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- W4207042010 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> Accurate and timely forecasts of sea ice conditions are crucial for safe shipping operations in the Canadian Arctic and other ice-infested waters. Given the recent declining trend of Arctic sea ice extent in past decades, seasonal forecasts are often desired. In this study machine learning (ML) approaches are deployed to provide accurate seasonal forecasts based on ERA5 data as input. This study, unlike previous ML approaches in the sea ice forecasting domain, provides daily spatial maps of sea ice presence probability in the study domain for lead times up to 90âd using a novel spatiotemporal forecasting method based on sequence-to-sequence learning. The predictions are further used to predict freeze-up/breakup dates and show their capability to capture these events within a 7âd period at specific locations of interest to shipping operators and communities. The model is demonstrated in hindcasting mode to allow for evaluation of forecasted predication. However, the design allows for the approach to be used as a forecasting tool. The proposed method is capable of predicting sea ice presence probabilities with skill during the breakup season in comparison to both Climate Normal and sea ice concentration forecasts from a leading subseasonal-to-seasonal forecasting system." @default.
- W4207042010 created "2022-01-26" @default.
- W4207042010 creator A5069799892 @default.
- W4207042010 date "2022-01-24" @default.
- W4207042010 modified "2023-09-30" @default.
- W4207042010 title "Reply on RC1" @default.
- W4207042010 doi "https://doi.org/10.5194/tc-2021-282-ac1" @default.
- W4207042010 hasPublicationYear "2022" @default.
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