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. Such problems may be exacerbated if there are rapid transitions between different precipitation phases within the same event. Previous studies have addressed this issue using precipitation-phase nowcasting techniques, often focusing on critical infrastructures such as airports. Very short-range forecasts are usually based on trends of observations and numerical weather prediction models. Nowcasting schemes considering the precipitation phase generally merge extrapolated surface observations, modelled vertical temperature profiles, and extrapolated weather radar precipitation fields. 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. In addition, three combinations of the previous algorithms were also evaluated. The nowcasting scheme was applied to a midlatitude region in the Northwestern Mediterranean to assess its performance during eight snowfall events. 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 results indicate that, although single and combined algorithms perform similarly, the latter can provide valuable information during event monitoring. Precipitation phase transitions were also analysed, finding that on average they can be forecast correctly with a lead time of 120 min. The proposed methodology can be readily applied to other regions where ground-based observations, weather radar data, and model forecasts are available.

中文翻译：

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结合天气雷达数据、地面观测和 NWP 模式预报，临近预报降水阶段

大雪事件会造成交通严重中断，并对社会经济产生负面影响，特别是在很少下雪的低海拔和中纬度地区。如果同一事件中不同降水阶段之间存在快速转变，则此类问题可能会加剧。以前的研究使用降水阶段临近预报技术解决了这个问题，通常侧重于机场等关键基础设施。超短期预报通常基于观测趋势和数值天气预报模型。考虑降水阶段的临近预报方案通常合并外推的地表观测、模拟的垂直温度剖面和外推的天气雷达降水场。在这项研究中，开发和评估了降水阶段临近预报方案，最初使用八种不同的算法将降水分类为雨、雨夹雪或雪，以及概率天气雷达数据外推技术。此外，还评估了先前算法的三种组合。临近预报方案应用于地中海西北部的中纬度地区，以评估其在八次降雪事件中的表现。当降水阶段的不确定性增加时，对单一算法和组合算法进行了比较，以确定它们在接近冰点的条件下的适用性。结果表明，虽然单一算法和组合算法的性能相似，但后者可以在事件监控期间提供有价值的信息。还分析了降水相变，发现平均而言，他们可以在 120 分钟的提前期正确预测。所提议的方法可以很容易地应用于其他可以获得地面观测、天气雷达数据和模型预报的地区。