doi:10.1007/s00376-015-5151-2

Cotton W. R., R. A. Anthes, 1993: Storm and Cloud Dynamic. China Meteorological Press, 311- 320.

Ge L. Y., Y. R. Jiang, H. M. Liang, S. W. Zhu, and E. Z. Lin, 1998: Discussion on the reason of the five day sustained fog on Hu-Ning region in the end of 1996. Scientia Meteorologica Sinica, 18( 2), 181- 188. (in Chinese)bf94943ef784ce00fb169e34d6d49cd5

http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-QXKX199802011.htm

http://en.cnki.com.cn/Article_en/CJFDTOTAL-QXKX199802011.htmSustained fog occurred in Jiangsu province during 27- 31 December 1996. After examined into the synoptic map, this paper analyses characters of synoptic type thatcaused the fog. The main physical variables are calculated from observation,and then theirspatial and temporal distribution and normal cross-section diagrams are made,alongside detailed discussion on the reasons of fog formation and long maintenance. The results may serveas useful reference for radiation fog forecasting in winter.

Grell G. A., S. E. Peckham, R. Schmitz, S. A. McKeen, G. Frost, W. C. Skamarock, and B. Eder, 2005: Fully coupled "online" chemistry within the WRF model. Atmos. Environ., 39( 37), 6957- 6975.4f810b02-a7b5-40b4-8e05-32a25ed9e07c

8b0355c6ddf4d645a8ea98cd391d6274

http%3A%2F%2Fams.confex.com%2Fams%2F84Annual%2Fwebprogram%2FPaper71176.html

http://ams.confex.com/ams/84Annual/webprogram/Paper71176.htmlThis modeling system has been evaluated with retrospective simulations on NOAA/FSL's massively parallel supercomputer with data from the 2002 New England Air Quality Study (NEAQS). It is also being run in real-time to predict air quality over the central and eastern US. In addition to describing the modeling system, we will show results from comparisons of air quality predictions to observations.

Gultepe I., Coauthors, 2007: Fog research: A review of past achievements and future perspectives. Pure Appl. Geophys., 164( 6-7), 1121- 1159.10.1007/s00024-007-0211-x

2f6f56eaffbb547c2c4307b78280cdbd

http%3A%2F%2Flink.springer.com%2F10.1007%2Fs00024-007-0211-x

http://onlinelibrary.wiley.com/resolve/reference/XREF?id=10.1007/s00024-007-0211-xThe scientific community that includes meteorologists, physical scientists, engineers, medical doctors, biologists, and environmentalists has shown interest in a better understanding of fog for years because of its effects on, directly or indirectly, the daily life of human beings. The total economic losses associated with the impact of the presence of fog on aviation, marine and land transportation can be comparable to those of tornadoes or, in some cases, winter storms and hurricanes. The number of articles including the word -渇og- in Journals of American Meteorological Society alone was found to be about 4700, indicating that there is substantial interest in this subject. In spite of this extensive body of work, our ability to accurately forecast/nowcast fog remains limited due to our incomplete understanding of the fog processes over various time and space scales. Fog processes involve droplet microphysics, aerosol chemistry, radiation, turbulence, large/small-scale dynamics, and surface conditions (e.g., partaining to the presence of ice, snow, liquid, plants, and various types of soil). This review paper summarizes past achievements related to the understanding of fog formation, development and decay, and in this respect, the analysis of observations and the development of forecasting models and remote sensing methods are discussed in detail. Finally, future perspectives for fog-related research are highlighted.

Guo E. M., J. Y. Zhang, 1991: Numerical simulation of the fog evaporation by the warm effect. Journal of the Air Force Institute of Meteorology, 12( 2), 11- 16. (in Chinese)

He H., X. L. Guo, H. Y. Li, H. Jin, and J. Z. Liu, 2011: Numerical simulation of the cold fog dissipation. Chinese Journal of Atmospheric Sciences, 35( 2), 272- 286. (in Chinese)10.3878/j.issn.1006-9895.2011.02.07

