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首页> 外文期刊>Advanced engineering informatics >Assessment and weighting of meteorological ensemble forecast members based on supervised machine learning with application to runoff simulations and flood warning
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Assessment and weighting of meteorological ensemble forecast members based on supervised machine learning with application to runoff simulations and flood warning

机译：基于监督机器学习的气象集合预报成员评估和加权及其在径流模拟和洪水预警中的应用

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摘要

Numerical weather forecasts, such as meteorological forecasts of precipitation, are inherently uncertain. These uncertainties depend on model physics as well as initial and boundary conditions. Since precipitation forecasts form the input into hydrological models, the uncertainties of the precipitation forecasts result in uncertainties of flood forecasts. In order to consider these uncertainties, ensemble prediction systems are applied. These systems consist of several members simulated by different models or using a single model under varying initial and boundary conditions. However, a too wide uncertainty range obtained as a result of taking into account members with poor prediction skills may lead to underestimation or exaggeration of the risk of hazardous events. Therefore, the uncertainty range of model-based flood forecasts derived from the meteorological ensembles has to be restricted. In this paper, a methodology towards improving flood forecasts by weighting ensemble members according to their skills is presented. The skill of each ensemble member is evaluated by comparing the results of forecasts corresponding to this member with observed values in the past. Since numerous forecasts are required in order to reliably evaluate the skill, the evaluation procedure is time-consuming and tedious. Moreover, the evaluation is highly subjective, because an expert who performs it makes his decision based on his implicit knowledge. Therefore, approaches for the automated evaluation of such forecasts are required. Here, we present a semi-automated approach for the assessment of precipitation forecast ensemble members. The approach is based on supervised machine learning and was tested on ensemble precipitation forecasts for the area of the Mulde river basin in Germany. Based on the evaluation results of the specific ensemble members, weights corresponding to their forecast skill were calculated. These weights were then successfully used to reduce the uncertainties within rainfall-runoff simulations and flood risk predictions.
机译：数值天气预报，例如降水的气象预报，本质上是不确定的。这些不确定性取决于模型物理以及初始条件和边界条件。由于降水预报是水文模型的输入，因此降水预报的不确定性会导致洪水预报的不确定性。为了考虑这些不确定性，应用了集成预测系统。这些系统由在不同的初始和边界条件下由不同模型或使用单个模型模拟的几个成员组成。但是，由于考虑到具有较差的预测技能的成员而获得的不确定性范围过大，可能会导致危险事件风险的低估或夸大。因此，必须限制基于气象集合的基于模型的洪水预报的不确定性范围。在本文中，提出了一种通过根据整体成员的权重加权来改进洪水预报的方法。通过将与该成员相对应的预测结果与过去的观察值进行比较来评估每个合奏成员的技能。由于需要大量的预测才能可靠地评估技能，因此评估过程既费时又乏味。此外，评估是高度主观的，因为执行评估的专家会根据他的隐性知识做出决定。因此，需要用于自动评估此类预测的方法。在这里，我们提出了一种用于降水预报集合成员评估的半自动化方法。该方法基于有监督的机器学习，并已在德国Mulde流域的整体降水预报中进行了测试。根据特定合奏成员的评估结果，计算与他们的预测技能相对应的权重。这些权重随后被成功用于减少降雨径流模拟和洪水风险预测中的不确定性。

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