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| Note |
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In the PECDv4.1, only three Global Climate Models (GCMs) were used. However, it is important to note that using a larger set of models is essential for adequately capturing the uncertainty inherent in climate projections. Future versions, such as PECDv4.2, will incorporate a wider range of models and scenarios to improve the representation of uncertainties. |
A detailed description of the filenames of the provided data is available in the Appendix.
Files will be provided in two format types: NetCDF and CSV. Please refer to Table 2.2, Table 2.13, Table 3.5 and Table 3.7 for more info on the file format of each variable.
A detailed description of the filenames of the provided data is available in the Appendix in Table 4.1, while Table 4.2 and Table 4.3 contain the description of all the ancillary NetCDF data used for PECDv4.1 and available in the Climate Data Store (CDS) under the widget "Weights and masks".
| Note |
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Please note that PECDv4.1 data will not be extended beyond the year 2021, as these datasets have been frozen prior to the start of the ERAA (European Resource Adequacy Assessment) studies 2023, in agreement with ENTSO-E. By the end of 2024, a new version, PECDv4.2, will be delivered, containing historical data from 1950 to the near present. The historical data for PECDv4.2 will be updated annually. These updates will add new data without modifying the existing datasets, thus maintaining the same version number. The plan agreed with ENTSO-E is to have PECDv4.2 available in 2025. Future versions will include more climate models, emission scenarios, extended time series, and changes in methodologies, aggregation zones, and other aspects according to ENTSO-E requirements. |
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The workflow depicting the historical stream is outlined in Figure 1.1. The retrieval of ERA5 data from the Climate Data Store ( CDS ) to C3S is accomplished using the CDS API (Application Programming Interface), which requires prior installation of Python and the CDS API Python package. Data is retrieved by specifying the required period and variables to be downloaded. Currently, retrievals are performed in monthly chunks. Each variable has been downloaded at a 1-hour resolution for the period 1980 to 2021, within the designated study region known as the PECD domain.
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[m] correspond to these two respective heights. The wind shear coefficient is computed at an hourly resolution and stratified by the twelve months of the year (24 hours x 12 months) and it was calculated using Python's Climate Data Operators (CDO) commands. The Alpha coefficients obtained with the procedure described above are then saved into a NetCDF file, which is available for download on the CDS (please refer to Table 4.2 and Table 4.3 for more details).
Alpha characterization
The mean value of the Alpha coefficient calculated over the geographical domain for each hour and month is represented in Figure 2.1. These results are consistent with previous studies, showing higher coefficient values during the cold and stable hours of the night. Conversely, during the day, when the boundary layer is generally well mixed, the Alpha coefficient is lower. For the same reason, the values of the Alpha coefficient are higher in winter compared to summer during the central (and warmer) hours of the day. However, when examining the distribution of the Alpha coefficient across each grid point and month of the year, a more complex picture emerges. Figure 2.2 shows the box plot for each hour over the entire domain, indicating that the interquartile range is broader for the night-time hours compared to the day-time hours. The Alpha coefficient reaches its most negative values, down to -0.4, during the night-time hours.
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). Raster values represent the number of inhabitants per cell, with sea/ocean pixels assigned to no data values according to the ESRI (Environmental Systems Research Institute) ASCII format. The path to NetCDF file of the population density mask is ‘/data/public/PECD/ANCI/POPM’obtained with the procedure described above is available for download on the CDS (please refer to Table 4.2 and Table 4.3 for more details).
Anchor Figure2_11 Figure2_11
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Figure 2.11: Population map from NASA Socioeconomic Data and Applications Center. The map is reported at 0.25° resolution and represents the number of inhabitants within each cell.
