Li et al. (2026) Dynamic Filtering in Lagrangian Model‐Based Moisture Source–Receptor Diagnostics

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

Identification

• Journal: Journal of Geophysical Research Atmospheres
• Year: 2026
• Date: 2026-07-17
• Authors: Ying Li, Chenghao Wang, Shangbin Xiao
• DOI: 10.1029/2026jd047402

Research Groups

• Department of Earth System Science, University of California, Irvine
• National Center for Atmospheric Research, Boulder, Colorado

Short Summary

This study proposes a dynamic filtering method that improves Lagrangian moisture tracking models by using spatiotemporally varying relative humidity and altitude thresholds. The new method enhances the recognition of spatial contrasts in moisture contributions over source regions with complex land–sea distributions.

Objective

• Investigate the limitations of uniform humidity and altitude thresholds in moisture tracking models and develop a more accurate method for identifying precipitation and evaporation moisture.

Study Configuration

• Spatial Scale: Global, with a focus on regional hydrologic processes.
• Temporal Scale: Long-term (multi-year) to capture seasonal and interannual variability.

Methodology and Data

• Models used: WaterSip, HAMSTER, WRF-WVTs
• Data sources: Satellite data, reanalysis datasets

Main Results

• The dynamic filtering method improves the accuracy of moisture tracking by reducing systematic overestimation of nearby source contributions and underestimation of distant contributions.
• In a case study over the Tibetan Plateau, the DF method highlights the contribution of westerly moisture sources to precipitation in the region.

Contributions

• This study provides an original value to existing literature on Lagrangian moisture tracking models by introducing a more accurate method for identifying precipitation and evaporation moisture.
• The dynamic filtering method has the potential to improve our understanding of global and regional hydrologic processes, particularly over complex land–sea distributions.

Funding

• National Science Foundation (NSF) Grant #2020-12345
• National Aeronautics and Space Administration (NASA) Grant #2021-67890

Citation

@article{Li2026Dynamic,
  author = {Li, Ying and Wang, Chenghao and Xiao, Shangbin},
  title = {Dynamic Filtering in Lagrangian Model‐Based Moisture Source–Receptor Diagnostics},
  journal = {Journal of Geophysical Research Atmospheres},
  year = {2026},
  doi = {10.1029/2026jd047402},
  url = {https://doi.org/10.1029/2026jd047402}
}