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3 results for “ACDC-ESM”
Weather Regime definition for the Euro-Atlantic sector (Daily, DFJM, 1979-2018) used for ACDC-ESM
<p><strong>Weather Regime definition for the Euro-Atlantic sector at daily resolution from 1979-2018 for December to March in CSV format</strong></p> <p> </p> <p><strong>TL;DR</strong>: this is the weather regime assignment based the method as set out by Swinda K.J. Falkena in 'Revisiting the identification of wintertime atmosphericcirculation regimes in the Euro-Atlantic sector' (<a href="https://doi.org/10.1002/qj.3818">10.1002/qj.3818</a>). Daily data is provided for December to March for the period 1979-2018.</p> <p> </p> <p><strong>Method Description </strong><br> The weather regime assignment can be obtained by applying <em>k</em>-means clustering to the full field data of geopotential height data. Following the observed circulation in reanalysis data, the optimal number of clusters is six. By incorporating a weak persistence constraint in the clustering procedure the assignment is stabilized, without changing the weather regime occurrence rates.</p> <p>The six regimes used have been labelled to indicate atmospheric state. Due to their symmetry, a name (Atlantic Ridge (AR), North Atlantic Oscillation (NAO) and Scandinavian Blocking (SB)) and state (positive (+) and negative (-)) are used to label each of the six weather regimes. It should be noted that the naming convention used, does not imply that the weather associated with these six weather regimes is similar to a definition that uses four or two clusters to classify the weather.</p> <p>Full details on the method can be found in: Swinda K.J. Falkena, et al, 'Revisiting the identification of wintertime atmosphericcirculation regimes in the Euro-Atlantic sector' (DOI:<a href="https://doi.org/10.1002/qj.3818">10.1002/qj.3818</a>). The original implementation and source code can be found on gitHub via: <a href="https://github.com/SwindaKJ/Regimes_Public">github.com/SwindaKJ/Regimes_Public</a>.</p> <p> </p> <p><strong>Data structure description</strong><br> The file is provided in CSV (.csv) format with a semicolon (;) as separator. The first row stores the column labels. The columns contain the following:</p> <ul> <li>first column (or A) contains the valid-time <ul> <li>Label: datetime</li> <li>Contents represent time with text as [DD/MM/YYYY])</li> </ul> </li> <li>second column (or B) contains the assigned weather regime <ul> <li>Label: WR</li> <li>Contents represent the assigned cluster as an interger in the range [0,5]</li> <li>Meaning: 0="SB-", 1="AR+", 2="NAO-", 3="SB+", 4="NAO+", 5="AR-"</li> </ul> </li> </ul> <p> </p> <p><strong>DISCLAIMER</strong>: <em>the content of this dataset has been created with the greatest possible care. However, we invite to use the original assignement of weather regimes for critical applications and studies. </em></p>
Energy Climate dataset consitent with ENTSO-E TYNDP2020 studies (CSV & NetCDF) for ACDC-ESM
<p><strong>Energy Climate dataset consistent with ENTSO-E Pan-European Climatic Database (PECD 2021.3) in CSV and netCDF format</strong></p> <p><strong>TL;DR</strong>: this is a tidy and friendly version of a recreation of ENTSO-E's PECD 2021.3 data by using ERA5: hourly capacity factors for wind onshore, offshore, solar PV and hourly electricity demand are provided. All the data is provided for 28-71 climatic years (1950-2020 for wind and solar, 1982-2010 for demand).</p> <p><strong>Description</strong><br> Country averages of energy-climate variables generated using the Python scripts, based on the <a href="https://2020.entsos-tyndp-scenarios.eu/">ENTSO-E's TYNDP 2020 study</a>. For the following scenario's data is available</p> <ul> <li>National trends 2025 (NT 2025)</li> <li>National trends 2030 (NT 2030)</li> <li>National trends 2040 (NT 2040)</li> <li>Distributed Energy 2030 (DE 2030)</li> <li>Distributed Energy 2040 (DE 2040)</li> <li>Global Ambitions (GA 2030)</li> <li>Global Ambitions (GA 2040)</li> </ul> <p>The time-series are at hourly resolution and the included variables are:</p> <ul> <li>Generation wind offshore (aggregated for all years per scenario in a .zip)</li> <li>Generation wind onshore (aggregated for all years per scenario in a .zip)</li> <li>Generation solar photovoltaic (aggregated for all years per scenario in a .zip)</li> <li>Total energy demand (all zones combined in single file per scenario)</li> </ul> <p>The Files are provided in CSV (.csv) & NetCDF (.nc). The data is given per ENTSO-E's bidding zone as used within the TYNDP2020.<br> </p> <p><strong>DISCLAIMER</strong>: <em>the content of this dataset has been created with the greatest possible care. However, we invite to use the original data for critical applications and studies. </em></p>
Hydropower dataset of hourly inflow values for European bidding zones for ACDC-ESM
