NOWCASTING THE RISK OF SNOWFALL AND FREEZING RAIN WITH RADAR AND GROUND DATA
W. Schmid, Alexander Mathis, U. Keller
Abstract
W. Schmid, Alexander Mathis, U. Keller
Abstract
For an optimal planning of snow and ice removal, a reliable forecast of the danger of snowfall or freezing rain is required. Risk forecasts in the time range of 0-2 hours would be especially useful. Salt could be distributed, or special actions to prevent accidents or traffic breakdowns could be initiated in time. Until now, forecast procedures are mainly based on local ground measurements of temperature, humidity and precipitation. Forecasts on the onset and evolution of precipitation are normally delivered by weather services or by monitoring system presenting weather maps, model forecasts, satellite or radar data. Few attempts have been made up to now to combine local data and short-term forecasts of precipitation based on satellite or radar data in a proper nowcasting system. This paper presents a new concept that combines various risk factors to the various risk forecasts of snowfall or freezing rain. These factors are: (1) the probability that precipitation stronger than a given level of intensity will occur in the subsequent 1-2 hours; (2) the probability that this precipitation will be snow or supercooled rain; and (3) the probability that liquid precipitation will fall on a frozen surface. The resulting nowcasting procedure is called raincast+. Various forecast products can be distributed for the users: forecast images, local risk forecasts and a warning message. The procedure allows selection of the desired warning regions and warning criteria in a highly flexible manner. The skill of the forecasts, based on a statistical analysis of large data samplings is promising and this system is now being installed for operational use in Switzerland, north of the Alps.
OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
For an optimal planning of snow and ice removal, a reliable forecast of the danger of snowfall or freezing rain is required. Risk forecasts in the time range of 0-2 hours would be especially useful. Salt could be distributed, or special actions to prevent accidents or traffic breakdowns could be initiated in time. Until now, forecast procedures are mainly based on local ground measurements of temperature, humidity and precipitation. Forecasts on the onset and evolution of precipitation are normally delivered by weather services or by monitoring system presenting weather maps, model forecasts, satellite or radar data. Few attempts have been made up to now to combine local data and short-term forecasts of precipitation based on satellite or radar data in a proper nowcasting system. This paper presents a new concept that combines various risk factors to the various risk forecasts of snowfall or freezing rain. These factors are: (1) the probability that precipitation stronger than a given level of intensity will occur in the subsequent 1-2 hours; (2) the probability that this precipitation will be snow or supercooled rain; and (3) the probability that liquid precipitation will fall on a frozen surface. The resulting nowcasting procedure is called raincast+. Various forecast products can be distributed for the users: forecast images, local risk forecasts and a warning message. The procedure allows selection of the desired warning regions and warning criteria in a highly flexible manner. The skill of the forecasts, based on a statistical analysis of large data samplings is promising and this system is now being installed for operational use in Switzerland, north of the Alps.
Key concepts: Nowcasting, Quantitative precipitation forecast, Snow, Precipitation, Meteorology, Environmental science, Radar, Quantitative precipitation estimation