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December 8, 2025Quarterly Journal of the Royal Meteorological SocietyOpen Access

Exploring strongly coupled land/atmosphere data assimilation for numerical weather prediction

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Authors

CDClara DraperJWJeffrey WhitakerMBMichael Barlage

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Overview

Observational analysis reduced root-mean-square difference using data assimilation for soil analysis, indicating improvements in numerical weather prediction.

Key Points

  • Adding soil states improved model fit, reducing root-mean-square difference for soil temperature and humidity.
  • Ensemble Kalman filter was utilized to test the data assimilation approach with significant results.
  • Strongly coupled land/atmosphere data assimilation reduced model errors by up to 7% for soil temperature and humidity.
  • Findings suggest both strongly and weakly coupled approaches can enhance numerical weather predictions.

Cite This Study

Draper et al. (2025) studied this question.

synapsesocial.com/papers/693624984fa91c937236c072https://doi.org/10.1002/qj.70033
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