APPLICATIONS OF NOVEL WEATHER INFORMATION: OPTIMIZATION FOR DEPLOYMENT OF SNOW REMOVAL GROUPS

Abstract

In winter, a lot of heavy snow events occur on the north side of Japan, because of significant meteorological characteristics of a convergence zone, Japan sea Polar air mass Convergence Zone; JPCZ. The weather forecasts about the location and the strength of the JPCZ are difficult, because the development of the snow clouds are very rapid and the convergence zone fluctuates with instability. As inherent characteristics of the snowfall around the JPCZ, the snowfall contaminates the water as well and the snow over the highways results in car accidents and stalled cars. Therefore, the East Nippon Expressway Co. Ltd. (NEXCO East) seeks to optimize the operating strategies of deployment, recombination, and rotation about multiple groups of snow removal in multiple bases. This optimization requires some important indexes in combination with the weather observations and forecasts. For example, the reformation of the snow removal groups needs the information about heavy snow needed with an additional equipment and light or no snow without any equipment. NEXCO East and WNI with the University of Oklahoma are researching an algorithm and analysis system since 2019. The most challenging part of this research is to improve the snow prediction, because there is a mismatch between numerical simulations and real - time observations. Therefore, the forecasts both with observational infrastructure such as meteorological satellites, operational wide - range weather radars, and polarimetric radars, and with frequently - updated analysis and forecast system through the artificial intelligence, need to be developed. As a result, we are supposed to build the forecasts beyond a statistical analysis. In this paper, we'll discuss the initial results of observation - based forecast systems and show the relationship between real - time estimation of snow amount and classification aloft, and the optimization of snow removal.