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Experiments in 1-6 h Forecasting of Convective Storms Using Radar Extrapolation and

Acknowledgements Mei Xu - MM5 Morris Weisman – WRF James Pinto –WRF, NCWF-6, computer support Steve Weygandt - RUC Tom Saxen – NCWF-6, Extrapolation Cindy Mueller – NCWF-6, Extrapolation, management Jenny Sun – Forecast VDRAS Dan Megenhardt – computer support

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Experiments in 1-6 h Forecasting of Convective Storms Using Radar Extrapolation and

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  1. Acknowledgements Mei Xu - MM5 Morris Weisman – WRF James Pinto –WRF, NCWF-6, computer support Steve Weygandt - RUC Tom Saxen – NCWF-6, Extrapolation Cindy Mueller – NCWF-6, Extrapolation, management Jenny Sun – Forecast VDRAS Dan Megenhardt – computer support Rita Roberts – Scientific advise Frank Hage – Display support Experiments in 1-6 h Forecasting of Convective Storms Using Radar Extrapolation and Numerical Weather Prediction

  2. Overarching Goal Blend Numerical Forecasting Methods and Observational methods To improve 1-6 h nowcasting

  3. Predictability Blended Extrapolation Forecast Skill NWP Best Forecast Length

  4. Challenge - How to blend extrapolation and model nowcast Numerical Model Forecast Extrapolation Forecast

  5. To meet this challenge – NCAR conducted a forecast extravaganza this summer 8 methods that produce 1-6h forecasts 4 numerical and 4 observational Forecasts evaluated with the objective of developing ideas for blending numerical and observational

  6. Study area June 2005

  7. Example Initiation case

  8. Observational Techniques Examined • Probabilities • Extrapolation plus smart trending • (synoptic situation and time of day) Extrapolation

  9. NWP Techniques Examined • Probabilities • 20 km grid • 3 h forecast cycle • ACARS, VAD, profiler, • GOES precip water) The point is- State of the art techniques were available • 4 km grid • 24h forecast cycle • initialized with 40km • ETA • nested grid • 3h forecast cycle • observational nudging • radar data assimilation • (conus mosaic of reflectivity)

  10. Subjective evaluation of forecast quality

  11. Forecast Quality Definitions Wilson subjective categories 1 – forecast and observed almost perfect overlap. 2 – majority of observed and forecast echoes overlap or offsets <50 km . 3- forecast and observed look similar but there are a number of echo offsets and several areas maybe missing or extra. 4 – the forecasts and observed are significantly different with very little overlap; but some features are suggestive of what actually occurred. 5- There is no resemblance to forecast and observed.

  12. Examples of Forecast Quality Forecast Observed Quality = 4.0 Quality = 2.0 Quality = 3.0 Quality = 5.0

  13. Quality of forecasts for echo • Existing at forecast time. • 2. Quality of NWP forecasts of • initiation • 3. Quality of NWP forecasts of • change in area size

  14. 1. Echo present at forecast time Forecast Quality Best Extrapolation NWP

  15. Forecast observed Quality = 4.0

  16. Cross over region GRID MESH 20 km Jun-Oct 2002 1.0 Accuracy of Rainfall Nowcasts .8 Extrapolation >1 mm/h .6 Critical Success Index (CSI) .4 .2 NWP 0 1 2 3 4 5 6 Forecast Length, hours Courtesy of Shingo Yamada JMA

  17. 2. Initiation Forecasts Best NWP Results

  18. observed forecast 3. Area Size Trend Forecasts 2, 4 and 6 hr forecasts of trend in area size 7 Trend Categories g+ large growth g medium growth g- small growth nc no change d- small dissipation d medium dissipation d+ large dissipation Error 2 categories

  19. observed forecast 3. Area Size Trend Forecasts 2, 4 and 6 hr forecasts of trend in area size 7 Trend Categories g+ large growth g medium growth g- small growth nc no change d- small dissipation d medium dissipation d+ large dissipation Error 2 categories

  20. observed forecast 3. Area Size Trend Forecasts 2, 4 and 6 hr forecasts of trend in area size 7 Trend Categories g+ large growth g medium growth g- small growth nc no change d- small dissipation d medium dissipation d+ large dissipation Error 6 categories

  21. 3. Area Size Trend Forecasts Best NWP results 6 h Best Worse

  22. Summary Overarching Goal Blend Numerical Forecasting Methods and Observational methods To improve 1-6 h nowcasting

  23. Summary 1. Model – frequent cycling (3hr), assimilate radar reflectivity 2. Initiation – Give full weight to model 3. Existing storms – Extrapolate and trend area size based on model trend (more weight for dissipation trend) Unfinished – examine model and extrapolation predictability stratified by precipitation organization, synoptic situation and time of day.

  24. Thank You

  25. 6-hour trend Number cases Trend Category Area Size Trends 2-hour trend 4-hour trend g+ large growth g medium growth g- small growth nc no change d- small dissipation d medium dissipation d+ large dissipation

  26. Quality = 1.5 Observed Forecast

  27. Example Initiation case

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