Modeling and Forecasting Marine Fog

  • Darko KoračinEmail author
Part of the Springer Atmospheric Sciences book series (SPRINGERATMO)


All problems inherent to models’ imperfection and generally insufficient vertical and horizontal resolution, as well as inability to obtain full and accurate initial and boundary conditions, amplify for fog predictions. This is due to a huge span of relevant parameters and processes ranging from aerosols to hemispheric synoptic conditions. Understanding fog characteristics and evolution as well as constructing accurate initial and boundary conditions for forecasting is severely hindered by absence of dense routine and also special measurements over the vast oceans. Fog modeling and forecasting has a long history from early methods based on persistence and synoptic indicators and later through weather analysis to contemporary methods using high-resolution numerical models on regional scales, mesoscales, and microscales. The main issues that are critical to understanding and forecasting marine fog include synoptic conditions and advection, local circulations and flow properties, turbulence, characteristics of inversion and subsidence, longwave and shortwave radiative fluxes, air-sea interaction, aerosols, microphysics, and coastal topography. The two main approaches to fog predictions are statistical and dynamical forecasting methods. Some of the statistical methods are based on various statistical analyses including regressions, correlations, classifications, and tree decision diagrams. Additional statistical methods include artificial neural networks and fuzzy logic, which can successfully treat nonlinear relationships between fog predictors and predictands. The statistical methods are applicable and useful when there is a sufficient archive of fog predictor parameters and fog observations at a location of interest. An advantage of using statistical methods is their computational efficiency allowing for fog nowcasting as an independent tool or in conjunction with operational forecasting models. A disadvantage is that the statistical approach does not take into account the actual three-dimensional weather structure and evolution. Dynamical models for fog forecasting use mathematical representations of basic conservation laws and parameterizations of physical processes including the ones relevant to fog. Early research in numerical studies has used one- (1D) and two-dimensional (2D) models, which can allow for high vertical resolutions and detailed parameterization schemes. Since 1D and 2D models cannot represent the full structure and evolution of atmospheric processes, three-dimensional (3D) models have been used for operational weather forecasts. An important objective of 3D modeling studies is understanding the path history of an air mass transformation that leads to fog or fog-free conditions. Some of the main drawbacks of 3D models include the high computational requirements, which usually result in inadequate horizontal and vertical resolution, and simplifications in physics parameterization schemes. It is obviously best to use the advantages of each of these types of models and to combine them into an integrated modeling system that can improve the accuracy of the forecast. Further improvement in marine fog forecasting is obtained by treating air-sea interaction with coupled atmospheric and ocean models. Besides a deterministic approach with a single fog forecast outcome, probabilistic methods based on an ensemble of solutions are emerging. The probabilistic forecasts are able to reveal uncertainties due to the initial and boundary conditions, physics parameterizations, and model structure and setup. Advanced modeling approaches such as the large-eddy simulation (LES) technique has been also used for fog predictions. LES can simulate high-resolution atmospheric fields including turbulence on limited domains, however, they have limitations in representing realistic synoptic processes. With the rapid development of measurement networks, and especially with satellite data, it has been shown that assimilation of data into the models can significantly improve the forecast accuracy. Although initially fog forecasts were developed and applied to marine areas of North America and Europe, it is encouraging that various fog forecasting methods are being developed and applied to marine areas of other continents. Of definite interest is to estimate projections of fog characteristics using regional climate models that are recently under rapid developments. In spite of their uncertainties in initial and boundary conditions as well as in emissions of aerosols and greenhouse gases, limited resolutions, and generally simplified physics parameterizations, they are valuable tools in assessing meteorological parameters relevant to future fog occurrence and evolution.

Due to the complex structure and evolution of marine fog as well as the generally significant influence of microlocations on fog processes, many studies show that a subjective forecaster’s experience still represents a valuable component in the final creation of an accurate fog forecast.


Model Output Statistic Marine Layer Longwave Cool Marine Inversion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Support for the writing of this chapter was partially provided by the U.S. Department of Energy grant DE-SC0001933 and the Croatian Science Foundation, project MARIPLAN (IP-2014-09-3606).


