Advertisement

Advances in Atmospheric Sciences

, Volume 36, Issue 6, pp 623–642 | Cite as

Evaluation of the Global and Regional Assimilation and Prediction System for Predicting Sea Fog over the South China Sea

  • Huijun HuangEmail author
  • Bin Huang
  • Li Yi
  • Chunxia Liu
  • Jing Tu
  • Guanhuan Wen
  • Weikang Mao
Original Paper
  • 5 Downloads

Abstract

In the South China Sea, sea fog brings severe disasters every year, but forecasters have yet to implement an effective sea-fog forecast. To address this issue, we test a liquid-water-content-only (LWC-only) operational sea-fog prediction method based on a regional mesoscale numerical model with a horizontal resolution of about 3 km, the Global and Regional Assimilation and Prediction System (GRAPES), hereafter GRAPES-3km. GRAPES-3km models the LWC over the sea, from which we infer the visibility that is then used to identify fog. We test the GRAPES-3km here against measurements in 2016 and 2017 from coastal-station observations, as well as from buoy data, data from the Integrated Observation Platform for Marine Meteorology, and retrieved fog and cloud patterns from Himawari-8 satellite data. For two cases that we examine in detail, the forecast region of sea fog overlaps well with the multi-observational data within 72 h. Considering forecasting for 0–24 h, GRAPES-3km has a 2-year-average equitable threat score (ETS) of 0.20 and a Heidke skill score (HSS) of 0.335, which is about 5.6% (ETS) and 6.4% (HSS) better than our previous method (GRAPES-MOS). Moreover, the stations near the particularly foggy region around the Leizhou Peninsula have relatively high forecast scores compared to other sea areas. Overall, the results show that GRAPES-3km can roughly predict the formation, evolution, and dissipation of sea fog on the southern China coast.

Key words

sea fog operational GRAPES model southern China coast forecast evaluation 

摘要

中国南海的海雾每年都带来严重的灾害, 但是预报员还缺乏有效的海雾预报方法. 为了实现这个目标, 我们评估了基于GRAPES模式, 利用液态含水量直接预报海雾的方法(以下称为GRAPES-3km). GRAPES-3km直接模拟海上大气的液体含水量分布, 利用经验关系转换为能见度. 我们利用沿海台站, 浮标, 海洋气象综合观测平台和葵花-8卫星反演的雾和低云资料等, 对比评估了GRAPES-3km在2016和2017年的预报评分. 评估结果表明, 对于0-24h 预报, GRAPES-3km两年平均的公正风险评分(ETS)为0.20, 海德克技巧评分(HSS)为0.335, 大约比我们之前的GRAPES-MOS预报方法高5.6%(ETS)和6.4%(HSS). 而且, 在雷州半岛周围这一海雾的高发区域, 有着相对较高的预报评分. 总体评估结果表明, GRAPES-3km可以大致预报出华南沿海海雾的形成, 演变和消散过程.

关键词

海雾 业务GRAPES模式 华南沿海 预报评估 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

We thank the editor and two anonymous reviewers for their constructive suggestions and comments. Special thanks to the crew of the Marine Meteorological Science Experiment Base at Bohe, for their help in conducting the field program and providing the data. This study was supported jointly by the National Natural Science Foundation of China (Grant Nos. 41675021, 41605006 and 41675019), the Meteorological Sciences Research Project (Grant No. GRMC2017M04), and the Innovation Team of Forecasting Technology for Typhoon and Marine Meteorology of the Weather Bureau of Guangdong Province.

