Ishibashi Toshiyuki
Meteorological Research Institute, Japan Meteorological Agency, Nagamine 1-1, Tsukuba, Ibaraki, Japan.
Sci Rep. 2024 Sep 27;14(1):22275. doi: 10.1038/s41598-024-71849-7.
Atmospheric state analysis is a difficult scientific problem due to the chaotic nature of the atmosphere. Data assimilation is a framework for generating an accurate state analysis of a physical system using probability density functions (PDFs) describing uncertainty of information on the state of the physical system. However, since PDFs cannot be deduced theoretically, those used in data assimilation of atmospheric state analysis are based on empirical tunings. This PDF uncertainty limits the theoretical consistency and accuracy of atmospheric state analysis and that of all atmospheric sciences. In this study, we constructed a highly accurate and theoretically consistent atmospheric state analysis by objectively estimating the PDFs of all datasets (forecasts and observations) under the Gaussian approximation. We show that an ensemble of data assimilations with 192 members using the four-dimensional variational method and sample statistics obtained with the data assimilation theory (Desroziers' method) can generate more accurate objective Gaussian PDFs, including flow-dependent forecast error structures. Numerical experiments of atmospheric state analysis and forecasts using objective PDFs were conducted and compared with those using conventional empirical PDFs. The objective PDFs had smaller error variances for most data (about 34% of those of CNTL on average) and larger observation error correlations for satellite radiances, where the strongest correlation was greater than 0.8. The analysed atmospheric states are systematically different, such as a cooler (exceeding 1.2 K) and wetter (exceeding 1.2 g/kg) low troposphere in regions characterized by low-level clouds off the west coast of the continents. The theoretical consistency evaluated by the chi-square-based tests showed a clear improvement from 16 to 95%. The forecast accuracy was improved globally up to 9%, with 95% statistical significance. The tropical cyclone track forecast accuracy was also improved about 20%.
由于大气的混沌性质，大气状态分析是一个困难的科学问题。数据同化是一种使用描述物理系统状态信息不确定性的概率密度函数（PDF）来生成物理系统精确状态分析的框架。然而，由于PDF无法从理论上推导出来，用于大气状态分析数据同化的那些PDF是基于经验调整的。这种PDF不确定性限制了大气状态分析以及所有大气科学的理论一致性和准确性。在本研究中，我们通过在高斯近似下客观估计所有数据集（预报和观测）的PDF，构建了一个高度准确且理论上一致的大气状态分析。我们表明，使用四维变分方法和通过数据同化理论（德罗齐埃方法）获得的样本统计量进行的192个成员的集合数据同化，可以生成更准确的客观高斯PDF，包括与流相关的预报误差结构。使用客观PDF进行了大气状态分析和预报的数值实验，并与使用传统经验PDF的实验进行了比较。客观PDF对于大多数数据具有较小的误差方差（平均约为CNTL的34%），对于卫星辐射具有较大的观测误差相关性，其中最强相关性大于0.8。分析得到的大气状态存在系统性差异，例如在大陆西海岸以低云为特征的区域，低层对流层更冷（超过1.2K）且更潮湿（超过1.2g/kg）。基于卡方检验评估的理论一致性从16%显著提高到95%。全球预报准确率提高了9%，具有95%的统计显著性。热带气旋路径预报准确率也提高了约20%。