  1. Ballard, S., Golding, B., & Smith, R. (1991). Mesoscale model experimental forecasts of the haar of northeast Scotland. Monthly Weather Review, 191, 2107–2123.CrossRefGoogle Scholar
  2. Bari, D., Bergot, T., & El Khlifi, M. (2015). Numerical study of a coastal fog event over Casablanca, Morocco. Quarterly Journal of the Royal Meteorological Society, 141, 1894–1905.CrossRefGoogle Scholar
  3. Barker, E. (1977). A maritime boundary-layer model for the prediction of fog. Boundary-Layer Meteorology, 11, 267–294.CrossRefGoogle Scholar
  4. Bartok, B., Bott, A., & Gera, M. (2012). Fog prediction for road traffic safety in a coastal desert region. Boundary-Layer Meteorology, 145(3), 485–506.CrossRefGoogle Scholar
  5. Bayler, G., & Lewit, H. (1992). The Navy Operational Global and Regional Atmospheric Prediction Systems at the Fleet Numerical Oceanography Center. Weather and Forecasting, 7, 273–279. doi: 10.1175/1520-0434(1992)007<0273:TNOGAR>2.0.CO;2.CrossRefGoogle Scholar
  6. Benoit, R., Desgagne, J. M., Pellerin, P., Pellerin, S., Chartier, Y., & Desjardins, S. (1997). The Canadian Mc2: A semi-Lagrangian, semi-implicit wide band atmospheric model suited for fine scale process studies and simulation. Monthly Weather Review, 125, 2382–2415.CrossRefGoogle Scholar
  7. Benz, R. F. (2003). Data mining atmospheric/oceanic parameters in the design of a long-range nephelometric forecast tool. Master’s Thesis, Department of Engineering Physics, Air Force Institute of Technology, pp. 42–47.Google Scholar
  8. Bonancina, L. C. W. (1925). Notes on the fog of January 10th–12th, 1925. Meteorological Magazine, 60, 7–8.Google Scholar
  9. Bott, A., & Trautmann, T. (2002). PAFOG—A new efficient forecast model of radiation fog and low-level stratiform clouds. Atmospheric Research, 64(1–4), 191–203.CrossRefGoogle Scholar
  10. Cho, Y.-K., Kim, M.-O., & Kim, B.-C. (2000). Sea fog around the Korean Peninsula. Journal of Applied Meteorology, 39, 2473–2479.CrossRefGoogle Scholar
  11. Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., et al. (2011). The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137, 553–597.CrossRefGoogle Scholar
  12. Dorman, C. E. (2017). Early and recent observational techniques for fog (Chap. 3). In D. Koračin & C. E. Dorman (Eds.), Marine fog: Challenges and advancements in observations, modeling and forecasting. New York: Springer.Google Scholar
  13. Dorman, C. E., Mejia, J., Koračin, D., & McEvoy, D. (2017). Worlwide marine fog occurrence and climatology (Chap. 2). In D. Koračin & C. E. Dorman (Eds.), Marine fog: Challenges and advancements in observations, modeling and forecasting. New York: Springer.Google Scholar
  14. Douglas, C. (1930). Cold fogs over the sea. Meteorological Magazine, 65, 133–135.Google Scholar
  15. Du, J., & Zhou, B. (2017). Ensemble fog prediction (Chap. 10). In D. Koračin & C. E. Dorman (Eds.), Marine fog: Challenges and advancements in observations, modeling and forecasting. New York: Springer.Google Scholar
  16. Edson, J., Crawford, T., Crescenti, J., Farrar, T., Frew, N., Gerbi, G., et al. (2007). The coupled boundary layers and air–sea transfer experiment in low winds. Bulletin of the American Meteorological Society, 88, 341–356. doi: 10.1175/BAMS-88-3-341.CrossRefGoogle Scholar
