Grid size dependency of short-term sea ice forecast and its evaluation during extreme Arctic cyclone in August 2016

被引:5
|
作者
De Silva, Liyanarachchi Waruna Arampath [1 ]
Yamaguchi, Hajime [1 ]
机构
[1] Univ Tokyo, Grad Sch Frontier Sci, Kiban Tou 6H1,5-1-5 Kashiwanoha, Kashiwa, Chiba 2778561, Japan
基金
日本学术振兴会;
关键词
Sea ice forecasting; Numerical simulation; Arctic cyclone; Arctic sea routes; MODEL;
D O I
10.1016/j.polar.2019.08.001
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Recently, cyclones are quite active and dramatically influence the sea ice distribution in the Arctic region. Therefore, precise prediction of sea ice distribution during the cyclones is crucial for safe and efficient navigation in the Arctic Ocean. A high-resolution (about 2.5 km) ice-ocean coupled model is developed for forecasting the short-term sea ice distribution along the Arctic sea routes. Since a commercial ship navigates to avoid the sea ice as much as possible, a factor to score the forecast skill is considered to be ice edge error which is an averaged distance between forecasted and observed ice edges. First, we discuss the influence of horizontal grid resolution on short-term sea ice predictions during May to November 2015. The model grid resolution is varied from 2.5 km, 5 km, 10 km and 15 km. This grid dependency study suggests that the 2.5 km resolution model predicts the ice edge accurately compared to the satellite observations. Second, the primary experiment was run from 2 August 2016 to 7 August 2016 under the extreme cyclone in the Laptev Sea using ensemble predictions. The ensembles are constructed by using forecasted atmospheric forcing data sets from THORPEX Interactive Grand Global Ensemble (TIGGE) project and European Center for Medium-Range Weather Forecast Interim (ERA-Interim) reanalysis data. The ensemble ice edge errors are computed from 2 August 2016 to 7 August 2016. The maximum forecast skill of ensemble average ice edge error in the ice-ocean coupled model is 11.74 +/- 0.54 km with the threshold of 15% ice concentration for the AMSR2 and model predicted ice edges. It can be said that the present model of 2.5 km grids satisfies the ship crew requirement of around 10 km ice edge error for 5-day forecast.
引用
收藏
页码:204 / 211
页数:8
相关论文
共 34 条
  • [1] Improvement of short-term sea ice forecast in the southern okhotsk sea
    Fujisaki, Ayumi
    Yamaguchi, Hahme
    Duan, Fengjun
    Sagawa, Genki
    [J]. JOURNAL OF OCEANOGRAPHY, 2007, 63 (05) : 775 - 790
  • [2] Improvement of short-term sea ice forecast in the southern Okhotsk Sea
    Ayumi Fujisaki
    Hajime Yamaguchi
    Fengjun Duan
    Genki Sagawa
    [J]. Journal of Oceanography, 2007, 63 : 775 - 790
  • [3] Extended-Range Arctic Sea Ice Forecast with Convolutional Long Short-Term Memory Networks
    Liu, Yang
    Bogaardt, Laurens
    Attema, Jisk
    Hazeleger, Wilco
    [J]. MONTHLY WEATHER REVIEW, 2021, 149 (06) : 1673 - 1693
  • [4] SHORT-TERM OPERATIONAL SEA ICE FORECASTING FOR ARCTIC SHIPPING
    Howell, Carl
    Richard, Martin
    Barnes, Joshua
    King, Tony
    [J]. PROCEEDINGS OF THE ASME 34TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, 2015, VOL 8, 2015,
  • [5] Artificial Neural Network for the Short-Term Prediction of Arctic Sea Ice Concentration
    Choi, Minjoo
    De Silva, Liyanarachchi Waruna Arampath
    Yamaguchi, Hajime
    [J]. REMOTE SENSING, 2019, 11 (09)
  • [6] Short-term Impacts of Arctic Summer Cyclones on Sea Ice Extent in the Marginal Ice Zone
    Finocchio, Peter M.
    Doyle, James D.
    Stern, Daniel P.
    Fearon, Matthew G.
    [J]. GEOPHYSICAL RESEARCH LETTERS, 2020, 47 (13)
  • [7] Short-term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the US Navy's Arctic Cap Nowcast/Forecast System
    Hebert, David A.
    Allard, Richard A.
    Metzger, E. Joseph
    Posey, Pamela G.
    Preller, Ruth H.
    Wallcraft, Alan J.
    Phelps, Michael W.
    Smedstad, Ole Martin
    [J]. JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2015, 120 (12) : 8327 - 8345
  • [8] Summer Cyclones and Their Association With Short-Term Sea Ice Variability in the Pacific Sector of the Arctic
    Finocchio, Peter M.
    Doyle, James D.
    [J]. FRONTIERS IN EARTH SCIENCE, 2021, 9
  • [9] Forecasting Arctic Sea Ice Concentration using Long Short-term Memory Networks
    Phutthaphaiboon, Thunchanok
    Siripongwutikorn, Peerapon
    Pusawiro, Priyakorn
    [J]. PROCEEDINGS OF 2023 8TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING TECHNOLOGIES, ICMLT 2023, 2023, : 121 - 126
  • [10] Short-term extreme ice loads prediction and fatigue damage evaluation for an icebreaker
    Chai, Wei
    Leira, Bernt J.
    Naess, Arvid
    [J]. SHIPS AND OFFSHORE STRUCTURES, 2018, 13 : 127 - 137