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Abstract
The ground-based-radar-derived raindrop size distribution (DSD) parameters—mass-weighted drop diameter D mass and normalized intercept parameter N W —are the sole resource for direct validation of the National Aeronautics and Space Administration (NASA) Global Precipitation Measurement (GPM) mission Core Observatory satellite-based retrieved DSD. Both D mass and N W are obtained from radar-measured reflectivity Z H and differential reflectivity Z DR through empirical relationships. This study uses existing relationships that were determined for the GPM ground validation (GV) program and directly compares the NASA S-band polarimetric radar (NPOL) observables of Z H and Z DR and derived D mass and N W with those calculated by two-dimensional video disdrometer (2DVD). The joint NPOL and 2DVD datasets were acquired during three GPM GV field campaigns conducted in eastern Iowa, southern Appalachia, and western Washington State. The comparative study quantifies the level of agreement for Z H , Z DR, D mass, and log(N W ) at an optimum distance (15–40 km) from the radar as well as at distances greater than 60 km from radar and over mountainous terrain. Interestingly, roughly 10%–15% of the NPOL Z H –Z DR pairs were well outside the envelope of 2DVD-estimated Z H –Z DR pairs. The exclusion of these pairs improved the comparisons noticeably.
Abstract
The ground-based-radar-derived raindrop size distribution (DSD) parameters—mass-weighted drop diameter D mass and normalized intercept parameter N W —are the sole resource for direct validation of the National Aeronautics and Space Administration (NASA) Global Precipitation Measurement (GPM) mission Core Observatory satellite-based retrieved DSD. Both D mass and N W are obtained from radar-measured reflectivity Z H and differential reflectivity Z DR through empirical relationships. This study uses existing relationships that were determined for the GPM ground validation (GV) program and directly compares the NASA S-band polarimetric radar (NPOL) observables of Z H and Z DR and derived D mass and N W with those calculated by two-dimensional video disdrometer (2DVD). The joint NPOL and 2DVD datasets were acquired during three GPM GV field campaigns conducted in eastern Iowa, southern Appalachia, and western Washington State. The comparative study quantifies the level of agreement for Z H , Z DR, D mass, and log(N W ) at an optimum distance (15–40 km) from the radar as well as at distances greater than 60 km from radar and over mountainous terrain. Interestingly, roughly 10%–15% of the NPOL Z H –Z DR pairs were well outside the envelope of 2DVD-estimated Z H –Z DR pairs. The exclusion of these pairs improved the comparisons noticeably.
Abstract
In this study, a nonparametric method to estimate precipitating ice from multiple-frequency radar observations is investigated. The method does not require any assumptions regarding the distribution of ice particle sizes and relies on an efficient search procedure to incorporate information from observed particle size distributions (PSDs) in the estimation process. Similar to other approaches rooted in optimal-estimation theory, the nonparametric method is robust in the presence of noise in observations and uncertainties in the forward models. Over 200 000 PSDs derived from in situ observations collected during the Olympic Mountains Experiment (OLYMPEX) and Integrated Precipitation and Hydrology Experiment (IPHEX) field campaigns are used in the development and evaluation of the nonparametric estimation method. These PSDs are used to create a database of ice-related variables and associated computed radar reflectivity factors at the Ku, Ka, and W bands. The computed reflectivity factors are used to derive precipitating ice estimates and investigate the associated errors and uncertainties. The method is applied to triple-frequency radar observations collected during OLYMPEX and IPHEX. Direct comparisons of estimated ice variables with estimates from in situ instruments show results consistent with the error analysis. Global application of the method requires an extension of the supporting PSD database, which can be achieved through the processing of information from additional past and future field campaigns.
Abstract
In this study, a nonparametric method to estimate precipitating ice from multiple-frequency radar observations is investigated. The method does not require any assumptions regarding the distribution of ice particle sizes and relies on an efficient search procedure to incorporate information from observed particle size distributions (PSDs) in the estimation process. Similar to other approaches rooted in optimal-estimation theory, the nonparametric method is robust in the presence of noise in observations and uncertainties in the forward models. Over 200 000 PSDs derived from in situ observations collected during the Olympic Mountains Experiment (OLYMPEX) and Integrated Precipitation and Hydrology Experiment (IPHEX) field campaigns are used in the development and evaluation of the nonparametric estimation method. These PSDs are used to create a database of ice-related variables and associated computed radar reflectivity factors at the Ku, Ka, and W bands. The computed reflectivity factors are used to derive precipitating ice estimates and investigate the associated errors and uncertainties. The method is applied to triple-frequency radar observations collected during OLYMPEX and IPHEX. Direct comparisons of estimated ice variables with estimates from in situ instruments show results consistent with the error analysis. Global application of the method requires an extension of the supporting PSD database, which can be achieved through the processing of information from additional past and future field campaigns.
Abstract
Two methods for deriving relationships between the equivalent radar reflectivity factor Z e and the snowfall rate S at three radar wavelengths are described. The first method uses collocations of in situ aircraft (microphysical observations) and overflying aircraft (radar observations) from two field programs to develop Z e –S relationships. In the second method, measurements of Z e at the top of the melting layer (ML), from radars on the Tropical Rainfall Measuring Mission (TRMM), Global Precipitation Measurement (GPM), and CloudSat satellites, are related to the retrieved rainfall rate R at the base of the ML, assuming that the mass flux through the ML is constant. Retrievals of R are likely to be more reliable than S because far fewer assumptions are involved in the retrieval and because supporting ground-based validation data are available. The Z e –S relationships developed here for the collocations and the mass-flux technique are compared with those derived from level 2 retrievals from the standard satellite products and with a number of relationships developed and reported by others. It is shown that there are substantial differences among them. The relationships developed here promise improvements in snowfall-rate retrievals from satellite-based radar measurements.
Abstract
Two methods for deriving relationships between the equivalent radar reflectivity factor Z e and the snowfall rate S at three radar wavelengths are described. The first method uses collocations of in situ aircraft (microphysical observations) and overflying aircraft (radar observations) from two field programs to develop Z e –S relationships. In the second method, measurements of Z e at the top of the melting layer (ML), from radars on the Tropical Rainfall Measuring Mission (TRMM), Global Precipitation Measurement (GPM), and CloudSat satellites, are related to the retrieved rainfall rate R at the base of the ML, assuming that the mass flux through the ML is constant. Retrievals of R are likely to be more reliable than S because far fewer assumptions are involved in the retrieval and because supporting ground-based validation data are available. The Z e –S relationships developed here for the collocations and the mass-flux technique are compared with those derived from level 2 retrievals from the standard satellite products and with a number of relationships developed and reported by others. It is shown that there are substantial differences among them. The relationships developed here promise improvements in snowfall-rate retrievals from satellite-based radar measurements.