Computation of Population-weighted temperature
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Anchor Section2_4_2 Section2_4_2
| Section2_4_2 | |
| Section2_4_2 |
Population-weighted temperature Population-weighted temperature TAW [°C] is computed by combining the population raster density mask at 0.25° resolution (see Table 4.2 and Table 4.3 for more details), which reports the inhabitants per cell, with the gridded temperature TA at the same resolution and over the same domain. TAWz of a zone z is calculated according to the equation:
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| Code | Description of the aggregation level | Source |
|---|---|---|
| ORIG | Not aggregated | Gridded data |
| BIAS | Not aggregated | Gridded data bias adjusted (CDFt method see Section 2.5) |
| NUT0 | Country | NUTS0+ADMIN0 |
| NUT2 | Sub Country/Provinces | NUTS2+ADMIN1 |
| SZON | Onshore Bidding Zones | Shapefile provided by ENTSO-E E* |
| SZOF | Offshore Bidding Zones | Shapefile provided by ENTSO-E* |
| PEON | Pan-European Onshore Zones | Shapefile provided by ENTSO-E* |
| PEOF | Pan-European Offshore Zones | Shapefile provided by ENTSO-E* |
*These shapefiles are not publicly available but the correspondent NetCDF masks are provided in the CDS under the widget "Weights and masks". Please see Table 4.2 and Table 4.3 for more details.
Anchor Figure2_13 Figure2_13
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Starting from the shapefiles listed in Table 2.1, floating point NetCDF masks were built to be used for data aggregation purposes, one for each level of aggregation: NUTS 0 regions mask, NUTS 2 regions mask, PEOF regions mask, PEON regions mask, SZOF regions mask, SZON regions mask.
The procedure requires different steps:
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Figure 2.14 shows an example of a country mask (Italy).
The NetCDF masks for the different levels of aggregation are available for download in the CDS under the widget "Weights and masks". Please refer to Table 4.2 and Table 4.3 for more info on these files, including their filenames' conventions and characteristics.
Anchor Figure2_14 Figure2_14
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Figure 2.14: Example of a float mask, for the Italian NUT0 administrative region, showing the fractions of land around the border and coastlines.
The NetCDF mask file will be structured as follows:
coordinates: latitude (PECD domain), longitude (PECD domain), mask (mask code of each polygon).
Spatial aggregation procedure
Spatial aggregation procedure
The spatial aggregation procedure is executed by a The spatial aggregation procedure is executed by a Python tool, following this script flow:
- Open the NetCDF file containing the data to be aggregated.
- Open the precalculated mask NetCDF file.
- Iterate over mask coordinates.
- For each mask, apply the mask (product of data array) required region mask to the NetCDF to be aggregated (one among SZON, SZOF, PEON, PEOF, NUT0, NUT2 regions masks), weighted by the cosine of latitude (the latitude weights mask). Please refer to Table 4.2 and Table 4.3 for more info on these files, including their filenames' conventions and characteristics.
- Calculate the average over the masked NetCDF.
- Store the result in a column of a data frame.
- Store the time axis of the NetCDF file in the same data frame of the aggregated result.
- Save the dataframe as csv file.
- Apply metadata to the CSV file according to the annex.
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Combinations of restricted areas were also considered for wind generation and PV modeling. The exclusion area masks/files have been created based on these criteria to accurately represent regions where energy production is not feasible or allowed. For the distance to shore areas, the exclusion layer was generated using the QGIS Buffer tool to create a distance buffer of 100 km in both directions from the continental edge, except for the North Sea, where a buffer of 200 nautical miles has been retained as per ENTSO-E’s specifications. The NetCDF files of the masks that have been used for PECDv4.1 are available for download in the CDS under the widget "Weights and masks" ("Wind power regions mask" and "Solar PV mask"). Please refer to Table 4.2 and Table 4.3 for more info on these files, including their filenames' convention and characteristics.
Table 2.3 provides a detailed description of each exclusion criterion, their sources, and the variables associated with them.
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- all wake modelling considers only intra-farm wakes (wakes between plants are not considered).
- Literature suggests a range of 5 % to 10 % for the other losses (Mortensen, 2018). The existing installations cover historical installations over tens of years with older technology, whereas the future installations are new installations (no wear-and-tear considered) with modern technology: it was thus considered fair to place them at the opposite sides of the loss range.
- A suitable mask is used is used to find the potential points for the Future wind technologies runs. This mask ("wind power regions mask") is available for download in the CDS. Please refer to Table 4.2 and Table 4.3 for more info.
- Locations of existing wind power plants are not Locations of existing wind power plants are not considered in the assessment of the 10-50 % best locations for each region. This is done because the decommissioning of old turbines is expected to free up more space for new installations in the future.
