Skip to main content
SpringerLink
Log in

A Comprehensive Dynamic Threshold Algorithm for Daytime Sea Fog Retrieval over the Chinese Adjacent Seas

  • Published:
Pure and Applied Geophysics Aims and scope Submit manuscript

Abstract

Sea fog influences human activities over oceans. It is somewhat difficult to separate sea fog from marine boundary stratus (low stratus and stratocumulus) by satellites due to their microphysical similarities and shared spectral features. For the purpose of improving sea fog detection over the Chinese adjacent seas where fog is common during the spring–summer seasons, the vertical structures of fog and stratus were analyzed using ground-based soundings, resulting in the observation of very explicit discrepancies between them, in terms of TAT − SST (TAT, the temperature at tops of fog or stratus; SST, the sea surface temperature). Based on these discrepancies and on previous related studies, we suggest a comprehensive dynamic threshold algorithm. The method combines real-time brightness temperature from Moderate Resolution Imaging Spectroradiometer channel 31 (~11 μm) with climatological monthly mean SSTs to produce a threshold that is monthly-dependent. The retrieved results are generally consistent with the observations from meteorological stations near the coast, on islands and from ships, and the scores of validation by conventional methods are promising. The distribution patterns of the retrieved sea fog frequency in May and June from 2006 to 2010 are both compatible with that from ship-based observations and exhibit more details that are consistent with our understanding of sea fog characteristics. This study is helpful for marine weather service and the improvement of models for sea fog forecasting.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Ahn, M. H., Sohn, E. H., and Hwang, B. J. (2003), A new algorithm for sea fog/stratus detection using GMS-5 IR data, Advances in Atmospheric Sciences 20, 899–913.

  • Bachmann, M., and Bendix, J. (1992), An improved algorithm for NOAA-AVHRR image referencing, International J. Remote Sensing 13, 3205–3215.

  • Bao, X. W., Wang, X., Sun, L. T., and Zhou, F.X. (2005), The weather proof detection system of sea fog by remote sensing and its applications, High Technol. Lett. 15 (1), 101–106 (in Chinese).

    Google Scholar 

  • Bendix, J., and Bachmann, M. (1991), A method for detection of fog using AVHRR imagery of NOAA satellites suitable for operational purposes, Meteorologische Rundschau 43, 169–178 (in German).

    Google Scholar 

  • Bendix, J. (2002), A satellite-based climatology of fog and low-level stratus in Germany and adjacent areas, Atmos. Res. 64, 3–18.

  • Bendix, J., Thies, B., and Čermak, J. (2004), Fog detection with TERRA-MODIS and MSG-SEVIRI. Proceeding of the 2003 Meteorological Satellite Data Users’ Conference, Weimar, Germany, European Organisation for the exploitation of meteorological satellites, 429–435.

  • Bendix, J., Čermak, J., and Thies, B. (2004), New perspectives in remote sensing of fog and low stratus-TERRA/AQUA-MODIS and MSG, Proceedings 3rd International Conference on Fog, Fog Collection and Dew, 11–15 Oct. 2004, Cape Town ZA, G2, 1–4.

  • Bendix, J., Thies, B., Čermak, J., Nauss, T. (2005), Ground Fog Detection From Space Based on MODIS Daytime Data—A Feasibility Study, Weather and Forecasting, 20, 989–1005.

  • Bendix, J., Thies, B., Nauß, T., and Čermak, J. (2006), A feasibility study of daytime fog and low stratus detection with TERRA/AQUA-MODIS over land, Meteorological Applications 13, 111–125.

  • Čermak, J., and Bendix, J. (2007), Dynamical nighttime fog/low stratus detection based on meteosat SEVIRI data–a feasibility study, Pure Appl. Geophys. 164, 1179–1192.

    Google Scholar 

  • Čermak, J., and Bendix, J. (2008), A novel approach to fog/low stratus detection using Meteosat 8 data, Atmospheric Research 87, 279–292.

    Google Scholar 

  • Čermak, J., Bendix, J. (2011), Detecting Ground Fog From Space—A Microphysics-Based Approach, International Journal of Remote Sensing 32, 12, 3345–3371.

