Averaging kernel prediction from atmospheric and surface state parameters based on multiple regression for nadir-viewing satellite measurements of carbon monoxide and ozone

A current obstacle to the observation system simulation experiments (OSSEs) used to quantify the potential performance of future atmospheric composition remote sensing systems is a computationally efficient method to define the scene-dependent vertical sensitivity of measurements as expressed by the retrieval averaging kernels (AKs). We present a method for the efficient prediction of AKs for multispectral retrievals of carbon monoxide (CO) and ozone (O₃) based on actual retrievals from MOPITT (Measurements Of Pollution In The Troposphere) on the Earth Observing System (EOS)-Terra satellite and TES (Tropospheric Emission Spectrometer) and OMI (Ozone Monitoring Instrument) on EOS-Aura, respectively.

This employs a multiple regression approach for deriving scene-dependent AKs using predictors based on state parameters such as the thermal contrast between the surface and lower atmospheric layers, trace gas volume mixing ratios (VMRs), solar zenith angle, water vapor amount, etc. We first compute the singular value decomposition (SVD) for individual cloud-free AKs and retain the first three ranked singular vectors in order to fit the most significant orthogonal components of the AK in the subsequent multiple regression on a training set of retrieval cases.