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Ronald L. Holle
,
Raúl E. López
,
Kenneth W. Howard
,
Kenneth L. Cummins
,
Mark D. Malone
, and
E. Philip Krider

An isolated lightning flash at 1436:52 UTC 11 February 1996 struck and destroyed a house in Burlington, Connecticut, injuring an occupant of the house. A flash detected simultaneously by the National Lightning Detection Network was within 1.1 km of the house. The flash was separated from any other flash by several hours and hundreds of kilometers and occurred during winter. Positive charge was lowered to ground by the flash, as has been found in previous studies of winter storms. Its estimated peak current of +76 kA was stronger than most positive flashes and nearly all negative cloud-to-ground flashes for the entire year in the same area. The incident is compared with other previously documented lightning casualty and damage statistics during wintertime for Connecticut and other regions of the United States. The importance of the flash is described in relation to the resulting material damage and personal injury, the handling of insurance claims, the use of flash data in forecasting and warning applications, and personal safety.

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Kenneth J. Voss
,
Howard R. Gordon
,
Stephanie Flora
,
B. Carol Johnson
,
Mark Yarbrough
,
Michael Feinholz
, and
Terrence Houlihan

Abstract

The upwelling radiance attenuation coefficient K Lu in the upper 10 m of the water column can be significantly influenced by inelastic scattering processes and thus will vary even with homogeneous water properties. The Marine Optical Buoy (MOBY), the primary vicarious calibration site for many ocean color sensors, makes measurements of the upwelling radiance L u at 1, 5, and 9 m, and uses these values to determine K Lu and to propagate the upwelling radiance directed toward the zenith, L u , at 1 m to and through the surface. Inelastic scattering causes the K Lu derived from the measurements to be an underestimate of the true K Lu from 1 m to the surface at wavelengths greater than 575 nm; thus, the derived water-leaving radiance is underestimated at wavelengths longer than 575 nm. A method to correct this K Lu, based on a model of the upwelling radiance including Raman scattering and chlorophyll fluorescence, has been developed that corrects this bias. The model has been experimentally validated, and this technique can be applied to the MOBY dataset to provide new, more accurate products at these wavelengths. When applied to a 4-month MOBY deployment, the corrected water-leaving radiance L w can increase by 5% (600 nm), 10% (650 nm), and 50% (700 nm). This method will be used to provide additional and more accurate products in the MOBY dataset.

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Fred V. Brock
,
Kenneth C. Crawford
,
Ronald L. Elliott
,
Gerrit W. Cuperus
,
Steven J. Stadler
,
Howard L. Johnson
, and
Michael D. Eilts

Abstract

The Oklahoma mesonet is a joint project of Oklahoma State University and the University of Oklahoma. It is an automated network of 108 stations covering the state of Oklahoma. Each station measures air temperature, humidity, barometric pressure, wind speed and direction, rainfall, solar radiation, and soil temperatures. Each station transmits a data message every 15 min via a radio link to the nearest terminal of the Oklahoma Law Enforcement Telecommunications System that relays it to a central site in Norman, Oklahoma. The data message comprises three 5-min averages of most data (and one 15-min average of soil temperatures). The central site ingests the data, runs some quality assurance tests, archives the data, and disseminates it in real time to a broad community of users, primarily through a computerized bulletin board system. This manuscript provides a technical description of the Oklahoma mesonet including a complete description of the instrumentation. Sensor inaccuracy, resolution, height with respect to ground level, and method of exposure are discussed.

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

Abstract

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

Abstract

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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David Kitzmiller
,
Suzanne Van Cooten
,
Feng Ding
,
Kenneth Howard
,
Carrie Langston
,
Jian Zhang
,
Heather Moser
,
Yu Zhang
,
Jonathan J. Gourley
,
Dongsoo Kim
, and
David Riley

Abstract

This study investigates evolving methodologies for radar and merged gauge–radar quantitative precipitation estimation (QPE) to determine their influence on the flow predictions of a distributed hydrologic model. These methods include the National Mosaic and QPE algorithm package (NMQ), under development at the National Severe Storms Laboratory (NSSL), and the Multisensor Precipitation Estimator (MPE) and High-Resolution Precipitation Estimator (HPE) suites currently operational at National Weather Service (NWS) field offices. The goal of the study is to determine which combination of algorithm features offers the greatest benefit toward operational hydrologic forecasting. These features include automated radar quality control, automated ZR selection, brightband identification, bias correction, multiple radar data compositing, and gauge–radar merging, which all differ between NMQ and MPE–HPE. To examine the spatial and temporal characteristics of the precipitation fields produced by each of the QPE methodologies, high-resolution (4 km and hourly) gridded precipitation estimates were derived by each algorithm suite for three major precipitation events between 2003 and 2006 over subcatchments within the Tar–Pamlico River basin of North Carolina. The results indicate that the NMQ radar-only algorithm suite consistently yielded closer agreement with reference rain gauge reports than the corresponding HPE radar-only estimates did. Similarly, the NMQ radar-only QPE input generally yielded hydrologic simulations that were closer to observations at multiple stream gauging points. These findings indicate that the combination of ZR selection and freezing-level identification algorithms within NMQ, but not incorporated within MPE and HPE, would have an appreciable positive impact on hydrologic simulations. There were relatively small differences between NMQ and HPE gauge–radar estimates in terms of accuracy and impacts on hydrologic simulations, most likely due to the large influence of the input rain gauge information.

