Use of the GPM Constellation for Monitoring Heavy Precipitation Events Over the Mediterranean Region

Panegrossi, Giulia; Casella, Daniele; Dietrich, Stefano; Marra, Anna Cinzia; Sanò, Paolo; Mugnai, A.; Baldini, Luca; Roberto, Nicoletta; Adirosi, Elisa; Cremonini, R.; Bechini, R.; Vulpiani, G.; Petracca, Matteo; Porcú, Federico

doi:10.1109/jstars.2016.2520660

Cited by 53 publications

(42 citation statements)

References 56 publications

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“…With 2 years' worth of calibrated and validated precipitation estimates, GPM's data are being used for scientific studies (e.g., Liu and Liu 2016;Wentz and Meissner 2016;Panegrossi et al 2016;Prakash et al 2016). Most of the science results are from investigations by members of the NASA Precipitation Measurement Missions (PMM) science team (in 2016 consisting of 60 principal investigators from NASA centers and U.S. universities funded by NASA Headquarters while the Japanese PMM science team consists of 41 principal investigators).…”

Section: Initial Scientific Investigations and Applicationsmentioning

confidence: 99%

The Global Precipitation Measurement (GPM) Mission for Science and Society

Skofronick-Jackson

Petersen

Berg

et al. 2017

Bulletin of the American Meteorological Society

619

381

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Precipitation is a key source of freshwater; therefore, observing global patterns of precipitation and its intensity is important for science, society, and understanding our planet in a changing climate. In 2014, the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA) launched the Global Precipitation Measurement (GPM) Core Observatory (CO) spacecraft. The GPM CO carries the most advanced precipitation sensors currently in space including a dual-frequency precipitation radar provided by JAXA for measuring the three-dimensional structures of precipitation and a well-calibrated, multifrequency passive microwave radiometer that provides wide-swath precipitation data. The GPM CO was designed to measure rain rates from 0.2 to 110.0 mm h−1 and to detect moderate to intense snow events. The GPM CO serves as a reference for unifying the data from a constellation of partner satellites to provide next-generation, merged precipitation estimates globally and with high spatial and temporal resolutions. Through improved measurements of rain and snow, precipitation data from GPM provides new information such as details on precipitation structure and intensity; observations of hurricanes and typhoons as they transition from the tropics to the midlatitudes; data to advance near-real-time hazard assessment for floods, landslides, and droughts; inputs to improve weather and climate models; and insights into agricultural productivity, famine, and public health. Since launch, GPM teams have calibrated satellite instruments, refined precipitation retrieval algorithms, expanded science investigations, and processed and disseminated precipitation data for a range of applications. The current status of GPM, its ongoing science, and its future plans are presented.

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Section: Initial Scientific Investigations and Applicationsmentioning

confidence: 99%

The Global Precipitation Measurement (GPM) Mission for Science and Society

Skofronick-Jackson

Petersen

Berg

et al. 2017

Bulletin of the American Meteorological Society

619

381

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“…Extreme precipitation leading to flood or landslide events, and the characterization of potential hazards, are a source of several GPM investigations (e.g. Petkovic and Kummerow, ; Kirschbaum et al ., ; Panegrossi et al ., ). The IMERG spatial and temporal resolutions provide a valuable product to examine precipitation extremes that may result in flooding, landslides or other meteorologically induced phenomena, and to support disaster response and recovery (Schumann et al ., ).…”

Section: Applications Of Gpm Data For Societal Benefitmentioning

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

136

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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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“…GPM is expected to provide accurate precipitation estimates thanks to the availability of the NASA/JAXA GPM Core Observatory (GPM-CO) (equipped with the GPM Microwave Imager (GMI) and the Dualfrequency Precipitation Radar (DPR)), a common, global observatory of 3-D precipitation structure at 5 km resolution, and to the exploitation of a constellation of international Low Earth Orbit (LEO) satellites equipped with microwave radiometers for precipitation observation, providing frequent measurements over most of the globe (3-hourly coverage between 65 • S and 65 • N) (Hou et al, 2014;Draper et al, 2015;Newell et al, 2014;Petković and Kummerow, 2015). A contribution of ATMS as part of the GPM constellation is foreseen in this direction, also in relation to the technological improvements over its predecessor sounders.…”

Section: Introductionmentioning

confidence: 99%

The new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars

et al. 2016

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Abstract. The objective of this paper is to describe the development and evaluate the performance of a completely new version of the Passive microwave Neural network Precipitation Retrieval (PNPR v2), an algorithm based on a neural network approach, designed to retrieve the instantaneous surface precipitation rate using the cross-track Advanced Technology Microwave Sounder (ATMS) radiometer measurements. This algorithm, developed within the EUMETSAT H-SAF program, represents an evolution of the previous version (PNPR v1), developed for AMSU/MHS radiometers (and used and distributed operationally within H-SAF), with improvements aimed at exploiting the new precipitationsensing capabilities of ATMS with respect to AMSU/MHS. In the design of the neural network the new ATMS channels compared to AMSU/MHS, and their combinations, including the brightness temperature differences in the water vapor absorption band, around 183 GHz, are considered. The algorithm is based on a single neural network, for all types of surface background, trained using a large database based on 94 cloud-resolving model simulations over the European and the African areas.The performance of PNPR v2 has been evaluated through an intercomparison of the instantaneous precipitation estimates with co-located estimates from the TRMM Precipitation Radar (TRMM-PR) and from the GPM Core Observatory Ku-band Precipitation Radar (GPM-KuPR). In the comparison with TRMM-PR, over the African area the statistical analysis was carried out for a 2-year (2013-2014) dataset of coincident observations over a regular grid at 0.5 • × 0.5 • resolution. The results have shown a good agreement between PNPR v2 and TRMM-PR for the different surface types. The correlation coefficient (CC) was equal to 0.69 over ocean and 0.71 over vegetated land (lower values were obtained over arid land and coast), and the root mean squared error (RMSE) was equal to 1.30 mm h −1 over ocean and 1.11 mm h −1 over vegetated land. The results showed a slight tendency to underestimate moderate to high precipitation, mostly over land, and overestimate moderate to light precipitation over ocean. Similar results were obtained for the comparison with GPM-KuPR over the European area (15 months, from March 2014 to May 2015 of coincident overpasses) with slightly lower CC (0.59 over vegetated land and 0.57 over ocean) and RMSE (0.82 mm h −1 over vegetated land and 0.71 mm h −1 over ocean), confirming a good agreement also between PNPR v2 and GPM-KuPR. The performance of PNPR v2 over the African area was also compared to that of PNPR v1. PNPR v2 has higher R over the different surfaces, with generally better estimation of low precipitation, mostly over ocean, thanks to improvements in the design of the neural network and also to the improved capabilities of ATMS compared to AMSU/MHS. Both versions of PNPR algorithm have shown a general consistency with the TRMM-PR.

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Use of the GPM Constellation for Monitoring Heavy Precipitation Events Over the Mediterranean Region

Cited by 53 publications

References 56 publications

The Global Precipitation Measurement (GPM) Mission for Science and Society

The Global Precipitation Measurement (GPM) Mission for Science and Society

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

The new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars

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