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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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气溶胶光学厚度作为描述气溶胶光学特性的重要参数之一, 被广泛应用于空气质量监测及辐射传输模型的大气订正等研究中。卫星遥感可快速反演获取大范围气溶胶信息, 但其产品通常因云覆盖或暗目标算法等原因而存在空间覆盖率较低的问题, 且产品时相受限于卫星过境时间。水平能见度作为描述气溶胶光学特性的另一重要参数, 由分布广泛的气象台站一日8 次固定时间多次发布。建立水平能见度与气溶胶光学厚度的转换关系, 可实现对卫星反演气溶胶光学厚度的有益补充。本文利用2001-2009 年的MODIS气溶胶光学厚度产品与中国华东地区71 个气象台站的水平能见度数据, 对描述两者转换关系的Peterson 模型进行区域优化。采用分区域高斯曲线拟合的方法, 对影响转换精度的主要参数气溶胶标高随时间变化规律开展研究和模拟。利用2010 年数据对优化模型进行精度及区域适用性验证。结果表明, 优化后模型的气溶胶光学厚度估算均方根误差为0.31, 低于原模型误差;精度基本上与单站点优化模型一致, 但在实用性方面优于单站点优化模型。 相似文献
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