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Abstract
Accurate calibration of radar reflectivity is integral to quantitative radar measurements of precipitation and a myriad of other radar-based applications. A statistical method was developed that utilizes the probability distribution of clutter area reflectivity near a stationary, ground-based radar to provide near-real-time estimates of the relative calibration of reflectivity data. The relative calibration adjustment (RCA) method provides a valuable, automated near-real-time tool for maintaining consistently calibrated radar data with relative calibration uncertainty of ±0.5 dB or better. The original application was to S-band data in a tropical oceanic location, where the stability of the method was thought to be related to the relatively mild ground clutter and limited anomalous propagation (AP). This study demonstrates, however, that the RCA technique is transferable to other S-band radars at locations with more intense ground clutter and AP. This is done using data from NASA’s polarimetric (NPOL) surveillance radar data during the Iowa Flood Studies (IFloodS) Global Precipitation Measurement (GPM) field campaign during spring of 2013 and other deployments. Results indicate the RCA technique is well capable of monitoring the reflectivity calibration of NPOL, given proper generation of an areal clutter map. The main goal of this study is to generalize the RCA methodology for possible extension to other ground-based S-band surveillance radars and to show how it can be used both to monitor the reflectivity calibration and to correct previous data once an absolute calibration baseline is established.
Abstract
Accurate calibration of radar reflectivity is integral to quantitative radar measurements of precipitation and a myriad of other radar-based applications. A statistical method was developed that utilizes the probability distribution of clutter area reflectivity near a stationary, ground-based radar to provide near-real-time estimates of the relative calibration of reflectivity data. The relative calibration adjustment (RCA) method provides a valuable, automated near-real-time tool for maintaining consistently calibrated radar data with relative calibration uncertainty of ±0.5 dB or better. The original application was to S-band data in a tropical oceanic location, where the stability of the method was thought to be related to the relatively mild ground clutter and limited anomalous propagation (AP). This study demonstrates, however, that the RCA technique is transferable to other S-band radars at locations with more intense ground clutter and AP. This is done using data from NASA’s polarimetric (NPOL) surveillance radar data during the Iowa Flood Studies (IFloodS) Global Precipitation Measurement (GPM) field campaign during spring of 2013 and other deployments. Results indicate the RCA technique is well capable of monitoring the reflectivity calibration of NPOL, given proper generation of an areal clutter map. The main goal of this study is to generalize the RCA methodology for possible extension to other ground-based S-band surveillance radars and to show how it can be used both to monitor the reflectivity calibration and to correct previous data once an absolute calibration baseline is established.
Abstract
As the Arctic sea ice thins and ultimately disappears in a warming climate, its insulating power decreases. This causes the surface air temperature to approach the temperature of the relatively warm ocean water below the ice. The resulting increases in air temperature, water vapor, and cloudiness lead to an increase in the surface downwelling longwave radiation (DLR), which enables a further thinning of the ice. This positive ice–insulation feedback operates mainly in the autumn and winter. A climate change simulation with the Community Earth System Model shows that, averaged over the year, the increase in Arctic DLR is 3 times stronger than the increase in Arctic absorbed solar radiation at the surface. The warming of the surface air over the Arctic Ocean during fall and winter creates a strong thermal contrast with the colder surrounding continents. Sea level pressure falls over the Arctic Ocean, and the high-latitude circulation reorganizes into a shallow “winter monsoon.” The resulting increase in surface wind speed promotes stronger surface evaporation and higher humidity over portions of the Arctic Ocean, thus reinforcing the ice–insulation feedback.
Abstract
As the Arctic sea ice thins and ultimately disappears in a warming climate, its insulating power decreases. This causes the surface air temperature to approach the temperature of the relatively warm ocean water below the ice. The resulting increases in air temperature, water vapor, and cloudiness lead to an increase in the surface downwelling longwave radiation (DLR), which enables a further thinning of the ice. This positive ice–insulation feedback operates mainly in the autumn and winter. A climate change simulation with the Community Earth System Model shows that, averaged over the year, the increase in Arctic DLR is 3 times stronger than the increase in Arctic absorbed solar radiation at the surface. The warming of the surface air over the Arctic Ocean during fall and winter creates a strong thermal contrast with the colder surrounding continents. Sea level pressure falls over the Arctic Ocean, and the high-latitude circulation reorganizes into a shallow “winter monsoon.” The resulting increase in surface wind speed promotes stronger surface evaporation and higher humidity over portions of the Arctic Ocean, thus reinforcing the ice–insulation feedback.
