多源数据融合高时空分辨率晴雨分类

高时空分辨率晴雨分类与交通、旅游、农业灌溉及人们日常出行都密切相关，然而"天有不测风云"，"东边日头西边雨"，准确的高时空分辨率晴雨分类是极具挑战性的问题.提出了一种基于多源数据的多视角学习晴雨分类方法，其中，多源数据包括雷达、卫星及地面观测因子及晴雨观测数据.该方法表述如下：首先，依据雷达观测因子构造了VisCAPPI视角和VisPPI视角，依据葵花卫星资料构造了VisSat视角，依据地面观测因子构造了VisGround视角；然后，对这4个视角特征进行组合获得组合视角VisCAPPI_PPI，VisRadar_Sat，VisRadar_Groumd，VisSat_Ground，VisRadar_Sat_Ground，应用随机森林机器学习方法分别对这些视角进行样本学习，获得这些视角的晴雨分类模型；最后，对这些视角晴雨分类模型估计进行融合，获得晴雨分类结果.主要贡献在于：（1）提出了雷达、卫星和地面观测因子多视角构建方法，构建了VisCAPPI，VisPPI，VisSat和VisGround晴雨分类视角及其组合视角；（2）提出了一种多视角方法（multi-view weight random forest，简称MVWRF），能够处理雷达、卫星和地面观测因子多源数据融合晴雨分类问题，提高1km×1km和6min时空分辨率晴雨分类准确率.在2016年10月7日和8日，泉州雷达覆盖的393个气象观测站上进行模型训练和测试，结果显示，该方法能够取得较高的晴雨分类准确率和较低的漏报率、空报率，优于对比方法.

Fusion of Multi-Source Data for Rain/No-Rain Classification with High Spatiotemporal Resolution

High spatiotemporal resolution rainfall estimation is closely related to transportation, tourism, agricultural irrigation and people''s daily travel. However, accurate high-resolution rain/no-rain classification is a very challenging problem. This paper proposes a multi-source data based multi-view learning method for rain/no-rain classification. The multiple source data used in this paper include radar, satellite and ground observation factors and rain/no-rain observation data. This method can be summarized as follows. Firstly, VisCAPPI view and VisPPI views are constructed according to the radar observation factors. VisSat view is constructed from the sunflower satellite data. VisGround view is constructed according to the ground observation factors. Secondly, the views of VisCAPPI_PPI, VisRadar_Sat, VisRadar_Groumd, VisSat_Ground, and VisRadar_Sat_Ground are obtained by combining features from preconstructed views. Random forest (RF) classification models are trained from these views using RF method. Finally, the rain/no rain classification results are obtained from the estimated results of these RF classification models. The main contributions of this paper arelisted as follows:(1) Present a method for constructing VisCAPPI, VisPPI, VisSat and VisGround views and their feature combined views for radar, satellite and ground observations; (2) A multi-view weight random forest method (MVWRF) is proposed. Multi-source data of radar, satellite and near surface observations are fused for rain/no-rain classification with temporal resolution of 6-minute and spatial resolution of 1km×1km in virtue of the proposed method. The experimental results show that the proposed method in this paper can obtain high precision of rain/no-rain classification after training and testing on 393 meteorological stations covered by radar in Quanzhou on October 7 and 8, 2016.