HRRR

RAP

NAM

NAMNST

GEM-RDPS

GEM-GDPS

ECMWF

GFS

CFSv2

SREF

GEFS

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NEXLAB FORECAST

Numerical Weather Prediction Data from NCEP, CMC and ECMWF

Deterministic Models

Short Range

HRRR

RAP

Medium Range

NAM

NAMNST

GEM-RDPS

Long Range

GEM-GDPS

ECMWF

GFS

CFSv2

Ensemble Models

Medium Range

SREF

Long Range

GEFS

Model Comparison Tools

Previous Runs

Current Models

Products w/ Height

NAM - Choose from available sectors

Large Sectors

A Brief Introduction...

This page contains numerical weather prediction data used to model the behavior of the atmosphere in the near future. The source data is generated by supercomputers ingesting current atmospheric conditions and solving complex physics calculations to produce a time-series of data. These models forecast deterministically; i.e create an exact expectation of the future state of the atmosphere. They come in a variety of types meant to aid forecasters in producing reliable and accurate forecasts. In all cases, the accuracy of these models diminishes the further out into the future they try to predict. As such, the solutions each model generates are re-run several times each day using updated current conditions. Forecast reliability can be improved by interrogating the degree to which the solution changes as newer runs are generated. Additionally, by cross-checking model solutions with other models covering the same domain and valid time, forecast uncertainty can be inferred.

Currently we offer a selection of models from NCEP, CMC and ECMWF that fall into two distinct categories; Deterministic and Ensemble. Deterministic models depict a single solution from the model and their data can be interpreted as a literal depiction of the future state of the atmosphere. However, it must never be overlooked that there is always an inherent aspect of inaccuracy as a natural result of never being able to perfectly sample, ingest and calculate the atmosphere and its physics at the smallest temporal and spatial scales. Ensemble models are an effort to remedy this and depict confidence in a given solution by running a number of simultaneous solutions where initial conditions or key aspects of the model physics are slightly altered to determine how sensitive the forecast solution is to variation. A convergent solution in an ensemble forecast adds confidence to that particular solution, and vice versa.

We generate our own customized renderings of this model data at a variety of output domain size. Some models come paired with point forecast sounding data, accessible by click/tap of applicable model data. Additionally we offer a variety of comparison tools; Previous Run and Model Comparisons to aid in determining forecast confidence, and Height Comparisons to aid in understanding the vertical profile of applicable parameters. All model date and time information is denoted in Zulu Time (Z) which is equivalent to Coordinated Universal Time (UTC) and Greenwich Mean Time (GMT).

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For further information, check the following twitter accounts for periodic updates on product generation status and website improvements; @CoDWXData and @CODMeteorology. Your feedback is always appreciated, so feel free to respond to tweets or send us an email using our Feedback page.

Legacy Forecast Models Page
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