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[End-to-End Test Code Sprint] Add JSON, YAML, Python code for GDASApp end-to-end testing of AHI Himawari-8 satwinds #741
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CoryMartin-NOAA
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Adding satwinds from the Advanced Himawari Imager (AHI) from Himawari-8 to GDASApp end-to-end testing new files include: parm/atm/obs/config/satwind_ahi_h8.yaml: QC filter YAML for AHI Himawari-8 satwinds parm/ioda/bufr2ioda/bufr2ioda_satwind_amv_ahi.json: JSON containing data format, sensor, and satellite information for AHI Himawari-8 satwinds ush/ioda/bufr2ioda/bufr2ioda_satwind_amv_ahi.py: bufr2ioda code for extracting AHI Himawari-8 satwinds from BUFR modified files include: ush/ioda/bufr2ioda/run_bufr2ioda.py: added `"satwind_amv_ahi"` to list `BUFR_py` See #741 for details on testing. JEDI/GSI comparisons look good with GSI thinning turned off, but there are large ob-count disparities both before and after QC when comparing GSI+thinning to JEDI+thinning. The tested `Gaussian Thinning` filter is included in the YAML but commented out with a note. --------- Co-authored-by: Brett Hoover <bhoover@Orion-login-1.HPC.MsState.Edu> Co-authored-by: Cory Martin <cory.r.martin@noaa.gov>
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Adding satwinds from the Advanced Himawari Imager (AHI) from Himawari-8 to GDASApp end-to-end testing
new files include:
parm/atm/obs/config/satwind_ahi_h8.yaml: QC filter YAML for AHI Himawari-8 satwinds
parm/ioda/bufr2ioda/bufr2ioda_satwind_amv_ahi.json: JSON containing data format, sensor, and satellite information for AHI Himawari-8 satwinds
ush/ioda/bufr2ioda/bufr2ioda_satwind_amv_ahi.py: bufr2ioda code for extracting AHI Himawari-8 satwinds from BUFR
modified files include:
ush/ioda/bufr2ioda/run_bufr2ioda.py: added
"satwind_amv_ahi"
to listBUFR_py
End-to-End Test Results
Himawari-8 satwinds consist of 3 observation-types: (LW)IR (252), VIS (242), and both clear-sky and cloud-top WV (250), which are given the same ob-type but can be differentiated by their
windComputationMethod
values of 3 and 5 respectively. Clear-sky WV winds from AHI/Himawari-8 are not assimilated in GSI.No thinning results
With horizontal and vertical thinning turned off in GSI, all AHI/Himawari-8 satwinds are assimilated:
(LW)IR satwinds
There are 46877 IR satwinds in the JEDI dataset and 45092 in the GSI dataset - the discrepancy is almost certainly due to the exclusion of IR satwinds over land north of 20 degrees latitude in the GSI, where these observations are removed from the dataset entirely in read_satwnd.f90 and do not carry through to the diag file. When filtering to only compare assimilated IR satwinds, JEDI assimilates 27530 observations and GSI assimilates 27454, a difference of roughly 0.3%. Some of the differences appear along coastlines and could be due to differences in whether the underlying surface-type is defined as land or water:
Comparison of the observations, the HofX, and the difference (ob minus HofX) look good:
VIS satwinds
There are 29814 VIS satwinds for both JEDI and GSI, and both systems assimilate 8992 of them (so I will skip that plot). The rest of the comparisons look good:
(cloud-top) WV satwinds
All clear-sky WV satwinds are rejected in JEDI for compliance with GSI. There are 49867 satwinds in the combined cloud-top and clear-sky observation type for both systems, JEDI assimilates 35190 and GSI assimilates 35080, a differences of less than 0.5%:
Other tests look good:
Thinning Tests
TBD
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