数据同化百年史:演化、里程碑和未来方向

A Century of Data Assimilation: Evolution, Milestones, and Future Directions

  • 摘要:
    在中国气象学会创立百年之际,站在历史的交汇点,回望过去,展望未来对未来的发展非常重要。这一百年的历程,是几代科学家们不懈努力、辛勤耕耘的结果。在这一具有里程碑意义的时刻,这个综述深入回顾气象及相关领域的发展历程,总结已有的科研成果,并探索未来的发展方向,以期加速我国气象事业的繁荣进步。
    数据同化,作为精确刻画大气、海洋和气候状态的关键科学问题,其概念早在一百多年前便已提出。然而,正是得益于近几十年来计算机技术的突飞猛进,一些尖端的方法和手段才得以实现和应用。数据同化是一门集大气科学、物理学、数学、计算机科学于一体的交叉应用科学,它的发展与这些学科的进步紧密相连。本文将数据同化的先进方法和理论,与这些学科的成果进行对比分析,以期对过去的工作进行总结,为未来的方向提供有意义的思路。本文从应用数学的视角,深入探讨主流同化算法的本质,澄清常见误解,并提出未来研究的关键方向。让我们携手并进,以数据同化为纽带,连接过去与未来,共同开启中国气象事业的新篇章。

     

    Abstract: As we mark the centennial of China’s meteorological endeavors, it is very important to reflect on the past and look ahead to the future at this threshold of history. This hundred-year journey is the result of relentless efforts and hard work of several generations of scientists. At this milestone moment, we must not only celebrate this significant occasion, but also take the opportunity to review the development history of meteorology and related fields, summarize scientific achievements we have made, and explore future development directions, with the aim of accelerating the prosperity and progress of China’s meteorological cause. Data assimilation, as a key scientific issue in accurately depicting the state of the atmosphere, oceans, and climate, was proposed over a hundred years ago. However, it is the rapid advancement of computer technology in recent decades that has enabled the implementation and application of some cutting-edge methods and techniques. Data assimilation is an interdisciplinary applied science closely related to meteorology, physics, mathematics, and computer science, and its development is closely linked to the progress of these disciplines. It would be of great interest to compare advanced methods and theories of data assimilation with achievements of these disciplines, hoping to summarize past progress and provide guidance for future development. This article, from the perspective of applied mathematics, delves into the essence of mainstream assimilation algorithms, clarifies common misconceptions, and proposes key directions for future research. Let us join hands to move forward, using data assimilation as a link between the past and the future, and jointly open a new chapter in China's meteorological endeavors.

     

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