Author(s): Yusuke Hiraga; Joaquin Meza
Linked Author(s):
Keywords: No Keywords
Abstract: Physical model benefits to updating Probable Maximum Precipitation (PMP) in data-sparse and complex terrain regions. This study first investigates the sensitivity of WRF model-simulated extreme precipitation to microphysics schemes in the Maipo River Basin, Chile. The Stony-Brook University scheme resulted in the least absolute error in the 72-hr MRB-average precipitation for the target precipitation event (-9.6 mm; -4.4 %), whereas some other microphysics schemes largely overestimated the precipitation, especially at the high terrain. This study then examined the applicability of the numerical model-based PMP estimation approaches to the basin as an initial analysis to physically estimate PMP. The proportional moisture increase and storm transposition physically increased the AR-induced heavy precipitation over the basin in Chile, indicating the high potential of the model-based PMP estimation over the region. Future studies need to perform ensemble runs on the moisture increment and shifting range to rigorously estimate PMP.
Year: 2024