Abstract
Ground radar rainfall, necessary for satellite rainfall product (e.g., TRMM and GPM) ground validation (GV) studies, is often retrieved using annual or climatological convective/stratiform Z–R relationships. Using the Kwajalein, Republic of the Marshall Islands (RMI), polarimetric S-band weather radar (KPOL) and gauge network during the 2009 and 2011 wet seasons, the robustness of such rain-rate relationships is assessed through comparisons with rainfall retrieved using relationships that vary as a function of precipitation regime, defined as shallow convection, isolated deep convection, and deep organized convection. It is found that the TRMM-GV 2A53 rainfall product underestimated rain gauges by −8.3% in 2009 and −13.1% in 2011, where biases are attributed to rainfall in organized precipitation regimes. To further examine these biases, 2A53 GV rain rates are compared with polarimetrically tuned rain rates, in which GV biases are found to be minimized when rain relationships are developed for each precipitation regime, where, for example, during the 2009 wet-season biases in isolated deep precipitation regimes were reduced from −16.3% to −4.7%. The regime-based improvements also exist when specific convective and stratiform Z–R relationships are developed as a function of precipitation regime, where negative biases in organized convective events (−8.7%) are reduced to −1.6% when a regime-based Z–R is implemented. Negative GV biases during the wet seasons lead to an underestimation in accumulated rainfall when compared with ground gauges, suggesting that satellite-related bias estimates could be underestimated more than originally described. Such results encourage the use of the large-scale precipitation regime along with their respective locally characterized convective or stratiform classes in precipitation validation endeavors and in development of Z–R rainfall relationships.
Abstract
Ground radar rainfall, necessary for satellite rainfall product (e.g., TRMM and GPM) ground validation (GV) studies, is often retrieved using annual or climatological convective/stratiform Z–R relationships. Using the Kwajalein, Republic of the Marshall Islands (RMI), polarimetric S-band weather radar (KPOL) and gauge network during the 2009 and 2011 wet seasons, the robustness of such rain-rate relationships is assessed through comparisons with rainfall retrieved using relationships that vary as a function of precipitation regime, defined as shallow convection, isolated deep convection, and deep organized convection. It is found that the TRMM-GV 2A53 rainfall product underestimated rain gauges by −8.3% in 2009 and −13.1% in 2011, where biases are attributed to rainfall in organized precipitation regimes. To further examine these biases, 2A53 GV rain rates are compared with polarimetrically tuned rain rates, in which GV biases are found to be minimized when rain relationships are developed for each precipitation regime, where, for example, during the 2009 wet-season biases in isolated deep precipitation regimes were reduced from −16.3% to −4.7%. The regime-based improvements also exist when specific convective and stratiform Z–R relationships are developed as a function of precipitation regime, where negative biases in organized convective events (−8.7%) are reduced to −1.6% when a regime-based Z–R is implemented. Negative GV biases during the wet seasons lead to an underestimation in accumulated rainfall when compared with ground gauges, suggesting that satellite-related bias estimates could be underestimated more than originally described. Such results encourage the use of the large-scale precipitation regime along with their respective locally characterized convective or stratiform classes in precipitation validation endeavors and in development of Z–R rainfall relationships.
Abstract
There are many applications in which the absolute and day-to-day calibrations of radar sensitivity are necessary. This is particularly so in the case of quantitative radar measurements of precipitation. While fine calibrations may be made periodically by a variety of techniques such as the use of antenna ranges, standard targets, and solar radiation, knowledge of variations that occur between such checks is required to maintain the accuracy of the data. This paper presents a method for this purpose using the radar on Kwajalein Atoll to provide a baseline calibration for the control of measurements of rainfall made by the Tropical Rainfall Measuring Mission (TRMM). The method uses echoes from a multiplicity of ground targets. The daily average clutter echoes at the lowest elevation scan have been found to be remarkably stable from hour to hour, day to day, and month to month within better than ±1 dB. They vary significantly only after either deliberate system modifications, equipment failure, or other unknown causes. A cumulative distribution function (CDF) of combined precipitation and clutter reflectivity (Ze in dBZ) is obtained on a daily basis, regardless of whether or not rain occurs over the clutter areas. The technique performs successfully if the average daily area mean precipitation echoes (over the area of the clutter echoes) do not exceed 45 dBZ, a condition that is satisfied in most locales. In comparison, reflectivities associated with the most intense clutter echoes can approach 70 dBZ. Thus, the level at which the CDF reaches 95% is affected only by the clutter and reflects variations only in the radar sensitivity. Daily calculations of the CDFs have recently been made beginning with August 1999 data and are used to correct 7.5 yr of measurements, thus enhancing the integrity of the global record of precipitation observed by TRMM. The method is robust and may be applicable to other ground-based radars.
