The influence of forest loss on rainfall remains poorly understood. Addressing this challenge Spracklen et al. recently presented a pan-tropical study of rainfall and land-cover that showed that satellite-derived rainfall measures were positively correlated with the degree to which model-derived air trajectories had been exposed to forest cover. This result confirms the influence of vegetation on regional rainfall patterns suggested in previous studies. However, we find that the conclusion of Spracklen et al. -- that differences in rainfall reflect air moisture content resulting from evapotranspiration while the circulation pattern remains unchanged -- appears undermined by methodological inconsistencies. We identify methodological problems with the underlying analyses and the quantitative estimates for rainfall change predicted if forest cover is lost in the Amazon. We discuss some alternative explanations that include the distinct role of forest evapotranspiration in creating low pressure systems that draw moisture from the oceans to the continental hinterland. Our analysis of meteorological data from three regions in Brazil, including the central Amazon forest, reveal a tendency for rainy days during the wet season with column water vapor (CWV) exceeding 50 mm to have higher pressure than rainless days; while at lower CWV rainy days tend to have lower pressure than rainless days. The coupling between atmospheric moisture content and circulation dynamics underlines that the danger posed by forest loss is greater than suggested by focusing only on moisture recycling alone.
Changes in Ugandan Climate Rainfall at the Village and Forest Level
2013年，美国国家海洋和大气局改进了非洲大陆的历史降雨量估计，并制作了非洲降雨气候2.0版（ARC 2）估计器。ARC 2提供了自1983年以来几乎完整的每日降雨量估计记录，分辨率为0.1 x 0.1度。尽管短期异常，在每年两次的雨季强度在乌干达，我们确定在过去的34年中，平均降雨量约12%的整体下降。从时空上看，乌干达中部和西部农业地区的降水量减少最多，但也反映了乌干达西南部布温迪森林大猩猩栖息地的降雨量减少。这些发现对农业、粮食安全和野生动物栖息地具有重要意义。
Cumulus parameterization controls simulated rainfall more than microphysics, but not at heavy thresholds: a 3 * 2 factorial WRF study over the Western Himalaya
Indian Institute of Technology Bhubaneswar（印度理工学院布巴内斯瓦尔分校）；Indian Institute of Technology Bombay（印度理工学院孟买分校）
Boundary-layer scheme selection outweighs microphysics in WRF simulations of the August 2018 Kerala extreme rainfall event
Indian Institute of Technology Bhubaneswar（印度布巴内斯瓦尔理工学院）；Indian Institute of Technology Bombay（印度孟买理工学院）；Cochin University of Science and Technology（科钦科技大学）
Deep-learning based down-scaling of summer monsoon rainfall data over Indian region
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences（印度热带气象研究所，地球科学部）；Indian Institute of Technology Bombay（印度理工学院孟买分校）；Ramrao Adik Institute of Technology（拉姆劳·阿迪克技术学院）；Defence Institute of Advanced Technology（国防高级技术研究所）
Fusion of rain radar images and wind forecasts in a deep learning model applied to rain nowcasting
CNRS（法国国家科学研究中心）；Nansen Environmental and Remote Sensing Center（南森环境与遥感中心）；Laboratoire d’océanographie et du climat（海洋学与气候实验室）；École nationale supérieure d'informatique pour l'industrie et l'entreprise（国立工业与企业计算机科学高等学院）
