Drought prediction based on the Standardized Precipitation Index (SPI)

The Standardized Precipitation Index (SPI) has been used routinely to classify the meteorological drought. Based on the precipitation (P) seasonal forecasts from the Climate Forecast System (CFS), we made 3-month, 6-month and 12-month SPI forecasts to predict the meteorological drought over the United States. Before predicting SPI, the P forecasts from a coarse resolution global model CFS were downscaled to a regional grid. Four different methods of statistical downscaling and error correction were tested. The four methods are: linear interpolation, a bias correction and spatial downscaling based on the probability distribution functions (BCSD), a linear regression method by John Schaake and the Bayesian method used by the Princeton University group. We tested cases with initial conditions in November, February, May and August. The downscaled CFS precipitation forecasts out to 6 months were appended to the precipitation analyses to form an extended P data set. The SPI was calculated from this extended time series. The skill is regionally and seasonally dependent. Overall, the 6 month SPI is skillful out to 3 months. For the first 3 month lead time, there is no statistical significant difference among different methods of downscaling. We have also tested the SPI forecasts based on dynamical downscaling and high resolution T382 model. The tests were performed for May initial conditions when all model runs were available. The dynamical downscaling is based on the 50-km regional spectral model (RSM) nested in the CFS forecasts over the United States. There is no statistical significant improvement in skill.