Weather and Climate Computing with Emerging Open-Science Tools and Platforms to Increasing STEM Education and Research

National meteorological services and international organizations, have been providing free accessibility to vast datasets such as satellite observations, climate model outputs, and historical weather records among many other in areas of atmospheric and ocean sciences, to advance weather forecasting, climate analysis, and environmental monitoring. The open-source software tools like Python libraries (e.g., Pandas, NumPy, SciPy, and Matplotlib), and availability of specialized meteorological and oceanographic packages (e.g., MetPy, Py-ART, and OceanPy), are facilitating the user needs for processing, analyzing, and visualizing complex data. Emerging platforms, such as the Climate-Learn, and Pangeo ecosystem, which integrates data management, cloud computing, and scalable analysis tools, are making easier for a user to interact with large datasets. This present work highlights emerging open-science tools and platforms that promote transparency, reusability, and efficiency in weather and climate analysis, with a particular focus on their applications in educational settings. We demonstrate a mini-project on the study of machine learning modeling to predict El Niño-Southern Oscillation (ENSO) using COBE sea surface temperatures (SST) data provided by the NOAA PSL, Boulder, Colorado, USA (from their website at https://psl.noaa.gov) and ENSO data from NCEP (https://www.cpc.ncep.noaa.gov/data/indices/oni.ascii.txt). The emerging open-science tools and platforms play a pivotal role in increasing education and research in the disciplines of Science, Technology, Engineering, and Mathematics (STEM). They provide hands-on learning experiences on integrating open data on atmospheric and ocean sciences, and software computing to analyze physical data and visualize systematic trends. Students will be able to perform mini-projects on selective topics of study to understand interplay between the physical mechanism and underlying principles of the subject of interest in atmospheric and ocean sciences. Students can engage directly with real-world data and cutting-edge analysis techniques, fostering critical thinking and practical skills. This present work is a follow-up on the ‘Innovations in Open Science (IOS) Planning Workshop: Community Expectations for a Geoscience Data Commons’ that was held from May 29-31, 2024 in Boulder, Colorado.