04ff193d-12f3-4d23-95ed-9e7761cf9fe8

4825320113526

ba9a95ae0080ee5e01d0e4ffcf90c588

http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-DQXK201102008.htm

http://en.cnki.com.cn/Article_en/CJFDTOTAL-DQXK201102008.htmBased on the dynamic frame of mesoscale model MM5 and Reisner2 explicit cloud scheme,a LN(liquid nitrogen) -seeding scheme was developed and used to simulate the cold fog dissipation for the cold fog event on 26 December 2007 in Beijing area.The seeding effect and the physical mechanism were studied.Furthermore,two sensitive experiments were performed to study the seeding effect under different seeding distances and seeding amounts.The results indicate that when the seeding operation lasts 10 min at seeding rate of 5g/s with distance being about 1-2 km on the upwind side of the target area,the seeding effect in the target area begins to appear at 9 min after seeding operation and the best effect appears at 24 min after seeding operation,the seeding effect can last about 20 min.The microphysical mechanism of the cold fog dissipation is because of the depletion of the water vapor due to the ice sublimation growth induced by seeding,which leads to the decrease of the water vapor condensation into fog droplets,meanwhile the decrease of the fog advection from the upstream region after seeding operation also contributes to the fog dissipation.In this case,seeding operation at seeding rate of 15 g/s with distance being about 5-6 km on the upwind side of the target area can make the target area have the most significant improvement in the visibility.

Houghton H. G., W. H. Radford, 1938: On the local dissipation of natural fog. Pap. Phys. Oceanogr. Meteor.,6(2), doi: 10.1575/1912/1094.

Hu Z. J., C. F. Yan, and Y. B. Wang, 1983: Numerical simulation of rain and seeding processes in warm layer clouds. Acta Meteorologica Sinica, 41( 1), 79- 88. (in Chinese)10.11676/qxxb1983.009

a19ff074-be0b-4f78-bbc5-a11c4fb22b01

55841983113

60e5642c4db584c1fa61f1db7dc1fdbb

http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTotal-QXXB198301008.htm

http://en.cnki.com.cn/Article_en/CJFDTotal-QXXB198301008.htmAn one-dimensional time-dependent model of precipitation in warm layer cloud is given. The influence of some factors on warm rain process and its initiation is tested. The process of rain enhancement by salt-seeding is simulated and discussed.

Huang P. Q., 1988: Numerical simulation of the fog dissipation by salt-seeding. Journal of the Air Force Institute of Meteorology, 9( 1), 33- 40. (in Chinese)a1a3eb3d-29a2-4894-aa32-4f171bd5fa7b

ba9a95ae0080ee5e01d0e4ffcf90c588

http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-DQXK201102008.htm

Kong F., 2002: An experimental simulation of a coastal fog-stratus case using COAMPS (tm) model. Atmos. Res., 64, 205- 215.10.1016/S0169-8095(02)00092-3

3008c57e-ce69-4591-a1bd-d37d24d294b6

3963b0472f0dae3ff876d217c6b84d23

http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0169809502000923

refpaperuri:(1a71892f63b8d75a9fc8e951457d9bca)

http://www.sciencedirect.com/science/article/pii/S0169809502000923ABSTRACT This paper presents an experimental simulation of a summer season marine fog-stratus case along the west coast of California using the US Navy COAMPS(tm) model. The purpose is to show the potential usefulness of mesoscale models in forecasting this type of marine boundary weather phenomenon. The role of data assimilation and the impacts of solar radiation, microphysics, and vertical resolution in improving the forecasts are examined. The model capability in forecasting the burn-off process over the San Francisco Bay area is also tested with very high horizontal resolution (2-km grid size) using the model's one-way nesting technique. The model demonstrates promising capacity in this case to replicate the temporal and spatial cloud coverage over the San Francisco Bay and surrounding area, shown in satellite imagery, despite a 2-h lag to complete clearing over the bay. This study also suggests that a better microphysics parameterization and proper representation of microphysics in the solar radiation scheme are both important for COAMPS(tm) to produce more realistic simulations and to improve the burn-off forecast.

Kornfeld P., 1970: Some numerical experiments for warm fog clearing by seeding with hygroscopic nuclei. J. Appl. Meteor., 9, 459- 463.10.1175/1520-0450(1970)009<0459:SNEFWF>2.0.CO;2

cdf66951264f42d5e32f08196f83e9de

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1970JApMe...9..459K

http://adsabs.harvard.edu/abs/1970JApMe...9..459KABSTRACT The effectiveness of warm fog clearing by seeding with hygroscopic nuclei was studied numerically. A parcel of air with a known distribution of fog droplets was assumed to be seeded with hygroscopic nuclei. While new drops were formed upon the seeding nuclei due to condensation, the fog droplets became smaller due to evaporation. The new distribution of drops and the corresponding visibility are given for 60 and 100 sec after seeding. The results indicate that seeding may be effective if the type of fog to be cleared is known as to droplet distribution and the seeding nuclei are chosen accordingly.

