2020-04-09#
The below notebook was adjusted from COMBLE-MIP.
It compares selected simulations against observational targets that were collected from satellite and ground-based retrievals.
In case of questions or concerns, please notify Ann Fridlind and Florian Tornow.
Please check out our description of this case.
%run functions_plotting.py
## select simulations to plot
sim_keyword = '20200409'
## load ERA5 fields
#era5_1d, era5_2d = load_era5(PATH='../data_files/')
era5_1d = load_era5csv(case=sim_keyword)
## load radiosondes
rs_dat = load_rs(case=sim_keyword,t_filter = 7.)
## load DOE ARM site statistics
kazrkollias_dat = load_kazrkollias(case=sim_keyword,t_filter = 6.,aux_dat=rs_dat) ## here using radiosonde profiles to estimate CTT
kazrclough_dat = load_kazrclough(case=sim_keyword,t_filter = 6.)
aeri_dat = load_aeri(case=sim_keyword,t_filter = 6.)
radflux_dat = load_radflux(case=sim_keyword,t_filter = 6.)
#srfflux_dat = load_flux(case=sim_keyword,t_filter = 6.)
## load CARRA surface fluxes
#carra_dat = load_carraflux()
## load MAC-LWP
maclwp_dat = load_maclwp(case=sim_keyword,t_filter = 0.)
## load MODIS, VIIRS, and SENTINEL retrievals
modis_dat = load_modis(case=sim_keyword,t_filter = 0.,sza_filter = 75.)
viirs_dat = load_viirs(case=sim_keyword,t_filter = 0.,sza_filter = 75.)
#sentinel_dat = load_sentinel(case=sim_keyword,t_filter = 0.,sza_filter = 65.)
## load CERES retrievals
ceres_dat = load_ceres(case=sim_keyword,t_filter = 0.)
## load CALIPSO retrievals (note the increased time window)
calipso_dat = load_calipso(case=sim_keyword,t_filter = 3.)
../data_files/anxsondewnpnM1.b1.20200409.233300.cdf
../data_files/anxsondewnpnM1.b1.20200409.172800.cdf
../data_files/anxsondewnpnM1.b1.20200409.112500.cdf
KAZR (Kollias): here using auxiliary field to estimate cloud-top temperature
/gpfsm/dhome/tflorian/LES-SCM/notebooks/functions_plotting.py:554: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead
aux_dat['zdiff'] = np.abs(aux_dat['zf'] - np.float64(p_df['zi']))
/gpfsm/dhome/tflorian/LES-SCM/notebooks/functions_plotting.py:555: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead
aux_dat['zdiff.25'] = np.abs(aux_dat['zf'] - np.float64(p_df['zi.25']))
/gpfsm/dhome/tflorian/LES-SCM/notebooks/functions_plotting.py:556: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead
aux_dat['zdiff.75'] = np.abs(aux_dat['zf'] - np.float64(p_df['zi.75']))
aeri_dat
| Unnamed: 0 | time.abs | altitude | qv | rh | temp | theta | time | ta | zf | ua | va | class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| time.abs | |||||||||||||
| 13.250000 | 1706 | 47699.999 | 0.000000 | 0.003363 | 82.787895 | 1.029313 | 273.233124 | 111600.0 | 274.179313 | 0.000000 | NaN | NaN | AERI |
| 13.250000 | 1707 | 47699.999 | 0.010000 | 0.003050 | 84.474457 | -0.604708 | 271.701843 | 111600.0 | 272.545292 | 10.000000 | NaN | NaN | AERI |
| 13.250000 | 1708 | 47699.999 | 0.021000 | 0.002950 | 81.821877 | -0.641333 | 271.772400 | 111600.0 | 272.508667 | 21.000000 | NaN | NaN | AERI |
| 13.250000 | 1709 | 47699.999 | 0.033100 | 0.002883 | 79.158386 | -0.523477 | 272.007812 | 111600.0 | 272.626523 | 33.100002 | NaN | NaN | AERI |