References

  1. Ballard, S. P., B. W. Golding, and R. N. B. Smith, 1991: Mesoscale model experimental forecasts of the haar of northeast Scotland. Mon. Wea. Rev., 119, 2107–2123, https://doi.org/10.1175/1520-0493(1991)119<2107:MMEFOT>2.0.CO;2.CrossRefGoogle Scholar
  2. Bergot, T., D. Carrer, J. Noilhan, and P. Bougeault, 2005: Improved site-specific numerical prediction of fog and low clouds: A feasibility study. Wea. Forecasting, 20, 627–646,  https://doi.org/10.1175/WAF873.1.CrossRefGoogle Scholar
  3. Bessho, K., and Coauthors, 2016: An introduction to Himawari-8/9—Japan’s new-generation geostationary meteorological satellites. Meteor. Soc. Japan, 94(2), 151–183,  https://doi.org/10.2151/jmsj.2016-009.CrossRefGoogle Scholar
  4. Chen, D. H., and Coauthors, 2008: New generation of multi-scale NWP system (GRAPES): General scientific design. Chinese Science Bulletin, 53(22), 3433–3445,  https://doi.org/10.1007/s11434-008-0494-Z.Google Scholar
  5. Chen, S. J., 1983: A preliminary study of the characteristics of the distribution of air and sea surface temperature in the South China Sea. Marine Science Bulletin, 2(4), 9–17. (in Chinese)Google Scholar
  6. Chen, Z. T., G. F. Dai, S. X. Zhong, Y. Y. Huang, Y. X. Zhang, D. S. Xu, and M. J. Li, 2016: Technical features and prediction performance of typhoon model for the South China Sea. Journal of Tropical Meteorology, 32(6), 831–840,  https://doi.org/10.16032/j.issn.1004-4965.2016.06.005.(in Chinese)Google Scholar
  7. Deardorff, J. W., 1978: Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation. J. Geophys. Res., 83, 1889–1903,  https://doi.org/10.1029/JC083iC04p01889.CrossRefGoogle Scholar
  8. Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc, 137, 553–597,  https://doi.org/10.1002/qj.828.CrossRefGoogle Scholar
  9. Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. Atmos. Sci., 46, 3077–3107, https://doi.org/10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2.CrossRefGoogle Scholar
  10. Emmons, G., and R. Montgomery, 1947: Note on the physics of fog formation. Meteor., 4, 206,  https://doi.org/10.1175/1520-0469(1947)004<0207:NOTPOF>2.0.CO;2.CrossRefGoogle Scholar
  11. Findlater, J., W. T. Roach, and B. C. McHugh, 1989: The haar of north-east Scotland. Quart. J. Roy. Meteor. Soc, 115, 581–608,  https://doi.org/10.1002/qj.49711548709.CrossRefGoogle Scholar
  12. Fu, G., T. Zhang, and F. X. Zhou, 2002: Three-dimensional numerical simulation of real sea fog event over the Yellow Sea. Journal of Ocean University of Qingdao, 32(6), 859–867,  https://doi.org/10.3969/j.issn.1672-5174.2002.06.002. (in Chinese)Google Scholar
  13. Gao, S. H., H. Lin, B. Shen, and G. Fu, 2007: A heavy sea fog event over the Yellow Sea in March 2005: Analysis and numerical modeling. Adv. Atmos. Sci., 24(1), 65–81,  https://doi.org/10.1007/s00376-007-0065-2.CrossRefGoogle Scholar
  14. Gao, S. H., Y. L. Qi, S. B. Zhang, and G. Fu, 2010: 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, 1–9,  https://doi.org/10.3969/j.issn.1672-5174.2010.10.001. (in Chinese)Google Scholar
  15. Gao, S. H., W. Wu, L. L. Zhu, G. Fu, and B. Huang, 2009: Detection of nighttime sea fog/stratus over the Huanghai Sea using MTSAT-1R IR data. Acta Oceanologica Sinica, 28(2), 23–35.Google Scholar
  16. Ghonima, M. S., H. D. Yang, C. K. Kim, T. Heus, and J. Kleissl, 2017: Evaluation of WRF SCM simulations of stratocumulus-topped marine and coastal boundary layers and improvements to turbulence and entrainment parameterizations. Journal of Advances in Modeling Earth Systems, 9, 2635–2653,  https://doi.org/10.1002/2017MS001092.CrossRefGoogle Scholar
