The Introduction and Evaluation of a Prototype GOES-R Fog/Low Stratus
Thickness Algorithm Using SEVIRI, GOES and SODAR Data
Corey G Calvert, Michael J Pavolonis*
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Cooperative Institute for Meteorological Satellite Studies, Madison, Wisconsin
*NOAA/NESDIS/Center for Satellite Applications and Research
Advanced Satellite Product Branch, Madison Wisconsin
Introduction
Previous Fog Thickness Algorithm
• Ellrod (1995) developed a
regression-based fog thickness
algorithm for GOES dependent on the
11-3.9 micron brightness temperature
differences (BTDs).
• A linear relationship between the fog
depth from PIREPs and BTDs was
found when the BTDs were between 26 K.
• The figure to the right shows the
linear relationship using counts, where
2 counts equal 1 K.
Current GOES-R Fog Thickness Approach
The previous operational fog/low cloud thickness algorithm for GOES relied on differences
between the 3.9 and 11-micron brightness temperatures. While this is a reliable approach
during the night, solar contamination of the 3.9-micron channel makes this unreliable during
the day, unless the solar component is accounted for. Here we present an algorithm that uses
the physical properties of clouds and an atmospherically-corrected 3.9 micron emissivity to
calculate the thickness of fog and low stratus developed for the Advanced Baseline Imager
(ABI) that will fly on GOES-R. This work uses an analysis software package called GEOCAT to
evaluate and characterize the performance of the prototype ABI fog thickness algorithm
applied to SEVIRI using GOES data and surface SODAR measurements.
• Use the liquid water path (LWP) produced by the GOES-R AWG
cloud application team (for solar zenith angles < 70º) and an assumed
liquid water content (LWC) to calculate the geometrical thickness (ΔZ)
of the fog layer.
• The LWC is assumed to be 0.06
g
m3
(Hett et al., 1998)
• The correlation between the SODAR measured thicknesses and the
GOES-R fog thicknesses yielded a correlation coefficient of 0.82 with
errors under 50 m in the bay area.
Figure from Ellrod (1995).
¾ No daytime fog thickness algorithm
• The figures below show an example of Ellrod’s experimental fog depth algorithm
applied to a GOES-12 scene (right), with the corresponding 3.9 micron emissivity
image (left). The dark areas in the 3.9 micron emissivity are areas of fog/low
stratus.
Image below obtained from http://www.star.nesdis.noaa.gov/smcd/opdb/aviation/fog.html
Table 1 below contains the visual flight rules pilots must abide by. For this study, fog/low
stratus will be defined as clouds with bases below ~900 m as this is when restrictions are
applied to pilots. Locations of these clouds are determined by a separate fog mask currently
under development.
Table 1: Aviation based fog/low cloud definition
Ceiling
> 3000 ft (914 m)
1000 ft (305 m) - 3000 ft (914 m)
500 ft (152 m) - 1000 ft (305 m)
< 500 ft (152 m)
Visual Flight Rules
Marginal Visual Flight Rules
Instrument Flight Rules
Low Instrument Flight Rules
SODAR Data
emsac(3.9μm) =
Rsfc(3.9μm)
B[3.9μm,BTsfc (11μm)]
• The acoustic SODAR (SOnic
where
Detection And Ranging) is an
upwardly pointing parabolic
antenna that emits an audible
signal whose return signal is
proportional to the vertical gradient
of air density.
R( λ) − Ratm( λ )
Rsfc (λ ) =
Tatm ( λ)
λ: wavelength
R(λ): observed radiance
Ratm(λ): atmospheric radiance
Tatm(λ): total atmospheric
transmittance
Rsfc(λ): surface radiance
εsfc(λ): surface emissivity
• The SODAR is capable of
detecting the base of the boundary
layer inversion, which defines the
top of the stratus deck.
BTsfc ( λ ) = B ( λ,Rsfc( λ))
−1
Rsfc(3.9μm): 3.9-μm surface
radiance
California
Italy
Nighttime:
Visibility
> 5 nm
3 nm - 5 nm
1 nm - 3 nm
< 1 nm
Atmospherically-Corrected 3.9 micron Emissivity
emsac(3.9μm) is computed as follows:
• A combination of ceilometer
and SODAR measurements are
used to infer the geometric
boundaries of low clouds in the
San Francisco Bay Area.
ΔZ = LWP /LWC
Daytime:
• Use a regression analysis based on the
relationship between the atmosphericallycorrected 3.9 micron emissivity and SODAR
thickness measurements.
ΔZ = A[emsac (3.9μm)] + B
• In the above equation, A = -1159.9 and
B = 1295.7, are constants determined by a
linear regression fit to a scatter plot of emsac(3.9μm) and SODAR
measured fog thickness in the San Francisco Bay area.
• The GOES-R fog thicknesses calculated using this method yield a
correlation coefficient of -0.85 with errors under 50 m in the bay area.
• While a relationship was seen with the emsac(3.9μm), the correlation
between the 3.9-11 micron BTDs and the SODAR thicknesses in the
bay area was found to be low. Please note that this data was limited to
a small area and may not be representative of the overall performance
of either the emsac(3.9μm) or 3.9-11 micron BTD approach.
B-1( ): inverse Planck Function
B[3.9μm,BTsfc(11μm)]: the 3.9-μm
blackbody surface radiance
California
relative to the 11-μm surface
brightness temperature
France
Why use atmospherically-corrected 3.9 micron emissivity
over 3.9-11 micron brightness temperature differences?
• Removes dependence on the 11 micron channel
fog depth
• Accounts for atmospheric water vapor
References
Bendix, J., B. Thies, J. Cermak, and T. Nauss, 2005: Ground Fog Detection from Space Based on MODIS Daytime Data - A Feasibility Study. Weather and Forecasting. 20, 989-1005.
• This network of instruments is
operated by the FAA and NWS.
I would like to thank the NWS San Francisco Bay Area Forecast Office for providing the
SODAR data. (Clark et al., 1997)
• Accounts for viewing zenith angle differences
Clark, D.A, and F.W. Wilson, 1997: “The San Francisco Marine Stratus Initiative”, 7^th Conference on Aviation, Range and Aerospace Meteorology, Long Beach, CA, pp. 384-389.
Ellrod, G.P., 2002: Estimation of Low Cloud Base Heights at Night from Satellite Infrared and Surface Temperature Data. National Weather Digest., 26, 39-44.
The fog mask currently being developed for GOES-R relies heavily on
the emsac(3.9μm), which results in cleaner detection of fog/low stratus
Ellrod, G.P., 1995: Advances in the Detection and Analysis of Fog at Night Using GOES Multispectral Infrared Imagery. Weather and Forecasting., 10, 606-619.
Hess, M., P. Koepke and I. Schult, 1998: Optical Properties of Aerosols and Clouds. Bull. Amer. Meteor. Soc., 79, 831-844.
Contact the author at corey.calvert@ssec.wisc.edu