Kunkel, B. A, 1984: Parameterization of droplet terminal velocity and extinction coefficient in fog models. J. Climate Appl. Meteor., 23( 1), 34- 41.cfc60b43529497d118f3d2c48b91438f

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1984JApMe..23...34K

/s?wd=paperuri%3A%28b68039b014c252db92062a136a48e34c%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1984JApMe..23...34K&ie=utf-8

Mason B. J., 1971: The Physics of Clouds. Clarendon Press, 305- 328.10.1002/qj.49707532306

046b6a27fc348d407c03d22b51bb9052

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1958PhT....11l..56M

http://adsabs.harvard.edu/abs/1958PhT....11l..56MCloud physics is concerned with those processes which are responsible for the formation of clouds and the release of precipitation. This classic book gives a comprehensive and detailed account of experimental and theoretical research on the microphysical processes of nucleation, condensation, droplet growth, initiation and growth of snow crystals, and the mechanisms of precipitation release. As a textbook it is designed to give the student the necessary background to carry out independent work. As a reference book for the research worker, it provides an integrated account of the major developments in this field. Although written primarily for the atmospheric physicist, it contains much of interest for those in the fields of nucleation phenomena, crystal growth, and aerosol physics.

Morrison H., J. Curry, and V. Khvorostyanov, 2005: A new double-moment microphysics parameterization for application in cloud and climate models. Part I: Description. J. Atmos. Sci., 62( 6), 1665- 1677.bcca90161eb1dd3cb83115f6fce9ac9a

http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2005JAtS...62.1665M%26db_key%3DPHY%26link_type%3DABSTRACT

/s?wd=paperuri%3A%28bae32659961a3733306de95f73c92bd3%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2005JAtS...62.1665M%26db_key%3DPHY%26link_type%3DABSTRACT&ie=utf-8

Reisner J., R. M. Rasmussen, and R. T. Bruintjes, 1998: Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model. Quart. J. Roy. Meteor. Soc., 124, 1071- 1107.10.1002/qj.49712454804

d554c322fa86eea0553dc38a884e191e

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fqj.49712454804%2Fabstract

http://onlinelibrary.wiley.com/doi/10.1002/qj.49712454804/abstractAbstract An explicit microphysical parametrization including ice physics was developed for use in the NCAR/Penn State Mesoscale Model Version 5 (MM5). This scheme includes three options of increasing complexity to represent the hydrometeor species. The scheme is evaluated by comparing model simulations with two well observed winter storms that occurred during the Winter Icing and Storms Project. The evaluation focused on the prediction of supercooled liquid water (SLW), which is of particular importance to aircraft icing. The intercomparisons showed that: 1 The double-moment microphysical scheme, in which both ice mixing ratios and number concentrations were predicted, performed best, with close agreement to the observed fields. 2 The single-moment schemes, in which the mixing ratio of ice species are predicted and number concentration specified, performed reasonably well if a diagnostic equation for N o, s , the Y -intercept of the assumed exponential snow distribution, is allowed to vary with snow mixing ratio. 3 Accurate microphysical simulations of SLW in shallow upslope clouds and cyclonic storms required accurate simulations of the kinematic and thermodynamic structure and evolution of the storms. Though the two storms were dynamically different, the SLW formed through a balance of the condensational growth of cloud water and the depletion of cloud water by deposition and riming of snow and/or graupel for both storms. The results of this study suggest that accurate prediction of SLW over limited areas of the country may be possible using the current microphysical parametrization and high-resolution grids (未蠂 <10 km).

