Data · dataset · 2026
A practical approach to sequential estimation of systematic error on near-surface mesoscale grids
Listed in National Center for Atmospheric Research
Statistical analysis arguments are used to construct an estimation algorithm for systematic error of near-surface temperatures on a mesoscale grid.
Description
The systematic error is defined as the observed running-mean error, and an averaging length of 7 days is shown to be acceptable. Those errors are spread over a numerical weather prediction model grid via the statistical analysis equation.
Two covariance models are examined: 1) a stationary, isotropic function tuned with the observed running-mean errors and 2) dynamic estimates derived from a recent history of running-mean forecasts. Prediction of error is possible with a diurnal persistence model, where the error at one time of day can be estimated from data with lags of 24-h multiples. The approach is tested on 6 months of 6-h forecasts with the fifth-generation Pennsylvania State University-NCAR Mesoscale Model (MM5) over New Mexico.
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Results show that for a quantity such as 2-m temperature, the systematic component of error can be effectively predicted on the grid. The gridded estimates fit the observed running-mean errors well. Cross validation shows that predictions of systematic error result in a substantial error reduction where observations are not available.
The error estimates show a diurnal evolution, and are not strictly functions of terrain elevation. Observation error covariances, localization operators, and covariance functions in the isotropic case must be tuned for a specific forecast system and observing network, but the process is straightforward. Taken together, the results suggest an effective method for systematic error estimation on near-surface mesoscale grids in the absence of a useful ensemble.
Correction for those errors may provide benefits to forecast users.
Links
Get the data
- Publisher page n2t.org/ark:/85065/d78g8n1s ↗
documentation · download · from data ucar edu
Where it is published
- data.ucar.edu /dataset/a-practical-approach-to-sequential-estimation-of-syste… ↗
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=21fbbf0f-4621-44e9-976c-5019a996d… ↗
metadata API · from data ucar edu
Topics
- From keywords
- Earth & Environmental Science · Ocean & Atmospheric Science
Provenance · 1 source records, 7 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| National Center for Atmospheric Research | 21fbbf0f-4621-44e9-976c-5019a996d5bd | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| 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 |