1. ROMAN: Real-Time Observation Monitor and Analysis Network John Horel, Mike Splitt, Judy Pechmann, Brian Olsen NOAA Cooperative Institute for Regional Prediction University of Utah Ed Delgado Eastern Great Basin Coordination Center San Diego Tribune. 28 Oct. 2003

2. Outline  Data quality: limitations of observations  Overview of ROMAN  Goals and Applications  ROMAN displays  Surface data assimilation: ADAS  Summary

3. Limitations of Observations- All That Is Labeled Data Is NOT Gold (Lockhart 2003)  References:  Challenges of Measurements. T. Lockhart (2003). Handbook of Weather, Climate and Water. Wiley & Sons. 695- 710.  Review of the RAWS Network. Zachariassen et al. (2003). USDA Tech. Report RMRS-GTR-119. GNI

4. Are All Observations Equally Bad?  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) SNZ

5. Are All Observations Equally Good?  Why was the sensor installed?  Observing needs and sampling strategies vary (air quality, fire weather, road weather)  Station siting results from pragmatic tradeoffs: power, communication, obstacles, access  Use common sense  Wind sensor in the base of a mountain pass will likely blow from only two directions  Errors depend upon conditions (e.g., temperature spikes common with calm winds)  Use available metadata  Topography  Land use, soil, and vegetation type  Photos  Monitor quality control information  Basic consistency checks  Comparison to other stations UT9

6. Documentation  ROMAN:  Horel et al. (2004) Submitted to International Journal of Wildland Fire. Jan. 2004  Text: http://www.met.utah.edu/jhorel/homepages/jhorel/ROMAN_text.pdf http://www.met.utah.edu/jhorel/homepages/jhorel/ROMAN_text.pdf  Figures: http://www.met.utah.edu/jhorel/homepages/jhorel/ROMAN_fig.pdf http://www.met.utah.edu/jhorel/homepages/jhorel/ROMAN_fig.pdf  Horel et al. (2004) IIPS Conference  MesoWest: Horel et al. (2002) Bull. Amer. Meteor. Soc. February 2002  ADAS:  Myrick and Horel (2004). Submitted to Wea. Forecasting. http://www.met.utah.edu/jhorel/cirp/WAF_Myrick.pdf http://www.met.utah.edu/jhorel/cirp/WAF_Myrick.pdf  Lazarus et al. (2003) Wea. Forecasting. 971-1000.  On-line help: http://www.met.utah.edu/droman/helphttp://www.met.utah.edu/droman/help

7. Goal  Provide real-time weather data around the nation to meteorologists and land managers  Display data in fast-loading formats tailored to the wildland fire community and accessible to:  Top-level managers using high speed networks  Fire-behavior analysts and IMETs in the field over slow dial- up connections San Diego Tribune. 28 Oct. 2003

8. 2003 Fire Locations (Red); ROMAN stations (Grey) Fire locations provided by Remote Sensing Applications Center from MODIS imagery

9. ROMAN Development  Software developed at University of Utah to assist entire fire weather community to obtain access to current surface weather information  Support for development of ROMAN from BLM and NWS  Tools designed for fire weather applications can be used for many other purposes  Tested during 2002 and 2003 summer fire seasons  Operational for 2004 summer fire season Geographic Area Coordination Centers

10. Applications of ROMAN  Fire behavior analysts, fire management officers, GACC meteorologists:  Monitor weather conditions for strategic and tactical decision making  Determine impacts of weather on fire behavior and fire fighting resources  NWS meteorologists at WFOs  Monitor conditions within County Warning Areas, issue spot fire and general forecasts, verifying forecasts  NWS Incident Meteorologists  Monitor weather conditions in vicinity of major wildland fires

11. Current ROMAN Web Portal: http://www.met.utah.edu/roman

12. States Top-Level Organization GACCs CWAs FWZsMODIS regions

13. ROMAN  Structured by  GACC Predictive Service Areas  NWS CWA Forecast Zones  NWS Fire Weather Zones  Counties within States  Intuitive, easily navigable interface  Clickable maps  Station Weather  Weather Summary  Trend Monitor  Weather Monitor  5 Day Temp/RH Summary  Precip Summary/Monitor  Weather Near Fires  Search by zip code, geographic location Triangles: RAWS Major data providers: NWS/FAA; SNOTEL; RAWS

14. What Weather Information is Available? Search by: maps (state, CWAs, GACCs, etc.)

15. Surface Weather Plots

16. Surface Data Plot

17. What Weather Information is Available? Search by: zip code, geographic location, latitude/longitude

18. What Has Been Happening Recently? 5-Day Max/Min Temperature, RH, Wind Speed

19. What Are the Current Conditions? Weather Summary

20. What Has Changed Since Yesterday? Trend Monitor

21. What Extreme Conditions Are Underway? Weather Monitor

22. How Much Precipitation Has Fallen? Monitor Summary

23. Weather Near Fires

24. Current Weather Near Fires

25. Archived Fires

26. Weather Near Fires: 31 October 2003

27. MODIS Base Maps October 29 October 31

28. Current Configuration at CIRP Database Preprocessing Web Server Data Streams Users

29. Proposed Configuration: 2004 Fire Season Database @WR Preprocessing @ WR Web Server @ UU Data Streams Users Database @Boise Preprocessing @ Boise Web Server @ Boise RAWS Other WR/WFO Apps

30. 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 grid at 2.5, 5, and 10 km spacing  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  ADAS helps provide additional quality control of MesoWest/ROMAN observations as well as applications to nowcasting and forecast verification

31. Current State of the Art  While surface data may have negligible or detrimental impacts to operational NWP, assimilation of surface data is critical for generating and verifying human-edited gridded forecasts  Current ADAS analyses are a compromise solution  Suffer from many fundamental problems due to nature of successive corrections/optimum interpolation approach  Flow does not adjust dynamically to terrain  Appropriate and practical constraints beyond mass balance are not clear for use in variational techniques  See Kalnay(2003) for further comparison of assimilation methods

33. Arctic Outbreak: 21-25 November 2003 NDFD 48 h forecastADAS Analysis

34. Summary  ROMAN under development for use by weather professionals and the public  ROMAN will be operated at the Boise WFO/ NIFC facility with 24/7 support this summer  Existing web resources can be used for many applications with access to both real-time and archival information  http://www.met.utah.edu/droman will continue as a testbed and ROMAN backup http://www.met.utah.edu/droman  Operational ROMAN will be located at http://roman.boi.noaa.gov  Feedback: mesowest@met.utah.edu