Editorial Type:
Article
Article Type:
Research Article

Assimilating MTSAT-Derived Humidity in Nowcasting Sea Fog over the Yellow Sea

Yongming Wang Key Laboratory of Physical Oceanography, Department of Atmospheric Science, Ocean University of China, Qingdao, China

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Shanhong Gao Key Laboratory of Physical Oceanography, Department of Atmospheric Science, Ocean University of China, Qingdao, China

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Gang Fu Key Laboratory of Physical Oceanography, Department of Atmospheric Science, Ocean University of China, Qingdao, China

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Jilin Sun Department of Atmospheric Science, Ocean University of China, Qingdao, China

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Suping Zhang Department of Atmospheric Science, Ocean University of China, Qingdao, China

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Print Publication:
01 Apr 2014
DOI:
https://doi.org/10.1175/WAF-D-12-00123.1
Page(s):
205–225
Restricted access

Abstract

An extended three-dimensional variational data assimilation (3DVAR) method based on the Weather Research and Forecasting Model (WRF) is developed to assimilate satellite-derived humidity from sea fog at its initial stage over the Yellow Sea. The sea fog properties, including its horizontal distribution and thickness, are retrieved empirically from the infrared and visible cloud imageries of the Multifunctional Transport Satellite (MTSAT). Assuming a relative humidity of 100% in fog, the MTSAT-derived humidity is assimilated by the extended 3DVAR assimilation method. Two sea fog cases, one spread widely over the Yellow Sea and the other spread narrowly along the coast, are first studied in detail with a suite of experiments. For the widespread-fog case, the assimilation of MTSAT-derived information significantly improves the forecast of the sea fog area, increasing the probability of detection and equitable threat scores by about 20% and 15%, respectively. The improvement is attributed to a more realistic representation of the marine boundary layer (MBL) and better descriptions of moisture and temperature profiles. For the narrowly spread coastal case, the model completely fails to reproduce the sea fog event without the assimilation of MTSAT-derived humidity. The extended 3DVAR assimilation method is then applied to 10 more sea fog cases to further evaluate its effect on the model simulations. The results reveal that the assimilation of MTSAT-derived humidity not only improves sea fog forecasts but also provides better moisture and temperature structure information in the MBL.

Corresponding author address: Shanhong Gao, Key Laboratory of Physical Oceanography, Dept. of Atmospheric Science, Ocean University of China, 5 Yushan Rd., Qingdao 266003, China. E-mail: gaosh@ouc.edu.cn

Abstract

An extended three-dimensional variational data assimilation (3DVAR) method based on the Weather Research and Forecasting Model (WRF) is developed to assimilate satellite-derived humidity from sea fog at its initial stage over the Yellow Sea. The sea fog properties, including its horizontal distribution and thickness, are retrieved empirically from the infrared and visible cloud imageries of the Multifunctional Transport Satellite (MTSAT). Assuming a relative humidity of 100% in fog, the MTSAT-derived humidity is assimilated by the extended 3DVAR assimilation method. Two sea fog cases, one spread widely over the Yellow Sea and the other spread narrowly along the coast, are first studied in detail with a suite of experiments. For the widespread-fog case, the assimilation of MTSAT-derived information significantly improves the forecast of the sea fog area, increasing the probability of detection and equitable threat scores by about 20% and 15%, respectively. The improvement is attributed to a more realistic representation of the marine boundary layer (MBL) and better descriptions of moisture and temperature profiles. For the narrowly spread coastal case, the model completely fails to reproduce the sea fog event without the assimilation of MTSAT-derived humidity. The extended 3DVAR assimilation method is then applied to 10 more sea fog cases to further evaluate its effect on the model simulations. The results reveal that the assimilation of MTSAT-derived humidity not only improves sea fog forecasts but also provides better moisture and temperature structure information in the MBL.

Corresponding author address: Shanhong Gao, Key Laboratory of Physical Oceanography, Dept. of Atmospheric Science, Ocean University of China, 5 Yushan Rd., Qingdao 266003, China. E-mail: gaosh@ouc.edu.cn
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  • Ahn, M. H., Sohn E. H. , and Hwang B. J. , 2003: A new algorithm for sea fog/stratus detection using GMS-5 IR data. Adv. Atmos. Sci., 20, 899–913.

    • Search Google Scholar
    • Export Citation

  • Anderson, J., 1931: Observations from airplanes of cloud and fog conditions along the southern California coast. Mon. Wea. Rev., 59, 264–270.