  17. Gultepe, I., and Coauthors, 2007: Fog research: A review of past achievements and future perspectives. Pure Appl. Geophys., 164, 1121–1159,  https://doi.org/10.1007/s00024-007-0211-x.CrossRefGoogle Scholar
  18. Haiden, T., and Coauthors, 2015: Evaluation of ECMWF forecasts, including 2014–2015 upgrades. Tech. Memo. No. 765.Google Scholar
  19. Heo, K. Y., and K. J. Ha, 2010: A coupled model study on the formation and dissipation of sea fogs. Mon. Wea. Rev., 138(4), 1186–1205,  https://doi.org/10.1175/2009MWR3100.1.CrossRefGoogle Scholar
  20. Hogan, R. J., C. A. T. Ferro, I. T. Jolliffe, and D. B. Stephenson, 2010: Equitability revisited: Why the “equitable threat score” is not equitable. Wea. Forecasting, 25, 710–726,  https://doi.org/10.1175/2009WAF2222350.1.CrossRefGoogle Scholar
  21. Hong, Y.S., and J. Lim, 2006: The WRF single-moment 6-class microphysics scheme (WSM6). Korean Meteor. Soc, 42, 129–151.Google Scholar
  22. Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318–2341,  https://doi.org/10.1175/MWR3199.1.CrossRefGoogle Scholar
  23. Hu, R. J., and F. X. Zhou, 1997: A numerical study on the effects of air-sea conditions on the process of seafog. Journal of Ocean University of Qingdao, 27(3), 282–290,  https://doi.org/10.16441/j.cnki.hdxb.1997.03.003. (in Chinese)Google Scholar
  24. Huang, J., and P. W. Chan, 2011: Progress of marine meteorological observation experiment at Maoming of south China. Journal of Tropical Meteorology, 17, 418–429,  https://doi.org/10.3969/j.issn.1006-8775.2011.04.012.Google Scholar
  25. Huang, B., T. Chen, J. Chen, and L. T. Deng, 2009a: Simulation and test of sea fog numerical prediction system for Yellow Sea and Bohai Sea. Meteorological Science and Technology, 37(3), 271–275,  https://doi.org/10.3969/j.issn.1671-6345.2009.03.003. (in Chinese)Google Scholar
  26. Huang, H. J., J. Huang, C. X. Liu, and W. K. Mao, 2017: Summary analysis of the forecast scores of GRAPES-MOS product in 2010–2017 (Internal report). Institute of Tropical and Marine Meteorology (ITMM), CMA, Guangzhou.Google Scholar
  27. Huang, H. J., H. N. Liu, W. M. Jiang, J. Huang, and W. K. Mao, 2011a: Characteristics of the boundary layer structure of sea fog on the coast of southern China. Adv. Atmos. Sci., 28(6), 1377–1389,  https://doi.org/10.1007/s00376-011-0191-8.CrossRefGoogle Scholar
  28. Huang, H. J., H. N. Liu, J. Huang, W. K. Mao, and X. Y. Bi, 2015: Atmospheric boundary layer structure and turbulence during sea fog on the southern China coast. Mon. Wea. Rev., 143, 1907–1923,  https://doi.org/10.1175/MWR-D-14-00207.l.CrossRefGoogle Scholar
  29. Huang, H. J., J. Huang, C. X. Liu, W. K. Mao, and X. Y. Bi, 2016a: Improvement of regional prediction of sea fog on Guangdong coastland using the factor of temperature difference in the near-surface layer. Journal of Tropical Meteorology, 22(1), 66–73,  https://doi.org/10.16555/j.1006-8775.2016.01.008.Google Scholar
  30. Huang, H. J., G. W. Zhan, C. X. Liu, J. Tu, and W. K. Mao, 2016b: A case study of numerical simulation of sea fog on the southern China coast. Journal of Tropical Meteorology, 22(4), 497–507,  https://doi.org/10.16555/j.1006-8775.2016.04.005.Google Scholar
  31. Huang, H. J., J. Huang, C. X. Liu, J. N. Yuan, W. K. Mao, and F. Liao, 2011b: Prediction of sea fog of Guangdong coastland using the variable factors output by GRAPES model. Journal of Tropical Meteorology, 17(2), 166–174,  https://doi.org/10.3969/j.issn.1006-8775.2011.02.009.Google Scholar
