NOAA Unified Forecast System (UFS)

Researchers at NOAA’s Physical Sciences Laboratory have developed a new “replay” dataset to support improvements to the Global Ensemble Forecast System (GEFSv13) and Global Forecast System (GFSv17). By nudging a coupled model toward ERA5 and ORAS5 reanalysis data, this dataset enhances the accuracy of retrospective forecasts and supports machine learning applications. The effort aims to improve medium-range global weather predictions and reduce model biases through innovative data assimilation techniques. To learn more

Researchers at NOAA’s Physical Sciences Laboratory are using the Global Forecast System (GFSv17) to generate experimental climate reconstructions that span from 1979 to the present. These reconstructions provide a consistent, gap-filled estimate of past atmospheric conditions, simulating the atmosphere as accurately as possible using modern data assimilation techniques and today’s best forecast models. A key diagnostic in this process is brightness temperature (TB)—a satellite-derived measurement that represents emitted radiation in temperature units. By comparing observed and simulated TB, researchers are evaluating the accuracy of the reconstructions and continuously refine the system for improved representation of Earth’s past climate.

The NOAA-NASA Joint Archive (NNJA) is a collaborative, experimental effort to provide a unified, curated dataset of Earth system observations from 1979 to the present, hosted on AWS S3. Designed to support Earth System reanalysis research, the archive enables consistent comparison across reanalysis efforts by minimizing variability in input observations. The dataset includes ocean, land, ice, and atmospheric data in formats such as BUFR, IODA, and NetCDF, along with quality control tools and metadata like observation errors and black/white lists. Future developments aim to expand data coverage to near real-time, enhance usability through APIs, and provide robust diagnostics, offering broad value to the Earth system science community.To learn more