  • Cho, Y. K., Kim, M. O., and Kim, B. C. (2000), Sea fog around the Korean Peninsula, J. Appl. Meteor. 39, 2473–2479.

    Google Scholar 

  • Dybbroe, A. (1993), Automatic detection of fog at night using AVHRR data, Proceeding of the 6th AVHRR Data Users’ Meeting 1993, Belgirate, Italy, European Organisation for the Exploitation of Meteorological Satellites, 245–252.

  • Eyre, J. R., Brownscombe, J. L., and Allam, R. J. (1984), Detection of fog at night using Advanced Very High Resolution Radiometer (AVHRR) imagery, J. Meteorology Magazine 113, 266–271.

    Google Scholar 

  • Fu, G., Guo, J. T., Xie, S. P., Duan, Y. H., and Zhang, M. G. (2006), Analysis and high-resolution modeling of a dense sea fog event over the Yellow Sea. Atmos. Res. 81, 293–303.

  • Gao, B. C., and Kaufman, Y., J. (2003), Water vapor retrievals using moderate resolution Imaging spectroradiometer (MODIS) near-infrared channels, Journal of geophysical research-atmospheres 108 (D13), 4389.

  • Gao, S. H., Lin, H., Shen, B., and Fu, G. (2007), A heavy sea fog event over the Yellow Sea in March 2005: analysis and numerical modeling, Adv. Atmos. Sci. 24, 65–81.

    Google Scholar 

  • Gao, S. H., Wu, W., Zhu, L. L., Fu, G., and Huang, B. (2009), Detection of nighttime sea fog/stratus over the Huang-hai Sea using MTSAT-1R IR data, Acta Oceanologica Sinica 28, 23–35.

    Google Scholar 

  • Gao, S. H., Qi, Y. L., Zhang, S. B., and Fu, G. (2010a), Initial Conditions Improvement of Sea Fog Numerical Modeling over the Yellow Sea by Using Cycling 3DVAR-Part I:WRF Numerical Experiments, Periodical of Ocean University of China 40, 001–009 (in Chinese).

  • Gao, S. H., Zhang, S. B., Qi, Y. L., and Fu, G. (2010b), Initial Conditions Improvement of Sea Fog Numerical Modeling over the Yellow Sea by Using Cycling 3DVAR-Part II: RAMS Numerical Experiments, Periodical of Ocean University of China 40, 001–010 (in Chinese).

  • Guan, L., and Kawamura, H. (2004), Merging satellite infrared and microwave SSTs: methodology and evaluation of the new SST, J. Oceanogr. 60, 905–912.

    Google Scholar 

  • Gultepe, I., Pagowski, M., and Reid, J. (2007), A Satellite-Based Fog Detection Scheme Using Screen Air Temperature. Wea. Forecasting 22, 444–456.

  • Gultepe, I., and Milbrandt, J. A. (2007), Microphysical observations and mesoscale model simulation of a warm fog case during FRAM project, Pure Appl. Geophys. 164, 1161–1178.

    Google Scholar 

  • Gultepe, I., Pearson, G., Mi Lbrandt, J. A., Hansen, B., Platni CK, S., Taylor, P., Gordon, M., Oakley, J. P. and Cober, S. G. (2009), The fog remote sensing and modeling field project, Bull. Amer. Meteor Soc. 90, 341–359.

  • Hunt, G. E. (1973), Radiative properties of terrestrial clouds at visible and infrared thermal window wavelengths, Quart. J. Roy. Meteor. Soc. 99, 346–359.

    Google Scholar 

  • Kaufman, Y. J., Tanri, D. L., Remer, A., Vermote, E. F., Chu, A., and Holbenl, B. N. (1997), Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer, J. Geophys. Res. 102, 17051–17067.

    Google Scholar 

  • King, M. D., Menzel, W. P., Kaufman, Y. J., Tanre, D., Gao, B. C., Platnick, S., Ackerman, S. A., Remer, L. A., Pincus, R., and Hubanks, P. A. (2003), Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS IEEE Trans Geosci. Remote Sensing 41, 442–458.