  17. Ellrod, G. P. (1995). Advances in the detection and analysis of fog at night using GOES multi-spectral infrared imagery. Weather and Forecasting, 10, 606–619.CrossRefGoogle Scholar
  18. Emmons, G., & Montgomery, R. B. (1947). Note on the physics of fog formation. Journal of Meteorology, 4, 206.CrossRefGoogle Scholar
  19. Filonczuk, M. K., Cayan, D. I. R., & Riddle, L. G. (1995). Variability of marine fog along the California coast. SIO-Reference, No 95-2. Climate Research Division, Scripps Institution of Oceanography, University of California, San Diego. Retrieved from
  20. Findlater, J., Roach, W., & McHugh, B. (1989). The haar of north-east Scotland. Quarterly Journal of the Royal Meteorological Society, 115, 581–608.CrossRefGoogle Scholar
  21. Fisher, E. L., & Caplan, P. (1963). An experiment in numerical prediction of fog and stratus. Journal of the Atmospheric Sciences, 20, 425–437.CrossRefGoogle Scholar
  22. Fitzgerald, J. W. (1978). A numerical model of the formation of droplet spectra in advection fogs at sea and its applicability to fogs off Nova Scotia. Journal of the Atmospheric Sciences, 35, 1522–1535.CrossRefGoogle Scholar
  23. Gao, S., Lin, H., Shen, B., & Fu, G. (2007). A heavy sea fog event over the Yellow Sea in March 2005: Analysis and numerical modeling. Advances in Atmospheric Sciences, 24, 65–81.CrossRefGoogle Scholar
  24. Garland, J. A. (1971). Some fog droplet size distributions obtained by an impaction method. Quarterly Journal of the Royal Meteorological Society, 97, 483–494.CrossRefGoogle Scholar
  25. Garreaud, R., Barichivich, J., Christie, D. A., & Maldonado, A. (2008). Interannual variability of the coastal fog at Fray Jorge relict forests in semiarid Chile. Journal of Geophysical Research: Biogeosciences, 113(G4), 2005–2012.Google Scholar
  26. Glahn, H. R., & Dallavalle, J. P. (2002). The new NWS MOS development and implementation systems. Preprints. In 16th Conference on Probability and Statistics in the Atmospheric Sciences (pp. 78–81). Orlando, FL: American Meteorological Society.Google Scholar
  27. Glahn, H. R., & Lowry, D. A. (1972). The use of model output statistics (MOS) in objective weather forecasting. Journal of Applied Meteorology, 11, 1202–1211.Google Scholar
  28. Golding, B. W. (1987). The U.K. Meteorological Office mesoscale model. Boundary-Layer Meteorology, 41, 97–107.CrossRefGoogle Scholar
  29. Grell, G. A., Dudhia, J., & Stauffer, D. R. (1994). A description of the fifth-generation Penn state/NCAR Mesoscale Model (MM5) (NCAR Tech. Note NCAR/TN-398+STR, 122pp).Google Scholar
  30. Gultepe, I., & Milbrandt, J. A. (2007). Microphysical observations and mesoscale model simulation of a warm fog case during FRAM project. Pure and Applied Geophysics, 164, 1161–1178.CrossRefGoogle Scholar
  31. Gultepe, I., Müller, M. D., & Boybeyi, Z. (2006). A new warm fog parameterization scheme for numerical weather prediction models. Journal of Applied Meteorology, 45, 1469–1480.CrossRefGoogle Scholar
  32. Gultepe, I., Milbrandt, J. A., & Zhou, B. (2017). Marine fog: A review on microphysics and visibility prediction (Chap. 7). In D. Koračin & C. E. Dorman (Eds.), Marine fog: Challenges and advancements in observations, modeling and forecasting. New York: Springer.Google Scholar
  33. Gultepe, I., Pearson, G., Milbrandt, J. A., Hansen, B., Platnick, S., Taylor, P., et al. (2009). The fog remote sensing and modeling (FRAM) field project. Bulletin of the American Meteorological Society, 90, 341–359.CrossRefGoogle Scholar
