DOI QR코드

DOI QR Code

A Remote Sensed Data Combined Method for Sea Fog Detection

  • Heo, Ki-Young (Division of Earth Environmental System, College of Natural Science, Pusan National University) ;
  • Kim, Jae-Hwan (Division of Earth Environmental System, College of Natural Science, Pusan National University) ;
  • Shim, Jae-Seol (Coastal Disaster Prevention Research Division, Korea Ocean Research & Development Institute) ;
  • Ha, Kyung-Ja (Division of Earth Environmental System, College of Natural Science, Pusan National University) ;
  • Suh, Ae-Sook (Environmental and Meteorological Satellite Division, Korean Meteorological Administration) ;
  • Oh, Hyun-Mi (Division of Earth Environmental System, College of Natural Science, Pusan National University) ;
  • Min, Se-Yun (Division of Earth Environmental System, College of Natural Science, Pusan National University)
  • Published : 2008.02.28

Abstract

Steam and advection fogs are frequently observed in the Yellow Sea from March to July except for May. This study uses remote sensing (RS) data for the monitoring of sea fog. Meteorological data obtained from the Ieodo Ocean Research Station provided a valuable information for the occurrence of steam and advection fogs as a ground truth. The RS data used in this study were GOES-9, MTSAT-1R images and QuikSCAT wind data. A dual channel difference (DCD) approach using IR and shortwave IR channel of GOES-9 and MTSAT-1R satellites was applied to detect sea fog. The results showed that DCD, texture-related measurement and the weak wind condition are required to separate the sea fog from the low cloud. The QuikSCAT wind data was used to provide the wind speed criteria for a fog event. The laplacian computation was designed for a measurement of the homogeneity. A new combined method, which includes DCD, QuikSCAT wind speed and laplacian computation, was applied to the twelve cases with GOES-9 and MTSAT-1R. The threshold values for DCD, QuikSCAT wind speed and laplacian are -2.0 K, $8m\;s^{-1}$ and 0.1, respectively. The validation results showed that the new combined method slightly improves the detection of sea fog compared to DCD method: improvements of the new combined method are $5{\sim}6%$ increases in the Heidke skill score, 10% decreases in the probability of false detection, and $30{\sim}40%$ increases in the odd ratio.

