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International Conference on Meteorological Data Assimilation and Modeling - (ICMDAM-26)

15th - 16th December 2026 , Copenhagen - Denmark

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Call For Papers

The (ICMDAM-26) emphasizes interdisciplinary collaboration by bringing together experts from diverse fields. It encourages research that integrates multiple perspectives to address complex global challenges.

Key areas such as Meteorology are explored to promote cross-domain knowledge exchange and collaborative innovation.

Authors are invited to submit papers addressing, but not limited to, the following areas:

  • Advancements in data assimilation techniques
  • Impact of data assimilation on forecasting accuracy
  • Integration of satellite data in modeling
  • Challenges in real-time data assimilation
  • Use of machine learning in data assimilation
  • Case studies of successful modeling applications
  • Public access to meteorological data
  • Collaboration between meteorological agencies
  • Future trends in meteorological modeling
  • Role of ground observations in data assimilation
  • Validation of model outputs against observations
  • Innovative approaches to data assimilation
  • Impact of climate change on data needs
  • Training for meteorologists in data assimilation
  • Data assimilation for extreme weather events
  • Ethical considerations in data usage
  • Community involvement in data collection
  • Use of big data in meteorological research
  • Impact of technology on data assimilation
  • Future challenges in meteorological data integration

Assessment

All submissions will undergo peer review to ensure quality and interdisciplinary relevance. Accepted papers will be presented and considered for publication in journals and conference proceedings.

Registration

Registering for the conference provides access to keynote sessions, technical presentations, and networking opportunities with global experts.

Publication

Publishing through the conference enhances the visibility of your research and connects your work with a broader academic audience.