New Statistical Techniques for Probabilistic Weather Forecasting - Stage II

行业、机构和公众在预测强降水、极端温度、暴风雪、洪水或其他破坏性天气现象时做出理性决策，需要有关用户可以在天气预报中放置的确定性程度的信息。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日益增长的期望，是至关重要的。本研究的长期目标是为下一代概率预测系统奠定方法学基础。具体目标是发展和测试(i)一套用于天气变化概率预测的统计技术，以及（ii）一套用于验证概率预测的性能指标。这项基本技术被称为贝叶斯输出处理器（BPO），它处理数值天气预报（NWP）模型的输出，并将其与气候数据最佳地融合在一起，以量化预测的不确定性。业务流程外包是在这个项目的第一阶段开发和测试的。评价业务流程流程的主要基准是目前在业务预测中使用的模式输出统计技术；BPO预测更准确，信息更丰富。第二阶段的目标是开发和测试一种扩展技术。它被称为贝叶斯集合处理器（BPE），它将处理NWP模式输出的集合，并将其与气候数据进行最佳融合，以便(i)获得每个预测的概率分布，（ii）调整集合，从而提供对即将到来的天气系统的空间、时间和变量间相关性的良好校准评估。BPE将利用贝叶斯统计理论、多元分布、估计方法和集合预测的最新进展。它将以两个版本进行开发和测试，用于(i)二元预测（例如，降水发生的指标）和（ii）连续预测（例如，降水数量取决于降水发生、温度、能见度、天花板高度、风速）。主要测试将包括制作和验证长达16天的概率定量降水预报（PQPFs）。评价BPE的主要基准将是目前在业务预测中使用的频率技术。本研究的预期智力价值将是贝叶斯统计理论的进步及其对复杂预测问题的适用性，以及对气象集合的随机特性的理解的进步-从NWP模型中提取更多预测信息的必要步骤。弗吉尼亚大学和国家环境预测中心之间的合作，包括交换数据和专业知识，测试BPE，并将研究成果转化为业务应用，将有助于THORPEX的目标-世界气象组织支持下的21世纪全球大气研究计划。这种新的、最先进的综合预报技术预计将产生更广泛的影响，改善对所有主要天气变化的预报，从而增加美国的经济和社会效益。特别是，由BPE制作的可靠和信息丰富的PQPFs，适合水文模型的要求，将能够产生概率河段预报、概率洪水预报和具有明确说明的探测概率的洪水警报。

Rational decision making by industries, agencies, and the public in anticipation of heavy precipitation, extreme temperature, snow storm, flood or other disruptive weather phenomena, requires information about the degree of certitude 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), processes output from a numerical weather prediction (NWP) model and optimally fuses it with climatic data in order to quantify uncertainty about a predictand. The BPO was developed and tested in Stage I of this project. The primary benchmark for evaluation of the BPO was the Model Output Statistics (MOS) technique used currently in operational forecasting; the BPO forecasts were better calibrated and more informative. The objective of Stage II is to develop and test an extended technique. Called Bayesian Processor of Ensemble (BPE), it will process an ensemble of the NWP model output and optimally fuse it with climatic data in order (i) to obtain a probability distribution of each predictand, and (ii) to adjust the ensemble and thus to provide a well-calibrated assessment of the spatial, temporal, and inter-variate correlation within the approaching weather system. The BPE will harness recent advances in Bayesian statistical theory, multivariate distributions, estimation methods, and ensemble forecasting. It will be developed and tested in two versions, for (i) binary predictands (e.g., indicator of precipitation occurrence), and (ii) 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 16 days ahead. The primary benchmark for evaluation of the BPE will be the frequentist technique used currently in operational forecasting. The expected intellectual merit of this research will be an advancement of the Bayesian statistical theory and its applicability to complex forecasting problems, and an advance in the understanding of the stochastic properties of meteorological ensembles - a necessary step toward extracting more predictive information from the NWP models. The collaboration between the University of Virginia and the National Centers for Environmental Prediction, with provisions for exchanging data and expertise, testing the BPE, and transferring research results into operational use, will contribute to the aims of THORPEX - A Global Atmospheric Research Programme for the 21st Century under the aegis of the World Meteorological Organization. The expected broader impacts of this new, state-of-the-art technique for ensemble forecasting will be improved forecasts of all major weather variates, and hence increased economic and societal benefits to the United States. In particular, reliable and informative PQPFs, produced by 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.