  32. Huang, H. J., J. Huang, C. X. Liu, J. N. Yuan, W. H. Lv, Y. Q. Yang, W. K. Mao, and F. Liao, 2009b: Microphysical characteristics of the sea fog in Maoming area. Acta Oceanologica Sinica, 31(2), 17–23,  https://doi.org/10.3321/j.issn:0253-4193.2009.02.003. (in Chinese)Google Scholar
  33. Jolliffe, I. T., and D. B. Stephenson, 2012: Forecast Verification: A Practitioner’s Guide in Atmospheric Science. 2nd ed., John Wiley & Sons Ltd, 288 pp.Google Scholar
  34. Kim, C. K., and S. S. Yum, 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(3), 481–505,  https://doi.org/10.1007/s10546-012-9706-9.CrossRefGoogle Scholar
  35. Köhler, M., M. Ahlgrimm, and A. Beljaars, 2011: Unified treatment of dry convective and stratocumulus-topped boundary layers in the ECMWF model. Quart. J. Roy. Meteorol. Soc., 137, 43–57,  https://doi.org/10.1002/qj.713.CrossRefGoogle Scholar
  36. Koračin, D., and C. E. Dorman, 2017: Marine Fog: Challenges and Advancements in Observations, Modeling, and Forecasting. Springer, 537 pp.CrossRefGoogle Scholar
  37. Koračin, D., J. A. Businger, C. E. Dorman, and J. M. Lewis, 2005: Formation, evolution, and dissipation of coastal sea fog. Bound.-Layer Meteor., 117, 447–478,  https://doi.org/10.1007/s10546-005-2772-5.CrossRefGoogle Scholar
  38. Koracin, D., C. E. Dorman, J. M. Lewis, J. G. Hudson, E. M. Wilcox, and A. Torregrosa, 2014: Marine fog: A review. Atmospheric Research, 143, 142–175,  https://doi.org/10.1016/j.atmosres.2013.12.012.CrossRefGoogle Scholar
  39. Lamb, H., 1943: Haars or North Sea fogs on the coasts of Great Britain. Meteorology Office Publication M.O. 50424.Google Scholar
  40. Leipper, D. F., 1948: Fog development at San Diego, California. J. Mar. Res., 7, 337–346.Google Scholar
  41. Lewis, J. M., D. Koračin, and K. T. Redmond, 2004: Sea fog research in the United Kingdom and United States: A historical essay including outlook. Bull. Amer. Meteor. Soc., 85, 395–408,  https://doi.org/10.1175/BAMS-85-3-395.CrossRefGoogle Scholar
  42. Li, P. Y., G. Fu, C. G. Lu, D. Fu, and S. Wang, 2012: The formation mechanism of a spring sea fog event over the Yellow Sea associated with a low-level jet. Wea. Forecasting, 27, 1538–1553,  https://doi.org/10.1175/WAF-D-11-00152.1.CrossRefGoogle Scholar
  43. Locarnini, R. A., A. V. Mishonov, J. I. Antonov, T. P. Boyer, and H. E. Garcia, 2006: Temperature. Vol. 1, World Ocean Atlas 2005, NOAA Atlas NESDIS 61, 182 pp.Google Scholar
  44. Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. Geophys. Res., 102(D14), 16 663–16 682,  https://doi.org/10.1029/97JD00237.CrossRefGoogle Scholar
  45. Murphy, A. H., 1996: The Finley affair: A signal event in the history of forecast verification. Wea. Forecasting, 11, 3–20, https://doi.org/10.1175/1520-0434(1996)011<0003:TFAASE>2.0.CO;2.CrossRefGoogle Scholar
  46. Petterssen, S., 1938: On the causes and the forecasting of the California fog. Bull. Amer. Meteor. Soc., 19, 49–55,  https://doi.org/10.1175/1520-0477-19.2.49.CrossRefGoogle Scholar
  47. Román-Cascón, C., G. J. Steeneveld, C. Yagüe, M. Sastre, J. A. Arrillaga, and G. Maqueda, 2016: Forecasting radiation fog at climatologically contrasting sites: Evaluation of statistical methods and WRF. Quart. J. Roy. Meteor. Soc., 142, 1048–1063,  https://doi.org/10.1002/qj.2708.CrossRefGoogle Scholar
  48. Skamarock, W. C., and Coauthors, 2008: A description of the advanced research WRF version 3. NCAR Technical Note NCAR/TN-475+STR,  https://doi.org/10.5065/D68S4MVH.Google Scholar