    • Search Google Scholar
    • Export Citation

  • Ballard, S. P., Golding B. W. , and Smith R. N. B. , 1991: Mesoscale model experimental forecasts of the haar of northeast Scotland. Mon. Wea. Rev., 119, 2107–2123.

    • Search Google Scholar
    • Export Citation

  • Bendix, J., Thies B. , Cermak J. , and Nauß T. , 2005: Ground fog detection from space based on MODIS daytime data—A feasibility study. Wea. Forecasting, 20, 989–996.

    • Search Google Scholar
    • Export Citation

  • Byers, H. R., 1930: Summer Sea Fogs of the Central California Coast. University of California Press, 338 pp.

  • Chen, F., and Dudhia J. , 2001: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model description and implementation. Mon. Wea. Rev., 129, 569–585.

    • Search Google Scholar
    • Export Citation

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

  • Coakley, J. A., and Bretherton F. P. , 1982: Cloud cover from high-resolution scanner data: Detection and allowing for partially filled fields of view. J. Geophys. Res., 87 (C7), 4917–4932.

    • Search Google Scholar
    • Export Citation

  • Deng, J., Bai J. , Liu J. W. , Wang X. W. , and Shi H. Y. , 2006: Detection of daytime fog using MODIS multispectral data (in Chinese). Mater. Sci. Technol., 34, 188–193.

    • Search Google Scholar
    • Export Citation

  • Ellrod, G. P., 1995: Advances in the detection and analysis of fog at night using GOES multispectral infrared imagery. Wea. Forecasting, 10, 606–619.

    • Search Google Scholar
    • Export Citation

  • Findlater, J., Roach W. T. , and McHugh B. C. , 1989: The haar of north-east Scotland. Quart. J. Roy. Meteor. Soc., 115, 581–608.

  • Fitzpatrick, M. F., Brandt R. E. , and Warren S. G. , 2004: Transmission of solar radiation by clouds over snow and ice surfaces: A parameterization in terms of optical depth, solar zenith angle, and surface albedo. J. Climate, 17, 266–275.

    • Search Google Scholar
    • Export Citation

  • Fu, G., Zhang M. G. , Duan Y. H. , Zhang T. , and Wang J. Q. , 2004: Characteristics of sea fog over the Yellow Sea and the East China Sea. Kaiyo Mon.,38, 99–108.

  • Fu, G., Guo J. , Xie S. P. , Duan Y. , and Zhang M. , 2006: Analysis and high-resolution modeling of a dense sea fog event over the Yellow Sea. Atmos. Res., 81, 292–303.

    • Search Google Scholar
    • Export Citation

  • Fu, G., Li P. Y. , Crompton J. G. , Guo J. , Gao S. H. , and Zhang S. P. , 2010: An observational and modeling study of a sea fog event over the Yellow Sea on 1 August 2003. Meteor. Atmos. Phys., 107, 149–159.

    • Search Google Scholar
    • Export Citation

  • Fu, G., Xu J. , and Zhang S. Q. , 2011: Comparison of modeling atmospheric visibility with visible satellite imagery (in Chinese). J. Ocean Univ. China,41, 001–010.

  • Fu, G., Zhang S. P. , Gao S. H. , and Li P. Y. , 2012: Understanding of Sea Fog over the China Seas. China Meteorological Press, 220 pp.

  • 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.

    • Search Google Scholar
    • Export Citation

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

    • Search Google Scholar
    • Export Citation

  • Gao, S. H., Qi Y. L. , Zhang S. B. , and Fu G. , 2010: Initial conditions improvement of sea fog numerical modeling over the Yellow Sea by using cycling 3DVAR. Part I: WRF numerical experiments (in Chinese). J. Ocean Univ. China,40, 001–009.

  • Gultepe, I., and Coauthors, 2007: Fog research: A review of past achievements and future perspectives. Pure Appl. Geophys., 164, 1121–1159.

    • Search Google Scholar
    • Export Citation

  • Heidinger, A. K., and Stephens G. L. , 2000: Molecular line absorption in a scattering atmosphere. Part II: Application to remote sensing in the O2 a band. J. Atmos. Sci., 57, 1615–1634.

    • Search Google Scholar
    • Export Citation

  • Hong, S. Y., and Lim J. O. J. , 2006: The WRF single-moment 6-class microphysics scheme (WSM6). J. Korean Meteor. Soc., 42, 129–151.

  • Hong, S. Y., Noh Y. , and Dudhia J. , 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318–2341.

    • Search Google Scholar
    • Export Citation

  • Hu, J. F., Guo K. C. , and Yan L. N. , 1996: Discriminate prediction of marine fog occurrence using a model output statistics scheme (in Chinese). J. Ocean Univ. Qingdao, 26, 439–445.

