Tang et al. (2025) Long‐Term Large‐Scale Atmospheric Forcing Data From Three‐Dimensional Constrained Variational Analysis for the ARM SGP Site

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

Identification

• Journal: Journal of Geophysical Research Atmospheres
• Year: 2025
• Date: 2025-11-22
• Authors: Shuaiqi Tang, Cheng Tao, Shaocheng Xie, Minghua Zhang
• DOI: 10.1029/2025jd044443

Research Groups

Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) site

Short Summary

This study introduces VARANAL3D, a 15-year three-dimensional large-scale forcing data set, demonstrating that its incorporated spatial variability significantly improves the representation of clouds and precipitation in single column model simulations compared to domain-mean forcing.

Objective

• To develop and evaluate a long-term (2004-2018) three-dimensional large-scale forcing data set (VARANAL3D) that incorporates spatial variability, and to demonstrate its value for investigating the impacts of this variability on atmospheric processes, particularly cloud and precipitation dynamics.

Study Configuration

• Spatial Scale: Large-scale (mesoscale synoptic systems), with focus on subdomain variability within the Southern Great Plains (SGP) site.
• Temporal Scale: 15 years (2004 to 2018).

Methodology and Data

• Models used: Single column model (SCM) simulations.
• Data sources: Derived from the same input data sets as the conventional continuous forcing data set (VARANAL), utilizing the three-dimensional constrained variational analysis (3DCVA) method.

Main Results

• VARANAL3D maintains overall consistency with VARANAL in domain-averaged fields while successfully introducing spatial variability.
• Evaluations across four cloud and precipitation regimes (Clear-sky, Shallow-clouds, Afternoon-precipitation, and Nocturnal-precipitation) show high consistency of domain-mean forcing data sets.
• Subdomain forcing variability is highlighted as particularly important in precipitating regimes.
• Single column model (SCM) simulations demonstrate that subdomain VARANAL3D forcing improves cloud and precipitation representation.
• Ensemble SCM simulations using VARANAL3D outperform simulations using domain-mean forcing in three cloudy and precipitating regimes.

Contributions

• Development of VARANAL3D, a novel long-term (15-year) three-dimensional large-scale forcing data set that explicitly incorporates spatial variability.
• Provides critical insights into the influence of mesoscale synoptic systems and spatial variability of large-scale forcing on cloud-related processes.
• Offers a valuable data set for evaluating model physics, advancing the development of scale-aware parameterizations, and deepening the understanding of cloud and precipitation dynamics.

Funding

Not specified in the abstract.

Citation

@article{Tang2025LongTerm,
  author = {Tang, Shuaiqi and Tao, Cheng and Xie, Shaocheng and Zhang, Minghua},
  title = {Long‐Term Large‐Scale Atmospheric Forcing Data From Three‐Dimensional Constrained Variational Analysis for the ARM SGP Site},
  journal = {Journal of Geophysical Research Atmospheres},
  year = {2025},
  doi = {10.1029/2025jd044443},
  url = {https://doi.org/10.1029/2025jd044443}
}