Heyvaert et al. (2025) Land data assimilation of satellite‐based surface soil moisture: Impact on atmospheric simulations over the contiguous United States

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

Identification

• Journal: Quarterly Journal of the Royal Meteorological Society
• Year: 2025
• Date: 2025-10-30
• Authors: Zdenko Heyvaert, Michel Bechtold, Wouter Dorigo, Jonas Mortelmans, Daniel Fiifi Tawia Hagan, Joseph A. Santanello, Gabriëlle De Lannoy
• DOI: 10.1002/qj.70052

Research Groups

• NASA Land Information System (LIS)
• NASA Unified WRF (NU-WRF) framework

Short Summary

This study investigates the effectiveness of surface soil moisture (SSM) data assimilation (DA) in enhancing land initialisation within coupled land-atmosphere models. It finds that SSM DA improves atmospheric predictions, particularly 2-meter air temperature, with greater impact in regions exhibiting stronger land-atmosphere coupling.

Objective

• To investigate the effectiveness of surface soil moisture (SSM) data assimilation (DA) in enhancing the land initialisation of coupled land-atmosphere models compared to a model-only initialisation.
• To determine if improved land initialisation through SSM DA leads to more accurate atmospheric predictions within such models.

Study Configuration

• Spatial Scale: Local impacts, with analysis focusing on areas of varying land-atmosphere coupling strength.
• Temporal Scale: Land reanalysis product used for initialisation, followed by atmospheric predictions extending up to 14 days.

Methodology and Data

• Models used:
  • Noah-MP land surface model
  • Weather Research & Forecasting (WRF) atmospheric model
  • NASA Land Information System (LIS) (for DA)
  • NASA Unified WRF (NU-WRF) framework (for coupled simulations)
  • One-dimensional ensemble Kalman filter (for DA)
• Data sources:
  • Soil Moisture Active Passive (SMAP) Level 2 product (for SSM retrievals)
  • Fifth-generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5) (for comparison of 2-meter air temperature predictions)

Main Results

• Surface soil moisture data assimilation (SSM DA) effectively enhances the land initialisation compared to a model-only initialisation.
• Atmospheric predictions, specifically 2-meter air temperature, initialised using SSM DA align more closely with ERA5 than those initialised using a model-only approach.
• The local impact of SSM DA on the atmosphere is primarily driven by differences in the soil moisture initial condition, which affects vegetation functioning.
• SSM DA shows more substantial impacts in areas with stronger land-atmosphere coupling, as quantified by the Liang-Kleeman information flow.

Contributions

• Provides important insights into the conditions under which numerical weather prediction (NWP) can most benefit from assimilating satellite-based surface soil moisture retrievals.
• Demonstrates a tangible improvement in atmospheric predictions (up to 14 days) through enhanced land initialisation via SSM DA in coupled models.
• Highlights the critical role of local land-atmosphere coupling strength in modulating the effectiveness and impact of soil moisture data assimilation on atmospheric forecasts.

Funding

• Not specified in the provided abstract.

Citation

@article{Heyvaert2025Land,
  author = {Heyvaert, Zdenko and Bechtold, Michel and Dorigo, Wouter and Mortelmans, Jonas and Hagan, Daniel Fiifi Tawia and Santanello, Joseph A. and Lannoy, Gabriëlle De},
  title = {Land data assimilation of satellite‐based surface soil moisture: Impact on atmospheric simulations over the contiguous United States},
  journal = {Quarterly Journal of the Royal Meteorological Society},
  year = {2025},
  doi = {10.1002/qj.70052},
  url = {https://doi.org/10.1002/qj.70052}
}