  49. Tang, Y. M., 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,  https://doi.org/10.1175/JAMC-D-11-0253.1.CrossRefGoogle Scholar
  50. Tao, S. Y., and L. X. Chen, 1987: A review of Recent Research on the East Asian Summer Monsoon. Monsoon Meteorology. C. P. Chang and T. N. Krishnamurti, Eds., Oxford University Press, 60–92.Google Scholar
  51. Tardif, R., 2007: The impact of vertical resolution in the explicit numerical forecasting of radiation fog: A case study. Pure Appl. Geophys., 164, 1221–1240,  https://doi.org/10.1007/s00024-007-0216-5.CrossRefGoogle Scholar
  52. Taylor, G. I., 1917: The formation of fog and mist. Quart. J. Roy. Meteor. Soc., 43, 241–268,  https://doi.org/10.1002/qj.49704318302.CrossRefGoogle Scholar
  53. Thiébaux, J., E. Rogers, W. Q. Wang, and B. Katz, 2003: A new high-resolution blended real-time global sea surface temperature analysis. Bull. Amer. Meteor. Soc., 84, 645–656,  https://doi.org/10.1175/BAMS-84-5-645.CrossRefGoogle Scholar
  54. Tuleya, R. E., 1994: Tropical storm development and decay: Sensitivity to surface boundary conditions. Mon. Wea. Rev., 122, 291–304, https://doi.org/10.1175/1520-0493(1994)122<0291:TSDADS>2.0.CO;2.CrossRefGoogle Scholar
  55. Wang, B. H., 1985: Sea Fog. China Ocean Press, 330 pp.Google Scholar
  56. Wang, Y. M., S. H. Gao, G. Fu, J. L. Sun, and S. P. Zhang, 2014: Assimilating MTSAT-derived humidity in nowcasting sea fog over the Yellow Sea. Wea. Forecasting, 29, 205–225,  https://doi.org/10.1175/WAF-D-12-00123.1.CrossRefGoogle Scholar
  57. Wilson, T. H., and R. G. Fovell, 2018: Modeling the evolution and life cycle of radiative cold pools and fog. Wea. Forecasting, 33(1), 203–220,  https://doi.org/10.1175/WAF-D-17-0109.1.CrossRefGoogle Scholar
  58. World Meteorological Organization, 2008: WMO-No.8: Guide to Meteorological Instruments and Methods of Observation. 7th ed., WMO, Part I: 14–3.Google Scholar
  59. Wu, X. J., and S. M. Li, 2014: Automatic sea fog detection over Chinese adjacent oceans using Terra/MODIS data. Int. J. Remote Sens., 35(21), 7430–7457,  https://doi.org/10.1080/01431161.2014.968685.CrossRefGoogle Scholar
  60. Xu, D. S., Z. T. Chen, S. X. Zhong, Y. J. Wen, and D. D. Xie, 2014: The limitation of cloud base mass flux in cumulus parameterization and its application in a high-resolution model. Journal of Tropical Meteorology, 30(3), 401–412,  https://doi.org/10.3969/j.issn.1004-4965.2014.03.001. (in Chinese)Google Scholar
  61. Xue, J. S., S. Y. Zhuang, G. F. Zhu, H. Zhang, Z. Q. Liu, Y. Liu, and Z. R. Zhuang, 2008: Scientific design and preliminary results of three-dimensional variational data assimilation system of GRAPES. Chinese Science Bulletin, 53(22), 3446–3457,  https://doi.org/10.1007/s11434-008-0416-0.Google Scholar
  62. Yang, Y., X. M. Hu, S. H. Gao, and Y. M. Wang, 2019: Sensitivity of WRF simulations with the YSU PBL scheme to the lowest model level height for a sea fog event over the Yellow Sea. Atmospheric Research, 215, 253–267,  https://doi.org/10.1016/j.atmosres.2018.09.004.CrossRefGoogle Scholar
  63. Yu, R. L., Y. N. Wang, and Y. P. Li, 2007: An operational objective forecast system for sea fog over the Yellow Sea and East China Sea. Atmospheric Science Research and Application, (2), 28–37. (in Chinese)Google Scholar
  64. Yuan, J. N., and J. Huang, 2011: An observational analysis and 3-dimensional numerical simulation of a sea fog event near the Pearl River Mouth in boreal spring. Acta Meteorologica Sinica, 69(5), 847–859,  https://doi.org/10.11676/qxxb2011.074. (in Chinese)Google Scholar
