23A.1 Evaluation of Single- and Dual-Wavelength Radar Rain Retrieval Algorithms by Using Measured DSD

Thursday, 31 August 2017: 4:00 PM
Vevey (Swissotel Chicago)
Liang Liao, Morgan State Univ., Greenbelt, MD; and R. Meneghini and A. Tokay

An important goal of the Dual-frequency Precipitation Radar (DPR), aboard the Global Precipitation Measurement (GPM) core satellite, is to derive rain rate by estimating parameters of the rain drop size distribution (DSD) that is often modeled by an analytical function, such as the gamma with two or three unknown parameters. The inability of the modeled DSD to represent actual hydrometeor spectra and to characterize their intrinsic variations in time and space lead to errors in the estimates of precipitation rate obtained from the DPR.

Since the DPR suffers attenuation while propagating in rain, its signal loss needs to be compensated for the radar echo at gates where rain estimates are made. For single-wavelength radar technique, the approach proposed by Hitschfeld and Bordan (HB method) has been widely used when additional constraints are used to stabilize its solutions, such as constraint of path integral attenuation (PIA) that is obtained independently by comparing radar surface returns between rain and rain-free regions. Several dual-wavelength radar retrieval techniques have recently been developed, some of which are based on the method that uses the difference of radar reflectivities between two wavelengths to first infer the DSD parameters, correct attenuation and then derive rain rate gate by gate either along radar beam (forward approach) or in reverse direction (backward approach). To improve robustness of the dual-wavelength retrieval for the GPM mission, a few optimal methods have been proposed in which one or more adjustment factors are used to modify some nominal relationships between radar and hydrometeor’s parameters (such as k-Z and R-Dm relations, where k, Z, R and Dm are specific attenuation, radar reflectivity, rain rate and mass-weighted diameter) to account for DSD variation in space and time. Adjustment factors at each gate/profile are determined by optimizing predefined cost functions that tend to satisfy dual-wavelength reflectivities and measured PIAs.

Because of spatial and temporal variations in DSD and also because of the fact that retrieval problems are under-constrained, i.e., more variables than measurements, none of the retrieval methods are perfect. Their accuracies depend on several factors that include DSD parameterization, vertical homogeneity of DSD profiles and accuracy of the PIA estimates as well as the nominal radar-hydrometeor relations used for retrieval. A physical evaluation of these techniques is necessary not only for understanding the uncertainties in rain rate estimation but also in gaining insight into ways to improve the algorithms. In this study, we will investigate performance of rain estimates using single- and dual-wavelength radar techniques with various assumed DSD models by comparing the radar and hydrometeor parameters retrieved with those directly derived from DSD measurements. The robustness of the algorithms with respect to SRT errors will also be studied. To construct realistic hydrometeor profiles used for our evaluation, the measured DSD data acquired from a variety of storm systems will be used. To test the algorithms for a wide range of vertical rain inhomogeneities, generated hydrometeor profiles are grouped into the categories with various degrees of vertical correlations, i.e., from perfectly correlated, partially correlated to uncorrelated DSD profiles. The relationships between radar reflectivity factors and specific attenuations at the Ku- and Ka-band frequencies at which the DPR operates, along with hydrometeor bulk and characteristic size parameters, will be derived and applied to the algorithms that require this information. The DSD data that will be used include the multiple Parsivel2 and 2DVD observations acquired during the Iowa Flooding Studies (IFloodS), the Integrated Precipitation Validation Experiment (IPHEx) and data from NASA Wallops Flight Facility in Wallops Island, Virginia as well as the Olympic Mountains Ground Validation Experiment (OLYMPEX) field campaign.

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