Articles | Volume 24, issue 3
19 May 2006
 | 19 May 2006

High-resolution vertical imaging of the troposphere and lower stratosphere using the new MU radar system

H. Luce, G. Hassenpflug, M. Yamamoto, and S. Fukao

Abstract. In the present paper, a new application of the range imaging technique called Frequency Interferometry Imaging (FII) or Range Imaging (RIM), performed in April 2005, is shown using the new 46.5-MHz Middle and Upper (MU) atmosphere radar system (Shigaraki, Japan). Height-time images of brightness distribution have been computed at the highest resolution ever obtained for imaging with VHF radars in the troposphere and, for the very first time, in the lower stratosphere, up to about 22 km. The images were produced by processing signals obtained with an initial range-resolution of Δr=150 m and five equally-spaced frequencies within Δf=1.0 MHz, with the adaptive Capon method. These values represent an improvement of a factor 2 over all the previous published experiments at VHF, which were performed with Δr=300 m and Δf=0.5 MHz. The Capon images present realistic and self-consistent features, and reveal many more organized structures than the height-time SNR plots at the initial range-resolution. For example, the Capon images show persistent enhanced brightness layers significantly thinner than 150 m in the stratosphere, which are impossible to track with the standard single-frequency mode owing to a lack of range resolution. These observations thus support the idea of strong stratification even at vertical scales much smaller than 100 m, as suggested by recent high-resolution temperature observations by balloons (Dalaudier et al., 1994). We also present comparisons of Capon images with patterns obtained from the dual-FDI technique and two parametric methods (the MUSIC algorithm and the newly-introduced Maximum Entropy Method based on an auto-regressive (AR) model). The comparisons confirm the insufficiencies of the dual-FDI technique and indicate that parametric methods such as MEM and the MUSIC algorithm can help to validate the Capon images when the parametric methods provide similar patterns.