New article!
Utilization of artificial intelligence and thermal cameras in material analysis for hot-summer Mediterranean climates
๐ https://bit.ly/4u1iBxQย
โ๏ธ Ahmet Benliay & ย Tรผrkan Azeri
Combining #AI & #thermal #imaging may be beneficial for #ecological and sustainable architectural design
New article!
Which meteorological parameters influence extreme wind speed in a wind farm? A heterogeneous Granger causality approach
๐https://bit.ly/405R00Iย
โ๏ธKateลina Hlavรกฤkovรก-Schindler, Rainer Wรถss, Irene Schicker & Claudia Plant
@univie.ac.atย @aswogeosphere.bsky.socialย #windspeed #windenergy
New article!
Actively inferring methane sources with drones
๐ https://bit.ly/4tAYwyr
โ๏ธAlouette van Hove, Kristoffer Aalstad & Norbert Pirk (@uio.no)
Part of the Connecting Data-Driven and Physical Approaches special issue.
#Bayesian #drones #methane
New article!
A machine learning approach using autoencoders to perform quality control on meteorological data
๐https://bit.ly/4qJuQNG
โ๏ธTeresa Kristine Spohn, Eoin Walsh, Kevin Horan (@maynoothuniversity.ie), John OโDonoghue (@unioflimerick.bsky.social), Tim Charnecki, Merlin Haslam,ย Sarah Gallagher
New article!
Uncertainty quantification for deep learning
๐ https://bit.ly/49MlJpa
โ๏ธ Peter Jan van Leeuwen, Jui-Yuan Christine Chiu & Chen-Kuang Kevin Yang (@csuatmossci.bsky.social)
Proposes a framework to improve consistency for #uncertaintyquantification in #deeplearning
#machinelearning
New article!
Language models for the analysis of and interaction with climate change documents
๐ https://bit.ly/48RjC1W
โ๏ธ Elena Volkanovska (@tuda.bsky.social)
Part of the Tackling Climate Change with Machine Learning special issue
#climatechange #MachineLearning
Calling all @britishecologicalsociety.org #BES2025 attendees!
Visitย @universitypress.cambridge.orgย at booth L15 in the Lennox Suite, floor -2, to find out more about Environmental Data Science journal & how to publish your research #openaccess!
Have a great conference!
#ecology #environment #data
New article!
Using Gaussian processes for spatial prediction of PM2.5 concentration based on calibrated data from distributed low-cost sensor networks
๐ https://bit.ly/496Ty42
โ๏ธLillian Muyama, Richard Sserunjogi, Deo Okure & Engineer Bainomugisha (
@airqo.bsky.social)
#airquality #airpollution
Exploring the Impact of 2025 @climformatics.bsky.social in the Global South - by @rblourenco.bsky.social
#CI2025 #ClimateInformatics #DataScience
www.cambridge.org/core/blog/20...
New article!
Prediction and uncertainty quantification of drought in North Benin
๐https://bit.ly/3Xhrrsc
Part of the Tackling #ClimateChange with #MachineLearning special issue.
Study underscoreing the importance of uncertainty quantification in #drought #forecasting.
๐ข CALL FOR PAPERS!
Solution-Based #DataScience for #Environmental #Biology Challenges
A special collection with @cu-esiil.bsky.socialย to advance data-intensive approaches to better understand today's environmental challengesย
๐๏ธ1 March-31 May 2026
โน๏ธhttps://bit.ly/4q0b68m
#TippingPoints
New article!
From winter storm thermodynamics to wind gust extremes: discovering interpretable equations from data
๐ https://bit.ly/4oPzZSN
โ๏ธ Frederick Iat-Hin Tam, Fabien Augsburger, Tom Beucler (@fgse-unil.bsky.social, @unil.bsky.socialย )
@climformatics.bsky.social #CI2025 #thermodynamics #wind
๐ข CALL FOR PAPERS: FINAL 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.
โฐ 31 October 2025
โน๏ธ https://bit.ly/4k09cBu
#climate #AI #forecasting
1 of 4 images created by Climate.us describing their mission. Text says: Our mission: Keeping trusted climate information up to date and easy to find.
2 of 4 images created by Climate.us describing their mission. Text says: Climate.us will be a nonprofit successor to Climate.gov, delivering climate data and information to promote public climate literacy and to equip people to turn knowledge into meaningful conversations and climate-conscious actions.
3 of 4 images created by Climate.us describing their mission. Text says: At the moment when critical climate information is being deleted or distorted, we are stepping up to rescue key climate resources and to ensure the public has continued easy access to the facts.
4 of 4 images created by Climate.us describing their mission. Text says: Our goal is to build an enduring, independent, and scientifically rigorous platform that the world can rely on for climate communication, education, and engagement. Stay up to date: Climate.us/#updates
We are Climate.us, a nonprofit successor to Climate(.)gov.
