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Steven M. Martinaitis
,
Stephen B. Cocks
,
Youcun Qi
,
Brian T. Kaney
,
Jian Zhang
, and
Kenneth Howard

Abstract

Precipitation gauge observations are routinely classified as ground truth and are utilized in the verification and calibration of radar-derived quantitative precipitation estimation (QPE). This study quantifies the challenges of utilizing automated hourly gauge networks to measure winter precipitation within the real-time Multi-Radar Multi-Sensor (MRMS) system from 1 October 2013 to 1 April 2014. Gauge observations were compared against gridded radar-derived QPE over the entire MRMS domain. Gauges that reported no precipitation were classified as potentially stuck in the MRMS system if collocated hourly QPE values indicated nonzero precipitation. The average number of potentially stuck gauge observations per hour doubled in environments defined by below-freezing surface wet-bulb temperatures, while the average number of observations when both the gauge and QPE reported precipitation decreased by 77%. Periods of significant winter precipitation impacts resulted in over a thousand stuck gauge observations, or over 10%–18% of all gauge observations across the MRMS domain, per hour. Partial winter impacts were observed prior to the gauges becoming stuck. Simultaneous postevent thaw and precipitation resulted in unreliable gauge values, which can introduce inaccurate bias correction factors when calibrating radar-derived QPE. The authors then describe a methodology to quality control (QC) gauge observations compromised by winter precipitation based on these results. A comparison of two gauge instrumentation types within the National Weather Service (NWS) Automated Surface Observing System (ASOS) network highlights the need for improved gauge instrumentation for more accurate liquid-equivalent values of winter precipitation.

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Stephen B. Cocks
,
Steven M. Martinaitis
,
Brian Kaney
,
Jian Zhang
, and
Kenneth Howard

Abstract

A recently implemented operational quantitative precipitation estimation (QPE) product, the Multi-Radar Multi-Sensor (MRMS) radar-only QPE (Q3RAD), mosaicked dual-polarization QPE, and National Centers for Environmental Prediction (NCEP) stage II QPE were evaluated for nine cool season precipitation events east of the Rockies. These automated, radar-only products were compared with the forecaster quality-controlled NCEP stage IV product, which was considered as the benchmark for QPE. Community Collaborative Rain, Hail and Snow Network (CoCoRaHS) 24-h accumulation data were used to evaluate product performance while hourly automated gauge data (quality controlled) were used for spatial and time series analysis. Statistical analysis indicated all three radar-only products had a distinct underestimate bias, likely due to the radar beam partially or completely overshooting the predominantly shallow winter precipitation systems. While the forecaster quality-controlled NCEP stage IV estimates had the best overall performance, Q3RAD had the next best performance, which was significant as Q3RAD is available in real time whereas NCEP stage IV estimates are not. Stage II estimates exhibited a distinct tendency to underestimate gauge totals while dual-polarization estimates exhibited significant errors related to melting layer challenges.

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Yadong Wang
,
Stephen Cocks
,
Lin Tang
,
Alexander Ryzhkov
,
Pengfei Zhang
,
Jian Zhang
, and
Kenneth Howard

Abstract

A prototype quantitative precipitation estimate (QPE) algorithm that utilizes specific attenuation A and specific differential phase K DP was developed for inclusion into the Multi-Radar Multi-Sensor (MRMS) system and the Weather Surveillance Radar-1988 Doppler (WSR-88D) network. Special attention is given to the optimization of the factor α used for computation of a path-integrated attenuation from a total span of differential phase along the propagation path in rain. It is suggested to estimate α from a slope of the Z DR dependence on Z in rain. The use of real-time adjusted α allows us to capture the variations of the drop size distributions, and therefore improve the QPE accuracy. It is demonstrated that the factor α is generally higher for tropical rain type compared to continental rain. Since the R(A) approach is only valid for pure rainfall, the R(K DP) relation is suggested as a complement in areas of hail contamination. The paper contains a description of the basic version of the R(A) and R(K DP) algorithm and recommendations for its further optimization.

