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| Type: | Resource | |
| Storage: | The size of this resource is 1.4 MB | |
| Created: | Oct 14, 2026 at 2:27 p.m. (UTC) | |
| Last updated: | Oct 15, 2026 at 7:40 p.m. (UTC) (Metadata update) | |
| Published date: | Oct 15, 2026 at 7:40 p.m. (UTC) | |
| DOI: | 10.4211/hs.33634cbd65db47329435e510e75b8d08 | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 36 |
| Downloads: | 2 |
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Abstract
The NOAA's National Water Model is run in multiple operational cycling and configurations and ultimately provide three main types of products: (1) Retrospective, (2) Analysis & Assimilation, and (3) Forecast. The resource provides an accessible reference for understanding how NWM configurations support hydrologic monitoring and forecasting across different time scales. The content includes visualizations of NWM configurations, including Standard Analysis, Extended Analysis, Long-range, Medium-range, Medium-range Blend, Short-range, and Retrospective, using radar-style diagrams developed from the NWM configuration documentation. The images summarize key differences in forecast output frequency, forecast cycle frequency, output destinations, data assimilation, forecast horizon, and uncertainty. These dimensions are described in the "Additional Metadata" section of the resource. This work was originally developed in 2025 as part of the Introduction to Seasonal Snow Observations, Modeling, and Analysis HydroLearn Module. The PowerPoint file containing the editable diagrams is also included.
Subject Keywords
Content
Additional Metadata
| Name | Value |
|---|---|
| Forcings | Indicate the meteorological input datasets used to drive the model, such as HRRR, RAP, NBM, or CFS. |
| Forecasts | These are generated using data from numerical weather prediction models (e.g., HRRR, NBM, CFS) and are initialized using Analysis & Assimilation outputs to simulate future hydrologic conditions such as streamflow and snowmelt. |
| Retrospective | These are historical model re-runs using past observations and consistent forcing inputs (AORC – Analysis of Record for Calibration). While not real-time, they offer high-quality, long-term datasets that are valuable for model evaluation, calibration, and research. |
| Cycle Frequency | Describes how often the model configuration is executed—for example, hourly or four times per day. |
| Output Frequency | Refers to how often model outputs (e.g., streamflow from routing module, snowpack from land surface module) are generated—such as hourly, 3-hourly, or daily. |
| Uses A&A Restart | Indicates whether the configuration is initialized from an Analysis and Assimilation (A&A) restart file, which helps ensure continuity and accuracy across model runs. |
| Output Description | Provides additional detail about the nature of the model outputs, such as whether different variables are generated at different intervals (e.g., hourly for streamflow, daily for land variables). |
| Output Uncertainty | Defines whether the configuration produces a single deterministic output or an ensemble of outputs that represent uncertainty. |
| Forecast / Lookback | Period refers to the time window the model covers. A forecast period extends into the future, while a lookback period refers to how far back in time the model ingests observational data to correct or initialize its state. |
| Type (Process Type) | Indicates whether the configuration serves a forecasting, analysis, or retrospective purpose. |
| Analysis and Assimilation (A&A) | This refers to a process that combines observed data with model simulations to produce the most accurate possible estimate of current conditions, known as the model state. These states are saved as restart files for forecast initialization. The model uses a lookback period (the amount of past time that a model considers) to ingest recent observations, improving streamflow estimates. Note: Only streamflow is assimilated as of version 3. |
Related Resources
| The content of this resource references | https://storymaps.arcgis.com/stories/c4964f08ffcf4d9286bd1fd545ddfbab |
| The content of this resource references | Garousi-Nejad, Irene and Johnson, Ryan., (2025) Introduction to Seasonal Snow Observations, Modeling, and Analysis. CIROH. https://edx.hydrolearn.org/courses/course-v1:CIROH_HydroLearn+CVEEN6001+2025/about |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| National Oceanic and Atmospheric Administration (NOAA) | Cooperative Institute Program to the Cooperative Institute for Research on Hydrology (CIROH) | NA22NWS4320003 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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