Stow Craig A, Jolliff Jason, McGillicuddy Dennis J, Doney Scott C, Allen J Icarus, Friedrichs Marjorie A M, Rose Kenneth A, Wallhead Philip
NOAA, Great Lakes Environmental Research Laboratory, 2205 Commonwealth Blvd., Ann Arbor, MI USA, 734-741-2055 (fax).
Naval Research Laboratory, Stennis Space Center, MS USA, 228-688-4149 (fax).
J Mar Syst. 2009 Feb 20;76(1-2):4-15. doi: 10.1016/j.jmarsys.2008.03.011. Epub 2008 May 24.
Coupled biological/physical models of marine systems serve many purposes including the synthesis of information, hypothesis generation, and as a tool for numerical experimentation. However, marine system models are increasingly used for prediction to support high-stakes decision-making. In such applications it is imperative that a rigorous model skill assessment is conducted so that the model's capabilities are tested and understood. Herein, we review several metrics and approaches useful to evaluate model skill. The definition of skill and the determination of the skill level necessary for a given application is context specific and no single metric is likely to reveal all aspects of model skill. Thus, we recommend the use of several metrics, in concert, to provide a more thorough appraisal. The routine application and presentation of rigorous skill assessment metrics will also serve the broader interests of the modeling community, ultimately resulting in improved forecasting abilities as well as helping us recognize our limitations.
海洋系统的耦合生物/物理模型有多种用途，包括信息综合、假设生成以及作为数值实验的工具。然而，海洋系统模型越来越多地用于预测，以支持高风险决策。在这类应用中，必须进行严格的模型技能评估，以便测试和了解模型的能力。在此，我们回顾了几种有助于评估模型技能的指标和方法。技能的定义以及给定应用所需技能水平的确定因具体情况而异，没有单一指标可能揭示模型技能的所有方面。因此，我们建议协同使用多种指标，以提供更全面的评估。严格的技能评估指标的常规应用和展示也将符合建模界的更广泛利益，最终提高预测能力，并帮助我们认识到自身的局限性。
