Environmental Data Science
@envdatascience.bsky.social
Environmental Data Science is an #OpenAccess journal @cambridgeup.bsky.social dedicated to the use of data science & AI to enhance our understanding of the environment.
Recently published! Interpretable machine learning for CMIP6 multi-model ensembles 👉https://cup.org/45FZTkm ✍️Siyi Wu and @steve.fediscience.org.ap.brid.gy Part of the Climate Informatics 2026 special issue. #CMIP6 #machinelearning #clustering #ensembleweighting #climate
Recently published! Interpretable machine learning for CMIP6 multi-model ensembles 👉https://cup.org/45FZTkm ✍️Siyi Wu and @steve.fediscience.org.ap.brid.gy Part of the Climate Informatics 2026 special issue. #CMIP6 #machinelearning #clustering #ensembleweighting
Recently published! A generative likelihood framework for high-resolution climate model evaluation 👉 https://cup.org/4fNN3Gw ✍️Lilli Johanna Freischem, @treichelt.bsky.social, Ronald Clark, @philipstier.bsky.social and @hannah325.bsky.social #climate #climatemodel
New article! SYNOPTICBENCH: evaluating vision-language models on generating weather forecast discussions of the future 👉https://cup.org/4x01Dkh ✍️Timothy Higgins, Antonios Mamalakis and Chirag Agarwal #visionlanguagemodel #multimodality #weatherforecasting
Recently published! Emulating non-differentiable metrics via knowledge-guided learning: Introducing the Minkowski image loss 👉 https://cup.org/3TppDyy ✍️Filippo Quarenghi, Ryan Cotsakis and Tom Beucler #differentiability #machinelearning #radarprecipitation
New article! One stone three birds: Three-dimensional implicit neural network for compression and continuous representation of multi-altitude climate data 👉https://cup.org/4wbBDm3 ✍️Alif Bin Abdul Qayyum, Xihaier Luo, Nathan M. Urban, Xiaoning Qian & Byung-Jun Yoon #climate
Recent article! Explainable machine learning highlights the role of diffuse radiation in ecosystem carbon uptake of boreal forest 👉https://cup.org/4wIPNLh ✍️Topi Markus Laanti, Aino Aarne, Maxime Durand et al. #machinelearning #borrealforest
New article! Calibrated conformal prediction intervals for microphysical process rates 👉https://cup.org/4w0sooz ✍️Miriam Simm, Corinna Hoose and Tom Beucler #cloud #microphysics #conformalprediction #machinelearning
New article! Precipitation nowcasting of satellite data using physically aligned neural networks 👉https://cup.org/4wfMkDp ✍️Antônio Catão, Leonardo Voltarelli, Melvin Poveda and Paulo Orenstein #precipitation #data #satellitedata #neuralnetworks #nowcasting
New article! Correcting dry/wet classification bias in precipitation downscaling via generative adversarial networks 👉 https://cup.org/4ugXdUr ✍️Shivam Singh, Simon Michael Papalexiou, Hebatallah M. Abdelmoaty, @tomhartvigsen.bsky.social & Antonios Mamalakis #data #generativemodels #precipitation
New article! Assessing the risk of future Dunkelflaute events for Germany using generative deep learning 👉https://cup.org/4nEHj4c ✍️ Felix Strnad, @schmidtjonathan.bsky.social, Fabian Mockert, @philipphennig.bsky.social & @nnludwig.bsky.social #CMIP6 #renewableenergy #generativeAI #climate
Recently published! How reliable are retrieval-augmented and standard ChatGPT models to support flood susceptibility mapping? 👉https://cup.org/4uoQb0F ✍️Ali Pourzangbar and Mário J. Franca (@kit.edu) #ChatGPT #LLMs #floods #machinelearning
How reliable are retrieval-augmented and standard ChatGPT models to support flood susceptibility mapping? | Environmental Data Science | Cambridge Core
How reliable are retrieval-augmented and standard ChatGPT models to support flood susceptibility mapping? - Volume 5
cup.org
Recently published! Data-driven discovery of meteotsunami patterns from sparse observations 👉 https://cup.org/4wcgN6l ✍️Ardiansyah Fauzi, Emiliano Renzi, Frederic Dias, Daniel Santiago Pelaez-Zapata & Tatjana Kokina #data #meteotsunami #coastalhazard @ucddublin.bsky.social
Recent article! Detecting unique wind field features in hurricane Sandy from topological data maps 👉 https://cup.org/3OEfL25 ✍️Justin Hoffmeier (@FLPolyU) Part of the Connecting Data-Driven and Physical Approaches special issue #weather #cyclones #hurricane #wind #dataanalysis
