Atmospheric Measurement Techniques (Nov 2011)
A 3-D tomographic retrieval approach with advection compensation for the air-borne limb-imager GLORIA
Abstract
Infrared limb sounding from aircraft can provide 2-D curtains of multiple trace gas species. However, conventional limb sounders view perpendicular to the aircraft axis and are unable to resolve the observed airmass along their line-of-sight. GLORIA (Gimballed Limb Observer for Radiance Imaging of the Atmosphere) is a new remote sensing instrument that is able to adjust its horizontal view angle with respect to the aircraft flight direction from 45° to 135°. This will allow for tomographic measurements of mesoscale structures for a wide variety of atmospheric constituents. <br><br> Many flights of the GLORIA instrument will not follow closed curves that allow measuring an airmass from all directions. Consequently, it is examined by means of simulations, what spatial resolution can be expected under ideal conditions from tomographic evaluation of measurements made during a straight flight. It is demonstrated that the achievable horizontal resolution in the line-of-sight direction could be reduced from over 200 km to around 70 km compared to conventional retrievals and that the tomographic retrieval is also more robust against horizontal gradients in retrieved quantities in this direction. In a second step, it is shown that the incorporation of channels exhibiting different optical depth can further enhance the spatial resolution of 3-D retrievals enabling the exploitation of spectral samples usually not used for limb sounding due to their opacity. <br><br> A second problem for tomographic retrievals is that advection, which can be neglected for conventional retrievals, plays an important role for the time-scales involved in a tomographic measurement flight. This paper presents a method to diagnose the effect of a time-varying atmosphere on a 3-D retrieval and demonstrates an effective way to compensate for effects of advection by incorporating wind-fields from meteorological datasets as a priori information.