用于能见度集合预报后处理的参数模型
Parametric model for post-processing visibility ensemble forecasts
AI总结：
针对能见度集合预报的校准与偏差问题，提出 gamma 与截断正态混合且右删失的参数后处理模型，并在 ECMWF 两个区域案例中验证其优于原始集合和贝叶斯模型平均。
摘要：
尽管基于集合的概率预报如今已是最先进的天气预报方法，但集合预报仍可能存在校准不足和/或表现出系统性偏差，因此需要进行某种后处理以提高其预报技巧。本文聚焦于能见度这一在航空、道路安全以及船舶导航等领域发挥关键作用的量，并提出一种参数模型，其预测分布是 gamma 分布与截断正态分布的混合，两者均在最大报告能见度值处进行右删失。新模型在两个案例研究中得到评估，这些研究基于欧洲中期天气预报中心的能见度集合预报，覆盖中欧和西欧两个不同区域以及两个不同时间段。案例研究结果表明，经后处理的预报显著优于原始集合预报；此外，所提出的混合模型始终优于作为参考后处理技术的 Bayesian model averaging（贝叶斯模型平均）方法。
英文摘要：
Although by now the ensemble-based probabilistic forecasting is the most advanced approach to weather prediction, ensemble forecasts still might suffer from lack of calibration and/or display systematic bias, thus require some post-processing to improve their forecast skill. Here we focus on visibility, which quantity plays a crucial role e.g. in aviation and road safety or in ship navigation, and propose a parametric model where the predictive distribution is a mixture of a gamma and a truncated normal distribution, both right censored at the maximal reported visibility value. The new model is evaluated in two case studies based on visibility ensemble forecasts of the European Centre for Medium-Range Weather Forecasts covering two distinct domains in Central and Western Europe and two different time periods. The results of the case studies indicate that post-processed forecasts are substantially superior to the raw ensemble; moreover, the proposed mixture model consistently outperforms the Bayesian model averaging approach used as reference post-processing technique.
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