%0 Journal Article %1 stringer2019improving %A Stringer, Nicky %A Knight, Jeff %A Thornton, Hazel %D 2019 %I American Meteorological Society %J Journal of Applied Meteorology and Climatology %K colleagues hydrology nao seasonal skill %R 10.1175/jamc-d-19-0094.1 %T Improving meteorological seasonal forecasts for hydrological modelling in European Winter %U https://doi.org/10.1175/jamc-d-19-0094.1 %X Recent advances in the skill of seasonal forecasts in the extra-tropics during winter mean they could offer improvements to seasonal hydrological forecasts. However the signal-to-noise paradox, whereby the variability in the ensemble mean signal is lower than would be expected given its correlation skill, prevents their use to force hydrological models directly. We describe a post-processing method to adjust for this problem, increasing the size of the predicted signal in the large-scale circulation. This reduces the ratio of predictable components in the North Atlantic Oscillation (NAO) from 3 to 1. We then derive a large ensemble of daily sequences of spatially gridded rainfall that are consistent with the seasonal mean NAO prediction, by selecting historical observations conditioned on the adjusted NAO forecasts. Over northern and southwestern Europe, where the NAO is strongly correlated with winter mean rainfall, the variability of the predicted signal in the adjusted rainfall forecasts is consistent with the correlation skill (they have a ratio of predictable components of around 1) and are as skilful as the unadjusted forecasts. The adjusted forecasts show larger predicted deviations from climatology and can be used to better assess the risk of extreme seasonal mean precipitation, as well as force hydrological models.

@article{stringer2019improving, abstract = {Recent advances in the skill of seasonal forecasts in the extra-tropics during winter mean they could offer improvements to seasonal hydrological forecasts. However the signal-to-noise paradox, whereby the variability in the ensemble mean signal is lower than would be expected given its correlation skill, prevents their use to force hydrological models directly. We describe a post-processing method to adjust for this problem, increasing the size of the predicted signal in the large-scale circulation. This reduces the ratio of predictable components in the North Atlantic Oscillation (NAO) from 3 to 1. We then derive a large ensemble of daily sequences of spatially gridded rainfall that are consistent with the seasonal mean NAO prediction, by selecting historical observations conditioned on the adjusted NAO forecasts. Over northern and southwestern Europe, where the NAO is strongly correlated with winter mean rainfall, the variability of the predicted signal in the adjusted rainfall forecasts is consistent with the correlation skill (they have a ratio of predictable components of around 1) and are as skilful as the unadjusted forecasts. The adjusted forecasts show larger predicted deviations from climatology and can be used to better assess the risk of extreme seasonal mean precipitation, as well as force hydrological models.}, added-at = {2019-12-21T09:49:35.000+0100}, author = {Stringer, Nicky and Knight, Jeff and Thornton, Hazel}, biburl = {https://www.bibsonomy.org/bibtex/2329cb2f77b88ae721bdbb111fe38d5f2/pbett}, doi = {10.1175/jamc-d-19-0094.1}, interhash = {d69bbcbe8ee0d21f03985be8d19ff51a}, intrahash = {329cb2f77b88ae721bdbb111fe38d5f2}, journal = {Journal of Applied Meteorology and Climatology}, keywords = {colleagues hydrology nao seasonal skill}, month = dec, publisher = {American Meteorological Society}, timestamp = {2019-12-21T09:49:35.000+0100}, title = {Improving meteorological seasonal forecasts for hydrological modelling in European Winter}, url = {https://doi.org/10.1175/jamc-d-19-0094.1}, year = 2019 }