Zidikheri et al. (2025) Increasing Atmospheric Surface Spread in an Ensemble Model Using Land Cover Fraction Perturbations

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Atmosphere
• Year: 2025
• Date: 2025-12-01
• Authors: Meelis J. Zidikheri, Peter Steinle, Imtiaz Dharssi
• DOI: 10.3390/atmos16121366

Research Groups

• Australian Bureau of Meteorology (BoM)

Short Summary

This study investigates perturbing land surface fraction values in an operational ensemble numerical weather prediction model to address underspread near the land surface, demonstrating that this method significantly increases ensemble spread for key surface variables and improves forecast skill.

Objective

• To increase ensemble spread near the land surface in operational numerical weather prediction models by perturbing land surface fraction values, thereby representing uncertainties in these estimates.

Study Configuration

• Spatial Scale: Global system, with specific focus on the Australian region and tropics.
• Temporal Scale: 75-day period during the Australian summer of 2017–2018.

Methodology and Data

• Models used: Australian Bureau of Meteorology’s (BoM) global operational ensemble numerical weather prediction system.
• Data sources: Existing land surface fraction estimates; reanalysis data (for verification of root-mean-square error).

Main Results

• Land surface fraction perturbations significantly increased ensemble spread for surface temperature, sensible heat flux, and latent heat flux, particularly in the tropics and over the Australian region.
• Screen-level temperature ensemble spread also increased, though to a lesser extent compared to surface temperature.
• Root-mean-square error values, relative to reanalysis data, were smaller in the perturbed runs.
• The methodology led to significantly improved spread-to-skill ratio values.

Contributions

• Introduces a novel and effective methodology for increasing ensemble spread near the land surface in operational numerical weather prediction models by perturbing land surface fraction values.
• Demonstrates that this approach not only increases spread but also improves forecast skill, addressing a common issue of underspread in ensemble systems.

Funding

• Not specified in the provided text.

Citation

@article{Zidikheri2025Increasing,
  author = {Zidikheri, Meelis J. and Steinle, Peter and Dharssi, Imtiaz},
  title = {Increasing Atmospheric Surface Spread in an Ensemble Model Using Land Cover Fraction Perturbations},
  journal = {Atmosphere},
  year = {2025},
  doi = {10.3390/atmos16121366},
  url = {https://doi.org/10.3390/atmos16121366}
}