Do the NAM and GFS have displacement biases in their MCS forecasts? - PowerPoint PPT Presentation

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

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

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  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

  4. 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

  5. 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

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

  7. 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

  8. August 18, 2009 – 12Z

  9. 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

  10. 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.

  11. 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

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

  13. Results

  14. “Eyeball” Test

  15. GFS Forecast Errors

  16. “Eyeball” Test

  17. NAM Forecast Errors

  18. 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?

  19. 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

  20. 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?