Abstract
There are many applications in which the absolute and day-to-day calibrations of radar sensitivity are necessary. This is particularly so in the case of quantitative radar measurements of precipitation. While fine calibrations may be made periodically by a variety of techniques such as the use of antenna ranges, standard targets, and solar radiation, knowledge of variations that occur between such checks is required to maintain the accuracy of the data. This paper presents a method for this purpose using the radar on Kwajalein Atoll to provide a baseline calibration for the control of measurements of rainfall made by the Tropical Rainfall Measuring Mission (TRMM). The method uses echoes from a multiplicity of ground targets. The daily average clutter echoes at the lowest elevation scan have been found to be remarkably stable from hour to hour, day to day, and month to month within better than ±1 dB. They vary significantly only after either deliberate system modifications, equipment failure, or other unknown causes. A cumulative distribution function (CDF) of combined precipitation and clutter reflectivity (Ze in dBZ) is obtained on a daily basis, regardless of whether or not rain occurs over the clutter areas. The technique performs successfully if the average daily area mean precipitation echoes (over the area of the clutter echoes) do not exceed 45 dBZ, a condition that is satisfied in most locales. In comparison, reflectivities associated with the most intense clutter echoes can approach 70 dBZ. Thus, the level at which the CDF reaches 95% is affected only by the clutter and reflects variations only in the radar sensitivity. Daily calculations of the CDFs have recently been made beginning with August 1999 data and are used to correct 7.5 yr of measurements, thus enhancing the integrity of the global record of precipitation observed by TRMM. The method is robust and may be applicable to other ground-based radars.
Abstract
Lake-effect snowstorms generally develop within convective boundary layers, which are induced when cold air flows over relatively warm lakes in fall and winter. Mesoscale circulations within the boundary layers largely control which communities near the downwind shores of the lakes receive the most intense snow. The lack of quantitative observations over the lakes during lake-effect storms limits the ability to fully understand and predict these mesoscale circulations. This study provides the first observations of the concurrent spatial and temporal evolution of the thermodynamic and microphysical boundary layer structure and mesoscale convective patterns across Lake Michigan during an intense lake-effect event. Observations analyzed in this study were taken during the Lake-Induced Convection Experiment (Lake-ICE).
Aircraft and sounding observations indicate that the lake-effect snows of 13 January 1998 developed within a convective boundary layer that grew rapidly across Lake Michigan. Boundary layer clouds developed within 15 km and snow developed within 30 km of the upwind (western) shoreline. Near the downwind shore, cloud cover was extensive and snow nearly filled the boundary layer. Extensive sea smoke in the surface layer, with disorganized (or cellular) and linear features, was observed visually across the entire lake. Over portions of northern Lake Michigan, where airborne dual-Doppler radar observations were obtained, the mesoscale circulation structure remained disorganized (random or cellular) across the lake. Given observed shear and stability conditions in this region, this structure is consistent with past theoretical and numerical modeling results. To the south, where surface winds were slightly stronger and lake–air temperature differences were less, wind-parallel bands indicative of rolls were often present.
The horizontal scale of the observed mesoscale convective structures grew across Lake Michigan, in agreement with most previous studies, but less rapidly than the increase of the boundary layer depth. The decreasing ratio of convective horizontal size to boundary layer depth (aspect ratio) is contrary to many recent studies that found a positive correlation between boundary layer depth and aspect ratio.
Abstract
Lake-effect snowstorms generally develop within convective boundary layers, which are induced when cold air flows over relatively warm lakes in fall and winter. Mesoscale circulations within the boundary layers largely control which communities near the downwind shores of the lakes receive the most intense snow. The lack of quantitative observations over the lakes during lake-effect storms limits the ability to fully understand and predict these mesoscale circulations. This study provides the first observations of the concurrent spatial and temporal evolution of the thermodynamic and microphysical boundary layer structure and mesoscale convective patterns across Lake Michigan during an intense lake-effect event. Observations analyzed in this study were taken during the Lake-Induced Convection Experiment (Lake-ICE).
