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February 27, 2026

Towards the assimilation of satellite radiances in the convection-permitting ICON model

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GMGrenzi M.

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Overview

Demonstrates improved precipitation forecasting in convective environments using satellite data assimilation in the ICON model.

Key Points

  • The aim is to improve weather predictions for extreme convective storms using advanced data assimilation techniques.
  • Utilized the ICON model at convection-permitting scale over Italy.
  • Employed an ensemble data assimilation scheme with conventional and radar data.
  • Tested model performance on an extreme convective storm event on September 15, 2022.
  • Demonstrated significant improvements in forecasting accuracy through data assimilation.
  • Observed substantial underestimation of precipitation despite the improvements.
  • Identified that low-level moisture convergence and topography are key factors in storm initiation.

Cite This Study

Grenzi M. (2025) studied this question.

synapsesocial.com/papers/69a1351ded1d949a99abeba3https://doi.org/10.1393/ncc/i2025-25178-x
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Enhancing severe convection forecasts with microwave radiance assimilation in limited‐area models2026
  2. 2Studies of Convection-Permitting Ensemble Forecasting for ICON-D2 with a 1km Nest over the Alps2024
  3. 3Improving predictability of Mesoscale Extreme Precipitation events with Convection-permitting models2024
  4. 4Characteristics of precipitating convection and moisture-convection relationships in global km-scale simulations2024
  5. 5Data Assimilation of Satellite-Derived Rain Rates Estimated by Neural Network in Convective Environments: A Study over Italy2024 · 4 citations