RAMMB Satellite Case Studies

GOES Imagery Analysis of the May 31st, 1996 Tornado Outbreak

Look at the low-level cumulus fields in detail, using all imaging channels. Identify the differences (e.g., why can't the low-level cumulus be seen in the 6.7 Ám imagery?). Can GOES imagery (all five channels) be utilized to identify and position different air masses, as well as the convergence boundaries separating them? Can you locate a dryline using the multispectral characteristics of the imagery? Is it colocated with the surface position of the dryline; and, if not, why?

This data set contains data collected at a much more frequent interval than the normal operating mode of every 15 minutes. Construct loops at the different imaging frequencies and note how much easier it is to track the clouds and to understand and anticipate storm development and evolution with the higher frequency images. What would be the optimum frequency interval for several different applications?

Once thunderstorms have developed, use the higher frequency imagery along with WSR-88D data to look at them and their surrounding environment. Do these two data sources, used together, enhance your understanding of thunderstorm intensity as compared to using either one alone?

Observe the cloud top temperatures in the 10.7 Ám IR window channel. Compare this with available radar data and determine what portion of the storm contains an active precipitation core. Compare the coldest regions at anvil top, as well as other portions of the anvil, with imagery from the other IR channels and look for differences. If there are, can you explain why they exist, and their meaning, with respect to active portions of a thunderstorm?

In your comparisons of IR and radar imagery of cloud tops, as suggested above, did you take into account the satellite parallax viewing and radar beam size and height-above-surface at the different viewing distances? Try using 30-sec. satellite imagery with 5-min. radar data.

Note that the developing thunderstorms in the central Plains form in preferred locations. The main activity is associated with an east-west warm front in central KS, a low pressure area in eastern CO and a dryline which stretches from western KS into west TX. Use individual frames and loops to study these boundaries. Specifically, look for clues of imminent storm development (e.g., VIS loops can help identify boundary development and the location of the center of the low).

Track the motion of storms using satellite imagery loops and notice the rightward deviation of the large storm in central CO. Use available supplementary data to compute how the storm-relative helicity for this storm differs from others in the region.

Use sequential imagery to track the cirrus clouds and compare your results with profiler data, if available. Identify cumulus and mid-level clouds and do the same thing. Finally, compare the cumulus motions with the motions that can be determined from the WSR-88D data.

Notice how some of the cumulus change from bright to dark in the 3.9 Ám imagery. Do you know why? (You may wish to refresh yourself on the nuances of this unique imaging channel by reviewing the CIRA-RAMM Team's GOES 3.9 Ám Channel Tutorial.) Are there corresponding differences in the WSR-88D imagery when bright-to-dark changes in the 3.9 Ám channel imagery occur? What about electrical activity reported by lightning detection systems?