Maximum Covariance Analysis of Typhoon Surface Wind and Rainfall Relationships in Taiwan

Abstract

The multivariate relationships between hourly surface wind and rainfall observations during typhoons affecting Taiwan have been investigated with maximum covariance analysis (MCA). Historical surface observations from 1987 to 2004 are used when typhoon centers were located inside the domain of 198-288N, 1178-1278E. The three leading MCA modes explain 70%, 20.6%, and 7.6% of the squared covariance fraction, and the correlation coefficients are 0.59, 0.48, and 0.49, respectively. The wind directions of the three leading positive modes are 1) northwesterly flow perpendicular to the Snow Mountain Range (SMR), 2) southwesterly flow toward the river valleys of the southwestern Central Mountain Range (CMR) and the southern SMR, and 3) easterly flow toward the northeastern SMR and the northern CMR. The rainfall patterns of the three principal modes reveal the contrast between the windward and the leeward sides of the mountain ranges. Based on the MCA singular vectors, historical typhoon surface wind patterns are categorized into major types. The results show that the three major wind types consist of 53% of the data, with 25%, 9%, and 19%, respectively, for these wind types. Furthermore, the analyses of the corresponding surface air temperatures, relative humidities, and air pressures also reveal contrasting patterns between the windward and leeward sides.

Effects of climate change on a mutualistic coastal species: Recovery from typhoon damages and risks of population erosion

“…Typhoons reduce the temperature and humidity in Southern Taiwan because of adiabatic warming (or foehn wind) on crossing the Central Mountain Range in Taiwan [ 52 ]. This phenomenon was strong during Typhoon Morakot [ 53 ].…”

“…Climate change strongly affects the uplifted coral reef remnant ecosystem of the Hengchun Peninsula. The aboveground vegetation was withered by Typhoon Morakot, and the posttyphoon days were severely dry and witnessed warm foehn-like winds [ 52 ], which reduced the humidity. Plants living in such harsh, uncertain environments have been selected for their ability to survive withering typhoons; however, because of global climate change, extreme events, such as strong typhoons and droughts, occurred increasingly and more powerfully [ 15 , 25 ].…”

Modelling of hillslope storage under temporally varied rainfall recharge

“…In nature the rainfall recharge is usually nonuniformly distributed, so temporal variation of it was considered. Referring to the works of Wartalska et al [25] and Shih [19], six recharge patterns over time shown in Figure 10 were introduced to discuss their effects on hillslope storage. Assuming the same aquifer and groundwater conditions and setting D = 5 m, θ = 5%, and γ = 0 for the simulated scenarios, Figure 11 shows the temporal variation of the water level at various durations under six recharge patterns for convergent hillslope.…”

Estimation of Hourly Rainfall during Typhoons Using Radar Mosaic-Based Convolutional Neural Networks

“…These researchers have revealed that when typhoons approach Taiwan from the east before passing through the CMR, their routes are more easily estimated, and early warnings can be provided for precaution against the corresponding wind and rain. By contrast, after typhoons pass through the CMR, the routes, arrival times of wind and rain, and delays in rainfall become unpredictable [14,15]. Therefore, efficiently estimating typhoon rainfall is crucial for Taiwan.…”