-i2',syears,leadRange,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False)\n", " elif expOption[1] == 'ANA1':\n", " fld2 = np.squeeze(nt.readHindcastLY(fieldMod,'dcppA-assim-i1',syears,leadRange,yearRange=modCoverage,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False))\n", " elif expOption[1] == 'ANA2':\n", " fld2 = nt.readHindcastLY(fieldMod,'dcppA-assim-i2',syears,leadRange,yearRange=modCoverage,memRange=memRange,levRange=levRange,suffix=tagRes,ensave=False) \n", " #\n", " tagExp = '_' + expOption[0] if expOption[1] == '' else '_{:s}-{:s}'.format(expOption[0],expOption[1])\n", " rfilePrefix = 'ACC10mem' + tagField + tagExp + tagYears + tagLead + tagRes \n", " mfilePrefix = 'MSSS10mem' + tagField + tagExp + tagYears + tagLead + tagRes \n", " print(rfilePrefix + ' ' + mfilePrefix)\n", " #\n", " print(fld1.shape)\n", " if expOption[1] != '':\n", " print(fld2.shape)\n", " print(obs.shape)\n", " titleString = expOption[0] if expOption[1] == '' else expOption[0] + ' - ' + expOption[1]\n", " title2 = tagLead[1:] if product == 'hindcast' else '' \n", " if doACC: \n", " fld = nt.corrMultiArrayYeager(fld1,obs) if expOption[1] == '' else nt.corrMultiArrayDiffYeager(fld1,obs,fld2)\n", " nt.plotACC(lon=lon,lat=lat,fld=fld,filePrefix=rfilePrefix,lbLabelBarOn=False,\n", " title=' ',title2=' ',landFill=landFill,plottype='ACC')\n", " if doMSSS: \n", " fld = nt.MSSSyeager(fld1,obs) if expOption[1] == '' else nt.MSSSyeager(fld1,obs,fld2)\n", " nt.plotACC(lon=lon,lat=lat,fld=fld,filePrefix=mfilePrefix,lbLabelBarOn=False,\n", " title=titleString,title2=title2,landFill=landFill,plottype='MSSS')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { 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