Improved seasonal prediction of UK regional precipitation using atmospheric circulation
ABSTRACT 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...
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Published in | International journal of climatology Vol. 38; no. S1; pp. e437 - e453 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Chichester, UK
John Wiley & Sons, Ltd
01.04.2018
Wiley Subscription Services, Inc |
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Abstract | ABSTRACT
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.
A simple multiple linear regression model is developed to relate atmospheric circulation patterns to UK regional winter precipitation. This is applied to seasonal hindcasts of mean sea‐level pressure and shown to provide skilful hindcasts of UK regional precipitation, with the largest improvement in western Scotland. |
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AbstractList | 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. ABSTRACT 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. A simple multiple linear regression model is developed to relate atmospheric circulation patterns to UK regional winter precipitation. This is applied to seasonal hindcasts of mean sea‐level pressure and shown to provide skilful hindcasts of UK regional precipitation, with the largest improvement in western Scotland. ABSTRACT 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. |
Author | Baker, L. H. Shaffrey, L. C. Scaife, A. A. |
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Cites_doi | 10.1002/2014GL061146 10.1002/wea.2101 10.1002/wea.2152 10.1016/S0065-2687(08)60219-8 10.1002/qj.49707633005 10.1006/asle.2000.0016 10.1002/met.5060010308 10.1088/1748-9326/10/4/044007 10.1175/JCLI3937.1 10.1175/1520-0493(1997)125<0292:BSAPIA>2.0.CO;2 10.1002/joc.3498 10.1038/nclimate2314 10.1002/j.1477-8696.1997.tb06323.x 10.1002/wea.2255 10.1002/wea.2469 10.1002/joc.670 10.3354/cr020189 10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2 10.1002/2014GL059637 10.1016/j.jhydrol.2004.06.017 10.1080/02626667.2012.754545 10.1002/wea.2471 10.1175/JAMC-D-15-0102.1 10.1002/qj.625 10.1088/1748-9326/10/6/064006 10.1002/joc.3370110308 10.1002/wea.2465 10.1016/j.jhydrol.2010.10.036 10.5194/hess-19-2353-2015 10.1002/qj.2396 10.1175/2008JCLI2459.1 10.1175/1520-0442(1994)007<0141:IVINAS>2.0.CO;2 10.3354/cr013173 |
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The aim of this study is to further our understanding of whether skilful seasonal forecasts of the large‐scale atmospheric circulation can be... 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... |
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SubjectTerms | Atmospheric circulation Atmospheric forcing Correlation downscaling North Atlantic Oscillation Ocean-atmosphere system Precipitation Precipitation variability Pressure Rainfall Regional development Regression analysis Regression models Sea level pressure Seasonal forecasting Seasons Variability Weather forecasting Winter Winter precipitation |
Title | Improved seasonal prediction of UK regional precipitation using atmospheric circulation |
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