A Year‐Round Subseasonal‐to‐Seasonal Sea Ice Prediction Portal
A Year‐Round Subseasonal‐to‐Seasonal Sea Ice Prediction Portal
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全年次季节到季节海冰预测门户
DOI：
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发表时间：
2019
影响因子：
5.2
通讯作者：
E. Blanchard‐Wrigglesworth
中科院分区：
文献类型：
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作者：
Nicholas E. Wayand;C. Bitz;E. Blanchard‐Wrigglesworth
了解和量化北极海冰预测的现有技能的一个重大障碍是缺乏一个中央数据库，使模型评估和相互比较。这项研究解决了这个问题，通过引入一个中央服务器和门户网站住房多模式集合预报。我们介绍了门户网站的概述，并提供了2018年预测技能的分析。在16个参与模式中，海冰密集度的预测差异很大，但多模式平均值通常提供了长达5个月的熟练预测。采用更先进方法同化观测浓度的模型平均表现优于其他模型。类似地，一个模型，其中包括卫星为基础的海冰厚度之后比较最有利的厚度测量沿着冰桥飞行轨迹。这些结果突出了多模式预测和同化海冰变量的好处，以及从业务预报的近真实的时间评估中获得的见解。
A significant barrier to understanding and quantifying current skill of Arctic sea ice forecasts is a lack of a central database to enable model evaluation and intercomparison. This study addresses this issue by introducing a central server and web portal housing multimodel ensemble forecasts. We present an overview of the portal and provide an analysis of 2018 forecast skill. Among the 16 participating models, forecasts of sea ice concentration varied widely; yet the multimodel mean generally offered skillful forecasts for up to 5 months. Models that assimilated observed concentrations with more advanced methods performed better on average than other models. Similarly, one model that incorporated satellite‐based sea ice thickness thereafter compared most favorably with thickness measured along IceBridge flight tracks. These results highlight the benefits from multimodel predictions and assimilating sea ice variables and the insights gained from near‐real‐time evaluation of operational forecasts.
影响因子：
5.2
作者：
Goessling, H. F.;Tietsche, S.;Jung, T.
通讯作者：
Jung, T.