| 13.250000 | 1710 | 47699.999 | 0.046410 | 0.002842 | 78.030869 | -0.543416 | 272.117615 | 111600.0 | 272.606584 | 46.410002 | NaN | NaN | AERI |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 13.333333 | 1811 | 47999.999 | 11.639103 | NaN | 2.661680 | -61.366245 | NaN | 111600.0 | NaN | 11639.102936 | NaN | NaN | AERI |
| 13.333333 | 1812 | 47999.999 | 12.813013 | NaN | 3.176039 | -61.878368 | NaN | 111600.0 | NaN | 12813.013077 | NaN | NaN | AERI |
| 13.333333 | 1813 | 47999.999 | 14.104314 | NaN | 3.351269 | -63.230335 | NaN | 111600.0 | NaN | 14104.313850 | NaN | NaN | AERI |
| 13.333333 | 1814 | 47999.999 | 15.524745 | NaN | 4.006438 | -64.948227 | NaN | 111600.0 | NaN | 15524.744987 | NaN | NaN | AERI |
| 13.333333 | 1815 | 47999.999 | 17.087219 | NaN | 4.477613 | -66.462296 | NaN | 111600.0 | NaN | 17087.219238 | NaN | NaN | AERI |
110 rows × 13 columns
## load all simulations located in subfolders of the given directory
var_vec_1d = ['z0','z0h','z0q','hfss','hfls','ts'] # variables with ERA5 (longer time axis)
var_vec_2d = ['theta','qv','ua','va']
df_col_1d_les,df_col_2d_les = load_sims('../output_les/',var_vec_1d,var_vec_2d,t_shift=0,keyword=sim_keyword,subfolder='s')
df_col_1d_scm,df_col_2d_scm = load_sims('../output_scm/',var_vec_1d,var_vec_2d,t_shift=0,keyword=sim_keyword,subfolder='s')
Loading variables: f(time)
../output_les/dharma/comble_20200409_diagNa.nc
../output_les/dharma/comble_20200409_progNa_noice.nc
../output_les/dharma/comble_20200409_progNa.nc
../output_les/dharma/comble_20200409_diagNa_noice.nc
Loading variables: f(time,height)
../output_les/dharma/comble_20200409_diagNa.nc
../output_les/dharma/comble_20200409_progNa_noice.nc
../output_les/dharma/comble_20200409_progNa.nc
../output_les/dharma/comble_20200409_diagNa_noice.nc
Loading variables: f(time)
../output_scm/modele/comble_20200409_diagNa.nc
../output_scm/modele/comble_20200409_diagNa_noice.nc
Loading variables: f(time,height)
../output_scm/modele/comble_20200409_diagNa.nc
../output_scm/modele/comble_20200409_diagNa_noice.nc
%run functions_plotting.py
## plot variables that have only time dependence
#plot_1d(pd.concat([df_col_1d,era5_1d,carra_dat,srfflux_dat]),var_vec_1d)dependence
#plot_1d(pd.concat([df_col_1d_les,df_col_1d_scm,era5_1d,carra_dat,srfflux_dat]),var_vec_1d)
plot_1d(pd.concat([df_col_1d_les,df_col_1d_scm,era5_1d]),var_vec_1d) #t0=-13,t1=18)
%run functions_plotting.py
## load all simulations located in subfolders of the given directory
var_vec_1d = ['lwpr','lwpc','iwp','od','clt','rlut','cf','nqc'] # variables without ERA5 (shorter time axis)
var_vec_2d_les = ['theta','qv','qlc','qlr','ta','ua','va','qic','qis','qig','ua','va','ta','prf']
var_vec_2d_scm = ['theta','qv','qlc','qlr','ta','ua','va','qicc','qics','qipc','qips','ua','va','ta','prf','fc']
df_col_1d_les,df_col_2d_les = load_sims('../output_les/',var_vec_1d,var_vec_2d_les,t_shift=0,keyword=sim_keyword,diag_zi_ctt=True)
df_col_1d_scm,df_col_2d_scm = load_sims('../output_scm/',var_vec_1d,var_vec_2d_scm,t_shift=0,keyword=sim_keyword,diag_zi_ctt=True)
df_col_1d_scm['clt'] = df_col_1d_scm['cf']/100.
