![]() ![]() However, surface precipitation phase discrimination remains challenging, especially at temperatures close to freezing point. Recent progress has also been reported regarding the use of medium-range forecasts (Gascón et al., 2018 Fehlmann et al., 2019).ĭiscrimination of the precipitation phase at the surface level has been widely studied using several approaches, including near-surface air-temperature threshold-based schemes (e.g., Koistinen and Saltikoff, 1998 Froidurot et al., 2014 Behrangi et al., 2018), decision-tree algorithms based on implicit assumptions about microphysical processes and vertical temperature profiles (e.g., Bourgouin, 2000 Schuur et al., 2012), explicit algorithms based on hydrometeor mixing ratios at the ground level provided by complex microphysics parameterisations (e.g., Ikeda et al., 2013 Benjamin et al., 2016), and machine-learning algorithms (e.g., McGovern et al., 2017 2019). For example, airports are critical infrastructures sensitive to snowfall, and several ad hoc nowcasting systems have been developed to address this issue specifically, such as the Canadian Airport Nowcasting system (CAN-Now: Isaac et al., 2014), Probabilistic Nowcasting of Winter Weather for Airports (PNOWWA: Saltikoff et al., 2018), and Winter Hazards in Terminal Environment for Munich Airport (WHITE: Keis, 2015). ![]() Nowcasting the precipitation phase can provide key information for meteorological services or emergency managers, who can then activate specific protocols to prevent or manage risks and harm to people and critical infrastructures (Vilaclara et al., 2010 Bonelli et al., 2011 Kann et al., 2015). Severe winter weather conditions can affect transport infrastructures, road safety, road maintenance, and everyday activities (Schmidlin, 1993 Andrey et al., 2003 Papagiannaki et al., 2013 Malin et al., 2019). The proposed methodology can be readily applied to other regions where ground-based observations, weather radar data, and model forecasts are available. Precipitation phase transitions were also analysed, finding that on average they can be forecast correctly with a lead time of 120 min. The results indicate that, although single and combined algorithms perform similarly, the latter can provide valuable information during event monitoring. Single and combined algorithms were compared to determine their suitability in conditions close to freezing point, when there is increased uncertainty about the precipitation phase. The nowcasting scheme was applied to a midlatitude region in the Northwestern Mediterranean to assess its performance during eight snowfall events. In addition, three combinations of the previous algorithms were also evaluated. In this study, a precipitation-phase nowcasting scheme was developed and evaluated, initially using eight different algorithms to classify precipitation into rain, sleet or snow, together with a probabilistic weather radar data extrapolation technique. Nowcasting schemes considering the precipitation phase generally merge extrapolated surface observations, modelled vertical temperature profiles, and extrapolated weather radar precipitation fields. Very short-range forecasts are usually based on trends of observations and numerical weather prediction models. Previous studies have addressed this issue using precipitation-phase nowcasting techniques, often focusing on critical infrastructures such as airports. Such problems may be exacerbated if there are rapid transitions between different precipitation phases within the same event. Heavy snowfall events can cause substantial transport disruption and exert a negative socioeconomic impact, particularly in low-altitude and midlatitude regions, where it seldom snows. ![]()
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