Create a mesoscale analysis that: Assimilates all available surface data at high time frequency

1. STMAS:Space-Time Mesoscale Analysis SystemSteve Koch, John McGinley, Yuanfu Xie, Steve Albers, Ning Wang, Patty Miller

2. STMAS Goal • Create a mesoscale analysis that: • Assimilates all available surface data at high time frequency • Performs data quality control • Has a very rapid product cycle (< 15 minutes) • Can sustain features with typical mesoscale structure (gust fronts, gravity waves, bores, sea breezes, etc) • Can be used for boundary identification and monitoring (SPC) • Candidate for automated processing • Is compatible with current workstation technology

3. STMAS utilizes surface data available through the FSL Meteorological Assimilation Data Ingest System (MADIS) • Surface Observations • Meteorological Aviation Reports (METARs) • Coastal Marine Automated Network (C-MAN) • Surface Aviation Observations (SAOs) • Modernized Cooperative Observer Program (COOP) • Many mesonetworks (constantly growing) • MADIS offers automated Quality Control • Gross validity checks • Temporal consistency checks • Internal (physical) consistency checks • Spatial (“buddy”) checks • MADIS Home Page: www-sdd.fsl.noaa.gov/MADIS • Real-Time Display: www-frd.fsl.noaa.gov/mesonet/

4. MADIS Mesonet Providers 5/1/2004 Mesonet DescriptionProvider NameNo. of SitesCoverage U.S. Army Aberdeen Proving Grounds Citizen Weather Observers Program AWS Convergence Technologies, Inc. Anything Weather Network Colorado Department of Transportation Florida Mesonet Ft. Collins Utilities Goodland WFO Miscellaneous Gulf of Maine Ocean Observing System FSL Ground-Based GPS Hydrometeorological Automated Data System Iowa Department of Transportation Iowa Environmental Mesonet Boulder WFO Miscellaneous Kansas Department of Transportation Multi-Agency Profiler Surface Observations Cooperative Mesonets in the Western U.S. Minnesota Department of Transportation National Ocean Service Physical Oceanographic Real-Time System National Weather Service Cooperative Observer Program Oklahoma Mesonet Remote Automated Weather Stations Radiometer Denver Urban Drainage & Flood Control Dist. Weather for You APG APRSWXNET AWS AWX CODOT FL - Meso FTCOLLINS GLDNWS GoMOOS GPSMET HADS IADOT IEM INTERNET KSDOT MAP MesoWest MNDOT NOS – PORTS NWS – COOP OK - Meso RAWS RDMTR UDFCD WXforYou 5 2195 5600 64 107 39 5 15 10 340 60 50 88 13 41 12 2552 92 34 100 116 1777 2 17 414 Maryland Global U.S. CONUS Colorado Florida Colorado CO/KS/NE Gulf of Maine U.S. New England Iowa Iowa Colorado Kansas CONUS West CONUS Minnesota CONUS New England Oklahoma U.S. U.S. Colorado U.S. Total = 13,748

5. MADIS collects data from over 13,000 sites presently (and still growing). Still, the data are largely distributed like “oases and deserts”.

6. Why conventional objective analysis schemes are inadequate to deal with the desert-oasis problem Successive correction (SC) schemes (e.g., Barnes used in LAPS) and optimum interpolation (OI) schemes (e.g., as used in MADIS RSAS/MSAS) suffer from common problems: • Inhomogeneous station distributions cause problems for a fixed value of the final weighting function or the covariance function scale • SC and OI schemes will introduce noise in the deserts if forced to try to show details that are resolvable in the data oases everywhere • None of the SC or OI schemes include the high-resolution time information explicitly, with the exception of the modified Barnes scheme of Koch and Saleeby (2001), which required assumptions about the advection vectors in time-to-space conversion approach

7. STMAS solution: Multi-scale space and time analysis capability • Ability to represent large scales resolvable by the data distribution characteristic of the desert regions • Two schemes tested: telescopic recursive filter and wavelet fitting • Recursive filter uses residual remaining after removal of the large-scale component for telescopic analysis: • Compute residual  reduce the filter scale  do analysis of residuals at this next smaller scale  repeat N times until analysis error falls below the expected error in observations (N = 3-6) • Include temporal weight in similar manner for the recursive filter • Variational cost function assures fit to observations • Wavelet fitting technique provides for locally variable levels of detail, non-isotropic searching, and temporal weighting (still under development, though tested on analytic functions)

