Data for satellite cloud climatologies

The motivation for presenting this material comes from work in developing cloud climatologies using MODIS imagery; these “climatologies” revealed many interesting patterns that were very closely tied to the underlying vegetation.  As such, this material should serve to motivate interest in various regions of the globe that are currently “less-well-known”.

The development of the cloud climatologies was through the use of MODIS visible imagery (provide details here).  One can also see similar climatologies at the ….

A discussion of the relative advantages and disadvantages of the two procedures can be found in our publication here.

Geostationary Satellite imagery

We will show many examples from geostationary satellite imagery and animations on this website because they illustrate various meteorological phenomena and their images can also generate estimates of mean cloudiness.  So some background is necessary to understand this source of information.  Although the Polar-orbiting, sun synchronous satellite perspective gives a nearly identical perspective of the cloudiness anywhere on Earth, it does have some limitations compared with the more familiar perspective from geostationary weather satellites.  The geostationary satellites, orbiting the earth once daily, appear fixed above the Equator and have the same perspective, day or night.  Thus imagery successive images can be animated to produce the familiar movies that we see frequently on television or the Internet.  This visualization is key to both understand the daily weather we experience and to make short-range forecasts by extrapolating cloudiness patters ahead in time.  (This doesn’t work well for many day forecasts but isn’t too bad for very short range forecasts.)

Although geostationary meteorological satellites have been around since 1974, they have gradually improved in their capabilities over the decades.  The latest series of these satellites provide visible imagery at 500m resolution (the size on earth of one pixel in the satellite image – at nadir (beneath the satellite on the Equator – looking straight down.  The pixel correspond to larger and larger areas the farther one looks away from nadir and such satellites do a very poor job of showing features at high latitudes.)  The frequency of imagery has also increased, and the number of radiation channels that are sensed has increased.  The details of these changes are not essential here, except to say that the latest satellites can provide a “full disk” perspective every 15 minutes with a 500m resolution; the previous generation could provide 1000m resolution every 30 minutes.

One somewhat important detail is that these newer satellites have their sensors/telescopes continually pointed towards the earth,  Older geostationary satellites were spun (at about 100 times per minute) to stabilize them and so could only see the earth for a short percentage of the time.  A downside of the continual pointing method is that fuel is required to ensure that the satellite continually points towards the earth – these attitude corrections are very minor but essential to ensure the pointing accuracy needed.  If this fails for whatever reason the earth will appear to slightly “wobble” when the images are animated and this affects the ability to average of the images to generate a mean cloud climatology.  (In principle this could be corrected for but even the georeferenced images showed this problem – these satellites were intended for weather forecasting objectives rather than subtle monitoring of climate.

While most of our illustrations will show examples of visible imagery (reflected from the earth’s surface in wavelengths around .6 microns (there are three visible bands detected with the newest GOES satellites) we will also show some infrared imagery examples.  These images display radiation leaving the earth with longer than visible wavelengths – about 11 microns (though the infrared (IR) ranges through a continuous spectrum).  This provides a measure of the temperature of the surface being observed – the warmer the surface the more radiation it emits.  One can generate climatologies from such images as well – though we will note (later) that major problems occur in the interpretation of such images and climatologies.