Quantitative Spatiotemporal Evaluation of Dynamically Downscaled MM5 Precipitation Predictions over the Tampa Bay Region, Florida

This research quantitatively evaluated the ability of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) to reproduce observed spatiotemporal variability of precipitation in the Tampa Bay region over the 1986–2008 period. Raw MM5 model results were positively biased; therefore, the raw model precipitation outputs were bias corrected at 53 long-term precipitation stations in the region using the cumulative distribution function (CDF) mapping approach. CDF mapping effectively removed the bias in the mean daily, monthly, and annual precipitation totals and improved the RMSE of these rainfall totals. Observed daily precipitation transition probabilities were also well predicted by the bias-corrected MM5 results. Nevertheless, significant error remained in predicting specific daily, monthly, and annual total time series. After bias correction, MM5 successfully reproduced seasonal geostatistical precipitation patterns, with higher spatial variance of daily precipitation in the wet season and lower spatial variance of daily precipitation in the dry season. Bias-corrected daily precipitation fields were kriged over the study area to produce spatiotemporally distributed precipitation fields over the dense grids needed to drive hydrologic models in the Tampa Bay region. Cross validation at the 53 long-term precipitation gauges showed that kriging reproduced observed rainfall with average RMSEs lower than the RMSEs of individually bias-corrected point predictions. Results indicate that although significant error remains in predicting actual daily precipitation at rain gauges, kriging the bias-corrected MM5 predictions over a hydrologic model grid produces distributed precipitation fields with sufficient realism in the daily, seasonal, and interannual patterns to be useful for multidecadal water resource planning in the Tampa Bay region.