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...     

Annual Frequency of Tropical Cyclones Directly Affecting Guangdong Province:Prediction Based on LSTM-FC

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.     

登陆华南台风强度的前兆信号分析及预测

自20世纪90年代中期后,登陆华南台风频数减少、强度转弱,这一方面受台风和大尺度环流系统自身的年际和年代际变化共同影响,另一方面,可能受太平洋年代尺度振荡(PDO)调制。进一步选用年际增量法剔除年代际变化得到,若前冬呈现东部型拉尼娜(中部型厄尔尼诺)状态,随后的后汛期120°~130°E越赤道气流偏强(弱),继而是强(弱)辐合带、季风槽偏强偏北(偏弱偏南),有(不)利于台风在华南地区活跃,则对应累计气旋能量指数(ACE)年指数更强(弱),体现了台风、海温、大气环流的年际变化影响。最后,采用基于年际增量法的主成分回归方法对ACE指数进行建模,结果得到近30年回报相关系数高达0.80,2014和2015年的预报效果与实况较为一致。     

近50年广东春运期间气温和降水协同变化的气候特征

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。