航空气象要素以及基于数值模式的低能见度和雾的预报
Aviation Weather and Model-Based Operational Forecasts of Low Visibility and Fog
中文摘要：
简述了对航空运输有危害的天气要素，包括航线要素和航站要素的业务预报。着重介绍了在美国国家环境预报中心（National Centers for Environmental Prediction，NCEP）业务模式后处理中，能见度的两种诊断方法和雾的三种后处理再诊断方法。能见度计算的第一种方法是Stoelinga-Warner方法。该方法要求预报模式有四种水溶性物质的输出。通过这些水凝性物质计算消光系数来估算水平能见度。第二种方法是第一种方法的改进，包括增加了更多的水凝性物质，用相对湿度估算高湿度时或霾条件下的能见度，考虑了白天和夜间能见度的不同等。还讨论了以机场跑道为背景的跑道能见度的估计方法。雾的再诊断包括美国联合包裹速递服务公司（United arcel Service，UPS）的方法，NCEP的多重规则法和根据雾内各物理过程间的平衡进行诊断的方法。UPS法根据低层大气稳定指数，多重规则法则根据模式输出的能见度、云、相对湿度和风速来诊断雾的发生，但两者均不能判断雾的强弱。物理过程平衡法是根据Zhou-Ferrier的雾层平衡理论所提出的雾发生及稳定时湍流强度小于某临界值的必要条件以及雾层内含水量的垂直分布公式对雾含水量及能见度进行诊断。后两种方法已被试验性地用于NCEP的短期集合预报中。预报验证表明，后处理中雾的再诊断方法可以有效地提高业务模式雾的预报能力。但总体而言，雾的预报与其他常规要素的预报相比，现仍处于较低的水平。最后讨论了模式对能见度和雾预报能力低下的原因和改善的途径。
中文关键词：
航空气象，数值预报，模式后处理诊断，能见度，雾
KeyWords：
aviation weather, numerical weather prediction, model post diagnosis, visibility, fog
Abstract：
This article introduces the operational predictions of hazardous weather elements to aviation both en-route and in TAF. Two visibility algorithms and three fog diagnostic schemes used at NCEP are discussed in particular. The first visibility algorithm is Stoelinga and Warner method. This method requires outputs of four hydrometeors from a model to estimate optical extinction coefficient and calculate visibility. The second visibility method is an upgrade to the first one by adding more species of hydrometeor. This method can also estimate visibility in high humidity and haze conditions without knowing hydrometers as well as considering the differences between daytime and nighttime. The concept of airport Runway Visibility Range (RVR) is introduced and the relationship between the RVR and horizontal visibility range (HVR) is given. The fog diagnosis includes the UPS method, multi-rule method and physical-process balanced method. The UPS method uses surface-layer stability index. The multi-rule method uses a combination of visibility, cloud, surface humidity and wind speed to diagnose fog occurrence. These two methods do not predict fog intensity. The third method can diagnose both fog occurrence and intensity. It is based on the turbulence condition for fog formation and persistence as well as the explicit formulation of fog liquid water content suggested by a fog’s physical balance theory. Both multi-rule and physical-process balance methods have been implemented operationally in the NCEP’s regional ensemble forecast systems and showed encouraging results. However, the current skill of low visibility and fog forecasts directly derived from a numerical weather prediction model is generally low comparing to the forecasts of other weather elements such as precipitation. The difficulties are discussed and the ways of improvement are also suggested.