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Verification of a downscaling approach for large area flood prediction over the Ohio River Basin

This study aims to predict streamflow and associated hydrologic variables for large river basins using a fully distributed hydrology model and ensemble weather forecasts. The objective is to improve spatial consistency, ungauge basins, and extend lead time up to 2 weeks.

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Verification of a downscaling approach for large area flood prediction over the Ohio River Basin

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  1. Verification of a downscaling approach for large area flood prediction over the Ohio River Basin N. Voisin, J.C. Schaake and D.P. Lettenmaier University of Washington, Seattle, WA AMS Annual Meeting, Phoenix AZ 11-15 Jan 2009

  2. Objective Predict streamflow and associated hydrologic variables, soil moisture, runoff, evaporation and snow water equivalent : • Applicable to large river basins, eventually globally: spatial consistency, ungauged basins • Using a fully distributed hydrology model • Using ensemble weather forecasts • Lead time up to 2 weeks

  3. Objective BCSD = Bias correction and statistical downscaling Forecast schematic Several years back Medium range forecasts (2 weeks) ECMWF EPS 50 ensemble members 2002-2008 Daily ERA-40 surrogate for near real time analysis fields 1979-2002 Daily ECMWF Analysis 2002-2008 BCSD to 0.25 degree BCSD with forecast calibration, 0.25 degree Atmospheric inputs VIC Hydrology Model Hydrologic model spinup 0.25 degree Hydrologic fcst (stream flow, soil moist., SWE, runoff ) Initial State Flow fcst calibration

  4. Objective Compare different downscaling techniques • Applicable at a global scale • For precipitation forecast • Improve or conserve the skill

  5. Outline • Existing downscaling methods • Analog technique and various variations of it • Forecast Verification at different spatial and temporal scales: • Mean errors • Predictability, reliability • Spatial rank structure

  6. 1. Downscaling techniques • MOS (Glahn and Lowry 1972, Clark and Hay 2004) • Bias correction followed by spatial and temporal resampling for seasonal forecast (Wood et al. 2002 and 2004) • National Weather Service (NWS) Ensemble Precipitation Processor (EPP) ( Schaake et al. 2007) • Analog techniques ( Hamill and Whitaker 2006)

  7. 2. Analog technique ( adapted from Hamill and Whitaker 2006) Retrosp. FCST dataset, +/- 45 days around day n 1 degree resolution Corresp. Observation (TRMM) 0.25 degree resolution FCST D DAY OBS D DAY Downscaled FCST day n 0.25 degree FCST D DAY OBS D DAY FCST D DAY OBS D DAY FCST D DAY OBS D DAY FCST D DAY OBS D DAY FCST D DAY OBS D DAY FCST D DAY OBS D DAY FCST D DAY OBS D DAY FCST D DAY OBS D DAY FCST D DAY OBS D DAY FCST D DAY OBS D DAY FCST D DAY OBS D DAY FCST n +/- 45 days Year-1 OBS n +/- 45 days Year-1 FCST day n 1 degree 5 degree • 3 methods for choosing the analog: • Closest in terms of RMSD, for each ensemble • 15 closest in terms of RMSD, to the ensemble mean fcst • Closest in terms of rank, for each ensemble 5 degree

  8. 2. Analog technique Spatial domain for the analog • Choose an analog for the entire domain (Maurer et al. 2008): entire US, or the globe • Ensure spatial rank structure • Need a long dataset of retrofcst-observation. • Moving spatial window (Hamill and Whitaker 2006): • 5x5 degree window (25 grid points) • Choose analog based on ΣRMSD, or Σ(Δrank) • Date of analog is assigned to the center grid point

  9. 2. Analog technique Ens. Mean Fcst, 20050713 Fcst 20050713 4 closest analogs in the retrospective forecast dataset Corresponding 0.25 degree TRMM for the analogs, Downscaled ensemble forecastmembers Downscaled ens. mean forecast TRMM (obs) ( adapted from Hamill and Whitaker 2006)

  10. 3. Forecast Verification • Evaluate the different analog techniques, simple interpolation, and basic resampling downscaling • Verification conditioned on the forecast: • Mean errors • Reliability • Predictability • Verification conditioned on the observation • Discrimination (ROC) For lead times 1,5 and 10 days at 0.25 and 1 degree spatial resolution, Daily and 5 day accumulation

  11. Mean Errors 0.25 degree Ohio Basin 2002-2006 TRMM as obs Upper tercile: improved bias

  12. Reliability of ens. spread 0.25 degree Ohio Basin 2002-2006 TRMM as obs Improved reliability

  13. Predictability 0.25 degree Ohio Basin 2002-2006 TRMM as obs Status quo or no improvement

  14. Discrimination ROC diagram 0.25 degree Ohio Basin 2002-2006 TRMM as obs Prob. of detection Or hit rate False alarm rate

  15. Spatial structure 2005, Jul 13th 75th Percentile basin daily acc., 2002-2006 TRMM

  16. Conclusions The analog technique with a moving spatial window • improves: • reliability (considerably), mean errors (slightly) • Status quo on: • discrimination,predictability • Results consistent at different spatial and temporal scales ( not shown, 1 degree and 5 day acc.) • More realistic precipitation patterns. • Spatial rank structure? • An analog technique with no moving spatial window would ensure it. Issue with short observed dataset. • Try the NWS EPP.

  17. Climatologies of forecasts Ohio Basin 2002-2006

  18. Mean Errors 0.25 degree Ohio Basin 2002-2006 TRMM as obs Upper tercile: improved bias

  19. Mean Errors 1 degree Ohio Basin 2002-2006 TRMM as obs Upper tercile: improved bias

  20. Mean Errors 0.25 degree 5 day acc. Ohio Basin 2002-2006 TRMM as obs Upper tercile: improved bias

  21. Reliability 0.25 degree Ohio Basin 2002-2006 TRMM as obs - Improved reliability - poor reliability for medium tercile - poor reliability lead time 10

  22. Reliability 1 degree Ohio Basin 2002-2006 TRMM as obs - Improved reliability - No reliability for medium tercile - No reliability lead time 10

  23. Reliability 0.25 degree 5 day acc Ohio Basin 2002-2006 TRMM as obs • - Improved reliability • No reliability for medium tercile • - Some reliability day 6-10

  24. Sharpness 0.25 degree Ohio Basin 2002-2006 TRMM as obs Improved sharpness for lower tercile

  25. Sharpness 1 degree Ohio Basin 2002-2006 TRMM as obs Improved sharpness for lower tercile

  26. Sharpness 0.25 degree 5 day acc Ohio Basin 2002-2006 TRMM as obs No improvement

  27. Predictability 0.25 degree Ohio Basin 2002-2006 TRMM as obs Status quo or no improvement

  28. Predictability 1 degree Ohio Basin 2002-2006 TRMM as obs Status quo or no improvement

  29. Predictability 0.25 degree 5 day acc Ohio Basin 2002-2006 TRMM as obs Status quo or no improvement

  30. Reliability of ens. spread 0.25 degree Ohio Basin 2002-2006 TRMM as obs

  31. Reliability of ens. spread 1 degree Ohio Basin 2002-2006 TRMM as obs

  32. Reliability of ens. spread 0.25 degree 5 day acc. Ohio Basin 2002-2006 TRMM as obs

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