2021
DOI: 10.1175/jhm-d-21-0075.1
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How well do multi-satellite products capture the space-time dynamics of precipitation? Part I: five products assessed via a wavenumber-frequency decomposition

Abstract: As more global satellite-derived precipitation products become available, it is imperative to evaluate them more carefully for providing guidance as to how well precipitation space-time features are captured for use in hydrologic modeling, climate studies and other applications. Here we propose a space-time Fourier spectral analysis and define a suite of metrics which evaluate the spatial organization of storm systems, the propagation speed and direction of precipitation features, and the space-time scales at … Show more

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Cited by 8 publications
(6 citation statements)
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“…These products use complicated algorithms to combine both the Global Precipitation Measurement mission (GPM) international constellation of passive microwave sensors and geostationary-based IR measurement and may integrate rain gauge information. Many studies have shown that these products can provide robust information down to resolution of about 50 km and a few hours (see Gosset et al 2018;Guilloteau et al 2016Guilloteau et al , 2021. Despite tremendous progress in the most recent products, high uncertainties remain in rainfall estimation based on satellite for high resolution (20 km or below and hourly or better) and/or for real-time applications when gauge data are not yet available to improve biases.…”
Section: Storm Monitoring and Rainfall Estimation From Satellitementioning
confidence: 99%
See 1 more Smart Citation
“…These products use complicated algorithms to combine both the Global Precipitation Measurement mission (GPM) international constellation of passive microwave sensors and geostationary-based IR measurement and may integrate rain gauge information. Many studies have shown that these products can provide robust information down to resolution of about 50 km and a few hours (see Gosset et al 2018;Guilloteau et al 2016Guilloteau et al , 2021. Despite tremendous progress in the most recent products, high uncertainties remain in rainfall estimation based on satellite for high resolution (20 km or below and hourly or better) and/or for real-time applications when gauge data are not yet available to improve biases.…”
Section: Storm Monitoring and Rainfall Estimation From Satellitementioning
confidence: 99%
“…The constellation of satellites currently dedicated to rainfall is denser than ever, with about a dozen passive microwave sensors (the GPM constellation), two radars (GPM DPR and Cloudsat) and high-resolution, multispectral geostationary satellites (Kidd et al 2021). However, the effective resolution of the products based on the constellation is around 50 km, 3 h (Guilloteau et al 2021) even if their nominal resolution is hourly/10 km. Going down to smaller scales would necessitate new algorithmic approaches or constellation of sensors allowing more frequent revisit while keeping high resolutions.…”
Section: Conclusion and Perspectivementioning
confidence: 99%
“…This product integrates data from multiple passive microwave (PMW) and infrared (IR) sensors, ensuring consistency and accuracy through intercalibration with state‐of‐the‐art precipitation measurement instruments onboard the GPM Core Observatory, and is ultimately adjusted by the monthly gauge analysis. IMERG is recognized as one of the most accurate high‐resolution satellite precipitation data sets available (Guilloteau et al., 2021; Pradhan et al., 2022; Tang et al., 2020), and has been widely employed in various applications (Nie & Sun, 2022; Orland et al., 2022; Zhang et al., 2023).…”
Section: Datamentioning
confidence: 99%
“…It combines data from 180 ground-based radar and about 7,000 rain gauges, along with model analyses (Zhang et al, 2016). Designed for GPM ground validation, GV-MRMS is further quality-controlled, adjusted, and integrated to the IMERG's resolution (0.1°, 0.5 h) (Kirstetter et al, 2012(Kirstetter et al, , 2014, and has been extensively used in GPM validation studies (e.g., Derin & Kirstetter, 2022;Guilloteau et al, 2021;Tan et al, 2022). Additionally, the Radar Quality Index (ranging from 0 to 100 (best)) generated concurrently with GV-MRMS QPE, is employed here to filter out low-quality estimates (Petersen et al, 2020).…”
Section: Datamentioning
confidence: 99%
“…The IMERG algorithm uses the CMORPH motion vector method to incorporate the microwave-derived precipitation measurements. However, in its final version, version 06, the total precipitable water vapor field gathered from numerical models is used to compute the motion vectors instead of the geostationary infrared (Geo-IR) imagery (Guilloteau et al, 2021). Additionally, IMERG assimilates the infrared precipitation measurements from the PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System) (Hong et al, 2004) using the Kalman filtering assimilation method.…”
Section: Imerg Precipitationmentioning
confidence: 99%