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Numerical Modeling of Ice Fog in Interior Alaska Using the Weather Research and Forecasting Model

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Abstract

An ice microphysics parameterization scheme has been modified to better describe and understand ice fog formation. The modeling effort is based on observations in the Sub-Arctic Region of Interior Alaska, where ice fog occurs frequently during the cold season due to abundant water vapor sources and strong inversions existing near the surface at extremely low air temperatures. The microphysical characteristics of ice fog are different from those of other ice clouds, implying that the microphysical processes of ice should be changed in order to generate ice fog particles. Ice fog microphysical characteristics were derived with the NCAR Video Ice Particle Sampler during strong ice fog cases in the vicinity of Fairbanks, Alaska, in January and February 2012. To improve the prediction of ice fog in the Weather Research and Forecasting model, observational data were used to change particle size distribution properties and gravitational settling rates, as well as to implement a homogeneous freezing process. The newly implemented homogeneous freezing process compliments the existing heterogeneous freezing scheme and generates a higher number concentration of ice crystals than the original Thompson scheme. The size distribution of ice crystals is changed into a Gamma distribution with the shape factor of 2.0, using the observed size distribution. Furthermore, gravitational settling rates are reduced for the ice crystals since the crystals in ice fog do not precipitate in a similar manner when compared to the ice crystals of cirrus clouds. The slow terminal velocity plays a role in increasing the time scale for the ice crystals to settle to the surface. Sensitivity tests contribute to understanding the effects of water vapor emissions as an anthropogenic source on the formation of ice fog.

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Abbreviations

a r_i :

7.22

b r_i :

–0.35

\( a_{\text{w,liq}} \) :

Water activity of solution in liquid phase

\( \varDelta a_{\text{w}} \) :

Difference of water activity between liquid and ice

D :

Diameter for an individual ice crystal

HPP:

Heat and power plant

IWC:

Ice water content

J h :

Nucleation rate of haze droplets

J s :

Nucleation rate of pure water

M :

Molality of solution

M w :

Molecular weight of pure water

MOD:

Experiment with modified Thompson scheme

MODIS:

Moderate resolution imaging spectroradiometer

MSLP:

Mean sea level pressure

MWMD:

Mass-weighted mean diameter

NARR:

North American Regional Reanalysis

NCEP:

National Center for Environment Prediction

NOE:

Experiment without water vapor emission

N c :

Number concentration of cloud droplets

N f,h :

Number concentration of haze droplets freezing in time step

N f,s :

Number concentration of cloud droplets freezing in time step

N h :

Number concentration of haze droplets

N i :

Number concentration of ice crystals

n(D):

Size distribution

OBS:

Observation

ORG:

Experiment with original Thompson scheme

q ve :

Water vapor mixing ratio emitted from the source

q v :

Water vapor mixing ratio

RAMS:

Regional atmospheric modeling system

RH:

Relative humidity

RRTMG:

Rapid Radiative Transfer Model for GCM application

r h :

Radius of haze droplet

SUCCESS:

SUbsonic aircraft: Contrail and Cloud Effects Special Study

T :

Air temperature

VIPS:

Video ice particle sampler

V l :

The volume of droplets

V h :

The volume of haze droplet

v t :

Terminal velocity of ice crystal

WRF:

Weather research and forecasting

YSU:

Yonsei University

α :

0.647 × 107

β :

1.73

Φ s :

Molal osmotic coefficient

λ :

Scale factor

μ :

Shape factor

θ e :

Equivalent potential temperature

υ :

Dissociation constant for solute

References

  • Benson, C. S., 1970: ICE FOG. Weather, 25, 11–18.

    Google Scholar 

  • Bigg, E. K., 1953: The formation of atmospheric ice crystals by the freezing of droplets. Quart. J. Roy. Meteor. Soc., 79, 510–519.

    Google Scholar 

  • Bigg, E. K., 1996: Ice forming nuclei in the high Arctic. Tellus, 48, 223–233.

    Google Scholar 

  • Bowling, S. A., T. Ohtake, and C. S. Benson, 1968: Winter Pressure Systems and Ice Fog in Fairbanks, Alaska. J. Appl. Meteor., 7, 961–968.

    Google Scholar 

  • Chelf, J. H. and S. T. Martin, 2001: Homogeneous ice nucleation in aqueous ammonium sulfate aerosol particles. Journal of Geophysical Research: Atmospheres, 106, 1215–1226.

    Google Scholar 

  • Chen, Y., P. J. DeMott, S. M. Kreidenweis, D. C. Rogers, and D. E. Sherman, 2000: Ice Formation by Sulfate and Sulfuric Acid Aerosol Particles under Upper-Tropospheric Conditions. J. Atmos. Sci., 57, 3752–3766.

    Google Scholar 

  • Cooper, W. A., 1986: Ice Initiation in Natural Clouds. Meteorological Monographs, 21, 29–32.

    Google Scholar 

  • Curry, J. A., F. G. Meyer, L. F. Radke, C. A. Brock, and E. E. Ebert, 1990: Occurrence and characteristics of lower tropospheric ice crystals in the arctic. Inter. J. Climatology, 10, 749–764.

