Chengdu University of Information Technology

Jianhong Gan

Yan Chen

Fei Luo

Yanqiong Hao

Tiangui Xiao

Wenjie Zhou

Wendong Hu

Yongkai Zhang

Jian Shao

Hongping Shu

Jingyi Tao

Balin Xu

Huahong Li

Juzhang Ren

Xiaohang Wen

Ying Zhang

Yu Peng

Chao Wang

Tao Zhang

Yaqiang Wang

Taisong Xiong

Yixue Deng

Yukun Yang

Zhuolin Chang

Zhiliang Shu

Jiajun Zhou

Qiyang Peng

Yang Cui

Chengchao Li

Provided is an automatic hail identification method based on radar image spatial mapping, including: obtaining a base reflectivity radar image; grading radar echoes using intensity values represented by different colors, and detecting radar center coordinates; performing spatial mapping, and establishing a dictionary for radar echo intensity in a new radar image space; after expanding the dictionary, transforming V-shaped notch and TBSS characteristics behind an original radar strong echo area into a standard rectangle and a triangle behind a strong echo in the new radar image space; determining whether there is a V-shaped notch and TBSS according to whether there is a standard rectangle or a triangle behind the strong echo; and if so, marking in the original radar image and calculating a position where hail occurs. A rapid and accurate method is provided for automation of hail monitoring and early warning and elimination of continuous high-intensity analysis pressure of forecasters.

Automatic hail identification method based on radar image spatial mapping

An automatic recognition method for a dry quasi-stationary front in Kunming, Yunnan, China is provided. The automatic recognition method includes: reading data; calculating, by a differential method, a temperature lapse rate between each layer of each point from a ground surface to 650 hPa, and acquiring a maximum inversion trend value; acquiring an initially selected inversion distribution based on the maximum inversion trend value; removing a nighttime clear sky radiation inversion, retaining only a frontal inversion, and binarizing (0, 1); finding a boundary between inversion and non-inversion; removing the abnormal candidate frontal points, and acquiring frontal nodes; removing meso- and micro-scale systems; and performing one-dimensional Gaussian filtering on longitude data of the frontal nodes, and connecting the filtered frontal nodes to acquire a quasi-stationary front in Kunming, Yunnan, China.

Automatic recognition method for dry quasi-stationary front in kunming, yunnan, china

The present disclosure discloses a method for automatically identifying south troughs by improved Laplace and relates to the technical field of meteorology. The method includes the following steps: acquiring grid data of a geopotential height field; calculating a gradient field of the geopotential height field in an x direction; searching for a turning point where a gradient value turned from being negative to being positive, and cleaning the gradient field; calculating a divergence of the x direction to obtain an improved Laplacian numerical value L′; performing 0,1 binarization processing on the L′ to obtain a black-and-white image and a plurality of targets of potential troughs, merging the black-and-white image and the plurality of targets of the potential troughs by expansion, recovering original scale through erosion, and selecting an effective target through an angle of direction of a contour and an axial ratio.

Method for automatically identifying south troughs by improved Laplace

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Chengdu, China

Jianhong Gan