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Sheng Chen
,
Jonathan J. Gourley
,
Yang Hong
,
P. E. Kirstetter
,
Jian Zhang
,
Kenneth Howard
,
Zachary L. Flamig
,
Junjun Hu
, and
Youcun Qi

Abstract

Quantitative precipitation estimation (QPE) products from the next-generation National Mosaic and QPE system (Q2) are cross-compared to the operational, radar-only product of the National Weather Service (Stage II) using the gauge-adjusted and manual quality-controlled product (Stage IV) as a reference. The evaluation takes place over the entire conterminous United States (CONUS) from December 2009 to November 2010. The annual comparison of daily Stage II precipitation to the radar-only Q2Rad product indicates that both have small systematic biases (absolute values > 8%), but the random errors with Stage II are much greater, as noted with a root-mean-squared difference of 4.5 mm day−1 compared to 1.1 mm day−1 with Q2Rad and a lower correlation coefficient (0.20 compared to 0.73). The Q2 logic of identifying precipitation types as being convective, stratiform, or tropical at each grid point and applying differential ZR equations has been successful in removing regional biases (i.e., overestimated rainfall from Stage II east of the Appalachians) and greatly diminishes seasonal bias patterns that were found with Stage II. Biases and radar artifacts along the coastal mountain and intermountain chains were not mitigated with rain gauge adjustment and thus require new approaches by the community. The evaluation identifies a wet bias by Q2Rad in the central plains and the South and then introduces intermediate products to explain it. Finally, this study provides estimates of uncertainty using the radar quality index product for both Q2Rad and the gauge-corrected Q2RadGC daily precipitation products. This error quantification should be useful to the satellite QPE community who use Q2 products as a reference.

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Stephen B. Cocks
,
Lin Tang
,
Pengfei Zhang
,
Alexander Ryzhkov
,
Brian Kaney
,
Kimberly L. Elmore
,
Yadong Wang
,
Jian Zhang
, and
Kenneth Howard

Abstract

The quantitative precipitation estimate (QPE) algorithm developed and described in Part I was validated using data collected from 33 Weather Surveillance Radar 1988-Doppler (WSR-88D) radars on 37 calendar days east of the Rocky Mountains. A key physical parameter to the algorithm is the parameter alpha α, defined as the ratio of specific attenuation A to specific differential phase K DP. Examination of a significant sample of tropical and continental precipitation events indicated that α was sensitive to changes in drop size distribution and exhibited lower (higher) values when there were lower (higher) concentrations of larger (smaller) rain drops. As part of the performance assessment, the prototype algorithm generated QPEs utilizing a real-time estimated and a fixed α were created and evaluated. The results clearly indicated ~26% lower errors and a 26% better bias ratio with the QPE utilizing a real-time estimated α as opposed to using a fixed value as was done in previous studies. Comparisons between the QPE utilizing a real-time estimated α and the operational dual-polarization (dual-pol) QPE used on the WSR-88D radar network showed the former exhibited ~22% lower errors, 7% less bias, and 5% higher correlation coefficient when compared to quality controlled gauge totals. The new QPE also provided much better estimates for moderate to heavy precipitation events and performed better in regions of partial beam blockage than the operational dual-pol QPE.

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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

Abstract

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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Pao-Liang Chang
,
Jian Zhang
,
Yu-Shuang Tang
,
Lin Tang
,
Pin-Fang Lin
,
Carrie Langston
,
Brian Kaney
,
Chia-Rong Chen
, and
Kenneth Howard

Abstract

Over the last two decades, the Central Weather Bureau of Taiwan and the U.S. National Severe Storms Laboratory have been involved in a research and development collaboration to improve the monitoring and prediction of river flooding, flash floods, debris flows, and severe storms for Taiwan. The collaboration resulted in the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) system. The QPESUMS system integrates observations from multiple mixed-band weather radars, rain gauges, and numerical weather prediction model fields to produce high-resolution (1 km) and rapid-update (10 min) rainfall and severe storm monitoring and prediction products. The rainfall products are widely used by government agencies and emergency managers in Taiwan for flood and mudslide warnings as well as for water resource management. The 3D reflectivity mosaic and QPE products are also used in high-resolution radar data assimilation and for the verification of numerical weather prediction model forecasts. The system facilitated collaborations with academic communities for research and development of radar applications, including quantitative precipitation estimation and nowcasting. This paper provides an overview of the operational QPE capabilities in the Taiwan QPESUMS system.

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