In this study, a framework is given by which air/space-borne dual-wavelength radar data can be used to estimate the characteristic parameters of hydrometeors. The focus of the study is on the Global Precipitation Meas...In this study, a framework is given by which air/space-borne dual-wavelength radar data can be used to estimate the characteristic parameters of hydrometeors. The focus of the study is on the Global Precipitation Measurement （GPM） precipitation radar, a dual-wavelength radar that will operate in the Ku （13.6 GHz） and Ka （35 GHz） bands. A key aspect of the retrievals is the relationship between the differential frequency ratio （DFR） and the median volume diameter, Do, and its dependence on the phase state of the hydrometeors. It is shown that parametric plots of Do and particle concentration in the plane of the DFR and the radar reflectivity factor in the Ku band can be used to reduce the ambiguities in deriving Do from DFR. A self-consistent iterative algorithm, which does not require the use of an independent pathattenuation constraint, is examined by applying it to the apparent radar reflectivity profiles simulated from a drop size distribution （DSD） model. For light to moderate rain, the self-consistent rain profiling approach converges to the correct solution only if the same shape factor of the Gamma distributions is used both to generate and retrieve the rain profiles. On the other hand, if the shape factors differ, the iteration generally converges but not to the correct solution. To further examine the dual-wavelength techniques, the selfconsistent iterative algorithm, along with forward and backward rain profiling algorithms, are applied to measurements taken from the 2nd generation Precipitation Radar （PR-2） built by the Jet Propulsion Laboratory. Consistent with the model results, it is found that the estimated rain profiles are sensitive to the shape factor of the size distribution when the iterative, self-consistent approach is used but relatively insensitive to this parameter when the forward- and backward-constrained approaches are used.展开更多
文摘In this study, a framework is given by which air/space-borne dual-wavelength radar data can be used to estimate the characteristic parameters of hydrometeors. The focus of the study is on the Global Precipitation Measurement （GPM） precipitation radar, a dual-wavelength radar that will operate in the Ku （13.6 GHz） and Ka （35 GHz） bands. A key aspect of the retrievals is the relationship between the differential frequency ratio （DFR） and the median volume diameter, Do, and its dependence on the phase state of the hydrometeors. It is shown that parametric plots of Do and particle concentration in the plane of the DFR and the radar reflectivity factor in the Ku band can be used to reduce the ambiguities in deriving Do from DFR. A self-consistent iterative algorithm, which does not require the use of an independent pathattenuation constraint, is examined by applying it to the apparent radar reflectivity profiles simulated from a drop size distribution （DSD） model. For light to moderate rain, the self-consistent rain profiling approach converges to the correct solution only if the same shape factor of the Gamma distributions is used both to generate and retrieve the rain profiles. On the other hand, if the shape factors differ, the iteration generally converges but not to the correct solution. To further examine the dual-wavelength techniques, the selfconsistent iterative algorithm, along with forward and backward rain profiling algorithms, are applied to measurements taken from the 2nd generation Precipitation Radar （PR-2） built by the Jet Propulsion Laboratory. Consistent with the model results, it is found that the estimated rain profiles are sensitive to the shape factor of the size distribution when the iterative, self-consistent approach is used but relatively insensitive to this parameter when the forward- and backward-constrained approaches are used.
