Bian et al. (2026) Improved Parameterization of Snow Albedo in WRF/Noah-MP: Methodology Based on Snow Ablation in Upper Yellow River

Identification

• Journal: Journal of Climate
• Year: 2026
• Date: 2026-10-14
• Authors: Zongzhen Bian, Shihao Chen, Lu Xiang, Baohui Men, Hongrui Wang
• DOI: 10.1175/jcli-d-25-0694.1

Research Groups

• School of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing, China
• CHN Energy Dadu River Production Command Center, Chengdu, Sichuan, China
• College of Water Sciences, Beijing Normal University, Beijing, China

Short Summary

This study developed an improved snow albedo scheme for the Noah-MP land surface model, dynamically integrating snow age, depth, and temperature, and demonstrated its superior accuracy in simulating a snow event in the Upper Yellow River basin compared to default schemes.

Objective

• To develop and validate an improved snow albedo (ISA) scheme for the Noah-MP land surface model that dynamically integrates snow age, depth, and temperature to refine the albedo decay process, thereby enhancing the accuracy of snow ablation simulations.

Study Configuration

• Spatial Scale: Upper Yellow River basin, characterized by complex terrain.
• Temporal Scale: January to March 2008, simulating a severe snow accumulation and ablation event.

Methodology and Data

• Models used: Weather Research and Forecasting (WRF) model, Noah Multi-Parameterization (Noah-MP) land surface model, with comparisons against default Noah-MP (CLASS/BATS) schemes.
• Data sources: Implied observational data for validation (e.g., snow cover fraction, surface albedo, air temperature), but specific sources are not detailed in the provided text.

Main Results

• The improved snow albedo (ISA) scheme significantly enhanced simulation accuracy compared to default Noah-MP (CLASS/BATS) schemes.
• The mean bias for snow cover fraction (SCF) was reduced to 4.19% (from greater than 20%).
• The surface albedo Root Mean Square Error (RMSE) decreased by at least 34% (to 0.069).
• The air temperature cold bias was substantially mitigated, with its RMSE cut by approximately 33.6% (1.1 K).
• A more physical representation of snow albedo is crucial for improving the surface energy balance and reducing the pervasive cold bias in regional climate simulations over complex terrain.

Contributions

• Development of a novel improved snow albedo (ISA) scheme for Noah-MP that dynamically incorporates snow age, depth, and temperature.
• Significant enhancement of snow process modeling accuracy in high-altitude regions, specifically demonstrated in the Upper Yellow River basin.
• Provides a validated and effective approach for improving surface energy balance and mitigating cold bias in regional climate models over complex terrain.

Funding

• Not specified in the provided text.

Citation

@article{Bian2026Improved,
  author = {Bian, Zongzhen and Chen, Shihao and Xiang, Lu and Men, Baohui and Wang, Hongrui},
  title = {Improved Parameterization of Snow Albedo in WRF/Noah-MP: Methodology Based on Snow Ablation in Upper Yellow River},
  journal = {Journal of Climate},
  year = {2026},
  doi = {10.1175/jcli-d-25-0694.1},
  url = {https://doi.org/10.1175/jcli-d-25-0694.1}
}