1. Analysis of Record Issues: Research Perspective John Horel NOAA Cooperative Institute for Regional Prediction Department of Meteorology University of Utah jhorel@met.utah.edu General reference: Atmospheric Modeling, Data Assimilation, and Predictability. Kalnay (2003)

2. Science, Technology, and Resources To what extent can the needs and requirements for objective analyses be met given existing scientific understanding, technologies, and resources? What are the critical scientific issues that must be faced in order to successfully develop quality analyses at high spatial/temporal resolution?

3. Data Assimilation vs. Objective Analysis Data Assimilation vs. Objective Analysis Data Assimilation –Determine best analysis from observations to minimize future model forecast errors Objective Analysis –Determine best analysis from observations subject to specified constraints

4. Objective Analysis Analysis value = Background value + observation Correction - A good analysis requires a good background field - Background fields can be supplied by a model forecast - Observation correction depends upon weighted differences between observations & background values at observation locations -Critical parameters and assumptions: - magnitude and relationship (covariance) between observational errors - magnitude and relationship (covariance) between background/model errors

5. Analysis Strategies depend upon goals Define microclimates? –Requires attention to details of geospatial information (e.g., minimize terrain smoothing) Resolve mesoscale/synoptic-scale features? –Requires good prediction from previous analysis

6. High terrain (dark),Flat (tan),Valleys (light) Microclimates: Diurnal Temperature Range

7. Is There One Answer? Each analysis approach has strengths and weaknesses What are the lessons that can be learned from all of the different analysis approaches?

8. What Are the Classes of Analyses? Observational error assumed small: Empirical (regression, curve fitting, successive corrections, Barnes) & Nudging Error covariances specified: Sequential (OI, Bratseth) & Variational (3DVAR, PSAS, 4DVAR) Error covariances predicted: Extended Kalman filter, Ensemble Kalman filters

9. Empirical Methods Observational error ignored Cressman/Barnes PRISM (OSU) –Background defined from geospatial information (elevation, slope) –Observations distance weighted MatchObsAll (Boise WFO) –Spline fit to differences between background and observations

10. Relevant PRISM Datasets Available Now http://www.ocs.oregonstate.edu/prism/ US and W Canada mean monthly climate grids All 50 states, plus YT,BC,AB,SK,MB Tmin, Tmax, Precip 1961-90 (1971-2000 update for CONUS) 4-km resolution Sequential monthly climate grids: “Monthly version of Analysis of Record” Jan 1895 – present (ongoing project) CONUS Tmin, Tmax, Precip, Dew Pt 4-km resolution Current methodology uses 1961-90 mean monthly grids as predictors

11. Rain Shadows: 1961-90 Mean Annual Precipitation Oregon Cascades Portland Eugene Sisters Redmond Bend Mt. Hood Mt. Jefferson Three Sisters N 350 mm/yr 2200 mm/yr 2500 mm/yr Dominant PRISM KBS Components Elevation Terrain orientation Terrain profile Moisture Regime

12. Match Obs All Developed to meet critical needs of forecasters June 9 00Z- Dewpoint Idaho 700 mb T RUC

13. Science, Technology, and Resources To what extent can the needs and requirements for objective analyses be met given existing scientific understanding, technologies, and resources? What are the critical scientific issues that must be faced in order to successfully develop quality analyses at high spatial/temporal resolution?

14. Selected Issues for AOR –What’s the best way to utilize the available surface observations? –Scales of severe weather phenomena are usually small. What are appropriate horizontal and temporal scales for the analysis to resolve such phenomena? –Nocturnal radiational inversions are difficult to analyze in basins/valleys. –Vertical decoupling from ambient flow of surface wind during night is difficult to analyze. Which is better guidance: match locally light surface winds or focus upon synoptic-scale forcing?

15. Are All Surface Observations Equally Good? All measurements have errors (random and systematic) Errors arise from many factors: –Siting (obstacles, surface characteristics) –Exposure to environmental conditions (e.g., temperature sensor heating/cooling by radiation, conduction or reflection) –Sampling strategies –Maintenance standards –Metadata errors (incorrect location, elevation)

16. Using Surface Observations in AORs Advocate using all available surface observations subject to some healthy caution Observing needs and sampling strategies vary (air quality, fire weather, road weather, COOP) Station siting results from pragmatic tradeoffs: power, communication, obstacles, access Accurate metadata are critical –Geospatial information must be utilized: terrain, exposure, land use, soil, vegetation type –Sensor type, installation, and maintenance Quality control procedures applied to data are very important Observations can be tagged with differing levels of uncertainty

17. Selected Issues for AOR –What’s the best way to utilize the available surface observations? –Scales of severe weather phenomena are usually small. What are appropriate horizontal and temporal scales for the analysis to resolve such phenomena? –Nocturnal radiational inversions are difficult to analyze in basins/valleys. –Vertical decoupling from ambient flow of surface wind during night is difficult to analyze. Which is better guidance: match locally light surface winds or focus upon synoptic-scale forcing?

18. Resolution Issues High resolution analysis based upon coarse background field and sparse data is simply downscaling/regressing to specified grid terrain High resolution analysis adds value if: –Quality data sources are available at high resolution –AND/OR a quality background field is available at high resolution To what extent can a single deterministic analysis be derived given the spatial variability at sub-grid scales and the temporal variability within 1 hour?

