GeoLibre v1.8.0 is here! - Live demo: viewer.geolibre.app - GitHub: github.com/opengeos/Geo... - Documentation: geolibre.app - Release notes: github.com/opengeos/Geo...

This week's MetPy Mondays tutorial shows how to create a Stüve diagram in Python using MetPy’s new Stüve plotting tools. buff.ly/LLryKAC

We're once again highlighting some lightning talks tomorrow for our Pangeo showcase close-out. It'll be a great line-up so we hope you'll join! discourse.pangeo.io/t/spring-sho.... 12 PM ET on May 13, see the announcement for more details

The Harmonized Landsat Sentinel-2 (HLS) Workshop is happening today, and I will present on using QGIS plugins for HLS data access, visualization, and analysis. The session will showcase practical workflows for exploring and analyzing HLS datasets directly within QGIS using open-source tools.

MetPy Mondays is back! John Leeman from NSF Unidata introduces what’s new in MetPy and previews a new 15-part MetPy Mondays series focused on practical meteorological data analysis, remote data access, and case study plotting. buff.ly/BQitB7I

MetPy Mondays #329 - What's New in MetPy? | NSF Unidata

This MetPy Monday episode introduces a new series focused on the latest features in MetPy and practical workflows for meteorological data analysis in Python. John highlights improvements to core…

unidata.ucar.edu

📝New eLearning: Supervised Machine Learning Readiness 📝 Get started with ML in the Earth systems sciences with this self-paced no- and low-code learning series. Enroll for free today on NSF Unidata eLearning! elearning.unidata.ucar.edu/course/view....

Due to the current gap in funding from the U.S. National Science Foundation (NSF), the NSF Unidata Program is pausing most operations effective 12 May 2025. Nearly all staff will be furloughed until funds from our existing NSF grant become available. For more information, see buff.ly/d2TC1Oy

NSF Unidata Pause in Most Operations

NSF Unidata Pause in Most Operations

unidata.ucar.edu

MetPy 1.7.0 is out! Highlights include saturation vapor pressure (including over ice) and LCL calculations, automated high/low identification, and clients for accessing S3 archives of NEXRAD L2/L3, GOES, and ML weather prediction output (from NOAA/CIRA), and more! github.com/Unidata/MetP...

Release 1.7.0 · Unidata/MetPy

Highlights New analytic LCL calculation based on Romps (2017) New saturation vapor pressure calculations, relative to both ice and liquid, using Ambaum (2020) Moisture/temperature dependent gas co...

github.com

Want a clean way to combine a Skew-T and time series in Python? This week’s #MetPyMonday shows how to wrangle GridSpec for a tight layout with MetPy + Siphon. youtu.be/fzEJvnaXYJw?...

MetPy Mondays #327 – Complicated Layouts with GridSpec

YouTube video by NSF Unidata

youtu.be

If you've been looking for a quick way to make some GOES composites (e.g. geocolor, airmass), here's a notebook showing how to do it using SatPy and AWS S3 data (optionally a little bit of the next @metpy.bsky.social release). gist.github.com/dopplershift...

Create GOES composite imagery products in Python using SatPy and data from public AWS S3 buckets.

Create GOES composite imagery products in Python using SatPy and data from public AWS S3 buckets. - GOES Composites with SatPy.ipynb

gist.github.com

Join us tomorrow, Wednesday April 16 at 12 PM ET for Pangeo lightning talks! The amazing set of topics includes climate data visualization, Parquet usage in oceanography, organizing model data with Zarr and STAC, speeding up Zarr, scaling geospatial zonal statistics, and rechunking 🤩

Spring showcase close-out: Lightning talks! (April 16, 2025 at 12 PM ET)

Title: “Spring showcase close-out: Lightning talks!!!” When: Wednesday, April 16, 2025 at 12 PM EST Where: Launch Meeting - Zoom Add to your calendar: Community calendar Details: ⚡ Join us next ...

discourse.pangeo.io

Imagine if all you had to do to serve geopspatial data over OGC APIs was to ingest @zarr.dev datasets into cloud object storage...and the platform handled the rest. Scaling, logging, metrics, auth, etc. That's Flux! Join our webinar if you want to learn more.

