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.

在空间和时间维度上对数据粒度进行统一是任何计量经济学和机器学习应用的重要前置步骤。研究者们若希望在统计测试中验证农业气象与经济成果之间的关系，常常发现农业气象数据通常以网格化和时间细节化的形式存储，而许多相关的经济成果仅以汇总水平提供。本数据集旨在通过提供1989年至2018年间13个欧洲国家NUTS 3区域级别的月度气象指标数据集，辅助实证研究。我们基于欧洲委员会联合研究中心提供的25公里 x 25公里网格的每日数据创建了此数据集。我们将原始数据与欧洲NUTS3区域行政边界地图相匹配，并进行了适当加权。随后，我们计算了以下变量的月度、区域平均值、方差和峰度：每日最高、最低、平均气温（摄氏度），每日降水量（毫米）和积雪深度（厘米）。同时，我们还报告了平均温度与降水量之间的协方差。