Cannon Alex J
Meteorological Service of Canada, Environment Canada, Vancouver, BC, Canada.
Neural Netw. 2007 May;20(4):444-53. doi: 10.1016/j.neunet.2007.04.002. Epub 2007 Apr 30.
Synoptic downscaling models are used in climatology to predict values of weather elements at one or more stations based on values of synoptic-scale atmospheric circulation variables. This paper presents a hybrid method for climate prediction and downscaling that couples an analog, i.e., k-nearest neighbor, model to an artificial neural network (ANN) model. In the proposed method, which is based on nonlinear principal predictor analysis (NLPPA), the analog model is embedded inside an ANN, forming its output layer. Nonlinear analog predictor analysis (NLAPA) is a flexible model that maintains the ability of the analog model to preserve inter-variable relationships and model non-normal and conditional variables (such as precipitation), while taking advantage of NLPPA's ability to define an optimal set of analog predictors that maximize predictive performance. Performance on both synthetic and real-world hydroclimatological benchmark tasks indicates that the NLAPA model is capable of outperforming other forms of analog models commonly used in synoptic downscaling.
天气学降尺度模型在气候学中用于根据天气尺度大气环流变量的值来预测一个或多个站点的气象要素值。本文提出了一种用于气候预测和降尺度的混合方法，该方法将一种类似物模型（即k近邻模型）与人工神经网络（ANN）模型相结合。在所提出的基于非线性主预测因子分析（NLPPA）的方法中，类似物模型被嵌入到一个ANN内部，形成其输出层。非线性类似物预测因子分析（NLAPA）是一种灵活的模型，它既保持了类似物模型保留变量间关系以及对非正态和条件变量（如降水量）建模的能力，同时又利用了NLPPA定义一组最优类似物预测因子以最大化预测性能的能力。在合成和实际水文气候基准任务上的表现表明，NLAPA模型能够优于天气学降尺度中常用的其他形式的类似物模型。
