Characterization of extrasolar terrestrial planets from diurnal photometric variability

The detection of massive planets orbiting nearby stars has become almost routine, but current techniques are as yet unable to detect terrestrial planets with masses comparable to the Earth's. Future space-based observatories to detect Earth-like planets are being planned. Terrestrial planets orbiting in the habitable zones of stars-where planetary surface conditions are compatible with the presence of liquid water-are of enormous interest because they might have global environments similar to Earth's and even harbor life. The light scattered by such a planet will vary in intensity and colour as the planet rotates; the resulting light curve will contain information about the planet's properties. Here we report a model that predicts features that should be discernible in light curves obtained by low-precision photometry. For extrasolar planets similar to Earth we expect daily flux variations up to hundreds of percent, depending sensitively on ice and cloud cover. Qualitative changes in surface or climate generate significant changes in the predicted light curves. This work suggests that the meteorological variability and the rotation period of an Earth-like planet could be derived from photometric observations. Other properties such as the composition of the surface (e.g., ocean versus land fraction), climate indicators (for example ice and cloud cover), and perhaps even signatures of Earth-like plant life could be constrained or possibly, with further study, even uniquely determined.Comment: Published in Nature. 9 pages including 3 figure

Feature Extraction from Pioneer Venus OCPP Data

. Scientific visualization provides means to explore data and highlight interesting features in the data. In this paper we will discuss the visualization of astrophysical data. Light properties of sunlight scattered by the atmosphere of Venus were measured by the Pioneer Venus Orbiter. One of the objectives of this mission was to determine the properties of the clouds and haze in the atmosphere. Given the amount and complexity of the data, it is important to be able to browse through the data and select maps with interesting features. We built a system that reads the raw data, prepares it and extracts cloud features. The feature extraction is achieved by the following steps: selection, clustering, attribute calculation and iconic mapping. After data exploration a number of consecutive images with coherent moving cloud features, is found. From the center position and the time between two frames, a qualitative measure for the cloud velocities is derived. The obtained velocitie..