The GOES-R AWG Cloud
Height Product
Andrew Heidinger (NOAA/NESDIS)
William Straka III, Anthony Schreiner,
Chang-Hwan Park, (UW-CIMSS)
What You Should Know by
the End of Training
• Which GOES-R cloud height algorithms are available
within AWIPS
• Product interpretation through examples of interest in
Alaska and OCONUS domains
• Strengths and limitations of the cloud height product
• Future improvements for the cloud height algorithm
Images courtesy of NASA ISS and STS-107
Introduction
• End user products viewable in AWIPS
• Cloud Top Height
• Cloud Top Temperature
• Effective Cloud Amount
• These products are retrieved using three infrared
observations. Temperature and moisture profiles and
surface temperatures from an NWP model such as the
GFS are also used.
• The GOES-R products will be made with the 11,12 and
13.3 um channels. On GOES-West (11) we use 6.5, 11
and 12 mm and on GOES-East we use 6.5, 11 and 13.3
um.
Why are Cloud products from
Satellites Important?
• Ground-based instruments are widely spaced apart (10 to
hundreds of km) and can look at a varying amount of sky.
• Satellites have a fixed pixels size, though at high viewing angles,
the spatial extent of a single pixel gets quite large
• Radiosondes are limited not only spatially, but temporally as
well
• Satellites provide information every 15 minutes
• Ground-based instruments do not necessarily provide
motion of systems as well as spatial growth of systems
• Satellites can provide information on trajectory and growth of
systems over a much broader area.
• Ground-based systems cannot provide information on the
properties (type, phase, cloud top temperature/height, etc.)
of clouds.
Why are Cloud Top Properties derived
from Satellites Important?
• Lots of information on cloud base and
sky cover are available (i.e. from
ASOS), but not much is available
regarding the tops of the clouds.
• High cloud top heights (low cloud top
temperatures) along with glaciated cloud
phases are a sign of severe weather,
which could pose a risk to aviation and
the ground.
Image courtesy of UCAR
• Cloud top temperature, along with a
supercooled water cloud phase, is an
indication of where icing may occur
What Does a Cloud Top Height (or
Temperature) from a Satellite Mean?
• Your ability to see a cloud-top depends on what data you use
to view it.
• LIDARS (lasers) are very sensitive to any particles and give a
very direct measure of the height of a cloud.
• Cloud Radars can’t see small ice particles and penetrate
deeper into clouds before reflecting.
• IR radiances (like ACHA uses) respond to profile of cloud
mass and temperature. The height the satellite sees from an
IR channel is the level where most of the emission you sense
came from (it’s an effective height/temperature).
The goal is to estimate the height of the highest cloud layer in the
column and to use all of our information to make that as accurate as
possible.
What Is Currently Available
Cloud Height/Temperature
•Infrared satellite imagery
•
•
11mm brightness temperatures provide an estimate of cloud top
temperatures
Used with skew-T (from RAOB or model data) to estimate cloud
height
•Radar estimated cloud heights
•
•
Limited to regions with radar coverage
Not necessarily easy to interpret (lots of effort needed to
determine height from multiple scans)
•Limited PIREP reports
Key: While there is information on cloud cover and the
height of the base of the cloud deck, there is extremely
limited information on cloud top height.
What Is Currently Available
How a single-channel 11 mm Cloud Height is Derived
http://www.rap.ucar.edu/projects/ocn/ref/
What Is Currently Available
What Is wrong with the current
approach
• It only works for thick clouds. For cirrus clouds, the
errors are very big.
• It does not account for the atmosphere above the
cloud. At large angles, like from GOES, this can be a
big source of error.
• Estimates are based on either radiosondes, which can
be hundreds of km away and hours old (recall
radiosondes are only launched at 00Z and 12Z), or
model data, which can be hours old and have the
inconsistencies of the particular model used.
AWG Cloud Height Algorithm (ACHA)
• AWG developed on IR-only solution that uses the multiple IR channels on ABI
to estimate cloud temperature, emissivity and particle size. Height and
Pressure are derived from the temperature and the NWP profiles (GFS).
Simultaneous estimation of cloud microphysics generates a better cloud height.
• AWG approach has been extend to many current imagers (AVHRR, GOES11,12,13, MODIS and VIIRS).
• On ABI and MODIS, we use 11, 12 and 13.3 mm.
• On GOES, we use 6.7, 11 and 12 mm or 6.7, 11 and 13.3 mm.
Validation (performed on MODIS – an Advanced Imager from NASA
with all the channel combinations we use.)
www.nasa.gov
Definition of Products
• Cloud Top Height
• The derived geopotential height of the detected cloud
• Units are km
• Cloud Top Temperature
• Derived temperature of the ambient atmosphere at the top of the
detected cloud
• Units are in C (converted from K)
• Effective Cloud Amount
• Mathematically, ECA is the cloud emissivity times cloud fraction
• At single pixel resolution, ACHA calculates the emissivity of a cloudy pixel, but
does not know anything about the cloud fraction.
• Measure of the transparency of a cloud
• Ranges from 0.0 to 1.0 where 0.0 is clear and 1.0 is an infinitely thick
opaque cloud.
Known Strengths
• Products are generated within minutes of receiving satellite
data.
• IR-only algorithm is consistent through the terminator
• ACHA runs on the current GOES and POES Imagers.
• Our analysis shows we meet the GOES-R specification on
current GOES though GOES-R ABI should be much better.
• We can generate products at raw GOES resolution (4 km)
which is higher than planned product resolution for GOESR (10 km).
Primary Limitations
• The ACHA algorithm relies on the knowledge of the cloud
phase. If a thin cirrus is labeled as a water cloud, a poor
result is likely.
• The ACHA algorithm does handle multi-layer situations if
the cloud typing algorithm detects. ACHA assumes that
height of the lower cloud deck can be determined by
looking at heights of surrounding and unobscured low
clouds. If this assumptions fails, a poor result is possible.
GOES-R Cloud Height Product:
What is Provided
GOES-W
GOES-E
GOES-E
Overlap Case
Cloud Dissipation Example
Building Convection Example
GOES-E
Convection Example
GOES-W
ECA Example
GOES-E
Near-Term Improvements
•Investigation of the temporal change in cloud top temperature
and height in relation to severe weather and winter weather
situations.
•End user feedback is essential and will be used to guide future
improvements
What You Should Know by
the End of Training
• Which GOES-R cloud height
algorithms are available within
AWIPS
• Product interpretation through
examples of interest
• Strengths and limitations of the
cloud height product
• Future improvements for the
cloud height algorithm
Contact
• Questions? Suggestions? Donations?
• Contact:
• Andrew Heidinger (Andrew.Heidinger@noaa,gov)
• William Straka (wstraka@ssec.wisc.edu)