An objective rain gauge quality control (QC) procedure is developed to minimize the amount of erroneous gauge data that is input to the MPE procedure. This objective scheme compares the gauge data with corresponding raw radar data. A rigorous examination of rain gauge data and the encompassing radar-derived rainfall estimates revealed four main scenarios that are included in the QC procedure. The scenario responsible for removing the most data occurs when a radar-derived estimate reports heavy rainfall (> 1 in./h), while the gauge within that radar grid cell reports a value near zero. The QC procedure removes most of these suspect gauge data, thereby limiting their corrupting effect on the bias calculations within the MPE procedure. The other three QC scenarios removed a smaller number of gauges that would have adversely affected the calculations.
An analysis of the different rainfall products produced by MPE demonstrates the positive impact of the extensive quality control efforts. An independent set of rain gauges is used to evaluate the MPE products statistically for selected periods during 1999 and 2001. Results show that the final MPE product (MMOSAIC) outperforms both the radar and gauge data alone. Gauges alone generally cannot accurately represent the spatial details of warm season convective type precipitation events. Conversely, radar data depict convective precipitation events quite well; however, radars do a poor job of detecting cold season stratiform precipitation events. Analyses reveal that the MMOSAIC product utilizes the strengths of the gauges and the radars in an optimum way. Seasonal case studies comparing the independent set of gauge observations to the final MPE product show that there is good agreement between the two hourly sources (i.e., biases °Ö -0.004 in., r °Ö 0.78, and RMSD °Ö 0.12 in.). Agreements are found to improve over daily and monthly accumulation periods. Results of this study describe the problems and uncertainties associated with quantitatively measuring Florida rainfall through a multisensor analysis. These data will be utilized in a GIS format in various hydrologic studies.
Supplementary URL: http://bertha.met.fsu.edu/