发表机构
University of Chicago; Columbia University; University of Witwatersrand; AfriClimate AI; University of Leeds; University of Oxford; African Centre of Meteorological Applications for Development; NORCE Research AS（芝加哥大学; 哥伦比亚大学; 威特沃特斯兰德大学; 非洲气候人工智能; 利兹大学; 牛津大学; 非洲气象应用发展中心; 挪威NORCE研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对小农户缺乏高质量天气预报的问题，提出一套评估农业相关预报的原则与协议，旨在建立标准以促进预报质量的可信传达，避免低质量预报挤占优质预报。
AI 中文摘要
人工智能天气预报（AIWP）模型使得利用有限的计算资源生成高质量的定制化预报成为可能。这一进展有望惠及低收入和中等收入国家中数以亿计无法获取关键天气现象预报的农民。然而，关键利益相关者可能难以评估预报质量，这可能导致“逐底竞赛”，即廉价但低质量的预报挤占了本可惠及农民的预报。我们提出了一套评估农业相关预报的原则和协议，作为制定标准的起点，使预报员能够可信地传达其预报质量。
英文摘要
Artificial-intelligence weather prediction (AIWP) models have made it possible to produce high-quality tailored forecasts with limited computational resources. This advance has the potential to benefit hundreds of millions of farmers in low- and middle-income countries who lack access to forecasts of critical weather phenomena. However, it can be difficult for key stakeholders to evaluate forecast quality, risking a "race to the bottom" as cheap but low-quality forecasts crowd out forecasts that would benefit farmers. We propose a set of principles and protocols for evaluating agriculturally-relevant forecasts as a starting point for standards that would let forecasters credibly convey their forecasts' quality.
Comments17 pages, updated October 7 to add acknowledgments
