Professor Myles Allen CBE FRS

Abstract:

The dynamic estimation of large-scale stochastic image sequences, as frequently encountered in remote sensing, is important in a variety of scientific applications. However, the size of such images makes conventional dynamic estimation methods, for example, the Kalman and related filters, impractical. In this paper, we present an approach that emulates the Kalman filter, but with considerably reduced computational and storage requirements. Our approach is illustrated in the context of a 512 x 512 image sequence of ocean surface temperature. The static estimation step, the primary contribution here, uses a mixture of stationary models to accurately mimic the effect of a nonstationary prior, simplifying both computational complexity and modeling. Our approach provides an efficient, stable, positive-definite model which is consistent with the given correlation structure. Thus, the methods of this paper may find application in modeling and single-frame estimation.

Abstract:

Observed globally-averaged land precipitation changes over the 20th century are compared with simulations of the HadCM3 climate model using an "optimal fingerprinting" method. We find that observed changes in precipitation are too large to be consistent with model-generated internal variability and are consistent with (attributable to) the combination of natural and anthropogenic forcings applied to the model. By comparing precipitation observations to shortwave and longwave forcing timeseries, we find that most of the forced variation in precipitation appears to be driven by natural shortwave forcings. We are unable to detect a response to anthropogenic longwave forcings in isolation. Finally, we seek to explain these results in terms of perturbations to the energy budget of the troposphere. Copyright 2004 by the American Geophysical Union.