8. Multi-scale Analysis using a Recursive Filter

9. Comparisons of hourly analyses of temperature and winds using LAPS and STMAS to surface and radar observationsHourly analyses: 1900 - 2200 UTC

10. 1900 UTC LAPS

11. 2000 UTC LAPS

12. 2100 UTC LAPS

13. 2200 UTC LAPS

14. 1900 UTC STMAS

15. 2000 UTC STMAS

16. 2100 UTC STMAS

17. 2200 UTC STMAS

18. 2155 UTC

19. Improvements to STMAS • Use of Spline Wavelets • Accommodate common meteorological structures • Improve analysis in data rich and data sparse areas • Data Quality Control Using a Kalman Scheme • Operate in observation space • Provide data projections for future cycles • Optimum model for each station

20. Scattered data fitting using Spline Wavelets • Basis functions: second order spline wavelets on bounded interval (by Chui and Quak) • Penalty function in variational formulation: a weighted combination of least square error and magnitude of the high order derivatives • Inner scaling functions control dilation and translation of the cardinal B-splines: • Boundary scaling functions control dilation of the cardinal B-spline with multiple knots at the endpoints

21. Wavelet functions • Inner wavelet functions: dilation and translation of the cardinal B-wavelets • Boundary wavelet functions: dilation of the special B-wavelets derived from cardinal B-splines and boundary scaling functions

22. Scaling and wavelet functions Approaching boundary

23. Comparison of four different analysis techniques Barnes Analysis Standard Recursive filter Telescopic Recursive filter Wavelet fitting

25. Kalman Filter for Surface Data • Provides a continuous station estimate of observation based on how a forecaster would perform observation projection: self trend, buddy trends, and NWP – use for quality checking • With missing obs – maintain constant station count Kalman ob Possible bad ob Station value Kalman continuous model Allowable Obs error Time Product time

26. Simulated Temp Traces Station 1- regular Station 2- occasional Station 3- synoptic Station 4- mesonet Station 5- data bursts Station 6- QC problem Time Needed Analysis Product Time

27. 44 40 36 No data Temperature and Dewpoint observations and as derived from Kalman Filter for 22 Mar 2001 Aurora, Nebraska Enid, Oklahoma 70 60 50 40 No data

28. Kalman Forecast Errors (F)(based on stations reappearing after not reporting for a time interval on x-axis) Temperature Dewpoint

29. More about STMAS • Ability to use background fields from a model (e.g., RUC) or a previous analysis (these features were adapted from LAPS and are important to have in the data-void desert regions) • Background fields are modified to account for very detailed terrain (another useful feature borrowed from LAPS) • Background field includes lake and sea surface temperatures and a land-weighting scheme to prevent situations such as warm land grid points having an influence on cooler water areas (via LAPS) • Currently, STMAS compares observations to background for its QC method. Kalman filter will provide both a much more sophisticated QC and the ability to fully utilize temporal detail in the data. • Reduced pressure calculation for a given reference height, as in LAPS (may see perturbation pressure sometime in the future) • Value of STMAS is being measured relative to the LAPS analysis • Analyses currently conducted over CIWS domain every 15 minutes on a 5-km grid (a variety of grid product fields are computed)

30. Severe Weather Event: 30-31 May 2004

31. 2000 UTC

32. 2100 UTC

33. 2200 UTC

34. 2300 UTC

35. 0000 UTC

36. 0100 UTC

37. 0200 UTC

38. 0300 UTC

39. 2000 UTC STMAS analysis of temperature and winds: 20 UTC 30 May - 01 UTC 31 May 2004

40. 2100 UTC

41. 2200 UTC

42. 2300 UTC

43. 0000 UTC

44. 2330 UTC

45. 2345 UTC

46. 2315 UTC

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48. 0030 UTC

49. 0045 UTC

50. 0100 UTC