Heavy Rainfall Mechanisms During the 2008 Terrain-influenced Monsoon Rainfall Experiment (TiMREX)

Colorado State University, Fort Collins CO

Investigators

Abstract

The Terrain-influenced Monsoon Rainfall Experiment (TiMREX), centered on Taiwan in the summer of 2008, collected special observations to improve understanding of the physical process associated with terrain-influenced heavy precipitation systems over this mountainous island and the monsoonal environment in which they are embedded. This award will allow researchers to implement quality control checks on potentially problematic rawinsonde data, combine these corrected data with other observations to create a more comprehensive gridded analysis, and then apply these data to investigate processes leading to heavy rainfall over and surrounding Taiwan. The Intellectual Merit of this research includes focus on analysis of special rawinsonde intercomparison launches with an eye toward sonde-dependent biases and ancillary satellite and ground-based remote , satellite and ground based remote sensing, and prior knowledge of sonde biases to reduce biases in moisture measurements taken by the several different atmospheric sounding systems used during the TiMREX campaign. The researchers will then conduct diagnostic studies of processes related to the production of heavy rainfall, including investigation of rainfall patterns upstream of and over Taiwan in relation to flow blocking, investigation of atmospheric boundary layer structure in the TiMREX region and its connection with aforementioned precipitation patterns, and use of the sounding network to diagnose properties of convection during the field experiment. Broader Impacts of the research will accrue through improved understanding of mechanisms (and ultimately prediction) of heavy rainfall, both specifically during the East Asian monsoon season and more generally in mountainous tropical and subtropical environments including those found in the Americas. A quality-controlled version of the TiMREX meteorological dataset will be disseminated for access and exploitation by the broader scientific community, and graduate student education will be supported.