Maximum likelihood representation of MIPAS profiles

T Von Clarmann,J Plieninger,N Glatthor

doi:10.5194/amt-8-2749-2015

Abstract

Abstract. In order to avoid problems connected with the content of a priori information in volume mixing ratio vertical profiles measured with the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), a user-friendly representation of the data has been developed which will be made available in addition to the regular data product. In this representation, the data will be provided on a fixed pressure grid coarse enough to allow a virtually unconstrained retrieval. To avoid data interpolation, the grid is chosen to be a subset of the pressure grids used by the Chemistry–Climate Model Initiative and the Data Initiative within the Stratosphere-troposphere Processes And their Role in Climate (SPARC) project as well as the Intergovernmental Panel of Climate Change climatologies and model calculations. For representation, the profiles have been transformed to boxcar base functions, which means that volume mixing ratios are constant within a layer. This representation is thought to be more adequate for comparison with model data. While this method is applicable also to vertical profiles of other species, the method is discussed using ozone as an example.

Highlights

The often ill-posed nature of inverse problems in remote sensing of the atmosphere is typically fought by formal regularization, i.e. by inclusion of prior information in a Bayesian or related sense
Still remain: (a) the need to communicate averaging kernels to the data user multiplies the amount of data to be transmitted; (b) many data users are not willing to consider averaging kernels in their analysis and prefer to take the data as they are, ignoring their content of prior information; and (c) averaging kernels vary with time, which leads to unsolved problems in trend analysis (e.g. Yoon et al, 2013) or analysis of annual cycles (e.g. Hegglin et al, 2013, and Schieferdecker et al, 2015, their Fig. 2 and related discussion)
It appears desirable to offer an alternative representation of the data which is user-friendly in a sense that the data user need not worry about averaging kernels, and no averaging kernels have to be provided to the user

Summary

Introduction

The often ill-posed nature of inverse problems in remote sensing of the atmosphere is typically fought by formal regularization, i.e. by inclusion of prior information in a Bayesian or related sense. Another common regularization method is that developed independently by Tikhonov (1963), Twomey (1963), and Phillips (1962) While all these problems provide profiles which are in some sense optimal, the major drawback is that the data product contains a certain amount of a priori information. The most user-friendly altitude grid is the one the data user works with, because this avoids interpolation problems and saves the data user from transforming the unity averaging kernel to the new grid For this reason, we use for our retrievals a well-established pressure grid as a “master vertical grid”, which has been used (except for the 700 and 400 hPa values newly added) in the context of the SPARC Chemistry–Climate Model Validation activities The larger peak values of the ML profiles compared to the regular representation where the peaks are more rounded is a typical feature of triangular base functions: their peak is slimmer, which is compensated by a larger peak value to be consistent with the same amount of molecules (Fig. 1)

Staircase profiles

How to use the ML data

Results

Discussion and conclusion