Earthmover@earthmover.io · last yr.

1/ Today we are launching Flux, a powerful new addition to the Earthmover platform. Flux is a high-performance gateway for exploring, querying, and visualizing geospatial data via standards-compliant APIs (EDR, WMS, and OPeNDAP).

I have to admit that I too was confused about how S3 prefixes and scalability work before reading this post by @functionth.bsky.social.

Earthmover@earthmover.io · last yr.

📣 Blog post alert! 𝐄𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 𝐈𝐜𝐞𝐜𝐡𝐮𝐧𝐤 𝐬𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲: 𝐮𝐧𝐭𝐚𝐧𝐠𝐥𝐢𝐧𝐠 𝐒𝟑'𝐬 𝐩𝐫𝐞𝐟𝐢𝐱 𝐬𝐭𝐨𝐫𝐲. This technical post by @functionth.bsky.social dives deep into the internals of how S3 shards data, showing that distributed Icechunk can easily perform 230,000 object reads/sec and beyond. earthmover.io/blog/explori...

🌍✨ Check out this demo of visualizing Earth Engine data on a 3D globe! It shows how to visualize nighttime light and land cover data, and you can easily adapt the code to visualize any #EarthEngine dataset. Notebook: geemap.org/workshops/GE... GitHub: github.com/gee-communit... #geospatial

📣 Tomorrow's Pangeo showcase presentation will be at an earlier than usual time! Join us on April 9 at 12 PM ET to learn about scaling Dask Array up for terabyte-level performance! Hope to see y'all there 🚀 discourse.pangeo.io/t/pangeo-sho...

Pangeo Showcase: "Dask Array: Scaling Up for Terabyte-Level Performance" (April 9, 2025 at 12 PM ET)

‼ Please note that this is happening at 12 PM ET even though it’s typically a “late” meeting week Title: “Dask Array: Scaling Up for Terabyte-Level Performance” Invited Speaker: Hendrik Makait When...

discourse.pangeo.io

🚀 Leafmap now supports visualizing geospatial data on a stunning 3D globe with MapLibre 🌍 🎥 Check out the full tutorial: youtu.be/D2bdwLkU1KQ 📓 Dive into the notebook: leafmap.org/maplibre/globe_control 🌐 Try the demo: maps.gishub.org/maplibre/globe_control_4.html #Geospatial #3DMapping #Python

The 28 March NITheCS Micro-School titled ‘Unlocking Earth Observation Data – Access and Analysis with Xarray Ecosystem Tools’ by Marco Wolsza (Friedrich Schiller University Jena, Germany) is now available to view online at buff.ly/1UJDQEC

🚀 GeoAI Tutorial 17: Create a Labeled Dataset for Deep Learning 🌍 Learn how to create a labeled dataset interactively in a Jupyter environment—no desktop GIS required. 🎥 Watch the full tutorial here: youtu.be/QqjhLFN8Hdc 📓 Explore the notebook: geoai.gishub.org/examples/cre...

No showcase this week, but join us on April 9 at 12 PM ET to learn about scaling Dask Array up for terabyte-level performance! We're also looking for folks to give lightning talks at our last showcase of the Spring on April 16 - sign up here - forms.gle/NA5zXdgmHHZs...

Pangeo lightning talks - Spring 2025

Sign up to give a lightning talk at the Pangeo showcase on April 16 at 12 PM ET You can give a demo, slides, rant, whatever you want to share - but keep it to 5 minutes or less please! We'll be stric...

forms.gle

This one was a lot of fun to work on! Why @xarray.bsky.social and @zarr.dev are faster than DuckDB and Parquet for gridded data...from first principles.

Earthmover@earthmover.io · last yr.

