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 1 st Forecast: most recent model forecast 2 nd Forecast: second most recent forecast 3 rd Forecast: third most recent forecast Example: 6Z to 12Z – 1 st Fore: 0Z – 6 to 12hr – 2 nd Fore: 12Z (previous day) – 18 to 24hr – 3 rd 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 ModelRadiusThreshold GFS4≥ 7.5 NAM6≥ 10 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 QuadrantNumber of PointsPercentage 1 (NE)4248% 2 (NW)2124% 3 (SW)911% 4 (SE)1517% TOTAL87100% MeanMedianStand. Dev. Distance (km)266.67216.80183.74

28. “Eyeball” Test

29. NAM Forecast Errors QuadrantNumber of PointsPercentage 1 (NE)2946% 2 (NW)2438% 3 (SW)610% 4 (SE)46% TOTAL63100% MeanMedianStand. Dev. Distance (km)249.19228.49134.45

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?