Our goal is to build an enduring, independent, and scientifically rigorous platform that the world can rely on for climate communication, education, and engagement. Stay up to date: Climate.us/#updates
๐ข 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.
โฐ 31 October 2025
โน๏ธ https://bit.ly/4k09cBu
#climate #AI #forecasting
New article!
Graph neural networks for hourly precipitation projections at the convection permitting scale with a novel hybrid imperfect framework
๐https://bit.ly/46RPjIF
โ๏ธValentina Blasone,
@erikacoppola.bsky.social, Guido Sanguinetti, Viplove Arora, Serafina Di Gioia & Luca Bortolussi
๐ข Solution-Based #DataScience for #Environmental #Biology Challenges
Announcing a new Call for Papers with @cu-esiil.bsky.social to advance data-intensive approaches to better understand today's environmental challenges:
โน๏ธhttps://bit.ly/4q0b68m
๐
31 May 2026
#TippingPoints #Resilience #Adaptation
We have exciting news! #CI2026 will be hosted by the รcole Polytechnique Fรฉdรฉrale de Lausanne (EPFL), in the period of April 27-30, 2026. More information in Tom Beucler's post:
www.linkedin.com/posts/tom-be...
๐ข CALL FOR PAPERS: Connecting Data-Driven and Physical Approaches: Application to Climate Modeling and Earth System Observation ย
Only 1 month left to submit to this special collection building upon a workshop at #EGU25.
โน๏ธ https://bit.ly/4k09cBu
#climate #AI #forecasting
Recently published!
Precipitation prediction over the upper Indus Basin from large-scale circulation patterns using Gaussian processes
๐ https://bit.ly/4nojwoo
โ๏ธ @kenzatazi.bsky.socialย , Andrew Orr, @scotthosking.bsky.socialย & Richard E. Turner
@theturing.bsky.social
@bas.ac.ukย ย
#precipitation
New article!
Toward accurate forecasting of renewable energy: Building datasets and benchmarking machine learning models for solar and wind power in France
๐ https://bit.ly/4mCtrWj
โ๏ธ Eloi Lindas, Yannig Goude & Philippe Ciais (@lsce-ipsl.bsky.social)
#climate #MachineLearning #forecasting
New article!
Air quality prediction from images in Indonesia: enhancing model explainability through visual explanation with AQI-net and grad-CAM
๐ https://bit.ly/4g434qD
โ๏ธ Muhammad Labib Alauddin, Novanto Yudistira & Muhammad Arif Rahman
@climformatics.bsky.socialโฌ #CI2025 #airquality
Recently published! ๐
Crafting desirable climate trajectories with reinforcement learning explored socio-environmental simulations
๐ https://bit.ly/4oVbLre
โ๏ธJames Rudd-Jones, Fiona Thendean & Marรญa Pรฉrez-Ortiz (@ucl.ac.ukโฌ)
#ClimateChange #ClimatePolicy
New article! ๐๐ฐ๏ธ
Discrete variational autoencoders for synthetic nighttime visible satellite imagery
๐ https://bit.ly/4oVjTb5
โ๏ธ Mickell D. Als, David Tomarov & @steve.fediscience.org.ap.brid.gyย (@uoftcompsci.bsky.socialโฌ)
Part of the @climformatics.bsky.socialย #CI2025 collection
#DeepLearning
New article!๐๐จ๐ก
Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6
๐ doi.org/10.1017/eds....
โ๏ธ Nina Effenberger & @nnludwig.bsky.social
This uses a global #climate model and shows #wind as a reliable #energy source with turbine location essential for projections.
Recent article! ๐ณ๐
Tree semantic segmentation from aerial image time series
๐ doi.org/10.1017/eds....
โ๏ธ Venkatesh Ramesh @arthurouaknine.bsky.social & @drolnick.bsky.social
Research that advances #forest monitoring using #deeplearning on aerial imagery time series.
๐ข CALL FOR PAPERS REMINDER!
Connecting #Data-Driven and Physical Approaches: Application to #Climate #Modeling and #Earth System Observation
Submit your work to this special collection in EDS (โช@universitypress.cambridge.org)
๐๏ธ 31st Oct 2025
โน๏ธ bit.ly/4k09cBu
@brajard.bsky.social #AI #ML #Weather
Article!
MoTiF: a self-supervised model for multi-source #forecasting with application to #tropical #cyclones ๐๐
โ๏ธClรฉment Dauvilliers & Claire Monteleoni (INRIA & colorado.eduโฌ)
๏ฟฝ๏ฟฝ https://doi.org/10.1017/eds.2025.1000014
Presents a new #deeplearning #AI method for forecasting #extremeweather.
Recent article!
Uncertainty bounds for long-term causal effects of perturbations in spatiotemporal systems
๐ https://bit.ly/3Idjb8x
By Kevin Debeire, Andreas Gerhardus, Renรฉe Bichler, @jakobrunge.bsky.socialโฌ and Veronika Eyring
#spatiotemporalmodels #uncertaintyestimations
