The Analog Ensemble (AnEn) technique has been shown effective on several weather problems. Unlike previous weather analogs that are sought within a large spatial domain and an extended temporal window, AnEn strictly confines space and time, and independently generates results at each grid point within a short time window. AnEn can find similar forecasts that lead to accurate and calibrated ensemble forecasts. The central core of the AnEn technique is a similarity metric that sorts historical forecasts with respect to a new target prediction. A commonly used metric is Euclidean distance. However, a significant difficulty using this metric is the definition of the weights for all the parameters. Generally, feature selection and extensive weight search are needed. This paper proposes a novel definition of weather analogs through a Machine Learning (ML) based similarity metric. The similarity metric uses neural networks that are trained and instantiated to search for weather analogs. This new metric allows incorporating all variables without requiring a prior feature selection and weight optimization. Experiments are presented on the application of this new metric to forecast wind speed and solar irradiance. Results show that the ML metric generally outperforms the original metric. The ML metric has a better capability to correct for larger errors and to take advantage of a larger search repository. Spatial predictions using a learned metric also show the ability to define effective latent features that are transferable to other locations.
翻译:模拟组合(AnEn) 技术在几个天气问题上表现得非常有效。 与以往在大空间领域和延长时间窗口中寻找的气象模拟不同, AnEn严格限制空间和时间, 并在短时间窗口中独立生成每个网格点的结果。 AnEn可以找到类似的预测, 导致准确和校准组合预测。 AnEn 技术的核心是一个相似性指标, 它对新目标预测进行历史预测的分类。 常用的尺度是Euclidean距离。 然而, 使用这一指标的一个重大困难是所有参数的重量定义。 一般而言, 需要特征选择和大量重力搜索。 本文建议通过基于类似性的机器学习(ML) 衡量标准对天气模拟每个网点作出新的定义。 类似性指标使用经训练并即时化的神经网络来搜索天气模拟。 这一新指标允许将所有变量都纳入,而无需事先的特性选择和重量优化。 在应用这一新指标来预测风速和太阳辐照度时, 实验是一个很大的困难。 一般来说, 特征选择和大规模重力搜索地点的特征选择。 也显示MLI 指标一般地标将更接近于原始的弹性预测能力, 测量到更高的空间定位能力, 向更强度能力, 测量能力向更强的精确到更精确到更精确性定位能力, 测量到原始定位能力, 测量到原始定位能力, 测量到原始定位能力, 测量到原始定位能力, 测量到更精确到更强的准。