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Found 30 publication(s)

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Pauli, E.; Andersen, H.; Bendix, J.; Cermak, J. & Egli, S. (2020): Determinants of fog and low stratus occurrence in continental central Europe – a quantitative satellite-based evaluation. Journal of Hydrology 591, 125451.
Cermak, J. & Bendix, J. (2008): Satellite climatology of fog and low stratus & retrieval of fog and fog properties. In: JACOBS, W., NIETOSVAARA, V., BOTT, A., BENDIX, J., CERMAK, J., MICHAELIDES, S. & GULTEPE, I. (eds.): EUR22978– Cost Action 722 – Earth system science and environmental management –short range forecasti ( ), COST office, Luxembourg, 55 – 62.
Bendix, J.; Fabian, P. & Rollenbeck, R., Gradients of fog and rain in a tropical montane cloud forest of southern Ecuador and its chemical composition(2004), pp. H7, 1-4.
Lehnert, L.; Thies, B. & Bendix, J. (2020): A new high spatial resolution low stratus/fog retrieval for the Atacama Desert. Remote Sensing of Environment 236, 111445.
Bendix, J.; Rollenbeck, R.; Nauss, T.; Göttlicher, D. & Fabian, P., Fog in a tropical mountain rain forest ecosystem of southern Ecuador(Proc. 4th Int. Conf. on Fog, Fog Collection and Dew, La Serena (Chile), 2007), pp. 407-410.
Egli, S.; Thies, B. & Bendix, J. (2019): A spatially explicit and temporally highly resolved analysis of variations in fog occurrence over Europe. Quarterly Journal of the Royal Meteorological Society 1, 1-20.
Lehnert, L.; Achilles, S.; Schmidt, J.; Büdel, B.; Osses, P.; Thies, B. & Bendix, J., Fog research in the southern Atacama: Measurement setup and first results of the new EarthShape project(2016).
Maier, F.; Bendix, J. & Thies, B. (2012): Simulating Z-LWC relations in natural fogs with radiative transfer calculations for future application to a cloud radar profiler. Pure and Applied Geophysics 169, 793–807.
Schulz, M.; Thies, B.; Chang, S. & Bendix, J. (2016): Detection of ground fog in mountainous areas from MODIS (Collection 051) daytime data using a statistical approach. Atmospheric Measurement Techniques 9, 1135 - 1152.
Cermak, J. & Bendix, J. (2007): Dynamical nighttime fog/low stratus detection based on Meteosat SEVIRI data: A feasibility study. Pure and Applied Geophysics 164(6), 1179-1192.
Li, Y.; Zhang, S.; Thies, B.; Trachte, K. & Bendix, J. (2014): Spatio-temporal detection of fog / low stratus top height over the geostationary satellite data as a precondition for ground fog detection – a feasibility study. Atmospheric Research 151, 212-223.
Bendix, J.; Eugster, W. & Klemm, O. (2011): Fog - boon or bane? . Erdkunde 65(3), 229-232.
Bendix, J., A fog climatology of Germany and the Alpine region based on AVHRR data(2001), pp. 414-419.
Egli, S.; Thies, B. & Bendix, J. (2018): A Hybrid Approach for Fog Retrieval Based on a Combination of Satellite and Ground Truth Data. Remote Sensing 10(4), 1-26.
Rollenbeck, R.; Bendix, J. & Fabian, P. (2011): Spatial and temporal dynamics of atmospheric water inputs in tropical mountain forests of South Ecuador. Hydrological Processes Vol. 25(Issue 3), 344–352.
Bendix, J. (1994): Fog climatology of the Po Valley. Rivista di meteorologia aeronautica 54(3-4), 25-36.
Bendix, J.; Bott, A.; Trautmann, T. & Jacobs, W. (2005): German report: Forecasting methods for fog and low clouds in Germany. In: JACOBS W. et al. (eds.): Short-range forecasting methods of fog, visibility and low clouds ( ), COST office Luxembourg, 74-80.
Jacobs, W.; Nietosvaara, V.; Bott, A.; Bendix, J.; Cermak, J.; Michaelides, S. & Gultepe, I. 2008: EUR 22978 – Cost Action 722 – Earth system science and environmental management – short range forecasting methods of fog, visibility and low cloud. (COST office, Luxembourg).
Jung, P.; Baumann, K.; Lehnert, L.; Samolov, E.; Achilles, S.; Schermer, M.; Wraase, L.M.; Eckhardt, K.; Bader, M.; Leinweber, P.; Karsten, U.; Bendix, J. & Büdel, B. (2020): Desert breath—How fog promotes a novel type of soil biocenosis, forming the coastal Atacama Desert’s living skin. Geobiology n/a(n/a), 1-12.
Rösner, B.; Egli, S.; Thies, B.; Beyer, T.; Callies, D.; Pauscher, L. & Bendix, J. (2020): Fog and Low Stratus Obstruction of Wind Lidar Observations in Germany—A Remote Sensing-Based Data Set for Wind Energy Planning. Energies 13(15), 3859.
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