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Tropical cyclone (TC) annual frequency forecasting is significant for disaster prevention and mitigation in Guangdong Province. Based on the NCEP-NCAR reanalysis and NOAA Extended Reconstructed global sea surface temperature (SST) V5 data in winter, the TC frequency climatic features and prediction models have been studied. During 1951-2019, 353 TCs directly affected Guangdong with an annual average of about 5.1. TCs have experienced an abrupt change from abundance to deficiency in the mid to late 1980 with a slightly decreasing trend and a normal distribution. 338 primary precursors are obtained from statistically significant correlation regions of SST, sea level pressure, 1000hPa air temperature, 850hPa specific humidity, 500hPa geopotential height and zonal wind shear in winter. Then those 338 primary factors are reduced into 19 independent predictors by principal component analysis (PCA). Furthermore, the Multiple Linear Regression (MLR), the Gaussian Process Regression (GPR) and the Long Short-term Memory Networks and Fully Connected Layers (LSTM-FC) models are constructed relying on the above 19 factors. For three different kinds of test sets from 2010 to 2019, 2011 to 2019 and 2010 to 2019, the root mean square errors (RMSEs) of MLR, GPR and LSTM-FC between prediction and observations fluctuate within the range of 1.05-2.45, 1.00-1.93 and 0.71-0.95 as well as the average absolute errors (AAEs) 0.88-1.0, 0.75-1.36 and 0.50-0.70, respectively. As for the 2010-2019 experiment, the mean deviations of the three model outputs from the observation are 0.89, 0.78 and 0.56, together with the average evaluation scores 82.22, 84.44 and 88.89, separately. The prediction skill comparisons unveil that LSTM-FC model has a better performance than MLR and GPR. In conclusion, the deep learning model of LSTM-FC may shed light on improving the accuracy of short-term climate prediction about TC frequency. The current research can provide experience on the development of deep learning in this field and help to achieve further progress of TC disaster prevention and mitigation in Guangdong Province. 相似文献
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Analysis of Precipitation Anomaly and a Failed Prediction During the Dragon-boat Rain Period in 2023
This study investigates the possible causes for the precipitation of Guangdong during dragon-boat rain period(DBRP) in 2022 that is remarkably more than the climate state and reviews the successes and failures of the prediction in2022. Features of atmospheric circulation and sea surface temperature(SST) are analyzed based on several observational datasets for nearly 60 years from meteorological stations and the NCEP/NCAR Global Reanalysis Data. Results show that fluctuation of the 200-h Pa weste... 相似文献
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This study investigates the variation and prediction of the west China autumn rainfall (WCAR) and their associated atmospheric circulation features, focusing on the transitional stages of onset and demise of the WCAR. Output from the 45-day hindcast by the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) and several observational data sets are used. The onset of WCAR generally occurs at pentad 46 and decays at pentad 56, with heavy rainfall over the northwestern China and moderate rainfall over the south. Before that, southerly wind changes into southeasterly wind, accompanied by a westward expansion and intensification of the western Pacific subtropical high (WPSH), favoring rainfall over west China. On the other hand, during the decay of WCAR, a continental cold high develops and the WPSH weakens and shifts eastward, accompanied by a demise of southwest monsoon flow, leading to decay of rainfall over west China. The CFSv2 generally well captures the variation of WCAR owing to the high skill in capturing the associated atmospheric circulation, despite an overestimation of rainfall. This overestimation occurs at all time leads due to the overestimated low-level southerly wind. The CFSv2 can pinpoint the dates of onset and demise of WCAR at the leads up to 5 days and 40 days, respectively. The lower prediction skill for WCAR onset is due to the unrealistically predicted northerly wind anomaly over the lower branch of the Yangtze River and the underestimated movement of WPSH after lead time of 5 days. 相似文献
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根据近50年(1971—2020年)广东省86个气象站的观测数据、NCEP/NCAR再分析数据和NOAA海温数据,采用线性趋势分析、合成分析等统计方法,研究了广东春运期间气温和降水的时空分布特征,从气温降水协同变化的角度切入划分了气候异常类型,并对比分析了其异常成因。结果表明：近50年来,广东省春运期间平均气温呈现显著上升趋势,珠江三角洲和粤东地区最明显。而降水日数则表现出显著减少趋势,粤西北、粤东和粤西沿海最明显。气温和降水协同变化的异常年(冷湿(4年)、冷干(6年)和暖干(11年))共有21年,占全部年份的42%。冷湿年和冷干年,欧亚大陆中高纬度都表现出经向环流特征,西伯利亚高压偏强,有利于冷空气活跃南下。不同的是冷湿年东亚西部地区“北高南低”,低纬度地区“东高西低”,对应的冷空气路径为中、西路,有利于水汽输送;而冷干年东亚东部地区“北高南低”,低纬度地区一致偏低,对应的冷空气路径偏东,不利于水汽输送。另外,冷湿年前期赤道中东太平洋偏暖,呈现El Ni?o状态,受其影响西太平洋副热带高压偏大偏强,西太暖池偏冷,在菲律宾海区域激发出一个反气旋性环流,有利于西南水汽输送到广东地区,降水偏多;而冷干年则相反。暖干年,东亚中高纬表现出“北低南高”的纬向环流分布,东亚大槽和西伯利亚高压偏弱,不利于冷空气的生成和南下,广东上空受反气旋式环流控制,辐散下沉,温高雨少。 相似文献
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