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Sampling techniques in high-dimensional spaces for the development of satellite remote sensing database

Summary: Sampling techniques in high-dimensional spaces for
the development of satellite remote sensing database
Filipe Aires1
and Catherine Prigent2
Received 3 January 2007; revised 10 May 2007; accepted 17 July 2007; published 17 October 2007.
[1] This study presents various strategies to sample databases from large atmospheric data
sets in high-dimensional spaces for satellite remote sensing applications. In particular, two
sampling algorithms are examined: the traditional uniform sampling that lists all possible
situations and the clustering sampling (K-means) that respects the natural variability
probability distribution functions. In order to assess the quality of both sampling methods,
the extracted databases are used to extract first guesses for satellite remote sensing
schemes. They are also employed as training databases for the calibration of statistical
retrieval algorithms. The analysis of these sampling algorithms is illustrated by
constructing both a first guess (FG) extraction and a retrieval databases of temperature and
water vapor profiles over sea for the Atmospheric Microwave Sounding Unit (AMSU)
instrument. The advantages and problems of each sampling approach are thoroughly
examined and sensitivity studies are conducted to analyze the impact on the FG extraction
and retrieval of various algorithmic parameters such as the distance being used, the size of
the databases, or the instrumental noise sensitivity. The K-means clustering algorithm, not
yet used for this kind of problems, is very efficient compared to the more traditional


Source: Aires, Filipe - Laboratoire de Météorologie Dynamique du CNRS, Université Pierre-et-Marie-Curie, Paris 6


Collections: Geosciences