FY08-10 GOES-R3 Project Proposal Title Page - PowerPoint PPT Presentation

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FY08-10 GOES-R3 Project Proposal Title Page

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  1. FY08-10 GOES-R3 Project Proposal Title Page • Title: GOES imager assimilation in GSI cloud analysis for Rapid Refresh • Status: Last year of 3-year project • Lead: • Stan Benjamin, NOAA/ESRL • Other Participants: • Steve Weygandt, NOAA/ESRL - presenting • Ming Hu, NOAA/ESRL • John Derber, NCEP/JCSDA

  2. Project Summary • Develop techniques to improve initialization of 3-d multi-species hydrometeor fields within GSI via assimilation of GOES imagery data. • Develop code to read in GOES imager data into GSI from NCEP prepBUFR • Test and refine cloud analysis technique using GOES imager data within GSI • Write plan for assimilation into sub-hourly imager data in GSI variational framework (jointly between ESRL/GSD and EMC)

  3. Methodology • NWP Models, Assimilation System, and Data Sets • NWP model - WRF configuration for Rapid Refresh (ARW core, Thompson microphysics (explicit forecasts of cloud water, ice, rain water, snow, graupel) • GSI for data assimilation • Build from initial cloud analysis module in GSI, built from previous software for GOES cloud-top product assimilation in the RUC (along with METAR cloud/visibiiity, 3-d radar reflectivity, lightning) • GOES imager data - visible, IR, near-IR • Exploratory Research • Develop software to calculate observation-background (O-B) differences between model background (1-h Rapid Refresh forecasts) and GOES imagery fields

  4. Key Progress in last 12 months • Addition of non-variational cloud-hydrometeor analysis into GSI for application to NCEP Rapid Refresh (replacing RUC in early FY11). • Assimilation of GOES cloud product including liquid and ice water path (via NASA Langley retrieval) as part of hourly-updated Rapid Refresh • Initial design and testing of cloud data as RH observations in fully variational scheme

  5. RUC to Rapid Refresh (RR) Real-time Rapid Refresh domain Current RUC-13 CONUS domain Cloud/hydrometeor analysis in GSI • CONUS domain (13km) • North American • domain (13km) • RUC model • WRF model • (RR version) • (ARW dynamic core) • RUC 3DVAR • GSI • (+ RR enhancements) • Cloud Analysis

  6. Cloud Analysis in GSI for RR • Gridpoint Statistical Interpolation (GSI) • NCEP operational data analysis • GFS, NAM, RTMA, Rapid Refresh • Add RUC-like features in GSI for designed for aviation and severe storm application • Cloud analysis (satellite, METAR, radar obs) • RUC-design modifications for surface assimilation • Assimilation of radar and lightning data (generate latent heating tendency for application in model DFI)

  7. Flow for RUC/RR hydrometeor assimilation Sat cloud-top Pres/temp/path METAR cloud/visib /current weather Radar 3-d reflectivity Lightning 1h fcst Qv, qc, qi • Force interconsistency between ob types • 3-d spatial mapping of Y/N/unknown (from obs) • for cloud hydrometeors, precip hydrometeors Analysis – RUC-3dvar or GSI-3dvar 3-d latent heating from radar + lightning (extendable to sat-estim precip or growing cumulus (“convective initiation”) Modified Qv, qc, qi 2-d convective Inhibition field Model – RUC model or WRF Application within model pre-forecast DFI Forecast integration

  8. RR/GSI vs. RUC 3h forecast of 3000’ ceiling (MVFR flight conditions)-probability of detection – verification against METAR observations over US every 3 h Introduction of new version of GSI hydrometeor analysis 2004 2005 2006 2007 2008 2009

  9. use of NASA Langley GOES cloud data in RR/GSI • introduced Nov 2009 • LaRC – uses 3.9, 10.8, 12.0 μchannels + 0.65 (daytime) Cloud Top Height Analysis RR with NASA data - over full RR domain RR with NESDIS data- only over RUC domain

  10. Real-time comparison of cloud analysis impact • Optimize and calibrate RR cloud analysis • compare results to RUC, fix deep details in algorithm • calibration period: Oct. 24 - Nov. 9, 2009 • Sync two parallel RR cycle running at GSD: • oprRR, devRR: 1-h partial cycling, GSI+cloud analysis • Turned off cloud analysis in devRR for 4-13 Jan 2010 • oprRR: GSI + cloud analysis • devRR: GSI only (NO cloud analysis)

