1. Real Time Mesoscale Analysis John Horel Department of Meteorology University of Utah john.horel@utah.edu RTMA Temperature 1500 UTC 14 March 2008

2. Acknowledgements –Dan Tyndall (Univ. of Utah) –Dave Myrick (WRH/SSD) –Manuel Pondeca (NCEP/EMC) References –Benjamin, S., J. M. Brown, G. Manikin, and G. Mann, 2007: The RTMA background – hourly downscaling of RUC data to 5-km detail. Preprints, 22 nd Conf. on WAF/18 th Conf. on NWP, Park City, UT, Amer. Meteor. Soc., 4A.6. –De Pondeca, M., and Coauthors, 2007: The status of the Real Time Mesoscale Analysis at NCEP. Preprints, 22 nd Conf. on WAF/18 th Conf. on NWP, Park City, UT, Amer. Meteor. Soc., 4A.5. –Horel, J., and B. Colman, 2005: Real-time and retrospective mesoscale objective analyses. Bull. Amer. Meteor. Soc., 86, 1477-1480. –Tyndall, D., 2008: Sensitivity of Surface Temperature Analyses to Specification of Background and Observation Error Covariances. M.S. Thesis. U/Utah

3. An Analysis of Record Forecasters have needs for higher resolution analyses than currently available –Localized weather forecasting –Gridded forecast verification –Climatological applications AOR program established in 2004 –Three phases 1.Real Time Mesoscale Analysis 2.Delayed analysis: Phase II 3.Retrospective reanalysis: Phase III

4. Real-Time Mesoscale Analysis (RTMA) Fast-track, proof-of-concept intended to: –Enhance existing analysis capabilities at the NWS and generate near real-time hourly analyses of surface observations on domains matching the NDFD grids. –Background errors can be defined using characteristics of background fields (terrain, potential temperature, wind, etc.) –Provide estimates of analysis uncertainty Developed at NCEP, ESRL, and NESDIS –Implemented in August 2006 for CONUS (and southernmost Canada) & recently for Alaska, Guam, Puerto Rico –Analyzed parameters: 2-m T, 2-m q, 2-m Td, sfc pressure, 10-m winds, precipitation, and effective cloud amount –5 km resolution for CONUS with plans for 2.5 km resolution

5. More Info… www.meted.ucar.edu

6. The Real-Time Mesoscale Analysis Several layers of quality control for surface observations Two dimensional variational surface analysis (2D- Var) using recursive filters Utilizes NCEP’s Gridpoint Statistical Interpolation software (GSI) Uses various surface observations and satellite winds –METAR, PUBLIC, RAWS, other mesonets –SSM/I and QuikSCAT satellite winds Analysis became available in 2006, and is being evaluated by WFOs and several universities

7. The actual ABCs… The RTMA analysis equation looks like: Covariances are error correlation measures between all pairs of gridpoints Background error covariance matrix can be extremely large –2,900 GB memory requirement for continental scale –Recursive filters significantly reduce this demand

9. RTMA Domains CONUSAlaskaHawaiiPuerto RicoGuam Resolution5-km~6-km2.5-km Background (downscaled) RUCNAM GFS Precip NCEP Stage II analysis No Effective Cloud Amount GOES sounder No

11. RTMA Vancouver Area CVOC & CVOI CVOD

12. Background Downscaling Benjamin et al. (2007) CONUS RTMA background = 1-h forecast from the NCEP-operational 13-km RUC downscaled to the 5-km NDFD terrain 1.Horizontal - bilinear interpolation 2.Vertical interpolation – varies by variable, for temperature it is based on near-surface stability and moisture from the RUC native data used to adjust to the RTMA 5-km terrain –If RTMA terrain lower than RUC, then local RUC lapse rate used (between dry adiabatic and isothermal) –If RTMA terrain higher than RUC, interpolated between native RUC vertical levels, but shallow, surface-based inversions maintained 3.Coastline sharpening

13. Real-time RTMA analysis begins ~30 min past the hour Getting the data in time is a challenge –RTMA uses obs taken (+/-12 min from top of hour) RAWS and Quickscat winds (-30 min to +12 min) Many remote obs still don’t make it in time, as well as some obs with longer latencies (Snotel = every 3 hrs) Surface Data Issues ObservationNetworkMADISNCEP

14. RTMA CONUS Temperature Analysis

15. Subjective Evaluation of the RTMA Temperature Development of web based graphics for many analyses over selected subdomains –Wasatch Valley, UT –Puget Sound, WA –Norman, OK –Shenandoah Valley, VA Analysis problems based on subjective evaluation –Non-physical treatment of temperature inversions –Smoothed features –Bull's-eye features around observations –Overfitting issues Case study –0900 UTC 22 October 2007 –Shenandoah Valley, VA

