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apply_obs_op_dart.py

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  • assim_synth_obs.py 11.04 KiB
    import os, sys, shutil
    import warnings
    import datetime as dt
    import numpy as np
    from scipy.interpolate import interp1d
    
    from config.cfg import exp, cluster
    from utils import symlink, copy, sed_inplace, append_file, mkdir, try_remove, print
    import create_obsseq as osq
    import wrfout_add_geo
    
    earth_radius_km = 6370
    
    # fit of Fig 7, Harnisch 2016
    x_ci = [0,   5, 10.5, 13, 16]
    y_oe = [1, 4.5,   10, 12, 13]  # Kelvin
    oe_73_linear = interp1d(x_ci, y_oe, assume_sorted=True)
    
    def oe_73(ci):
        if ci < 13:
            return oe_73_linear(ci)
        else:
            return 16.
    
    def cloudimpact_73(bt_mod, bt_obs):
        """
        follows Harnisch 2016
        """
        biascor_obs = 0
        bt_lim = 255  # Kelvin for 7.3 micron WV channel
    
        ci_obs = max(0, bt_lim-(bt_obs - biascor_obs))
        ci_mod = max(0, bt_lim-bt_mod)
        ci = (ci_obs+ci_mod)/2
        return ci
    
    def read_prior_obs(f_obs_prior):
        """
        docstring
        """
        obsseq = open(f_obs_prior, 'r').readlines()
        OBSs = []
        # read observations from obs_seq.final
        for i, line in enumerate(obsseq):
            if ' OBS ' in line:
                observed = float(obsseq[i+1].strip())
                truth = float(obsseq[i+2].strip())
                prior_ensmean = float(obsseq[i+3].strip())
                prior_enssd = float(obsseq[i+4].strip())
                prior_ens = []
                for j in range(5, 5+exp.n_ens):
                    prior_ens.append(float(obsseq[i+j].strip()))
    
                OBSs.append(dict(observed=observed, truth=truth, prior_ens=np.array(prior_ens)))
        return OBSs
    
    def read_obsseqout(f):
        obsseq = open(f, 'r').readlines()
        true = []
        obs = []
        # read observations from obs_seq.out
        for i, line in enumerate(obsseq):
            if ' OBS ' in line:
                observed = float(obsseq[i+1].strip())
                truth = float(obsseq[i+2].strip())
                true.append(truth)
                obs.append(observed)
        return true, obs
    
    
    def set_DART_nml(sat_channel=False, cov_loc_radius_km=32, cov_loc_vert_km=False,
                     just_prior_values=False):
        """descr"""
        cov_loc_radian = cov_loc_radius_km/earth_radius_km
        
        if just_prior_values:
            template = cluster.scriptsdir+'/../templates/input.eval.nml'
        else:
            template = cluster.scriptsdir+'/../templates/input.nml'
        copy(template, cluster.dartrundir+'/input.nml')
    
        # options are overwritten with settings
        options = {'<n_ens>': str(int(exp.n_ens)),
                   '<cov_loc_radian>': str(cov_loc_radian)}
    
        if cov_loc_vert_km:
            cov_loc_vert_rad = cov_loc_vert_km*1000/cov_loc_radian
            options['<horiz_dist_only>'] = '.false.'
            options['<vert_norm_hgt>'] = str(cov_loc_vert_rad)
        else:
            options['<horiz_dist_only>'] = '.true.'
            options['<vert_norm_hgt>'] = '50000.0'  # dummy value
    
        for key, value in options.items():
            sed_inplace(cluster.dartrundir+'/input.nml', key, value)
    
        # input.nml for RTTOV
        if sat_channel > 0:
            if sat_channel in [1, 2, 3, 12]:  # VIS channels
                rttov_nml = cluster.scriptsdir+'/../templates/obs_def_rttov.VIS.nml'
            else:  # IR channels
                rttov_nml = cluster.scriptsdir+'/../templates/obs_def_rttov.IR.nml'
            append_file(cluster.dartrundir+'/input.nml', rttov_nml)
        else:
            # append any rttov segment, needs to exist anyway
            rttov_nml = cluster.scriptsdir+'/../templates/obs_def_rttov.IR.nml'
            append_file(cluster.dartrundir+'/input.nml', rttov_nml)
    
    def obs_operator_ensemble(istage):
        # assumes that prior ensemble is already linked to advance_temp<i>/wrfout_d01
        print('running obs operator on ensemble forecast')
        os.chdir(cluster.dartrundir)
    
        if sat_channel:
            list_ensemble_truths = []
    
            for iens in range(1, exp.n_ens+1):
                print('observation operator for ens #'+str(iens))
                # ens members are already linked to advance_temp<i>/wrfout_d01
                copy(cluster.dartrundir+'/advance_temp'+str(iens)+'/wrfout_d01',
                     cluster.dartrundir+'/wrfout_d01')
                # DART may need a wrfinput file as well, which serves as a template for dimension sizes
                symlink(cluster.dartrundir+'/wrfout_d01', cluster.dartrundir+'/wrfinput_d01')
                