  11. Cloud Analysis Verification: PODy 500 ft. ceiling -- LIFR oprRR:with cloud analysis devRR: no cloud analysis after Jan 4, 2010 Analysis 1h fcst 3h fcst 9h fcst 6h fcst 12h fcst

  12. 17z 21 Jan 2010 Rapid Refresh Cloud top height Hourly assimilation of GOES cloud-top pressure/temp, retrieved liquid/ice water path (from NASA LaRC)

  13. 17z 21 Jan 2010 Rapid Refresh Outgoing LW rad Hourly assimilation of GOES cloud-top pressure/temp, retrieved liquid/ice water path (from NASA LaRC)

  14. 17z 21 Jan 2010 Rapid Refresh reflectivity Hourly assimilation of GOES cloud-top pressure/temp, retrieved liquid/ice water path (from NASA LaRC)

  15. 17z 21 Jan 2010 Rapid Refresh Cloud top height Hourly assimilation of GOES cloud-top pressure/temp, retrieved liquid/ice water path (from NASA LaRC)

  16. Variational cloud analysis – initial steps • Step 1 - Create forward model for cloud obs • Start with METAR cloud observations • Forward model • Set RH=1.0 (saturation) observation just above cloud base. • Create subsaturation observation at model level below cloud base. • Two observations for each METAR cloud observation • Step 2 - Observation error • Set same as for METAR in-situ observation error (0.05) • Step 3 – Test environment • Use development RUC cycle run at ESRL/GSD. • Run from Feb - June 2010 in real-time cycle.

  17. Init cldRHvar technique Revised cldRHvar technique Higher is better for TSS (Total Skill Score)

  18. Init cldRHvar technique Revised cldRHvar technique Lower is better for rms error (vs. raobs)

  19. Variational cloud analysis (cloud-RH-var) – initial steps • Next steps • Study result to eliminate higher RH forecast error • Current treatment – biased toward cloudy/saturation conditions since forward model applied only for METARs with BKN/OVC conditions • Determine why ceiling forecasts are improved • Add GOES-based cloud-top and above-cloud-top RH obs using similar design • Apply to GSI in Rapid Refresh environment • Expand cloud/moisture variational analysis design applicable to various time scales (3h12h3day range) • meet requirement for improved very short-range ceiling forecasts for aviation and energy requirements (improve over those currently shown by the RUC)

  20. Plan: Variational analysis of clouds • Possibilities of analysis control variables • Total water content (qc + qi + qr + qs + qg + qv) • Normalized RH w.r.t. total water but not precip condensate = ((qv + qc + qi) / qs(bkg)) • Corresponds to RH* (Holm) for water vapor – (qv / qs(bkg)) • Initial step for Mar-Jun 2010 with RUC-dev • Condensate only (qc + qi + qr + qs + qg) • Cloud condensate (qc + qi) and precipitation condensate (qr + qs + qg) • Cloud condensate (qc + qi) • Binary cloud indicator (Y/N) • Cloud fraction (0-1) (related to qc, qi; but not to qr, qs, qg)

  21. Project Outcomes • Improved short-range cloud-top and cloud-base (ceiling) forecasts for Rapid Refresh • Improved surface radiation short-range forecasts from improved initial cloud fields (not yet validated but expected to be shown from future energy-related/DOE project) • Initial GOES imager assimilation capability within GSI for improved initial hydrometeor fields for other NCEP models using GSI • Plan under development for direct variational assimilation of GOES cloud imager data within GSI • Initial RUC-based experiments using cloud-RH-var forward model show promise. • GSI with hydrometeor analysis using GOES imager-based cloud data planned for implementation with initial Rapid Refresh in Q2 FY11.

  22. 5. Summary of Previous Results • Revised GSI cloud analysis code in testing now at ESRL/GSD and at NCEP/EMC in experimental hourly-updated Rapid Refresh • Revisions to operational RUC cloud analysis for • assimilation of 3-d radar reflectivity (17 Nov 2008) • improved use of GOES and METAR cloud data and retention (17 Nov 08, 31 March 09). • 2002 - Initial GOES-cloudtop product based cloud analysis introduced to operational RUC (using previous 1h forecast as background) • 2005 - Major revision to RUC cloud analysis to add assimilation of METAR multi-level cloud and visibility observations