    Google Scholar 

  • Koračin, D., Lewis, J., Thompson, W. T., Dorman, C. E., and Businger, J. A. (2001), Transition of stratus into fog along the California coast: observations and modeling, J. Atmos. Sci. 58, 1714–1731.

    Google Scholar 

  • Leipper, D. F. (1994), Fog on the U.S. west coast: A review, Bull. Amer. Meteor. Soc. 75, 229–240.

    Google Scholar 

  • Lewis, J., Koračin, D., Rabin, R., and Businger, J. (2003), Sea fog off the California Coast: viewed in the context of transient weather systems, J. Geophys. Res. 108, 4457–4473.

    Google Scholar 

  • Lewis, J., Koračin, D., and Redmond, K. (2004), Sea fog research in the UK and USA: historical essay including outlook, Bull. Amer. Meteor. Soc. 85, 395–408.

    Google Scholar 

  • Meng, X. G., and Zhang, S. P. (2012), The effect of cold SST on summer atmosphere boundary layer and sea fog over the Yellow Sea, Periodical of Ocean University of China, accepted (in Chinese).

  • Pavolonis, M. J., Key, J. R. and Cassano, J. J. (2004), A study of the Antarctic surface energy budget using a polar regional atmospheric model forced with satellite-derived cloud properties. Monthly Weather Review 132 (2), 654–661.

    Google Scholar 

  • Platnick, S., King, M. D., Ackerman, S. A., Menzel, W. P., Baum, B. A., Riedi, J. C., and Frey, R. A. (2003), The MODIS cloud products: algorithms and examples from Terra, Geoscience and Remote Sensing, IEEE Transactions on 41, 459–473.

  • Putsay, M., Kerényi, J., Szenyán, I., Sebok, I., Németh, P., and Diószeghy, M. (2001), Nighttime fog and low cloud detection in NOAA-16 AVHRR images and validation with ground observed SYNOP data and radar measurements, In the Proceeding of 2001 EUMETSAT Meteorological Satellite Conference, EUM P33, 365–373, EUMETSAT, Antalya, Turkey.

  • Qu, J. J., Gao, W., Kafatos, M., Murphy, R., E., and Salomonson, V. V., Earth Science Satellite Remote Sensing vol. 1/2 (Springer-Verlag, New York, 2006).

  • Ricchiazzi, P. (1998), SBDART: A research and teaching software tool for plane-parallel radiative transfer in the earth’s atmosphere, Bulletin of the American Meteorological Society 79, 2101–2114.

  • Salomonsona, V. V., and Appel, I. (2004), Estimating fractional snow cover from MODIS using the normalized difference snow index, Remote Sensing of Environment 89, 351–360.

    Google Scholar 

  • Slutz, R. J., Luber, S. L., Woodruff, S. D., Jenne, R. L., Joseph, D. H., Steurer, P. M., and Elms, J. D. (1985), Comprehensive Ocean-Atmosphere Data Set; Release 1, NOAA Environmental Research Laboratories, Climate Research Program, Boulder, CO, pp 268.

  • Tanré, D., Kaufman, Y. J., Herman, M., and Mattoo, S. (1997), Remote sensing of aerosol properties over oceans using the MODIS/EOS spectral radiances, Journal of Geophysical Research 102, 16971–16988.

    Google Scholar 

  • The National Climate Center, China Meteorological Administration (1995), Marine Climatological Atlas for Continental and Adjacent Sea Areas of China, China Meteorological Press, Beijing, pp 124.

  • Tokinaga, H., Xie, S. P. (2009), Ocean tidal cooling effect on summer sea fog over the Okhotsk Sea, J. Geophys. Res. 114, D14102, doi:10.1029/2008JD011477.

  • Turner, J., Allama, R. J., and Maine, D. R. (1986), A case study of the detection of fog at night using channels 3 and 4 on the Advanced Very High Resolution Radiometer (AVHRR), Meteorol. Mag. 115, 285–297.

  • Underwood, S. J., Ellrod, G. P., and Kuhnert, A. L. (2004), A multiple-case analysis of nocturnal radiation-fog development in the Central Valley of California utilizing the GOES nighttime fog product, J. Appl. Meteor. 43, 297–311.

    Google Scholar 

  • Wang, B. H., Sea Fog (China Ocean Press, Beijing, 1985) (in Chinese).

