Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 74 tok/s
Gemini 2.5 Pro 55 tok/s Pro
GPT-5 Medium 19 tok/s Pro
GPT-5 High 24 tok/s Pro
GPT-4o 109 tok/s Pro
Kimi K2 212 tok/s Pro
GPT OSS 120B 464 tok/s Pro
Claude Sonnet 4 37 tok/s Pro
2000 character limit reached

Modeling Tangential Vector Fields on a Sphere (1612.08062v1)

Published 23 Dec 2016 in stat.ME and stat.AP

Abstract: Physical processes that manifest as tangential vector fields on a sphere are common in geophysical and environmental sciences. These naturally occurring vector fields are often subject to physical constraints, such as being curl-free or divergence-free. We construct a new class of parametric models for cross-covariance functions of curl-free and divergence-free vector fields that are tangential to the unit sphere. These models are constructed by applying the surface gradient or the surface curl operator to scalar random potential fields defined on the unit sphere. We propose a likelihood-based estimation procedure for the model parameters and show that fast computation is possible even for large data sets when the observations are on a regular latitude-longitude grid. Characteristics and utility of the proposed methodology are illustrated through simulation studies and by applying it to an ocean surface wind velocity data set collected through satellite-based scatterometry remote sensing. We also compare the performance of the proposed model with a class of bivariate Mat\'ern models in terms of estimation and prediction, and demonstrate that the proposed model is superior in capturing certain physical characteristics of the wind fields.

Summary

We haven't generated a summary for this paper yet.

Lightbulb On Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube