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Ensemble forecasting

机译：合奏预测

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Numerical weather prediction models as well as the atmosphere itself can be viewed as nonlinear dynamical systems in which the evolution depends sensitively on the initial conditions. The fact that estimates of the current state are inaccurate and that numerical models have inadequacies, leads to forecast errors that grow with increasing forecast lead time. The growth of errors depends on the flow itself. Ensemble forecasting aims at quantifying this flow-dependent forecast uncertainty. The sources of uncertainty in weather forecasting are discussed. Then, an overview is given on evaluating probabilistic forecasts and their usefulness compared with single forecasts. Thereafter, the representation of uncertainties in ensemble forecasts is reviewed with an emphasis on the initial condition perturbations. The review is complemented by a detailed description of the methodology to generate initial condition perturbations of the Ensemble Prediction System (EPS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). These perturbations are based on the leading part of the singular value decomposition of the operator describing the linearised dynamics over a finite time interval. The perturbations are flow-dependent as the linearisation is performed with respect to a solution of the nonlinear forecast model. The extent to which the current ECMWF ensemble prediction system is capable of predicting flow-dependent variations in uncertainty is assessed for the large-scale flow in mid-latitudes. (c) 2007 Elsevier Inc. All rights reserved.
机译：数值天气预报模型以及大气本身可以看作是非线性动力学系统，其中演化敏感地取决于初始条件。当前状态的估计不准确并且数值模型存在不足这一事实会导致预测误差随预测提前期的增加而增加。错误的增长取决于流程本身。集合预测旨在量化这种与流量相关的预测不确定性。讨论了不确定性的来源。然后，概述了评估概率预测及其与单次预测相比的有用性。此后，对总体预报中不确定性的表示进行了审查，重点是初始条件扰动。审查辅以对欧洲中距离天气预报中心（ECMWF）的集合预报系统（EPS）产生初始条件扰动的方法的详细描述。这些扰动基于算子的奇异值分解的前导部分，该部分描述了有限时间间隔内的线性化动力学。由于相对于非线性预测模型的解执行了线性化，因此扰动与流量有关。对于中纬度地区的大规模水流，评估了当前ECMWF集合预报系统能够预测不确定性中水流相关变化的程度。 （c）2007 Elsevier Inc.保留所有权利。

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