一个按网格经济活动加权的预处理气候数据统一存储库
A unified repository for pre-processed climate data weighted by gridded economic activity
AI总结：
该研究构建了一个统一框架，将网格化气候数据按人口密度或夜间灯光强度加权聚合至国家和区域层面，并验证数据可靠性，以支持环境与经济研究。
摘要：
尽管多个来源提供了高分辨率的网格化气候变量，但在环境和经济研究中，按经济活动指标加权的国家和区域特定气候数据的需求正变得越来越普遍。我们处理来自不同气候数据源的可用信息，以提供具有全球覆盖的空间聚合数据，涵盖国家（GADM0分辨率）和区域（GADM1分辨率）以及多种气候指标（平均降水量、平均气温、平均SPEI）。在聚合之前，我们按人口密度或夜间灯光强度（两者均为经济活动的代理指标）对网格化气候数据进行加权。气候变量按日、月和年测量，覆盖（取决于数据源）从1900年（最早）到2023年的时间窗口。我们将所有预处理流程整合到一个统一框架中，并通过开放获取的加权气候数据存储库Web应用共享。最后，我们通过与领先的气候影响研究中使用的数据进行系统比较来验证我们的数据。
英文摘要：
Although high-resolution gridded climate variables are provided by multiple sources, the need for country and region-specific climate data weighted by indicators of economic activity is becoming increasingly common in environmental and economic research. We process available information from different climate data sources to provide spatially aggregated data with global coverage for both countries (GADM0 resolution) and regions (GADM1 resolution) and for a variety of climate indicators (average precipitations, average temperatures, average SPEI). We weigh gridded climate data by population density or by night light intensity -- both proxies of economic activity -- before aggregation. Climate variables are measured daily, monthly, and annually, covering (depending on the data source) a time window from 1900 (at the earliest) to 2023. We pipeline all the preprocessing procedures in a unified framework, which we share in the open-access Weighted Climate Data Repository web app. Finally, we validate our data through a systematic comparison with those employed in leading climate impact studies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