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Jian Zhang
,
Lin Tang
,
Stephen Cocks
,
Pengfei Zhang
,
Alexander Ryzhkov
,
Kenneth Howard
,
Carrie Langston
, and
Brian Kaney

Abstract

A new dual-polarization (DP) radar synthetic quantitative precipitation estimation (QPE) product was developed using a combination of specific attenuation A, specific differential phase K DP, and reflectivity Z to calculate the precipitation rate R. Specific attenuation has advantages of being insensitive to systematic biases in Z and differential reflectivity Z DR due to partial beam blockage, attenuation, and calibration while more linearly related to R than other radar variables. However, the R(A) relationship is not applicable in areas containing ice. Therefore, the new DP QPE applies R(A) in areas where radar is observing pure rain, R(K DP) in regions potentially containing hail, and R(Z) elsewhere. Further, an evaporation correction was applied to minimize false light precipitation related to virga. The new DP QPE was evaluated in real time over the conterminous United States and showed significant improvements over previous radar QPE techniques that were based solely on R(Z) relationships. The improvements included reduced dry biases in heavy to extreme precipitation during the warm season. The new DP QPE also reduced errors and spatial discontinuities in regions impacted by partial beam blockage. Further, the new DP QPE reduced wet bias for scattered light precipitation in the southwest and north central United States where there is significant boundary layer evaporation.

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Charles L. Dempsey
,
Kenneth W. Howard
,
Robert A. Maddox
, and
Daniel H. Phillips

The National Severe Storms Laboratory, the Salt River Project (SRP), and the Electric Power Research Institute have been involved in a multiyear tailored collaboration (TC) research project. The project was jointly supported by all three agencies and had the goal of exploring potential benefits that the power industry could realize by incorporating new weather information, resulting from the National Weather Service's modernization program, into their operational decision-making process. The SRP, which is one of the nation's largest public utilities and located in the greater Phoenix metropolitan area, served as a test bed for a variety of experimental techniques that could easily be emulated in the future. Activities during this TC were focused upon weather-related problems experienced during the summer monsoon months when thunderstorms can threaten or impact SRP's operations on a daily basis. Weather information and special forecasts were introduced to and shared with several of SRP's operational divisions through the course of this TC; their degree of utilization and subsequent improvements to SRP's operational efficiency are summarized in this paper.

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Ami T. Arthur
,
Gina M. Cox
,
Nathan R. Kuhnert
,
David L. Slayter
, and
Kenneth W. Howard

The National Basin Delineation Project (NBDP) was undertaken by the National Severe Storms Laboratory to define flash-flood-scale basin boundaries for the country in support of the National Weather Service (NWS) Flash Flood Monitoring and Prediction (FFMP) system. FFMP-averaged basin rainfall calculations allow NWS forecasters to monitor precipitation in flash-flood-scale basins, improving their ability to make accurate and timely flash-flood-warning decisions. The NBDP was accomplished through a partnership with the U.S. Geological Survey Earth Resources Observation Systems (EROS) Data Center (EDC). The one-arc-second (approximately 30 m)-resolution digital terrain data in the EDC's National Elevation Dataset provided the basis for derivation of the following digital maps using a geographic information system: 1) a grid of hydrologically conditioned elevation values (all grid cells have a defined flow direction), 2) a grid of flow direction indicating which of eight directions water will travel based on slope, 3) a grid of flow accumulation containing a count of the number of upstream grid cells contributing flow to each grid cell, 4) synthetic streamlines derived from the flow accumulation grid, and 5) flash-flood-scale basin boundaries. Special techniques were applied in coastal areas and closed basins (basins with no outflow) to ensure the accuracy of derived basins and streams. Codifying each basin with a unique identifier and including hydrologic connectivity information produced a versatile, seamless dataset for use in FFMP and other national applications.

