For pilots of gliders or sailplanes, the \'thermal\' is the most important pheno

Question

For pilots of gliders or sailplanes, the 'thermal' is the most important phenomena of the air. A thermal is classically described as an upward flow of air caused by ground level heating of air that rises in bubbles or a connected stream of warmed air. Given sufficient velocity of the rising air, a gliding craft, bird or even trash and debris can be lifted thousands of feet. It can be also noted that 'dust devils' can result from especially vigorous flows and that other even stronger phenomena like tornadoes and cyclonic storms are related.

But in the absence of markers like dust, the air flow is generally invisible until possibly the flow reaches an altitude where the water vapor contained, condenses to form a cloud. And again if the flow is strong enough and contains enough water, a thunder storm is possible.

So, the question. Given all of the above is it possible to see the mass of air that makes up the thermal? Is there anything in the difference between the thermal and the surrounding air that could be detected and presented graphically?

Explanation / Answer

Air of different temperature and pressure has different refractive index - but the difference is very small.

If you think of putting a piece of glass into water and trying to see it. The difference between glass (1.5) and water (1.33) is pretty large, air at different temperatures has refractive index differences of parts-per-million

Astronomers at least measure the refractive index profile to design adaptive optics systems, but it takes a lot of equipment (lasers and large telescopes) - I don't know if meteorologists do it much.

On second thought, there is no reason we should be limited by the precision of human perception (especially that of a person who is also trying to fly a plane simultaneously). Perhaps a detection algorithm hooked to a remote sensor would be able to pick up the air-dependent micro-warping that our eyes and distracted brains cannot? And then there is spectral analysis, which can reveal achieve jaw-droppingly precise observations from noisy data. I don't know, just throwing out ideas. Also, I think the connection to adaptive optics suggested by Martin Beckett below is really promising.

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