  34. Gultepe, I., Tardif, R., Michaelides, S. C., Cermak, J., Bott, A., Bendix, J., et al. (2007). Fog research: A review of past achievements and future perspectives. Pure and Applied Geophysics, 164, 1121–1159.CrossRefGoogle Scholar
  35. Gutiérrez, A. G., Barbosa, O., Christie, D. A., del-Val, E., Ewing, H. A., Jones, C. G., et al. (2008). Regeneration patterns and persistence of the fog dependent Fray Jorge forest in semiarid Chile during the past two centuries. Global Change Biology, 14, 161–176. doi: 10.1111/j.13652486.2007.01482.x.Google Scholar
  36. Heo, K.-Y., & Ha, K.-J. (2010). A coupled model study on the formation and dissipation of sea fogs. Monthly Weather Review, 138, 1186–1205.CrossRefGoogle Scholar
  37. Heo, K.-Y., Ha, K. J., Mahrt, L., & Shim, J.-S. (2010). Comparison of advection and steam fogs: From direct observation over the sea. Atmospheric Research, 98, 426–437. doi: 10.1016/j.atmosres.2010.08.004.CrossRefGoogle Scholar
  38. Hodur, R. M. (1997). The Naval Research Laboratory’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). Monthly Weather Review, 125, 1414–1430.CrossRefGoogle Scholar
  39. Hodur, R. M., Hong, X., Doyle, J. D., Pullen, J., Cummings, J., Martin, P., et al. (2002). The Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). Oceanography, 15, 88–98.CrossRefGoogle Scholar
  40. Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed., p. 375). Hoboken, NJ: Wiley.CrossRefGoogle Scholar
  41. Huang, H., Huang, J., Liu, C., Yuan, J., Mao, W., & Liao, F. (2011). Prediction of sea fog of Guangdong coastland using the variable factors output by GRAPES model. Journal of Tropical Meteorology, 17, 166–174.Google Scholar
  42. Huang, H., Liu, H., Huang, J., Mao, W., & Bi, X. (2015). Atmospheric boundary layer structure and turbulence during sea fog on the southern China Coast. Monthly Weather Review, 143, 1907–1923. doi: 10.1175/MWR-D-14-00207.1.CrossRefGoogle Scholar
  43. Huang, H., Liu, H., Jiang, W., Huang, J., & Mao, W. (2011). Characteristics of the boundary layer structure of sea fog on the coast of Southern China. Advances in Atmospheric Sciences, 28(6), 1377–1389.CrossRefGoogle Scholar
  44. Hudson, J. G. (1980). Relationship between fog condensation nuclei and fog microstructure. Journal of the Atmospheric Sciences, 37, 1854–1867.CrossRefGoogle Scholar
  45. Ishida, H., Miura, M., Matsuda, T., Ogawara, K., Goto, A., Matsuura, K., et al. (2014). Investigation of low-cloud characteristics using mesoscale numerical model data for improvement of fog-detection performance by satellite remote sensing. Journal of Applied Meteorology and Climatology, 53, 2246–2263.CrossRefGoogle Scholar
  46. Johnstone, J. A., & Dawson, T. E. (2010). Climatic context and ecological implications of summer fog decline in the coast redwood region. PNAS, 107(10), 4533–4538. doi: 10.1073/pnas.0915062107.CrossRefGoogle Scholar
  47. Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., et al. (1996). The NCEP/NCAR 40-Year Reanalysis Project. Bulletin of the American Meteorological Society, 77, 437–471.CrossRefGoogle Scholar
  48. Kim, C.-K., & Yum, S.-S. (2010). Local meteorological and synoptic characteristics of fogs formed over Incheon international airport in the west coast of Korea. Advances in Atmospheric Sciences, 27, 761–776.CrossRefGoogle Scholar
