Light Detection and Ranging (LIDAR)

Lidar stands for "light detection and ranging" and is a remote sensing technology. The principle is simple. A pulse of intense laser light is emitted, reflected at an obstacle and received by the detector. The time between emission and reception of the light can be easily converted into a distance as the speed of light is constant. Therefore, a lidar can measure the distance to an obstacle. Similar concepts such as radar or sonar are probably familiar from old spy movies. Imagine the obstacle is optically thin, meaning not the whole laser pulse is reflected but only a small portion of it. The rest of the light travels further unhindered. This is the case when shooting a green laser pulse into the atmosphere. The obstacles are air molecules. Only a small fraction of them scatters the light directly back to the detector, the rest flashes to higher altitudes. The great advantage: Not only one distance to some air molecules is measured but a whole range of distances as light is scattered from air molecules at all altitudes up until the air is so thin and the laser light is so strongly extinguished that no more light is coming back.

The DLR has build a Rayleigh lidar that is capable of measuring the atmospheric density up to an altitude of 100km! And not only that. By assuming that the atmospheric is in hydrostatic balance, meaning that gravity is balanced by the pressure gradient, it is even possible to derive atmospheric temperatures up to these incredible altitudes.

Question: What is the best way to illustrate the lidar temperature measurement? The data I plotted is just temperature as function of altitude. Each temperature value has an uncertainty. In a, b, and c one might be tempted to interpret the smaller scale structures above 85km as dynamically interesting features. However, at the same height uncertainty values climb up and so more noise contaminates the temperature profile. I am wondering what might be a good approach to visualize the data in such a way that it is not prone to over interpretation.