Loading variables: f(time)
../output_les/dharma/comble_20200409_diagNa.nc
cf not found in ../output_les/dharma/comble_20200409_diagNa.nc
../output_les/dharma/comble_20200409_progNa_noice.nc
iwp shows NAN values in ../output_les/dharma/comble_20200409_progNa_noice.nc
cf not found in ../output_les/dharma/comble_20200409_progNa_noice.nc
../output_les/dharma/comble_20200409_progNa.nc
cf not found in ../output_les/dharma/comble_20200409_progNa.nc
../output_les/dharma/comble_20200409_diagNa_noice.nc
iwp shows NAN values in ../output_les/dharma/comble_20200409_diagNa_noice.nc
cf not found in ../output_les/dharma/comble_20200409_diagNa_noice.nc
Loading variables: f(time,height)
../output_les/dharma/comble_20200409_diagNa.nc
../output_les/dharma/comble_20200409_progNa_noice.nc
qic shows NAN values in ../output_les/dharma/comble_20200409_progNa_noice.nc
qis shows NAN values in ../output_les/dharma/comble_20200409_progNa_noice.nc
qig shows NAN values in ../output_les/dharma/comble_20200409_progNa_noice.nc
../output_les/dharma/comble_20200409_progNa.nc
../output_les/dharma/comble_20200409_diagNa_noice.nc
qic shows NAN values in ../output_les/dharma/comble_20200409_diagNa_noice.nc
qis shows NAN values in ../output_les/dharma/comble_20200409_diagNa_noice.nc
qig shows NAN values in ../output_les/dharma/comble_20200409_diagNa_noice.nc
computing inversion height, cloud-top height, and cloud-top temperature
using liquid(-ice) potential temperature
dharma/comble_20200409_diagNa.nc
dharma/comble_20200409_diagNa_noice.nc
dharma/comble_20200409_progNa.nc
dharma/comble_20200409_progNa_noice.nc
Loading variables: f(time)
../output_scm/modele/comble_20200409_diagNa.nc
../output_scm/modele/comble_20200409_diagNa_noice.nc
iwp shows NAN values in ../output_scm/modele/comble_20200409_diagNa_noice.nc
Loading variables: f(time,height)
../output_scm/modele/comble_20200409_diagNa.nc
../output_scm/modele/comble_20200409_diagNa_noice.nc
qicc shows NAN values in ../output_scm/modele/comble_20200409_diagNa_noice.nc
qics shows NAN values in ../output_scm/modele/comble_20200409_diagNa_noice.nc
qipc shows NAN values in ../output_scm/modele/comble_20200409_diagNa_noice.nc
qips shows NAN values in ../output_scm/modele/comble_20200409_diagNa_noice.nc
computing inversion height, cloud-top height, and cloud-top temperature
modele/comble_20200409_diagNa.nc
modele/comble_20200409_diagNa_noice.nc
%run functions_plotting.py
## plot variables that have only time dependence
#plot_1d(pd.concat([df_col_1d,maclwp_dat,modis_dat,viirs_dat,sentinel_dat,kazrkollias_dat,kazrclough_dat,calipso_dat,radflux_dat]),['zi','od','lwp','iwp','ctt'])
#plot_1d(pd.concat([df_col_1d,maclwp_dat,kazrkollias_dat,kazrclough_dat,calipso_dat,radflux_dat]),['cth','od','lwp','iwp','ctt'],longnames=['cloud-top height','cloud optical depth','liquid water path','ice water path','cloud-top temperature'],units=['m','','kg m^-2','kg m^-2','C'])
#plot_1d(pd.concat([df_col_1d_les,df_col_1d_scm,maclwp_dat,kazrkollias_dat,kazrclough_dat,calipso_dat,radflux_dat]),['cth','od','lwp','iwp','ctt'],longnames=['cloud-top height','cloud optical depth','liquid water path','ice water path','cloud-top temperature'],units=['m','','kg m^-2','kg m^-2','C'])
plot_1d(pd.concat([df_col_1d_les,df_col_1d_scm,maclwp_dat,kazrkollias_dat,kazrclough_dat,calipso_dat,modis_dat,viirs_dat,radflux_dat,ceres_dat]),['clt','cth','od','nqc','lwp','iwp','ctt','rlut'],
longnames=['cloud cover','cloud-top height','cloud optical depth','cloud droplet number concentration','liquid water path','ice water path','cloud-top temperature','LW upward flux at TOA'],
units=['','m','','cm-3','kg m^-2','kg m^-2','(°C','W m^-2']) #t0=-13,t1=18)
## plot variables that have only time dependence (for now excluding imager retrievals that are mostly SZA > 70)
#plot_1d(pd.concat([df_col_1d,maclwp_dat,kazrkollias_dat,kazrclough_dat,calipso_dat,gongiwp_dat,radflux_dat]),['od','lwp','iwp']) #'zi','od','lwp','iwp
/usr/local/other/anaconda/GEOSpyD/24.3.0-0/2024-08-29/envs/py3.12/lib/python3.12/site-packages/matplotlib/cbook.py:1699: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead
return math.isfinite(val)
%run functions_plotting.py
## plot all variables that have time and height dependence
#plot_2d(df_col_2d,var_vec_2d,[0,3,6,9,12,15,18],z_max=6000)
## example where ERA5 and radiosonde are included
#plot_2d(pd.concat([df_col_2d,era5_2d,rs_dat,aeri_dat]),var_vec = ['ta','theta','qv','ws','wd'],times=[-1.5,0,4,8,18],z_max=6000)
#plot_2d(pd.concat([df_col_2d_les,df_col_2d_scm,era5_2d,rs_dat,aeri_dat]),var_vec = ['ta','theta','qv','ws','wd'],times=[-1.5,0,4,8,18],z_max=6000)
plot_2d(pd.concat([df_col_2d_les,df_col_2d_scm,rs_dat,aeri_dat]),var_vec = ['ta','theta','qv','ws','wd','fc'],times=[0.5,8,16,24,31],z_max=6000)
Computing wind speed
Computing wind direction
temporal averaging over 1.0 h interval
<Figure size 1000x600 with 0 Axes>