  49. Kim, C.-K., & Yum, S.-S. (2012). A numerical study of sea-fog formation over cold sea surface using a one-dimensional turbulence model coupled with the weather research and forecasting model. Boundary-Layer Meteorology, 143, 481–505.CrossRefGoogle Scholar
  50. Kim, C. K., & Yum, S. S. (2017a). Turbulence in marine fog (Chap. 4). In D. Koračin & C. E. Dorman (Eds.), Marine fog: Challenges and advancements in observations, modeling and forecasting. New York: Springer.Google Scholar
  51. Kim, C. K., & Yum, S. S. (2017b). Radiation in marine fog (Chap. 5). In D. Koračin & C. E. Dorman (Eds.), Marine fog: Challenges and advancements in observations, modeling and forecasting. New York: Springer.Google Scholar
  52. Koračin, D., Businger, J. A., Dorman, C. E., & Lewis, J. M. (2005). Formation, evolution, and dissipation of coastal sea fog. Boundary-Layer Meteorology, 117, 447–478.CrossRefGoogle Scholar
  53. Koračin, D., Dorman, C. E., Lewis, J. M., Hudson, J. G., Wilcox, E. M., & Torregrosa, A. (2014). Marine fog: A review. Atmospheric Research, 143, 142–175.CrossRefGoogle Scholar
  54. Koračin, D., Dorman, C. E., & Dever, E. P. (2004). Coastal perturbations of marine layer winds, wind stress, and wind stress curl along California and Baja California in June 1999. Journal of Physical Oceanography, 34, 1152–1173.CrossRefGoogle Scholar
  55. Koračin, D., Leipper, D. F., & Lewis, J. M. (2005). Modeling sea fog on the U.S. California coast during a hot spell event. Geofizika, 22, 59–82.Google Scholar
  56. Koračin, D., Lewis, J., Thompson, W. T., Dorman, C. E., & Businger, J. A. (2001). Transition of stratus into fog along the California coast: Observations and modeling. Journal of the Atmospheric Sciences, 58, 1714–1731.CrossRefGoogle Scholar
  57. Kunkel, B. A. (1984). Parameterization of droplet terminal velocity and extinction coefficient in fog models. Journal of Climate and Applied Meteorology, 23, 34–41.CrossRefGoogle Scholar
  58. Leipper, D. (1948). Fog development at San Diego, California. Journal of Marine Research, 7, 337–346.Google Scholar
  59. Leipper, D. F. (1994). Fog on the U.S. West Coast, a review. Bulletin of the American Meteorological Society, 72, 229–240.CrossRefGoogle Scholar
  60. Lewis, D. M. (2004). Forecasting advective sea fog with the use of classification and regression tree analysis for Kunsan Air Base. M.S. thesis, Air Force Institute of Technology, Wright-Patterson Air Force Base, OH. Retrieved from
  61. Lewis, J. M., Koračin, D., & Redmond, K. T. (2004). Sea fog research in the United Kingdom and United States: A historical essay including outlook. Bulletin of the American Meteorological Society, 85, 395–408.CrossRefGoogle Scholar
  62. Li, P., Fu, G., Lu, C., Fu, D., & Wang, S. (2012). The formation mechanism of a spring sea fog event over the Yellow Sea associated with a low-level jet. Weather and Forecasting, 27, 1538–1553.CrossRefGoogle Scholar
  63. Marzban, C., Leyton, S., & Colman, B. (2007). Ceiling and visibility forecasts via neural nets. Weather and Forecasting, 22(3), 466–479.CrossRefGoogle Scholar
  64. Mensbrugghe, V. (1892). The formation of fog and of clouds, translated from Ciel et Terre. Symons’s Monthly Meteorological Magazine, 27, 40–41.Google Scholar
