Global Precipitation Measurement (GPM) Ground Validation (GV) Prototype in the Korean Peninsula

Kim, Jihye; Ou, Mi-Lim; Park, Jun-Dong; Morris, Kenneth R.; Schwaller, M.; Wolff, David B.

doi:10.1175/jtech-d-13-00193.1

Cited by 30 publications

(22 citation statements)

References 60 publications

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“…Islam et al, 2012) and checks on surface radar calibration stability in preparation for the GPM era (e.g. Kim et al, 2014). In the GPM era the aforementioned types of study, now using the DPR instead of TRMM's Precipitation Radar (PR) continue, but the VN has also directly supported verification of mission science requirements , DSD retrieval algorithms (e.g.…”

Section: Direct Gv Datasetsmentioning

confidence: 99%

The Global Precipitation Measurement (GPM) mission's scientific achievements and societal contributions: reviewing four years of advanced rain and snow observations

Skofronick-Jackson

Kirschbaum

Petersen

et al. 2018

Quart J Royal Meteoro Soc

142

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Precipitation represents a life‐critical energy and hydrologic exchange between the Earth's atmosphere and its surface. As such, knowledge of where, when and how much rain and snow falls is essential for scientific research and societal applications. Building on the 17‐year success of the Tropical Rainfall Measurement Mission (TRMM), the Global Precipitation Measurement (GPM) Core Observatory (GPM‐CO) is the first US National Aeronautical and Space Administration (NASA) satellite mission specifically designed with sensors to observe the structure and intensities of both rain and falling snow. The GPM‐CO has proved to be a worthy successor to TRMM, extending and improving high‐quality active and passive microwave observations across all times of day. The GPM‐CO launched in early 2014, is a joint mission between NASA and the Japanese Aerospace Exploration Agency (JAXA), with sensors that include the NASA‐provided GPM Microwave Imager and the JAXA‐provided Dual‐frequency Precipitation Radar. These sensors were devised with high accuracy standards enabling them to be used as a reference for inter‐calibrating a constellation of partner satellite data. These inter‐calibrated partner satellite retrievals are used with infrared data to produce merged precipitation estimates at temporal scales of 30 min and spatial scales of 0.1° × 0.1°. Precipitation estimates from the GPM‐CO and partner constellation satellites, provided in near real time and later reprocessed with all ancillary data, are an indispensable source of precipitation data for operational and scientific users. Advances have been made using GPM data, primarily in improving sensor calibration, retrieval algorithms, and ground validation measurements, and used to further our understanding of the characteristics of liquid and frozen precipitation and the science of water and hydrological cycles for climate/weather forecasting. These advances have extended to societal benefits related to water resources, operational numerical weather prediction, hurricane monitoring, prediction, and disaster response, extremes, and disease.

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Section: Direct Gv Datasetsmentioning

confidence: 99%

The Global Precipitation Measurement (GPM) mission's scientific achievements and societal contributions: reviewing four years of advanced rain and snow observations

Skofronick-Jackson

Kirschbaum

Petersen

et al. 2018

Quart J Royal Meteoro Soc

142

View full text Add to dashboard Cite

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“…This is more significant in Group 2. Several studies have discovered that as a result of low BB height, large path-integrated attenuation (PIA) and strong correction, PR is likely to overestimate the convective rain rate in the Korean Peninsula and some other regions compared with ground radars [68,69]. Chen et al [70] found that due to incorrect rain type classification, overcorrection of radar signals, poor performance of the Surface Reference Technique (SRT) module, ground clutter and geometric effects, TRMM PR overestimates the frequency of moderate rain rates.…”

Section: Comparison Of Overall Precipitationmentioning

confidence: 99%

“…GPM DPR was found to underestimate precipitation rates for rates smaller than 10 mm/h and overestimate for rates above 10 mm/h by using at-launch code and synthetic data based on the TRMM PR data [71]. and some other regions compared with ground radars [68,69]. Chen et al [70] found that due to incorrect rain type classification, overcorrection of radar signals, poor performance of the Surface Reference Technique (SRT) module, ground clutter and geometric effects, TRMM PR overestimates the frequency of moderate rain rates.…”

Section: Comparison Of Overall Precipitationmentioning

confidence: 99%

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

2017

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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.

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“…On that basis, we collected 225, 378, and 295 TRMM overpasses for 2012, 2013, and 2014, respectively. For GPM, there were 178, 224, and 211 for 2014.…”

mentioning

confidence: 99%

“…We have to assume that the fluctuations are a cumulative result of various 10 effects that might include e.g. hardware instability, fluctuations in the refractive index of the atmosphere, dynamic changes in the atmosphere in the time interval between SR overpass and GR sweep, non-meteorological echoes, residual errors in the geometric intersection of the volume samples, and other random error components, as noted in other studies (Anagnostou et al, 2001;Kim et al, 2014;Schwaller and Morris, 2011;Wang and Wolff, 2009;Warren et al, 2017). (3) Effect of quality weighting on bias estimation: Figure 8b and c together illustrate the benefit of taking into account GR 25 data quality (i.e.…”

mentioning

confidence: 99%

Enhancing the consistency of spaceborne and ground-based radar comparisons by using quality filters

Crisologo

Warren

Mühlbauer

et al. 2018

Preprint

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Abstract.Coinciding monsoon and typhoon seasons in the Philippines cause torrential rainfall, and associated hazards such as flooding and landslides. While early warning systems require accurate radar-based rainfall estimates, low-density rain gauge networks in the Philippines make it challenging to monitor the calibration of the ground-based radars (GRs). As an alternative, we explore the potential of spaceborne radar (SR) observations from the Ku-band precipitation radars on board the TRMM and GPM 5 satellites as a reference to quantify the calibration bias of an S-band GR in the Philippines. To this end, the 3D volume-matching algorithm proposed by Schwaller and Morris (2009) is implemented and applied to five years (2012-2016) of observations. We further extend the procedure by a framework to take into account the data quality of each ground radar bin. Through these methods, we are able to assign a quality index to each matching SR-GR volume, and thus compute the GR calibration bias as a quality-weighted average of reflectivity differences in any sample of matching GR-SR volumes. We exemplify the idea of 10 quality-weighted averaging by using the beam blockage fraction as a basis of a quality index. As a result, we can increase the consistency of SR and GR observations, and thus the precision of calibration bias estimates. The remaining scatter between GR and SR reflectivity, as well as the variability of bias estimates between overpass events indicate, however, that other error sources are not yet fully addressed. Still, our study provides a framework to introduce any other quality variables that are considered relevant in a specific context. The code that implements our analysis is based on the open source software library 15 wradlib, and is, together with the data, publicly available to monitor radar calibration, or to scrutinize long series of archived radar data back to December 1997, when TRMM became operational.Copyright statement.

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Global Precipitation Measurement (GPM) Ground Validation (GV) Prototype in the Korean Peninsula

Cited by 30 publications

References 60 publications

The Global Precipitation Measurement (GPM) mission's scientific achievements and societal contributions: reviewing four years of advanced rain and snow observations

The Global Precipitation Measurement (GPM) mission's scientific achievements and societal contributions: reviewing four years of advanced rain and snow observations

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

Enhancing the consistency of spaceborne and ground-based radar comparisons by using quality filters

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