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CU-ESIIL · Feb 9, 2025 · 36569e4 · 36569e4
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@@ -9,7 +9,7 @@ Harmonizing Natural Methane Datasets using Knowledge-Guided Machine Learning
 Atmospheric methane (CH4) is the second most powerful greenhouse gas after carbon dioxide and grew at the fastest rate ever recorded in 2020-2022. Slowing or reversing the accelerating growth in atmospheric CH4 will require an improved understanding of the global CH4 budget, which is currently underconstrained. Natural CH4 budgets are responsible for ~40% of the total global CH4 budgets but remain the most uncertain factor. This AI for natural CH4 working group aims to build a novel framework that integrates scientific knowledge and machine learning to harmonize simulated and observed datasets from global wetlands and soil sinks to quantify the spatial and temporal changes of global natural CH4 fluxes. Specifically, we will harmonize every possible form of the global natural CH4 datasets, including field-based CH4 fluxes from chamber and eddy-covariance measurements and simulated CH4 fluxes from bottom-up process-based models and top-down atmospheric assimilation models. As an output of this working group, we will generate and publicly share harmonized measurement datasets and global natural CH4 flux products from 1980 to present.
 
 
-## Project Proposal: <https://drive.google.com/file/d/1ZbBGjVluDXpoL1RCdHnk7FJqvz5pHdtu/view?usp=drive_link>
+## Project Poster:[https://drive.google.com/file/d/16X5Xw-yC25NDlBHGlQiNzXGygjl5bGMY/view]
 
 ## Principal Investigators