Aircraft and sounding observations indicate that the lake-effect snows of 13 January 1998 developed within a convective boundary layer that grew rapidly across Lake Michigan. Boundary layer clouds developed within 15 km and snow developed within 30 km of the upwind (western) shoreline. Near the downwind shore, cloud cover was extensive and snow nearly filled the boundary layer. Extensive sea smoke in the surface layer, with disorganized (or cellular) and linear features, was observed visually across the entire lake. Over portions of northern Lake Michigan, where airborne dual-Doppler radar observations were obtained, the mesoscale circulation structure remained disorganized (random or cellular) across the lake. Given observed shear and stability conditions in this region, this structure is consistent with past theoretical and numerical modeling results. To the south, where surface winds were slightly stronger and lake–air temperature differences were less, wind-parallel bands indicative of rolls were often present.
The horizontal scale of the observed mesoscale convective structures grew across Lake Michigan, in agreement with most previous studies, but less rapidly than the increase of the boundary layer depth. The decreasing ratio of convective horizontal size to boundary layer depth (aspect ratio) is contrary to many recent studies that found a positive correlation between boundary layer depth and aspect ratio.
Abstract
Projected climatic warming has direct implications for future disturbance regimes, particularly fire-dominated ecosystems at high latitudes, where climate warming is expected to be most dramatic. It is important to ascertain the potential range of climate change impacts on terrestrial ecosystems, which is relevant to making projections of the response of the Earth system and to decisions by policymakers and land managers. Computer simulation models that explicitly model climate–fire relationships represent an important research tool for understanding and projecting future relationships. Retrospective model analyses of ecological models are important for evaluating how to effectively couple ecological models of fire dynamics with climate system models. This paper uses a transient landscape-level model of vegetation dynamics, Alaskan Frame-based Ecosystem Code (ALFRESCO), to evaluate the influence of different driving datasets of climate on simulation results. Our analysis included the use of climate data based on first-order weather station observations from the Climate Research Unit (CRU), a statistical reanalysis from the NCEP–NCAR reanalysis project (NCEP), and the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5). Model simulations of annual area burned for Alaska and western Canada were compared to historical fire activity (1950–2000). ALFRESCO was only able to generate reasonable simulation results when driven by the CRU climate data. Simulations driven by the NCEP and MM5 climate data produced almost no annual area burned because of substantially colder and wetter growing seasons (May–September) in comparison with the CRU climate data. The results of this study identify the importance of conducting retrospective analyses prior to coupling ecological models of fire dynamics with climate system models. The authors’ suggestion is to develop coupling methodologies that involve the use of anomalies from future climate model simulations to alter the climate data of more trusted historical climate datasets.
Abstract
Projected climatic warming has direct implications for future disturbance regimes, particularly fire-dominated ecosystems at high latitudes, where climate warming is expected to be most dramatic. It is important to ascertain the potential range of climate change impacts on terrestrial ecosystems, which is relevant to making projections of the response of the Earth system and to decisions by policymakers and land managers. Computer simulation models that explicitly model climate–fire relationships represent an important research tool for understanding and projecting future relationships. Retrospective model analyses of ecological models are important for evaluating how to effectively couple ecological models of fire dynamics with climate system models. This paper uses a transient landscape-level model of vegetation dynamics, Alaskan Frame-based Ecosystem Code (ALFRESCO), to evaluate the influence of different driving datasets of climate on simulation results. Our analysis included the use of climate data based on first-order weather station observations from the Climate Research Unit (CRU), a statistical reanalysis from the NCEP–NCAR reanalysis project (NCEP), and the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5). Model simulations of annual area burned for Alaska and western Canada were compared to historical fire activity (1950–2000). ALFRESCO was only able to generate reasonable simulation results when driven by the CRU climate data. Simulations driven by the NCEP and MM5 climate data produced almost no annual area burned because of substantially colder and wetter growing seasons (May–September) in comparison with the CRU climate data. The results of this study identify the importance of conducting retrospective analyses prior to coupling ecological models of fire dynamics with climate system models. The authors’ suggestion is to develop coupling methodologies that involve the use of anomalies from future climate model simulations to alter the climate data of more trusted historical climate datasets.