16. 0900 UTC 22 October 2007RTMA

17. 01002003004005006007008009001000

18. Sterling, VA Atmospheric Sounding 1200 UTC 22 October 2007 KIAD

19. RUC 1-hr Forecast Valid 0900 UTC 22 October 2007

20. RTMA Temperature Analysis 0900 UTC 22 October 2007

21. RTMA Temperature Analysis Increments 0900 UTC 22 October 2007

22. Observations Surface observations: METAR, RAWS, PUBLIC, OTHER Observations must fall in ±12 min time window (−30/+12 min for RAWS) Observations undergo quality control by MADIS and internal checks by RTMA Approximately 11,000 observations for almost 900,000 gridpoints for CONUS

23. Observation Density METAR16/59/1,744 PUBLIC215/575/6,486 OTHER10/75/1,961 RAWS3/11/1,301

24. Observation and Background Error Variances Ratio of σ o 2 /σ b 2 estimated by accumulating statistics over a time period for entire CONUS –8 May 2008 – 7 June 2008 RTMA used σ o 2 /σ b 2 = 1 for METAR observations and σ o 2 /σ b 2 = 1.2 for mesonet observations Statistics computed in our research suggest σ o 2 /σ b 2 between 2 and 3 –Background should be “trusted” more than observations

25. Estimation of Observation and Background Error Covariances Temperature errors at two gridpoints may be correlated with each other Error covariances specify over what distance an observation increment should be “spread” RTMA used decorrelation lengths of: –Horizontal (R): 40 km –Vertical (Z): 100 m

26. Error Correlation Example – KOKV R = 40 km, Z = 100 m

27. Error Correlation Example – KOKV R = 80 km, Z = 200 m

28. Local Surface Analysis RTMA experiments run on NCEP’s Haze supercomputer but limited computer time available Development of a local surface analysis (LSA) –Same background field –Same observation dataset, but without internal quality control –Similar 2D-Var method, but doesn’t use recursive filters –Smaller domain –Control run uses same characteristics of RTMA (decorrelation length scales; observation to background error ratio) at time of study

29. LSA Temperature Analysis R = 40 km, Z = 100 m, σ o 2 /σ b 2 = 1

30. RTMA Temperature Analysis 0900 UTC 22 October 2007

31. Data Denial Experiments Evaluation of analyses done by splitting up all observations randomly into 10 groups Two error measures: –Root-mean-square error calculated using the withheld observations only vs. using the observations assimilated into the analysis –To avoid overfitting, want RMSE at withheld locations to be similar to RMSE at locations used in the analysis –Root-mean-square sensitivity computed at all gridpoints –Want analyses to be less sensitive to withheld observations

32. Withholding Observations Group 5R = 40 km, Z = 100 m, σ o 2 /σ b 2 = 1

33. 00.51-0.5 2.54.56.58.510.512.514.516.5 R = 40 km, Z = 100 m, σ o 2 /σ b 2 = 1

34. 00.51-0.5 2.54.56.58.510.512.514.516.5 R = 40 km, Z = 100 m, σ o 2 /σ b 2 = 1

35. RTMA Temperature Analysis Sensitivity Group 1RTMA

36. Suggests overfitting Overly sensitive to withheld observations

37. How good does the RTMA have to be? Is the downscaled RUC background field good enough? –RUC RMSE approximately 2°C to all observations in CONUS How much does a 2D-Var analysis improve RMSE? –0.1-0.2°C based on LSA results

38. Verifying Forecasts with Analyses Motivating Question: Is there a way we can define what constitutes a “good enough” forecast? Problem: Concerns about grid-based verification Reality: Forecasters need feedback for entire forecast grids not just selected key observation locations

39. Forecaster Concerns Quality of the verifying analysis Penalty for adding mesoscale detail to grids in areas unresolved by analysis Bad (mesonet) observations influencing analysis Analysis in remote areas – driven mostly by the background model

40. Using RTMA Analyses and RTMA Uncertainty Estimates for Forecast Verification RTMA uncertainty estimate grids are experimental products under development for temperature, moisture, wind Goal: –Higher uncertainty in data sparse areas or areas with larger representativeness errors –Lower uncertainty in data dense areas or areas with smaller representativeness errors Basic premise –Forecasters not penalized as much where the analysis is more uncertain

41. 6 Temperature ( o C) Forecast Verification Example ForecastRTMA RTMA Uncertainty 7 8 9 8 10 12 1 2 1 2 3 4 Valley w many obs Mountains w unrep obs

42. 6 Temperature ( o C) Forecast Verification Example ForecastRTMA RTMA Uncertainty 7 8 9 8 10 12 1 2 1 2 3 4 Valley w many obs Mountains w unrep obs No color= Differences between the forecast and analysis are less than analysis uncertainty Red = abs(RTMA – Forecast) > Uncertainty

43. Summary Improving current analyses such as RTMA requires improving observations, background fields, and analysis techniques –Increase number of high-quality observations available to the analysis –Improve background forecast/analysis from which the analyses begin –Adjust assumptions regarding how background errors are related from one location to another Future approaches –Treat analyses like forecasts: best solutions are ensemble ones rather than deterministic ones –Depend on assimilation system to define error characteristics of modeling system including errors of the background fields –Improve forward operators that translate how background values correspond to observations