Improvement of dust parameterization with data-assimilation-based parameter estimation

虽然沙尘现象已经在气候和地球系统模式（esm）中实现，以估计全球尺度上的潜在风险，但全球模拟结果表明，沙尘排放、沉积和浓度在esm之间分别存在较大的偏差。特别是东亚地区。本研究的重点是开发一个基于高精度尘埃气候数据同化的系统。本研究分为三个部分：(1)[改进的扬尘方案]：我联系了著名扬尘研究人员、扬尘方案开发人员邵教授，参观了他的实验室，并根据他的建议对植被参数进行了测试。(2)[更新气象模型中改进的扬尘方案]：我联系了SCALE-Chem开发研究员，正在通过添加植被参数改进他们的模型编码。它将在今年夏天结束。(3)[通过基于数据分析的参数估计优化理想u*t]: SCALE-LETKF正在RIKEN建立，我正在测试一些天气现象（如台风和飑线）的模拟，并将结果反馈给开发团队。

Although the dust phenomenon has been implemented in the climate and Earth system models (ESMs) to estimate potential risk at the global scale, global simulation results indicated large biases of dust emission, deposition, and concentrations among ESMs, respectively. Particularly, the East Asian area.This study focuses on developing a high-precision dust-climate-Data-assimilation-based system. There are three parts in this study:(1) [An improved dust scheme]: I contacted Prof. Shao, a famous dust researcher and dust scheme developer, and visited his lab and tested the vegetation parameters based on his advice.(2) [Updating the improved dust scheme in the meteorological model]: I contacted with SCALE-Chem development researcher, and am working on improving their model coding by adding vegetation parameters. It will finish this summer.(3) [Optimizing the ideal u*t by DA-based parameter estimation]: SCALE-LETKF is being established at RIKEN, and I am testing some simulations for weather phenomena (such as typhoon and squall line) and feedbacking the results to the development team.