Abstract
Rain within the footprint of the SeaWinds scatterometer on the QuikSCAT satellite causes more significant errors than existed with its predecessor, the NASA scatterometer (NSCAT) on Advanced Earth Observing Satellite-I (ADEOS-I). Empirical relations are developed that show how the rain-induced errors in the scatterometer wind magnitude depend on both the rain rate and on the wind magnitude. These relations are developed with collocated National Data Buoy Center (NDBC) buoy measurements (to provide accurate sea surface winds) and simultaneous Next Generation Weather Radar (NEXRAD) observations of rain reflectivity. An analysis, based on electromagnetic scattering theory, interprets the dependence of the scatterometer wind errors on volumetric rain rate over a range of wind and rain conditions. These results demonstrate that the satellite scatterometer responds to rain in a manner similar to that of meteorological radars, with a Z–R relationship. These observations and results indicate that the combined (wind and rain) normalized radar cross section will lead to erroneously large wind estimates when the rain-related radar cross section exceeds a particular level that depends on the rain rate and surface wind speed.
Abstract
Rain within the footprint of the SeaWinds scatterometer on the QuikSCAT satellite causes more significant errors than existed with its predecessor, the NASA scatterometer (NSCAT) on Advanced Earth Observing Satellite-I (ADEOS-I). Empirical relations are developed that show how the rain-induced errors in the scatterometer wind magnitude depend on both the rain rate and on the wind magnitude. These relations are developed with collocated National Data Buoy Center (NDBC) buoy measurements (to provide accurate sea surface winds) and simultaneous Next Generation Weather Radar (NEXRAD) observations of rain reflectivity. An analysis, based on electromagnetic scattering theory, interprets the dependence of the scatterometer wind errors on volumetric rain rate over a range of wind and rain conditions. These results demonstrate that the satellite scatterometer responds to rain in a manner similar to that of meteorological radars, with a Z–R relationship. These observations and results indicate that the combined (wind and rain) normalized radar cross section will lead to erroneously large wind estimates when the rain-related radar cross section exceeds a particular level that depends on the rain rate and surface wind speed.
Abstract
Premodification of the atmosphere by upwind lakes is known to influence lake-effect snowstorm intensity and locations over downwind lakes. This study highlights perhaps the most visible manifestation of the link between convection over two or more of the Great Lakes lake-to-lake (L2L) cloud bands. Emphasis is placed on L2L cloud bands observed in high-resolution satellite imagery on 2 December 2003. These L2L cloud bands developed over Lake Superior and were modified as they passed over Lakes Michigan and Erie and intervening land areas. This event is put into a longer-term context through documentation of the frequency with which lake-effect and, particularly, L2L cloud bands occurred over a 5-yr time period over different areas of the Great Lakes region.
Abstract
Premodification of the atmosphere by upwind lakes is known to influence lake-effect snowstorm intensity and locations over downwind lakes. This study highlights perhaps the most visible manifestation of the link between convection over two or more of the Great Lakes lake-to-lake (L2L) cloud bands. Emphasis is placed on L2L cloud bands observed in high-resolution satellite imagery on 2 December 2003. These L2L cloud bands developed over Lake Superior and were modified as they passed over Lakes Michigan and Erie and intervening land areas. This event is put into a longer-term context through documentation of the frequency with which lake-effect and, particularly, L2L cloud bands occurred over a 5-yr time period over different areas of the Great Lakes region.
Abstract
The first detailed observations of the interaction of a synoptic cyclone with a lake-effect convective boundary layer (CBL) were obtained on 5 December 1997 during the Lake-Induced Convection Experiment. Lake-effect precipitation and CBL growth rates were enhanced by natural seeding by snow from higher-level clouds and the modified thermodynamic structure of the air over Lake Michigan due to the cyclone. In situ aircraft observations, project and operational rawinsondes, airborne radar, and operational Weather Surveillance Radar-1988 Doppler data were utilized to document the CBL and precipitation structure for comparison with past nonenhanced lake-effect events. Despite modest surface heat fluxes of 100–200 W m−2, cross-lake CBL growth was greatly accelerated as the convection merged with an overlying reduced-stability layer. Over midlake areas, CBL growth rates averaged more than twice those previously reported for lake-effect and oceanic cold-air outbreak situations. Regions of the lake-effect CBL cloud deck were seeded by precipitation from higher-level clouds over the upwind (western) portions of Lake Michigan before the CBL merged with the overlying reduced-stability layer. In situ aircraft observations suggest that in seeded regions, the CBL was deeper than in nonseeded regions. In addition, average water-equivalent precipitation rates for all of the passes with seeded regions were more than an order of magnitude greater in seeded regions than nonseeded regions because of higher concentration of snow particles of all sizes. A maximum snowfall rate of 4.28 mm day−1 was calculated using aircraft particle observations in seeded regions, comparable to snowfall rates previously reported for lake-effect events, often with much larger surface heat fluxes, but not interacting with synoptic cyclones.
