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Xiaoyong Xu
,
Kenneth Howard
, and
Jian Zhang

Abstract

A radar-based automated technique for the identification of tropical precipitation was developed to improve quantitative precipitation estimation during extreme rainfall events. The technique uses vertical profiles of reflectivity to identify the potential presence of warm rain (i.e., tropical rainfall) microphysics and delineates the tropical rainfall region to which the tropical ZR relationship is applied. The performance of the algorithm is examined based on case studies of five storms that produced extreme precipitation in the United States. Results demonstrate relative improvements in radar-based quantitative precipitation estimation through the automated identification of tropical rainfall and the subsequent adaptation of the tropical ZR relation to account for the potential warm rain processes.

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Jian Zhang
,
Youcun Qi
,
David Kingsmill
, and
Kenneth Howard

Abstract

This study explores error sources of the National Weather Service operational radar-based quantitative precipitation estimation (QPE) during the cool season over the complex terrain of the western United States. A new, operationally geared radar QPE was developed and tested using data from the National Oceanic and Atmospheric Administration Hydrometeorology Testbed executed during the 2005/06 cool season in Northern California. The new radar QPE scheme includes multiple steps for removing nonprecipitation echoes, constructing a seamless hybrid scan reflectivity field, applying vertical profile of reflectivity (VPR) corrections to the reflectivity, and converting the reflectivity into precipitation rates using adaptive ZR relationships. Specific issues in radar rainfall accumulations were addressed, which include wind farm contaminations, blockage artifacts, and discontinuities due to radar overshooting. The new radar QPE was tested in a 6-month period of the 2005/06 cool season and showed significant improvements over the current operational radar QPE (43% reduction in bias and 30% reduction in root-mean-square error) when compared with gauges. In addition, the new technique minimizes various radar artifacts and produces a spatially continuous rainfall product. Such continuity is important for accurate hydrological model predictions. The new technique is computationally efficient and can be easily transitioned into operations. One of the largest remaining challenges is obtaining accurate radar QPE over the windward slopes of significant mountain ranges, where low-level orographic enhancement of precipitation is not resolved by the operational radars leading to underestimation. Additional high-resolution and near-surface radar observations are necessary for more accurate radar QPE over these regions.

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Jian Zhang
,
Youcun Qi
,
Carrie Langston
,
Brian Kaney
, and
Kenneth Howard

Abstract

High-resolution, accurate quantitative precipitation estimation (QPE) is critical for monitoring and prediction of flash floods and is one of the most important drivers for hydrological forecasts. Rain gauges provide a direct measure of precipitation at a point, which is generally more accurate than remotely sensed observations from radar and satellite. However, high-quality, accurate precipitation gauges are expensive to maintain, and their distributions are too sparse to capture gradients of convective precipitation that may produce flash floods. Weather radars provide precipitation observations with significantly higher resolutions than rain gauge networks, although the radar reflectivity is an indirect measure of precipitation and radar-derived QPEs are subject to errors in reflectivity–rain rate (ZR) relationships. Further, radar observations are prone to blockages in complex terrain, which often result in a poor sampling of orographically enhanced precipitation. The current study aims at a synergistic approach to QPE by combining radar, rain gauge, and an orographic precipitation climatology. In the merged QPE, radar data depict high-resolution spatial distributions of the precipitation and rain gauges provide accurate precipitation measurements that correct potential biases in the radar QPE. The climatology provides a high-resolution background of the spatial precipitation distribution in the complex terrain where radar coverage is limited or nonexistent. The merging algorithm was tested on heavy precipitation events in different areas of the United States and provided a superior QPE to the individual components. The new QPE algorithm is fully automated and can be easily implemented in an operational system.

