Data · dataset · 2026
The Anemomilosprediction methodology for Dst
Listed in National Center for Atmospheric Research
This paper describes new capabilities for operational geomagnetic Disturbance storm time (Dst) index forecasts.
Description
We present a data-driven, deterministic algorithm called Anemomilos for forecasting Dst out to a maximum of 6 days for large, medium, and small storms, depending upon transit time to the Earth. This capability is used for operational satellite management and debris avoidance in Low Earth Orbit (LEO).
Anemomilos has a 15âmin cadence, 1âh time granularity, 144âh prediction window (+6 days), and up to 1âh latency. A new finding is that nearly all flare events above a certain irradiance threshold, occurring within a defined solar longitude/latitude region and having sufficient estimated liftoff velocity of ejected material, will produce a geoeffective Dst perturbation. Three solar observables are used for operational Dst forecasting: flare magnitude, integrated flare irradiance through time, and event location.
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Magnitude is a proxy for ejecta quantity or mass and, combined with speed derived from the integrated flare irradiance, represents the kinetic energy. Speed is estimated as the line-of-sight velocity for events within 45° radial of solar disk center. Storms resulting from high-speed streams emanating from coronal holes are not modeled or predicted.
A new result is that solar disk, not limb, observable features are used for predictive techniques. Comparisons between Anemomilos predicted and measured Dst for every hour over 25 months in three continuous time frames between 2001 (high solar activity), 2005 (low solar activity), and 2012 (rising solar activity) are shown. The Anemomilos operational algorithm was developed for a specific customer use related to thermospheric mass density forecasting.
It is an operational space weather technology breakthrough using solar disk observables to predict geomagnetically effective Dst up to several days at 1âh time granularity. Real-time forecasts are presented at sol.spacenvironment.net/~sam_ops/index.html?
Links
Get the data
- Publisher page n2t.org/ark:/85065/d751403w ↗
documentation · download · from data ucar edu
Where it is published
- data.ucar.edu /dataset/the-anemomilosprediction-methodology-for-dst ↗
National Center for Atmospheric Research dataset page
landing page · from data ucar edu
Catalogue records · 1
- CKAN API data.ucar.edu/api/3/action/package_show?id=bedfa0e3-f01c-4a11-97ed-c2bbebcd5… ↗
metadata API · from data ucar edu
Topics
- From keywords
- Earth & Environmental Science · Ocean & Atmospheric Science
- Inferred from text
- Geoinformatics 72%
Provenance · 1 source records, 8 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| National Center for Atmospheric Research | bedfa0e3-f01c-4a11-97ed-c2bbebcd5992 | 11 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:3704 | enrichment · data ucar edu | taxonomy-embedding@1.1.0 | title+keywords+description (72%) |
| concepts[field].local:field:earth-environmental | mapping · data ucar edu | connector:data_ucar_edu@1.0.0 | |
| concepts[field].local:field:ocean-atmospheric | mapping · data ucar edu | connector:data_ucar_edu@1.0.0 | |
| created_date | source · data ucar edu | connector:data_ucar_edu@1.0.0 | |
| description | source · data ucar edu | connector:data_ucar_edu@1.0.0 | /notes |
| publication_date | source · data ucar edu | connector:data_ucar_edu@1.0.0 | |
| title | source · data ucar edu | connector:data_ucar_edu@1.0.0 | /title |
| updated_date | source · data ucar edu | connector:data_ucar_edu@1.0.0 |