Improved Sea Ice Forecasting Through Spatiotemporal Bias Correction

Improved Sea Ice Forecasting Through Spatiotemporal Bias Correction AbstractWe propose a new method, called contour-shifting, for correcting the bias in forecasts of contours such as sea ice concentration above certain thresholds. Retrospective comparisons of observations and dynamical model forecasts are used to build a statistical spatiotemporal model of how predicted contours typically differ from observed contours. Forecasted contours from a dynamical model are then adjusted to correct for expected errors in their location. The statistical model changes over time to reflect the changing error patterns that result from reducing sea ice cover in the satellite era in both models and observations. For an evaluation period from 2001-2013, these bias-corrected forecasts are on average more accurate than the unadjusted dynamical model forecasts for all forecast months in the year at four different lead times. The total area which is incorrectly categorized as containing sea ice or not is reduced by 3.3 × 105 km2 (or 21.3%) on average. The root mean squared error of forecasts of total sea ice area is also reduced for all lead times. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Climate American Meteorological Society

Improved Sea Ice Forecasting Through Spatiotemporal Bias Correction

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0442
D.O.I.
10.1175/JCLI-D-17-0185.1
Publisher site
See Article on Publisher Site

Abstract

AbstractWe propose a new method, called contour-shifting, for correcting the bias in forecasts of contours such as sea ice concentration above certain thresholds. Retrospective comparisons of observations and dynamical model forecasts are used to build a statistical spatiotemporal model of how predicted contours typically differ from observed contours. Forecasted contours from a dynamical model are then adjusted to correct for expected errors in their location. The statistical model changes over time to reflect the changing error patterns that result from reducing sea ice cover in the satellite era in both models and observations. For an evaluation period from 2001-2013, these bias-corrected forecasts are on average more accurate than the unadjusted dynamical model forecasts for all forecast months in the year at four different lead times. The total area which is incorrectly categorized as containing sea ice or not is reduced by 3.3 × 105 km2 (or 21.3%) on average. The root mean squared error of forecasts of total sea ice area is also reduced for all lead times.

Journal

Journal of ClimateAmerican Meteorological Society

Published: Aug 28, 2017

References

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