.. _energy_indicators_technical_documentation: Technical Documentation ======================= This page describes the technical workflow used to produce the Energy Indicators, including data extraction, processing, application execution, and HPC integration. Integration in the workflow --------------------------- 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, 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 **Figure 1:** 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 fields, such as 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 and unified metadata. Before reaching the application, the climate data are retrieved from the GSV through the `GSV interface `_, which supports spatial reduction, regridding onto a regular latitude/longitude grid, and conversion to NetCDF format. The extracted and processed data serve as input to the application, which generates outputs tailored for renewable energy assessments. For a comprehensive description of all available indicators and their definitions, refer to the :ref:`energy_indicators_data_description` section. One-pass layer -------------- The workflow then processes the data retrieved from the GSV through the One_Pass layer (Grayson et al., 2025) :cite:`Grayson2025`, a Python package designed for memory-efficient computation of statistical summaries directly from streamed climate simulation data. It performs temporal reduction, derives statistical summaries, and extracts several climate variables: * Wind components (100u, 100v) at 100 m height for wind resource assessments. * Surface downwelling shortwave solar radiation (sdswrf) * Wind components (10u, 10v) at 10 m height for PV potential calculation. * 2 m air temperature (2t) for demand-related metrics and 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: Lacima-Nadolnik et al. (preprint). **Figure 2:** 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 in :ref:`energy_indicators_data_description`. HPC integration --------------- Within the Climate DT, this approach allows the indicators to be computed at model runtime, 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 `_.