Keywords

References

  1. Anthis, A. L. and A. P. Cracknell, 1999. Use of satellite images for fog detection (Sensing, 20: 1107-1124.AVHRR) and forecast of fog dissipation (METEOSAT) over lowland Thessalia, Hellas. Int. J. Remote https://doi.org/10.1080/014311699212876
  2. Bader, M. J., J. R. Forbes, J. R. Grant, R. B. Lilley, and A. J. Waters, 1995. Images in weather forecasting: practical guide for interpreting satellite and radar data. University Press, Cambridge.
  3. Bendix, J., B. Thies, J. Cermak, and T. Nauss, 2005. Fog detection from space based on MODIS daytime data - A feasibility study. Wea. Forecasting, 20: 989-1005. https://doi.org/10.1175/WAF886.1
  4. Byers, H. R., 1959. General Meteorology, 3rd Edition, McGraw Hill Book Co., Inc, 540pp.
  5. Cermak, J. and J. Bendix, 2005. Fog / low stratus detection and discrimination using satellite data. Proceeding of COST722 mid-term workshop on short-range forecasting methods of fog, visibility and low clouds, 20 October 2005, Langen, Germany.
  6. Cho, Y. K., M. O. Kim, and B. C. Kim, 2000. Sea fog around the Korean Peninsula. J. Appl. Meteor., 39: 2473-2479. https://doi.org/10.1063/1.1656583
  7. Coakley, J. A. and F. P. Bretherton, 1982. Cloud cover from high-resolution scanner data: detecting and allowing for partially filled fields of view. J. Geophys. Res., 87: 4917- 4932. https://doi.org/10.1029/JC087iC07p04917
  8. Croft, P. J., R. L. Pfost, J. M. Medlin, and G. A. Johnson, 1997. Fog forecasting for the southern region: A conceptual model approach. Wea. Forecasting, 12: 545-566. https://doi.org/10.1175/1520-0434(1997)012<0545:FFFTSR>2.0.CO;2
  9. Ellrod, G. P., 1995. Advances in the detection and analysis of fog at night using GOES multispectral infrared imagery. Wea.Forecasting, 10: 606-619. https://doi.org/10.1175/1520-0434(1995)010<0606:AITDAA>2.0.CO;2
  10. Ellrod, G. P., 2000. Proposed improvements to the GOES nighttime fog product to provide ceiling and visibility information. Preprints, 10th conf. on satellite meteorology and oceanography, Long Beach, CA, Amer. Meteor. Soc., 454-456.
  11. Eyre, J. R., J. L. Brownscombe, and R. J. Allam, 1984. Detection of fog at night using Advanced Very High Resolution Radiometer (AVHRR) imagery. Meteorol. Mag., 113: 265-271.
  12. Fu, G., J. Guo, S.-P. Xie, Y. Duan, and M. Zhang, 2006. Analysis and high-resolution modeling of a dense sea fog event over the Yellow Sea. Atmos. Res., 81: 293-303. https://doi.org/10.1016/j.atmosres.2006.01.005
  13. Guidard, V. and D. Tzanos, 2005. Discrimination between fog and low clouds using a combination of satellite data and ground observations. Proceeding of COST722 midterm workshop on short-range forecasting methods of fog, visibility and low clouds, 20 October 2005, Langen, Germany.
  14. Heo, K. Y. and K. J. Ha, 2004. Classification of synoptic pattern associated with coastal fog around the Korean Peninsula. J. Korean Metor. Soc., 40: 541-556 (in Korean with English abstract).
  15. Hilliker, J. L. and J. M. Fritsch, 1999. An observations-based statistical system for warm-season hourly probabilistic forecasts of low ceiling at the San Francisco International Airport. J. Appl. Meteor., 38: 1692-1705. https://doi.org/10.1175/1520-0450(1999)038<1692:AOBSSF>2.0.CO;2
  16. Hunt, G. E., 1973. Radiative properties of terrestrial clouds at visible and infra-red thermal window wavelengths. Quarterly Journal of Royal Meteorological Society, 99: 346-369.
  17. Klein, S. A. and D. L. Hartmann, 1993. The seasonal cycle of low stratiform cloud. J. Climate, 6: 1587-1606. https://doi.org/10.1175/1520-0442(1993)006<1587:TSCOLS>2.0.CO;2
  18. Lee, T. F., F. J. Turk and K. Richardson, 1997. Stratus and fog products using GOES-8-9 3.9${\mu}m$ Data. Wea. Forecasting, 12: 606-619.
  19. Liu, W. T., W. Tang, and R. Polito, 1998. NASA scatterometer provides global ocean-surface wind fields with more structures than numerical weather prediction. Geophys. Res. Lett., 25: 761-764. https://doi.org/10.1029/98GL00544
  20. Liu, W. T., H. Hu, and S. Yueh, 2000. Interplay between wind and rain observed in hurricane Floyd. Eos, Trans. Amer. Geophys. Union, 81: 253-257.
  21. Liu, W. T., H. Hu, Y. T. Song, and W. Tang, 2001. Improvement of scatterometer wind vectorsimpact on hurricane and coastal studies, in Proc. of WCRP/SCOR Workshop on Intercomparison and Validation of Ocean-Atmosphere flux Fields, World Climate Research Programme, Geneva, 197-200.
  22. Meteorological Satellite Center, 2002. Analysis and use of meteorological satellite images. Meteorological Satellite Center, 1st Edition, 195pp.
  23. Nakajima, T. and M. Tanaka, 1988. Algorithms for radiative intensity calculations in moderately thick atmospheres using a truncation approximation, J. Quant. Spectrosc. Radiat. Transfer, 40: 51-69. https://doi.org/10.1016/0022-4073(88)90031-3
  24. Park, H. S., Y. H. Kim, A. S. Suh, and H. H. Lee, 1997. Detection of fog and the low stratus cloud at night using derived dual channel difference of NOAA/AVHRR data. Proceeding of the 18th Asian conference on remote sensing, 20-24 Oct. Kuala lumpur, Malaysia.
  25. Roach, W. T., 1994. Back to basics: Fog: Part 1 - Definitions and basic physics. Weather, 49: 411-415. https://doi.org/10.1002/j.1477-8696.1994.tb05962.x
  26. Sakaida, F. and H. Kawamura, 1996. HIGHERS_The AVHRR-based higher spatial resolution sea surface temperature data set intended for studying the ocean south of Japan. J. Oceanogr., 52: 441-455. https://doi.org/10.1007/BF02239048
  27. Scorer, R. S., 1986. Cloud investigation by satellite. Ellis Horwood Ltd., 314 pp.
  28. Uesawa, D., 2006. Status of Japanese Meteorological Satellites and Recent Activities of MSC, Proceedings of the 2006 EUMETSAT Meteorological Satellite Conference, Helsinki, Finland, June 12-16, 2006.
  29. Wentz, F. J., D. K. Smith, C. A. Mears, and C. L. Gentemann, 2001. Advanced algorithms For QuikSCAT and SeaWinds/AMSR. Proceedings of IEEE 2001 International Geoscience and Remote Sensing Symposium, 9-13 July 2001, Sydney, Australia.
  30. Yoo, J. M., M. J. Jeong, and M. Y. Yun, 2005. Optical Characteristics of Fog in Satellite Observation (MODIS) and Numerical Simulation; Effect of Upper Clouds in Nighttime Fog Detection. J. Korean Metor. Soc., 41: 639-650 (in Korean with English abstract).
  31. Yum, S. S., S. N. Oh, J. Y. Kim, C. K. Kim, and J. C. Nam, 2004. Measurements of Cloud Droplet Size Spectra Using a Forward Scattering Spectrometer Probe (FSSP) in the Korean Peninsula. J. Korean Metor. Soc., 40: 623- 631 (in Korean with English abstract).