New Statistical Techniques for Probabilistic Weather Forecasting

行业、机构和公众在预测强降水、暴风雪、洪水或其他破坏性天气现象时的理性决策需要关于用户可以在天气预报中放置的置信度的信息。 因此，提高气象学家量化预报不确定性的能力以满足社会对可靠信息不断增长的期望是至关重要的。本研究的长期目标是为下一代概率预报系统奠定方法学基础。 具体目标是开发和测试（一）一套统计技术的概率预测的天气变量和（二）一套性能措施的概率预测的验证。 基本技术，称为贝叶斯输出处理器（BPO），将处理数值天气预报（NWP）模型的输出，并将其与气候数据进行最佳融合，以量化预测的不确定性。 扩展的技术称为贝叶斯集合处理器（Bayesian Processor of Ensemble，BPE），将处理数值预报模式输出的集合，该技术将利用贝叶斯统计理论、多变量分布、估计方法和集合预报的最新进展。 每种技术将在三个版本中开发和测试，用于（i）二进制预测（例如，降水发生的指示器），（ii）多类别预测值（例如，降水类型的指示符），以及（iii）连续的预测值（例如，降水量取决于降水发生、温度、能见度、天花板高度、风速）。 初步测试将涉及未来3天的概率定量降水预报（PQPF）的制作和验证。 评估新技术的主要基准，将是目前在业务预报中使用的已有30年历史的模式输出统计技术。这些新的、最先进的概率预报技术的预期影响，将是改善所有主要天气变量的预报，从而增加社会效益。 特别是，由BPO或BPE产生的、适合水文模型要求的可靠且信息丰富的PQPF将能够产生具有明确规定的检测概率的概率性河流水位预报、概率性洪水预报和洪水警报。

Rational decision making by industries, agencies, and the public in anticipation of heavy precipitation, snow storm, flood or other disruptive weather phenomenon, requires information about the degree of confidence that the user can place in a weather forecast. It is vital, therefore, to advance the meteorologist's capability of quantifying forecast uncertainty to meet the society's rising expectations for reliable information.The long-term goal of this research is to lay down a methodological foundation for the next generation of probabilistic forecasting systems. The specific objective is to develop and test (i) a set of statistical techniques for probabilistic forecasting of weather variates and (ii) a set of performance measures for verification of probabilistic forecasts. The basic technique, called Bayesian Processor of Output (BPO), will process output from a numerical weather prediction (NWP) model and optimally fuse it with climatic data in order to quantify uncertainty about a predictand. The extended technique, called Bayesian Processor of Ensemble (BPE), will process an ensemble of the NWP model output.The techniques will harness recent advances in Bayesian statistical theory, multivariate distributions, estimation methods, and ensemble forecasting. Each technique will be developed and tested in three versions, for (i) binary predictands (e.g., indicator of precipitation occurrence), (ii) multi-category predictands (e.g., indicator of precipitation type), and (iii) continuous predictands (e.g., precipitation amount conditional on precipitation occurrence, temperature, visibility, ceiling height, wind speed). The primary test will involve the production and verification of probabilistic quantitative precipitation forecasts (PQPFs) for up to 3 days ahead. The primary benchmark for evaluation of the new techniques will be the 30-year old Model Output Statistics (MOS) technique used currently in operational forecasting.The expected impacts of these new, state-of-the-art techniques for probabilistic forecasting will be improved forecasts of all major weather variates, and hence increased societal benefits. In particular, reliable and informative PQPFs, produced by the BPO or the BPE and suited to requirements of hydrologic models, will enable the production of probabilistic river stage forecasts, probabilistic flood forecasts, and flood warnings with explicitly stated detection probabilities.