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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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RGB Image

Apr 14, 2025 01:17 PM

RGBs Recommendations for Weather Forecasting

At the 2025 Red-Green-Blue (RGBs) Experts and Developers Workshop (1–3 April), CISESS Scientist and Satellite Liaison to the NWS Weather Prediction Center (WPC) and Ocean Prediction Center (OPC), Christopher Smith, presented a talk on “RGBs in testbeds for U.S . Weather Offices.”

Framework for the stream water temperature model

Apr 14, 2025 11:58 AM

CISESS Seed Grant: Developing a Basic Model Interface (BMI) for Stream Water Temperature to Enhance Water Quality Predictions in NOAA's Next Generation Water Resources Modeling Framework

Our CISESS Seed Grant Project will develop a Basic Model Interface (BMI) for stream water temperature modules, enabling seamless integration with NOAA’s NextGen framework. Key objectives include: • Developing a BMI that enables integration of the stream water temperature model with the NextGen platform. • Conducting rigorous testing and validation of the BMI to ensure model accuracy and reliability. • Providing real-time predictions of stream temperature dynamics.

Downscaling of vegetation products

Apr 14, 2025 11:06 AM

CISESS Seed Grant: Developing High Spatiotemporal Resolution Vegetation Datasets from Multi-Sensor Satellite Observations for Enhanced Ecological and Agricultural Monitoring

This seed grant project focuses on balancing the spatial resolution of LEO satellites and the temporal resolution of GEO satellites to daily high-resolution vegetation datasets (10–30m). We will use a pre-calibrated Look-Up Tables for seamless integration of satellite observations and a a framework ready for operational deployment, aligning with NOAA's mission to deliver actionable Earth observation data.

Results: 147 Articles found.

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