Droughts and flash droughts (rapidly developing droughts; FDs) remain impactful events that are known to desiccate landscape and destroy crops. In particular, droughts in Africa are often more impactful than in other locations, such as the United States or Europe, due to many regions in Africa heavily depending on local agriculture for sustenance. In recent years, large machine learning (ML) models, such as GraphCast and AIFS, have emerged as effective tools for global weather prediction. However, sparse data observations and few ML studies in Africa have left it unclear if these ML models retain their skill when focused on Africa. As such, this project seeks to examine the predictability of drought and FD in Africa using a CrossFormer model based on the Community Research Earth Digital Intelligence Twin (CREDIT) framework developed by NSF NCAR. Our CrossFormer model, termed DroughtFormer, incorporates variables from the ERA5 and GLDAS2 reanalyses and the IMERG and MODIS satellite observations, and employs dry air mass and moisture conservation, to predict soil moisture, vegetation health, and other drought-related surface variables. While DroughtFormer displayed lower accuracy in predicting precipitation and FD indices, it showed significant skill in predicting the remaining variables, delivering stable and skillful forecasts out to 90-day lead times (either beating out or having comparable skill to climatology). In particular, DroughtFormer skillfully represented climate anomalies for key variables, such as soil moisture (though it struggled with the magnitude of the anomalies). Thus, DroughtFormer showed significant promise in representing and predicting agricultural level drought in a region that is heavily impacted by drought events.

翻译：干旱和骤旱（快速发展的干旱）是导致土地干涸和作物损毁的重大灾害事件。由于非洲许多地区严重依赖本地农业维持生计，非洲干旱往往比美国或欧洲等地区更具破坏性。近年来，以GraphCast和AIFS为代表的大型机器学习模型已成为全球天气预报的有效工具。然而，非洲观测数据稀疏且相关机器学习研究匮乏，导致这些模型在聚焦非洲区域时是否保持预测效能尚不明确。为此，本研究基于美国国家大气研究中心（NSF NCAR）开发的社区研究地球数字智能孪生（CREDIT）框架，采用CrossFormer模型探讨非洲干旱与骤旱的可预测性。我们所提出的CrossFormer模型（命名为DroughtFormer）融合了ERA5与GLDAS2再分析资料、IMERG与MODIS卫星观测变量，并引入干空气质量与水分守恒机制，用于预测土壤湿度、植被健康及其他与干旱相关的地表变量。尽管DroughtFormer在降水和骤旱指数预测中精度较低，但其在其余变量预测中展现出显著效能，可提供长达90天超前时间内的稳定且技能优异的预测（性能优于或堪比气候态预报）。特别是在关键变量的气候异常表征方面，DroughtFormer具备良好能力（尽管在异常幅度预测上存在困难）。因此，DroughtFormer在受干旱事件严重影响的区域中，针对农业尺度干旱的表征与预测展现出显著潜力。