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Friday, April 3, 2026

Wildfires Disrupt the Atmospheric Nitrogen Cycle in Ecosystems

CISESS Consortium Scientists Patrick Campbell, Daniel Tong, Youhua Tang (GMU) and coauthors discuss multi-decadal fire activity and how it influences changes in reactive nitrogen emissions across the contiguous United States in their paper recently published in the journal Communications Earth & Environment.

Monday, March 30, 2026

New Chemistry for the Unified Forecasting System

CISESS Scientists Kai Yang (AOSC), Zachary Moon and colleagues have created a generalized global configuration to NOAA’s official Unified Forecasting System (UFS) to better simulate the full tropospheric and stratospheric chemistry needed for global air quality applications. The new Configurable ATmospheric Chemistry (CATChem) library and modeling component is described in their paper published in the Journal of Advances in Modeling Earth Systems.

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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.

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.

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.

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