GOES-16 has generated a great deal of excitement
in the operational forecasting community.
However, its value for some critical warning
and forecasting decisions is impeded by one
drawback – the effect of parallax.
The good news is we can overcome this drawback
by understanding its causes and then applying
some simple mitigation techniques.
Parallax is defined as the apparent displacement
of an object due to differences in observing
platform locations.
For geostationary meteorological satellites,
parallax is attributable to the angle between
the satellite sub-point and the specific position
of the target object.
There are three factors that influence parallax
associated with weather satellites in geostationary
orbit: latitude, longitude, and the height
of the feature above the earth’s surface.
To visualize how these factors translate into
parallax displacement, let’s consider a
conceptual cartoon.
This is a simplified illustration of a spacecraft
in the GOES-R series.
Let’s pretend the cartoon represents GOES-16,
since that satellite is already in orbit.
GOES-16, like any geostationary satellite,
is positioned over the equator, orbiting at
an altitude of roughly 22,000 miles (36,000
km).
Therefore, when the satellite detects objects
located at mid-latitudes, it is viewing them
at an angle, not from directly overhead.
Additionally, the satellite sensor has no
explicit information about the height of objects
above the ground.
It simply captures images and geo-locates
individual elements as if they are surface-based
objects.
Imagine a thunderstorm, which has formed at
40 degrees north latitude, with a line of
towering cumulus clouds on its southern flank.
For the sake of demonstration, let’s say
the cumulonimbus cloud is 15 km in width at
its base, and approximately the same height
above the ground.
This thunderstorm is in a mature phase, with
a well-developed anvil and a strong, broad
updraft that it is supporting a 50 dbZ reflectivity
core aloft, as detected by a nearby radar.
When depicted on a plan view map, the reflectivity
core will be located accurately, directly
beneath the position where the radar beam
intercepts it.
However, the corresponding satellite image
will be displaced poleward.
The distance of that displacement is a function
of its height.
Here’s why.
While GOES-16 detects the reflectance – or
brightness temperature – of the thunderstorm
top, it assumes that feature is at the earth’s
surface.
Therefore, rather than placing it below the
back edge of a 50,000-foot structure, the
satellite geo-locates the feature as though
it were positioned at the point where the
beam intercepts the ground.
That pixel will then be placed at this perceived
position, several kilometers north of its
true location.
A similar displacement occurs in the longitudinal
direction.
That is, if a tall cloud feature is located
to the west or east of the satellite’s longitudinal
sub-point, it will be positioned farther to
the west or east, respectively, as a consequence
of parallax.
One final point related to the effects of
parallax on thunderstorms.
Because of the satellite’s viewing angle,
and the spatial resolution of the spectral
band, the equatorward side of the storm is
partially visible, as well as its top.
Thus, the image you observe is slightly stretched
out, and “tilted” from vertical.
To further reinforce this concept, let’s
look at a real example.
Here is a radar reflectivity image of a storm
about 40 miles west of Topeka, Kansas.
The date and time don’t really matter, because
I’m just using this case to illustrate a
point.
The image depicts reflectivity along the 0.5-degree
scan from the KTWX radar.
The white line represents a NW-SE cross-section
that cuts through the strongest portion of
the updraft.
Details about the storm structure are more
easily visualized in this vertical cross-section
along that line.
Reflectivity values are relatively low where
the inflow is occurring near the surface,
but higher reflectivities are suspended aloft
by the strong updraft.
In fact, there is even evidence of an overshooting
top above the equilibrium level.
Now let’s superimpose the radar reflectivity…
…on top of the corresponding image from
the GOES-16 0.64 micron visible channel.
Note that the overshooting top in the visible
satellite image is displaced to the north
and west of the location we just analyzed
from the radar cross-section.
This displacement is also clearly evident
in the infrared, as seen here.
The coldest cloud top pixel on the 10.3 micron
(Clean IR) channel is placed north and west
of the position where radar indicated the
location of the main updraft.
While these distances are not great, they
are significant enough to impact certain operational
decisions that require precision.
For example, returning to our radar image
west of Topeka, let’s assume that after
carefully interrogating reflectivity, velocity,
Dual Pol products, and the near-storm environment,
you have decided this storm poses an imminent
tornado threat.
Based on your determination that the storm
will likely remain anchored to the proximity
of the boundary, and accounting for some southeastward
motion of that boundary over the next half-hour
or so, you generate this tornado warning polygon,
Overlaying the corresponding GOES-16 visible
image, we can see that the tornado warning
does not encompass the areas where satellite
imagery would suggest the highest severe threats
are located.
The overshooting top is completely outside
the bounds of the tornado threat we identified
on radar, and even the region of vigorous
inflow is barely within the northern periphery
of the polygon.
For this very reason, some have concluded
satellite is not a useful tool for convective
warning decisions.
However, we would argue for a different perspective.
As long as you recognize and make adjustments
for the effects of parallax, it is possible
to use GOES-16 products…
along with other observational data and predictive
fields, not only to improve your situational
intelligence, but to inform critical short-term
forecasting decisions – even convective
warnings.
With practice, effective integration of high-resolution
data sets can enhance a forecaster’s abilities
in many ways: assessing the near-storm environment,
anticipating rather than reacting to development,
and in some cases, drawing or refining warning
polygons while waiting for the next radar
volume scan.
In radar-sparse regions, forecasters are increasingly
employing GOES-16 imagery as a crucial component
to their convective warning decisions.
This will likely become even more prevalent
once the GOES-16 GLM data are operational.
The key is to understand, and account for,
the effects of parallax when using satellite
imagery to determine and communicate specific
threats.
By learning and practicing a few simple techniques,
forecasters CAN learn to make effective adjustments
to the slight geo-location displacements produced
by parallax.
As your ability to make such adjustments improves,
you may well find GOES-R series satellite
imagery and derived products to be valuable
tools that can:
enhance your situational awareness of the
environment;
increase the accuracy, quality, and timeliness
of your forecasts;
strengthen your confidence in those forecasts;
and improve your communication with core partners
who need your expertise to make effective
risk management decisions
