Similarities and Improvements of GPM Dual-Frequency Precipitation Radar (DPR) upon TRMM Precipitation Radar (PR) in Global Precipitation Rate Estimation, Type Classification and Vertical Profiling

Abstract

Spaceborne precipitation radars are powerful tools used to acquire adequate and highquality precipitation estimates with high spatial resolution for a variety of applications in hydrological research. The Global Precipitation Measurement (GPM) mission, which deployed the first spaceborne Ka-and Ku-dual frequency radar (DPR), was launched in February 2014 as the upgraded successor of the Tropical Rainfall Measuring Mission (TRMM). This study matches the swath data of TRMM PR and GPM DPR Level 2 products during their overlapping periods at the global scale to investigate their similarities and DPR's improvements concerning precipitation amount estimation and type classification of GPM DPR over TRMM PR. Results show that PR and DPR agree very well with each other in the global distribution of precipitation, while DPR improves the detectability of precipitation events significantly, particularly for light precipitation. The occurrences of total precipitation and the light precipitation (rain rates < 1 mm/h) detected by GPM DPR are~1.7 and~2.53 times more than that of PR. With regard to type classification, the dual-frequency (Ka/Ku) and single frequency (Ku) methods performed similarly. In both inner (the central 25 beams) and outer swaths (1-12 beams and 38-49 beams) of DPR, the results are consistent. GPM DPR improves precipitation type classification remarkably, reducing the misclassification of clouds and noise signals as precipitation type "other" from 10.14% of TRMM PR to 0.5%. Generally, GPM DPR exhibits the same type division for around 82.89% (71.02%) of stratiform (convective) precipitation events recognized by TRMM PR. With regard to the freezing level height and bright band (BB) height, both radars correspond with each other very well, contributing to the consistency in stratiform precipitation classification. Both heights show clear latitudinal dependence. Results in this study shall contribute to future development of spaceborne radar precipitation retrievals and benefit hydrological and meteorological research.

Exploring Deep Neural Networks to Retrieve Rain and Snow in High Latitudes Using Multisensor and Reanalysis Data

“…Compared to IR, MW provides more direct and accurate precipitation measurements because of its sensitivity to rain/ice particles, whereas IR‐based algorithms are often built on empirical and indirect links between cloud top brightness temperature ( T b ) and surface precipitation (Huffman et al, ; Kidd et al, ; Sorooshian et al, ). Active microwave sensors, that is, spaceborne radars, often provide more direct and accurate precipitation estimates than passive microwave (PMW) sensors (Behrangi et al, ; Gao et al, ; Tang et al, ; Tang, Long, et al, ). So far, there have been three popular spaceborne radars for observing precipitation, that is, the Ku‐band precipitation radar onboard the Tropical Rainfall Measuring Mission (TRMM) satellite operating from 1997 to 2015, the W‐band Cloud Profiling Radar (CPR) onboard the CloudSat operating from 2006 to the present, and the Dual‐frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) Core Observatory operating from 2014 to the present.…”

Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview

“…However, there could be errors of the classification results due to some limitations of PR including low sensitivity for light rain and snow, single-frequency detection at Ku-band, and the coarse horizontal resolution above the ground. This problem has already been noticed [159,160], and could be improved by future algorithms and the next-generation space-borne precipitation radar [161][162][163][164].…”

Evaluating Simulated Microphysics during OLYMPEX Using GPM Satellite Observations

“…Tian et al (2018) demonstrated that GPM rain retrievals generally overestimated light rainfall events and underestimated heavy events over land. In spite of this, studies indicate that GPM is more accurate than TRMM when determining whether precipitation is occurring (Gao et al 2017) and light precipitation rates over complex terrain (Ma et al 2016). In one study over the Swiss Alps using surface radars and rain gauges, Speirs et al (2017) showed that GPM retrievals generally underestimate precipitation during the winter, being most accurate when liquid-phase precipitation is occurring.…”