Abstract
The first detailed observations of the interaction of a synoptic cyclone with a lake-effect convective boundary layer (CBL) were obtained on 5 December 1997 during the Lake-Induced Convection Experiment. Lake-effect precipitation and CBL growth rates were enhanced by natural seeding by snow from higher-level clouds and the modified thermodynamic structure of the air over Lake Michigan due to the cyclone. In situ aircraft observations, project and operational rawinsondes, airborne radar, and operational Weather Surveillance Radar-1988 Doppler data were utilized to document the CBL and precipitation structure for comparison with past nonenhanced lake-effect events. Despite modest surface heat fluxes of 100–200 W m−2, cross-lake CBL growth was greatly accelerated as the convection merged with an overlying reduced-stability layer. Over midlake areas, CBL growth rates averaged more than twice those previously reported for lake-effect and oceanic cold-air outbreak situations. Regions of the lake-effect CBL cloud deck were seeded by precipitation from higher-level clouds over the upwind (western) portions of Lake Michigan before the CBL merged with the overlying reduced-stability layer. In situ aircraft observations suggest that in seeded regions, the CBL was deeper than in nonseeded regions. In addition, average water-equivalent precipitation rates for all of the passes with seeded regions were more than an order of magnitude greater in seeded regions than nonseeded regions because of higher concentration of snow particles of all sizes. A maximum snowfall rate of 4.28 mm day−1 was calculated using aircraft particle observations in seeded regions, comparable to snowfall rates previously reported for lake-effect events, often with much larger surface heat fluxes, but not interacting with synoptic cyclones.
Assessing Dual-Polarization Radar Estimates of Extreme Rainfall during Hurricane HarveyAbstract
Hurricane Harvey hit the Texas Gulf Coast as a major hurricane on 25 August 2017 before exiting the state as a tropical storm on 29 August 2017. Left in its wake was historic flooding, with some locations measuring more than 60 in. (150 cm) of rain over a 5-day period. The WSR-88D radar (KHGX) maintained operations for the entirety of the event. Rain gauge data from the Harris County Flood Warning System (HCFWS) was used for validation with the full radar dataset to retrieve daily and event-total precipitation estimates for the period 25–29 August 2017. The KHGX precipitation estimates were then compared with the HCFWS gauges. Three different hybrid polarimetric rainfall retrievals were used, along with attenuation-based retrieval that employs the radar-observed differential propagation. An advantage of using a attenuation-based retrieval is its immunity to partial beam blockage and calibration errors in reflectivity and differential reflectivity. All of the retrievals are susceptible to changes in the observed drop size distribution (DSD). No in situ DSD data were available over the study area, so changes in the DSD were interpreted by examining the observed radar data. We examined the parameter space of two key values in the attenuation retrieval to test the sensitivity of the rain retrieval. Selecting a value of α = 0.015 and β = 0.600 provided the best overall results, relative to the gauges, but more work needs to be done to develop an automated technique to account for changes in the ambient DSD.
Abstract
Hurricane Harvey hit the Texas Gulf Coast as a major hurricane on 25 August 2017 before exiting the state as a tropical storm on 29 August 2017. Left in its wake was historic flooding, with some locations measuring more than 60 in. (150 cm) of rain over a 5-day period. The WSR-88D radar (KHGX) maintained operations for the entirety of the event. Rain gauge data from the Harris County Flood Warning System (HCFWS) was used for validation with the full radar dataset to retrieve daily and event-total precipitation estimates for the period 25–29 August 2017. The KHGX precipitation estimates were then compared with the HCFWS gauges. Three different hybrid polarimetric rainfall retrievals were used, along with attenuation-based retrieval that employs the radar-observed differential propagation. An advantage of using a attenuation-based retrieval is its immunity to partial beam blockage and calibration errors in reflectivity and differential reflectivity. All of the retrievals are susceptible to changes in the observed drop size distribution (DSD). No in situ DSD data were available over the study area, so changes in the DSD were interpreted by examining the observed radar data. We examined the parameter space of two key values in the attenuation retrieval to test the sensitivity of the rain retrieval. Selecting a value of α = 0.015 and β = 0.600 provided the best overall results, relative to the gauges, but more work needs to be done to develop an automated technique to account for changes in the ambient DSD.
