Global weather forecasts depend on petabyte scale datasets and are generated on some of the world’s largest supercomputers. Until now, the resources required have severely limited the number of organizations capable of producing global weather forecasts. Using generative AI, we have developed EarthNet, a multi-modal foundation model for global data assimilation directly from Earth observations w

Zeus AI is pleased to announce a new funding award from the U.S. Department of Energy’s Small Business Innovation Research (SBIR) Program. Our Phase I project, A Meteorological Foundation Model for Gap-filled High-Resolution Data in Urban Environments, will leverage generative models trained on multimodal earth observations to seek breakthroughs in neighborhood-level heat monitoring. The urba

[video width="936" height="832" mp4="https://myzeus.ai/wp-content/uploads/2023/09/goes-mesoscaleradarwinds.mp4"][/video] Radar is not available in most ocean regions which can make monitoring hurricane intensity challenging prior to landfall. LENS solves that problem using mesoscale satellite data to synthesize radar to enhance spatial and temporal coverage of tropical storms at crucial times. M

[video width="4400" height="2000" mp4="https://myzeus.ai/wp-content/uploads/2023/08/Boston.mp4"][/video] Scattered showers pass over the Northeast on Sunday August 13, 2023. The progression of cloud cover, synthetic radar reflectivity, and surface solar irradiance were captured in near-real time by LENS-Analysis. This summer’s severe precipitation events, wildfires, and persistent heatwaves ha

See our near real-time LENS-Analysis Demo covering the Caribbean region. We show wind speed, temperature, relative humidity, radar reflectively composite, and cloud cover percentage updated every 15-minutes. The visualization is shown at a reduced spatial resolution for performance purposes. Zeus AI is pleased to announce the release of LENS-Cast, a new weather nowcasting product, offering

Sequences of GOES-16 tropospheric water vapor are used to calculate NOAA’s derived motion winds (b; in black). Atmospheric winds help to reduce initial condition uncertainty when assimilated into weather models. Dense feature tracking using deep optical flow provides more complete information about the atmospheric wind speed and direction (c), but produces too much data to be handled by trad

Geostationary (GEO) satellites orbit the Earth at the same rate that the Earth rotates, which allows them to remain over the same spot on the planet at all times. This makes them ideal for monitoring weather patterns and tracking the movement of storms. Space agencies around the world fly GEO satellites carrying visible/thermal imagers, infrared sounders, and lightning mapping instruments. These s

Zeus AI was selected for a phase II award through NASA’s Small Business Innovation Research (SBIR) program. The phase II award of $850k will enable Zeus AI to continue development and commercialize our 3D atmospheric analysis product for applications to numerical weather prediction and energy forecasting. Leveraging advances I geostationary satellite imagery in combination with advanced machine

Zeus AI has been selected for funding through NASA’s Small Business Innovation Research (SBIR) program. In the Phase I project, titled “Predicting 3D atmospheric structure from geostationary satellites”, Zeus will address current observing system gaps by developing artificial intelligence models to predict dense and accurate atmospheric winds. Timely and accurate wind data has important appl