Abstract submissions are now open for the 30th AMOS National Conference, which will be taking place at the Hyatt Hotel, Canberra, on 5-9 February 2024.
We are currently inviting abstracts for the "Section 24: Advances in remote sensing and modelling for land surface processes " for oral and poster presentations. We encourage contributions in (a) model developments that improve our understanding of land-atmosphere interactions; (b) innovative techniques like data assimilation and machine learning to incorporate Earth observations into land surface models, and (c) diagnostic and analysis providing insights into model performance and guiding future improvements. A detailed session description is provided below.
The last date for submission is 1st September 2023. Abstracts must be submitted through the Oxford Abstracts submission portal. There will be no deadline extension.
Please circulate this info with your team members and others who might be interested in submitting an abstract to this session.
We look forward to seeing you at AMOS next February.
On behalf of the session convenors,
Siyuan Tian (BoM) (@siyuan ), Yohanna Villalobos Cortes (CSIRO), Abhirup Dikshit (UNSW)
Advances in remote sensing and modelling for land surface processes
The land surface is crucial in shaping Earth’s energy, carbon, and water cycles, while also interacting with the atmosphere and global climate. Understanding the underlying physical and physiological mechanisms, including anthropogenic influences, is essential for assessing the role of land surface in the Earth system and its capacity to mitigate climate change. Enhancing land surface models requires improved representation of near-surface processes and effective utilization of observations at different scales to constrain model estimates. This session aims to highlight recent advancements in modelling and monitoring land surface processes and land-atmosphere interactions across various components (vegetation, soil, land cover, etc.) at different spatio-temporal scales. We encourage contributions in (a) model developments that improve our understanding of land-atmosphere interactions; (b) innovative techniques like data assimilation and machine learning to incorporate Earth observations into land surface models, and (c) diagnostic and analysis providing insights into model performance and guiding future improvements.