  65. Miao, Y., Potts, R., Huang, X., Elliot, G., & Rivett, R. (2012). A fuzzy logic fog forecasting model for Perth airport. Pure and Applied Geophysics, 169, 1107–1119.CrossRefGoogle Scholar
  66. Milbrandt, J. A., & Yau, M. K. (2005a). A multimoment bulk microphysics parameterization. Part I: Analysis of the role of the spectral shape parameter. Journal of the Atmospheric Sciences, 62, 3051–3064.CrossRefGoogle Scholar
  67. Milbrandt, J. A., & Yau, M. K. (2005b). A multimoment bulk microphysics parameterization. Part II: A proposed three-moment closure and scheme description. Journal of the Atmospheric Sciences, 62, 3065–3081.CrossRefGoogle Scholar
  68. Nakanishi, M. (2000). Large-eddy simulation of radiation fog. Boundary-Layer Meteorology, 94, 461–493.CrossRefGoogle Scholar
  69. Neumann, J. (1989). Forecasts of fine weather in the literature of classical antiquity. Bulletin of the American Meteorological Society, 70, 46–48.Google Scholar
  70. O’Brien, T. A., Chuang, P. Y., Sloan, L. C., Faloona, I. C., & Rossiter, D. L. (2012). Coupling a new turbulence parametrization to RegCM adds realistic stratocumulus clouds. Geoscience Model Development, 5(4), 989–1008.CrossRefGoogle Scholar
  71. O’Brien, T. A., Sloan, L. C., Chuang, P. Y., Faloona, I. C., & Johnstone, J. A. (2013). Multidecadal simulation of coastal fog with a regional climate model. Climate Dynamics, 40, 2801–2812.CrossRefGoogle Scholar
  72. Oliver, D., Lewellen, W., & Williamson, G. (1978). The interaction between turbulent and radiative transport in the development of fog and low-level stratus. Journal of the Atmospheric Sciences, 35, 301–316.CrossRefGoogle Scholar
  73. Pagowski, M., Gultepe, I., & King, P. (2004). Analysis and modeling of an extremely dense fog event in Southern Ontario. Journal of Applied Meteorology, 43, 3–16. doi: 10.1175/1520-0450(2004)043b0003:AAMOAEN2.0.CO;2.CrossRefGoogle Scholar
  74. Petterssen, S. V. (1936). On the causes and forecasting of the California fog. Journal of the Aeronautical Sciences, 3, 305–309.CrossRefGoogle Scholar
  75. Petterssen, S. V. (1938). On the causes and forecasting of the California fog. Bulletin of the American Meteorological Society, 19, 49–55.Google Scholar
  76. Petterssen, S. (1939). Some aspects of formation and dissipation of fog. Geofysiske Publikasjoner, 12, 15–22.Google Scholar
  77. Pilié, R. J., Mack, E. J., Rogers, C. W., Katz, U., & Kocmond, W. C. (1979). The formation of marine fog and the development of fog-stratus systems along the California coast. Journal of Applied Meteorology, 18, 1275–1286.CrossRefGoogle Scholar
  78. Roach, W., Brown, R., Caughey, S. J., Garland, J. A., & Readings, C. J. (1976). The physics of radiation fog: I—A field study. Quarterly Journal of the Royal Meteorological Society, 102, 313–333.Google Scholar
  79. Scott, R. H. (1894). Fogs reported with strong winds during the 15 years 1876–90 in the British Isles. Quarterly Journal of the Royal Meteorological Society, 20(92), 253–262.CrossRefGoogle Scholar
  80. Shchepetkin, A. F., & McWilliams, J. C. (2004). The regional oceanic modeling system: A split-explicit, free-surface, topography-following-coordinate ocean model. Ocean Modelling, 9, 347–404.CrossRefGoogle Scholar
  81. Skamarock, W., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D., Duda, M. G., et al. (2008). A Description of the Advanced Research WRF Version 3 (NCAR Technical Note NCAR/TN-475+STR). doi: 10.5065/D68S4MVH.
