A Mesoscale Analysis of Vertical Motion Producing Extreme Rainfall during PRECIP 2022

Extreme rainfall events can cause severe impacts to people across the globe. Yet the forecasting of these events is extremely challenging, due in part to deficient understanding of the physical and dynamical processes that give rise to extreme rainfall in the first place. One fundamental dynamical question that remains unresolved is which spatial scales and their interactions are the most important for producing vertical motion in extreme rainfall. To answer this question, a mesoscale analysis of an intensive observation period during the Prediction for Rainfall Extremes Campaign in the Pacific (PRECIP) in 2022 is performed with a focus on diagnosing the vertical motion associated with the heavy rainfall. From 6 June 2022 to 12 June 2022, NCAR’s S-band dual polarization Doppler radar (S-Pol) observed the evolution of a multi-day extreme rainfall event on- and off-shore of Taiwan during an Intensive Observing Period (IOP #3). The extreme rainfall was produced via scale interactions along the mei-yu front, a semi-stationary large-scale boundary extending from eastern China to southern Japan. Mesoscale convective systems that advect eastward along the mei-yu front are associated with rainfall extremes, with localized rain rates sometimes surpassing 100 mm/hr. In this study, a variational analysis technique called SAMURAI is used to retrieve the 3-dimensional wind field by combining radar data from S-Pol with the National Central University TEAM-R and Central Weather Administration operational radars. Numerical simulations from the Pennsylvania State University ensemble Kalman Filter (PSU-enKF) Weather Research and Forecasting model are also utilized to analyze the event. The observed and modeled vertical velocity will be analyzed to better understand the importance of vortical and shearing flows at different spatial scales and their interactions in producing the extreme rainfall. An improved understanding of the underlying dynamics will help to improve quantitative precipitation forecasting and help to understand the mechanisms behind extreme rainfall events in other environments around the world.