王東明

摘 要：課題采用流場分析和統(tǒng)計學(xué)習(xí)的方法，建立基于神經(jīng)網(wǎng)絡(luò)系統(tǒng)辨識和支持向量機的風(fēng)場反演校正模型，計算出未受干擾風(fēng)場中的風(fēng)速風(fēng)向數(shù)據(jù)，減小干擾風(fēng)場與未受干擾風(fēng)場風(fēng)速風(fēng)向之間的偏差；利用徑向基概率神經(jīng)網(wǎng)絡(luò)和支持向量機方法進行分析研究，借助機器學(xué)習(xí)方法得到氣象要素數(shù)據(jù)奇異值剔除模型，從而提高監(jiān)測數(shù)據(jù)的有效性；應(yīng)用區(qū)域平滑濾波和閾值剔除技術(shù)，采用基于雷達反射率閾值的識別算法，實現(xiàn)雷暴等危險天氣的識別；采用MCT耦合器技術(shù)及消息傳遞的并行計算方式，實現(xiàn)區(qū)域海氣模式耦合；采用動力診斷、支持向量機、多指標(biāo)疊套等預(yù)報方法，建立海上雷暴、云的船用預(yù)報模型。

關(guān)鍵詞：風(fēng)場反演校正 支持向量機 船用預(yù)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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