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Marine Fog: A Review on Microphysics and Visibility Prediction

  • Ismail Gultepe
  • Jason A. Milbrandt
  • Binbin Zhou
Chapter
Part of the Springer Atmospheric Sciences book series (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.

Keywords

Numerical Weather Prediction Liquid Water Content Numerical Weather Prediction Model Cloud Condensation Nucleus Polar Warming Amplification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Nomenclature

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

Notes

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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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Ismail Gultepe
    • 1
  • Jason A. Milbrandt
    • 2
  • Binbin Zhou
    • 3
  1. 1.Cloud Physics and Severe Weather ResearchEnvironment and Climate Change CanadaTorontoCanada
  2. 2.Atmospheric Numerical Weather Prediction ResearchEnvironment and Climate Change CanadaDorvalCanada
  3. 3.I. M. Systems Group and NOAA/NCEP/Environmental Modeling CenterCollege ParkUSA

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