%0 Journal Article %1 baker2018improved %A Baker, L. H. %A Shaffrey, L. C. %A Scaife, A. A. %D 2018 %J International Journal of Climatology %K circulation colleagues forecasting lambweathertypes precip seasonal skill weathertypes %N S1 %P e437-e453 %R 10.1002/joc.5382 %T Improved seasonal prediction of UK regional precipitation using atmospheric circulation %V 38 %X 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.

@article{baker2018improved, 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. }, added-at = {2018-06-19T22:45:15.000+0200}, author = {Baker, L. H. and Shaffrey, L. C. and Scaife, A. A.}, biburl = {https://www.bibsonomy.org/bibtex/266bae6e95f5f97cf0590a6a80bee3b0e/pbett}, doi = {10.1002/joc.5382}, interhash = {56784131b90ca3759a632689fb23a4ad}, intrahash = {66bae6e95f5f97cf0590a6a80bee3b0e}, journal = {International Journal of Climatology}, keywords = {circulation colleagues forecasting lambweathertypes precip seasonal skill weathertypes}, month = apr, number = {S1}, pages = {e437-e453}, timestamp = {2018-06-19T22:45:31.000+0200}, title = {Improved seasonal prediction of UK regional precipitation using atmospheric circulation}, volume = 38, year = 2018 }