  • Wang, X., Huang, F., and Zhou, F. X. (2006), Climatic characteristics of sea fog formation of the Huanghai Sea in summer, Acta Oceanologica Sinica 28, 26–34 (in Chinese).

    Google Scholar 

  • Wang, Y. L., Wang, J. and Gong, D. L.(2001), The macro-micro structure analysis of Jianghuai cyclone and cold front, Quarterly journal of applied meteorology 12 (suppl.), 30–38 (in Chinese).

  • Wu, X. J., Zhang, S-P., Li, S. M., Zhang, M. S., Ren, S. L., Cao, Z. Q., and Liu, Y. C. (2010), Sea fog retrieval methods based on polar-orbiting meteorological satellite during day and night, China patent, ZL200810249748.X.

  • Xie, S., P., Hafner, J., Tanimoto, Y., Liu, W. T., Tokinaga, H., and Xu, H. (2002), Bathymetric effect on the winter sea surface temperature and climate of the Yellow and East China Seas, Geophys. Res. Lett. 29, 2228, doi:10.1029/2002GL015884.

  • Yang, D., Ritchie, H., and Gultepe, I. (2010), High-resolution GEM-LAM application in marine fog prediction: evaluation and diagnosis, Weather and Forecasting 25, 727–748.

  • Yoo, J. M., Jeong, M. J., Hur, Y. M., and Shin, D. B. (2010), Improved fog detection from satellite in the presence of clouds, Asia-Pacific J. Atmos. Sci. 46, 29–40.

  • Zhang, D. C., Artificial influence on weather (Meteorological press, Beijing, 1992) (in Chinese).

  • Zhang, G. W., Zhang, S-P., Wu, X. J., Liu, Y. C., and Liu, J. W. (2009), The research on Yellow Sea fog based on MODIS data: Sea fog properties retrieval and spatial-temporal distribution, Periodical of ocean university of China 39 (sup.), 311–318 (in Chinese).

  • Zhang, S. P., Ren, Z. P., Yang, Y. Q., and Wang, X. G. (2008), Variations in the lower level of the PBL associated with the Yellow Sea fog-New observations by L-Band radar, J. Ocean Univ. Chin. (Oceanic and Coastal Sea Research) 7, 353–361.

  • Zhang, S. P., Xie, S. P., Liu, Q. Y., Yang, Y. Q., Wang, X. G., and Ren, Z. P. (2009), Seasonal variations of Yellow Sea fog: Observations and mechanisms, J. Climate 22, 6758–6772.

  • Zhang, S. P., and Ren, Z. P. (2010), The Influence of thermal effects of underlaying surface on the spring sea fog over the Yellow Sea-observations and numerical simulation, Acta Meteorologica Sinica 68, 116–125 (in Chinese).

    Google Scholar 

  • Zhang, S. P., Li, M., Meng, X. G., Fu, G., Ren, Z. P., and Gao, S. H. (2012), A comparison study between spring and summer fogs in the Yellow Sea-observations and mechanisms, Pure Appl. Geophys. 169, 1001–1017, doi:10.1007/s00024-011-0358-3.

    Google Scholar 

  • Zhou, F. X., Wang, X., and Bao, X. W. (2004), Climatic characteristics of sea fog formation of the Huanghai Sea in spring, Acta Oceanologica Sinica 26, 28–37.

    Google Scholar 

Download references

Acknowledgments

This work is supported by NSFS 41175006, National Basic Research Program of China No. 2012CB955602 Programs, and GYHY (QX) 2007-6-31.

Author information

Authors and Affiliations

  1. Physical Oceanography Laboratory, Ocean-Atmosphere Interaction and Climate Laboratory, Ocean University of China, Qingdao, China

    Suping Zhang & Li Yi

Authors
  1. Suping Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Suping Zhang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, S., Yi, L. A Comprehensive Dynamic Threshold Algorithm for Daytime Sea Fog Retrieval over the Chinese Adjacent Seas. Pure Appl. Geophys. 170, 1931–1944 (2013). https://doi.org/10.1007/s00024-013-0641-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00024-013-0641-6

Keywords

Search

Navigation