In weather forecasting, traditional deterministic local atmosphere-driven models (NWP) rely on physical equations to simulate atmospheric behavior.
Reanalysis models combine historical observations with modeling techniques to create consistent, long-term datasets. They often achieve SOTA performance in evaluations but are less practical for real-time applications. In contrast, observation-based models directly utilize current data, offering greater practicality despite lower accuracy.
Additionally, global models excel at capturing large-scale atmospheric patterns, while local models focus on finer, region-specific predictions, catering to localized needs.
Weather Forecast Models
Weather Forecast Notion

Monitoring Space Weather From the Ground
A number of private space missions are underway, and thousands of satellites are being readied to join the thousands already in orbit. etermining the weather in space as important to the success of the global space industry as terrestrial weather forecasting is for Earthbound industries.
https://spectrum.ieee.org/space-weather

Waymo's robotaxis are basically mobile weather stations now
Autonomous vehicles have difficulty navigating bad weather. Heavy rain, snow, and fog can do a lot to scramble an AV's perception systems, which largely rely on cameras, radar, and lidar to "see" the world around it. Wet roads can create reflections that confuse cameras. Fog can screw up sensor data.
https://www.theverge.com/2022/11/14/23453478/waymo-av-autonomous-bad-weather-fog-sf-station
IBM and NASA Release Open-Source AI Model on Hugging Face for Weather and Climate Applications
IBM announced a new AI foundation model for a variety of weather and climate use cases, available in open-source to the scientific, developer, and business communities.
https://newsroom.ibm.com/2024-09-23-ibm-and-nasa-release-open-source-ai-model-on-hugging-face-for-weather-and-climate-applications

hf
openclimatefix (Open Climate Fix)
Using computers to fix climate change.
https://huggingface.co/openclimatefix

Seonglae Cho