利用线性回归、EOF分解等方法对广东省1951—2016年"龙舟水"时空特征进行分析,同时对"龙舟水"期间的大气环流背景和南海夏季风进行对比,结果发现:在全球气候持续变暖的背景下,2005—2016年以来广东的"龙舟水&q...利用线性回归、EOF分解等方法对广东省1951—2016年"龙舟水"时空特征进行分析,同时对"龙舟水"期间的大气环流背景和南海夏季风进行对比,结果发现:在全球气候持续变暖的背景下,2005—2016年以来广东的"龙舟水"整体呈现出雨量、雨日明显增多,雨强明显增强的新特点;降水偏多年500 h Pa广东高度场偏低,东亚大槽较深,反之偏少年广东高度场偏高,东亚大槽较浅;南海夏季风强度与广东"龙舟水"雨量并不是正相关关系,"龙舟水"偏多(偏少)的多数年份中南海夏季风反而偏弱(偏强)。展开更多
利用广东省2015年86个台站的降水观测资料,采用小波分析、相关分析、合成分析等方法分析了2015年5月广东省的降水时空特征;并采用NCEP/NCAR再分析资料中的500 h Pa高度场资料,以及国家气候中心气候监测室提供的130项环流指数对降水场进...利用广东省2015年86个台站的降水观测资料,采用小波分析、相关分析、合成分析等方法分析了2015年5月广东省的降水时空特征;并采用NCEP/NCAR再分析资料中的500 h Pa高度场资料,以及国家气候中心气候监测室提供的130项环流指数对降水场进行分析。结果发现:广东省2015年5月的降水除了雷州半岛、粤东部分地区,其余大部分地区较常年异常偏多,为有气象记录以来历史同期最多。造成该月降水偏多的主要原因是受ENSO信号的影响;5月环流形势由前期的纬向型向经向型的转型,500 h Pa高度场在内蒙古北部、东北上空及日本海地区存在一个正-负-正的偶极子模型,同时配有来自南海充足的水汽条件,从而造成5月降水异常偏多。展开更多
The occurrence of most major basin-wide floods is closely related to persistent heavy rainfall(PHR).In June 2022,a PHR event that lasted twenty days hit the Beijiang River Basin(BRB)in South China.The record-breaking ...The occurrence of most major basin-wide floods is closely related to persistent heavy rainfall(PHR).In June 2022,a PHR event that lasted twenty days hit the Beijiang River Basin(BRB)in South China.The record-breaking rainfall led to major floods and caused tremendous losses.This study first reviews the spatiotemporal distribution of the precipitation and the flooding process of this PHR event and then analyzes the atmospheric circulation patterns associated with the event based on the hourly reanalysis data from the European Centre for Medium-Range Weather Forecasts(ERA5).The results show that the establishment and stabilization of mid-to high-latitude blockings provided a favorable background for the“2022.06”PHR event in the BRB.The convergence of water vapor at the low level,the release of unstable energy,and the development of stronger vertical ascending movement provided the necessary dynamic conditions.The vertical circulation of water vapor was much stronger than that of climatology,while the vertical ascending movement was also more active in the BRB.The heavy rainfall belt in the BRB was formed in a region with apparently stronger divergence,which also coincided with regions of higher-than-normal updraft velocity and specific humidity.展开更多
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 tempera...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.展开更多
文摘利用线性回归、EOF分解等方法对广东省1951—2016年"龙舟水"时空特征进行分析,同时对"龙舟水"期间的大气环流背景和南海夏季风进行对比,结果发现:在全球气候持续变暖的背景下,2005—2016年以来广东的"龙舟水"整体呈现出雨量、雨日明显增多,雨强明显增强的新特点;降水偏多年500 h Pa广东高度场偏低,东亚大槽较深,反之偏少年广东高度场偏高,东亚大槽较浅;南海夏季风强度与广东"龙舟水"雨量并不是正相关关系,"龙舟水"偏多(偏少)的多数年份中南海夏季风反而偏弱(偏强)。
文摘利用广东省2015年86个台站的降水观测资料,采用小波分析、相关分析、合成分析等方法分析了2015年5月广东省的降水时空特征;并采用NCEP/NCAR再分析资料中的500 h Pa高度场资料,以及国家气候中心气候监测室提供的130项环流指数对降水场进行分析。结果发现:广东省2015年5月的降水除了雷州半岛、粤东部分地区,其余大部分地区较常年异常偏多,为有气象记录以来历史同期最多。造成该月降水偏多的主要原因是受ENSO信号的影响;5月环流形势由前期的纬向型向经向型的转型,500 h Pa高度场在内蒙古北部、东北上空及日本海地区存在一个正-负-正的偶极子模型,同时配有来自南海充足的水汽条件,从而造成5月降水异常偏多。
基金National Natural Science Foundation of China(U2142205)Science and Technology Research Project of the Guangdong Meteorological Service(GRMC2021M13,GRMC2023M11)。
文摘The occurrence of most major basin-wide floods is closely related to persistent heavy rainfall(PHR).In June 2022,a PHR event that lasted twenty days hit the Beijiang River Basin(BRB)in South China.The record-breaking rainfall led to major floods and caused tremendous losses.This study first reviews the spatiotemporal distribution of the precipitation and the flooding process of this PHR event and then analyzes the atmospheric circulation patterns associated with the event based on the hourly reanalysis data from the European Centre for Medium-Range Weather Forecasts(ERA5).The results show that the establishment and stabilization of mid-to high-latitude blockings provided a favorable background for the“2022.06”PHR event in the BRB.The convergence of water vapor at the low level,the release of unstable energy,and the development of stronger vertical ascending movement provided the necessary dynamic conditions.The vertical circulation of water vapor was much stronger than that of climatology,while the vertical ascending movement was also more active in the BRB.The heavy rainfall belt in the BRB was formed in a region with apparently stronger divergence,which also coincided with regions of higher-than-normal updraft velocity and specific humidity.
基金National Key R&D Program of China(2017YFA0605004)Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)+4 种基金National Basic R&D Program of China(2018YFA0606203)Special Fund of China Meteorological Administration for Innovation and Development(CXFZ2021J026)Special Fund for Forecasters of China Meteorological Administration(CMAYBY2020-094)Graduate Independent Exploration and Innovation Project of Central South University(2021zzts0477)Science and Technology Planning Program of Guangdong Province(20180207)。
文摘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.