  82. Stage, S. A., & Businger, J. A. (1981). A model for entrainment into a cloud-topped marine boundary-layer. Part II: Discussion of model behaviour and comparison with other models. Journal of the Atmospheric Sciences, 38, 2230–2242.Google Scholar
  83. Tang, Y. (2012). The effect of variable sea surface temperature on forecasting sea fog and sea breezes: A case study. Journal of Applied Meteorology and Climatology, 51, 986–990.CrossRefGoogle Scholar
  84. Tardif, R., & Rasmussen, R. M. (2007). Event-based climatology and typology of fog in the New York City region. Journal of Applied Meteorology and Climatology, 46(8), 1141–1168.CrossRefGoogle Scholar
  85. Tardif, R., & Rasmussen, R. M. (2008). Process-oriented analysis of environmental conditions associated with precipitation fog events in the New York City region. Journal of Applied Meteorology and Climatology, 47, 1681–1703.CrossRefGoogle Scholar
  86. Tardif, R. (2017). Precipitation and fog (Chap. 8). In D. Koračin & C. E. Dorman (Eds.), Marine fog: Challenges and advancements in observations, modeling and forecasting. New York: Springer.Google Scholar
  87. Taylor, G. I. (1917). The formation of fog and mist. Quarterly Journal of the Royal Meteorological Society, 43, 241–268.CrossRefGoogle Scholar
  88. Teixeira, J., & Miranda, P. M. A. (2001). Fog prediction at Lisbon airport using a one-dimensional boundary layer model. Meteorological Applications, 8, 497–505.CrossRefGoogle Scholar
  89. Thompson, W. T., Burk, S. D., & Lewis, J. (2005). Fog and low clouds in a coastally trapped disturbance. Journal of Geophysical Research, 110, D18213.CrossRefGoogle Scholar
  90. U.S. Department of Agriculture. (1938). Atlas of the climatic charts of the oceans. Publication No. 1247, Prepared under the supervision of W. F. McDonald, 130 charts. U.S. Weather Bureau.Google Scholar
  91. van Schalkwyk, L., & Dyson, L. (2013). Climatological characteristics of fog at Cape Town International Airport. Weather and Forecasting, 28(3), 631–646.CrossRefGoogle Scholar
  92. Vautard, R., Yiou, P., & van Oldenborgh, G. J. (2009). Decline of fog, mist and haze in Europe over the past 30 years. Nature Geoscience, 2, 115–119.CrossRefGoogle Scholar
  93. Wang, Y., Gao, S., Fu, G., Sun, J., & Zhang, S. (2014). Assimilating MTSAT-derived humidity in nowcasting sea fog over the Yellow Sea. Weather and Forecasting, 29, 205–225.CrossRefGoogle Scholar
  94. Wilcox, E. M. (2017). Multi-spectral remote sensing of sea fog with simultaneous passive infrared and microwave sensors (Chap.  11). In D. Koračin & C. E. Dorman (Eds.), Marine fog: Challenges and advancements in observations, modeling and forecasting. New York: Springer.
  95. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.CrossRefGoogle Scholar
  96. Zhang, S.-P., Xie, S.-P., Liu, Q.-L., Yang, Y.-Q., Wang, X.-G., & Ren, Z.-P. (2009). Seasonal variations of Yellow Sea fog: Observations and mechanisms. Journal of Climate, 22(24), 6758–6772.Google Scholar
  97. Zhang, S., & Yi, L. (2013). A comprehensive dynamic threshold algorithm for daytime sea fog retrieval over the Chinese adjacent seas. Pure and Applied Geophysics, 170, 1931–1944.CrossRefGoogle Scholar
  98. Zhang, S., & Lewis, J. M. (2017). Synoptic processes (Chap. 6). In D. Koračin & C. E. Dorman (Eds.), Marine fog: Challenges and advancements in observations, modeling and forecasting. New York: Springer.Google Scholar
  99. Zhou, B., & Du, J. (2010). Fog prediction from a multimodel mesoscale ensemble prediction system. Weather and Forecasting, 25, 303–322.CrossRefGoogle Scholar
  100. Zhou, B., Du, J., McQueen, J., Dimego, G., Manikin, G., Ferrier, B., et al. (2004) An introduction to NCEP SREF aviation project. Preprint. In 11th Conference 27 on Aviation, Range, and Aerospace, Oct 4–8, 2004. Hyannis: American Meteorological Society. Paper 9.15.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Faculty of Science, Department of PhysicsUniversity of SplitSplitCroatia
  2. 2.Department of Atmospheric SciencesDesert Research InstituteRenoUSA

Personalised recommendations