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Assimilation of Shortwave IR Data for Global NWP

This study examines the potential impact of assimilating shortwave IR data from hyperspectral sounders on global numerical weather prediction models. The findings suggest that incorporating shortwave IR data can enhance the accuracy and reliability of the global observation system.

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Assimilation of Shortwave IR Data for Global NWP

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  1. Satellite and Information Service 26 Sep 2019 Assimilation of shortwave IR data from hyperspectral sounders in NOAA global NWP Kevin Garrett NOAA/NESDIS Center for Satellite Applications and Research Erin Jones, Yingtao Ma, Kayo Ide (University of Maryland/CISESS) Chris Barnet (STC) Sid Boukabara (NOAA/NESDIS/STAR) Fall 2019 NASA Sounder Science Team Meeting, College Park, Maryland

  2. Motivation for Assimilating SWIR data • Cubesat technology supports an alternative/cost effective/agile constellation of shortwave/midwave IR (SW/MWIR) sensors to provide temperature and water vapor profile information to numerical weather prediction (NWP). • A SWIR constellation could compliment LWIR sounders and add robustness to the global observation system • Shortwave IR data (4 µm CO2 band), however, is not operationally assimilated in NWP models (LWIR only) • To help shape future satellite global observing system architecture, can a SWIR-only solution achieve or exceed positive impact provided by LWIR radiances in medium-range, global NWP? • Additional operational outcomes- • Extend NOAA global NWP to assimilation SWIR data (CrIS/IASI) • Improved radiance forward operator for SWIR (CRTM) • Does SWIR bring additional positive impact to LWIR assimilation? IASI AIRS CrIS NCEP Ensemble Forecast Sensitivity to Observations Impact (EFSOI) showing reduction in 24-hour forecast errors per observation type. [Mahajan et al, 2017]

  3. Current NWP Operational State for SWIR NOAA Global FV3GFS uses the Gridpoint Statistical Interpolation (GSI) with 4DEnVar data assimilation CrIS NPP 2380 cm-1 QC Flags CrIS NPP 690 cm-1 QC Flags Gross check • GSI ingests CrIS FSR 431 channel subset • 92 LWIR channels assimilated (642 – 1095 cm-1), 8 MWIR • SWIR channels are monitored only, with strict QC • Similar status for IASI and AIRS (using channel subsets) Cloud Cloud Daytime obs over ocean Current GSI Quality Control flags for a Longwave IR channel (left) and Shortwave IR Channel (right)

  4. Outlook on SWIR assimilation SWIR channels show similar information content as LWIR temperature (CO2) channels. • Data assimilation system enhancements needed to enable SWIR: • Forward operator, Community Radiative Transfer Model (CRTM) • Modeling of BRDF • NLTE correction • Cloud detection scheme • SWIR channels in place of LWIR • New observation errors • From fixed 1 K to scene-dependent • Overhaul quality control based on above • Activate 52 SWIR channels (2380 – 2507 cm-1) • Disable 92 LWIR channels CrIS LW Kernel Functions CrIS SW Kernel Functions Temperature (LW) Sounding Channels (SW) Courtesy Chris Barnet ~65 dof for LW (black) ~35 dof for SW (red) ~35 dof for LW (690-790 cm-1, black dots)

  5. Forward operator enhancement: NLTE Bias, O-A_ecmwf Bias, O-A_ecmwf NLTE Correction After Fix NLTE Correction Before Fix • Non-Local Thermodynamic Equilibrium (NLTE): Assess if the correction for NLTE in CRTM is adequate to improve modeling of affected channels for data assimilation • Only a few channels in the 431 subset are affected by NLTE • However the NLTE correction in CRTM is negatively affecting channels not impacted by NLTE NLTE NLTE Correction On NLTE Correction Off NLTE Correction On NLTE Correction Off Inter-channel correlation Inter-channel correlation STD, O-A_ecmwf STD, O-A_ecmwf NLTE Correction On NLTE Correction Off NLTE Correction On NLTE Correction Off Over-correction Disable NLTE between 2390 and 2400 cm-1

