1. Real-Time Mesoscale Analysis (RTMA) - A First Step Towards an Analysis of Record Geoff DiMego, Ying Lin, Manuel Pondeca, Seung-Jae Lee, Wan-Shu Wu, David Parrish, and Stacie Bender Mesoscale Modeling Branch National Centers for Environmental Prediction Geoff.DiMego@noaa.gov 301-763-8000 ext 7221 NOAA Science Center - Room 205 5200 Auth Road Camp Springs, MD 20746-4304

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

4. I promise to try to avoid achieving this result.

5. Analysis of Record A comprehensive set of the best possible analyses of the atmosphere at high spatial and temporal resolution with particular attention placed on weather and climate conditions near the surface

6. Glahn-Livesey Verification Meeting: Need to Verify NDFD – Grid vs Analysis Insufficient density of obs for grid vs point verification of NDFD alone Need centrally produced “analysis of record” (DiMego’s application of a term used in FGGE) No funds available No 00 hr analysis in NDFD 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

7. Opinions/Concerns Expressed Cliff Mass (2003), Weather and Forecasting WR 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

8. First Steps Toward an AOR A Community Meeting on Real-time and Retrospective Mesoscale Objective Analysis Convened by NWS’ Brad Colman & UofUtah’s John Horel June 2004 in Boulder Strawman – 3DVar based 4DDA AOR program should develop and implement suite of consistent sensible weather analysis products using current and future technologies. Mesoscale Analysis Committee (MAC) established August 2004 by Director, NWS Office of Science and Technology

9. Robert Aune, NOAA/NESDIS University of Wisconsin Space Sciences and Engineering Center Stanley Benjamin, Forecast Systems Laboratory Craig Bishop, Naval Research Laboratory Keith A. Brewster, Center for Analysis and Prediction of Storms The University of Oklahoma Brad Colman (Committee Co-chair), NOAA/National Weather Service -- Seattle Christopher Daly, Spatial Climate Analysis Climate Service Oregon State University Geoff DiMego, NOAA/ National Weather Service National Centers for Environmental Prediction Joshua P. Hacker, National Center for Atmospheric Research John Horel (Committee Co-chair), Department of Meteorology, University of Utah Dongsoo Kim, National Climatic Data Center Steven Koch, Forecast Systems Laboratory Steven Lazarus, Florida Institute of Technology Jennifer Mahoney, Aviation Division Forecast Systems Laboratory Tim Owen, National Climatic Data Center John Roads, Scripps Institution of Oceanography David Sharp, NOAA/National Weather Service -- Melbourne Ex Officio: Andy Edman, Science & Technology Committee representative LeRoy Spayd, Meteorological Services Division representative Gary Carter, Office of Hydrology representative Kenneth Crawford, COOP/ISOS representative Mesoscale Analysis Committee (MAC)

10. Steps Toward an AOR Strategy  MAC Committee meeting in Silver Spring in October 2004 to define needs and development strategy for AOR  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

11. Three Phases of AOR Phase I – Real-time Mesoscale Analysis Hourly within 30 minutes Prototype for AOR  NCEP and FSL volunteer to build first phase  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

12. NWS’ Integrated Work Team (IWT) Lee Anderson (co-chair), OST PMB Brad Colman (co-chair), WFO SEA Fred Branski, OCIO Geoff DiMego, NCEP EMC Brian Gockel, OST MDL Dave Kitzmiller, OHD Chuck Kluepfel, OCWWS Performance Branch Art Thomas, OCWWS Al Wissman, OOS

13. Subsequent Context Admiral Lautenbacher “Observations alone are often meaningless without the actions that provide economic and societal benefit.” A comprehensive weather and climate observing system requires integration and synthesis of observations into gridded analyses of current and past states of the atmosphere NWS has critical need to produce real-time and retrospective analyses at high resolution to help create and verify gridded forecasts as part of NWS Digital Services Program