  65. Zhang, D. L., and R. A. Anthes, 1982: A high-resolution model of the planetary boundary layer-sensitivity tests and comparisons with SESAME-79 data. Appl. Meteor., 21, 1594–1609, https://doi.org/10.1175/1520-0450(1982)021<1594:AHRMOT>2.0.CO;2.CrossRefGoogle Scholar
  66. Zhang, G. C., 2016: The progress of fog forecast operation in China. Advances in Meteorological Science and Technology, 6(2), 42–48.  https://doi.org/10.3969/j.issn.2095-1973.2016.02.004. (in Chinese)Google Scholar
  67. Zhang, S. P., and Z. P. Ren, 2010: The influence of the thermal effect of underlaying surface on the spring sea fog over the Yellow Sea: Observations and numerical simulations. Acta Meteorologica Sinica, 68(4), 439–449,  https://doi.org/10.11676/qxxb2010.043. (in Chinese)Google Scholar
  68. Zhang, S. P., and L. Yi, 2013: A comprehensive dynamic threshold algorithm for daytime sea fog retrieval over the Chinese adjacent seas. Pure Appl. Geophys., 170(11), 1931–1944,  https://doi.org/10.1007/s00024-013-0641-6.CrossRefGoogle Scholar
  69. Zhang, S. P., J. C. Long, Y. J. Yin, W. Y. Yang, and W. B. Yang, 2014: Analysis of the process of a local sea fog lifted into low cloud in Eastern China. Periodical of Ocean University of China, 44(2), 1–10,  https://doi.org/10.16441/j.cnki.hdxb.2014.02.001. (in Chinese)Google Scholar
  70. Zhang, S. P., S. P. Xie, Q. Y. Liu, Y. Q. Yang, X. G. Wang, and Z. P. Ren, 2009: Seasonal variations of Yellow Sea fog: Observations and mechanisms. Climate, 22, 6758–6772,  https://doi.org/10.1175/2009JCLI2806.1.CrossRefGoogle Scholar
  71. Zhang, X. B., Q. L. Wan, J. S. Xue, W. Y. Ding, and H. R. Li, 2015: The impact of different physical processes and their parameterizations on forecast of a heavy rainfall in south China in annually first raining season. Journal of Tropical Meteorology, 21(2), 194–210,  https://doi.org/10.16555/j.1006-8775.2015.02.010.Google Scholar
  72. Zhang, X.B., Y.L. Luo, Q. L. Wan, W. Y. Ding, and J. X. Sun, 2016: Impact of assimilating wind profiling radar observations on convection-permitting quantitative precipitation forecasts during SCMREX. Wea. Forecasting, 31, 1271–1292,  https://doi.org/10.1175/WAF-D-15-0156.1.CrossRefGoogle Scholar
  73. Zhong, S. X., and Z. T. Chen, 2015: Improved wind and precipitation forecasts over South China using a modified orographic drag parameterization scheme. Meteor. Res., 29(1), 132–143,  https://doi.org/10.1007/s13351-014-4934-1.CrossRefGoogle Scholar
  74. Zhou, B. B., 2011: Introduction to a new fog diagnostic scheme. NCEP Office Note 466, 43 pp.Google Scholar
  75. Zhou, B. B., and J. Du, 2010: Fog prediction from a multimodel mesoscale ensemble prediction system. Wea. Forecasting, 25, 303–322,  https://doi.org/10.1175/2009WAF2222289.1.CrossRefGoogle Scholar
  76. Zhou, B. B., J. Du, I. Gultepe, and G. Dimego, 2012: Forecast of low visibility and fog from NCEP: Current status and efforts. Pure Appl. Geophys., 169, 895–909,  https://doi.org/10.1007/s00024-011-0327-x.CrossRefGoogle Scholar

Copyright information

© Institute of Atmospheric Physics/Chinese Academy of Sciences, and Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Huijun Huang
    • 1
    Email author
  • Bin Huang
    • 2
  • Li Yi
    • 3
  • Chunxia Liu
    • 1
  • Jing Tu
    • 4
  • Guanhuan Wen
    • 1
  • Weikang Mao
    • 1
  1. 1.Institute of Tropical and Marine MeteorologyChina Meteorological AdministrationGuangzhouChina
  2. 2.National Meteorological CentreBeijingChina
  3. 3.Ocean University of ChinaQingdaoChina
  4. 4.Guangdong Meteorological ObservatoryGuangzhouChina

Personalised recommendations