    • Search Google Scholar
    • Export Citation

  • Hu, R. J., and Zhou F. X. , 1997: A numerical study of the effects of air–sea conditions on the process of sea fog (in Chinese). J. Ocean Univ. Qingdao, 27, 282–290.

    • Search Google Scholar
    • Export Citation

  • Hu, R. J., and Zhou F. X. , 1998: Effects of advection, turbulence and radiation on formation of sea fog (in Chinese). J. Ocean Univ. Qingdao, 20, 25–30.

    • Search Google Scholar
    • Export Citation

  • Huang, J., and Zhou F. X. , 2006: The cooling and moistening effect on the formation of sea fog in the Huanghai Sea. Acta Oceanol. Sin., 25, 49–62.

    • Search Google Scholar
    • Export Citation

  • Hunt, G. E., 1973: Radiative properties of terrestrial clouds at visible and infrared thermal window wavelengths. Quart. J. Roy. Meteor. Soc., 99, 346–369.

    • Search Google Scholar
    • Export Citation

  • Iacono, M. J., Delamere J. S. , Mlawer E. J. , Shephard M. W. , Clough S. A. , and Collins W. D. , 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, doi:10.1029/2008JD009944.

    • Search Google Scholar
    • Export Citation

  • Kain, J. S., and Fritsch J. M. , 1990: A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 2784–2802.

    • Search Google Scholar
    • Export Citation

  • Kästner, M., Kriebel K. T. , Meerkötter R. , Renger W. , Ruppersberg G. H. , and Wendling P. , 1993: Comparison of cirrus height and optical depth derived from satellite and aircraft measurements. Mon. Wea. Rev., 121, 2708–2715.

    • Search Google Scholar
    • Export Citation

  • Kim, C. K., and Yum S. S. , 2010: Local meteorological and synoptic characteristics of fogs formed over Incheon international airport in the west coast of Korea. Adv. Atmos. Sci., 27, 761–776.

    • Search Google Scholar
    • Export Citation

  • Kim, C. K., and 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. Bound.-Layer Meteor., 143, 481–505.

    • Search Google Scholar
    • Export Citation

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

    • Search Google Scholar
    • Export Citation

  • Koračin, D., Businger J. A. , Dorman C. E. , and Lewis J. M. , 2005a: Formation, evolution, and dissipation of coastal sea fog. Bound.-Layer Meteor., 117, 447–478.

    • Search Google Scholar
    • Export Citation

  • Koračin, D., Leipper D. F. , and Lewis J. M. , 2005b: Modeling sea fog on the U.S. California coast during a hot spell event. Geofizika, 22, 59–82.

    • Search Google Scholar
    • Export Citation

  • Kunkel, B., 1984: Parameterization of droplet terminal velocity and extinction coefficient in fog models. J. Climate Appl. Meteor., 23, 34–41.

    • Search Google Scholar
    • Export Citation

  • Lee, T. F., Turk F. J. , and Richardson K. , 1997: Stratus and fog productions using GOES-8–9 3.9-μm data. Wea. Forecasting, 12, 606–619.

    • Search Google Scholar
    • Export Citation

  • Leipper, D., 1948: Fog development at San Diego, California. J. Mar. Res., 7, 337–346.

  • Lewis, J. M., 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, doi:10.1029/2002JD002833.

    • Search Google Scholar
    • Export Citation

  • Lewis, J. M., Koračin D. , and Redmond K. T. , 2004: Sea fog research in the United Kingdom and United States. Bull. Amer. Meteor. Soc., 85, 395–408.

    • Search Google Scholar
    • Export Citation

  • Li, R., Gao S. H. , and Wang Y. M. , 2012: Numerical study on direct assimilation of satellite radiances for sea fog over the Yellow Sea (in Chinese). J. Ocean Univ. China,42, 10–20.

  • Liang, W. F., and Hou Z. X. , 2001: The characteristics and forecasts of heavy fog occurred over Qingdao (in Chinese). Shandong Meteor., 84, 11–17.

    • Search Google Scholar
    • Export Citation

  • Lim, K. S. S., and Hong S. Y. , 2010: Development of an effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon. Wea. Rev., 138, 1587–1612.

    • Search Google Scholar
    • Export Citation

  • Lin, Y. L., Farley R. D. , and Oriville H. D. , 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22, 1065–1092.

    • Search Google Scholar
    • Export Citation

  • Liu, X., and Hu X. Q. , 2008: Sea fog automatic detection over the East China Sea using MTSAT data (in Chinese). J. Oceanogr. Taiwan, 27, 112–117.

