1. 1 RUA Relevant Excerpts From & Extensions Of: Real-Time Mesoscale Analysis (RTMA) – A First Step Towards an Analysis of Record first presented 5 October 2005 Geoff DiMego, Ying Lin, Manuel Pondeca, Geoff Manikin, Seung-Jae Lee, Wan-Shu Wu, David Parrish Mesoscale Modeling Branch National Centers for Environmental Prediction Stan Benjamin Forecast Systems Laboratory (aka GSD) NOAA Science Center - Room 205 5200 Auth Road Camp Springs, MD 20746-4304

2. 2 Topics Background Three Phase Plan Phase I: RTMA Whys + wherefores RUC first guess 2DVar / GSI Precip Sky Future Phases

3. 3 Glahn-Livesey Verification Meeting 10/2002 Need to Verify NDFD – Grid vs Analysis Insufficient density of obs for grid vs point verification of NDFD alone No 00 hr analysis in NDFD Need centrally produced “analysis of record” [AOR] (DiMego’s application of a term used in FGGE) No funds available National Digital Forecast Database (NDFD) A national database of digital weather forecast information Designed to meet the basic weather information needs of industry, media, commercial weather services, academia, and the public 3 hour to 7 day lead time

4. 4 Opinions and Concerns Expressed Cliff Mass (2003), Weather and Forecasting August 2003 NWS’ Western Region SOO/DOH IFPS White Paper provided recommendations: Develop a national real-time, gridded verification system Provide full-resolution NCEP model grids Produce objective, bias-corrected model grids for WFO use Implement methods to objectively downscale forecast grids Incorporate climatology grids into the GFE process Deliver short and medium-range ensemble grids Produce NDFD-matching gridded MOS Modify the GFE software to ingest real-time data Optimize ways to tap forecaster expertise

5. 5 First Steps Toward an AOR A Community Meeting on Real-time and Retrospective Mesoscale Objective Analysis Convened by NWS’ Seattle SOO Brad Colman and University of Utah’s John Horel June 2004 in Boulder AOR program should develop and implement suite of consistent sensible weather analysis products using current and future technologies. Mesoscale Analysis Committee (MAC) was established August 2004 by Jack Hayes Director, NWS Office of Science and Technology No funds available

6. 6 Next Steps Toward an AOR Strategy  MAC Committee meeting in Silver Spring in October 2004 to define needs and development strategy for AOR  Three distinct requirements become clear:  Real-time for forecasters – hourly within ~30 min  Best analysis for verification – time is no object  Long-term history for local climatology  Little or no funds available

7. 7 Three Phases of AOR Phase I – Real-Time Mesoscale Analysis [RTMA] Hourly within ~30 minutes Prototype for AOR NCEP/EMC and GSD volunteer to build first phase: NCEP/EMC’s Stage II National Precipitation Analysis NCEP/EMC 2D-Var of 2m Temperature, 2m Dew Point, 10 m wind, and surface pressure plus analysis uncertainty GSD provide downscaled (RUC 13 –> NDFD 5) first guess NESDIS provide GOES-based Equivalent Cloud Amount Phase II – Analysis of Record Best analysis possible Time is no object Phase III – Reanalysis Apply mature AOR retrospectively 30 year time history of AORs

8. 8 Diverse Needs For AOR Forecaster situational awareness, gridded forecast preparation and verification Aviation and surface transportation Environmental issues from the coastal zone to fire management Mesoscale modeling (AOR must be 3D) Homeland security Dispersion modeling for transport of hazardous materials and pollutants Impacts of climate change on a regional scale

9. 9 Terrain Grid Variations (issue? … yup!) MDLWRF SI

10. Anisotropic covariance functions 10 Error Correlations for Valley Ob (SLC) Location Plotted Over Utah Topography Isotropic Correlation: obs' influence extends up mountain slope Anisotropic Correlation: obs' influence restricted to areas of similar elevation