14. Societal Benefits Climate Variability and Change Ocean Resources Disasters Energy Health Agriculture Ecosystem Water Resources GEOSS - Creating a “System of Systems” Global Observing Systems GCOS GOOS GTOS WHYCOS World Weather IGBP IOOS CEOS IGOS Global Observing Systems GCOS GOOS GTOS WHYCOS World Weather IGBP IOOS CEOS IGOS National/Multinational Observing Systems Satellites Surface Obs. Radar Aircraft Ocean Observations Paleo-data National/Multinational Observing Systems Satellites Surface Obs. Radar Aircraft Ocean Observations Paleo-data Private Sector Observing Systems Satellites Mesonets Lightning Commercial Aircraft Private Sector Observing Systems Satellites Mesonets Lightning Commercial Aircraft 14 Proposed Ongoing Analysis of the Climate System/Analysis of Record P. Arkin Global Earth Observing System of Systems

15. 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

16. Phase I: The Real-Time Mesoscale Analysis (RTMA)

17. Real-Time Mesoscale Analysis The RTMA is a fast-track, proof-of-concept effort intended to: leverage and enhance existing analysis capabilities in order to generate experimental CONUS-scale hourly NDFD- matching analyses OPERATIONAL at NCEP and on AWIPS with OB7 by end of FY2006 establish a real-time process that delivers a sub-set of fields to allow preliminary comparisons to NDFD forecast grids Hourly temp, wind, moisture plus precip & sky on 5 km NDFD grid also provide estimates of analysis uncertainty establish benchmark for future AOR efforts build constituency for subsequent AOR development activities

18. RTMA Procedure Temperature & dew point at 2 m & wind at 10 m RUC forecast/analysis (13 km) is downscaled by FSL to 5 km NDFD grid Downscaled RUC used as first-guess in NCEP’s 2DVar analysis of ALL surface observations Estimate of analysis error/uncertainty Precipitation – NCEP Stage II analysis Sky cover – NESDIS GOES sounder effective cloud amount

19. RTMA Logistics Hourly within ~30 minutes 5 km NDFD grid in GRIB2 Operational at NCEP Q3 FY2006 Distribution of analyses and estimate of analysis error/uncertainty via AWIPS SBN as part of OB7 upgrade – end of FY2006 Archived at NCDC

20. Why not another solution? Overarching Constraints: No funding available – must rely on volunteered resources Need to generate centrally as part of NCEP operations RSAS, OI, SCM or Barnes – legacy techniques with known issues where obs density changes ADAS, LAPS or STMAS – built around non-NCEP environments, too long & costly to adapt, no expertise at NCEP (for O&M and evolution) Ensemble Kalman Filter – brand new technique (risk), does not scale to large number of obs, too long & costly to adapt, no expertise at NCEP

21. Why 2DVar solution? 2DVar is subset of NCEP’s more general 3DVar Grid- point Statistical Interpolation (GSI) Connected to state-of-the-art unified GSI development at NCEP / JCSDA 2DVar is already running in NAM (low risk) Anisotropy built into 2DVar provides way to restrict influence of obs based on Elevation (terrain height – NAM & ADAS in WR) Flow (wind) Air mass (potential temperature) 2DVar is more than fast enough to run overtop of hourly RUC in tight NCEP Production suite Can provide estimate of analysis error as well as “accuracy” via built-in cross-validation

23. RTMA Background/First Guess Hourly RUC 1 hr forecast (or analysis) Downscale from 13 km RUC to 5 km NDFD using simple techniques like SMARTINIT Ten (10) parameters currently provided Temperature at 2m Dew point and specific humidity at 2m Wind components at 10 m Surface pressure and elevation Wind gust at 10 m Cloud base Visibility at 2 m Primary contact: Stan Benjamin (FSL) Implemented by Geoff Manikin (EMC/NCEP)

24. Downscaled 2 m Temperature Original 13 kmDownscaled 5 km

25. Downscaled 10 m Wind Original 13 kmDownscaled 5 km

26. Terrain Grid Variants (issue?) MDLWRF SI

27. NCEP’s Gridpoint Statistical Interpolation (GSI) http://wwwt.emc.ncep.noaa.gov/gmb/treadon/gsi/documents/presentations/1st_gsi_orientation/ 1 st GSI User Orientation 4-5 January 2005

28. NCEP obtains full complement of observations Conventional through TOC Mesonets through MADIS (FSL) MesoWest will be critical alternate path to MADIS during AOR due to their ability to store and forward “old” data transmitted in bursts from some sites/networks (may have better QC as well)

