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Detailed airborne and ground-based measurements, including radar and stereocamera data, were collected over an isolated mountain in Arizona to study the dynamics of cumulus clouds evolving from shallow to deep convection.

This paper describes a technique for photogrammetric analysis of stereo pairs of images that is applied to the study of orographic convection. The technique is designed for use with digital images and assumes detailed knowledge of the camera properties (focal length and imaging chip) and that the position and orientation are known as a first guess. An iterative scheme using known landmarks on the frame is used to determine the camera orientation. The scheme is accurate to 10–100 m at a distance of 15 km from the camera pair. The transition from shallow to deep convection over the Santa Catalina Mountains in southern Arizona on 26 July 2005 is presented. The three-dimensional structure of the visible portion of the cloud is determined and compared with the composite reflectivity from the National Weather Service Weather Surveillance Radar-1988 Doppler radar and the tropopause height from the 1200 UTC sounding in Tucson, Arizona, providing additional validation of the scheme. The shallow to deep transition is characterized by tracking individual turrets and determining the maximum height of the cloud top. The cloud tops were limited to beneath 6000 m MSL for the first 1.5 h followed by the development of deep convection. The motion of the turrets and location of the eventual deep convection were consistent with the idea that moistening by shallow convection conditions the atmosphere for further development.

A technique for calibrating digital cameras for stereo photogrammetry of cumulus clouds is presented. It has been applied to characterize the formation of summer thunderstorms observed during the Cumulus Photogrammetric, In Situ, and Doppler Observations (CuPIDO) project. Starting from gross measurements of locations, orientations of cameras, and landmark surveys, accurate locations and orientations of the cameras are obtained by minimizing a geometric error (GE). Once accurate camera parameters are obtained, 3D positions of cloud-feature points are computed by triangulation. The main contributions of this paper are as follows. First, it is proven that the GE has only one minimum in the neighborhood of the real parameters of a camera. In other words, searching the minimum of the GE enables the authors to find the right camera parameters even if there are significant differences between the initial measurements and their true values. Second, a new coarse-to-fine iterative algorithm is developed that minimizes the GE and finds the camera parameters. Numerical experiments show that the coarse-to-fine algorithm is efficient and effective. Third, a new landmark survey based on a geographic information system (GIS) rather than field measurements is presented. The GIS landmark survey is an effective and efficient way to obtain landmark world coordinates for camera calibrations in these experiments. Validation of this technique is achieved by the data collected by a NASA/Earth Observing System satellite and an instrumented aircraft. This paper builds on previous research and details the calibration and 3D reconstructions.

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