Silverman B. A., B. A. Kunkel, 1970: A numerical model of warm fog dissipation by hygroscopic particle seeding. J. Appl. Meteor., 9, 627- 633.10.1175/1520-0450(1970)0092.0.CO;2

71835c711a96d9931bacd7992a2da8ad

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1970JApMe...9..627S

http://adsabs.harvard.edu/abs/1970JApMe...9..627SAbstract A numerical model has been developed which simulates the modification of warm fog caused by the condensation-coalescence growth of monodispersed hygroscopic particles introduced into the top of a fog layer. With this model, the effects of the quantity and size of NaCl particles on the visibility in logs of various drop size spectra and liquid water contents were determined. The results show that the optimum seeding particle radius is approximately 10 . For fogs with a relatively high turbulence level where time is an important factor, particles > 10 may be necessary. To achieve the same visibility improvement, the seeding rate is directly proportional to the fog liquid water content and inversely proportional to the fog drop size. Because of the large payloads and dispensing rates required, the results indicate that there is no advantage in using saturated solution droplets instead of dry particles.

Skamarock W.C, Coauthors, 2008: A description of the advanced research WRF version 3. NCAR Tech. Note NCAR/ TN-475+STR. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/arwv3.pdf.]10.5065/D68S4MVH

6e1e8ed5238484bf7e6021f9957054e6

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F244955031_A_Description_of_the_Advanced_Research_WRF_Version_2

http://www.researchgate.net/publication/244955031_A_Description_of_the_Advanced_Research_WRF_Version_2The development of the Weather Research and Forecasting (WRF) modeling system is a multiagency effort intended to provide a next-generation mesoscale forecast model and data assimilation system that will advance both the understanding and prediction of mesoscale weather and accelerate the transfer of research advances into operations. The model is being developed as a collaborative effort ort among the NCAR Mesoscale and Microscale Meteorology (MMM) Division, the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Environmental Prediction (NCEP) and Forecast System Laboratory (FSL), the Department of Defense's Air Force Weather Agency (AFWA) and Naval Research Laboratory (NRL), the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma, and the Federal Aviation Administration (FAA), along with the participation of a number of university scientists. The WRF model is designed to be a flexible, state-of-the-art, portable code that is an efficient in a massively parallel computing environment. A modular single-source code is maintained that can be configured for both research and operations. It offers numerous physics options, thus tapping into the experience of the broad modeling community. Advanced data assimilation systems are being developed and tested in tandem with the model. WRF is maintained and supported as a community model to facilitate wide use, particularly for research and teaching, in the university community. It is suitable for use in a broad spectrum of applications across scales ranging from meters to thousands of kilometers. Such applications include research and operational numerical weather prediction (NWP), data assimilation and parameterized-physics research, downscaling climate simulations, driving air quality models, atmosphere-ocean coupling, and idealized simulations (e.g boundary-layer eddies, convection, baroclinic waves).*WEATHER FORECASTING

Tardif R., 2007: The impact of vertical resolution in the explicit numerical forecasting of radiation fog: A case study. Pure Appl. Geophys. 164, 1221- 1240.10.1007/s00024-007-0216-5

5c1949119bdeb1284d88689386ae6749

http%3A%2F%2Flink.springer.com%2F10.1007%2Fs00024-007-0216-5

http://link.springer.com/10.1007/s00024-007-0216-5ABSTRACT Numerical experiments are performed with a comprehensive one-dimensional boundary layer/fog model to assess the impact of vertical resolution on explicit model forecasts of an observed fog layer. Two simulations were performed, one using a very high resolution and another with a vertical grid typical of current high-resolution mesoscale models. Both simulations were initialized with the same profiles, derived from observations from a fog field experiment. Significant differences in the onset and evolution of fog were found. The results obtained with the high-resolution simulation are in overall better agreement with available observations. The cooling rate before the appearance of fog is better represented, while the evolution of the liquid water content within the fog layer is more realistic. Fog formation is delayed in the low resolution simulation, and the water content in the fog layer shows large-amplitude oscillations. These results show that the numerical representation of key thermodynamical processes occurring in fog layers is significantly altered by the use of a grid with reduced vertical resolution.

WMO, 1992: International Meteorological Vocabulary.WMO/ OMM/BMO-No, 782 pp.3261cfeb4c0368c360ec56cbcf2c0d13

http%3A%2F%2Fci.nii.ac.jp%2Fnaid%2F10013126297%2F

http://ci.nii.ac.jp/naid/10013126297/International Meteorological Vocabulary WMO WMO/OMM/BMO 784, 1992

Zhao Q. Y., 1989: Numerical simulation of warm fog dissipation by salt-seeding. Journal of Tropical Meteorology, 5( 2), 245- 252. (in Chinese)