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Friday, July 31, 2026

The Network for Detection of Atmospheric Composition Change

CISESS Scientist Jeannette Wild (retired) co-authored a new article in the journal Atmospheric Chemistry and Physics on the Network for Detection of Atmospheric Composition Change (NDACC). The articles covers the history, satellites, observations, scientific achievements and future directions of the organization.
Friday, July 24, 2026

Assessing Design and Deployment Strategies for Future Microwave-Sounding Missions

CISESS Scientist Dr. Zaizhong Ma and co-authors, including CISESS Deputy Director Dr. Hugo Berbery, recently published a study on the expected forecast impact of future microwave sounding instruments planned under NOAA’s Near Earth Orbit Network (NEON) program. This work was done using the Ensemble of Data Assimilations (EDA) for the Integrated Forecasting System in collaboration with the European Centre for Medium-Range Weather Forecasts (ECMWF).

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Apr 24, 2025 09:21 AM

CISESS Announces 2025 Seed Grants

Deputy Director Hugo Berbery has just announced the CISESS Seed Grants to be funded in 2025. While four projects have been funded for the last four years, this year CISESS will have six Seed Grants for the first time. In addition, two prior seed grants, Hu Yang's Remote Sensing Lab and Guangyang Fan's 3D Virtual Reality Weather Maps, have now been given permanent funding.

 
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Latent Heat Profile
Apr 23, 2025 02:31 PM

CISESS Seed Grant: Retrieving Latent Heat from Passive Microwave Satellite Observations

The goal of this CISESS Seed Grant Project is to demonstrate the feasibility of retrieving latent heating profiles of the atmospheric column using passive microwave satellite observations. This advance could potentially provide the foundation for unique cloud system analyses and new insights into cloud processes, potential for advances in numerical prediction, and a better understanding of energy and water budgets at global and regional scales.

 
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PACE satellite
Apr 23, 2025 01:59 PM

CISESS Seed Grant: Machine Learning-based Hyperspectral Sensor Data Retrieval at the CISESS Remote Sensing Laboratory

The CISESS Seed Project takes hyperspectral data from NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Ocean Color Instrument (OCI). This data is processed with both supervised-learning and self-teaching machine learning to classify hyperspectral data to ocean composition and phytoplankton types. This approach will be validated using hyperspectral radiometer data from field and lab experiments using the CISESS Remote Sensing Lab (RSL) instruments.

 
 
Results: 156 Articles found.
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