- The assumed locations of wind power plant installations within a region significantly impact the expected capacity factor on the aggregate level (Swisher et al., 2022). At this point, only one ‘resource grade’ (i.e., the 10-50 % best locations) is simulated; however, simulations covering also the 10 % best locations and the 50 % worst locations (or in principle any other distribution split between 0 and 100 %) could be provided in a later version of the PECD in consultation with ENTSO-E. However, this would multiply the amount of Future wind technology time series.
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For future wind installations, the starting point is the ERA5 grid points. Masking, based on the Exclusion layers exclusion layers ("wind power regions mask", please refer to Table 4.2 and Table 4.3 for more info) presented in Section 2.9, is then applied to these points to select potential future WPP locations. The potential points are shown in Figure 2.21. After selecting the 10-50% best points (based on 100 m mean wind speeds), the resulting final future installation simulation points can be seen for onshore and offshore wind in Figure 2.22. The selection of 10-50% best points is the average ‘resource grade’ selection following from the work done by Swisher et al. (2022), where also the best 10 % and worst 50 % selection of points were simulated for each region. Similar additional runs can be performed at a later stage in the project in agreement with ENTSO-E, to model the decrease in capacity factor as more and more of the best wind resource locations are used. However, this would multiply the number of wind time series related to future installations.
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Once the PV capacity factor product is generated for the PECD-constrained ERA5 grid, regional estimates for bidding and PECD zones are calculated through a spatial average. However, it is important to note that particular (restricted) areas were masked in both the grid-like and regional-based products to produce more accurate results. Specifically, sea and ocean areas (thus, off-shore PV), polar and protected areas, as well as locations with high elevation (above 2000 m a.s.l.) or slope (higher than 10%) were excluded from the computation. While high elevation may be unsuitable as an exclusion criterion at a global scale (notably for Chile), we found that for the PECD area this does not pose issues in terms of final PV estimates. The information to identify such regions was obtained from a range of sources (see Section 2.9). The mask used for PECDv4.1 ("Solar PV mask") is available for download in the CDS under the widget "Weights and masks" (please refer to Table 4.2 and Table 4.3 for more info).
Improvements over the previous methodology
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Low scores are mainly due to few years of available data for the training (e.g. 3 years), or to irregularities in the time series of generation which reduce its seasonality. This can be caused by artificial regulations or faulty data records. In some cases, low scores were obtained due to a loss of seasonality in the time series brought by significant changes in the Installed Capacity for that country / bidding zone. The new installations can cause abrupt changes or gradual shifts in the mean observed signal. Since the model is based solely on climate data, it cannot predict this behaviour. A possible solution that’s been attempted is to model directly Capacity Factors (CF), hence normalizing the provided generation data by the annual series of country-aggregated Installed Capacities (IC). This improves the results for some countries, but generally worsens them for countries where the IC doesn’t change significantly with time. This means that generation may not reflect the actual IC at one time. Changes to the IC can occur at the beginning of the reporting year or at any time during the year, therefore likely introducing step changes in the IC. However, a data collection was launched by ENTSO-E to retrieve monthly Installed Capacity time series from the TSOs and some were able to provide them. Therefore, where new installations visibly affected the TSO generation time series, these were normalized with the corresponding monthly IC data provided, the model was trained on the normalized time series, and the output was then multiplied back by the same IC series to re-obtain a generation/inflow time series. This procedure was applied to timeseries of Albania, Switzerland, Hungary, Poland, and Portugal and must be taken into account when comparing projection energy values to historical ones, since in these cases the anomalies are not only due to changes in climate variables, but also to the known changes in IC. It is also important to note that the assumption made for this procedure is that TSO generation and TSO monthly installed capacity series provided for these countries were compatible. Therefore, any inconsistency that may be found between model outputs and expected historical values may come from discrepancies between generation and installed capacity initial input data.
Other time series displayed irregularities arguably attributable to changes in IC but were not provided with monthly IC series. In such cases, the RF model was trained on a recent restricted time window (at least 4 years) of close-to-constant IC. The latter is hence assumed unvaried in time.
