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  • Author or Editor: Kenneth W. Howard x
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Jonathan J. Gourley, Robert A. Maddox, Kenneth W. Howard, and Donald W. Burgess


Implementation of the National Weather Service Weather Surveillance Radar-1988 Doppler (WSR-88D) radar network provides the potential to monitor rainfall and snowfall accumulations at fine spatial and temporal resolutions. An automated, operational algorithm called the Precipitation Processing System (PPS) uses reflectivity data to estimate precipitation accumulations. The utility of these estimates has yet to be quantified in the Intermountain West during winter months. The accuracy of precipitation estimates from the operational PPS during cool-season, stratiform-precipitation events in Arizona is examined. In addition, a method, with the potential for automation, is developed to improve estimates of precipitation by calibrating infrared data (10.7-μm band) from Geostationary Operational Environmental Satellite-9 using reflectivity-derived rainfall rates from WSR-88D radar. The “multisensor” approach provides more accurate estimates of rainfall across lower elevations during cool-season extratropical storms. After the melting layer has been manually identified using volumetric radar reflectivity data, reflectivity measured in or above it is discarded. Melting-layer heights also indicate the altitude of the rain–snow line. This information is used to delineate and map frozen versus liquid precipitation types. Rain gauges are used as an independent, ground-based source to assess the magnitude of improvements made over PPS rainfall products. Although the technique is designed and evaluated over a limited area in Arizona, it may be applicable to many mountainous regions.

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Steven M. Martinaitis, Stephen B. Cocks, Andrew P. Osborne, Micheal J. Simpson, Lin Tang, Jian Zhang, and Kenneth W. Howard


Hurricane Harvey in 2017 generated one of the most catastrophic rainfall events in United States history. Numerous gauge observations in Texas exceeded 1200 mm, and the record accumulations resulted in 65 direct fatalities from rainfall-induced flooding. This was followed by Hurricane Florence in 2018, where multiple regions in North Carolina received over 750 mm of rainfall. The Multi-Radar Multi-Sensor (MRMS) system provides the unique perspective of applying fully automated seamless radar mosaics and locally gauge-corrected products for these two historical tropical cyclone rainfall events. This study investigates the performance of various MRMS quantitative precipitation estimation (QPE) products as it pertains to rare extreme tropical cyclone rainfall events. Various biases were identified in the radar-only approaches, which were mitigated in a new dual-polarimetric synthetic radar QPE approach. A local gauge correction of radar-derived QPE provided statistical improvements over the radar-only products but introduced consistent underestimation biases attributed to undercatch from tropical cyclone winds. This study then introduces a conceptual methodology to bulk correct for gauge wind undercatch across the numerous gauge networks ingested by the MRMS system. Adjusting the hourly gauge observations for wind undercatch resulted in increased storm-total accumulations for both tropical cyclones that better matched independent gauge observations, yet its application across large network collections highlighted the challenges of applying a singular wind undercatch correction scheme for significant wind events (e.g., tropical cyclones) while recognizing the need for increased metadata on gauge characteristics.

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Steven M. Martinaitis, Stephen B. Cocks, Micheal J. Simpson, Andrew P. Osborne, Sebastian S. Harkema, Heather M. Grams, Jian Zhang, and Kenneth W. Howard


This study describes recent advancements in the Multi-Radar Multi-Sensor (MRMS) automated gauge ingest and quality control (QC) processes. A data latency analysis for the combined multiple gauge collection platforms provided guidance for a multiple-pass generation and delivery of gauge-based precipitation products. Various advancements to the gauge QC logic were evaluated over a 21-month period, resulting in an average of 86% of hourly gauge observations per hour being classified as useful. The fully automated QC logic was compared to manual human QC for a limited domain, which showed a >95% agreement in their QC reasoning categories. This study also includes an extensive evaluation of various characteristics related to the gauge observations ingested into the MRMS system. Duplicate observations between gauge collection platforms highlighted differences in site coordinates; moreover, errors in Automated Surface Observing System (ASOS) station site coordinates resulted in >79% of sites being located in a different MRMS 1-km grid cell. The ASOS coordinate analysis combined with examinations of other limitations regarding gauge observations highlight the need for robust and accurate metadata to further enhance the quality control of gauge data.

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Steven M. Martinaitis, Andrew P. Osborne, Micheal J. Simpson, Jian Zhang, Kenneth W. Howard, Stephen B. Cocks, Ami Arthur, Carrie Langston, and Brian T. Kaney


Weather radars and gauge observations are the primary observations to determine the coverage and magnitude of precipitation; however, radar and gauge networks have significant coverage gaps, which can underrepresent or even miss the occurrence of precipitation. This is especially noticeable in mountainous regions and in shallow precipitation regimes. The following study presents a methodology to improve spatial representations of precipitation by seamlessly blending multiple precipitation sources within the Multi-Radar Multi-Sensor (MRMS) system. A high spatiotemporal resolution multisensor merged quantitative precipitation estimation (QPE) product (MSQPE) is generated by using gauge-corrected radar QPE as a primary precipitation source with a combination of hourly gauge observations, monthly precipitation climatologies, numerical weather prediction short-term precipitation forecasts, and satellite observations to use in areas of insufficient radar coverage. The merging of the precipitation sources is dependent upon radar coverage based on an updated MRMS radar quality index, surface and atmospheric conditions, topography, gauge locations, and precipitation values. Evaluations of the MSQPE product over the western United States resulted in improved statistical measures over its individual input precipitation sources, particularly the locally gauge-corrected radar QPE. The MSQPE scheme demonstrated its ability to sufficiently fill in areas where radar alone failed to detect precipitation due to significant beam blockage or poor coverage while minimizing the generation of false precipitation and underestimation biases that resulted from radar overshooting precipitation.

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