%0 Journal Article %1 Brands2014Predicting %A Brands, Swen %D 2014 %J Environmental Research Letters %K wind statistics seasonal wave forecasting snow %N 4 %P 045006+ %R 10.1088/1748-9326/9/4/045006 %T Predicting average wintertime wind and wave conditions in the North Atlantic sector from Eurasian snow cover in October %U http://dx.doi.org/10.1088/1748-9326/9/4/045006 %V 9 %X The present study assesses the lead–lag teleconnection between Eurasian snow cover in October and the December-to-February mean boreal winter climate with respect to the predictability of 10 m wind speed and significant wave heights in the North Atlantic and adjacent seas. Lead–lag correlations exceeding a magnitude of 0.8 are found for the short time period of 1997/98–2012/13 ( n = 16) for which daily satellite-sensed snow cover data is available to date. The respective cross-validated hindcast skill obtained from using linear regression as a statistical forecasting technique is similarly large in magnitude. When using a longer but degraded time series of weekly snow cover data for calculating the predictor variable (1979/80–2011/12, n = 34), hindcast skill decreases but yet remains significant over a large fraction of the study area. In addition, Monte-Carlo field significance tests reveal that the patterns of skill are globally significant. The proposed method might be used to make forecast decisions for wind and wave energy generation, seafaring, fishery and offshore drilling. To exemplify its potential suitability for the latter sector, it is additionally applied to DJF frequencies of significant wave heights exceeding 2 m, a threshold value above which mooring conditions at oil platforms are no longer optimal.

@article{Brands2014Predicting, abstract = {The present study assesses the lead–lag teleconnection between Eurasian snow cover in October and the December-to-February mean boreal winter climate with respect to the predictability of 10 m wind speed and significant wave heights in the North Atlantic and adjacent seas. Lead–lag correlations exceeding a magnitude of 0.8 are found for the short time period of 1997/98–2012/13 ( n = 16) for which daily satellite-sensed snow cover data is available to date. The respective cross-validated hindcast skill obtained from using linear regression as a statistical forecasting technique is similarly large in magnitude. When using a longer but degraded time series of weekly snow cover data for calculating the predictor variable (1979/80–2011/12, n = 34), hindcast skill decreases but yet remains significant over a large fraction of the study area. In addition, Monte-Carlo field significance tests reveal that the patterns of skill are globally significant. The proposed method might be used to make forecast decisions for wind and wave energy generation, seafaring, fishery and offshore drilling. To exemplify its potential suitability for the latter sector, it is additionally applied to DJF frequencies of significant wave heights exceeding 2 m, a threshold value above which mooring conditions at oil platforms are no longer optimal.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Brands, Swen}, biburl = {https://www.bibsonomy.org/bibtex/2446519043699a8c275e245e8ccd99b2a/pbett}, citeulike-article-id = {13132312}, citeulike-linkout-0 = {http://dx.doi.org/10.1088/1748-9326/9/4/045006}, citeulike-linkout-1 = {http://iopscience.iop.org/1748-9326/9/4/045006}, day = 01, doi = {10.1088/1748-9326/9/4/045006}, interhash = {b5359fb2ce24506c36c13f27d961f0bb}, intrahash = {446519043699a8c275e245e8ccd99b2a}, issn = {1748-9326}, journal = {Environmental Research Letters}, keywords = {wind statistics seasonal wave forecasting snow}, month = apr, number = 4, pages = {045006+}, posted-at = {2014-04-10 12:45:59}, priority = {2}, timestamp = {2018-06-22T18:34:20.000+0200}, title = {Predicting average wintertime wind and wave conditions in the North Atlantic sector from Eurasian snow cover in October}, url = {http://dx.doi.org/10.1088/1748-9326/9/4/045006}, volume = 9, year = 2014 }