New article! Combined effects of site and model parameterization for soil respiration components in a Canadian wildfire chronosequence 👉https://cup.org/3NxKQEc ✍️John Zobitz, Xuan Zhou, Heidi Aaltonen, Egle Köster, Frank Berninger , Jukka Pumpanen & Kajar Köster #carbon #microbes #modelling #soil
🌳 Do you want to contribute to research on how humans perceive forests? Take this quick, anonymous 10-min survey 🌲 👉 www.biodiful.org#/forest This will help us explore how people experience forest biodiversity! Please share on 🦋 & tag @biodiful.bsky.social to reach more participants 🙏💚 🌐🌍🦤🦑🪴🍁🧪
Recent article! Skillful subseasonal Indian Ocean marine heatwave forecasts using a neural network 👉 https://bit.ly/40c6P6k ✍️Lucas Howard, Aneesh C. Subramanian, Jithendra Raju Nadimpalli, Donata Giglio & Ibrahim Hoteit Part of Connecting Data-Driven and Physical Approaches Issue #machinelearning
Skillful subseasonal Indian Ocean marine heatwave forecasts using a neural network | Environmental Data Science | Cambridge Core
Skillful subseasonal Indian Ocean marine heatwave forecasts using a neural network - Volume 5
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New article! Utilization of artificial intelligence and thermal cameras in material analysis for hot-summer Mediterranean climates 👉 https://bit.ly/4u1iBxQ ✍️ Ahmet Benliay & Türkan Azeri Combining #AI & #thermal #imaging may be beneficial for #ecological and sustainable architectural design
Utilization of artificial intelligence and thermal cameras in material analysis for hot-summer Mediterranean climates | Environmental Data Science | Cambridge Core
Utilization of artificial intelligence and thermal cameras in material analysis for hot-summer Mediterranean climates - Volume 5
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New article! Which meteorological parameters influence extreme wind speed in a wind farm? A heterogeneous Granger causality approach 👉https://bit.ly/405R00I ✍️Kateřina Hlaváčková-Schindler, Rainer Wöss, Irene Schicker & Claudia Plant @univie.ac.at @aswogeosphere.bsky.social #windspeed #windenergy
New article! Actively inferring methane sources with drones 👉 https://bit.ly/4tAYwyr ✍️Alouette 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 👉https://bit.ly/4qJuQNG ✍️Teresa Kristine Spohn, Eoin Walsh, Kevin Horan (@maynoothuniversity.ie), John O’Donoghue (@unioflimerick.bsky.social), Tim Charnecki, Merlin Haslam, Sarah Gallagher
New article! Uncertainty quantification for deep learning 👉 https://bit.ly/49MlJpa ✍️ 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 👉 https://bit.ly/48RjC1W ✍️ Elena Volkanovska (@tuda.bsky.social) Part of the Tackling Climate Change with Machine Learning special issue #climatechange #MachineLearning
Calling all @britishecologicalsociety.org #BES2025 attendees! Visit @universitypress.cambridge.org 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 👉 https://bit.ly/496Ty42 ✍️Lillian 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...
Climate Informatics 2025: Exploring Climate Science and Data Science in the Global South « Earth & Environmental Science# « Cambridge Core Blog
As many readers will know, COP30, the UN climate conference, got underway in Belém, Brazil this month. In this blog post, we’re pleased to report on another conference that was successfully held in Br...
cambridge.org
New article! Prediction and uncertainty quantification of drought in North Benin 👉https://bit.ly/3Xhrrsc Part of the Tackling #ClimateChange with #MachineLearning special issue. Study underscoreing the importance of uncertainty quantification in #drought #forecasting.
📢 CALL FOR PAPERS! Solution-Based #DataScience for #Environmental #Biology Challenges A special collection with @cu-esiil.bsky.social to advance data-intensive approaches to better understand today's environmental challenges 🗓️1 March-31 May 2026 ℹ️https://bit.ly/4q0b68m #TippingPoints
New article! From winter storm thermodynamics to wind gust extremes: discovering interpretable equations from data 👉 https://bit.ly/4oPzZSN ✍️ Frederick Iat-Hin Tam, Fabien Augsburger, Tom Beucler (@fgse-unil.bsky.social, @unil.bsky.social ) @climformatics.bsky.social #CI2025 #thermodynamics #wind
📢 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. ⏰ 31 October 2025 ℹ️ https://bit.ly/4k09cBu #climate #AI #forecasting