    Google Scholar 

  • Curry, J. A., and Coauthors, 2000: FIRE Arctic Clouds Experiment. Bull. Amer. Meteor. Soc., 81, 5–29.

  • Cziczo, D. J., D. M. Murphy, P. K. Hudson, and D. S. Thomson, 2004: Single particle measurements of the chemical composition of cirrus ice residue during CRYSTAL-FACE. J. Geophy. Res.: Atmospheres, 109, doi:10.1029/2003jd004032.

  • DeMott, P. J. and D. C. Rogers, 1990: Freezing Nucleation Rates of Dilute Solution Droplets Measured between −30° and −40°C in Laboratory Simulations of Natural Clouds. J. Atmos. Sci., 47, 1056–1064.

    Google Scholar 

  • DeMott, P. J., M. P. Meyers, and W. R. Cotton, 1994: Parameterization and Impact of Ice initiation Processes Relevant to Numerical Model Simulations of Cirrus Clouds. J. Atmos. Sci., 51, 77–90.

    Google Scholar 

  • Girard, E. and J.-P. Blanchet, 2001: Microphysical Parameterization of Arctic Diamond Dust, Ice Fog, and Thin Stratus for Climate Models. J. Atmos. Sci., 58, 1181–1198.

    Google Scholar 

  • Girard, E. and J.-P. Blanchet, 2001: Simulation of Arctic Diamond Dust, Ice Fog, and Thin Stratus Using an Explicit Aerosol–Cloud–Radiation Model. J. Atmos. Sci., 58, 1199–1221.

    Google Scholar 

  • Grell G.A., S.E. Peckham, R. Schmitz, S.A. McKeen, G. Frost, W.C. Skamarock, and B Eder. 2005. Fully coupled ‘online’ chemistry in the WRF model. Atmos. Environ., 39:6957–6976.

    Google Scholar 

  • Gultepe, I., T. Kuhn, M. Pavolonis, C. Calvert, J. Gurka, A. J. Heymsfield, P. S. K. Liu, B. Zhou, R. Ware, B. Ferrier, J. Milbrandt, and B. Bernstein, 2013: ICE FOG IN ARCTIC DURING FRAM-ICE FOG PROJECT: AVIATION AND NOWCASTING APPLICATIONS. Bull. Amer. Meteor. Soc. In Print.

  • Heymsfield, A., D. Baumgardner, P. DeMott, P. Forster, K. Gierens, and B. Kärcher, 2010: Contrail Microphysics. Bull. Amer. Meteor. Soc., 91, 465–472.

  • Heymsfield, A. J. and L. M. Miloshevich, 1993: Homogeneous Ice Nucleation and Supercooled Liquid Water in Orographic Wave Clouds. J. Atmos. Sci., 50, 2335-2353.

    Google Scholar 

  • Heymsfield, A. J., R. P. Lawson, and G. W. Sachse, 1998: Growth of ice crystals in a precipitating contrail. Geophy. Res. Lett., 25, 1335–1338.

    Google Scholar 

  • 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.

    Google Scholar 

  • Huffman, P. J. and T. Ohtake, 1971: Formation and Growth of Ice Fog Particles at Fairbanks, Alaska. J. Geophys. Res., 76, 657–665.

    Google Scholar 

  • Kärcher, B. and U. Lohmann, 2002: A parameterization of cirrus cloud formation: Homogeneous freezing of supercooled aerosols. J. Geophys. Res., 107, AAC 4-1-AAC 4-10.

    Google Scholar 

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

    Google Scholar 

  • Koop, T., B. Luo, A. Tsias, and T. Peter, 2000: Water activity as the determinant for homogeneous ice nucleation in aqueous solutions. Nature, 406, 611–614.

    Google Scholar 

  • Kumai, M., 1966: Electron Microscopic Study of Ice-Fog and Ice-Crystal Nuclei in Alaska. J. Meteor. Soc. of Japan., 44, 185–194.

    Google Scholar 

  • Kunkel, B. A., 1969: Comments on “A Generalized Equation for the Solution effect in Droplet Growth”. J. Atmos. Sci., 26, 1344–1344.

    Google Scholar 

  • Kunkel, B. A., 1984: Parameterization of Droplet Terminal Velocity and Extinction Coefficient in Fog Models. J. Appl. Meteor., 23, 34–41.

    Google Scholar 

  • Liu, X., J. E. Penner, S. J. Ghan, and M. Wang, 2007: Inclusion of Ice Microphysics in the NCAR Community Atmospheric Model Version 3 (CAM3). J. Climate, 20, 4526–4547.

    Google Scholar 

  • Lohmann, U. and B. Kärcher, 2002: First interactive simulations of cirrus clouds formed by homogeneous freezing in the ECHAM general circulation model. J. Geophys. Res., 107, AAC 8-1-AAC 8-13.

  • Low, R. D. H., 1969: A Generalized Equation for the Solution Effect in Droplet Growth. J. Atmos. Sci., 26, 608–611.

