Adding satwinds from the Visible Infrared Imaging Radiometer Suite (VIIRS) from SNPP/NOAA-20 to GDASApp end-to-end testing
new files include:
parm/atm/obs/config/satwind_viirs_npp.yaml.j2: QC filter YAML for VIIRS SNPP satwinds (jinja2 standard)
parm/atm/obs/config/satwind_viirs_n20.yaml.j2: QC filter YAML for VIIRS NOAA-20 satwinds (jinja2 standard)
parm/ioda/bufr2ioda/bufr2ioda_satwind_amv_viirs.json: JSON containing data format, sensor, and satellite information for VIIRS SNPP/NOAA-20 satwinds
ush/ioda/bufr2ioda/bufr2ioda_satwind_amv_viirs.py: bufr2ioda code for extracting VIIRS SNPP/NOAA-20 satwinds from BUFR
End-to-End Test Results
VIIRS satwinds consist of (LW)IR (type=260) from SNPP and NOAA-20 satellites that are tanked and dumped into BUFR subset NC005091. No thinning is applied to these tests in either GSI or JEDI by regular convention.
SNPP LW(IR) Satwinds (type=260, subtype=224)
There are 39240 SNPP VIIRS LW(IR) satwinds in the UFO test dataset and 39236 in the GSI. The outstanding 4 satwinds that appear in the UFO diag file but not in the GSI diag file are all at pressures less than 125 hPa and are rejected by a pressure-check filter. There are no VIIRS observations at pressures less than 125 hPa in the GSI diag file. There are 27529 satwinds assimilated in JEDI and 27560 in GSI, a difference of roughly 0.1%.
Accepted observations are distributed similarly between GSI and JEDI:
The windEastward and windNorthward values, their HofX values, and the OmB look good comparing GSI and JEDI:
Overall error comparisons between JEDI and GSI look good - there are a few outstanding differences where GSI assigns a higher error to an observation than JEDI, but these are infrequent and are likely due to differences in duplicate error inflation since GSI is processing all AMVs on a single processor and can identify duplicates across types while JEDI processes types on separate processors and the cross-type duplicates are invisible.
plan map distribution:
vertical profile:
NOAA-20 LW(IR) Satwinds (type=260, subtype=225)
There are 44849 NOAA-20 VIIRS LW(IR) satwinds in the UFO test dataset and 44847 in the GSI. The outstanding 2 satwinds that appear in the UFO diag file but not in the GSI diag file are all at pressures less than 125 hPa and are rejected by a pressure-check filter. There are no VIIRS observations at pressures less than 125 hPa in the GSI diag file. There are 31839 satwinds assimilated in JEDI and 31863 in GSI, a difference of roughly 0.07%.
Accepted observations are distributed similarly between GSI and JEDI:
The windEastward and windNorthward values, their HofX values, and the OmB look good comparing GSI and JEDI:
Overall error comparisons between JEDI and GSI look good - there are a few outstanding differences where GSI assigns a higher error to an observation than JEDI, but these are infrequent and are likely due to differences in duplicate error inflation since GSI is processing all AMVs on a single processor and can identify duplicates across types while JEDI processes types on separate processors and the cross-type duplicates are invisible.
Adding satwinds from the Visible Infrared Imaging Radiometer Suite (VIIRS) from SNPP/NOAA-20 to GDASApp end-to-end testing
new files include: parm/atm/obs/config/satwind_viirs_npp.yaml.j2: QC filter YAML for VIIRS SNPP satwinds (jinja2 standard) parm/atm/obs/config/satwind_viirs_n20.yaml.j2: QC filter YAML for VIIRS NOAA-20 satwinds (jinja2 standard) parm/ioda/bufr2ioda/bufr2ioda_satwind_amv_viirs.json: JSON containing data format, sensor, and satellite information for VIIRS SNPP/NOAA-20 satwinds ush/ioda/bufr2ioda/bufr2ioda_satwind_amv_viirs.py: bufr2ioda code for extracting VIIRS SNPP/NOAA-20 satwinds from BUFR
End-to-End Test Results
VIIRS satwinds consist of (LW)IR (type=260) from SNPP and NOAA-20 satellites that are tanked and dumped into BUFR subset NC005091. No thinning is applied to these tests in either GSI or JEDI by regular convention.
SNPP LW(IR) Satwinds (type=260, subtype=224)
There are 39240 SNPP VIIRS LW(IR) satwinds in the UFO test dataset and 39236 in the GSI. The outstanding 4 satwinds that appear in the UFO diag file but not in the GSI diag file are all at pressures less than 125 hPa and are rejected by a pressure-check filter. There are no VIIRS observations at pressures less than 125 hPa in the GSI diag file. There are 27529 satwinds assimilated in JEDI and 27560 in GSI, a difference of roughly 0.1%.
Accepted observations are distributed similarly between GSI and JEDI:
The
windEastwardandwindNorthwardvalues, their HofX values, and the OmB look good comparing GSI and JEDI:Overall error comparisons between JEDI and GSI look good - there are a few outstanding differences where GSI assigns a higher error to an observation than JEDI, but these are infrequent and are likely due to differences in duplicate error inflation since GSI is processing all AMVs on a single processor and can identify duplicates across types while JEDI processes types on separate processors and the cross-type duplicates are invisible.
plan map distribution:
vertical profile:
NOAA-20 LW(IR) Satwinds (type=260, subtype=225) There are 44849 NOAA-20 VIIRS LW(IR) satwinds in the UFO test dataset and 44847 in the GSI. The outstanding 2 satwinds that appear in the UFO diag file but not in the GSI diag file are all at pressures less than 125 hPa and are rejected by a pressure-check filter. There are no VIIRS observations at pressures less than 125 hPa in the GSI diag file. There are 31839 satwinds assimilated in JEDI and 31863 in GSI, a difference of roughly 0.07%.
Accepted observations are distributed similarly between GSI and JEDI:
The
windEastwardandwindNorthwardvalues, their HofX values, and the OmB look good comparing GSI and JEDI:Overall error comparisons between JEDI and GSI look good - there are a few outstanding differences where GSI assigns a higher error to an observation than JEDI, but these are infrequent and are likely due to differences in duplicate error inflation since GSI is processing all AMVs on a single processor and can identify duplicates across types while JEDI processes types on separate processors and the cross-type duplicates are invisible.
plan map distribution:
vertical profile:
