a conceptually simple modeling approach for jason-1 sea state bias correction based on 3 parameters exclusively derived from altimetric information

Clicks: 192
ID: 161537
2016
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #394 of 499 articles by views in Journal of pharmacological sciences

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 499 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
A conceptually simple formulation is proposed for a new empirical sea state bias (SSB) model using information retrieved entirely from altimetric data. Nonparametric regression techniques are used, based on penalized smoothing splines adjusted to each predictor and then combined by a Generalized Additive Model. In addition to the significant wave height (SWH) and wind speed (U10), a mediator parameter designed by the mean wave period derived from radar altimetry, has proven to improve the model performance in explaining some of the SSB variability, especially in swell ocean regions with medium-high SWH and low U10. A collinear analysis of scaled sea level anomalies (SLA) variance differences shows conformity between the proposed model and the established SSB models. The new formulation aims to be a fast, reliable and flexible SSB model, in line with the well-settled SSB corrections, depending exclusively on altimetric information. The suggested method is computationally efficient and capable of generating a stable model with a small training dataset, a useful feature for forthcoming missions.
Reference Key
pires2016remotea Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Nelson Pires;M. Joana Fernandes;Christine Gommenginger;Remko Scharroo
Journal Journal of pharmacological sciences
Year 2016
DOI
10.3390/rs8070576
URL
http://www.mdpi.com/2072-4292/8/7/576
Keywords
satellite altimetry gamscience

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.