Anchor Figure2_32 Figure2_32
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Model | Originator | Model code | node URL |
CMCC-CM2-SR5 | CMCC (Centro Euro-Mediterraneo sui Cambiamenti Climatici) | CMR5 | |
EC-Earth3 | ECEC (European community Earth System Model) | ECE3 | |
MPI-ESM1-2-HR | MPI- (Max Planck Institute) | MEHR |
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Spatial interpolation
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Starting from a common 100 km nominal spatial resolution and global domain, each model has its own grid, necessitating spatial interpolation to the PECD domain at 0.25° x 0.25°. This interpolation uses the bilinear method as implemented in the CDO
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Anchor Table3_7 Table3_7
Table 3.7: Energy indicators provided in the PECDv4.1 for the projection stream. Files provided at ORIG spatial aggregation are gridded (NetCDF format), while all the other levels of aggregation are provided in CSV format.
| Variable | Type | Period | Source | Domain/ spatial resolution | Temporal resolution | Spatial aggregation | Technology | Units |
|---|---|---|---|---|---|---|---|---|
| Wind power onshore (WON) | Capacity factor | 2015 - 2065 | CMIP6 projection | PECD/0.25° x 0.25° | hourly | PEON | Onshore Existing technologies, Onshore SP199_HH100, Onshore SP199_HH150, Onshore SP199_HH200, Onshore SP277_HH100, Onshore SP277_HH150, Onshore SP277_HH200, Onshore SP335_HH100, Onshore SP335_HH150, Onshore SP335_HH200 | MW/MW |
| Wind power offshore (WOF) | Capacity factor | 2015 - 2065 | CMIP6 projection | PECD/0.25° x 0.25° | hourly | PEOF | Offshore Existing technologies, Offshore SP316_HH155, Offshore SP370_HH155 | MW/MW |
| Solar generation (SPV) | Capacity factor | 2015 - 2065 | CMIP6 projection | PECD/0.25° x 0.25° | hourly | ORIG, NUT0, NUT2, SZON, PEON | --- | MW/MWp |
| Concentrated solar generation (CSP) | Capacity factor | 2015 - 2065 | CMIP6 projection | PECD/0.25° x 0.25° | hourly | PEON | storage_0_hours_preDispatch, storage_0_hours_storageDispatched, storage_7p5_hours_preDispatch, storage_7p5_hours_storageDispatched | MW/MW |
| Hydropower reservoirs generation energy (HRG) | Energy | 2015 - 2065 | CMIP6 projection ENTSO-E TP* TSO** | PECD/0.25° x 0.25° | weekly | SZON | --- | MWh |
| Hydropower reservoirs inflow energy (HRI) | Energy | 2015 - 2065 | CMIP6 projection ENTSO-E TP* TSO** PECDv3.1*** | PECD/0.25° x 0.25° | weekly | SZON | --- | MWh |
| Hydropower run-of-river generation energy (HRO) | Energy | 2015 - 2065 | CMIP6 projection ENTSO-E TP* TSO** PECDv3.1*** | PECD/0.25° x 0.25° | weekly | SZON | --- | MWh |
| Hydropower run-of-river inflow energy (HRR) | Energy | 2015 - 2065 | ERA5 reanalysis ENTSO-E TP* TSO** PECDv3.1*** | PECD/0.25° x 0.25° | weekly | SZON | --- | MWh |
| Hydropower run-of-river with pondage generation energy (HPO) | Energy | 2015 - 2065 | CMIP6 projection ENTSO-E TP* TSO** PECDv3.1*** | PECD/0.25° x 0.25° | weekly | SZON | --- | MWh |
| Hydropower run-of-river with pondage inflow energy (HPI) | Energy | 2015 - 2065 | CMIP6 projection ENTSO-E TP* TSO** PECDv3.1*** | PECD/0.25° x 0.25° | weekly | SZON | --- | MWh |
| Hydropower open-loop pumped storage inflow energy (HOL) | Energy | 2015 - 2065 | CMIP6 projection ENTSO-E TP* TSO** PECDv3.1*** | PECD/0.25° x 0.25° | weekly | SZON | --- | MWh |
*Energy data from ENTSO-E Transparency Platform
**Energy data from Transmission System Operators specific for each country
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Appendix
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Filenames convention and characteristics
This paragraph aims to explain the filename convention of the PECD datasets. Table 4.1 details the structure and possible fields of the filenames. Specifically, the last column indicates the corresponding section of the CDS catalogue where users can personalize their choice. If "Not applicable" is indicated, it means that the user cannot modify this field, and the data are downloaded with fixed characteristics that are not customizable. Table 4.2 details the structure and filenames of the ancillary NetCDF files that have been used for PECDv4.1 and that are available in the CDS under the widget "Weights and masks".
