Gou et al. (2026) A performance-based multivariable weighting framework for WRF parameterization-scheme selection

Identification

• Journal: Geoscientific model development
• Year: 2026
• Date: 2026-10-15
• Authors: Tian‐Yu Gou, Jun Niu, Shaozhong Kang
• DOI: 10.5194/gmd-19-9879-2026

Research Groups

• State Key Laboratory of Efficient Utilization of Agricultural Water Resources, Beijing 100083, China
• National Field Scientific Observation and Research Station on Efficient Water Use of Oasis Agriculture in Wuwei of Gansu Province, Wuwei 733009, China
• Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China

Short Summary

This study proposes a performance-based multivariable weighting framework for WRF parameterization-scheme evaluation. The framework combines mean relative error and the coefficient of variation of absolute errors to give greater influence to variables that are both difficult to simulate and informative for distinguishing candidate schemes.

Objective

• Evaluate the performance of different physical parameterization schemes in WRF simulations using a multivariable weighting framework.
• Identify regionally optimal configurations of WRF parameterization schemes for two contrasting regions, the arid Northwest China and the coastal Southeast China.

Study Configuration

• Spatial Scale: Regional climate modeling at 9 km horizontal resolution covering the arid region of Northwest China (73–106° E, 35–50° N) and the coastal region of Southeast China.
• Temporal Scale: Daily simulations from May to August for the years 2007 in Northwest China and 2009 in Southeast China.

Methodology and Data

• Models used: Advanced Research WRF model version 4.3.3 with a near-complete ensemble of parameterization schemes available within the WRF model.
• Data sources: ERA5 reanalysis dataset for initial and boundary conditions, GLC_FCS30D land use dataset for surface input data, and daily meteorological observation data from 49 stations in Northwest China and 38 stations in Southeast China.

Main Results

• The proposed MRE × CV weighting framework assigns greater weights to variables that are difficult to simulate, such as precipitation and 10 m wind.
• The method helps identify physically interpretable and well-performing parameterization-scheme combinations under the prescribed evaluation framework.
• Compared with equal weighting and established statistical weighting methods, the proposed MRE × CV weighting helped identify parameterization-scheme combinations that achieved the lowest integrated cMIEI in both representative screening years and in the independent 2010 extreme-year validation.

Contributions

• The study provides a critical scientific basis for future research on water resource management in arid regions and the development of rainstorm and flood early warning systems in coastal urban clusters.
• The proposed multivariable weighting framework can be applied to other climate models and evaluation studies to improve model performance and reduce uncertainty.

Funding

• This study was funded by the National Natural Science Foundation of China (Grant No. 52109223) and the Key Research and Development Program of Gansu Province (Grant No. 20ZD2RA001).

Citation

@article{Gou2026performancebased,
  author = {Gou, Tian‐Yu and Deng, Yaoyang and Niu, Jun and Kang, Shaozhong},
  title = {A performance-based multivariable weighting framework for WRF parameterization-scheme selection},
  journal = {Geoscientific model development},
  year = {2026},
  doi = {10.5194/gmd-19-9879-2026},
  url = {https://doi.org/10.5194/gmd-19-9879-2026}
}