Meteorological indicator dataset for selected European NUTS 3 regions

The harmonization of data granularity in spatial and temporal terms is an important pre-step to any econometric and machine learning applications. Researchers, who wish to statistically test hypotheses on the relationship between agro-meteorological and economic outcomes, often observe that agro-meteorological data is typically stored in gridded and temporally detailed form, while many relevant economic outcomes are only available on an aggregated level. This dataset intends to aid empirical investigations by providing a dataset with monthly meteorological indicators on a European NUTS 3 regional level for 13 countries for the period from 1989 to 2018. We created this dataset from daily data in a grid of 25km x 25km provided by the Joint Research Centre of the European Commission. We matched the map with the raw data to a map with the administrative boundaries of European NUTS3 regions. After appropriately weighting, we calculated the monthly, regional mean, variance and kurtosis of the following variables: daily maximum, minimum, average air temperature in degrees Centigrade, sum of precipitation in mm per day and snow depth in cm. We report the covariance between the average temperature and the precipitation as well.

在空间与时间维度上统一数据粒度，是所有计量经济学与机器学习应用的重要前置步骤。旨在通过统计方法检验农业气象与经济产出间关联假说的研究者，常会发现农业气象数据通常以网格化且高时间分辨率的形式存储，而多数相关经济产出数据仅能获取聚合后的版本。本数据集旨在助力实证研究，为13个国家1989年至2018年期间欧洲地区的欧盟统计区域三级单元（NUTS 3）提供月度气象指标数据集。本数据集源自欧洲委员会联合研究中心（Joint Research Centre of the European Commission）提供的25km×25km网格化逐日数据。我们将搭载原始数据的地理格网，匹配至欧洲地区欧盟统计区域三级单元的行政边界地图。经过合理加权处理后，我们针对以下变量计算了月度区域均值、方差与峰度：日最高气温、日最低气温、日平均气温（单位：摄氏度）、日降水量总和（单位：毫米）以及积雪深度（单位：厘米）。此外，我们还一并提供了平均气温与降水量间的协方差数据。