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Simulating rainfall in the Chehalis river basin using the WRF model

Presentation by Laura Read and Fernando Salas May 5, 2009. Simulating rainfall in the Chehalis river basin using the WRF model. Presentation Outline. Motivation for modeling heavy rainfall Introduction to Weather Research & Forecasting model The Chehalis River Basin and storm event

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Simulating rainfall in the Chehalis river basin using the WRF model

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  1. Presentation by Laura Read and Fernando Salas May 5, 2009 Simulating rainfall in the Chehalis river basin using the WRF model

  2. Presentation Outline • Motivation for modeling heavy rainfall • Introduction to Weather Research & Forecasting model • The Chehalis River Basin and storm event • Experimental design and methods • Model validation • Results and conclusions • Future work

  3. Motivation: Places Flood Adjusted Flood Damage Costs in the U.S. from 1900-2007 Flood Forecasting System Weather Research & Forecast Model (WRF) Runoff Wind Flooding HEC-HMS Courtesy of John E. Strack et al.

  4. Climate Change A call to action Evidence for more rain, stronger storms • IPCC report on regional climate conditions • National recognition among the NRC, NOAA • Uncertainty in current projections • Higher temperatures = More moisture flux and convergence in the atmosphere

  5. The Weather Research & Forecasting Model Who, What, Where, When Simulation Process • Developed by NCAR, NOAA, NCEP, FAA, Air Force and Navy Research • Regional version of a GCM—finer resolution, set boundaries and initializations • Uses physical equations to conduct research, forecast weather (numeric weather prediction) http://www.dtcenter.org/wrfnmm/users/docs/user_guide/V3/users_guide_nmm_chap3_files/image001.gif WRF Post-Processing ARWpost

  6. Storm Event Justification • Why Washington? • 31 of 43 Disaster Declarations are flood related • NEXRAD storm database lists heavy precipitation events and its associated damage costs http://www4.ncdc.noaa.gov/cgi-win/wwcgi.dll?wwEvent~Storms

  7. The Chehalis River Basin • Basin Area: 2,660 sq mi • Length: 115 mi • Annual Average Discharge: 11,208 cfs 200 in/yr 45 in/yr http://www.ngdc.noaa.gov/mgg/topo/img/wa.jpg http://www.chehalislandtrust.org/_borders/MAP1.JPG

  8. Chehalis River Basin • USGS collects continuous observational stream-flow data nationwide • Nov 3rd storm event eclipsed flood stage in Chehalis River • 2007 and 2008 both had record breaking floods in the South-Central region…is this a trend? http://waterdata.usgs.gov/wa/nwis/dv/?dd_cd=02_00065_00003&format=img_default&site_no=12027500&set_logscale_y=0&begin_date=20061103&end_date=20061109

  9. Storm Event: Nov 3, 2006 to Nov 9, 2006 http://water.weather.gov/

  10. Storm Event: Nov 3, 2006 to Nov 9, 2006

  11. Storm Event: Nov 3, 2006 to Nov 9, 2006

  12. Storm Event: Nov 3, 2006 to Nov 9, 2006

  13. Storm Event: Nov 3, 2006 to Nov 9, 2006

  14. Storm Event: Nov 3, 2006 to Nov 9, 2006

  15. Storm Event: Nov 3, 2006 to Nov 9, 2006

  16. WRF Domain 52º N 43º N 128º W 112º W Courtesy of Google Earth

  17. Experimental Design

  18. Model Validation • Atmospheric – 32 km NARR • Geo-potential height • Specific Humidity/Water vapor mixing ratio • Wind and wind direction • Precipitation – 4 km NEXRAD • Accumulated rainfall • Temporal Distribution • Spatial Distribution

  19. Geo-Potential Height: 120 W Over Time WRF: 01_02 Simulation NARR

  20. Wind and Wind Speed: 700 mb WRF: 01_02 Simulation at 00z7nov2006 NARR

  21. Specific Humidity: Constant Latitude 01_02 Simulation: 00z7nov2006 NARR Pressure (mb) Pressure (mb) Pressure (mb)

  22. NEXRAD Validation Process of Importing Our Simulations

  23. Model Validation with NEXRAD: Daily

  24. Model Validation • Atmospheric – 32 km NARR • Geo-potential height • Specific Humidity/Water vapor mixing ratio • Wind and wind direction • Precipitation – 4 km NEXRAD • Accumulated rainfall • Temporal Distribution • Spatial Distribution

  25. Example of 6-Hour Error Analysis

  26. Model Validation with NEXRAD: 6 Hours

  27. Parameterization Results Summary • 02_02: Betts-Miller-Janjic, Lin et al. • 01_02: Kain-Fritsch, Lin et al. • 02_05: Kain-Fritsch, new Eta Lin et al. microphysics scheme produced lowest percent errors compared with NEXRAD

  28. Histogram of Precipitation Distribution

  29. Testing for Statistical Significance NEXRAD 01_02 Simulation

  30. Test for Homogeneity and T-Stats F-Test: Datasets are not homogeneous T-test: reject the null hypothesis (small p)

  31. Model Validation • Atmospheric – 32 km NARR • Geo-potential height • Specific Humidity/Water vapor mixing ratio • Wind and wind direction • Precipitation – 4 km NEXRAD • Accumulated rainfall • Temporal Distribution • Spatial Distribution

  32. Temporal Distribution Results Time period with the lowest amount of accumulated rainfall (06z7nov2006) has the highest associated percent error

  33. Total Precipitation: 12z6nov2006 to 12z7nov2006 **Note scale difference The simulations do not capture the magnitude or the trend of the rainfall over the 24 hours

  34. Model Validation • Atmospheric – 32 km NARR • Geo-potential height • Specific Humidity/Water vapor mixing ratio • Wind and wind direction • Precipitation – 4 km NEXRAD • Accumulated rainfall • Temporal Distribution • Spatial Distribution

  35. Spatial Results: Percent Error 0102 Simulation 0205 Simulation

  36. Spatial Error Analysis: Percent Error 6 Hour 12 Hour 18 Hour 24 Hour

  37. Conclusions • We need to improve our model • WRF underestimates rainfall for all parameterizations tested • BMJ and Lin et al. model physics work best • Spatial conclusions • Need to learn about the dynamics of the atmosphere

  38. Future Work (that likely will not happen) • Complete spatial analysis—error maps • Run simulations at higher resolution • With better results, look into feeding modeled precipitation into a river basin model (HEC-HMS) • How do WRF errors translate into stream-flow errors for flood forecasting?

  39. Questions? Comments? No Comments? • “Do you like statisticians?” **”Probably”

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