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Spatial aggregation is the process of computing regional statistics (e.g., means, sums) from gridded (NetCDF) climate or energy indicators. These gridded datasets typically cover the globe at uniform spatial resolution (e.g., 0.25° or 1.00°). Spatial aggregation enables users to derive regional statistics over standard administrative areas, supporting downstream climate or energy analyses.
Administrative Boundaries: ADM0 and ADM1
Two levels of spatial aggregation are supported:
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This enables analyses at multiple scales of governance. The administrative zones used for aggregation are derived from standard shapefiles (e.g., Natural Earth), and are shown in Figure 1.1.
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Figure 1.1: Global administrative regions used for spatial aggregation.
Top: ADM0 (country-level boundaries).
Bottom: ADM1 (first-level subdivisions such as states or provinces).
Aggregation Methodology
The spatial aggregation procedure transforms gridded climate or energy indicators into regional averages or totals over predefined administrative areas (ADM0 and ADM1). This is achieved through a two-step process: the generation of the regional floating masks and the application of those masks to average the data.
Generation of Regional Float Masks
Each administrative unit (country or subnational region) is associated with a spatial mask, created using the corresponding ADM0 or ADM1 shapefile. The steps for generating the float masks are:
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This results in a fractional float mask with weights between 0 and 1 for each region and grid cell. These masks match the spatial resolution of the input gridded data and are precomputed to speed up the aggregation process.
Figure 2.1 illustrates an example of such a float mask for Italy at ADM0 level, showing how coastal and border grid cells are fractionally weighted.
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Figure 2.1: Example of a float mask for the Italian ADM0 administrative region, showing fractional grid cell coverage along borders and coastlines.
Application of Masks and Aggregation
Once the float masks are computed, spatial aggregation is carried out as follows:
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Grid cells with missing or masked values are automatically excluded from the aggregation.
Aggregation for a region is skipped or flagged if fewer than a minimum percentage (typically 80%) of its grid cells contain valid data.
Output Format
The final outputs are regional time series, provided in CSV format, with rows corresponding to time steps and columns to administrative units. The following information is included:
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The temporal resolution of the output matches that of the input gridded dataset.
Special case for the Energy Degree Days (EDD) indicator
For the Energy Degree Days (EDD) indicator—used as a proxy for energy demand—an adapted spatial aggregation method is employed to account for population distribution more accurately, particularly in coastal areas.
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To overcome this, grid cells that intersect a country’s administrative boundary are fully assigned to that country, even if partially over the sea. This ensures that population-weighted averages include all relevant urban areas near the coast. This adjustment is critical for countries like Italy, Japan, or the Netherlands, where a significant portion of the population resides in coastal cities.
Figure 2.2 shows an example of this modified float mask, where all coastal-intersecting grid cells are fully assigned to the Italian national boundary, regardless of sea overlap.
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Figure 2.2: Modified float mask for the Italian ADM0 region used in the aggregation of Energy Degree Days (EDD).
All grid cells intersecting national boundaries—including those partially over the sea—are fully attributed to the country to preserve accuracy in population-weighted energy demand estimates, particularly in coastal zones.
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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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