mult_inf_range = np.arange(1.01,1.110,0.01)
ntemp_range = np.arange(1,5,1)
total_analysis_rmse = np.zeros( (len(mult_inf_range),len(ntemp_range)) )
total_analysis_sprd = np.zeros( (len(mult_inf_range),len(ntemp_range)) )
total_forecast_rmse = np.zeros( (len(mult_inf_range),len(ntemp_range)) )
total_forecast_sprd = np.zeros( (len(mult_inf_range),len(ntemp_range)) )
for iinf , mult_inf in enumerate( mult_inf_range ) :
for intemp , ntemp in enumerate( ntemp_range ) :
conf.DAConf['InfCoefs']=np.array([mult_inf,0.0,0.0,0.0,0.0])
conf.DAConf['NTemp']=int(ntemp)
results.append( ahm.assimilation_hybrid_run( conf ) )
print('Multiplicative Inflation',mult_inf)
print('Tempering iteraations',ntemp)
print('Analisis RMSE: ',np.mean(results[-1]['XASRmse']))
print('Forecast RMSE: ',np.mean(results[-1]['XFSRmse']))
print('Analisis SPRD: ',np.mean(results[-1]['XASSprd']))
print('Forecast SPRD: ',np.mean(results[-1]['XFSSprd']))
total_analysis_rmse[iinf,intemp] = np.mean(results[-1]['XASRmse'])
total_forecast_rmse[iinf,intemp] = np.mean(results[-1]['XFSRmse'])
total_analysis_sprd[iinf,intemp] = np.mean(results[-1]['XASSprd'])
total_forecast_sprd[iinf,intemp] = np.mean(results[-1]['XFSSprd'])
f=open(out_filename,'wb')
pickle.dump([results,mult_inf_range,ntemp_range,total_analysis_rmse,total_forecast_rmse,total_analysis_sprd,total_forecast_sprd],f)
'Twin'] = True #When True, model configuration will be replaced by the model configuration in the nature run.
conf.ModelConf['Coef'] = np.array(
[8.0]
) #Coefficient of parametrized forcing (polynom coefficients starting from coef[0]*x^0 + coef[1]*x ... )
conf.DAConf[
'AddaptiveTemp'] = True #Enable addaptive tempering time step in pseudo time.
conf.DAConf['NTemp'] = 1 #Number of temper iterations
conf.DAConf['InfCoefs'] = np.array([1.6, 0.0, 0.0, 0.0, 0.0])
conf.DAConf[
'RejuvParam'] = 0.0 #Global particle rejuvenestion (For the ETPF only)
results = gmm.assimilation_gm_run(conf)
print('Multiplicative Inflation', conf.DAConf['InfCoefs'][0])
print('Tempering iteraations', conf.DAConf['NTemp'])
print('Analisis RMSE: ', np.mean(results['XASRmse']))
print('Forecast RMSE: ', np.mean(results['XFSRmse']))
print('Analisis SPRD: ', np.mean(results['XASSprd']))
print('Forecast SPRD: ', np.mean(results['XFSSprd']))
conf.DAConf['InfCoefs'] = np.array([1.06, 0.0, 0.0, 0.0, 0.0])
conf.DAConf[
'BridgeParam'] = 0.4 #Global particle rejuvenestion (For the ETPF only)
results = hym.assimilation_hybrid_run(conf)
print('Multiplicative Inflation', conf.DAConf['InfCoefs'][0])
print('Tempering iteraations', conf.DAConf['NTemp'])
print('Analisis RMSE: ', np.mean(results['XASRmse']))
print('Forecast RMSE: ', np.mean(results['XFSRmse']))
print('Analisis SPRD: ', np.mean(results['XASSprd']))
print('Forecast SPRD: ', np.mean(results['XFSSprd']))
