多元动态逐步回归方法在北京地区能见度预报中的应用

Visibility Forecast in Beijing Based on Dynamically Multivariate Stepwise Regression Method

基于2016年冬季的观测资料和ECMWF细网格240 h气象要素预报资料,选用与能见度变化相关的水汽、动力、热力等因素作为预报因子,利用多元动态逐步回归方程对北京地区未来10 d的能见度进行预报研究。同时将能见度分为3个等级:<1 km、1~10 km(低能见度)和≥10 km,并从区域平均、空间分布及3次低能见度过程个例进行预报效果检验。多元动态逐步回归方法对北京地区的能见度及其变化趋势均有一定预报能力且效果稳定,其中≥10 km等级的能见度预报效果最好,TS评分为64.2%,其次是1~10 km,TS评分为53.1%,最后是<1 km,TS评分为51.3%;两个低能见度等级中平原地区预报效果优于山区,表现为从东南向西北递减的特征;而≥10 km等级的呈相反变化,预报效果山区优于平原地区;北京地区3次雾霾过程个例预报也证实动态逐步回归方法能够较好预报北京地区持续性低能见度过程。

Based on meteorology observation data and ECMWF numerical prediction products in winter 2016,a dynamically multivariate stepwise regression model was established for visibility prediction in Beijing by selecting some possible impact factors(water vapor,dynamic and thermodynamic factors and so on)as independent variables.Visibility was divided into three levels,namely visibility lower than 1 km,from 1 to 10 km(low visibility levels)and higher than or equal to 10 km,and the relevant forecast effect evaluation was conducted from the views of regional average,spatial distribution and cases analysis,respectively.The model had certain stable forecast capability for visibility and its change trend.The threat score(TS)of forecasted visibility greater than and equal to 10 km was largest(64.2%),followed by the visibility of 1-10 km(53.1%),and the TS of visibility lower than 1 km was smallest(51.3%).The forecasting was better in plain areas than mountain areas for the lower two grades visibility,showing the decline distribution from the northeast to the southwest.On the contrary,the forecast capability of visibility more than 10 km increased from the northeast to the southwest.Besides,forecasts of three cases also confirmed that this model had a relatively good forecasting ability of persistent fog-haze episodes occurring in Beijing.