Anchor Table4_1 Table4_1
Table 4.1: Filename convention used in the PECDv4.1.
Position in the filename | Possible substrings for each position in the filename | Description | Option in the CDS download form |
0 | H (historicalHistorical), P (Future projection) | Data streams | Stream |
1 | ERA5 (ERA5 reanalysis), CMI6 (CMIP6 Projection) | Model | Origin (Reanalysis or Climate models) |
2 | ECMW (ECMWF), CMCC (Centro Euro-Mediterraneo sui Cambiamenti Climatici), ECEC (European community Earth System Model), MPI- (Max Planck Institute) | Model | Origin (Reanalysis or Climate models) |
3 | T639 (ERA5 data), CMR5 (CMCC-CM2-SR5 r1i1p1f1), ECE3 (EC-Earth3 r1i1p1f1), MEHR (MPI-ESM1-2-HR r1i1p1f1) | Model | Origin (Reanalysis or Climate models) |
4 | TA- (2m temperature), TAW (Population-weighted temperature), TP- (Total precipitation), GHI (Surface solar radiation downwards), WS- (10m wind speed and 100m wind speed) | Variable | Variable (Climate) |
SPV (Solar generation capacity factor), CSP (Concentrated solar generation capacity factor), WON (Wind power onshores capacity factor), WOF (Wind power offshores capacity factor), HOL (Hydropower open-loop pumped storage inflow energy), HPI (Hydropower run-of-river with pondage inflow energy), HPO (Hydropower run-of-river with pondage generation energy), HRG (Hydropower reservoirs generation energy), HRI (Hydropower reservoirs inflow energy), HRO (Hydropower run-of-river generation energy), HRR (Hydropower run-of-river inflow energy) | Variable | Variable (Energy) | |
5 | 0000m, 0002m, 0010m, 0100m | Level (meters above sea level) | Not applicable |
6 | Pecd (ENTSO-E PECD domain) | Region | Not applicable |
7 | 025d (0.25°), NUT0 (NUTS 0), NUT2 (NUTS 2), PEOF (Pan-European Offshore Zones), PEON (Pan-European Onshore Zones), SZOF (Offshore Bidding Zones), SZON (Onshore Bidding Zones) | Spatial resolution | Gridded Regional aggregated timeseries |
8 | SYYYYMMDDhhmm (starting year, month, day, hour, minute) | Start date | Year Month |
9 | EYYYYMMDDhhmm (ending year, month, day, hour, minute) | End date | Year Month |
10 | ACC (accumulated), INS (Instantaneous), CFR (Capacity factor), NRG (Energy) | Type | Not applicable |
11 | MAP (gridded data), TIM (time series) | View | Not applicable |
12 | 01h (1 hour), 01d (1 day), 07d (7 days) | Temporal resolution | Not applicable |
13 | NA- | Lead time | Not applicable |
14 | noc (no correction), cdf (Cumulative distribution fn), mbc (mean bias correction) | Bias adjustment method | Not applicable |
15 | NA-, org (original data), avg (mean) | Statistics | Not applicable |
16 | NA, 20 (Offshore wind turbine: Existing technologies), 21 (Offshore wind turbine: SP316 HH155), 22 (Offshore wind turbine: SP370 HH155), 30 (Onshore wind turbine: Existing technologies), 31 (Onshore wind turbine: SP199 HH100), 32 (Onshore wind turbine: SP199 HH150), 33 (Onshore wind turbine: SP199 HH200), 34 (Onshore wind turbine: SP277 HH100), 35 (Onshore wind turbine: SP277 HH150), 36 (Onshore wind turbine: SP277 HH200), 37 (Onshore wind turbine: SP335 HH100), 38 (Onshore wind turbine: SP335 HH150), 39 (Onshore wind turbine: SP335 HH200), 40 (Concentrated solar power: Pre-dispatch, no storage), 41 (Concentrated solar power: Dispatched, no storage), 42 (Concentrated solar power: Pre-dispatch, 7-hours of storage), 43 (Concentrated solar power: Dispatched, 7-hours of storage) | Technological specification | Technological specification (Offshore wind turbine, Onshore wind turbine, Concentrated solar power) |