                # add geodata, if istage>0, wrfout is DART output (has coords)
                if istage == 0:
                    wrfout_add_geo.run(cluster.dartrundir+'/geo_em.d01.nc', cluster.dartrundir+'/wrfout_d01')
    
                # run perfect_model obs (forward operator)
                os.system('mpirun -np 12 ./perfect_model_obs > /dev/null')
    
                # truth values in obs_seq.out are H(x) values
                true, _ = read_obsseqout(cluster.dartrundir+'/obs_seq.out')
                list_ensemble_truths.append(true)
            
            n_obs = len(list_ensemble_truths[0])
            np_array = np.full((exp.n_ens, n_obs), np.nan)
            for i in range(exp.n_ens):
                np_array[i, :] = list_ensemble_truths[i]
            return np_array
        else:
            raise NotImplementedError()
    
    def obs_operator_nature(time):
        print('running obs operator on nature run')
        prepare_nature_dart(time)
        run_perfect_model_obs()
        true, _ = read_obsseqout(cluster.dartrundir+'/obs_seq.out')
        return true
    
    
    def link_nature_to_dart_truth(time):
        # get wrfout_d01 from nature run
        shutil.copy(time.strftime(cluster.nature_wrfout),
                    cluster.dartrundir+'/wrfout_d01')
        # DART may need a wrfinput file as well, which serves as a template for dimension sizes
        symlink(cluster.dartrundir+'/wrfout_d01', cluster.dartrundir+'/wrfinput_d01')
    
    
    def prepare_nature_dart(time):
        link_nature_to_dart_truth(time)
        wrfout_add_geo.run(cluster.dartrundir+'/geo_em.d01.nc', cluster.dartrundir+'/wrfout_d01')
    
    
    def calc_obserr_WV73(Hx_nature, Hx_prior):
    
        n_obs = len(Hx_nature)
        OEs = np.ones(n_obs)
        for iobs in range(n_obs):
    
            bt_y = Hx_nature[iobs]
            bt_x_ens = Hx_prior[:, iobs]
            CIs = [cloudimpact_73(bt_x, bt_y) for bt_x in bt_x_ens]
            mean_CI = np.mean(CIs)
    
            oe_nature = oe_73(mean_CI)
            print('oe_nature:', oe_nature, ', bt_y:', bt_y, ', mean_CI:', mean_CI)
            OEs[iobs] = oe_nature
        return OEs
    
    def run_perfect_model_obs():
        os.chdir(cluster.dartrundir)
        try_remove(cluster.dartrundir+'/obs_seq.out')
        if not os.path.exists(cluster.dartrundir+'/obs_seq.in'):
            raise RuntimeError('obs_seq.in does not exist in '+cluster.dartrundir)
        os.system('mpirun -np 12 ./perfect_model_obs > log.perfect_model_obs')
        if not os.path.exists(cluster.dartrundir+'/obs_seq.out'):
            raise RuntimeError('obs_seq.out does not exist in '+cluster.dartrundir, 
                               '\n look for '+cluster.dartrundir+'log.perfect_model_obs')
    
    def assimilate(nproc=96):
        print('running filter')
        os.chdir(cluster.dartrundir)
        try_remove(cluster.dartrundir+'/obs_seq.final')
        os.system('mpirun -genv I_MPI_PIN_PROCESSOR_LIST=0-'+str(int(nproc)-1)+' -np '+str(int(nproc))+' ./filter > log.filter')
    
    def archive_diagnostics(archive_dir, time):
        print('archive obs space diagnostics')
        mkdir(archive_dir)
        fout = archive_dir+time.strftime('/%Y-%m-%d_%H:%M_obs_seq.final')
        copy(cluster.dartrundir+'/obs_seq.final', fout)
        print(fout, 'saved.')
    
