pydartdiags 0.0.3b0

Observation Sequence Diagnostics for DART

pyDARTdiags

pyDARTdiags is a python library for obsevation space diagnostics for the Data Assimilation Research Testbed (DART).

pyDARTdiags is under initial development, so please use caution. The MATLAB observation space diagnostics are available through DART.

pyDARTdiags can be installed through pip. We recommend installing pydartdiags in a virtual enviroment:

python3 -m venv dartdiags
source dartdiags/bin/activate
pip install pydartdiags

Example importing the obs_sequence and plots modules

from pydartdiags.obs_sequence import obs_sequence as obs_seq
from pydartdiags.plots import plots

Examining the dataframe

obs_seq = obs_seq.obs_sequence('obs_seq.final.ascii')
obs_seq.df.head()

	obs_num	observation	prior_ensemble_mean	prior_ensemble_spread	prior_ensemble_member_1	prior_ensemble_member_2	prior_ensemble_member_3	prior_ensemble_member_4	prior_ensemble_member_5	prior_ensemble_member_6	...	latitude	vertical	vert_unit	type	seconds	days	time	obs_err_var	bias	sq_err
0	1	230.16	231.310652	0.405191	231.304725	231.562874	231.333915	231.297690	232.081416	231.051063	...	0.012188	23950.0	pressure (Pa)	ACARS_TEMPERATURE	75603	153005	2019-12-01 21:00:03	1.00	1.150652	1.324001
1	2	18.40	15.720527	0.630827	14.217207	15.558196	15.805599	16.594644	14.877743	16.334438	...	0.012188	23950.0	pressure (Pa)	ACARS_U_WIND_COMPONENT	75603	153005	2019-12-01 21:00:03	6.25	-2.679473	7.179578
2	3	1.60	-4.932073	0.825899	-5.270562	-5.955998	-4.209766	-5.105016	-4.669405	-4.365305	...	0.012188	23950.0	pressure (Pa)	ACARS_V_WIND_COMPONENT	75603	153005	2019-12-01 21:00:03	6.25	-6.532073	42.667980
3	4	264.16	264.060532	0.035584	264.107192	264.097270	264.073212	264.047718	264.074140	264.019895	...	0.010389	56260.0	pressure (Pa)	ACARS_TEMPERATURE	75603	153005	2019-12-01 21:00:03	1.00	-0.099468	0.009894
4	5	11.60	10.134115	0.063183	10.067956	10.078798	10.120263	10.084885	10.135112	10.140610	...	0.010389	56260.0	pressure (Pa)	ACARS_U_WIND_COMPONENT	75603	153005	2019-12-01 21:00:03	6.25	-1.465885	2.148818

5 rows × 97 columns

Find the numeber of assimilated (used) observations vs. possible observations by type

obs_seq.possible_vs_used(obs_seq.df)

	type	possible	used
0	ACARS_TEMPERATURE	175429	128040
1	ACARS_U_WIND_COMPONENT	176120	126946
2	ACARS_V_WIND_COMPONENT	176120	127834
3	AIRCRAFT_TEMPERATURE	21335	13663
4	AIRCRAFT_U_WIND_COMPONENT	21044	13694
5	AIRCRAFT_V_WIND_COMPONENT	21044	13642
6	AIRS_SPECIFIC_HUMIDITY	6781	0
7	AIRS_TEMPERATURE	19583	7901
8	GPSRO_REFRACTIVITY	81404	54626
9	LAND_SFC_ALTIMETER	21922	0
10	MARINE_SFC_ALTIMETER	9987	0
11	MARINE_SFC_SPECIFIC_HUMIDITY	4196	0
12	MARINE_SFC_TEMPERATURE	8646	0
13	MARINE_SFC_U_WIND_COMPONENT	8207	0
14	MARINE_SFC_V_WIND_COMPONENT	8207	0
15	RADIOSONDE_SPECIFIC_HUMIDITY	14272	0
16	RADIOSONDE_SURFACE_ALTIMETER	601	0
17	RADIOSONDE_TEMPERATURE	29275	22228
18	RADIOSONDE_U_WIND_COMPONENT	36214	27832
19	RADIOSONDE_V_WIND_COMPONENT	36214	27975
20	SAT_U_WIND_COMPONENT	107212	82507
21	SAT_V_WIND_COMPONENT	107212	82647

Example plotting

rank histogram

• Select only observations that were assimliated (QC === 0).
• plot the rank histogram

df_qc0 = obs_seq.select_by_dart_qc(obs_seq.df, 0) 
plots.plot_rank_histogram(df_qc0)

plot profile of RMSE and Bias

• Chose levels
• Select only observations that were assimliated (QC === 0).
• plot the profiles

hPalevels = [0.0, 100.0,  150.0, 200.0, 250.0, 300.0, 400.0, 500.0, 700, 850, 925, 1000]# float("inf")] # Pa?
plevels = [i * 100 for i in hPalevels]

df_qc0 = obs_seq.select_by_dart_qc(obs_seq.df, 0)  # only qc 0
df_profile, figrmse, figbias = plots.plot_profile(df_qc0, plevels)

Contributing

Contributions are welcome! If you have a feature request, bug report, or a suggestion, please open an issue on our GitHub repository.

License

DartLabPlot is released under the Apache License 2.0. For more details, see the LICENSE file in the root directory of this source tree or visit Apache License 2.0.