Will an improved land surface model enhance seasonal prediction of drought?

改进的地表模型会增强干旱的季节性预测吗？

• 批准号: DE200100086
• 负责人: Dr Anna Ukkola
• 金额: $ 27.01万
• 依托单位: The University of New South Wales
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2020
• 资助国家: 澳大利亚
• 起止时间: 2020-12-18 至 2025-02-01
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE200100086
• 关键词: Will; improved; land; surface; model

This project aims to increase the predictability of seasonal droughts that cause major socio-economic losses in rural Australia. The capacity to predict drought, and in particular its impacts on the land, is currently limited by the low skill of forecast models. Using novel observations, the project expects to quantify the vulnerability of Australian agricultural lands to seasonal droughts. The new knowledge will then be used to modify land processes in the Bureau of Meteorology’s seasonal prediction system to better reflect Australian conditions. This project is expected to improve forecasts of high impact droughts, crucial to mitigate socio-economic risks, and should benefit decision-making in agriculture and other industries.

该项目旨在提高季节性干旱的可预测性，这些干旱在澳大利亚农村地区造成重大社会经济损失。预测干旱的能力，特别是干旱对土地的影响，目前受到预测模型技术水平低的限制。利用新的观测结果，该项目预计将量化澳大利亚农业用地对季节性干旱的脆弱性。新的知识将用于修改气象局季节预测系统中的陆地过程，以更好地反映澳大利亚的情况。预计该项目将改善对高影响干旱的预测，这对减轻社会经济风险至关重要，并应有利于农业和其他行业的决策。