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Matthias Grzeschik, Hans-Stefan Bauer, Volker Wulfmeyer, Dirk Engelbart, Ulla Wandinger, Ina Mattis, Dietrich Althausen, Ronny Engelmann, Matthias Tesche, and Andrea Riede

instrument by using the provided error profile to define the observation error covariance matrix ๐—ฅ i . Additionally, this continuous data assimilation technique takes into account the physics of the atmospheric processes while minimizing the cost function J : where x and x b are the state vectors of the model and background field variables, respectively; ๐—• is the background error covariance matrix; y i are the observations and x i the model forecasts both valid at times i ; ๐—› i is the

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Ulrich Lรถhnert, S. Crewell, O. Krasnov, E. Oโ€™Connor, and H. Russchenberg

needed to investigate BL processes in detail. These are extremely relevant for assessing the performance of NWP models, as well as for investigating the potential for data assimilation of such observations. Therefore, we describe an instrument combination method which is capable of continuously profiling the lower troposphere with special emphasis on an accurate boundary layer description. The technique described here is an advancement of the integrated profiling technique (IPT) described and

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Daniela Nowak, Dominique Ruffieux, Judith L. Agnew, and Laurent Vuilleumier

1. Introduction Precise forecasting of the formation, evolution, and erosion of fog and low stratus is a major challenge for meteorology, especially in complex topography. One of the goals of the COST 720 Temperature, Humidity, and Cloud (TUC) winter experiment undertaken in Switzerland in 2003/04 ( Ruffieux et al. 2006 ) was to provide a dataset for determining the base and top of low clouds using a simple combination of ground-based remote sensing instruments. Frequent and detailed

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P. C. S. Devara, P. E. Raj, K. K. Dani, G. Pandithurai, M. C. R. Kalapureddy, S. M. Sonbawne, Y. J. Rao, and S. K. Saha

. Proc. 22nd Int. Laser Radar Conf. (ILRC), Matera, Italy, European Space Agency, 631โ€“634 . Houghton, J. T. , Ding Y. , Griggs D. J. , Noguer M. , van der Linden P. J. , Dai X. , Maskell K. , and Johnson C. A. , 2001 : Climate Change 2001: The Scientific Basis . Cambridge University Press, 881 pp . Kamineni, R. , Krishnamurti T. N. , Ferrare R. A. , Ismail S. , and Browell E. V. , 2003 : Impact of high-resolution water vapor cross-sectional data on hurricane forecasting

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