Improved seasonal prediction of UK regional precipitation using atmospheric circulation
Improved seasonal prediction of UK regional precipitation using atmospheric circulation
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DOI：
10.1002/joc.5382
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发表时间：
2018-04-01
期刊：
影响因子：
--
通讯作者：
Scaife, A. A.
中科院分区：
文献类型：
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作者：
Baker, L. H.;Shaffrey, L. C.;Scaife, A. A.
本研究的目的是进一步了解是否可以缩小规模的大尺度大气环流的季节性预报，以提供熟练的区域降水的季节性预报。一个简单的多元线性回归模型来描述英国9个地区的冬季降水变化。每个区域的模型是一个线性组合的两个平均海平面气压（MSLP）为基础的指数，这是来自苏格兰西北部和英格兰东南部降水的MSLP相关模式。第一个指数是气压偶极子，类似于北大西洋涛动，但向东移动;第二个指数是以英国为中心的MSLP异常。多元线性回归模型描述了高达76%的观测到的降水变化在每个区域，并给出了更高的相关性与降水比单独使用两个指数。英国气象局的季节性预报系统（GloSea 5）被发现在预测冬季的两个MSLP指数方面具有重要的技能，在11月初左右开始的预测中。应用多元线性回归模型的GloSea 5后报显示，GloSea 5的降水预测，在苏格兰的最大改善，以提高技能。
The aim of this study is to further our understanding of whether skilful seasonal forecasts of the large-scale atmospheric circulation can be downscaled to provide skilful seasonal forecasts of regional precipitation. A simple multiple linear regression model is developed to describe winter precipitation variability in nine UK regions. The model for each region is a linear combination of two mean sea-level pressure (MSLP)-based indices which are derived from the MSLP correlation patterns for precipitation in northwest Scotland and southeast England. The first index is a pressure dipole, similar to the North Atlantic Oscillation but shifted to the east; the second index is the MSLP anomaly centred over the UK. The multiple linear regression model describes up to 76% of the observed precipitation variability in each region and gives higher correlations with precipitation than using either of the two indices alone. The Met Office's seasonal forecast system (GloSea5) is found to have significant skill in forecasting the two MSLP indices for the winter season, in forecasts initialized around the start of November. Applying the multiple linear regression model to the GloSea5 hindcasts is shown to give improved skill over the precipitation forecast by the GloSea5, with the largest improvement in Scotland.