11. 11 Estimation of Observation Error Wu, W.-S.,, and S.-W. Joo, 1996: The change of the observational errors in the NCEP SSI analysis. Pp84-85 in Proceedings of the 11th Conference on Numerical Weather Prediction, 19-23 August 1996, Norfolk, Virginia. Desroziers, G., and S. Ivanov, 2001: Diagnosis and adaptive tuning of observation-error parameters in a variational assimilation. Q. J. R. Meteorol. Soc., 127, 1433-1452

12. 12 Estimates of RTMA Analysis Error/Uncertainty Reflect Obs density, Obs quality and Background quality Not direct from GSI but it will be possible to estimate it:

13. 13 Estimate of RTMA Analysis “Accuracy” via Cross-validation NWP data assimilation gauges quality of initial conditions via model forecast skill Cross-validation is really only way: Withhold observations from analysis Measure ability of analysis system to reproduce their values GSI will have Cross-Validation built in Withhold ~5-10% of each type of obs for 1 st ~65% iterations Measure fit of solution to withheld obs Add obs back in for another ~35% iterations Plan to do this periodically (not routinely)

14. NCEP RTMA Precipitation Analysis NCEP Stage II (real-time) and Stage IV (delayed) precipitation analyses are produced on the 4-km Hydrologic Rainfall Analysis Project grid The existing multi-sensor (gauge and radar) Stage II precipitation analysis available 35 minutes past the hour RTMA is mapped to the 5 km NDFD grid and converted to GRIB2 Upgrade plan including OHD analysis + improved gauge QC from FSL Primary contact: Ying Lin, NCEP/EMC http://wwwt.emc.ncep.noaa.gov/mmb/ylin/pcpanl/ ORIGINALNDFD GRIB2

15. Derived ECA from GOES-12 ECA from GRIB2 file – 5km grid GOES-12 IR image (11um) Effective Cloud Amount (ECA, %) Derived from GOES sounder Mapped onto 5-km NDFD grid Converted to GRIB2 for NDGD Contact: Robert Aune, Advanced Satellite Products Branch, NESDIS (Madison, WI) (b) (a) (c) Sky Cover: Effective Cloud Amount

16. Implementation Chronology - 1 Aug. 2006 – Initial RTMA 5 km CONUS, analyses of T & Td at 2m, pressure at surface and wind at 10 m along with estimate of analysis ‘error’, uselist from GSD, distributed with AWIPS build OB7.2 and on NDGD site, archived at NCDC, clearing all OSIP barriers … Aug. 2006 16

17. June 2007 – tune & sharpen background error covariances, add coastline escarpment June 2007 Dec. 2007 – GEFS uses RTMA to downscale to 5km April 2008 – Add 5.9 km Alaska Oct. 2008 – Add 2.5 km Hawaii & 2.5km Puerto Rico 17 Implementation Chronology - 2

18. Sep. 2010 – Add 2.5km for Guam and 2.5km for CONUS, realign Hawaii, FGAT, use scat & sat winds, broaden obs window Sep. 2010 Oct. 2011 – RTMA declared operational Oct. 2011 Oct. 2011 – NAM/NDAS uses RTMA’s mesonet qc lists Aug. 2012 – SREF uses RTMA to downscale / bias correct 2m T to 5km 18 Implementation Chronology - 3

19. Jan. 2014 – Added visibility & 10m wind gust, add 3km for Alaska, add URMA, stage IV precip, cross-validation for CONUS, mesonet QC lists: diurnal reject for T & Td and dir-dep accept for wind Jan. 2014 April 2015 – Added sky cover, HRRR & NAM-nest as background, improved QC, improved URMA 6hr precip April 2015 19 Implementation Chronology - 4

20. 20 Moving Beyond the ‘Prototype’ RTMA Phase II Needs for AOR somewhat met by URMA Will be fully met by an RUA Phase III Once done with Blender upgrades (Q1FY2016) Perform ~3 year reanalysis for Blender calibration using archive of HRRR & NAM nest Perform 30 year reanalysis supported by USFS using downscaled NARR & CFSRR These should be considered simply 1 st iteration and should be repeated with mature RUA Still few funds available – massive & creative leveraging necessary … but I’m not bitter …