Techniques for determining bias and stability in satellite altimeters

The case for monitoring large-scale sea level variability is established in the context of the estimation of the extent of anthropogenic climate change. Satellite altimeters are identified as having the potential to monitor this change with high resolution and accuracy. Possible sources of systematic errors and instabilities in these instruments which would be hurdles to the most accurate monitoring of such ocean signals are examined. Techniques for employing tide gauges to combat such inaccuracies are proposed and developed. The tide gauge at Newhaven in Sussex is used in conjunction with the nearby satellite laser ranger and high-resolution ocean models to estimate the absolute bias of the TOPEX, Poseidon, ERS 1 and ERS 2 altimeters. The theory which underlies the augmentation of altimeter measurements with tide gauge data is developed. In order to apply this, the tide gauges of the World Ocean Circulation Experiment are assessed and their suitability for altimeter calibration is determined. A reliable subset of these gauges is derived. A method of intra-altimeter calibration is developed using these tide gauges to remove the effect of variability over long time scales. In this way the long-term instability in the TOPEX range measurement is inferred and the drift arising from the on-board ultra stable oscillator is thus detected. An extension to this work develops a method for inter-altimeter calibration, allowing the systematic differences between unconnected altimeters to be measured. This is applied to the TOPEX and ERS 1 altimeters.

Random field models and priors on wind

This report seeks to make concrete some of the ideas we have been discussing about sensible priors for winds over the ocean. In particular, random field models are reviewed, as are permissible covariance functions. The criteria which these covariance functions must satisfy in order that vorticity and divergence exist and are continuous are defined. The use of Helmholtz theorem is discussed, and possible choices for the covariances are suggested.

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.

Online approximations for wind-field models

We study online approximations to Gaussian process models for spatially distributed systems. We apply our method to the prediction of wind fields over the ocean surface from scatterometer data. Our approach combines a sequential update of a Gaussian approximation to the posterior with a sparse representation that allows to treat problems with a large number of observations.

Improved neural network scatterometer forward models

Current methods for retrieving near-surface winds from scatterometer observations over the ocean surface require a forward 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 CMOD4, but incorporates greater flexibility, allowing a better fit to the observations. By introducing a separate model for the midbeam 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. Copyright 2001 by the American Geophysical Union.