17 | NA---, SP245 (ssp 245) | Emission scenario | Emissions |
18 | NA--- | Energy scenario | Not applicable |
19 | NA---, StRnF (Statistical model/Random Forests), PhM01 (Physical Model/method1), PhM02 (Physical Model/method2), PhM03 (Physical Model/method3) | Transfer function | Not applicable |
20 | PECD4.1 | Version of PECD database | Not applicablePECD version |
21 | fv1 | File version | Not applicable |
22 | .nc (NetCDF) .csv (comma-separated values) | File formats | Not applicable |
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This NetCDF file (.nc) contains historical data (H) from ERA5 reanalysis (ERA5 and 7639) originated by ECMWF (ECMW); the variable is total precipitation (TP-) at 0m height (0000m), the coverage is PECD domain (Pecd) with a 0.25° spatial resolution (025d). Data span from 01/01/1985 at 00:00 UTC (S198501010000) to 31/01/1985 at 00:00 UTC (E198501310000). The data are accumulated (ACC), gridded (MAP), with a daily temporal resolution (01d). The lead time is not available (NA-), data are not bias-corrected (noc) and they are original (org). The ensemble number, emission scenario, energy scenario and transfer function are not available (NA_NA---_NA---_NA---). The PECD version is 4.1 (PECD4.1) while the file version is fv1.
Metadata
Anchor Table4_2 Table4_2
Table 4.2: Filename convention for ancillary data used in the PECDv4.1 and that are available in the CDS under the widget "Weights and masks".
Position in the filename | Possible substrings for each position in the filename | Description | Option in the CDS download form |
|---|---|---|---|
| 0 | ANCI (Ancillary) | Category | Not applicable |
| 1 | LAT-mask (latitude-weight mask), SZON-mask (SZON aggregation mask), PEON-mask (PEON aggregation mask), PEOF-mask (PEOF aggregation mask), NUT0-mask (NUT0 aggregation mask), NUT2-mask (NUT2 aggregation mask), WPM-mask (wind power model mask), PVM-mask (photovoltaic model mask), ALP-coef (alpha coefficients mask), POP-mask (population mask for population-weighted temperature) | Variable | Variable (Weights and masks) |
| 2 | PECD4.1 | Version of PECD database | PECD version |
| 3 | fv1 | File version | Not applicable |
| 4 | .nc (NetCDF) | File formats | Not applicable |
Example of filename for the ancillary data: ANCI_LAT-mask_PECD4.1_fv1.nc
This NetCDF file (.nc) contains ancillary data (ANCI) used to adjust the gridded data with the proper latitudinal weights (LAT-mask) during the spatial aggregation procedure. The PECD version is 4.1 (PECD4.1) and the file version is fv1.
Anchor Table4_3 Table4_3
Table 4.3: Description of the ancillary NetCDF data and their characteristics. These files are available for download in the CDS under the widget "Weights and masks".
