Statistical atmospheric downscaling for rainfall estimation in Kyushu Island, Japan

International audienceThe present paper develops linear regression models based on singular value decomposition (SVD) with the aim of downscaling atmospheric variables statistically to estimate average rainfall in the Chikugo River Basin, Kyushu Island, southern Japan, on a 12-hour basis. Models were designed to take only significantly correlated areas into account in the downscaling procedure. By using particularly precipitable water in combination with wind speeds at 850 hPa, correlation coefficients between observed and estimated precipitation exceeding 0.8 were reached. The correlations exhibited a seasonal variation with higher values during autumn and winter than during spring and summer. The SVD analysis preceding the model development highlighted three important features of the rainfall regime in southern Japan: (1) the so-called Bai-u front which is responsible for the majority of summer rainfall, (2) the strong circulation pattern associated with autumn rainfall, and (3) the strong influence of orographic lifting creating a pronounced east-west gradient across Kyushu Island. Results confirm the feasibility of establishing meaningful statistical relationships between atmospheric state and basin rainfall even at time scales of less than one day. Keywords: atmospheric downscaling, precipitation, rainfall, singular value decomposition, southern Japa

Statistical atmospheric downscaling for rainfall estimation in Kyushu Island, Japan

The present paper develops linear regression models based on singular value decomposition (SVD) with the aim of downscaling atmospheric variables statistically to estimate average rainfall in the Chikugo River Basin, Kyushu Island, southern Japan, on a 12-hour basis. Models were designed to take only significantly correlated areas into account in the downscaling procedure. By using particularly precipitable water in combination with wind speeds at 850 hPa, correlation coefficients between observed and estimated precipitation exceeding 0.8 were reached. The correlations exhibited a seasonal variation with higher values during autumn and winter than during spring and summer. The SVD analysis preceding the model development highlighted three important features of the rainfall regime in southern Japan: (1) the so-called Bai-u front which is responsible for the majority of summer rainfall, (2) the strong circulation pattern associated with autumn rainfall, and (3) the strong influence of orographic lifting creating a pronounced east-west gradient across Kyushu Island. Results confirm the feasibility of establishing meaningful statistical relationships between atmospheric state and basin rainfall even at time scales of less than one day. Keywords: atmospheric downscaling, precipitation, rainfall, singular value decomposition, southern Japa

Neural Networks for rainfall forecasting by atmospheric downscaling

Several studies have used artificial neural networks (NNs) to estimate local or regional recipitation/rainfall on the basis of relationships with coarse-resolution atmospheric variables. None of these experiments satisfactorily reproduced temporal intermittency and variability in rainfall. We attempt to improve performance by using two approaches: (1) couple two NNs in series, the first to determine rainfall occurrence, and the second to determine rainfall intensity during rainy periods; and (2) categorize rainfall into intensity categories and train the NN to reproduce these rather than the actual intensities. The experiments focused on estimating 12-h mean rainfall in the Chikugo River basin, Kyushu Island, southern Japan, from large-scale values of wind speeds at 850 hPa and precipitable water. The results indicated that (1) two NNs in series may greatly improve the reproduction of intermittency; (2) longer data series are required to reproduce variability; (3) intensity categorization may be useful for probabilistic forecasting; and (4) overall performance in this region is better during winter and spring than during summer and autumn