Theorie, Modellierung
Convection-permitting models
Vorschlag: Christian Keil
Model dynamics
Vorschlag: Tijana Janjic Pfander
Data assimilation
Fabry, F., and J. Sun, 2010: For how long should what data be assimilated for the mesoscale forecasting of convection and why? Part I: On the propagation of initial condition errors and their implications for data assimilation. Mon. Wea. Rev., 138, 242–255.
Fabry, F., 2010: For how long should what data be assimilated for the mesoscale forecasting of convection and why? Part II: On the observation signal from different sensors. Mon. Wea. Rev., 138, 256–264.
Vorschlag: Martin Weissmann
Superparameterization and MCS propagation
Randall, D., DeMott, C., Stan, C., Khairoutdinov, M., Benedict, J., McCrary, R., Thayer-Calder, K. and Branson, M., 2016. Simulations of the tropical general circulation with a multiscale global model. Meteorological Monographs, 56, pp.15-1.
Pritchard, M.S., Moncrieff, M.W. and Somerville, R.C., 2011. Orogenic propagating precipitation systems over the United States in a global climate model with embedded explicit convection. Journal of the Atmospheric Sciences, 68(8), pp.1821-1840.
Vorschlag: Stephan Rasp
Aerosol und Chemie
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Strahlung, Fernerkundung
Aerosol microphysical properties retrieval from polarimetric measurements
Xu, X and J. Wang, Retrieval of aerosol microphysical properties from AERONET photo-polarimetric measurements: 1. Information content analysis, J. Geophys. Res., 120, 7059-7078, doi:10.1002/2015JD023108, 2015.
Xu, X., J. Wang , J. Zeng, R. Spurr, X. Liu, O. Dubovik, L. Li, Z. Li, M. Mishchenko, A. Sinyuk, and B. Holben, Retrieval of aerosol microphysical properties from AERONET photo-polarimetric measurements: 2. A new research algorithm and case demonstration, J. Geophys. Res., 120, 7079-7098, doi:10.1002/2015JD023113, 2015.
Vorschlag: Claudia Emde
Remote sensing of the fractal dimension of clouds
Féral, Laurent, and Henri Sauvageot. „Fractal identification of supercell storms.“ Geophysical Research Letters 29.14 (2002).
Gotoh, Kazuo, and Yasuhiko Fujii. „A fractal dimensional analysis on the cloud shape parameters of cumulus over land.“ Journal of Applied Meteorology 37.10 (1998): 1283-1292.
Vorschlag: Josef Schröttle
Remote sensing in the oxygen-A band
Preusker, R. and Lindstrot, R., 2009: Remote Sensing of Cloud-Top Pressure Using Moderately Resolved Measurements within the Oxygen A Band - A Sensitivity Study. J. Appl. Met. Climatol., 48, 1562-1574, DOI: 10.1175/2009JAMC2074.1
Lindstrot, R., Preusker, R., Fischer, J., 2009: The Retrieval of Land Surface Pressure from MERIS Measurements in the Oxygen A Band. J. Atmos. Oceanic Technol., 26, 1367-1377, DOI: 10.1175/2009JTECHA1212.1
Vorschlag: Lucas Höppler
Determination of the mixing layer height
Pal, S. and Haeffelin, M. (2015): Forcing mechanisms governing diurnal, seasonal, and interannual variability in the boundary layer depths: Five years of continuous lidar observations over a suburban site near Paris, J. Geophys. Res. Atmos., 120, 11,936–11,956, doi:10.1002/2015JD023268.
Su, T., J. Li, C. Li, P. Xiang, A. K.-H. Lau, J. Guo, D. Yang, and Y. Miao (2017): An intercomparison of long-term planetary boundary layer heights retrieved from CALIPSO, ground-based lidar, and radiosonde measurements over Hong Kong, J. Geophys. Res. Atmos., 122, 3929–3943, doi:10.1002/2016JD025937.
Vorschlag: Matthias Wiegner
Wolken-Strahlungs-Wechselwirkung
Convective Self-Organization and Radiation
Muller, C. and Bony, S. (2015). What favors convective aggregation and why? Geophysical Research Letters, 42(13): 5626-5634.
Muller, C. J. and Held, I. M. (2012). Detailed investigation of the self-aggregation of convection in cloud-resolving simulations. Journal of the Atmospheric Sciences, 69:2551-2565.
Vorschlag: Caro Klinger, Fabian Jakub
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