A Cloud Detection Algorithm Based Upon FPI Measurements

Physics

Scientific paper

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[2494] Ionosphere / Instruments And Techniques, [3369] Atmospheric Processes / Thermospheric Dynamics

Scientific paper

Fabry-Perot Interferometers (FPIs) can be used to estimate winds and temperatures in the thermosphere by observing the 630.0-nm redline emission. From a ground-based FPI, the availability of measurements depends on the sky conditions at the local observatory. When clouds are overhead, they can both reflect the lights from nearby cities and filter out the 630.0-nm emission. In either case, analysis of the observed FPI ring patterns could lead to erroneous results. An explicit way to detect clouds is by looking at the images taken by a collocated imaging system to determine the sky conditions. However, such coordinated observations are not always available. We present a more robust method for cloud detection dependent only on FPI observations, specifically estimates of the intensity of the spectral background. This parameter is estimated as part of the inversion process used to analyze the FPI observations. This background measurement parameter can be decomposed into two components, the sky continuum and the fluctuations in the intensity due to clouds obscuring the observation of the 630.0-nm emission. Case studies from the FPI data obtained by the Remote Equatorial Nighttime Observatory of Ionospheric Regions (RENOIR) in northeastern Brazil are presented. Results are verified through comparisons with images taken by a collocated CCD all-sky camera. In addition, we present monthly averages of winds and temperatures constructed after cloudy conditions have been removed from the data.

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