Environmental Data Science

Jackson School of Geosciences at The University of Texas at Austin Department of Earth and Planetary Sciences is recruiting. My esteemed colleagues, would you please help share/distribute the ad below to those who are interested? Jay Famiglietti Christa Peters-Lidard Ana Barros Reed Maxwell Joshua Fisher Peter van Oevelen Peter Gleick Peter Cox Pierre Gentine Xin Li Thorsten Wagener Lawrence Band Vasco Mantas Karen Mohr John Bolten Hyunglok Kim Eunsang Cho Venkataraman Lakshmi Lulin Xue Sujay Kumar Yan Xue Z. George Xue Zoltán Sylvester Daniella Rempe Danny Stockli Michael Mann Martin Heimann Youngsoo Choi Claudia Mora Michael Young Chaopeng Shen Chaoshun Hu I am at #AGU24 through Saturday and would be happy to meet with anyone who is interested.

The AI for High-Precision Weather Forecasting Showcase event last week demonstrated the crucial role AI technology is playing in addressing environmental challenges and improving our understanding of weather systems and the changing climate. The new Machine Learning model for weather prediction – FastNet developed by Met Office and The Alan Turing Institute represents the next step in weather prediction and the UK’s commitment to remain at the cutting-edge of weather forecasting. https://lnkd.in/eCpP7g8u #MetOfficeXTuring 

Dezenove vagas de pesquisadores em uma dos melhores institutos de pesquisa do Brasil (Museu Emílio Goeldi | MCTI). Jovens pesquisadores, esta oportunidade é simplesmente imperdível!! https://lnkd.in/e6ag2B6m #brazil #biodiversity #amazon

New article! A high-accuracy ionospheric foF2 critical frequency forecast using long short-term memory LSTM 👉 https://bit.ly/4i0VZaD Accurately predicting changes in the ionosphere is crucial for a variety of applications, including communication, navigation, remote sensing, and climate change monitoring. Traditional models rely on physical and empirical models, which have limitations in capturing the dynamic nature of the ionosphere. However, the development of machine learning methods has created an opportunity to significantly improve prediction accuracy. The long short-term memory (LSTM) model has been developed to provide accurate short-term predictions during maximum and minimum solar activity. The results demonstrate an efficient and reliable method for predicting ionospheric variations, which can have a significant impact on various applications. By using machine learning methods, researchers can gain new insights into the complex dynamics of the ionosphere, ultimately improving the quality of life for people worldwide. By Alexandra Denisenko-Floyd, Meric Yucel & Burak Berk Üstündağ #MachineLearning #ionosphere #forecasting #LSTM

🌍 Welcome to Pangeo’s LinkedIn! 🌟 We’re excited to join the LinkedIn community to share our journey in advancing open, scalable, and reproducible tools for tackling Big Data challenges in Earth Observation, Climate Modeling, and beyond. Pangeo is an open community leveraging user-friendly, community-developed tools like Xarray, Dask, and Jupyter, from laptops to cloud and high-performance computing (HPC), to empower collaborative, large-scale data analysis in Earth System Science.  ✅ Follow us for: - Highlights from the Pangeo community 🎉 - Tutorials, webinars, and real-world applications 📚 - Opportunities to get involved 🤝 We’re passionate about open science and supporting researchers , educators, practitioners, and industry professionals in tackling the planet’s biggest challenges. If this resonates with you, follow us to stay connected! 💡 Have a Pangeo story or project? Share it with us—we’d love to feature it! #Pangeo #OpenScience #EarthSystemScience #BigEarthData #HPC #Cloud #DataAnalysis #EO

🌏 Equitable Open Access Publishing 🔓 🤝 Any author accepted in Environmental Data Science (EDS) can publish their work on an #openaccess basis, irrespective of their funding situation or their institutional affiliation. Open access publishing at Cambridge University Press & Assessment is primarily funded by Transformative Agreements with institutions, rather than by individual-level article processing charges. However, if you are not based at an institution with an agreement, you don't need to worry. We can guarantee that any author without an agreement or funding who meets the peer review standard can still publish in EDS on an open access basis. ℹ️ 📺 To read more and watch a video about Cambridge University Press & Assessment's equitable approach to open access, see: https://lnkd.in/eV3GT4Hx #Equity #OpenScience #OpenResearch #Funding #DataScience #AI #Environment

Recently published! Identifying probabilistic weather regimes targeted to a local-scale impact variable 👉 https://bit.ly/3CFtZta This research paper presents a novel approach to #machinelearning that can identify atmospheric circulation patterns, known as #weather regimes, which affect local-scale variables like extreme precipitation. By doing so, it can improve the usefulness of weather regimes for #climate-related applications such as sub-seasonal forecasting or climate model output downscaling. The paper is co-authored by meteorology and computer science experts and aims to introduce the new method in an accessible way to researchers from both fields. Additionally, it aims to highlight the similarities, differences, and trade-offs between this new machine learning method and established statistical approaches for dimensionality reduction and clustering in weather and climate science. By Fiona Spuler, Marlene Kretschmer, Yevgeniya Kovalchuk, Magdalena Alonso Balmasea & Theodore G. Shepherd #extremeevents #weatherregimes #generativemodels

New article! Leveraging heterogeneous LiDAR data to model successional stages at tree species level in temperate forests 👉 https://bit.ly/4i3dqY8 Large-scale regional or national data sets, that are not collected for a specific study, often pose significant challenges due to their heterogeneity. However, these data sets can provide valuable ecological insights and serve as readily available resources. This study highlights the benefits of using heterogeneous light detection and ranging (LiDAR) data for ecological modeling and mapping. The research emphasizes the advantages of leveraging "data-at-hand" rather than disregarding it in anticipation of more refined data sources. By Lisa Bald, Alice Ziegler, Jannis Gottwald, Tiziana L. Koch, Marvin Ludwig, Hanna Meyer, Stephan Wöllauer, Dirk Zeuss and Nicolas Frieß #LiDAR #biodiversity #ecosystem #heterogeneousdata

🌅 SSI project manager Kirsty Pringle's Air Quality Stripes has been recently feature on BBC Future. Find out more about the project and access the images at https://lnkd.in/eWckNZaJ

📰 AI in weather prediction takes centre stage at Turing and Met Office event 💬 “We’re in the midst of an AI revolution and it’s happening at just the right time”, according to Professor Kirstine Dale, Chief AI Officer at the Met Office. Integrating AI and data-driven approaches with well-established physics-based numerical models presents technical challenges – particularly in ensuring robustness, reliability and trustworthiness. The event highlighted FastNet, an innovative machine learning model developed to improve UK weather forecasting by integrating AI with traditional approaches, drawing together Met Office AI projects and Turing expertise. ➡️ Learn more: https://bit.ly/4fZykpv European Centre for Medium-Range Weather Forecasts | Open Climate Fix | UK Research and Innovation #AI #AiInnovation #Climate #ClimateScience #DataScience #Sustainability #WeatherForecasting