Case Studies of Low‐Visibility Forecasting in Falling Snow With WRF Model

Abstract

Accurate low‐visibility forecasts in falling snow are critical to the safety and efficiency of air traffic. The Weather and Research Forecast (WRF) model successfully captured two unusual snowstorms occurred in Urumqi. On this basis, the quality of 15 parameterizations for predicting visibility in snow is evaluated, using both observations and forecasts of the meteorological variables from WRF model. The parameterizations are mainly based on the relations between the extinction efficient (β) or visibility (Vis) and the snowfall rate (S). Comprehensive evaluations show that most of these parameterizations (13 of 15) are skillful as well as having the ability to predict low visibilities to some extent. Among them, the parameterization Vis = 0.62S−0.59 performs the best, followed by the approach of Stoelinga and Warner. It is also found that the visibility forecasts based on the observations always have considerably higher quality than the visibility forecasts from WRF model. The results suggest that more than one parameterization is promising if the WRF model is able to provide accurate predictions of the relevant meteorological variables. Furthermore, the forecast accuracy of low visibilities strongly depends on the accurate predictions of snowfall rate of greater than or equal to 1.0 mm h−1.

Visibility Prediction over South Korea Based on Random Forest

“…Importantly, precipitation and air pollution can have significant impacts on visibility, with low-visibility conditions of a few kilometers not uncommon [6], and such effects have impacts on weather and climate change [7,8]. In addition, low visibility is linked to socioeconomic losses, including road and air traffic accidents and public health risks [9][10][11].…”

“…NWP models predict visibility based on various meteorological variables including the liquid/ice water content of clouds and water droplets, aerosol concentrations, and rain/snow, which are included in the parameterization of cloud physics and microphysical processes [14][15][16]. However, because visibility is sensitive to a range of variables, prediction based on NWP is challenging, yielding poor predictive performance compared to other meteorological variables such as precipitation [3,10,12,[17][18][19]. Therefore, various studies have been reported that focus on parameterization improvement [14,20,21], data assimilation [3], and ensemble construction [19].…”

Comparison of accuracy of forecasting methods of convective precipitation

“…The second approach is based on a quantitative evaluation of conditions of convective precipitation clouds using NWP models and statistics historical situations. The quantitative assessment focuses on the estimation of future weather developments for a longer forecast lead time, ranging from 6 to 24 hours [10,11,12]. This approach has also been developed in the Algorithm of Storm Prediction, which implements the prediction of convective precipitation and dangerous phenomena.…”