29. ALL Surface Obs = 89126 total

30. SFCSHP+ADPSFC Obs = 10843

31. of which METAR Obs = 8543

32. All Mesonet Obs = 78283

33. of which AWS Obs = 35565

34. Mesonet Issues Mesonets comprise majority of obs but they are not as “good” as the other conventional sfc ob sources Winds not used in current RUC due to biases FSL has constructed a “Uselist” of acceptable networks based on overall siting strategies etc. Seung-Jae Lee (KMA visitor at NCEP) working with these mesonet data in GSI context

35. Seung-Jae Lee Projects Sensitivity to regional background error Understanding the characteristics of innovations (guess-obs) of the sfc mesonet data A complex forward operator

36. Mesonet data: QC NCEP currently honors quality markers provided by the FSL-MADIS. Bad quality marks can be put on the mesonet data based on the SDM ’ s reject-list. Subjective QC is too costly. NCEP will convert FSL ’ s use-list to reject-list. Some gross checks result in bad quality marks for data that are outside reasonable limits, data with mising lat/lon etc.

37. Background error treatment Old: Downscaled version of background error derived from NCEP global model New: background error computed from auto-covariances based on 8 km forecasts of WRF-NMM (W.-S. Wu 2005, personal communication)

38. Experimental design analysis use of mesonet data background error Note AN1 Noglobaloperation (control run) AN2 Yesglobalimpact of mesonet data AN3 Yes regional (WRF-NMM) impact of new regional background error statistics

39. Analysis increments (case 1) A -G < 0 over much of the domain

40. Analysis Increments (case 2) - Smaller and detailed str - Positive in the N-eastern region where Low is located 6 th -7 th : 920-900hPa

41. Analysis increments (case 3) Unstable sfc

42. Characteristics of innovations (observed – guess) of the near-surface data Long-term statistics: 1 month Scatter diagram of (O-G) during May 2005 for nighttime (0600 UTC), early morning western (1200 UTC), daytime eastern (1800 UTC) domains. Linear regression, bias, rmse etc.

43. Western domain (0600 UTC) Sfc mesonet T data have a considerable amount of outliers compared with other land sfc T data.  Bad obs or local effects Some stations can be seen to produce the same values regardless of model fcsts.

44. Many outliers This can mean quality markers placed on the data by the FSL-MADIS QC are of little value not only for wind but also T data.  Better QC is needed. Very few outliers in METAR  possibility of local effect Central domain (1200 UTC)

45. Eastern domain (1800 UTC) Slope and correlation coeff. of conventional surface land T data are good and similar in all three domains.

46. Motivation for Forward Model x : an N-component vector of analysis increment B : the N by N previous forecast-error covariance matrix O : the M by M observational (instrumental)-error covariance matrix F : the M by M representativeness (forward operator)-error covariance matrix H : a linear or nonlinear transformation operator (aka forward model) y : an M-component vector of observational residuals; that is, M : the number of degrees of freedom in the analysis; and N : the number of observations. Cost function

47. H, the forward model Converts the analysis variables to the observation type and location (May include variable transformations in case of, for example, radar or satellite data assimilations). The quality of simulated observation, the model state converted to “ observation units ”, depends on the accuracy of the forward operator: Inaccurate pseudo- observations give rise to errors in observation increments leading to bad analyses. The accuracy of analysis is dependent on the effectiveness of algorithm used to match obs with the background values

48. New Forward Model Uses a similarity theory based on Dyer and Hicks formula which has been adopted in model PBL parameterization such as surface layer process in MRF PBL scheme (Hong and Pan 1996). realized in the MM5-3DVAR system (Barker et al. 2004). It has been used for surface data assimilation in operational analysis systems and has proven to be encouraging in many contexts (e.g., Shin et al. 2002; Hwang 2005).

49. Number of sites with large innovations is reduced Old simple interpol. New fwd model

50. GSI: Nonlinear Quality Control  Next to be tried will be the GSI’s nonlinear QC procedure

51. Anisotropic covariance functions

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

53. NCEP GSI-2DVAR

54. Terrain-following auto-correlation function for temperature for a test point located at (x=360,y=420). Terrain shown as color contours. Units on x and y axis are “grid units.”

