Temporal aggregation is the process of summarising high-frequency time series data—such as hourly or daily values—into lower-frequency intervals like daily, monthly, seasonal, or annual values. This step enables users to analyse long-term trends, seasonal variability, and interannual changes across climate and energy indicators.
The aggregation method depends on the nature of the variable:
Continuous variables (e.g., temperature, wind speed, capacity factors) are aggregated using the mean, which captures typical values over a given period.
Accumulated variables (e.g., precipitation, solar radiation, or energy production) are aggregated using the sum, representing total accumulation across the time window.
Only indicators that represent cumulative processes are summed; all others are averaged accordingly.
To optimise storage, in this dataset, gridded data are generally provided at their original high temporal resolution (typically hourly), and temporal aggregation is applied primarily to spatially aggregated datasets (e.g., at administrative unit level). Users may perform additional custom aggregation on gridded outputs as needed.
Daily
Monthly
Seasonal (e.g., DJF, MAM, JJA, SON)
Annual
Time series of gridded indicators (NetCDF format)
Time series of spatially aggregated indicators (CSV format)
Temporal resolution: typically hourly, daily, weekly, or monthly, depending on the indicator
Temporally aggregated datasets are provided in two formats:
Gridded data retain their original spatial resolution and are typically distributed at hourly (or sub-daily) resolution in NetCDF format. Users can apply custom aggregation as needed.
Spatially aggregated data are delivered as CSV files for administrative units (ADM0 and ADM1). Each row includes:
Region identifier (e.g., ISO code)
Timestamp (e.g., YYYY-MM, YYYY)
Aggregated values for each indicator
For hydropower indicators, a tailored aggregation strategy is adopted due to the nature of their original resolution and data structure:
European Hydropower Indicators (HRG, HRI, HRO):
These indicators are generated at a weekly resolution. To enable consistent monthly, seasonal, and annual statistics, the weekly time series is first converted to a daily resolution by evenly distributing the weekly value across seven days (i.e., assuming constant production within each week). Daily values are then aggregated using standard temporal operations.
Note: The intermediate daily values are not delivered, as they do not add informational value beyond the weekly dataset.
Global Hydropower Proxy (Installed capacity Weighted Precipitation – IWP):
IWP is computed on a monthly basis. However, the number of months over which precipitation is accumulated to derive IWP varies by country (depending on local storage capacity assumptions). Therefore:
The monthly values are reported in units of mm/months.
When seasonal or annual aggregations are performed, a simple arithmetic mean is computed (not a sum), so that units remain consistent (mm/n-months).
As with the European indicators, daily IWP values are not provided, since the data are derived from n-month accumulations and daily disaggregation would be artificial.
These adjustments ensure that hydropower indicators remain temporally consistent with other energy variables, while preserving the integrity of their source format.
For more information on the hydropower models please refer to the page Hydro Power Conversion Models.
For energy demand proxies based on Energy Degree Days (EDD)—specifically Heating Degree Days (HDD) and Cooling Degree Days (CDD)—temporal aggregation follows a different logic. Instead of averaging as done for most climate and energy indicators, monthly HDD and CDD values are summed to derive seasonal (3-month) and annual totals. This approach aligns with the methodology adopted by the European Climate Adaptation Platform Climate-ADAPT, and ensures that cumulative heating and cooling requirements over longer periods are correctly represented. This summation method reflects the additive nature of degree-day indicators, which are intended to quantify cumulative deviations from thermal comfort thresholds over time. For more information on the energy demand model please refer to the page Electricity and Energy Demand Models.
This document has been produced in the context of the Copernicus Climate Change Service (C3S). The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation Agreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view. |
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