18A.3 Using a Calibrated Mesoscale Ensemble to Improve Precipitation and Hydrological Forecasts over the Northeast U.S

Friday, 5 June 2009: 9:30 AM
Grand Ballroom East (DoubleTree Hotel & EMC - Downtown, Omaha)
Michael J. Erickson, Stony Brook University, Stony Brook, NY; and B. A. Colle, J. S. Tongue, A. M. Cope, and J. T. Ostrowski

The goal of this project is to utilize a multi-model atmospheric ensemble to improve hydrologic forecasts for the Northeast U.S. The atmospheric ensemble deficiencies are elucidated for short-term (< 48 hour) quantitative precipitation forecasts (QPF) and the ensemble members are weighted based on their past performance. The post-processed QPF data can be ingested into hydrological models for streamflow and stage height forecasts, such as the National Weather Service River Forecast System (NWSRFS) at the Mid-Atlantic River Forecast Center and the Site Specific Hydrologic Predictor (SSHP) system at the National Weather Service Forecast Offices.

The ensemble for this study includes the 21 member Short Range Ensemble Forecast (SREF) system at 32-km to 45-km grid spacing run at the National Centers for Environmental Prediction (NCEP) as well as the 13 members run daily at Stony Brook University (SBU) from the Weather Research and Forecasting (WRF-ARW) and Penn-State-NCAR Mesoscale Model (MM5) at 12-km grid spacing. These models include different initial conditions and physical parameterizations (convective parameterization, boundary layer, and microphysics). The precipitation forecasts were verified over the Northeast U.S. from 2006-2008 using the Stage IV (4 km grid spacing) precipitation data interpolated to the model grid. The raw ensemble model forecasts are typically biased and underdispersed. For example, all models over-predict precipitation on the average, but models with the Mellor-Yamada (MY) or Mellor-Yamada-Janjic (MYJ) planetary boundary layer (PBL) schemes exhibit greater overprediction. Bayesian Model Averaging (BMA) is applied to simultaneously correct bias and calibrate the ensemble while weighting each member based on its past performance. BMA has been shown to improve probabilistic QPFs over the Pacific Northwest, but it has not been tested for a large ensemble over the Northeast U.S. This talk will highlight the QPF errors within the atmospheric ensemble over the Northeast, show the impacts of the BMA approach, and how the post-processing influences the hydrologic forecasts for particular flooding events.

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