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Alan Gerard
,
Steven M. Martinaitis
,
Jonathan J. Gourley
,
Kenneth W. Howard
, and
Jian Zhang

Abstract

The Multi-Radar Multi-Sensor (MRMS) system is an operational, state-of-the-science hydrometeorological data analysis and nowcasting framework that combines data from multiple radar networks, satellites, surface observational systems, and numerical weather prediction models to produce a suite of real-time, decision-support products every 2 min over the contiguous United States and southern Canada. The Flooded Locations and Simulated Hydrograph (FLASH) component of the MRMS system was designed for the monitoring and prediction of flash floods across small time and spatial scales required for urban areas given their rapid hydrologic response to precipitation. Developed at the National Severe Storms Laboratory in collaboration with the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) and other research entities, the objective for MRMS and FLASH is to be the world’s most advanced system for severe weather and storm-scale hydrometeorology, leveraging the latest science and observation systems to produce the most accurate and reliable hydrometeorological and severe weather analyses. NWS forecasters, the public, and the private sector utilize a variety of products from the MRMS and FLASH systems for hydrometeorological situational awareness and to provide warnings to the public and other users about potential impacts from flash flooding. This article will examine the performance of hydrometeorological products from MRMS and FLASH and provide perspectives on how NWS forecasters use these products in the prediction of flash flood events with an emphasis on the urban environment.

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Katherine M. Willingham
,
Elizabeth J. Thompson
,
Kenneth W. Howard
, and
Charles L. Dempsey

Abstract

During the 2008 North American monsoon season, 140 microburst events were identified in Phoenix, Arizona, and the surrounding Sonoran Desert. The Sonoran microbursts were studied and examined for their frequency and characteristics, as observed from data collected from three Doppler radars and electrical power infrastructure damage reports. Sonoran microburst events were wet microbursts and occurred most frequently in the evening hours (1900–2100 local time). Stronger maximum differential velocities (20–25 m s−1) were observed more frequently in Sonoran microbursts than in many previously documented microbursts. Alignment of Doppler radar data to reports of wind-related damage to electrical power infrastructure in Phoenix allowed a comparison of microburst wind damage versus gust-front wind damage. For these damage reports, microburst winds caused more significant damage than gust-front winds.

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Robert A. Maddox
,
Jian Zhang
,
Jonathan J. Gourley
, and
Kenneth W. Howard

Abstract

Terrain and radar beam-elevation data are used to examine the spatial coverage provided by the national operational network of Doppler weather radars. This information is of importance to a wide variety of users, and potential users, of radar data from the national network. Charts generated for radar coverage at 3 and 5 km above mean sea level show that radar surveillance near 700 and 500 hPa is very limited for some portions of the contiguous United States. Radar coverage charts at heights of 1, 2, and 3 km above ground level illustrate the extent of low-level radar data gathered above the actual land surface. These maps indicate how restricted the national radar network coverage is at low levels, which limits the usefulness of the radar data, especially for quantitative precipitation estimation. The analyses also identify several regions of the contiguous United States in which weather phenomena are sampled by many adjacent radars. Thus, these regions are characterized by very comprehensive radar information that could be used in many kinds of research studies.

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Lin Tang
,
Jian Zhang
,
Carrie Langston
,
John Krause
,
Kenneth Howard
, and
Valliappa Lakshmanan

Abstract

Polarimetric radar observations provide information regarding the shape and size of scatterers in the atmosphere, which help users to differentiate between precipitation and nonprecipitation radar echoes. Identifying and removing nonprecipitation echoes in radar reflectivity fields is one critical step in radar-based quantitative precipitation estimation. An automated algorithm based on reflectivity, correlation coefficient, and temperature data is developed to perform reflectivity data quality control through a set of physically based rules. The algorithm was tested with a large number of real data cases across different geographical regions and seasons and showed a high accuracy (Heidke skill score of 0.83) in segregating precipitation and nonprecipitation echoes. The algorithm was compared with two other operational and experimental reflectivity quality control methodologies and showed a more effective removal of nonprecipitation echoes and a higher computational efficiency. The current methodology also demonstrated a satisfactory performance in a real-time national multiradar and multisensor system.

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