We present our #CVPR2024 work on #EarthVision: Deep Generative Data Assimilation in Multimodal Setting that calibrates Earth system model state🌎 with diverse observations🛰️📡 using diffusion
Paper: https://t.co/whx4xScmLn
Code: https://t.co/KCyF77ZO6S
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Excited to share our new paper using transfer learning to better predict pCO2 in data sparse conditions (in the real world), by Shaun Kim, @juannat7, Zhewen Hou and @tz33cu https://t.co/NIOymrdaez
Glad to see our paper on using data-driven reduced order modeling to develop cloud microphysics schemes published in JAMES this week! https://t.co/77DGjN6COB
@LeapStc@CUSEAS@columbiaclimate
🏆🥇 Happy to share that our work on SLAMS won the Best Student Paper Award at @EarthVisionWS#CVPR2024. We will present it in ✈️Seattle next week.
Also, happy to have ☕️chats with folks interested in #AI4Science broadly!
We present our #CVPR2024 work on #EarthVision: Deep Generative Data Assimilation in Multimodal Setting that calibrates Earth system model state🌎 with diverse observations🛰️📡 using diffusion
Paper: https://t.co/whx4xScmLn
Code: https://t.co/KCyF77ZO6S
🧵1/6
1/2 We hear a lot about "Earth twins" these days. There are still major challenges on the way before we can get to Earth's twins whether using high-resolution Earth system models or AI-based models but progress is happening fast, especially on the AI front. Some comparisons:
We present our #CVPR2024 work on #EarthVision: Deep Generative Data Assimilation in Multimodal Setting that calibrates Earth system model state🌎 with diverse observations🛰️📡 using diffusion
Paper: https://t.co/whx4xScmLn
Code: https://t.co/KCyF77ZO6S
🧵1/6
🎟️Bonus: we perform feature importance study to identify the relative contribution of each observation modality. For example, assimilating ex-situ OLR🌞, rather than in-situ precipitation🌧️, better constrain top-of-the-atmosphere temperature 🌡️
5/6
Excited to share our new global terrestrial carbon flux product based on metalearning led by Juan Nathaniel, a great student in our group: https://t.co/MXfNLgu5KV, called MetaFlux. This product has improved capacity in data poor regions such as the tropics and dry regions.