SVM方法在霾识别和能见度预报中的应用
Application of SVM Method to Identification of Haze and Prediction of Visibility
中文摘要：
选用2013—2014年地面自动站资料、探空气象资料以及大气污染物浓度的数据，采用支持向量机（Support Vector Machine，SVM）方法分别建立金华SVM霾识别预报模型和14时能见度SVM回归预报模型来进行实证研究。通过预报结果检验发现：1）金华地区SVM霾识别预报模型的TS评分均在0.65以上，且8个最优模型判断完全错误的天数只有3d，占2.7%，表明模型分类结果较好，可在实际业务预报中推广应用；2）金华地区14时能见度SVM回归预报模型得到的预报值集中在6～16km，预报值较为集中，而实况值波动较大，即模型对极值预报能力较弱，表明模型对中度霾和重度霾天气预报的指导意义不大。
中文关键词：
SVM方法，霾识别预报，14时能见度预报
KeyWords：
SVM method, the identification of haze, visibility forecast at 1400 (BT)
Abstract：
Based on the data in 2013-2014 at automatic weather station, radiosonde and the concentrations of air pollutants, the identification models of haze and the prediction models of visibility at 1400 BT were respectively carried out by using the Support Vector Machine (SVM) at Jinhua Meteorological Bureau. The results show that: 1) The identification models of haze may be used in the actual business forecast, because the satisfied TS scores were all over 0.65, except for that only three days were judged completely wrong by eight optimal models; 2) The prediction of the visibility forecast models for haze-day at 1400 BT in Jinhua were concentrated in 6-16km, which is much small than the actural range, however. That means the models have little cability to forecast the extreme values, so have a little guidance to distinguish the moderate and severe haze.