    • Search Google Scholar
    • Export Citation

  • Nakanishi, M., and Niino H. , 2006: An improved Mellor–Yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog. Bound.-Layer Meteor., 119, 397–407.

    • Search Google Scholar
    • Export Citation

  • Nicholls, S., 1984: The dynamics of stratocumulus: Aircraft observation and comparison with a mixed layer model. Quart. J. Roy. Meteor. Soc., 110, 783–820.

    • Search Google Scholar
    • Export Citation

  • Otkin, J. A., and Greenwald T. J. , 2008: Comparison of WRF model-simulated and MODIS-derived cloud data. Mon. Wea. Rev., 136, 1957–1970.

    • Search Google Scholar
    • Export Citation

  • Parrish, D. F., and Derber J. C. , 1992: The National Meteorological Center’s spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 1747–1763.

    • Search Google Scholar
    • Export Citation

  • Petterssen, S., 1936: On the causes and the forecasting of the California fog. J. Aeronaut. Sci., 3, 305–309.

  • Petterssen, S., 1938: On the causes and the forecasting of the California fog. Bull. Amer. Meteor. Soc., 19, 49–55.

  • Skamarock, W., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN–475+STR, 113 pp. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/arw_v3_bw.pdf.]

  • Sorli, B., Pascal-Delannoy F. , Giani A. , Foucaran A. , and Boyer A. , 2002: Fast humidity sensor for high range 80%–95% RH. Sens. Actuators, 100A, 24–31.

    • Search Google Scholar
    • Export Citation

  • Stoelinga, M. T., and Thomas T. W. , 1999: Nonhydrostatic, mesobeta-scale model simulations of cloud ceiling and visibility for an East Coast winter precipitation event. J. Appl. Meteor., 38, 385–403.

    • Search Google Scholar
    • Export Citation

  • Taylor, G., 1917: The formation of fog and mist. Quart. J. Roy. Meteor. Soc., 43, 241–268.

  • Thompson, G., Field P. R. , Rasmussen R. M. , and Hall W. D. , 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 5095–5115.

    • Search Google Scholar
    • Export Citation

  • Trémant, M., 1987: La prèvision du brouilliard en mer. Meteorologie Maritime et Activities, Oceanograpiques Connexes Paport, Vol. 20, WMO, 127 pp.

  • Wang, B. H., 1985: Sea Fog. China Ocean Press, 330 pp.

  • Wang, X., Barker D. , Snyder C. , and Hamill T. M. , 2008a: A hybrid ETKF–3DVAR data assimilation scheme for the WRF model. Part I: Observing system simulation experiment. Mon. Wea. Rev., 136, 5116–5131.

    • Search Google Scholar
    • Export Citation

  • Wang, X., Barker D. , Snyder C. , and Hamill T. M. , 2008b: A hybrid ETKF–3DVAR data assimilation scheme for the WRF model. Part II: Real observation experiments. Mon. Wea. Rev., 136, 5132–5147.

    • Search Google Scholar
    • Export Citation

  • WMO, 1966: International Meteorological Vocabulary.World Meteorological Organization, 276 pp.

  • 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-Pac. J. Atmos. Sci., 46, 29–40.

    • Search Google Scholar
    • Export Citation

  • Zhang, H. Y., Zhou F. X. , and Zhang X. H. , 2005: Interannual change of sea fog over the Yellow Sea in spring (in Chinese). Oceanol. Limnol. Sin., 36, 36–42.

    • Search Google Scholar
    • Export Citation

  • Zhang, S. P., and Bao X. W. , 2008: The main advances in sea fog research in China (in Chinese). J. Ocean Univ. China, 38, 359–366.

  • 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.

    • Search Google Scholar
    • Export Citation

  • Zhao, Y. P., Chen Y. L. , and Wang P. G. , 1997: Analysis of atmospheric and oceanic conditions for marine fog formation over the Yellow Sea and East China Seas (in Chinese). Stud. Mar. Sin., 38, 69–79.

    • Search Google Scholar
    • Export Citation

  • Zhou, B., and Du J. , 2010: Fog prediction from a multimodel mesoscale ensemble prediction system. Wea. Forecasting, 25, 303–322.

  • Zhou, F. X., and Liu L. T. , 1986: Comprehensive survey and research report on the water areas adjacent to the Changjing River estuary and Chejudo Island marine fog (in Chinese). J. Shandong Coll. Oceanol., 16, 114–131.

    • Search Google Scholar
    • Export Citation

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

    • Search Google Scholar
    • Export Citation
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