.. _technical_description: Technical Description ======================= Integration in the workflow (Energy Indicators) ----------------------------------------------- The Energy Indicators application is integrated into the Climate DT workflow through a data processing pipeline, which is visually summarised in the figure below. This pipeline extracts climate data and computes energy-specific metrics in streaming mode (i.e., while the climate simulation is running). .. image:: ../../figures/energy_indicators_workflow.svg :alt: Conceptual scheme of the Energy Indicators application integration into the Climate DT workflow. :width: 100% :align: center Conceptual scheme of the Energy Indicators application integration into the Climate DT workflow. Data Extraction and Processing ------------------------------ In the Climate DT workflow, the climate models output km-scale high-frequency (i.e., hourly) fields, which are temporarily stored in GRIB format in a `Fields DataBase `_ (FDB). In this process, the native model data is homogenised into a generic state vector (GSV), with a common `HEALPix `_ grid (Górski et al. 2005) and unified metadata. Before reaching the application, the climate data are retrieved from the GSV through the `GSV interface `_, which supports spatial reduction (i.e., selecting a specific region), regridding onto a regular latitude/longitude grid and conversion to netCDF format. One-pass layer ^^^^^^^^^^^^^^ The workflow then processes the data retrieved from the GSV through the one-pass layer (Grayson et al. 2025), which performs a temporal reduction, deriving statistical summaries (e.g., percentile) with a specific output frequency (e.g., daily, weekly, monthly), and extracts several climate variables: * Wind components (100u, 100v) at 100m height for wind resource assessments. * Wind components (10u, 10v) at 10m height for PV potential calculation. * 2-meter air temperature (2t) for demand-related metrics (such as heating and cooling degree days) and for PV potential calculation. * Wind speed statistics. .. image:: ../../figures/energy_indicators_streaming.svg :width: 100% :alt: Conceptual scheme of the post-processing applied by the one-pass layer within the Climate DT workflow. Source: Doblas et al. 2026. Conceptual scheme of the post-processing applied by the one-pass layer within the Climate DT workflow. Source: Lacima-Nadolnik et al. (preprint). Energy Indicators computation ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The processed climate data is then used by the Energy Indicators application to compute the indicators described above. Workflow integration ^^^^^^^^^^^^^^^^^^^^ Within the Climate DT, this approach allows for the indicators to be computed at model runtime (i.e., while the simulation advances). As a result, the produced climate metrics are directly tailored for the renewable energy sector. The applications run inside dedicated containers on high-performance computing (HPC) platforms from the `EuroHPC consortium `_. Additional resources -------------------- * Energy Indicators application overview (DestinE Climate DT energy indicators repository) * Workflow Overview (DestinE Climate DT Workflow repository) * `User story `_ from ECMWF. .. rubric:: References Doblas-Reyes, F. J., Kontkanen, J., Sandu, I., Acosta, M., Al Turjmam, M. 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Grayson, K., Thober, S., Lacima-Nadolnik, A., Alsina-Ferrer, I., Lledó, L., Sharifi, E., & Doblas-Reyes, F. (2025). Statistical summaries for streamed data from climate simulations: one-pass algorithms. Geoscientific Model Development, 18(17), 5873–5890. https://doi.org/10.5194/gmd-18-5873-2025 Lacima-Nadolnik, Aleksander and Grayson, Katherine and Roura-Adserias, Francesc and Ghosh, Sushovan and Keller, Kai and Batlle, Marc and Gonzalez-Yeregi, Iker and Samsó-Cabré, Margarida and Soret, Albert and Doblas-Reyes, Francisco Javier, Near-term streamed climate information from kilometre-scale global climate models for the wind energy sector. Available at SSRN: https://ssrn.com/abstract=5509245 or http://dx.doi.org/10.2139/ssrn.5509245