Flash floods are among the most devastating natural weather hazards in the United States, causing an average of more than 225 deaths and $4 billion in property damage annually. As a result, prediction of flash floods in an accurate and timely fashion is one of the most important challenges in weather prediction. Data from geostationary and polar-orbiting satellites are significant sources of information for the diagnosis and prediction of heavy precipitation and flash floods. Geostationary satellites are especially important for their unique ability simultaneously to observe the atmosphere and its cloud cover from the global scale down to the storm scale at high resolution in both time (every 15 min) and space (1-4 km). This capability makes geostationary satellite data ideally suited for estimating and predicting heavy precipitation, especially during flash-flood events. Presented in this paper are current and future efforts in the National Environmental Satellite, Data, and Information Service that support National Weather Service River Forecast Centers and Weather Forecast Offices during extreme-precipitation events.

what: 50 participants from precipitation research community met to develop a list of research priorities and recommendations in the field of remote sensing of precipitation whEn: 15-17 March 2010 whErE: University of California, Irvine Overview Of recOmmendatiOns (i) Uncertainty of merged products and multisensor observations warrants a great deal of research. Quantification of uncertainties and their propagation into combined products is vital for future development. (ii) Future improvements in satellite-based precipitation retrieval algorithms will rely on more indepth research on error properties in different climate regions, storm regimes, surface conditions, seasons, and altitudes. Given such information, precipitation algorithms for retrieval, downscaling, and data fusion can be optimized for different situations. (iii) Based on the currently available data, global multichannel precipitation estimates with spatial and temporal resolutions of 4 km and 30 min can be considered as the target dataset that can be achieved in the near future. At high resolutions, however, achieving desirable accuracy is the main challenge. Extensive development and validation efforts are required to make such a dataset available to the community for research and applications. (iv) Development of metrics for validation and uncertainty analysis are of great importance. Various metrics with emphasis on different aspects of performance are required so that users can decide which product fits their purposes/applications best. Furthermore, developing diagnostic statistics (shifting and rotation) will help to capture the systematic deficiency inherent in precipitation retrieval algorithms.

To date, little objective verification has been performed for rainfall predictions from numerical forecasts of landfalling tropical cyclones. Until 2001, digital output from the operational version of the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane forecast model was available only on a 1°grid. The GFDL model was rerun or reanalyzed for 25 U.S. landfalling tropical cyclones from 1995 to 2002 to obtain higher resolution (1/3°) output. Several measures of forecast quality were used to evaluate the predicted rainfall from these runs, using daily rain gauge data as ground truth. The overall quality was measured by the mean error and bias averaged over all the gauge sites. An estimate of the quality of the forecasted pattern was obtained through the correlation coefficient of the model versus gauge values. In addition, more traditional precipitation verification scores were calculated including equitable threat and bias scores. To evaluate the skill of the rainfall forecasts, a simple rainfall climatology and persistence (R-CLIPER) model was developed, where a climatological rainfall rate is accumulated along either the forecasted or observed storm track. Results show that the R-CLIPER and GFDL forecasts had comparable mean absolute errors of ϳ0.9 in. (23 mm) for the 25 cases. The GFDL model exhibited a higher pattern correlation with observations than R-CLIPER, but still only explained ϳ30% of the spatial variance. The GFDL model also had higher equitable threat scores than R-CLIPER, partially because of a low bias of R-CLIPER for rainfall amounts larger than 0.5 in. (13 mm). A large case-to-case variability was found that was dependent on both synoptic conditions and track error.

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A multisensor applications development and evaluation system at the National Severe Storms Laboratory addresses significant gaps in both our knowledge and capabilities for accurate high-resolution precipitation estimates at the national scale. W ater is a precious resource and, when excessive or in short supply, a source of many hazards. It is essential to monitor and predict water-related hazards, such as floods, droughts, debris flows, and water quality, and to determine current and future availability of water resources. Accurate quantitative precipitation estimates (QPE) and very short term quantitative precipitation forecasts (VSTQPF) provide key input to these assessments. [QPE and VSTQPF are hereafter referred to collectively as quantitative precipitation information (QPl}.] To meet these needs at the national scale, accurate QPI is needed at various temporal and spatial scales for the entire United States, its territories, and immediate surrounding areas. Temporal scales range from minutes to several hours for tiash flood prediction. QPI products can then be aggregated to support longer-term applications for water supply prediction. Spatial scales range from a few square kilometers or less for urban flash flood predictit)n.