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Using GOES-R to Diagnose NWP: Multi-layer Clouds
Andrew Heidinger, Michael Pavolonis* and Dan Tarpley
NOAA/NESDIS, Office of Research and Applications
*UW/CIMSS, Madison, WI
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Motivation
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EXAMPLE APPLICATION
These images show an example of the multi-layer cloud detection applied to the GOES-10 Imager.
The multi-layer cloud detection is part of a larger cloud typing algorithm using the cloud type
definitions used in the NESDIS Clouds from AVHRR (CLAVR-x) system
•Many GOES products are not directly used in NWP but
may help in diagnosing problems in forecasted fields.
Dr Andrew Heidinger
NOAA/NESDIS Office of Research and Applications
1225 Dayton Street
Madison, WI 53706
ph. 608-263-6757
email: [email protected]
COMPARISON TO NWP
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The images below show a comparison of the number of cloud layers from a 12 hour forecast by the NCEP GFS
model (valid at March 28, 2004 0Z). The number of cloud layers were computed by looking for clear layers in the
cloud mixing ratio profiles. Multiple cloud layers were ignored when the total column cloud liquid water path was
less than 0.05 kg/m^2 because this situation would not be detected as multi-layer cloud by GOES.
•One example is the GOES cloud classification product
run at NESDIS that includes a new multi-layer cloud
detection scheme.
The GOES imager data from GOES-10 full disk scans were processed through the NESDIS GOES Solar
Insolation Project (GSIP-fd) with a spatial resolution of 0.125 degree. The GOES results were spatially averaged
to the model resolution and show the fraction of all pixels that were classified as multi-layer cloud.
•Validation with RADAR/LIDAR has demonstrated the
high skill of satellites to detect multiple cloud layers
under many conditions.
•Knowledge of the presence of multiple cloud layers is
important to interpreting satellite data properly and in
the ability to compute radiative fluxes.
•During GOES-R, we expect great improvements in the
accuracy and temporal resolution of this information.
•Through the JCSDA, NESDIS and NCEP are working on
meaningful ways to compare satellite derived and
forecasted cloud fields. This is one example.
GOES-10
GFS 12hr FORECAST
Algorithm Description
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The detection of multi-layer cloud is the detection of
clouds that do not behave radiatively as single layer clouds.
The current algorithm uses the 0.6 micron reflectance
with the split window brightness temperature difference
(T11-T12). Basically, as illustrated in the figure below, as
single layer clouds become brighter, the T11- T12 should
decrease. Multi-layer clouds can be detected by looking
for deviations from this behavior.
Low Level Water Cloud
High Level Ice Cloud
Cirrus over water cloud (tau=10)
Threshold function
ALGORITHM VALIDATION
Example case of strong
overlap from ARM data
Comparison with surface based RADAR/LIDAR at ARM sites revealed:
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Fraction of GFS grid points with multiple cloud layers where GOES detected at least 10% multi-layer cloud = 52%
Fraction of GFS grid points without multiple cloud layers where GOES detected more than 10% multi-layer cloud = 18%
• when single layer cloud is present, the average occurrence of
overlap was only 3% (false alarm rate)
Fraction of GOES cells with more than 10% multi-layer cloud where GFS had less than 2 cloud layers = 65%
•The amount of cirrus overlap detected was larger in the strong than
the weak radar-derived overlap scenes.
Jay Mace’s Figure
APPLICATION TO THE ADVANCED BASELINE IMAGER
MODIS provides today comparable channels to what ABI will provide on GOES-R. As this example using MODIS data during a tropical cyclone in the
Indian Ocean shows, the GOES-R algorithm detects more multi-layer cloud (orange) and is generally better at detecting thin cirrus (red). The
current algorithm often classifies thin cirrus as water cloud (blue)
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• GOES cloud products continue to evolve and offer new aids
in diagnosing NWP performance.
•The GOES multi-layer cloud product provides insight into the
vertical structure of forecast cloud fields. It shows some
similar features but also some significant differences in the
distribution of multi-layer cloud
•Work is ongoing to develop meaningful comparisons between
satellite derived and forecasted cloud fields.
Motivation
References
•On GOES-R, use of the 1.38 and 8.5 micron channel will offer
more capability.
•Pavolonis, Michael J., A. K. Heidinger, 2004: Daytime Cloud Overlap
Detection from AVHRR and VIIRS. J. Applied. Meteor.., 43, 5, 762--778
•On GOES-12, the 11- 6.7 micron difference is used in place of
the 11 – 12 micron difference. (requires TPW knowledge)
•Nighttime detection is also possible using 3.9, 11 and 12 micron.
Conclusions
RGB FROM MODIS
CLOUD TYPE USING
“GOES-R”
CLOUD TYPE USING
“GOES-10”
•Heidinger, A. K., 2003: Rapid Day-time Estimation of Cloud Properties
over a Large Area from Radiance Distributions, J. Atmos. Ocean. Tech.,
20, 1237-1250
•Heidinger, A. K., M. J. Pavolonis, 2004: Global Daytime Distribution of
Multi-layer Cloud, submitted to Journal of Climate