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Constructing a meteorological indicator dataset for selected European NUTS 3 regions
Denitsa Angelova1, Norman Blanco Lupio2
1Technical University of Munich, School of Management.
Data in Brief
|June 20, 2020
Summary
This study harmonizes agro-meteorological data to the NUTS 3 regional level across Europe. This facilitates research on European policy outcomes by providing monthly weather indicators, aiding studies on weather volatility and extreme events.
Area of Science:
- Agricultural Science
- Environmental Science
- Data Science
Background:
- Harmonizing data granularity is crucial for econometric and machine learning applications.
- Agro-meteorological data is often gridded and temporally detailed, while policy outcomes are aggregated.
- A gap exists in accessible, harmonized datasets for analyzing weather and policy relationships.
Purpose of the Study:
- To provide a harmonized dataset of monthly meteorological indicators at the European Nomenclature of Territorial Units for Statistics level 3 (NUTS 3) regional level.
- To enable statistical testing of hypotheses on the relationship between agro-meteorological factors and European policy outcomes.
- To support research on weather volatility and the probability of extreme weather events.
Main Methods:
- Derived monthly meteorological indicators from daily gridded data (25 km x 25 km) provided by the European Commission's Joint Research Centre.
- Matched gridded meteorological data to European NUTS 3 administrative boundaries for 13 countries (1989-2018).
- Calculated weighted monthly regional means, variances, and kurtosis for temperature, precipitation, and snow depth, reporting covariance between temperature and precipitation.
Main Results:
- A comprehensive dataset of monthly meteorological indicators at the NUTS 3 regional level is now available.
- The dataset includes mean, variance, and kurtosis for maximum, minimum, and average air temperature, precipitation, and snow depth.
- Covariance between average temperature and precipitation is also reported, offering insights into weather patterns.
Conclusions:
- The harmonized dataset bridges the granularity gap between detailed weather data and aggregated policy information.
- This resource empowers researchers to investigate complex relationships between weather patterns and policy impacts across European regions.
- Facilitates empirical investigations into weather volatility, extreme events, and their connection to policy outcomes.
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