Calibration of the Dual-Frequency Precipitation Radar Onboard the Global Precipitation Measurement Core Observatory

Masaki, Takeshi; Iguchi, Toshio; Kanemaru, Kaya; Furukawa, Katsuko; Yoshida, N.; Kubota, Takuji; Oki, Riko

doi:10.1109/tgrs.2020.3039978

Cited by 30 publications

(10 citation statements)

References 25 publications

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“…Importantly, the GPM mission also benefits from extensive field experiments undertaken as part of the ground validation program, including in situ ground and aircraft validation of the products of the GPM mission. By comparing different approaches for the GPM Ku-band radar calibration, Masaki et al (2020) demonstrated that the accuracy of the radar was well within the ±1 dB requirement. In our study, Version 5 of the GPM 2AKu product was used for all comparisons (Kidd et al, 2017), which includes the latest calibration from Masaki et al (2020) and contains attenuation-corrected Ku-band reflectivities.…”

Section: The Radars Of This Studymentioning

confidence: 93%

“…By comparing different approaches for the GPM Ku-band radar calibration, Masaki et al (2020) demonstrated that the accuracy of the radar was well within the ±1 dB requirement. In our study, Version 5 of the GPM 2AKu product was used for all comparisons (Kidd et al, 2017), which includes the latest calibration from Masaki et al (2020) and contains attenuation-corrected Ku-band reflectivities. GPM attenuation correction is achieved using a hybrid approach combining the traditional Hitschfeld-Bordan technique (Hitschfeld and Bordan, 1954) and the so-called surface reference technique (Meneghini et al, 2004).…”

Section: The Radars Of This Studymentioning

confidence: 93%

“…As discussed previously, the GPM Ku-band radar measurements are considered as the reference for the calibration of all radars in this study. The GPM radar calibration procedure, described in detail by Masaki et al (2020), is inherited from years of calibration work undertaken as part of the previous satellite radar mission, the Tropical Rainfall Measurement Mission (TRMM). This calibration comprises an internal calibration (monitoring closely the gains and losses of each component of the radar) and an external calibration procedure using a ground-based calibrator and sea surface of well-known backscatter.…”

Section: The Radars Of This Studymentioning

confidence: 99%

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Three-way calibration checks using ground-based, ship-based, and spaceborne radars

et al. 2022

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Abstract. This study uses ship-based weather radar observations collected from research vessel Investigator to evaluate the Australian weather radar network calibration monitoring technique that uses spaceborne radar observations from the NASA Global Precipitation Mission (GPM). Quantitative operational applications such as rainfall and hail nowcasting require a calibration accuracy of ±1 dB for radars of the Australian network covering capital cities. Seven ground-based radars along the western coast of Australia and the ship-based OceanPOL radar are first calibrated independently using GPM radar overpasses over a 3-month period. The calibration difference between the OceanPOL radar (used as a moving reference for the second step of the study) and each of the seven operational radars is then estimated using collocated, gridded, radar observations to quantify the accuracy of the GPM technique. For all seven radars the calibration difference with the ship radar lies within ±0.5 dB, therefore fulfilling the 1 dB requirement. This result validates the concept of using the GPM spaceborne radar observations to calibrate national weather radar networks (provided that the spaceborne radar maintains a high calibration accuracy). The analysis of the day-to-day and hourly variability of calibration differences between the OceanPOL and Darwin (Berrimah) radars also demonstrates that quantitative comparisons of gridded radar observations can accurately track daily and hourly calibration differences between pairs of operational radars with overlapping coverage (daily and hourly standard deviations of ∼ 0.3 and ∼ 1 dB, respectively).

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Section: The Radars Of This Studymentioning

confidence: 93%

Section: The Radars Of This Studymentioning

confidence: 93%

Section: The Radars Of This Studymentioning

confidence: 99%

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Three-way calibration checks using ground-based, ship-based, and spaceborne radars

et al. 2022

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“…The attenuations accumulated from previous range gates, which are in general very small for dry snow, are computed from the derived PSD, and then, are used to compensate the measured reflectivities for the attenuation. To conform with the DPR measurements, the thresholds consistent with the minimum detectable reflectivities of 15.46 and 19.18 dBZ at Ku-and Ka-band, respectively [4], are adopted for filtering the data. In other words, only the DPR data with measured reflectivities exceeding these thresholds are selected.…”

Section: Comparison Of the Dfr Retrieval With The Dpr Productmentioning

confidence: 99%

“…As a result, matched Ku-and Ka-band measurements are achieved for the full swath. The footprint of DPR on the surface is about 5 km in diameter and the minimum detectable radar reflectivities are 15.46, 19.18, and 13.71 dBZ for the KuPR, KaMS, and KaHS, respectively [4].…”

Section: Introductionmentioning

confidence: 99%

GPM DPR Retrievals: Algorithm, Evaluation, and Validation

Liao

Meneghini

2022

Remote Sensing

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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.

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Vertical microphysical structures of summer heavy rainfall in the Yangtze-Huaihe River Valley from GPM DPR data

Hu,

Kou,

Wang

et al. 2025

Atmospheric Research

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Calibration of the Dual-Frequency Precipitation Radar Onboard the Global Precipitation Measurement Core Observatory

Cited by 30 publications

References 25 publications

Three-way calibration checks using ground-based, ship-based, and spaceborne radars

Three-way calibration checks using ground-based, ship-based, and spaceborne radars

GPM DPR Retrievals: Algorithm, Evaluation, and Validation

Vertical microphysical structures of summer heavy rainfall in the Yangtze-Huaihe River Valley from GPM DPR data

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