Cohn, S.: A New Edition of the International Cloud Atlas, WMO Bulletin, World Meteorological Organization, Geneva, 66, 2–7, 2017. a
Emde, C., Buras-Schnell, R., Kylling, A., Mayer, B., Gasteiger, J., Hamann, U., Kylling, J., Richter, B., Pause, C., Dowling, T., and Bugliaro, L.: The
libRadtran software package for radiative transfer calculations (version 2.0.1), Geosci. Model Dev., 9, 1647–1672,
https://doi.org/10.5194/gmd-9-1647-2016, 2016.
a
Franklin, C. N., Sun, Z., Bi, D., Dix, M., Yan, H., and Bodas-Salcedo, A.:
Evaluation of clouds in ACCESS using the satellite simulator package COSP:
Global, seasonal, and regional cloud properties, J. Geophys. Res.-Atmos., 118, 732–748, 2013.
a,
b
Geer, A. J., Lonitz, K., Weston, P., Kazumori, M., Okamoto, K., Zhu, Y., Liu,
E. H., Collard, A., Bell, W., Migliorini, S., Chambon, P., Fourrié, N., Kim, M.-J., Köpken-Watts, C., and Schraff, C.: All-sky satellite data assimilation at operational weather forecasting centres, Q. J. Roy. Meteorol. Soc., 144, 1191–1217, 2018. a
Geiss, S., Scheck, L., de Lozar, A., and Weissmann, M.: Understanding the
model representation of clouds based on visible and infrared satellite
observations – a data set [data set],
https://doi.org/10.5281/zenodo.4548922, 2021.
a
Giles, D. M., Sinyuk, A., Sorokin, M. G., Schafer, J. S., Smirnov, A.,
Slutsker, I., Eck, T. F., Holben, B. N., Lewis, J. R., Campbell, J. R.,
Welton, E. J., Korkin, S. V., and Lyapustin, A. I.: Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for
Sun photometer aerosol optical depth (AOD) measurements, Atmos. Meas. Tech., 12, 169–209,
https://doi.org/10.5194/amt-12-169-2019, 2019.
a
Gustafsson, N., Janjić, T., Schraff, C., Leuenberger, D., Weissmann, M.,
Reich, H., Brousseau, P., Montmerle, T., Wattrelot, E., Bučánek, A., Mile, M., Hamdi, R., Lindskog, M., Barkmeijer, J., Dahlbom, M., Macpherson, B., Ballard, S., Inverarity, G., Carley, J., Alexander, C., Dowell, D., Liu, S., Ikuta, Y., and Fujita, T.: Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres, Q. J. Royal Meteorol. Soc., 144, 1218–1256,
https://doi.org/10.1002/qj.3179, 2018.
a,
b
Honda, T., Miyoshi, T., Lien, G.-Y., Nishizawa, S., Yoshida, R., Adachi, S. A., Terasaki, K., Okamoto, K., Tomita, H., and Bessho, K.: Assimilating all-sky Himawari-8 satellite infrared radiances: A case of Typhoon Soudelor (2015), Mon. Weather Rev., 146, 213–229, 2018. a
IEA: Solar PV, IEA, Paris, available at:
https://www.iea.org/reports/solar-pv (last access: 14 August 2021), 2020. a
Keil, C., Baur, F., Bachmann, K., Rasp, S., Schneider, L., and Barthlott, C.:
Relative contribution of soil moisture, boundary-layer and microphysical
perturbations on convective predictability in different weather regimes, Q. J. Roy. Meteorol. Soc., 145, 3102–3115, 2019. a
Keller, M., Fuhrer, O., Schmidli, J., Stengel, M., Stöckli, R., and
Schär, C.: Evaluation of convection-resolving models using satellite
data: The diurnal cycle of summer convection over the Alps, Meteorol. Z., 25, 165–179, 2016. a
Köhler, C., Steiner, A., Saint-Drenan, Y.-M., Ernst, D., Bergmann-Dick, A., Zirkelbach, M., Bouallègue, Z. B., Metzinger, I., and Ritter, B.:
Critical weather situations for renewable energies – Part B: Low stratus risk for solar power, Renew. Energy, 101, 794–803, 2017. a
Kurzrock, F., Cros, S., Chane-Ming, F., Otkin, J., Hutt, A., Linguet, L.,
Lajoie, G., and Potthast, R.: A review of the use of geostationary satellite
observations in regional-scale models for short-term cloud forecasting,
Meteorol. Z., 27, 277–298, 2018. a
Marseille, G.-J. and Stoffelen, A.: Toward scatterometer winds assimilation in the mesoscale HARMONIE Model, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 10, 2383–2393, 2017. a
McNally, A.: A note on the occurrence of cloud in meteorologically sensitive
areas and the implications for advanced infrared sounders, Q. J. Roy. Meteorol. Soc., 128, 2551–2556, 2002. a
Meirink, J. F., Roebeling, R. A., and Stammes, P.: Inter-calibration of polar imager solar channels using SEVIRI, Atmos. Meas. Tech., 6, 2495–2508,
https://doi.org/10.5194/amt-6-2495-2013, 2013.
