NCEP/NLDAS Seasonal Drought Prediction over the Continental US Using the Seasonal Forecast System Developed by Princeton University and University of Washington
The uncoupled ensemble seasonal prediction mode utilizes the following three independent approaches for generating downscaled ensemble seasonal forecasts of surface forcing: (1) Ensemble Streamflow Prediction, (2) NCEP CFS ensemble dynamical model prediction, and (3) CPC Official Seasonal Climate Outlook. For the first two approaches, twenty ensemble members of forcing realizations are generated using a Bayesian merging algorithm developed by Princeton University, and for third approach, twenty ensemble members of forcing realizations are produced by an algorithm developed by University of Washington. The forcing generated from three forcing methods is then used to run VIC model over fourteen large river basins that together span the CONUS domain. One to six month ensemble seasonal prediction products such as air temperature, precipitation, soil moisture, snowpack, total runoff, evaporation and streamflow are derived for each forcing approach. The anomalies and percentiles of the predicted products for each forcing approach are used for the purpose of US drought prediction. In addition, a probability forecast for total soil moisture is also presented. One key application of the realtime monthly updates is drought forecast over CONUS, shown at the "NLDAS Forecast" tab of the NLDAS web site. The above seasonal prediction approach, currently applied to the VIC land model, will be expanded to include the other three four land models used in NLDAS drought monitor (NCEP/Noah, OHD/SAC, NASA/Mosaic) in the future.