Environmental Data Science
@EnvDataScience
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Environmental Data Science is an #OpenAccess @CambridgeUP journal dedicated to the use of data science & AI to enhance our understanding of the environment.
Cambridge, UK
Joined September 2020
New article! From winter storm thermodynamics to wind gust extremes: discovering interpretable equations from data 👉 https://t.co/6ULPQCrbWx ✍️ Frederick Iat-Hin Tam, Fabien Augsburger & Tom Beucler (@FGSE_UNIL) @Climformatics #CI2025 #thermodynamics #wind #windgusts
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📢 CALL FOR PAPERS: Last day to submit! Connecting Data-Driven and Physical Approaches: Application to Climate Modeling and Earth System Observation A special collection building upon a workshop at #EGU25. ⏰ 31 October 2025 ℹ️ https://t.co/yAgthoo6eu
@brajard #climate #AI
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📢 CALL FOR PAPERS: Closing soon! Connecting Data-Driven and Physical Approaches: Application to Climate Modeling and Earth System Observation A special collection building upon a workshop at #EGU25. ⏰ 31 October 2025 ℹ️ https://t.co/yAgthooE42
@brajard #climate #AI
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New article! Graph neural networks for hourly precipitation projections at the convection permitting scale with a novel hybrid imperfect framework 👉 https://t.co/khWxOSZjvE ✍️ Valentina Blasone,@ErikaCoppolaE, Guido Sanguinetti, Viplove Arora, @SerafinaDiG & Luca Bortolussi
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📢Solution-Based #DataScience for #Environmental #Biology Challenges Announcing a new Call for Papers with @CU_ESIIL to advance data-intensive approaches to better understand today's environmental challenges: ℹ️ https://t.co/pCWcRS5aLc
#TippingPoints #Resilience #Adaptation
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📢 CALL FOR PAPERS: Connecting Data-Driven and Physical Approaches: Application to Climate Modeling and Earth System Observation Only 1 month left to submit to this special collection building upon a workshop at #EGU25. ℹ️ https://t.co/yAgthooE42
@brajard #climate #AI
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Recently published! Precipitation prediction over the upper Indus Basin from large-scale circulation patterns using Gaussian processes 👉 https://t.co/FiKYeFdWAJ ✍️ @KenzaxTazi, Andrew Orr, J. Scott Hosking & Richard E. Turner (@BAS_News, @Cambridge_Uni, @turinginst)
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New article! Toward accurate forecasting of renewable energy: Building datasets and benchmarking machine learning models for solar and wind power in France 👉 https://t.co/zYLgBrNGEV ✍️ Eloi Lindas, @YannigGoude & @ciais_philippe
@LSCE_IPSL #climate #MachineLearning
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New article! Air quality prediction from images in Indonesia: enhancing model explainability through visual explanation with AQI-net and grad-CAM 👉 https://t.co/mWpn4aLgcf ✍️ Muhammad Labib Alauddin, Novanto Yudistira & Muhammad Arif Rahman @Climformatics #CI2025 #airquality
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Recently published! 🌍 Crafting desirable climate trajectories with reinforcement learning explored socio-environmental simulations 👉 https://t.co/lPMmOS5GMR ✍️James Rudd-Jones, Fiona Thendean & @MPerezOrtiz_ (@ai_ucl, @uclcs) #ClimateChange #ClimatePolicy
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New article! 🌍🛰️ Discrete variational autoencoders for synthetic nighttime visible satellite imagery 👉 https://t.co/SlHEY5fMX0 ✍️ Mickell D. Als, David Tomarov & Steve Easterbrook (@UofTCompSci) Part of the @Climformatics #CI2025 collection #DeepLearning #RemoteSensing
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New article!🌍💨💡 Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6 👉 https://t.co/iGYy9vbTrP ✍️ Nina Effenberger & Nicole Ludwig (@uni_tue) #ClimateChange #WindPower #Energy
cambridge.org
Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6 - Volume 4
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New article! 🌳🌍 Tree semantic segmentation from aerial image time series 👉 https://t.co/9UYIRnPXU7 ✍️Venkatesh Ramesh @ArthurOuaknine @david_rolnick Research that advances #forest monitoring using #deeplearning on aerial imagery time series.
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📢 CALL FOR PAPERS REMINDER! Connecting #Data-Driven and #Physical Approaches: Application to #Climate #Modeling and #EarthSystem Observation 🗓️ 31st Oct 2025 ℹ️ https://t.co/xxRDfDhWHS Consider submitting your work! @brajard #EGU2025
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MoTiF: a self-supervised model for multi-source #forecasting with application to #tropical #cyclones 🌀🌍 ✍️Clément Dauvilliers & Claire Monteleoni (@inria_paris & @CUBoulder) 👉 https://t.co/rKYNyGbjvG Presents a new #deeplearning #AI method for forecasting #extremeweather.
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In honour of the 31st ACM/SIGKDD Knowledge and Data Discovery Conference this week, enjoy free access to our curated article collection - featuring content from @EnvDataScience, @Data_and_Policy, @DCE_Journal and @nws_journal. Explore the full collection: https://t.co/pe3NuY6NXB
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📢 CALL FOR PAPERS: Connecting Data-Driven and Physical Approaches: Application to Climate Modeling and Earth System Observation This special collection will build upon a workshop at #EGU25. 🗓️31st Oct 2025 ℹ️ https://t.co/yAgthoo6eu
@brajard #climate #AI #forecasting
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Discover Environmental Data Science, an #OpenAccess journal focused on #data-driven methods for studying #environmental processes like #climatechange & supporting sustainable decision-making. Check out our 2024 metrics and learn more at: https://t.co/UzlckaqHvc
#datascience
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See our call for papers for more info on how to submit your manuscript to the special collection: 🗓️ 2nd February 2026 ℹ️ https://t.co/DnSE9uJ4wT Guest Editors: @yuguangc92 and Emre Eftelioglu @AI4Good @kdd_news #KDD2025 #AI #Climate
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