Precipitation from Space: Advancing Earth System Science

Kucera, Paul A.; Ebert, Elizabeth E.; Turk, F. Joseph; Levizzani, Vincenzo; Kirschbaum, Dalia; Tapiador, Francisco J.; Loew, Alexander; Borsche, Michael

doi:10.1175/bams-d-11-00171.1

Cited by 177 publications

(121 citation statements)

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“…IMERG is starting to be used as an input for forecasts in other regions of the world, especially areas lacking adequate ground-based coverage. Selected applications are reported upon in Kirschbaum et al (2017), Ward et al (2015), Kucera et al (2013), and Kirschbaum and Patel (2016).…”

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

632

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

632

381

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“…In recent years, remote sensing sensors are increasingly employed in many parts of the world, mainly in poorly gauged areas, for estimating rainfall through different algorithms and sensor types (Hou et al, 2014). Despite this, satellite rainfall products often fail in reproducing the rainfall patterns because of the indirect nature of satellitebased observations (Kucera et al, 2013). An alternative source for rainfall estimation is provided by short-range forecasts from numerical weather prediction models (Ebert et al, 2007) that, however, suffers from the known limitations of modelled data (e.g., model structure, parameterization, input data).…”

Section: Introductionmentioning

confidence: 99%

Rainfall estimation from in situ soil moisture observations at several sites in Europe: an evaluation of the SM2RAIN algorithm

Brocca

Massari

Ciabatta

et al. 2015

Journal of Hydrology and Hydromechanics

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Rain gauges, weather radars, satellite sensors and modelled data from weather centres are used operationally for estimating the spatial-temporal variability of rainfall. However, the associated uncertainties can be very high, especially in poorly equipped regions of the world. Very recently, an innovative method, named SM2RAIN, that uses soil moisture observations to infer rainfall, has been proposed by Brocca et al. (2013) with very promising results when applied with in situ and satellite-derived data. However, a thorough analysis of the physical consistency of the SM2RAIN algorithm has not been carried out yet. In this study, synthetic soil moisture data generated from a physically-based soil water balance model are employed to check the reliability of the assumptions made in the SM2RAIN algorithm. Next, high quality and multiyear in situ soil moisture observations, at different depths (5-30 cm), and rainfall for ten sites across Europe are used for testing the performance of the algorithm, its limitations and applicability range.SM2RAIN shows very high accuracy in the synthetic experiments with a correlation coefficient, R, between synthetically generated and simulated data, at daily time step, higher than 0.940 and an average Bias lower than 4%. When real datasets are used, the agreement between observed and simulated daily rainfall is slightly lower with average R-values equal to 0.87 and 0.85 in the calibration and validation periods, respectively. Overall, the performance is found to be better in humid temperate climates and for sensors installed vertically. Interestingly, algorithms of different complexity in the reproduction of the underlying hydrological processes provide similar results. The average contribution of surface runoff and evapotranspiration components amounts to less than 4% of the total rainfall, while the soil moisture variations (63%) and subsurface drainage (30%) terms provide a much higher contribution. Overall, the SM2RAIN algorithm is found to perform well both in the synthetic and real data experiments, thus offering a new and independent source of data for improving rainfall estimation, and consequently enhancing hydrological, meteorological and climatic studies.

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“…The utility of current satellite rainfall products in determining spatially distributed runoff and renewable freshwater supplies is still limited (Fekete et al 2004;Kucera et al 2013). The enhanced measurement and sampling capabilities of GPM will help us better understand how changing precipitation patterns at multiple scales translate into changes in hydrologic fluxes and states (e.g., runoff, evapotranspiration, soil moisture, and groundwater recharge) both directly and through data assimilation into land process models (e.g., Rodell et al 2004).…”

mentioning

confidence: 99%