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: https://pypi.org/project/pydartdiags/
Documenation : https://ncar.github.io/pyDARTdiags/
We recommend installing pydartdiags in a virtual enviroment:
python3 -m venv dartdiags
source dartdiags/bin/activate
pip install pydartdiagsFor developers of pyDARTdiags, we recommend installing pyDARTdiags as a local project in “editable” mode in your virtual environment:
git clone https://github.com/NCAR/pyDARTdiags
python -m pip install -e pyDARTdiagsExample importing the obs_sequence and plots modules
from pydartdiags.obs_sequence import obs_sequence as obsq
from pydartdiags.plots import plotsExamining the dataframe
obs_seq = obsq.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
obsq.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 = obsq.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 = obsq.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
pyDARTdiags 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.