55. Example of 2DVar/RTMA analysis increment for temp Isotropic Anisotropic

56. Wind-following auto-correlation function for moisture for a test point located at (x=140,y=180)

57. Example of 2DVar/RTMA analysis increment for moisture Isotropic Anisotropic

58. 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

59. Obs Error Tuning results aircft3.63.29 asdar35.65 acars2.52.32 Metar3.51.58 sfcshp2.52.13 synop41.50 Nopsfc72.63 metar62.26 Wind oberr Temp oberr RH20%5.9% Psfc1.60.54 Ob type 180-189

60. blue red Impact of oberr tuning on forecasts 3 hr forecast RMS fit to obs at end of 12-hr cycle (cntl/exp) blue : positive: red : negative psfcqtu/v 1.71 1.84/1.71 7.74 8.49/7.74 2.11 2.25/2.114.19/4.19 1.04 1.12/1.04 12.94 13.37/12.94 2.16 2.18/2.16 4.03 4.05/4.03 1.37 1.68/1.37 12.17 12.41/12.17 2.07 2.12/2.07 4.11 4.18/4.11 1.39 1.75/1.39 10.82 11.33/10.82 2.04 2.12/2.04 4.29 4.36/4.29 1.28 1.50/1.28 13.23 13.79/13.23 1.99 2.04/1.99 4.50 4.52/4.50 1.58 1.75/1.58 12.21 12.63/12.21 2.07 2.15/2.07 4.24 4.31/4.24 1.46 1.45/1.46 13.50 13.76/13.50 2.08 2.10/2.08 4.64 4.74/4.64 1.50 1.72/1.50 11.55 11.72/11.55 2.13 2.15/2.13 4.31 4.29/4.31 1.39 1.65/1.39 11.52 11.64/11.52 2.07 2.13/2.07 4.14 4.24/4.14 1.28 1.60/1.28 12.58 13.03/12.58 2.19 2.21/2.19 4.61 4.58/4.61 1.25 1.69/1.25 11.98 12.54/11.98 2.18 2.22/2.18 4.69 4.72/4.69 1.12 1.35/1.12 12.71 13.24/12.71 2.16 2.19/2.16 4.48 4.43/4.48 1.32 1.69/1.32 11.75 12.10/11.75 2.05 2.11/2.05 4.76 4.81/4.76 1.20 1.20/1.20 12.15 13.53/12.15 1.99 2.13/1.99 4.50 4.61/4.50

61. 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:

62. Estimates of RTMA Analysis “Accuracy” - 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 ~10% of each type of obs for 1 st ~85% iterations Measure fit of solution to withheld obs Add obs back in for another ~35% iterations Plan to do this periodically (not routinely)

63. Evaluation plans (Nov-Jun) NCEP will establish webpage displaying 2DVar/RTMA NCEP will attempt to acquire gridded form of: ADAS from UofUtah / WR STMAS from FSL Steve Koch MatchOb from AWIPS Steve Lazarus (ADAS expert) to help with intercomparison of 2DVar with other analyses

64. 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

65. Hourly Gages Available for Stage II Precipitation Analysis

66. 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

67. Phase II: Analysis of Record Moving Beyond the Prototype RTMA Best possible analysis requires 4-Dimensional Data Assimilation Model to move info through space & time – WRF Analysis capable of using ALL observations – GSI More data collection time than RTMA to acquire all obs – 18-48 hours Should use obs precip to drive evolution of land-states lower boundary condition – EDAS/NDAS 4DVar might be feasible but only with MAJOR funding increase and essentially new computer hence $10M Three levels scoped out $2.5M (4DDA w/ PBL emphasis only), $4.5M (4DDA w/ 3DVar or equivalent, and $10M (4DVar or equivalent – will take years longer too) RTMA will remain in place to support near-real-time needs of forecaster, but support ($100K) needed for O&M and extra efforts to include all NDFD parameters

68. Phase II: Analysis of Record Moving Beyond the Prototype RTMA Support for an AOR suite of mesoscale analyses needs to be broadened (established!) Considerable research, development, and operational infrastructure (computing) required for complete suite of analysis products of sufficient accuracy to verify NDFD Initial steps underway to address NWS AOR funding requirements for FY 2008 and beyond, emphasizing critical role played by EMC/NCEP and partners (OSIP, PPBES, etc.) Additional funding sources for broader community efforts to develop next-generation AOR need to be identified