Earth Observation Data Group

Abstract:

Observations from the International Satellite Cloud Climatalogy Project (ISCCP) are used to demonstrate that the 19-level HadAM3 version of the United Kingdom Met Office Unified Model does not simulate sufficient high cloud over land. By using low-altitude winds, from the European Centre for Medium Range Weather Forecasting (ECMWF) Re-Analysis from 1979 to 1994 (ERA-15) to predict the areas of maximum likelihood of orographic wave generation, it is shown that much of the deficiency is likely to be due to the lack of a representation of the orographic cirrus generated by sub-grid scale orography. It is probable that this is a problem in most GCMs.

Abstract:

A new method for the retrieval of the spectral refractive indices of micrometer-sized particles from infrared aerosol extinction spectra has been developed. With this method we use a classical damped harmonic-oscillator model of molecular absorption in conjunction with Mie scattering to model extinction spectra, which we then fit to the measurements using a numerical optimal estimation algorithm. The main advantage of this method over the more traditional Kramers-Kronig approach is that it allows the full complex refractive-index spectra, along with the parameters of the particle size distribution, to be retrieved from a single extinction spectrum. The retrieval scheme has been extensively characterized and has been found to provide refractive indices with a maximum uncertainty of similar to10% (with a minimum of similar to0.1%). Comparison of refractive indices calculated from measurements of a ternary solution of HNO3, H2SO4, and H2O with those published in J. Phys. Chem. A 104, 783 (2000) show similar differences as found by other authors. (C) 2005 Optical Society of America.

Abstract:

This work is focused on the application of neural network based models to the analysis of total ozone (TO) time series. Processes that affect total ozone are extremely non linear, especially at the considered European mid-latitudes. Artificial neural networks (ANNs) are intrinsically non-linear systems, hence they are expected to cope with TO series better than classical statistics do. Moreover, neural networks do not assume the stationarity of the data series so they are also able to follow time-changing situations among the implicated variables. These two features turn NNs into a promising tool to catch the interactions between atmospheric variables, and therefore to extract as much information as possible from the available data in order to make, for example, time series reconstructions or future predictions. Models based on NNs have also proved to be very suitable for the treatment of missing values within the data series. In this paper we present several models based on neural networks to fill the missing periods of data within a total ozone time series, and models able to reconstruct the data series. The results released by the ANNs have been compared with those obtained by using classical statistics methods, and better accuracy has been achieved with the non linear ANNs techniques. Different network structures and training strategies have been tested depending on the specific task to be accomplished. © European Geosciences Union 2004.