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Marine Fog: A Review on Microphysics and Visibility Prediction

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Marine Fog: Challenges and Advancements in Observations, Modeling, and Forecasting

Part of the book series: Springer Atmospheric Sciences ((SPRINGERATMO))

Abstract

Marine fog occurs commonly over the world due to the various physical, chemical, dynamical, and radiative processes active at various time and space scales. These processes are affected by local topographical conditions such as surface height and irregularities, slope, and ocean-land boundaries and sea surface conditions as well as atmospheric physical conditions such as pollution as a source of cloud condensation nuclei, cooling rates, and moisture and heat fluxes. Marine fog is usually the result of the advection of warm air masses over cold surfaces or vice versa. Marine fog impacts transportation and shipping, aviation, and the Earth ecosystem because of reduced visibilities and increased moisture availability. Recent studies suggest that the occurrence of fog is decreasing in many part of the world over the lands but not over the ocean. Its prediction using numerical weather prediction (NWP) models includes large uncertainties on small space scales over the short time periods. In this review, first, fog observations are summarized, and second microphysics of fog and visibility were described. Fog prediction issues related to NWP model uncertainties and observational issues are then provided. In the end, future challenges related to marine fog observations and NWP model based prediction, as well as fog and climate change issues are summarized.

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Notes

  1. 1.

    A nomenclature in the end of paper is provided for symbols used in this work.

Abbreviations

A:

Regression constant in Eq. (7.19)

Ac :

Cross sectional area

ADDS:

Aviation Digital Data Service

C:

3.6 s h−1 L g−1; unit conversion factor

CCN:

Cloud condensation nuclei

CCSM4:

Community Climate System Model 4

CN:

Condensation Nuclei

CONUS:

Contiguous United Sates

CO2 :

Carbon dioxide

c:

A constant for airmasses for droplet activation; 1000 cm−3 for airmasses over land

cm :

A constant in mass-size relationships (page 368), depending on particle shape

D:

Droplet diameter

Di :

Droplet diameter in bin i

DMT:

Droplet Measurement Technologies

FAA:

Federal Aviation Administration

FDS:

Fog droplet settling

FMD:

FM120

Fμ :

Kinematic viscosity

GCIP:

Ground Cloud Imaging Probe

GEM:

Global Environmental Multi-scale model

IC:

Initial conditions for a model

IN:

Ice nuclei

IFN:

Ice forming nuclei

IRSW:

Infrared shortwave part in ch2 of GOES data

Ki :

Dielectric constant for ice crystals

Kw :

Dielectric constant for water droplets

K:

Size parameters

K:

A constant for airmasses for droplet activation; 1 for airmasses over land

km :

A constant in mass-size relationships (page 368), depending on particle shape

LEPS:

Local Ensemble Prediction System

LVP:

Low visibility procedure

LWC:

Liquid water content

MOR:

Meteorological observing range

MSC:

Meteorological Service of Canada

NaO :

Aerosol number concentration over ocean

NaL :

Aerosol number concentration over land

Nd :

Droplet number concentration

Ndi :

Droplet number concentration in spectral bin i

Ndt :

Total droplet number concentration

NAM:

North American Model

NCAR:

National Center for Atmospheric Research

NCEP:

National Centers for Environmental Prediction (NCEP)

NESDIS:

National Environmental Satellite, Data, and Information Systems

NWP:

Numerical Weather Prediction

NWS:

National Weather Service

No :

The intercept parameter in gamma distribution

N(D):

Droplet spectral concentration

Ndt :

Total droplet number concentration

n(r):

Droplet spectral value at size r

n(S):

Droplet number concentration at a specific supersaturation ration

PBL:

Planetary boundary layer

PWA:

Polar warming amplification

Qdep :

Fog deposition rate on a mesh

Qext :

Extinction efficiency

qw :

Water mixing ratio

Reff :

Effective droplet size

RDPS:

Regional Deterministic Prediction System

RHw :

Relative humidity with respect to water

r:

Droplet size

reff :

Effective droplet size

HRDPS:

High resolution deterministic prediction system model

S:

Supersaturation with respect to water

SAAWSO:

Satellite Application for Arctic Weather and SAR Operations

SAR:

Search and rescue

SPN1:

Solar radiation sensor

SR:

Settling rate (or deposition rate) of fog droplets

SREF:

Short Range Ensemble Forecast

T:

Air temperature

To :

273.15 K

TPS:

Total precipitation sensor

Uh :

Horizontal wind speed

Vis:

Visibility

Viso :

Observed visibility

Vism :

Model based visibility

Vt :

Droplet terminal velocity

Vts :

Fog droplet settling velocity

wa :

Vertical air velocity

wm :

Vertical air velocity after taking mean or trend out

wo :

Observed vertical air velocity

w′:

wa fluctuations

X:

An empirical constant in Eq. (7.8) e.g., 0.49

x and y:

Constants in Eq. (7.19)

Z:

Radar reflectivity factor

Ze :

Radar equivalent reflectivity factor

α:

An empirical constant in Eq. (7.8) e.g., 0.877

η:

Dispersion parameter for size distribution (sd/mean)

μ:

Spectral shape parameter for gamma distribution or dynamic viscosity coefficient in Eq. (7.16)

λ:

Visible wavelength

λs :

Gamma distribution slope parameter

ε:

0.05 for MOR

βext :

Extinction of a visible light

γce :

Collection efficiency

ρa :

Air density

ρw :

Water droplet density

ΔTs:

Surface temperature change

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Acknowledgments

This work is funded by the DND SAR Office during FRAM and SAAWSO projects and focused on fog, low visibilities, and Arctic weather. Additional financial and logistic support was also provided by EC Cloud Physics and Research Section, Toronto, Ontario, Canada.

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Authors and Affiliations

  1. Cloud Physics and Severe Weather Research, Environment and Climate Change Canada, Toronto, ON, Canada

    Ismail Gultepe

  2. Atmospheric Numerical Weather Prediction Research, Environment and Climate Change Canada, Dorval, QC, Canada

    Jason A. Milbrandt

  3. I. M. Systems Group and NOAA/NCEP/Environmental Modeling Center, College Park, MD, USA

    Binbin Zhou

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Correspondence to Ismail Gultepe .

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  1. Faculty of Science Department of Physics, University of Split, Split, Croatia

    Darko Koračin

  2. Integrative Oceanography Division Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California, USA

    Clive E. Dorman

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Gultepe, I., Milbrandt, J.A., Zhou, B. (2017). Marine Fog: A Review on Microphysics and Visibility Prediction. In: Koračin, D., Dorman, C. (eds) Marine Fog: Challenges and Advancements in Observations, Modeling, and Forecasting. Springer Atmospheric Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-45229-6_7

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