Abstract:

For promoting the development of local tourism industry and satisfying the demand for tourism meteorological services, the three-dimensional (3D) meteorological observation and the forecast experiments for the Ha'ni cloud sea landscape in Yuanjiang county were conducted.Based on the meteorological observation data from the weather station in the studied area during 2016-2019, the dichotomous prediction experiments for the cloud sea landscape appearing or not were carried out by using the empirical forecasting, the logistics regression, the support vector machine (SVM), the decision tree analysis and other analysis methods.The results show that the overall forecast accuracies of the training samples based on the different prediction methods are between 74.3% and 82.2% with a small difference, but the prediction indicator of the traditional empirical prediction based on the research background of the cloud sea mechanism is of a definite physical meaning.With the accumulation of the prediction experience, the TS score of empirical prediction i.e.54.8 in 2019 is better than that i.e.46.0 in 2016-2018, and also better than that based on the statistical learning algorithm using only local data.In addition, the TS scores for the test sample employing the other several learning forecast methods are lower than that for the training sample.The occurrence of cloud sea landscape requires the coordination of both water vapor and atmospheric static stability.Besides, the collected 3D climate data from the local cloud sea observatory is beneficial for improving the forecast index system, increasing forecast accuracy and promoting the development of local tourism industry.

Key words: Cloud sea, Forecast, Logistics regression, Decision tree, Supporot vector machine (SVM)