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Youcun Qi
,
Jian Zhang
,
Brian Kaney
,
Carrie Langston
, and
Kenneth Howard

Abstract

Quantitative precipitation estimation (QPE) in the West Coast region of the United States has been a big challenge for Weather Surveillance Radar-1988 Doppler (WSR-88D) because of severe blockages caused by the complex terrain. The majority of the heavy precipitation in the West Coast region is associated with strong moisture flux from the Pacific that interacts with the coastal mountains. Such orographic enhancement of precipitation occurs at low levels and cannot be observed well by WSR-88D because of severe blockages. Specifically, the radar beam either samples too high above the ground or misses the orographic enhancement at lower levels, or the beam broadens with range and cannot adequately resolve vertical variations of the reflectivity structure. The current study developed an algorithm that uses S-band Precipitation Profiler (S-PROF) radar observations in northern California to improve WSR-88D QPEs in the area. The profiler data are used to calculate two sets of reference vertical profiles of reflectivity (RVPRs), one for the coastal mountains and another for the Sierra Nevada. The RVPRs are then used to correct the WSR-88D QPEs in the corresponding areas. The S-PROF–based VPR correction methodology (S-PROF-VPR) has taken into account orographic processes and radar beam broadenings with range. It is tested using three heavy rain events and is found to provide significant improvements over the operational radar QPE.

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Jonathan J. Gourley
,
Robert A. Maddox
,
Kenneth W. Howard
, and
Donald W. Burgess

Abstract

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

Abstract

Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation (QPE) radar only (Q3RAD), Q3RAD local gauge corrected (Q3gc), dual polarization (Dual Pol), legacy Precipitation Processing System (PPS), and National Centers for Environmental Prediction (NCEP) stage IV product performance were evaluated for data collected east of the Rockies during the 2014 warm season. For over 22 000 radar QPE–gauge data pairs, Q3RAD had a higher correlation coefficient (0.85) and a lower mean absolute error (9.4 mm) than the Dual Pol (0.83 and 10.5 mm, respectively) and PPS (0.79 and 10.8 mm, respectively). Q3RAD performed best when the radar beam sampled precipitation within or above the melting layer because of its use of a reflectivity mosaic corrected for brightband contamination. Both NCEP stage IV and Q3gc showed improvement over the radar-only QPEs; while stage IV exhibited the lower errors, the performance of Q3gc was remarkable considering the estimates were automatically generated in near–real time. Regional analysis indicated Q3RAD outperformed Dual Pol and PPS over the southern plains, Southeast/mid-Atlantic, and Northeast. Over the northern United States, Q3RAD had a higher wet bias below the melting layer than both Dual Pol and PPS; within and above the melting layer, Q3RAD exhibited the lowest errors. The Q3RAD wet bias was likely due to MRMS’s overestimation of tropical rain areas in continental regions and applying a high yield reflectivity–rain-rate relationship. An adjustment based on precipitation climatology reduced the wet bias errors by ~22% and will be implemented in the operational MRMS in the fall of 2016.

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Steven M. Martinaitis
,
Heather M. Grams
,
Carrie Langston
,
Jian Zhang
, and
Kenneth Howard

Abstract

Precipitation values estimated by radar are assumed to be the amount of precipitation that occurred at the surface, yet this notion is inaccurate. Numerous atmospheric and microphysical processes can alter the precipitation rate between the radar beam elevation and the surface. One such process is evaporation. This study determines the applicability of integrating an evaporation correction scheme for real-time radar-derived mosaicked precipitation rates to reduce quantitative precipitation estimate (QPE) overestimation and to reduce the coverage of false surface precipitation. An evaporation technique previously developed for large-scale numerical modeling is applied to Multi-Radar Multi-Sensor (MRMS) precipitation rates through the use of 2D and 3D numerical weather prediction (NWP) atmospheric parameters as well as basic radar properties. Hourly accumulated QPE with evaporation adjustment compared against gauge observations saw an average reduction of the overestimation bias by 57%–76% for rain events and 42%–49% for primarily snow events. The removal of false surface precipitation also reduced the number of hourly gauge observations that were considered as “false zero” observations by 52.1% for rain and 38.2% for snow. Optimum computational efficiency was achieved through the use of simplified equations and hourly 10-km horizontal resolution NWP data. The run time for the evaporation correction algorithm is 6–7 s.

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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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