        # try:  # what are regression diagnostics?!
        #     print('archive regression diagnostics')
        #     copy(cluster.dartrundir+'/reg_diagnostics', archive_dir+'/reg_diagnostics')
        # except Exception as e:
        #     warnings.warn(str(e))
    
    def recycle_output():
        print('move output to input')
        for iens in range(1, exp.n_ens+1):
            os.rename(cluster.dartrundir+'/filter_restart_d01.'+str(iens).zfill(4),
                      cluster.dartrundir+'/advance_temp'+str(iens)+'/wrfout_d01')
    
    def archive_output(archive_stage):
        print('archiving output')
        mkdir(archive_stage)
        copy(cluster.dartrundir+'/input.nml', archive_stage+'/input.nml')
    
        # single members
        # for iens in range(1, exp.n_ens+1):
        #     savedir = archive_stage+'/'+str(iens)
        #     mkdir(savedir)
        #     filter_in = cluster.dartrundir+'/preassim_member_'+str(iens).zfill(4)+'.nc'
        #     filter_out = cluster.dartrundir+'/filter_restart_d01.'+str(iens).zfill(4)
    
        # copy mean and sd to archive
        for f in ['output_mean.nc', 'output_sd.nc']:
            copy(cluster.dartrundir+'/'+f, archive_stage+'/'+f)
    
    
    if __name__ == "__main__":
    
        """Generate observations (obs_seq.out file)
        as defined in config/cfg.py
        for a certain timestamp (argument) of the nature run (defined in config/clusters.py)
    
        Workflow:
        for each assimilation stage (one obs_seq.in and e.g. one observation type):
        1) prepare nature run for DART
        optional: 2) calculate obs-error from parametrization
        3) create obs_seq.in with obs-errors from 2)
        4) generate actual observations (obs_seq.out) with obs_seq.in from 3)
    
        - calculate obs-error from parametrization
            1) create obs_seq.in with obs error=0
            2) calculate y_nat = H(x_nature) and y_ens = H(x_ensemble) 
            3) calculate obs error as function of y_nat, y_ensmean
        
        Assumptions:
        - x_ensemble is already linked for DART to advance_temp<iens>/wrfout_d01
        """
    
        time = dt.datetime.strptime(sys.argv[1], '%Y-%m-%d_%H:%M')
        archive_time = cluster.archivedir()+time.strftime('/%Y-%m-%d_%H:%M/')
    
        os.chdir(cluster.dartrundir)
        os.system('rm -f obs_seq.in obs_seq.out obs_seq.final')  # remove any existing observation files
    
        n_stages = len(exp.observations)
        for istage, obscfg in enumerate(exp.observations):
    
            archive_stage = archive_time + '/assim_stage'+str(istage)
            n_obs = obscfg['n_obs']
            sat_channel = obscfg.get('sat_channel', False)
            obscfg['folder_obs_coords'] = archive_stage+'/obs_coords.pkl'
    
            set_DART_nml(sat_channel=sat_channel, 
                         cov_loc_radius_km=obscfg['cov_loc_radius_km'],
                         cov_loc_vert_km=obscfg.get('cov_loc_vert_km', False))
    
            use_error_parametrization = obscfg['err_std'] == False
            if use_error_parametrization:
                if sat_channel != 6:
                    raise NotImplementedError('sat channel '+str(sat_channel))
    
                osq.create_obsseq_in(time, obscfg, zero_error=True)  # zero error to get truth vals
    
                Hx_nat = obs_operator_nature(time) 
                Hx_prior = obs_operator_ensemble(istage)  # files are already linked to DART directory
                
                obscfg['err_std'] = calc_obserr_WV73(Hx_nat, Hx_prior)
            else:
                obscfg['err_std'] = np.ones(n_obs) * obscfg['err_std']  # fixed stderr
    
            osq.create_obsseq_in(time, obscfg)  # now with correct errors
            prepare_nature_dart(time)
            run_perfect_model_obs()
    
            assimilate()
            dir_obsseq = cluster.archivedir()+'/obs_seq_final/assim_stage'+str(istage)
            archive_diagnostics(dir_obsseq, time)
    
            if istage < n_stages-1:
                # recirculation: filter output -> input
                recycle_output()
                archive_output(archive_stage)
    
            elif istage == n_stages-1:
                # last assimilation, continue integration now
                archive_output(archive_stage)
    
            else:
                RuntimeError('this should never occur?!')