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.

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 (Z–R) 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.

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.

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.

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.

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.

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.

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.

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.

The National Mosaic and Multi-sensor QPE (Quantitative Precipitation Estimation), or “NMQ”, system was initially developed from a joint initiative between the National Oceanic and Atmospheric Administration's National Severe Storms Laboratory, the Federal Aviation Administration's Aviation Weather Research Program, and the Salt River Project. Further development has continued with additional support from the National Weather Service (NWS) Office of Hydrologic Development, the NWS Office of Climate, Water, and Weather Services, and the Central Weather Bureau of Taiwan. The objectives of NMQ research and development (R&D) are 1) to develop a hydrometeorological platform for assimilating different observational networks toward creating high spatial and temporal resolution multisensor QPEs for f lood warnings and water resource management and 2) to develop a seamless high-resolution national 3D grid of radar reflectivity for severe weather detection, data assimilation, numerical weather prediction model verification, and aviation product development.

Through about ten years of R&D, a real-time NMQ system has been implemented (http://nmq.ou.edu). Since June 2006, the system has been generating high-resolution 3D reflectivity mosaic grids (31 vertical levels) and a suite of severe weather and QPE products in real-time for the conterminous United States at a 1-km horizontal resolution and 2.5 minute update cycle. The experimental products are provided in real-time to end users ranging from government agencies, universities, research institutes, and the private sector and have been utilized in various meteorological, aviation, and hydrological applications. Further, a number of operational QPE products generated from different sensors (radar, gauge, satellite) and by human experts are ingested in the NMQ system and the experimental products are evaluated against the operational products as well as independent gauge observations in real time.

The NMQ is a fully automated system. It facilitates systematic evaluations and advances of hydrometeorological sciences and technologies in a real-time environment and serves as a test bed for rapid science-to-operation infusions. This paper describes scientific components of the NMQ system and presents initial evaluation results and future development plans of the system.