PDF《of炉he tropical oceans》

s Republic of?China

5 Present Address: Department of?Physical Oceanography, Woods Hole Oceanographic Institution, Woods Hole, MA, USA You 2014). Thus, accurately predicting HRV heavy rainfall events is of vital importance to reduce the economic loss caused by flooding and downpours. However, previous studies on rainfall prediction focused primarily on the seasonal mean precipitation, which might not be readily adaptable to heavy rainfall events. Specifi- cally, the statistical behavior of heavy rainfall events are determined by the tail of probability distributions (Katz and Brown 1992), which is substantially different from that of the mean (Gumble 1954;

Wilson and Toumi 2005). Further- more, it has been noticed that heavy rainfall events respond differently to climate variability and climate change com- pared to the seasonal mean precipitation (Chen et?al. 2012;

Chou et?al. 2012;

Qian et?al. 2007;

Wu and Fu 2013). Con- sequently, the factors responsible for seasonal mean precip- itation over the HRV, including the sea surface temperature anomaly (SSTA) patterns (Chang et?al. 2000;

Huang and Sun 1992;

Yang and Lau 2004), springtime soil moisture content (Meng et?al. 2014;

Zhang and Zuo 2011), and Arc- tic sea ice extent (Li and Leung 2013;

Vihma 2014), might not be used in the same way as for the prediction of heavy rainfall events. In order to improve seasonal prediction of heavy rain- fall events, the prediction models should be updated and the climate predictors suitable for the HRV heavy rainfall events should be identified. On top of that, a reliable sta- tistical inference on the HRV heavy rainfall events and a better understanding of the related physical processes are needed. This study advances climate prediction of HRV heavy rainfall by applying a novel statistical framework. Unlike traditional statistical prediction models, the framework does not require predefinition of distribution kernels, and thus largely reduces the biases in rainfall statistical mod- els due to their subjective selection of distribution kernels (Li and Li 2013). By objectively identifying heavy rain- fall events, the framework can better model the statistical behavior of heavy rainfall, and thus help to improve the understanding of related physical mechanisms. The prob- ability behavior of heavy rainfall events are then linked to preceding SSTA pattern to identify the potential climate predictors. Two statistical prediction models, one linear model and the other machine learning algorithm, are con- structed to assess the predictability of the HRV heavy rain- fall events using the identified SSTA predictors. The rest of the paper is organized as following. In Sect.?2, data, analysis methods, and the statistical frame- work are described. The Bayesian inference on the HRV summertime heavy rainfall events is presented in Sect.?3. The SSTA predictors of the HRV heavy rainfall are identi- fied based on exploratory data analysis in Sect.?4. Further- more, the atmospheric circulation is analyzed to create the physical linkage between preceding SSTA and summertime HRV heavy rainfall events. In Sect.?5, two statistical pre- diction models, multiple linear regression model and sup- port vector machine (SVM) algorithm, are constructed to test the predictability of the HRV heavy rainfall events using the SSTA predictors identified in Sect.?4. Concluding remarks are presented in the last section. 2? Data and?methods 2.1?Data The data used in this study includes gridded daily precipita- tion data archived by China Meteorological Administration (CMA) (Xie et?al. 2007). The temporal period analyzed in this study is 1961C2012 in order to avoid the potential inac- curate of statistical inference due to the sparse data cover- age prior to 1960s. The HRV covers a geographical domain of 30.5°NC36.5°N;