19. Selected Issues for AOR –What’s the best way to utilize the available surface observations? –Scales of severe weather phenomena are usually small. What are appropriate horizontal and temporal scales for the analysis to resolve such phenomena? –Nocturnal radiational inversions are difficult to analyze in basins/valleys. –Vertical decoupling from ambient flow of surface wind during night is difficult to analyze. Which is better guidance: match locally light surface winds or focus upon synoptic-scale forcing?

20. Selected Issues for AOR –What’s the best way to utilize the available surface observations? –Scales of severe weather phenomena are usually small. What are appropriate horizontal and temporal scales for the analysis to resolve such phenomena? –Nocturnal radiational inversions are difficult to analyze in basins/valleys. –Vertical decoupling from ambient flow of surface wind during night is difficult to analyze. Which is better guidance: match locally light surface winds or focus upon synoptic-scale forcing?

21. RUC SLP & MesoWest Observations 12Z 10 Oct. 2003 Weak winds reflect local blocking and other terrain effects that result in decoupling surface winds from synoptic forcing

22. Temperature and Wind RUC Analysis: 12 Z 10 Oct. 2003 Temperature (C)Vector Wind and Speed (m/s) Analyzed strong pre/post frontal winds consistent with synoptic-scale forcing

23. Temperature and Wind ADAS Analysis: 12 Z 10 Oct. 2003 Temperature (C)Vector Wind and Speed (m/s) ADAS analysis, forced by local obs, weakens RUC winds: which is correct?

24. NDFD 12 H Forecast: VT 12Z 10 Oct. NDFD Temperature NDFD Wind

25. Science, Technology, and Resources To what extent can the needs and requirements for objective analyses be met given existing scientific understanding, technologies, and resources? What are the critical scientific issues that must be faced in order to successfully develop quality analyses at high spatial/temporal resolution?

26. RUC Temperature Decorrelation DJF 2003-2004 Covariance Distance (km)

27. ADAS: ARPS Data Assimilation System ADAS is run in near-real time to create analyses of temperature, relative humidity, and wind over the western U. S. (Lazarus et al. 2002 WAF) Analyses on NWS GFE grid at 5 km spacing in the West Test runs made for lower 48 state NDFD grid at 5 km spacing Typically > 2000 surface temperature and wind observations available via MesoWest for analysis (5500 for lower 48) The 20km Rapid Update Cycle (RUC; Benjamin et al. 2002) is used for the background field Background and terrain fields help to build spatial & temporal consistency in the surface fields Efficiency of ADAS code improved significantly Anisotropic weighting for terrain and coasts added (Myrick et al. 2004) Current ADAS analyses are a compromise solution; suffer from many fundamental problems due to nature of optimum interpolation approach

28. RUC Temp. Analysis 12UTC 18 March 2004

29. ADAS Temp. Analysis 12UTC 18 March 2004 Sensitivity to Obs. Errors

30. MesoWest MesoWest: Cooperative sharing of current weather information around the nation Real-time and retrospective access to weather information through state-of-the-art database http://www.met.utah. edu/mesowest http://www.met.utah. edu/mesowest Distributing environmental information to government agencies and the public for protection of life and property Horel et al. (2002) Bull. Amer. Meteor. Soc. February 2002

31. Nudging Requires empirically determined time constants to relax model towards observations Observational uncertainty ignored The NCAR/ATEC Real-Time Four-Dimensional Data Assimilation and Forecast (RTFDDA) System: Basics, operation and future development Yubao Liu. NCAR/RAP An Automated Humvee-Operated Meteorological Nowcast/Prediction System for the U. S. Army (MMS- Profiler) David Stauffer, Aijun Deng, Annette Gibbs, Glenn Hunter, George Young, Anthony Schroeder and Nelson Seaman http://www.met.psu.edu/dept/research/

32. Sequential/Variational Sequential: find the optimal weights that minimizes the analysis error covariance matrix Variational: find the optimal analysis that minimizes a scalar cost function MSAS and RSAS Surface Analysis Systems. Patricia A. Miller and Michael F. Barth (NOAA Forecast Systems Laboratory) Analysis of Record. Geoff DiMego An FSL-RUC/RR proposal for the Analysis of Record. Stan Benjamin, Dezso Devenyi, Steve Weygandt, John Brown

33. Kalman Filters Estimate forecast error covariance Assimilation of Fixed Screen-Height Observations in a Parameterized PBL. Joshua Hacker NCAR Ensemble Filters for Data Assimilation: Flexible, Powerful, and Ready for Prime-Time? Jeff Anderson. NCAR Toward a Real-time Mesoscale Ensemble Kalman Filter. Greg Hakim. U. Washington A New Approach for Mesoscale Surface Analysis: The Space-Time Mesoscale Analysis System. John McGinley, Steven Koch, Yuanfu Xie, Ning Wang, Patricia Miller, and Steve Albers

34. Upper Level Ridging and Surface Cold Pools: 14 January 2004 NDFD 48 h forecast Analysis

35. Surface Cold Pool Event: 14 January 2004 NDFD 48 h forecastADAS Analysis NDFD and ADAS DJF 2003-2004 seasonal means removed

36. Sensitivity of OI/3DVar Solutions to Specification of Error Covariance Myrick et al. (2004) Sample of 1000 analyses with random observations and background fields Background errors strongly correlated Background errors anticorrelated

37. Mean background, OI, 3DVAR, and Bratseth solutions for 1000 case sample Myrick et al. 2004