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Water Vapor Radiance Assimilation in the NCEP Global Data Assimilation System

Water Vapor Radiance Assimilation in the NCEP Global Data Assimilation System. James Jung Cooperative Institute for Meteorological Satellite Studies Jim.Jung@noaa.gov In collaboration with NCEP/EMC, NASA/GMAO, NESDIS/STAR, NESDIS/JPSS, CAWCR, etc. Outline. Recent Projects

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Water Vapor Radiance Assimilation in the NCEP Global Data Assimilation System

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  1. Water Vapor Radiance Assimilation in the NCEP Global Data Assimilation System James Jung Cooperative Institute for Meteorological Satellite Studies Jim.Jung@noaa.gov In collaboration with NCEP/EMC, NASA/GMAO, NESDIS/STAR, NESDIS/JPSS, CAWCR, etc.

  2. Outline • Recent Projects • Supersaturation removal • Using q instead of RH for background error • Addition to Baseline Observing System Experiments • Current Work • 2014 Global Data Assimilation System transition • Future Projects • Water Vapor Radiance Assimilation • Addition to Baseline Observing System Experiments

  3. Water Vapor Assimilation

  4. Background • May 2012 version of the GDAS/GFS • Hybrid (80 ensembles, T254) • T574, NCEP operational resolution • Summer and winter seasons • No changes to observations • Counts / fits differences due to atmosphere changes • Forecast scores verified against own analysis

  5. Background Supersaturation removal • NCEP Ticket #346 Supersaturation removal experiments • Subversion branch r29873 with updates • T574 hybrid (~May 2012 version ported to JIBB) • No changes to observations • Perturbations derived from the ensembles used RH • Namelist variable added (clip_supersaturation=.true.) • Factqmax=50.0 (penalize minimization for generating supersaturation)

  6. Backgroundrelative humidity vs specific humidity • NCEP ticket #338 Use q instead of RH perturbations for the moisture component in the ensembles. • Includes the changes from the supersaturation removal experiment. • RH background error derived from ensemble specific humidity • RH perturbations are computed from q only (Removes ΔT) • From Daryl Kleist • Namelist variable (q_hyb_ens=.true.)

  7. Effect on supersaturation counts Supersaturation counts before each outer loop from the control SUPERSAT RH COUNT,RMS= 770051 0.118376 SUPERSAT RH COUNT,RMS= 1494196 0.103566 SUPERSAT RH COUNT,RMS= 1585750 0.101427 Supersaturation counts before each outer loop from the experiment SUPERSAT RH COUNT,RMS= 0 0.00000 SUPERSAT RH COUNT,RMS= 235489 0.266668E-01 SUPERSAT RH COUNT,RMS= 160906 0.716671E-02 Supersaturation counts accumulate with each outer loop in the control. Counts and RMS are an order of magnitude higher in the control

  8. Latitude – Height Analysis DifferencesRelative Humidity 20120801 - 20120915 20130101 - 20130215 About equally less relative humidity in both experiments

  9. Latitude – Height Analysis DifferencesCloud Water 20120801 - 20120915 20130101 - 20130215 About equally less cloud water in both experiments

  10. Latitude – Height Analysis DifferencesGeopotential Height 20120801 - 20120915 20130101 - 20130215 Lower heights in the enkf_q experiment

  11. Latitude – Height Analysis DifferencesTemperature 20120801 - 20120915 20130101 - 20130215 Significantly colder over both poles

  12. Analysis DifferencesNear Surface Temperature 20120801 - 20120915 20130101 - 20130215 Significantly colder over both poles

  13. North Pole Rawinsonde Comparisons Black = control, green = experiment Courtesy A. Collard

  14. South Pole Rawinsonde Comparisons Black = control, green = experiment Courtesy A. Collard

  15. Analysis troposphere fit to rawinsondes Control: solid Experiment: dash Analysis: black 6-hr guess: red Experiment is generally cooler

  16. Analysis troposphere fit to rawinsondes Control: solid Experiment: dash Analysis: black 6-hr guess: red Experiment generally drier

  17. 12 & 36 hr forecast fit to rawinsondes Control: solid Experiment: dash 12-hr: black 36-hr: red Experiment remains cooler in troposphere but drifting back to control

  18. 12 & 36 hr forecast fit to rawinsondes Control: solid Experiment: dash 12-hr: black 36-hr: red Experiment remains drier

