Do the NAM and GFS have displacement biases in their MCS forecasts?

1. Do the NAM and GFS have displacement biases in their MCS forecasts? Charles Yost Russ Schumacher Department of Atmospheric Sciences Texas A&M University Research supported by COMET grant #Z10-83387

2. Outline • Brief Background • Data and Methodology • Results • Case Studies • Future Work

3. Background • Mesoscale Convective Systems (MCSs) are responsible for a large percentage of rain during the warm season • Researchers and forecasters noticed the NAM and GFS consistently predicted these events too far north • HPC and Texas A&M University collaborated to investigate

10. Cases • Searched April through August of 2009 and 2010 using • Radar to identify MCSs • Stage IV to analyze amounts • 29 unique 6 hour intervals • Ranging from April 13 to August 18 • Several cases outside of initial time frame

11. Data • Stage IV • 6 hourly multi-sensor precipitation analyses • North American Mesoscale Model • 0Z and 12Z model runs • 6 hourly precipitation forecast • Global Forecast System Model • 0Z and 12 Z model runs • 6 hourly precipitation forecast

12. Methodology • “Eyeball” Test • Method for Object-Based Diagnostic Evaluation (MODE)

13. Note on terminology • 1st Forecast: most recent model forecast • 2nd Forecast: second most recent forecast • 3rd Forecast: third most recent forecast • Example: 6Z to 12Z • 1st Fore: 0Z – 6 to 12hr • 2nd Fore: 12Z (previous day) – 18 to 24hr • 3rd Fore: 0Z (previous day) – 30 to 36hr • Note: 0Z and 12Z are 12 hour forecasts, 6Z and 18Z are 6 hour forecasts

14. August 18, 2009 – 12Z

17. Method for Object-Based Diagnostic Evaluation Tool (MODE) • Resolves objects in observed and forecasted fields • Provides detailed information about the objects • Centroid location, object area, length, width • Axis angle, aspect ratio, curvature, intensity • Can pair observed and forecasted objects

18. Process for Resolving Objects Davis, C., B. Brown, and R. Bullock, 2006a: Object-based verifica- tion of precipitation forecasts. Part I: Methods and application to mesoscale rain areas. Mon. Wea. Rev., 134, 1772–1784.

19. MODE Tool Settings • GFS was re-gridded to the 212 grid. NAM remained at the 218 grid • Stage IV was regridded to the corresponding forecast’s grid • Radii and thresholds were selected to match what a human would draw

20. MODE Tool Output • Fields used: • Centroid (center of mass) • Area • Length • Width • Determine forecast error: “Forecast – Observed”

23. Results

25. “Eyeball” Test

26. GFS Forecast Errors

28. “Eyeball” Test

29. NAM Forecast Errors

30. Forecasting Questions • Is there a correlation between forecast error (distance) and forecast area? • Is there a correlation between forecast error (distance) and forecast width? • Is there a correlation between forecast error (distance) and forecast length?

40. Conclusions • “Eyeball” test and MODE test are consistent with each other • Clear northern bias in the NAM • 84% of cases • No temporal bias • GFS northern bias present, not as strong • 72% of cases • Tends to move system through early (65%) • No clear bias with area, width, or length

41. Future Work • Expand the time period to include more years and cases • Does this bias exist in higher resolutions? • NSSL WRF • What are the causes of this bias?