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Advancing Precipitation Estimation, Prediction, and Impact Studies

2020; American Meteorological Society; Volume: 101; Issue: 9 Linguagem: Inglês

10.1175/bams-d-20-0014.1

1520-0477

Efi Foufoula‐Georgiou, Clément Guilloteau, Phu Nguyen, Amir AghaKouchak, Kuolin Hsu, Antonio J. Busalacchi, F. Joseph Turk, C. D. Peters‐Lidard, Taikan Oki, Qingyun Duan, Witold F. Krajewski, R. Uijlenhoet, Ana P. Barros, Pierre‐Emmanuel Kirstetter, William Logan, T. S. Hogue, Hoshin V. Gupta, Vincenzo Levizzani,

Soil Moisture and Remote Sensing

recipitation exhibits a large variability over a wide range of space and time scales: from seconds to years and decades in time and from the millimeter scale of microphysical processes to regional and global scales in space.It also exhibits a large variability in magnitude and frequency, from low extremes resulting in prolonged droughts to high extremes resulting in devastating floods.Improving precipitation estimation and prediction has great societal impact for decision support in water resources management, infrastructure protection and design under accelerating climate extremes, quantifying water and energy balances at the regional to global scales, and predicting hurricanes, tornadoes, floods, and droughts that affect the economy and security around the world (e.g., Blunden and Arndt 2019).Yet, despite significant advances in observations and physical understanding, precipitation still remains one of the most challenging variables to model and predict at local, regional, and global scales with significant implications for our ability to quantify water and energy cycle dynamics, inform decision-making, and predict hydrogeomorphic hazards in response to precipitation extremes (e.g., Maggioni and Massari 2019).Observations of precipitation from ground-based and satellite sensors are paramount for advancing precipitation science and for monitoring Earth's water cycle, weather, and climate.The Tropical Rainfall Measuring Mission (TRMM) (Kummerow et al. 1998) was the first satellite to be equipped with both an active and a passive sensor dedicated to precipitation measurement forging a new era of developing passive microwave (PMW) retrieval algorithms (Huffman et al. 2007).Currently, the international constellation of satellites of the Global Precipitation Measurement (GPM) mission, including the GPM Core Observatory launched in 2014 (Hou et al. 2014;Skofronick-Jackson et al. 2017), offers almost global coverage of precipitation (Tan et al. 2019).However, challenges still exist, among them the detection and estimation of precipitation and snowfall over complex topography, snow-and ice-covered regions, at high latitudes and along land margins (coast lines and lakes), and in estimating heavy precipitation from convective weather systems [e.g., Decadal Survey; National Academies of Science, Engineering, and Medicine (NASEM); NASEM 2018, chapter 6].The past and future successes of precipitation observation from space rely on the synergy and complementarity with ground and airborne measurements (for calibration and validation in particular, see Kirstetter et al. 2012;Kidd et al. 2018;Houze et al. 2017;Duan et al. 2015).Over the past decade, significant advances have also been made in numerical weather prediction (NWP) models and global circulation models (GCMs).Yet, accurate prediction of