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

通过基于数据同化的参数估计改进灰尘参数化

• 批准号: 22K21337
• 负责人: 江 嘉敏
• 金额: $ 1.83万
• 依托单位: Institute of Physical and Chemical Research
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Research Activity Start-up
• 财政年份: 2022
• 资助国家: 日本
• 起止时间: 2022-08-31 至 2024-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-22K21337/
• 关键词: dust simulation; data assimilation; parameter estimation; dead vegetation cover; threshold friction speed

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.

虽然沙尘现象已经在气候和地球系统模型（ESM）中实现，以估计全球范围内的潜在风险，但全球模拟结果表明，ESM之间的沙尘排放，沉积和浓度分别存在很大的偏差。本研究的重点是建立一个高精度的沙尘气候资料同化系统。本研究分为三个部分：（1）[一个改进的沙尘方案]：我联系了著名的沙尘研究者和沙尘方案开发者邵教授，参观了他的实验室，并根据他的建议测试了植被参数。(2)[更新气象模型中改进的沙尘方案]：我联系了SCALE-Chem开发研究员，正在通过添加植被参数来改进他们的模型编码。它将在今年夏天结束。(3)[通过基于DA的参数估计优化理想u*t]：RIKEN正在建立SCALE-LETKF，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。

项目成果

Sensitive experiments of data assimilation localization scales for the pre-preparation of cumulus parameter estimation

积云参数估计前期准备的数据同化定位尺度敏感实验

• 发表时间: 2022
• 作者: Kaman Kong;Arata Amemiya;Kenta Sueki;Hirofumi Tomita
• 通讯作者: Hirofumi Tomita

University of Cologne(ドイツ)

科隆大学（德国）