  6. Forward operator enhancement: BRDF BRDF performs well for most SWIR surface channels except for sun glint correction. • QC: Do not assimilate scenes affected by sun glint using glint angle threshold BRDF On BRDF Off BRDF Correction reduces bias in SW surface channels NLTE

  7. SWIR cloud detection for clear-sky radiances Based on (Eyre and Menzel, 1988), The scheme is to find cloud top height and that minimizes GSI uses the tangent linear approach. Cloud Fraction using LW Cloud Fraction using SW Number of Observations Number of Observations SWIR-based cloud detection allows more SWIR observations to pass cloud check – likely due to increased vertical resolution. Wavenumber (cm-1) Wavenumber (cm-1)

  8. Scene-Dependent Observation Errors Non-linearity in the Planck function -> noise dependent on scene temperature 690 cm-1Obs Error SWIR has strong scene sensitivity ~Constant 2380 cm-1Obs Error LWIR, is very insensitive Courtesy Chris Barnet Observation error scaled from error at reference scene temperature using derivative of Planck function Scene-Dep

  9. Observing System Experiment setup • Use current operational FV3GFS 4DEnVar at C384 (~25 km) • Experiments run 2018-12-01 to 2019-01-31 • Observations assimilated per table; AIRS/IASI turned off to enhance signal of CrIS LW and SW impact • Verified against Baseline analysis • Verification is using analyses/forecasts from 2019-12-08-2019-12-18 so far…

  10. Observation minus Background/Analysis Improved SWIR QC and forward model show increased number of observations assimilated, and reduced O-B/O-A statistics LW Region Data Count SW Region Data Count

  11. Background fit to ATMS sounding channels No significant impact on O-B statistics for other sounders (ATMS). (i.e. 6-hour background field has not been degraded) x Control + SW Exp Significance intervals

  12. Temperature analysis differences/forecast impact SW – LW Temp Anl. 200 hPa Temperature RMSE (GL) 200 hPa Temperature Bias (GL) 200 hPa Worse Better 500 hPa Forecast Hour Forecast Hour Small global 200 hPatemperature analysis differences Small differences in 200 hPa temperature forecast at all times 850 hPa

  13. Temperature analysis differences/forecast impact SW – LW Temp Anl. SWIR assimilation shows bias in 850 hPa bias in SH Significant bias in 850 hPa forecast but neutral RMSE 200 hPa 850hPa Temperature RMSE (SH) 850hPa Temperature Bias (SH) 500 hPa Worse Better Significance intervals 850 hPa Forecast Hour Forecast Hour

  14. Forecast impact on height anomaly correlation Height AC, 500 hPa, NH Height AC, 500 hPa, SH No degradation in Anomaly Correlation with SW IR enabled Northern Hemisphere Southern Hemisphere Better Significance intervals Worse Better Worse Forecast Hour Forecast Hour 500 mb Anomaly Correlation as a function of forecast hour (top). Change in AC score relative to Baseline (bottom).

  15. Forecast impact on tropical winds Wind RMSE, 200 hPa Wind RMSE, 850 hPa No degradation in RMSE with SW IR enabled Tropics Tropics Worse Significance intervals Better Worse Better Forecast Hour Forecast Hour 200 and 850 hPa Tropical Wind RMSE as a function of forecast hour (top). Change in AC score relative to Baseline (bottom).

  16. Conclusions Acknowledgement: This project is funded by the NESDIS/OPPA Technology Maturation Program • Short forecast runs demonstrate so far that SWIR can maintain skill of LWIR • Forecast experiments continue to run and are being accelerated to complete by end of September • Next steps: • Evaluating more analysis and forecast metrics • Including cumulative statistics • Run experiment assimilating both SW and LW • Working on improved NLTE to assimilate CrIS FSR 2211 channel set

  17. Acknowledgements • These projects are funded by the NESDIS/OPPA Technology Maturation Program

  18. Impact of new quality control Observed - Background (K) 2380 cm-1 No QC w/ QC QC flag Observed - Background (K) 2390 cm-1 w/ QC No QC QC flag w/ QC

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