船載氣象監測預報系統研發課題2013年度科技報告

王東明

摘 要：課題采用流場分析和統計學習的方法，建立基于神經網絡系統辨識和支持向量機的風場反演校正模型，計算出未受干擾風場中的風速風向數據，減小干擾風場與未受干擾風場風速風向之間的偏差；利用徑向基概率神經網絡和支持向量機方法進行分析研究，借助機器學習方法得到氣象要素數據奇異值剔除模型，從而提高監測數據的有效性；應用區域平滑濾波和閾值剔除技術，采用基于雷達反射率閾值的識別算法，實現雷暴等危險天氣的識別；采用MCT耦合器技術及消息傳遞的并行計算方式，實現區域海氣模式耦合；采用動力診斷、支持向量機、多指標疊套等預報方法，建立海上雷暴、云的船用預報模型。

關鍵詞：風場反演校正 支持向量機 船用預報模型

Abstract：This subject introduced flow field analysis and statistical learning methods to build wind retrieval and calibration model based on neural networks identification and support vector machine， calculated the wind speed and direction data of undisturbed wind field， reduced the deviation of wind speed and direction between disturbed wind field and wind field that wasn't disturbed. This subject made use of radial basis probabilistic neural networks and support vector machine method to analyze and research， used machine learning methods to get the exclusion model of meteorological data to improve the effectiveness of monitoring data. This subject used smoothing and threshold eliminating techniques， adopted radar reflectivity threshold identification algorithm to realize the identification of dangerous weather， such as thunderstorm. MCT coupler technology and message passing parallel computing was used to achieve regional air-sea mode coupling. Forecasting methods such as dynamic diagnosis， support vector machines， multi-index nesting were adopted to establish a shipborne forecasting model of maritime thunderstorm and clouds.

Key Words：Wind retrieval and calibration model； Support vector machine； Shipborne forecasting model

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