Abstract Improving accuracy of downscaling rainfall by combining predictions of different statistical downscale models
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Improving accuracy of downscaling rainfall by combining predictions of different statistical downscale models

机译：结合不同统计降尺度模型的预测来提高降尺度降雨的精度

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Abstract A flexible framework of multi-model of three statistical downscaling approaches was established in which predictions from these models were used as inputs to Artificial Neural Network (ANN). Traditional ANN, Simple Average Method (SAM), and combining models (SDSM, Multiple Linear Regressions (MLR), Generalized Linear Model (GLM)) were applied to a studied site in North-western England. Model performance criteria of each of the primary and combining models were evaluated. The obtained results indicate that different downscaling methods can gain diverse usefulness and weakness in simulating various rainfall characteristics under different circumstances. The combining ANN model showed more adaptability by acquiring better overall performance, while GLM, MLR and showed comparable results and the SDSM reveals relatively less accurate results in modelling most of the rainfall amount. Furthermore traditional ANN has been tested and showed poor performance in reproducing the observed rainfall compared with above methods. The results also show that the superiority of the combining approach model over the single models is promising to be implemented to improve downscaling rainfall at a single site.
机译： 摘要 建立了三种统计缩减方法的多模型灵活框架，其中将这些模型的预测用作人工神经网络（ANN）的输入。将传统的人工神经网络，简单平均法（SAM）和组合模型（SDSM，多重线性回归（MLR），广义线性模型（GLM））应用于英格兰西北部的研究地点。评价了每个主要模型和组合模型的模型性能标准。所得结果表明，不同的降尺度方法在模拟不同情况下的各种降雨特征时可获得多种有用性和弱点。组合的ANN模型通过获得更好的总体性能表现出更大的适应性，而GLM，MLR并显示出可比的结果，而SDSM在模拟大多数降雨量时显示出相对不准确的结果。此外，传统的人工神经网络已经过测试，与上述方法相比，在再现观测到的降雨方面表现较差。结果还表明，组合方法模型优于单个模型的优势有望实现，以改善单个站点的降尺度降雨。

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