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- Author or Editor: Steven M. Martinaitis x
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Abstract
Statistical evaluations of tornado warnings issued during recent tropical cyclone events yielded an above-average false alarm ratio. This study analyzed tornado-warned convection associated with Tropical Storms Debby (2012) and Andrea (2013) using superresolution and dual-polarization data from Weather Surveillance Radar-1988 Doppler radars located throughout the Florida peninsula to identify precursor characteristics and signatures that would distinguish tornadic events prior to tornadogenesis. A series of radar-based interrogation guidance at varying ranges from radar is presented to help facilitate the reduction of the tornado-warning false alarm ratio without compromising the probability of detection. For convection within 74.1 km from the nearest radar, low-level velocity characteristics include a rotational velocity ≥ 10.3 m s−1 (20 kt), shear across the rotation ≥ 0.010 s−1, and a contracting rotation diameter. The convection should also exhibit supercell reflectivity signatures and at least a mesocyclone velocity enhancement signature or horizontal separation of greater Z DR and K DP values. Guidance at a range from 74.1 to 129.6 km is similar except for not requiring the presence of a supercell reflectivity signature and the change of the rotational velocity guidance to ≥7.7 m s−1 (15 kt) at the 0.5°-elevation angle. Convection at a range beyond 129.6 km only requires a rotational velocity ≥ 7.7 m s−1 (15 kt) at the 0.5°-elevation angle. Evaluation of the radar interrogation guidance for tornadic events and tornado-warned convection for six tropical cyclones reduced the number of false alarm events by 28.9% and reduced the false alarm ratio from 0.740 to 0.669.
Abstract
Statistical evaluations of tornado warnings issued during recent tropical cyclone events yielded an above-average false alarm ratio. This study analyzed tornado-warned convection associated with Tropical Storms Debby (2012) and Andrea (2013) using superresolution and dual-polarization data from Weather Surveillance Radar-1988 Doppler radars located throughout the Florida peninsula to identify precursor characteristics and signatures that would distinguish tornadic events prior to tornadogenesis. A series of radar-based interrogation guidance at varying ranges from radar is presented to help facilitate the reduction of the tornado-warning false alarm ratio without compromising the probability of detection. For convection within 74.1 km from the nearest radar, low-level velocity characteristics include a rotational velocity ≥ 10.3 m s−1 (20 kt), shear across the rotation ≥ 0.010 s−1, and a contracting rotation diameter. The convection should also exhibit supercell reflectivity signatures and at least a mesocyclone velocity enhancement signature or horizontal separation of greater Z DR and K DP values. Guidance at a range from 74.1 to 129.6 km is similar except for not requiring the presence of a supercell reflectivity signature and the change of the rotational velocity guidance to ≥7.7 m s−1 (15 kt) at the 0.5°-elevation angle. Convection at a range beyond 129.6 km only requires a rotational velocity ≥ 7.7 m s−1 (15 kt) at the 0.5°-elevation angle. Evaluation of the radar interrogation guidance for tornadic events and tornado-warned convection for six tropical cyclones reduced the number of false alarm events by 28.9% and reduced the false alarm ratio from 0.740 to 0.669.
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.
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.
A Temporal Gauge Quality Control Algorithm as a Method for Identifying Potential Instrumentation MalfunctionsAbstract
There are multiple reasons as to why a precipitation gauge would report erroneous observations. Systematic errors relating to the measuring apparatus or resulting from observational limitations due to environmental factors (e.g., wind-induced undercatch or wetting losses) can be quantified and potentially corrected within a gauge dataset. Other challenges can arise from instrumentation malfunctions, such as clogging, poor siting, and software issues. Instrumentation malfunctions are challenging to quantify as most gauge quality control (QC) schemes focus on the current observation and not on whether the gauge has an inherent issue that would likely require maintenance of the gauge. This study focuses on the development of a temporal QC scheme to identify the likelihood of an instrumentation malfunction through the examination of hourly gauge observations and associated QC designations. The analyzed gauge performance resulted in a temporal QC classification using one of three categories: GOOD, SUSP, and BAD. The temporal QC scheme also accounts for and provides an additional designation when a significant percentage of gauge observations and associated hourly QC were influenced by meteorological factors (e.g., the inability to properly measure winter precipitation). Findings showed a consistent percentage of gauges that were classified as BAD through the running 7-day (2.9%) and 30-day (4.4%) analyses. Verification of select gauges demonstrated how the temporal QC algorithm captured different forms of instrumental-based systematic errors that influenced gauge observations. Results from this study can benefit the identification of degraded performance at gauge sites prior to scheduled routine maintenance.
Significance Statement
This study proposes a scheme that quality controls rain gauges based on its performance over a running history of hourly observational data and quality control flags to identify gauges that likely have an instrumentation malfunction. Findings from this study show the potential of identifying gauges that are impacted by issues such as clogging, software errors, and poor gauge siting. This study also highlights the challenges of distinguishing between erroneous gauge observations based on an instrumentation malfunction versus erroneous observations that were the result of an environmental factor that influence the gauge observation or its quality control classification, such as winter precipitation or virga.
Abstract
There are multiple reasons as to why a precipitation gauge would report erroneous observations. Systematic errors relating to the measuring apparatus or resulting from observational limitations due to environmental factors (e.g., wind-induced undercatch or wetting losses) can be quantified and potentially corrected within a gauge dataset. Other challenges can arise from instrumentation malfunctions, such as clogging, poor siting, and software issues. Instrumentation malfunctions are challenging to quantify as most gauge quality control (QC) schemes focus on the current observation and not on whether the gauge has an inherent issue that would likely require maintenance of the gauge. This study focuses on the development of a temporal QC scheme to identify the likelihood of an instrumentation malfunction through the examination of hourly gauge observations and associated QC designations. The analyzed gauge performance resulted in a temporal QC classification using one of three categories: GOOD, SUSP, and BAD. The temporal QC scheme also accounts for and provides an additional designation when a significant percentage of gauge observations and associated hourly QC were influenced by meteorological factors (e.g., the inability to properly measure winter precipitation). Findings showed a consistent percentage of gauges that were classified as BAD through the running 7-day (2.9%) and 30-day (4.4%) analyses. Verification of select gauges demonstrated how the temporal QC algorithm captured different forms of instrumental-based systematic errors that influenced gauge observations. Results from this study can benefit the identification of degraded performance at gauge sites prior to scheduled routine maintenance.
Significance Statement
This study proposes a scheme that quality controls rain gauges based on its performance over a running history of hourly observational data and quality control flags to identify gauges that likely have an instrumentation malfunction. Findings from this study show the potential of identifying gauges that are impacted by issues such as clogging, software errors, and poor gauge siting. This study also highlights the challenges of distinguishing between erroneous gauge observations based on an instrumentation malfunction versus erroneous observations that were the result of an environmental factor that influence the gauge observation or its quality control classification, such as winter precipitation or virga.
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
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
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 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
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.
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.
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.
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.
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.
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.
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
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.
