Improved multi-beam neural network scatterometer forward models


Current methods for retrieving near surface winds from scatterometer observations over the ocean surface require a foward sensor model which maps the wind vector to the measured backscatter. This paper develops a hybrid neural network forward model, which retains the physical understanding embodied in ¸mod, but incorporates greater flexibility, allowing a better fit to the observations. By introducing a separate model for the mid-beam and using a common model for the fore- and aft-beams, we show a significant improvement in local wind vector retrieval. The hybrid model also fits the scatterometer observations more closely. The model is trained in a Bayesian framework, accounting for the noise on the wind vector inputs. We show that adding more high wind speed observations in the training set improves wind vector retrieval at high wind speeds without compromising performance at medium or low wind speeds.

Divisions: ?? 50811700Jl ??
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Uncontrolled Keywords: surface winds,scatterometer observations,foward sensor model,wind vector,measured backscatter,neural network forward model,Bayesian framework,wind speeds
ISBN: NCRG/2000/008
Last Modified: 15 Jul 2024 08:34
Date Deposited: 14 Sep 2009 14:53
PURE Output Type: Technical report
Published Date: 2000
Authors: Cornford, Dan (ORCID Profile 0000-0001-8787-6758)
Nabney, Ian T. (ORCID Profile 0000-0003-1513-993X)
Ramage, Guillaume


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