Inhaltsverzeichnis

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

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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