  19. Anomaly Correlations500 hPa Northern Hemisphere 20120801 - 20120915 20130101 - 20130215

  20. Anomaly Correlations500 hPa Southern Hemisphere 20120801 - 20120915 20130101 - 20130215

  21. Tropical Wind Vector RMSE 20120801 - 20120915 20130101 - 20130215

  22. Summary • Reduce supersaturation counts and RMS (wrt RH) by an order of magnitude • Troposphere - drier / stratosphere – wetter • Troposphere cooler, less drift in stratosphere temperature • Less clouds at upper levels • Convergence and penalty marginally worse (factqmax) • Benchmarks / scores are mixed to postitive. • ENKF_Q better than SUPERSAT.

  23. Addition to baseline Observing System Experiments

  24. Background • May 2012 version of the GDAS/GFS • Hybrid (80 ensembles, T254) • T574, NCEP operational resolution • Summer and winter seasons • Baseline is conventional data and GPS-RO • Add single instruments • ATMS (SNPP) • AMSUA, MHS (NOAA-19) • AIRS (Aqua) • Verified against a control analysis with all operational data (including NOAA-19, SNPP, and Aqua)

  25. Latitude – HeightAnalysis Differences All_Data SNPP-ATMS – All_Data Aqua-AIRS – All_Data N19-AMSU/MHS – All_Data Base – All_Data Relative Humidity 00Z 20120801 - 20120920

  26. Latitude – HeightAnalysis Differences All_Data SNPP-ATMS – All_Data Aqua-AIRS – All_Data N19-AMSU/MHS – All_Data Base – All_Data Cloud Water 00Z 20120801 - 20120920

  27. Latitude – HeightAnalysis Differences All_Data SNPP-ATMS – All_Data Aqua-AIRS – All_Data N19-AMSU/MHS – All_Data Base – All_Data Temperature 00Z 20120801 - 20120920

  28. 500 hPa AC scores for 00Z 20120801-20120930 Base SNPP-ATMS All_DataAqua-AIRS N19-AMSU/MHS Base SNPP-ATMS All_DataAqua-AIRS N19-AMSU/MHS Northern Hemisphere Southern Hemisphere

  29. 1000 hPa AC scores for 00Z 20120801 – 20120930 Base SNPP-ATMS All_DataAqua-AIRS N19-AMSU/MHS Base SNPP-ATMS All_DataAqua-AIRS N19-AMSU/MHS Northern Hemisphere Southern Hemisphere

  30. Tropical Vector Wind RMSE for 00Z 20120801 - 20120930 200 hPa 850 hPa Base SNPP-ATMS All_DataAqua-AIRS N19-AMSU/MHS Base SNPP-ATMS All_DataAqua-AIRS N19-AMSU/MHS

  31. Hurricane Statistics20120801 - 20120915

  32. Summary(WRT the control) • Increased cloud water in the tropics. • Baseline has the most clouds • Higher geopotential heights at upper levels. • Baseline and SNPP-ATMS are the highest • Greater RH in Southern Hemisphere upper troposphere • Except for Aqua-AIRS • Anomaly correlation scores: • SNPP-ATMS and Aqua-AIRS are generally equal • N19-AMSU/MHS is slightly lower • Tropical wind vector RMSE: • Aqua-AIRS is best (first 24 hours) • Baseline worst throughout • Aqua-AIRS generally best hurricane stats

  33. Future Projects

  34. Water Vapor Radiance Assimilation • Build from previous work. Control will including these namelist changes: • clip_supersaturation=.true. • factqmax=50.0 • q_hyb_ens=.true. • Due to all of the recent changes in both the analysis and forecast model, a review of the QC procedures for MW and IR water vapor channels currently used by GDAS is in order. • Adjust gross error check • Adjust assimilation weights • Review water vapor channel selection for AIRS, IASI, and CrIS. • Remove AIRS stratospheric channels ( ~11) • Add tropospheric channels for IASI and CrIS. • Two season impact tests. • Operations resolution (T1534?) • Wiki page for progress updates. • Ticket #394 • Branch jung_wv_chans

  35. Observing System ExperimentsData Additions • Control • Lower resolution semi-Lagrangian (T670?) • All available data • Baseline • Conventional data only (unless unstable) • Experiments • AIRS • IASI • CrIS • If time permits • ATMS • SSMIS

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