Evaluation of LES against satellite and ground-based observations (ProgNa)#
The below notebook 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 (ann.fridlind@nasa.gov), Timothy Juliano (tjuliano@ucar.edu), and Florian Tornow (ft2544@columbia.edu).
%run functions_plotting.py
## select simulations to plot
sim_keyword = 'dx100_ProgNa.'
## load ERA5 fields
era5_1d, era5_2d = load_era5(PATH='../../data_files/')
## load radiosondes
rs_dat = load_rs(t_filter = 7.)
## load DOE ARM site statistics
kazrkollias_dat = load_kazrkollias(t_filter = 6.,aux_dat=rs_dat) ## here using radiosonde profiles to estimate CTT
kazrclough_dat = load_kazrclough(t_filter = 6.)
aeri_dat = load_aeri(t_filter = 0.5)
radflux_dat = load_radflux(t_filter = 6.)
srfflux_dat = load_flux(t_filter = 6.)
## load CARRA surface fluxes
carra_dat = load_carraflux()
## load MAC-LWP
maclwp_dat = load_maclwp(t_filter = 0.)
gongiwp_dat = load_iwpgong(t_filter = 0.)
## load MODIS, VIIRS, and SENTINEL retrievals
modis_dat = load_modis(t_filter = 0.,sza_filter = 65.)
viirs_dat = load_viirs(t_filter = 0.,sza_filter = 65.)
sentinel_dat = load_sentinel(t_filter = 0.,sza_filter = 65.)
## load CERES retrievals
ceres_dat = load_ceres(t_filter = 0.)
## load CALIPSO retrievals (note the increased time window)
calipso_dat = load_calipso(t_filter = 3.)
../../data_files/theta_temp_rh_sh_uvw_sst_along_trajectory_era5ml_28h_end_2020-03-13-18.nc
../../data_files/anxsondewnpnM1.b1.20200313.052700.cdf
../../data_files/anxsondewnpnM1.b1.20200313.112600.cdf
../../data_files/anxsondewnpnM1.b1.20200313.172600.cdf
../../data_files/anxsondewnpnM1.b1.20200313.232200.cdf
KAZR (Kollias): here using auxiliary field to estimate cloud-top temperature
## load all simulations located in subfolders of the given directory
var_vec_1d = ['hfss','hfls','ts'] # variables with ERA5 (longer time axis)
var_vec_2d = ['theta','qv','ua','va']
df_col_1d,df_col_2d = load_sims('../../output_les/',var_vec_1d,var_vec_2d,t_shift=-2,keyword=sim_keyword,subfolder='sand') #,ignore='ICON')
Loading variables: f(time)
../../output_les/dharma/sandbox/DHARMA_Lx25_dx100_ProgNa.nc
../../output_les/icon/sandbox/ICON_Lx25_dx100_ProgNa.nc
../../output_les/DALES/sandbox/DALES_Lx25_dx100_ProgNa.nc
Loading variables: f(time,height)
../../output_les/dharma/sandbox/DHARMA_Lx25_dx100_ProgNa.nc
../../output_les/icon/sandbox/ICON_Lx25_dx100_ProgNa.nc
../../output_les/DALES/sandbox/DALES_Lx25_dx100_ProgNa.nc
## plot variables that have only time dependence
plot_1d(pd.concat([df_col_1d,era5_1d,carra_dat,srfflux_dat]),var_vec_1d)
/opt/conda/lib/python3.10/site-packages/matplotlib/axes/_axes.py:1185: RuntimeWarning: All-NaN axis encountered
miny = np.nanmin(masked_verts[..., 1])
/opt/conda/lib/python3.10/site-packages/matplotlib/axes/_axes.py:1186: RuntimeWarning: All-NaN axis encountered
maxy = np.nanmax(masked_verts[..., 1])
%run functions_plotting.py
## load all simulations located in subfolders of the given directory
var_vec_1d = ['lwpr','lwpc','iwp','od'] # variables without ERA5 (shorter time axis)
var_vec_2d = ['theta','qv','qlc','qlr','ta','ua','va'] #,'qic','qis','qig','ua','va','ta']
