Dennis Miller

The WSR-88D Rainfall Algorithm

Abstract A detailed description of the operational WSR-88D rainfall estimation algorithm is presented. This algorithm, called the Precipitation Processing System, produces radar-derived rainfall products in real time for forecasters in support of the National Weather Service’s warning and forecast missions. It transforms reflectivity factor measurements into rainfall accumulations and incorporates rain gauge data to improve the radar estimates. The products are used as guidance to issue flood watches and warnings to the public and as input into numerical hydrologic and atmospheric models. The processing steps to quality control and compute the rainfall estimates are described, and the current deficiencies and future plans for improvement are discussed.

Real-Time Adjustment of Range-Dependent Biases in WSR-88D Rainfall Estimates due to Nonuniform Vertical Profile of Reflectivity

Abstract A procedure for real-time adjustment of range-dependent biases in Weather Surveillance Radar-1988 Doppler version (WSR-88D) rainfall estimates due to nonuniform vertical profile of reflectivity is proposed. Using volume-scan measurements of effective reflectivity, the procedure estimates conditional mean and variance profiles of point reflectivity in the vertical and calculates, as a function of elevation angle and slant range, a multiplicative adjustment factor to be applied to the apparent radar rain rate. As a by-product, the maximum effective coverage of radar is also delineated, outside of which radar rainfall estimates are subject to beam overshooting. To evaluate the procedure, unadjusted and adjusted radar rainfall estimates from a Pacific Northwest winter storm are examined and compared with rain gauge data.

Radar and Multisensor Precipitation Estimation Techniques in National Weather Service Hydrologic Operations

This paper describes techniques used operationally by the National Weather Service (NWS) to prepare gridded multisensor (gauge, radar, and satellite) quantitative precipitation estimates (QPEs) for input into hydrologic forecast models and decision- making systems for river forecasting, flood and flash flood warning, and other hydrologic monitoring purposes. Advanced hydrologic prediction techniques require a spatially continuous representation of the precipitation field, and remote sensor input is critical to achiev- ing this continuity. Although detailed descriptions of individual remote sensor estimation algorithms have been published, this review provides a summary of how the estimates from these various sources are merged into finished products. Emphasis is placed on the Weather Surveillance Radar-1988 Doppler (WSR-88D) Precipitation Processing System (PPS) and the Advanced Weather Interactive Processing System (AWIPS) Multisensor Precipitation Estimator (MPE) algorithms that utilize a combination of in situ rain gauges and remotely sensed measurements to provide a real-time suite of gridded radar and multisensor precipitation products. These two algorithm suites work in series to combine both computer-automated and human-interactive techniques, and they are used routinely at NWS field offices (river forecast centers (RFCs) and weather forecast offices (WFOs)) to support NWSs broader hydrologic missions. The resulting precipitation products are also available to scientists and engineers outside the NWS; a summary of charac- teristics and sources of these products is presented. DOI: 10.1061/(ASCE)HE.1943-5584.0000523.

Radar-Based Precipitation Processing for NWS Hydrologic Forecast Services

The National Weather Service (NWS) runs an operational, multi-stage, precipitation processing system which determines rainfall accumulation estimates over a variety of spatial and temporal scales for use in forecasting, warning and numerical modeling applications and for dissemination to the general public. The primary input data to this system are radar reflectivity factor returns provided by the Weather Surveillance Radar-1988 Doppler (WSR-88D) radars of the Next Generation Weather Radar (NEXRAD) program. Since 1991 over 160 of these radars have been deployed, providing nearly contiguous coverage across most of the United States. Rain gage data are also incorporated at various stages of the system, principally to provide calibration of radar rainfall estimates. As the processing proceeds across NWS venues from a local to a regional to a national level, numerous quality control operations are performed, radar rainfall data are composited together spatially (mosaicked), and a wide variety of products are generated including alphanumeric, graphics-display, and high-resolution, digital-data. The products, which are updated as often as every five minutes, provide guidance to forecasters and input to hydrologic and other numerical models. In some instances, they are made available to users outside the Weather Service, as well.

Application of Pre-NEXRAD Reflectivity Data to Hourly Precipitation Analyses

AbstractMultidecadal records of hourly precipitation estimates are needed to provide forcings for the simulation of subdaily processes in hydrologic models. Existing climatological datasets, such as the North American Land Data Assimilation System version 2 (NLDAS2), determine hourly rainfall estimates via the temporal downscaling (disaggregation) of far-more-plentiful daily rain gauge reports. The National Weather Service’s (NWS) National Water Center (NWC) compared NLDAS2 hourly precipitation estimates in the period since 1996, when Next Generation Weather Radar (NEXRAD) Stage-II hourly data became available for use in achieving the disaggregation, with analogous estimates from before 1996, which were disaggregated without the use of radar data. For 20 independently selected days/cases with substantial precipitation during each of the two periods, the post-1996 NLDAS2 hourly amounts were found to be far more highly correlated with cooperative (COOP) rain gauge reports, used for verification, than were t...

High Resolution Radar Precipitation Evaluation

Since the summer of 2008, the Weather Surveillance Radar-1988 Doppler (WSR-88D) has had the capability to process base reflectivity data at eight times higher spatial resolution than previously (250 m x 0.5o vs. 1000 m x 1.0o). While this “super-resolution” is only being applied to some base radar products during the initial phase of implementation, we wish to determine what benefits may be achieved from its application to rainfall accumulation products such as those of the WSR-88D Precipitation Processing System (PPS). For example, higher radar spatial resolution may be expected to result in better detection of small-scale, heavy rain patterns and, in turn, better distribution of that rain to topographic features such as small-scale stream basins. However, these potential benefits might be offset by factors known to cause discrepancies between quantitative precipitation estimates (QPE) determined aloft and the amounts and distribution of rainfall realized at the ground, including subbeam advection, evaporation, and hydrometeor interactions. Indeed, the impact of these factors may be exacerbated when QPEs are analyzed at finer spatial scales.