Advancing Precipitation Estimation, Prediction, and Impact Studies
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
Abstract
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).