GPM DPR Retrievals: Algorithm, Evaluation, and Validation

Abstract

The primary goal of the dual-frequency precipitation radar (DPR) aboard the Global Precipitation Measurement (GPM) Core Observatory satellite is to infer precipitation rate and raindrop/particle size distributions (DSD/PSD). The focus of this paper is threefold: (1) to describe the DPR retrieval algorithm that uses an adjustable relationship between rain rate (R) and the mass-weighted diameter (Dm) or an R-Dm relationship in solving for R and Dm simultaneously; (2) to evaluate the DPR algorithm based on the physical simulations that employ measured DSD/PSD to understand the mechanism and error characteristics of the retrieval method; (3) to review ground validation studies for DPR products as well as to analyze the strengths and weaknesses of ground radar and rain gauge/disdrometer validations. Overall, the DPR Version 6 algorithm provides reasonably accurate estimates of R and Dm in rain. Non-uniformity in the rain profile, however, tends to degrade the accuracy of the R and Dm estimates to some extent as the range-independent assumption of the adjustable parameter (ε) of the R-Dm relation is not able to fully account for natural variation of DSD in the vertical profile. The DPR snow rate is underestimated as compared with the independent dual-frequency ratio (DFR) technique. This is possibly the result of the constraint associated with the path integral attenuation (PIA)/differential PIA (δPIA) used in the DPR algorithm to find the best ε and range-independent ε assumption. A range-variable ε model, proposed in the DPR Version 7 algorithm, is expected to improve rain and snow retrieval.

Micro Rain Radar and Radiometric Measurements to Unravel Contrasting Features of Rain Microstructure Below and Above the Boundary Layer

“…In this scenario, investigation of the DSD profile is only possible with ground and space‐based radar measurements. Space‐borne radar like Global Precipitation Measurement (GPM) cannot provide continuous measurements of precipitation profiles over a particular location (Liao & Meneghini, 2022; Maitra et al., 2022; Ryu et al., 2021). Whereas ground‐based radars like micro rain radar (MRR) enable thorough monitoring of rain events through the measurements of evolving rain microstructure profiles using Doppler principle (Maitra et al., 2022).…”

“…Hence this R ‐ D m relation requires consideration of the physical processes that may also impact the evolution of the three parameters along the rain profile (Barthes & Mallet, 2013; Maitra et al., 2022). Accordingly, the R ‐ D m relationships adopted by the spaceborne radars are inadequate to fully accommodate the natural variation in rain DSD (Liao & Meneghini, 2022; Maitra et al., 2022).…”

“…Also, the mass‐weighted diameter ( D m ) which is determined by the ratio of fourth to third moment of DSD, is a common representative to reveal the N ( D ) in an integrated manner (Ryu et al., 2021). In this context, it may be mentioned that the determination of rain rate and rain microphysical parameters from reflectivity measurements of the space‐borne dual‐frequency precipitation radar aboard the GPM satellite relies on the adjustable power‐law relationship ( D m = aR b ) between rain rate ( R ) and D m (Liao & Meneghini, 2022). Hence this R ‐ D m relation requires consideration of the physical processes that may also impact the evolution of the three parameters along the rain profile (Barthes & Mallet, 2013; Maitra et al., 2022).…”

Microphysical Characteristics of Cyclonic Rainfall: A GPM‐DPR Based Study Over the Arabian Sea

“…Combining the observations of two precipitating radars (PR) with two different frequency bands mounted on the GPM satellite is helpful in studying the three‐dimensional structure of precipitating systems and the associated microphysical processes. The dual‐frequency precipitation radar (DPR) mounted on the GPM core satellite enhanced its sensitivity of the measurement, and lower and heavy rain rates can be captured more accurately (Gao et al., 2017; Hamada & Takayabu, 2016; Iguchi et al., 2012; Liao & Meneghini, 2022; Mazzoglio et al., 2019). The spatial and temporal distributions of the rain‐integral parameters, like mass‐weighted mean diameter (D m ) and the normalized intercept parameter (N w ), can be derived from the reflectivity difference (Z e ) between the two PR observations.…”

Joint Collaboration on Comparing NOAA’s Ground-Based Weather Radar and NASA–JAXA’s Spaceborne Radar

“…However, comparisons of total rainfall amounts do not reveal the capabilities and deficiencies of the GRs and SRs. Numerous studies have focused on comparing the raw radar reflectivities from GRs and SRs and have explored possible reasons for the observed differences, including calibration procedure and methods of attenuation correction (Anagnostou et al, 2001;Biswas et al, 2018;Cao et al, 2013;Chandrasekar et al, 2003;Keem et al, 2019;Liao et al, 2022;Masaki et al, 2020;Protat et al, 2022) used as a valid source to calibrate GRs because of the stability of the DPR calibration over time (Protat et al, 2022;Warren et al, 2018;Masaki et al, 2015Masaki et al, , 2020.…”