<p><strong>Energy Climate dataset consistent with ENTSO-E Pan-European Climatic Database (PECD 2021.3) in CSV and netCDF format</strong></p> <p> </p> <p><strong>TL;DR</strong>: this is a nationally aggregated hourly dataset for the capacity factors per unit installed capacity for storage hydropower plants and run-of-river hydropower plants in the European region. All the data is provided for 30 climatic years (1981-2010).</p> <p> </p> <p><strong>Method Description </strong><br> The hydro inflow data is based on historical river runoff reanalysis data simulated by the E-HYPE model. E-HYPE is a pan-European model developed by The Swedish Meteorological and Hydrological Institute (SMHI), which describes hydrological processes including flow paths at the subbasin level. E-hype only provides the time series of daily river runoff entering the inlet of each European subbasin over 1981-2010. To match the operational resolution of the dispatch model, we linearly downscale these time series to hourly. By summing up runoff associated with the inlet subbasins of each country, we also obtain the country-level river runoff.</p> <p>The hydro inflow time series per country is defined as the normalized energy inflows (per unit installed capacity of hydropower) embodied in the country-level river runoff. A dispatch model can be used to decides whether the energy inflows are actually used for electricity generation, stored, or spilled (in case the storage reservoir is already full).</p> <p><strong>Data coverage</strong><br> This dataset considers two types of hydropower plants, namely storage hydropower plant (STO) and run-of-river hydropower plant (ROR). Not all countries have both types of hydropower plants installed (see table). </p> <p>The countries and their acronyms for both technologies included in this dataset are:</p> <table> <thead> <tr> <th scope="col">Country</th> <th scope="col">Run-of-River </th> <th scope="col">Storage</th> </tr> </thead> <tbody> <tr> <td>Austria</td> <td>AT_ROR</td> <td>AT_STO</td> </tr> <tr> <td>Belgium</td> <td>BE_ROR</td> <td>BE_STO</td> </tr> <tr> <td>Bulgaria</td> <td>BG_ROR</td> <td>BG_STO</td> </tr> <tr> <td>Switzerland</td> <td>CH_ROR</td> <td>CH_STO</td> </tr> <tr> <td>Cyprus</td> <td>CZ_ROR</td> <td>CZ_STO</td> </tr> <tr> <td>Germany</td> <td>DE_ROR</td> <td>DE_STO</td> </tr> <tr> <td>Denmark</td> <td>DK_ROR</td> <td> </td> </tr> <tr> <td>Estonia</td> <td>EE_ROR</td> <td> </td> </tr> <tr> <td>Greece</td> <td>EL_ROR</td> <td>EL_STO</td> </tr> <tr> <td>Spain</td> <td>ES_ROR</td> <td>ES_STO</td> </tr> <tr> <td>Finland</td> <td>FI_ROR</td> <td>FI_STO</td> </tr> <tr> <td>France</td> <td>FR_ROR</td> <td>FR_STO</td> </tr> <tr> <td>Great Britain</td> <td>GB_ROR</td> <td>GB_STO</td> </tr> <tr> <td>Croatia</td> <td>HR_ROR</td> <td>HR_STO</td> </tr> <tr> <td>Hungary</td> <td>HU_ROR</td> <td>HU_STO</td> </tr> <tr> <td>Ireland</td> <td>IE_ROR</td> <td>IE_STO</td> </tr> <tr> <td>Italy</td> <td>IT_ROR</td> <td>IT_STO</td> </tr> <tr> <td>Luxembourg</td> <td>LU_ROR</td> <td> </td> </tr> <tr> <td>Latvia</td> <td>LV_ROR</td> <td> </td> </tr> <tr> <td>the Netherlands</td> <td>NL_ROR</td> <td> </td> </tr> <tr> <td>Norway</td> <td>NO_ROR</td> <td>NO_STO</td> </tr> <tr> <td>Poland</td> <td>PL_ROR</td> <td>PL_STO</td> </tr> <tr> <td>Portugal</td> <td>PT_ROR</td> <td>PT_STO</td> </tr> <tr> <td>Romania</td> <td>RO_ROR</td> <td>RO_STO</td> </tr> <tr> <td>Sweden</td> <td>SE_ROR</td> <td>SE_STO</td> </tr> <tr> <td>Slovenia</td> <td>SI_ROR</td> <td>SI_STO</td> </tr> <tr> <td>Slovakia</td> <td>SK_ROR</td> <td>SK_STO</td> </tr> </tbody> </table> <p> </p> <p><strong>Data structure description</strong><br> The files is provided in CSV (.csv) format with a comma (,) as separator and double-quote mark (") as text indicator. The first row stores the column labels. The columns contain the following:</p> <ul> <li>first column (or A) contains the row number <ul> <li>Label: unlabeled</li> <li>Contents: interger range [1,262968]</li> </ul> </li> <li>second column (or B) contains the valid-time <ul> <li>Label: T1h</li> <li>Contents represent time with text as [DD/MM/YYYY HH:MM])</li> </ul> </li> <li>column 3-52 (or C-AY) each contain the capacity factor for each valid combination of a country and hydropower plant type <ul> <li>Label: XX_YYY the two letter country code (XX) and the hydropower plant type (YYY) acronym for storage hydropower plant (STO) and run-of-river hydropower plant (ROR)</li> <li>Contents represent the capacity factor as a floating value in the range [0,1], the decimal separator is a point (.).</li> </ul> </li> </ul> <p><strong>DISCLAIMER</strong>: <em>the content of this dataset has been created with the greatest possible care. However, we invite to use the original data for critical applications and studies. </em></p>
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