a
Necker, T., Weissmann, M., and Sommer, M.: The importance of appropriate
verification metrics for the assessment of observation impact in a
convection-permitting modelling system, Q. J. Roy. Meteorol. Soc., 144, 1667–1680, 2018. a
Necker, T., Geiss, S., Weissmann, M., Ruiz, J., Miyoshi, T., and Lien, G.-Y.: A convective-scale 1,000-member ensemble simulation and potential applications, Q. J. Roy. Meteorol. Soc., 146, 1423–1442, 2020. a
Otkin, J. A. and Greenwald, T. J.: Comparison of WRF model-simulated and
MODIS-derived cloud data, Mon. Weather Rev., 136, 1957–1970, 2008.
a,
b
Pfeifer, M., Yen, W., Baldauf, M., Craig, G., Crewell, S., Fischer, J., Hagen, M., Hühnerbein, A., Mech, M., Reinhardt, T., Schröder, M., and Seifert, A.: Validating precipitation forecasts using remote sensor synergy: A case study approach, Meteorol. Z., 19, 601–617,
https://doi.org/10.1127/0941-2948/2010/0487, 2010.
a
Piper, D., Kunz, M., Ehmele, F., Mohr, S., Mühr, B., Kron, A., and Daniell, J.: Exceptional sequence of severe thunderstorms and related flash floods in May and June 2016 in Germany – Part 1: Meteorological background, Nat. Hazards Earth Syst. Sci., 16, 2835–2850,
https://doi.org/10.5194/nhess-16-2835-2016, 2016.
a,
b
Reinhardt, T. and Seifert, A.: A three-category ice scheme for LMK, COSMO
Newsletter No. 6, Deutscher Wetterdienst, Offenbach, Germany, 115–120, 2006. a
Rossow, W. B. and Schiffer, R. A.: ISCCP cloud data products, B. Am. Meteorol. Soc., 72, 2–20, 1991.
a,
b
Saunders, R., Hocking, J., Rundle, D., Rayer, P., Havemann, S., Matricardi, M., Geer, A., Lupu, C., Brunel, P., and Vidot, J.: RTTOV v12 science and
validation report, 78 pp., available at:
https://nwp-saf.eumetsat.int/site/download/documentation/rtm/docs_rttov12/rttov12_svr.pdf
(last access: 14 August 2021), 2017.
a,
b
Saunders, R., Hocking, J., Turner, E., Rayer, P., Rundle, D., Brunel, P., Vidot, J., Roquet, P., Matricardi, M., Geer, A., Bormann, N., and Lupu, C.: An update on the RTTOV fast radiative transfer model (currently at version 12), Geosci. Model Dev., 11, 2717–2737,
https://doi.org/10.5194/gmd-11-2717-2018, 2018.
a,
b,
c,
d
Scheck, L., Frèrebeau, P., Buras-Schnell, R., and Mayer, B.: A fast
radiative transfer method for the simulation of visible satellite imagery, J. Quant. Spectrosc. Ra., 175, 54–67, 2016.
a,
b
Scheck, L., Weissmann, M., and Mayer, B.: Efficient Methods to Account for
Cloud-Top Inclination and Cloud Overlap in Synthetic Visible Satellite Images, J. Atmos. Ocean. Tech., 35, 665–685, 2018.
a,
b,
c,
d,
e
Schmetz, J., Pili, P., Tjemkes, S., Just, D., Kerkmann, J., Rota, S., and
Ratier, A.: An introduction to Meteosat second generation (MSG), B. Am. Meteorol. Soc., 83, 977–992, 2002. a
Schröttle, J., Weissmann, M., Scheck, L., and Hutt, A.: Assimilating
visible and infrared radiances in idealized simulations of deep convection,
Mon. Weather Rev., 148, 4357–4375, 2020. a
Segal, Y. and Khain, A.: Dependence of droplet concentration on aerosol
conditions in different cloud types: Application to droplet concentration
parameterization of aerosol conditions, J. Geophys. Res.-Atmos., 111, D15204,
https://doi.org/10.1029/2005JD006561, 2006.
a
Seifert, A. and Beheng, K. D.: A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description, Meteorol. Atmos. Phys., 92, 45–66, 2006. a
Senf, F. and Deneke, H.: Uncertainties in synthetic Meteosat SEVIRI infrared
brightness temperatures in the presence of cirrus clouds and implications for
evaluation of cloud microphysics, Atmos. Res., 183, 113–129,
https://doi.org/10.1016/j.atmosres.2016.08.012, 2017.
