A B S T R A C T ADM-Aeolus, the wind Lidar under development at ESA, is a High Spectral Resolution Lidar that additionally provides separated information on particles (Mie channel) and molecules (Rayleigh channel). Lidar signals will be accumulated in vertical range bins in order to reach sufficient signal-to-noise ratio for reliable wind estimates. The vertical range bin integration may vary from 250 m near the surface up to 2 km in the upper troposphere and lower stratosphere. Significant attenuation in a range bin changes the nature of the retrieval problem. The commonly used Lidar inversion techniques appear to be inadequate to process bin-accumulated signals. This paper presents the 'L2A processor', conceived to use ADM-Aeolus signals to provide information on aerosol and cloud layers optical properties. The altitude, geometrical depth, optical depth, backscatter-to-extinction ratio and scattering ratio are to be retrieved. The L2A processor algorithms provide a new formulation to the inverse problem for various filling cases of a range bin and it includes a credibility criterion (CC) in order to select the best filling approximation. The effective vertical resolution can be two to four times better than the ADM-Aeolus range bins. The basic concept, the processing algorithms, numerical examples and sensitivity tests are here presented.
Aeolus is primarily a research and demonstration mission flying the first Doppler wind lidar in space. Flexible data processing tools are being developed for use in the operational ground segment and by the meteorological community. We present the algorithms developed to retrieve accurate and representative wind profiles, suitable for assimilation in numerical weather prediction. The algorithms provide a flexible framework for classification and weighting of measurement-scale (1-10 km) data into aggregated, observation-scale (50 km) wind profiles for assimilation. The algorithms account for temperature and pressure effects in the molecular backscatter signal, and so the main remaining scientific challenge is to produce representative winds in inhomogeneous atmospheric conditions, such as strong wind shear, broken clouds, and aerosol layers. The Aeolus instrument provides separate measurements in Rayleigh and Mie channels, representing molecular (clear air) and particulate (aerosol and clouds) backscatter, respectively. The combining of information from the two channels offers possibilities to detect and flag difficult, inhomogeneous conditions. The functionality of a baseline version of the developed software has been demonstrated based on simulation of idealized cases.
This paper shows the first results of a more complete study on the comparison of interpolation methods applied to conically scanning and across-track scanning radiometers.The goal here is to present the performances of two interpolation algorithms for the interpolation of the raw measurements (samples) onto non-overlapping pixels, regularly localized along a scan of an across-track radiometer.The originality of the approach is the use of an end-to-end simulator including:• high resolution 2D realistic brightness temperature (TB) scenes computed from geophysical fields thanks to a radiative transfer model when previous studies have used synthetic 1D profiles • 2D convolution of the scene by Gaussian or measured antenna patterns at any pointing angle (defined by azimuth and elevation) when previous studies have used 1D convolution of synthetic antenna patterns pointing at nadir • notion of temporal integration when computing the raw radiometric measurements when previous studies have used instantaneous fields of view (IFOV) Both synthetic and high resolution realistic scenes, including or not radiometric noise, are used as referenced fields to assess the performances of these algorithms in term of radiometric accuracy and radiometric sensitivity.The simulation of the measurements is based on the convolution of the scene by the antenna patterns and takes into account the notion of Effective Field Of View (EFOV) [1].The two interpolation processes are: • a purely geometric process based on the surface intersection between measurements (samples) and pixels -3dB beams projected on Earth. • the well-known Backus-Gilbert algorithm [2]-[3] The two methods show the same performance in term of radiometric accuracy when the Backus-Gilbert allows a better reduction of the radiometric noise.
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