Reanalyzed datasets can be used to create a coherent, long-term record of past weather by compensating for the inherent biases of the different instruments used to take measurements at different points in the history of weather observation. Reanalysis involves a variety of data synthesis methods that are used to incorporate different datasets into one regularly spaced grid. This ‘gridding’ process makes it easier to compare observations from different datasets, and preserves the original data collection model to ensure that the historical record isn’t influenced by artificial factors. Visit NOAA’s National Center for Environmental Prediction (NCEP) website to learn more about NOAA

The release of this dataset marks a significant advancement in NOAA’s ability to more equitably serve the nation’s coastal and maritime communities, regardless of their proximity to a NOAA tide gauge. CORA paves the way for NOAA to provide coastal flooding predictions between every 500 meters along U.S. coasts.

Through the combination of Advanced CIRCulation (ADCIRC) and Simulating WAves Nearshore (SWAN) modeling, and atmospheric data from ERA5 in addition to National Water Level Observation Network (NWLON), CORA models: Water level elevation and maximums, atmospheric pressure, wind speed and direction, wave heights, direction, and peaks. More details can be found in the Model Output section of the Glossary & Key Terms guide.

CORA datasets are created by combining Advanced CIRCulation (ADCIRC) and Simulating WAves Nearshore (SWAN) modeling attributes between observations from the National Water Level Observation Network (NWLON). CORA is unique because it also incorporates long-term historical water level observations to inform and improve accuracy of modeled datasets.

CORA datasets are produced as both centroids and grids to provide flexibility for both analysis and visualization. Centroids are the traditional point-based results of modeling that enhance the resolution of modeled historical data from miles to meters. Translating centroids onto a grid creates continuous spatial resolution of modeled datasets, as well as uniform infrastructure for visualizing a variety of modeled datasets at the same resolution. This feature is helpful for mapping and geospatial analysis of CORA datasets.

CORA includes detailed meteorological data from a resource called ERA5, a reanalysis for the global climate and weather for the past 8 decades developed by European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5 combines global weather observations—like wind speed, wind direction, and air pressure—with computer models to create a complete and consistent view of past weather patterns from around the world.

CORA datasets can be accessed through NOAA’s Open Data Dissemination (NODD) Platform.

CORA will be periodically updates as modeling resources are available. The dataset covers 1979–2022, and the next planned update will include data from 2023.

Yes! The reanalysis is being completed in phases. NOAA is currently conducting a reanalysis of the Pacific and Hawaii, slated to be released in late 2025. After that, there will be a reanalysis for Alaska and the Pacific Islands, slated for release in late 2026.

Historical data is at the heart of all NOAA’s inundation forecast and flood predictions, which helps identify long-term patterns in flood frequency, intensity, and duration. By analyzing decades of water level records, NOAA can better understand how often flooding occurs and under what conditions, such as high tides or storms.

Yes! SWAN simulates nearshore waves, which allows CORA to incorporate wave direction and height.

Hindcastasts simulate past weather conditions using a numerical model without incorporating real-time observations, while a reanalysis combines historical observations with a model to create a more accurate representation of past weather conditions through a process called data assimilation, resulting in a more comprehensive picture of the past climate state.

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