    Google Scholar 

  • Milbrandt, J. A. and M. K. Yau, 2005: A Multimoment Bulk Microphysics Parameterization. Part II: A Proposed Three-Moment Closure and Scheme Description. J. Atmos. Sci., 62, 3065–3081.

    Google Scholar 

  • Ohtake, T. and P. J. Huffman, 1969: Visual Range in Ice Fog. J. Appl. Meteor., 8, 499–501.

    Google Scholar 

  • Prenni, A. J., P. J. Demott, D. C. Rogers, S. M. Kreidenweis, G. M. McFarquhar, G. Zhang, and M. R. Poellot, 2009: Ice nuclei characteristics from M-PACE and their relation to ice formation in clouds. Tellus, 61, 436–448.

    Google Scholar 

  • Prenni, A. J., P. J. DeMott, S. M. Kreidenweis, J. Y. Harrington, A. Avramov, J. Verlinde, M. Tjernström, C. N. Long, and P. Q. Olsson, 2007: Can Ice-Nucleating Aerosols Affect Arctic Seasonal Climate? Bull. Amer. Meteor. Soc., 88, 541–550.

    Google Scholar 

  • Pruppacher, H. and J. klett, 1997: Microphysics of cloud and precipitation. Kluwer Academic, 955 pp.

  • Rogers, R. R. and M. K. Yau, 1989: A short course in cloud physics. 3 ed. Butterworth-Heinemann, 290 pp.

  • Sassen, K. and G. C. Dodd, 1989: Haze Particle Nucleation Simulations in Cirrus Clouds, and Applications for Numerical and Lidar Studies. J. Atmos. Sci., 46, 3005–3014.

  • Schmitt, C., M. Stuefer, A. Heymsfield, and C. K. Kim, 2013: The microphysical properties of ice fog measured in urban environments of Interior Alaska. J. Geophy. Res., VOL. 118, 1–12, 2013 doi:10.1002/jgrd.50822.

  • Schoenberg Ferrier, B., 1994: A Double-Moment Multiple-Phase Four-Class Bulk Ice Scheme. Part I: Description. J. Atmos. Sci., 51, 249–280.

  • Shaw, G. E., 1983: On the Aerosol Particle Size Distribution Spectrum in Alaskan Air Mass Systems: Arctic Haze and Non-Haze Episodes. J. Atmos. Sci., 40, 1313–1320.

    Google Scholar 

  • Shulski, M. and G. Wendler, 2007: The Climate of Alaska. University of Alaska Press, 216 pp.

  • Skamarock, W. C., and Coauthours, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475 + STR, 113 pp.

  • Straka, J., 2009: Cloud and Precipitation Microphysics: principles and parameterization. Cambridge University Press, 392 pp.

  • Thompson, G., R. M. Rasmussen, and K. Manning, 2004: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis. Mon. Wea. Rev., 132, 519–542.

    Google Scholar 

  • Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 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.

    Google Scholar 

  • Thuman, W. C. and E. Robinson, 1954: STUDIES OF ALASKAN ICE-FOG PARTICLES. J. of Meteor., 11, 151–156.

    Google Scholar 

  • Walko, R. L., W. R. Cotton, M. P. Meyers, and J. Y. Harrington, 1995: New RAMS cloud microphysics parameterization part I: the single-moment scheme. Atmos. Res., 38, 29–62.

    Google Scholar 

  • Welch, R. M., M. G. Ravichandran, and S. K. Cox, 1986: Prediction of Quasi-Periodic Oscillations in Radiation Fogs. Part I: Comparison of Simple Similarity Approaches. J. Atmos. Sci., 43, 633–651.

    Google Scholar 

  • Young, K. C., 1974: A Numerical Simulation of Wintertime, Orographic Precipitation: Part I. Description of Model Microphysics and Numerical Techniques. J. Atmos. Sci., 31, 1735–1748.

    Google Scholar 

  • Zhou, B., 2011: Introduction to A New Fog Diagnostic Scheme. NCEP Office Note 466.

  • Zhou, B., and B. S. Ferrier, 2008: Asymptotic Analysis of Equilibrium in Radiation Fog. J. Appl. Meteor. and Climat., 47, 1704–1722.

    Google Scholar 

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Acknowledgments

This work was funded by an award from the U.S. Air Force (Award Number, FA 9550-11-1-0006).

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  1. Chang Ki Kim

    Present address: Department of Atmospheric Sciences, University of Arizona, 1118 E 4th Street, Tucson, AZ, 85721-0081, USA

Authors and Affiliations

  1. Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USA

    Chang Ki Kim & Martin Stuefer

  2. National Center for Atmospheric Research, Boulder, CO, USA

    Carl G. Schmitt, Andrew Heymsfield & Greg Thompson

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  1. Chang Ki Kim
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Correspondence to Chang Ki Kim.

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Kim, C.K., Stuefer, M., Schmitt, C.G. et al. Numerical Modeling of Ice Fog in Interior Alaska Using the Weather Research and Forecasting Model. Pure Appl. Geophys. 171, 1963–1982 (2014). https://doi.org/10.1007/s00024-013-0766-7

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