Research interests

Mesoscale convective systems (MCSs) are crucial components of the hydrological cycle and often produce flash floods. Given their impact, it is important to understand how they will change under a warming climate. This study uses a satellite- and radar-based MCS tracking algorithm on convection-permitting climate model simulations and examines changes in MCS properties and precipitation structures between historical and future simulations. An underestimation in MCS total precipitation is evident in historical simulation compared to observations, due to model's depiction of MCS precipitation area and summertime occurrence frequency. Under pseudo-global warming, increases in MCS frequency and total warm season precipitation are observed, most notably in the southern U.S. The precipitation intensity and precipitating area generated by future MCSs also rises and results in an increase in precipitation volume. MCS precipitation structures are further classified into convective core and stratiform regions to understand how change in these structures contributes to future rainfall changes. In a warmer climate, the stratiform region demonstrates minimal change in size, but increases in mean precipitation rate and mean maximum precipitation rate by 15% and 29% are noted, respectively. A more robust future response is observed in the convective core region, with its size, mean precipitation rate and mean maximum precipitation rate increasing significantly by 24%, 37% and 42%, respectively. Finally, by examining the environmental properties of MCS initial condition, future intensification of convective rain may be attributed to a combined effect of substantial increases in atmospheric instability and moisture availability.

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This study uses machine-learning methods, specifically the random-forests (RF) method, on a radar-based mesoscale convective system (MCS) tracking dataset to classify the five types of linear MCS morphology in the contiguous United States during the period 2004–16. The algorithm is trained using radar- and satellite-derived spatial and morphological parameters, along with reanalysis environmental information from a 5-yr manually identified nonlinear mode and five linear MCS modes. The algorithm is then used to automate the classification of linear MCSs over 8 years with high accuracy, providing a systematic, long-term climatology of linear MCSs. Results reveal that nearly 40% of MCSs are classified as linear MCSs, of which one-half of the linear events belong to the type of system having a leading convective line. The occurrence of linear MCSs shows large annual and seasonal variations. On average, 113 linear MCSs occur annually during the warm season (March–October), with most of these events clustered from May through August in the central eastern Great Plains. MCS characteristics, including duration, propagation speed, orientation, and system cloud size, have large variability among the different linear modes. The systems having a trailing convective line and the systems having a back-building area of convection typically move more slowly and have higher precipitation rate, and thus they have higher potential for producing extreme rainfall and flash flooding. Analysis of the environmental conditions associated with linear MCSs show that the storm-relative flow is of most importance in determining the organization mode of linear MCSs.

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During monsoon season (May – July), East Asia is characterized by a quasi-stationary rainband that extends from the foothills of the Tibetan Plateau to Japan, the rainband is known as Meiyu in China and Baiu in Japan. The quasi-stationarity of the Meiyu front is favorable for the development of organized mesoscale disturbances and convective cloud clusters that propagate along the Meiyu front and produce localized heavy rainfall. This study analyzed 5-year MCSs occurring in central and southern China, we found MCSs contribute 20% to 60% of the total rainfall amount during the Meiyu period, and regional contribution can be more than 90%. We also found MCS number, lifetime and precipitation intensity have large inter-annual variations. After examing the synoptic patterns during the weak and strong MCS activity years, we found several key environmental factors that affect MCS activity. Including the strengths of the southwesterly low-level jet and mid-tropospheric westerly jet, the location of subtropical high pressure center.

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