df_col_1d,df_col_2d = load_sims('../../output_les/',var_vec_1d,var_vec_2d,t_shift=-2,keyword=sim_keyword,diag_zi_ctt=True,subfolder='sand') #,ignore='ICON')
Loading variables: f(time)
../../output_les/dharma/sandbox/DHARMA_Lx25_dx100_ProgNa.nc
../../output_les/icon/sandbox/ICON_Lx25_dx100_ProgNa.nc
../../output_les/DALES/sandbox/DALES_Lx25_dx100_ProgNa.nc
iwp not found in ../../output_les/DALES/sandbox/DALES_Lx25_dx100_ProgNa.nc
od not found in ../../output_les/DALES/sandbox/DALES_Lx25_dx100_ProgNa.nc
Loading variables: f(time,height)
../../output_les/dharma/sandbox/DHARMA_Lx25_dx100_ProgNa.nc
../../output_les/icon/sandbox/ICON_Lx25_dx100_ProgNa.nc
../../output_les/DALES/sandbox/DALES_Lx25_dx100_ProgNa.nc
computing inversion height, cloud-top height, and cloud-top temperature
DALES/sandbox/DALES_Lx25_dx100_ProgNa.nc
dharma/sandbox/DHARMA_Lx25_dx100_ProgNa.nc
icon/sandbox/ICON_Lx25_dx100_ProgNa.nc
## 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 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
/opt/conda/lib/python3.10/site-packages/matplotlib/axes/_axes.py:1185: RuntimeWarning: All-NaN axis encountered
miny = np.nanmin(masked_verts[..., 1])
/opt/conda/lib/python3.10/site-packages/matplotlib/axes/_axes.py:1186: RuntimeWarning: All-NaN axis encountered
maxy = np.nanmax(masked_verts[..., 1])
era5_2d
| class | time | pf | ua | va | w | theta | qv | zf | ta | |
|---|---|---|---|---|---|---|---|---|---|---|
| 18.0 | ERA5 | 64800.0 | 1012.049377 | 3.761155 | -8.429794 | -0.027666 | 270.269867 | 0.002308 | 18.167820 | 271.197296 |
| 17.0 | ERA5 | 61200.0 | 1012.049377 | 3.726061 | -11.233538 | 0.034034 | 270.648346 | 0.002132 | 13.275071 | 271.577057 |
| 16.0 | ERA5 | 57600.0 | 1012.049377 | 4.188862 | -11.726610 | 0.019582 | 270.504089 | 0.002079 | 8.482681 | 271.432312 |
| 15.0 | ERA5 | 54000.0 | 1012.049377 | 3.789669 | -12.985072 | 0.001795 | 270.084229 | 0.001972 | 8.890698 | 271.011017 |
| 14.0 | ERA5 | 50400.0 | 1012.049377 | 3.932238 | -14.237088 | 0.017544 | 269.661469 | 0.001863 | 9.654793 | 270.586792 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| -6.0 | ERA5 | -21600.0 | 0.010000 | 6.649825 | -22.005390 | -0.000058 | 5420.681152 | 0.000001 | 76277.929688 | 201.397369 |
| -7.0 | ERA5 | -25200.0 | 0.010000 | 2.635956 | -17.873898 | -0.000058 | 5438.761230 | 0.000001 | 76239.414062 | 202.069107 |
| -8.0 | ERA5 | -28800.0 | 0.010000 | -1.987671 | -13.578048 | -0.000058 | 5432.479492 | 0.000001 | 76177.406250 | 201.835709 |
| -9.0 | ERA5 | -32400.0 | 0.010000 | -6.703418 | -13.392743 | -0.000058 | 5469.329102 | 0.000001 | 76284.093750 | 203.204803 |
| -10.0 | ERA5 | -36000.0 | 0.010000 | -15.424261 | -10.653453 | -0.000058 | 5540.691895 | 0.000001 | 76272.406250 | 205.856186 |
3973 rows × 10 columns
## 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)
Computing wind speed
Computing wind direction
temporal averaging over 1.0 h interval
<Figure size 1000x600 with 0 Axes>
'ua' in df_col_2d.columns
True
df_col_2d.columns
Index(['class', 'time', 'zf', 'theta', 'qv', 'qlc', 'qlr', 'ta', 'ua', 'va',
'colflag'],
dtype='object')