a,
b,
c
Senf, F., Voigt, A., Clerbaux, N., Hünerbein, A., and Deneke, H.:
Increasing Resolution and Resolving Convection Improve the Simulation of
Cloud-Radiative Effects Over the North Atlantic, J. Geophys. Res.-Atmos., 125, e2020JD032667,
https://doi.org/10.1029/2020JD032667, 2020.
a
Sissenwine, N., Dubin, M., and Wexler, H.: The US standard atmosphere, 1962,
J. Geophys. Res., 67, 3627–3630, 1962. a
Stamnes, K., Tsay, S.-C., Wiscombe, W., and Laszlo, I.: Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media, Appl. Optics, 27, 2502–2509, 1988. a
Tiedtke, M.: Representation of clouds in large-scale models, Mon. Weather Rev., 121, 3040–3061, 1993. a
Tselioudis, G. and Jakob, C.: Evaluation of midlatitude cloud properties in a
weather and a climate model: Dependence on dynamic regime and spatial resolution, J. Geophys. Res.-Atmos., 107, AAC 14-1–AAC 14-10,
https://doi.org/10.1029/2002JD002259, 2002.
a
Tuohy, A., Zack, J., Haupt, S. E., Sharp, J., Ahlstrom, M., Dise, S., Grimit,
E., Mohrlen, C., Lange, M., Casado, M. G., Black, J., Marquis, M., and Collier, C.: Solar forecasting: Methods, challenges, and performance, IEEE Power Energ. Mag., 13, 50–59, 2015.
a,
b
Vidot, J., Brunel, P., Dumont, M., Carmagnola, C., and Hocking, J.: The VIS/NIR Land and Snow BRDF Atlas for RTTOV: Comparison between MODIS MCD43C1 C5 and C6, Remote Sens., 10, 21,
https://doi.org/10.3390/rs10010021, 2018.
a
Wapler, K. and Mayer, B.: A fast three-dimensional approximation for the
calculation of surface irradiance in large-eddy simulation models, J. Appl. Meteorol. Clim., 47, 3061–3071, 2008. a
Webb, M., Senior, C., Bony, S., and Morcrette, J.-J.: Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate models, Clim. Dynam., 17, 905–922, 2001. a
Weissmann, M., Gober, M., Hohenegger, C., Janjic, T., Keller, J., Ohlwein, C., Seifert, A., Tromel, S., Ulbrich, T., Wapler, K., Bollmeyer, C., and Deneke, H.: Initial phase of the Hans-Ertel Centre for Weather Research – A virtual centre at the interface of basic and applied weather and climate research, Meteorol. Z., 23, 193–208, 2014. a
Yang, P., Wei, H., Huang, H.-L., Baum, B. A., Hu, Y. X., Kattawar, G. W.,
Mishchenko, M. I., and Fu, Q.: Scattering and absorption property database
for nonspherical ice particles in the near- through far-infrared spectral
region, Appl. Optics, 44, 5512–5523,
https://doi.org/10.1364/AO.44.005512, 2005.
a
Yuter, S. E. and Houze Jr., R. A.: Three-dimensional kinematic and microphysical evolution of Florida cumulonimbus. Part II: Frequency distributions of vertical velocity, reflectivity, and differential reflectivity, Mon. Weather Rev., 123, 1941–1963, 1995. a
Zack, J.: Current Status and Challenges of Solar Power Production Forecasting, available at:
http://www.ercot.com/gridinfo/ettsArchive/solar/04_-_AWS_John_Zack_ERCOT_workshop_25apr11.pdf (last access: May 2020), 2011. a
Zängl, G., Reinert, D., Rípodas, P., and Baldauf, M.: The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M:
Description of the non-hydrostatic dynamical core, Q. J. Roy. Meteorol. Soc., 141, 563–579, 2015.
a,
b,
c
Zeng, Y., Janjić, T., de Lozar, A., Blahak, U., Reich, H., Keil, C., and
Seifert, A.: Representation of Model Error in Convective-Scale Data
Assimilation: Additive Noise, Relaxation Methods, and Combinations, J. Adv. Model. Earth Syst., 10, 2889–2911, 2018. a
Zhang, M., Lin, W., Klein, S., Bacmeister, J., Bony, S., Cederwall, R.,
Del Genio, A., Hack, J., Loeb, N., Lohmann, U., Minnis, P., Musat, I., Pincus, R., Stier, P., Suarez, M. J., Webb, M. J., Wu, J. B., Xie, S. C., Yao, M.-S., and Zhang, J. H.: Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements, J. Geophys. Res.-Atmos., 110, D15S02,
https://doi.org/10.1029/2004JD005021, 2005.
a,
b
