Retrieval of tangent pressures from EOS-MLS radiances using a neural network for use in an assimilation scheme

Limb sounding instruments provide high vertical resolution data on the temperature and composition of the atmosphere. Their data are, therefore, valuable for assimilating into general circulation models of the atmosphere. Direct assimilation of radiances from limb sounders is more complex in practice than from nadir sounders due to the need to know the tangent pressures of the measurements. This paper discusses the practical implications of tangent pressures in direct radiance assimilation of limb sounding radiances and demonstrates that a neural network can be used to und these tangent pressures for the Earth Observing System Microwave Limb Sounder with a root mean-square error of σ = 50 m, which is comparable with that in traditional retrieval techniques.