Ian C. Cornejo and Angela K. Rowe and Kristen L. Rasmussen and Jennifer C. DeHart, : Orographic Controls on Extreme Precipitation Associated with a Mei-Yu Front. Monthly Weather Review, 152 , https://doi.org/10.1175/MWR-D-23-0170.1
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Abstract
Taiwan regularly receives extreme rainfall due to seasonal mei-yu fronts that are modi ed by Taiwan’s com- plex topography. One such case occurred between 1 and 3 June 2017 when a mei-yu front contributed to ooding and land- slides from over 600 mm of rainfall in 12 h near the Taipei basin, and over 1500 mm of rainfall in 2 days near the Central Mountain Range (CMR). This mei-yu event is simulated using the Weather Research and Forecasting (WRF) Model with halved terrain as a sensitivity test to investigate the orographic mechanisms that modify the intensity, duration, and location of extreme rainfall. The reduction in WRF terrain height produced a decrease in rainfall duration and accumulation in northern Taiwan and a decrease in rainfall duration, intensity, and accumulation over the CMR. The reductions in northern Taiwan are linked to a weaker orographic barrier jet resulting from a lowered terrain height. The reductions in rainfall intensity and dura- tion over the CMR are partially explained by a lack of orographic enhancements to mei-yu frontal convergence near the ter- rain. A prominent feature missing with the reduced terrain is a redirection of postfrontal westerly winds attributed to orographic deformation, i.e., the redirection of ow due to upstream topography. Orographically deforming winds converge with prefrontal ow to maintain the mei-yu front. In both regions, the decrease in mei-yu front propagation speed is linked to increased rainfall duration. These orographic features will be further explored using observations captured during the 2022 Prediction of Rainfall Extremes Campaign in the Paci c (PRECIP) eld campaign.
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Acknowledgments
This study was supported by the NOAA Cooperative Institute for Meteorological Satellite Studies through their graduate student education program and National Science Foundation Awards AGS-2013743, AGS-1854399, and AGS-1854559. We would like to acknowledge high-performance computing support from Cheyenne (doi: 10.5065/D6RX99HX) provided by NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation. The authors thank Alison Nugent, Tianqi Zuo, Zoe Douglas, Rung Panasawatwong, and Hungjui Yu for their helpful feedback as well as the Central Weather Administration for providing QPESUMS and radar data. The authors would also like to thank the two anonymous reviewers for their insightful feedback and comments.