⭐ We just released the first post in our Fundamentals series. This one is called 𝐓𝐞𝐧𝐬𝐨𝐫𝐬 𝐯𝐬. 𝐓𝐚𝐛𝐥𝐞𝐬 - 𝐖𝐡𝐲 𝐭𝐚𝐛𝐮𝐥𝐚𝐫 𝐭𝐨𝐨𝐥𝐬 𝐭𝐫𝐢𝐩 𝐨𝐯𝐞𝐫 𝐠𝐫𝐢𝐝𝐝𝐞𝐝 𝐝𝐚𝐭𝐚. earthmover.io/blog/tensors...

aerobulk-python 0.4.2 (Pre-Alpha) General Circulation Model Postprocessing with xarray Author: aerobulk-python Developers 🏠Homepage

Need to know which METAR station is closest to your point of interest? Checkout this week's #metpymonday We show you how to: ✅ Build a clean station list from MetPy ✅ Find the nearest station using KDTree ✅ Find distance with GeoPandas #Python youtu.be/nMTFb-8e1Nk?...

MetPy Mondays #326 - Use KDTree and MetPy to Find the Nearest METAR

YouTube video by NSF Unidata

youtu.be

🚀 GeoAI Tutorial 14: Search and Download Data From Microsoft Planetary Computer 🌍 Learn how to search, visualize, and download geospatial data from Microsoft Planetary Computer. 🎥 Watch the full tutorial here: youtu.be/bnY5isQApW0 📓 Explore the notebook: geoai.gishub.org/examples/pla...

The 14 March NITheCS Micro-School titled ‘Introduction to Xarray: Empowering Multidimensional Data Analysis’ by Marco Wolsza (Friedrich Schiller University Jena, Germany) may now be viewed online at buff.ly/jLJ4KKI.

🌐 Scaling geoscience workloads with Dask and Xarray is crucial for analyzing massive datasets. Hendrik Makait explores the challenges and improvements in Dask, making it easier to scale from prototyping to petabyte-scale analyses. 📊 Watch the talk:

Hendrik Makait - Geoscience at Massive Scale | PyData Paris 2024

When scaling geoscience workloads to large datasets, many scientists and developers reach for Dask, a library for distributed computing that plugs seamlessly...

buff.ly

🚀 GeoAI Tutorial 12: Wetland Mapping with Deep Learning and GeoAI! 🌍 🎥 Watch the full tutorial here: youtu.be/ed79i3DTWT0 📓 Explore the notebook: geoai.gishub.org/examples/wet... #GeoAI #geospatial #AI #Python #DeepLearning

🌟 Leafmap Tutorial 105: Visualizing Vector Data with Time Attributes! This demo showcases the evolution of median home values in California from 2000 to 2025. 📈🏡 🔗 Watch on YouTube: youtu.be/FiP3sKkoyKs 📓 Notebook: leafmap.org/notebooks/10... 🌐 Try the PyCafe Web App: py.cafe/giswqs/vecto...

Highlighting a country on a map with #Python, #Matplotlib and #Cartopy scipython.com/blog/highlig...

🚀 Exciting news for xarray & Pangeo users! Brockmann Consult has released XRLint, a linter for xarray datasets. Validate Dataset & DataTree with configurable rules via CLI or Python API. Check it out 🚀: 🔗 Repo: github.com/bcdev/xrlint 📖 Docs: bcdev.github.io/xrlint

𝗚𝗲𝗼𝘀𝗽𝗮𝘁𝗶𝗮𝗹 𝗣𝘆𝘁𝗵𝗼𝗻 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹𝘀 SpatialThoughts provides tutorials covering a broad range of geospatial topics and technologies, e.g., #GeoPandas, #XArray, #dask, and more. Each technology is described in a notebook with step-by-step explanation. Check it out: buff.ly/4ha3HOo

Geospatial Python Tutorials

Welcome to Spatial Analysis and Remote Sensing Tutorials by Spatial Thoughts. These tutorials complement our Python courses and are suitable for learners who want to advance their skills.

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