Skip to content

This repository contains Jupyter notebooks for conducting scientific assessment and uncertainty quantification based on various coupled simulations within the Climate DT framework for the Energy-indicator application.

Notifications You must be signed in to change notification settings

DestinE-Climate-DT/Notebooks-D15.1.3-Energy-indicator

Repository files navigation

Scientific Assessment and Uncertainty Quantification (UQ) for the Energy Indicator Application (DE340.15.1.3)

This repository provides Jupyter notebooks for assessing the scientific robustness and quantifying the uncertainty of wind energy indicators derived from Climate Digital Twin (Climate DT) simulations. These notebooks form part of Deliverable D15.1.3 within the DestinE project.


Objective

To evaluate how well the climate variables and indicators tailored to the wind energy sector from the Climate DT simulations capture the spatio-temporal characteristics of wind patterns, compared to reference data from ERA5.


Climate DT Simulations Used

  1. Freeze run + Historical Ensembles

    • Simulations: Freeze run (a24r) and 3-member historical ensemble
    • Period: 1990–2014 (25 years)
    • Model: IFS-NEMO
    • Resolution: Global, 10 km horizontal
  2. Freeze Run Control

    • Simulation: Control run (a267)
    • Period: 1990–2019 (30 years)
    • Model: IFS-NEMO
    • Resolution: Global, 10 km horizontal
  3. E-suite control simulation (o005)

    • Period: 1990–1994 (5 years)
    • Model: IFS-NEMO
    • Domain: North Sea (5°W–10°E, 50°N–60°N)
    • Resolution: 5 km horizontal
    • Indicator: Capacity Factor class -S
  4. O-suite control simulation (o007)

    • Period:1990–2005 (15 years)
    • Model: IFS-NEMO
    • Domain: North Sea (5°W–10°E, 50°N–60°N)
    • Resolution: 5 km horizontal
    • Indicator: Capacity Factor class -S

Scientific Assessment and UQ Components

  • Comparison with ERA5
  • Mean Bias (MB)
  • Root Mean Square Error (RMSE)
  • Pairwise differences between each ensemble member and the freeze run

Methodology

  • Climate DT simulations are regridded to the ERA5 grid using a conservative regridding technique to ensure compatibility.
  • Primary variable of interest: wind speed at 100m and capacity factor

Reference:
Ramon et al. (2019), Conservative regridding technique for climate fields
🔗 https://doi.org/10.1002/qj.3616


Repository Contents

  • Energy_indicator_UQ_global_ws_DE34015_1_3_Notebook_1.ipynb
    → Global wind speed UQ and comparison across members

  • Energy_indicator_UQ_north_sea_cf_s_DE340_D15_1_3_Notebook_2.ipynb
    → North Sea capacity factor assessment and diagnostics


Contact


License

To be added — choose based on BSC/DestinE policy (e.g., MIT, Apache 2.0).


About

This repository contains Jupyter notebooks for conducting scientific assessment and uncertainty quantification based on various coupled simulations within the Climate DT framework for the Energy-indicator application.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published