| Filename | Variable | Grid | Description | Corresponding name in the widget "Weights and masks" |
|---|---|---|---|---|
| ANCI_LAT-mask_PECD4.1_fv1.nc | lat_weights(latitude, longitude) | PECD domain (latitude, longitude) | Each grid cell contains the cosine of the latitude for the correspondent grid cell. See Section 2.5 for more details. | Latitude weights |
| ANCI_SZON-mask_PECD4.1_fv1.nc | mask(region, latitude, longitude) | PECD domain (latitude, longitude) level (region) | For each level (region in SZON), every grid cell contains a floating point value between 0 and 1. A value of 0 indicates that the grid cell is outside the region, while a value of 1 means the cell is fully within the region. In other cases, the value represents the fraction of the grid cell’s area that lies within the region. See Section 2.5 for more details. | SZON regions mask |
| ANCI_SZOF-mask_PECD4.1_fv1.nc | mask(region, latitude, longitude) | PECD domain (latitude, longitude) level (region) | For each level (region in SZOF), every grid cell contains a floating point value between 0 and 1. A value of 0 indicates that the grid cell is outside the region, while a value of 1 means the cell is fully within the region. In other cases, the value represents the fraction of the grid cell’s area that lies within the region. See Section 2.5 for more details. | SZOF regions mask |
| ANCI_PEON-mask_PECD4.1_fv1.nc | mask(region, latitude, longitude) | PECD domain (latitude, longitude) level (region) | For each level (region in PEON), every grid cell contains a floating point value between 0 and 1. A value of 0 indicates that the grid cell is outside the region, while a value of 1 means the cell is fully within the region. In other cases, the value represents the fraction of the grid cell’s area that lies within the region. See Section 2.5 for more details. | PEON regions mask |
| ANCI_PEOF-mask_PECD4.1_fv1.nc | mask(region, latitude, longitude) | PECD domain (latitude, longitude) level (region) | For each level (region in PEOF), every grid cell contains a floating point value between 0 and 1. A value of 0 indicates that the grid cell is outside the region, while a value of 1 means the cell is fully within the region. In other cases, the value represents the fraction of the grid cell’s area that lies within the region. See Section 2.5 for more details. | PEOF regions mask |
| ANCI_NUT0-mask_PECD4.1_fv1.nc | mask(region, latitude, longitude) | PECD domain (latitude, longitude) level (region) | For each level (region in NUT0), every grid cell contains a floating point value between 0 and 1. A value of 0 indicates that the grid cell is outside the region, while a value of 1 means the cell is fully within the region. In other cases, the value represents the fraction of the grid cell’s area that lies within the region. See Section 2.5 for more details. | NUTS 0 regions mask |
| ANCI_NUT2-mask_PECD4.1_fv1.nc | mask(region, latitude, longitude) | PECD domain (latitude, longitude) level (region) | For each level (region in NUT2), every grid cell contains a floating point value between 0 and 1. A value of 0 indicates that the grid cell is outside the region, while a value of 1 means the cell is fully within the region. In other cases, the value represents the fraction of the grid cell’s area that lies within the region. See Section 2.5 for more details. | NUTS 2 regions mask |
| ANCI_WPM-mask_PECD4.1_fv1.nc | m_rest(latitude, longitude) | PECD domain (latitude, longitude) | Each grid cell contains a boolean value: 1 indicates that the cell is unsuitable for potential future wind power installations, while 0 indicates that the cell could potentially be used as a site for such installations. See Section 2.9.1 for more details. | Wind power regions mask |
| ANCI_PVM-mask_PECD4.1_fv1.nc | PVmask(latitude, longitude) | PECD domain (latitude, longitude) | Each grid cell contains a boolean value: 1 indicates that the cell is unsuitable for potential future solar photovoltaic power installations, while 0 indicates that the cell could potentially be used as a site for such installations. See Section 2.9.2 for more details. | Solar PV mask |
| ANCI_ALP-coef_PECD4.1_fv1.nc | alpha(time, latitude, longitude) | PECD domain (latitude, longitude) levels (time) | For each level (time), every grid cell contains the power law's alpha coefficient. Each grid cell contains in total 12*24 alpha coefficients, one for each month of the year and each hour of the day. See Section 2.2 for more details. | Power law coefficients |
| ANCI_POP-mask_PECD4.1_fv1.nc | population_mask(latitude, longitude) | PECD domain (latitude, longitude) | Each grid cell contains the number of people living in that area. See Section 2.4.2 for more details. | Population density mask |
Metadata
The header of the time series The header of the time series CSV files will contain the following metadata descriptors. An example of an air temperature variable is presented below, provided as a CSV file with the filename:
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### The original data sources are ECMWF ERA5 Reanalysis (available at: https://cds.climate.copernicus.eu) #
How to cite the data*
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*If the dataset is to be published in the CDS or ADS, then this may not be needed. Please check with the CUS team. |
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