Ten years of MIPAS measurements with ESA Level 2 processor V6 – Part 1: Retrieval algorithm and diagnostics of the products

Abstract

Abstract. The MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) instrument on the Envisat (Environmental satellite) satellite has provided vertical profiles of the atmospheric composition on a global scale for almost ten years. The MIPAS mission is divided in two phases: the full resolution phase, from 2002 to 2004, and the optimized resolution phase, from 2005 to 2012, which is characterized by a finer vertical and horizontal sampling attained through a reduction of the spectral resolution. While the description and characterization of the products of the ESA processor for the full resolution phase has been already described in previous papers, in this paper we focus on the performances of the latest version of the ESA (European Space Agency) processor, named ML2PP V6 (MIPAS Level 2 Prototype Processor), which has been used for reprocessing the entire mission. The ESA processor had to perform the operational near real time analysis of the observations and its products needed to be available for data assimilation. Therefore, it has been designed for fast, continuous and automated analysis of observations made in quite different atmospheric conditions and for a minimum use of external constraints in order to avoid biases in the products. The dense vertical sampling of the measurements adopted in the second phase of the MIPAS mission resulted in sampling intervals finer than the instantaneous field of view of the instrument. Together with the choice of a retrieval grid aligned with the vertical sampling of the measurements, this made ill-conditioned the retrieval problem of the MIPAS operational processor. This problem has been handled with minimal changes to the original retrieval approach but with significant improvements nonetheless. The Levenberg–Marquardt method, already present in the retrieval scheme for its capability to provide fast convergence for nonlinear problems, is now also exploited for the reduction of the ill-conditioning of the inversion. An expression specifically designed for the regularizing Levenberg–Marquardt method has been implemented for the computation of the covariance matrices and averaging kernels of the retrieved products. The regularization of the Levenberg–Marquardt method is controlled by the convergence criteria and is deliberately kept weak. The resulting oscillations of the retrieved profile are a posteriori damped by an innovative self-adapting Tikhonov regularization. The convergence criteria and the weakness of the self-adapting regularization ensure that minimum constraints are used and the best vertical resolution obtainable from the measurements is achieved in all atmospheric conditions. Random and systematic errors, as well as vertical and horizontal resolution are compared in the two phases of the mission for all products, namely: temperature, H2O, O3, HNO3, CH4, N2O, NO2, CFC-11, CFC-12, N2O5 and ClONO2. The use in the two phases of the mission of different optimized sets of spectral intervals ensures that, despite the different spectral resolutions, comparable performances are obtained in the whole MIPAS mission in terms of random and systematic errors, while the vertical resolution and the horizontal resolution are significantly better in the case of the optimized resolution measurements.

A multi-wavelength classification method for polar stratospheric cloud types using infrared limb spectra

“…The satellite operated on a sun synchronous orbit (inclination 98.4 • ) and allowed geographical coverage up to both poles due to additional poleward tilt of the primary mirror. The high spectral resolution of 0.025 cm −1 (HR: high-resolution mode) was reduced in 2004 to 0.0625 cm −1 (OR: optimized-resolution mode) due to technical problems with the interferometer (Raspollini et al, 2013). Consequently, the level 1b radiance data from the measurement period July 2002 to March 2004 (Phase 1) were measured in the HR mode and from January 2005 to March 2012 (Phase 2) in the OR mode.…”

Characterization of model errors in the calculation of tangent heights for atmospheric infrared limb measurements

“…In order to assess the influence of the newly calculated tangent heights on the level 2 products, we retrieved orbit 43442 starting from the recalculated tangent heights using the ESA operational algorithm (Ridolfi et al, 2000;Raspollini et al, 2006Raspollini et al, , 2013. On average, the differences in the retrieved profiles are much smaller than the noise error, with no real improvement in the final χ 2 of the fit.…”

“…Our implementation is however planned for inclusion in the ESA retrieval model for the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) routine data processing (Ridolfi et al, 2000;Raspollini et al, 2006Raspollini et al, , 2013, which assumes that all limb scans lie in the orbit plane. In a forthcoming evolution of this algorithm, the atmosphere will be represented with a 2-D discretization in the orbit plane and will be considered constant in the direction perpendicular to this plane.…”

“…-the US Standard Atmosphere, 1976 (US Gov., 1976), -the IG2 atmosphere (Remedios et al, 2007), -the atmosphere retrieved in a previous processing version of MIPAS data (Raspollini et al, 2013),…”

Tomographic retrieval of water vapour and temperature around polar mesospheric clouds using Odin-SMR

“…The reason for this is that the final solution (and hence the AVK) is independent of γ (for further discussion see e.g. Ceccherini and Ridolfi, 2010 and the discussion thereof, and Raspollini et al, 2013).…”