Developing an Aerosol Layer Height Retrieval Algorithm for Passive Space-Based Sensors

Abstract

Aerosols are the source of the largest uncertainties in our climate models, blurring our outlook of the future. This has been attributed to the complexity of measuring their properties, which vary over time and space. Atmospheric circulation spreads aerosols across the globe from a point source, which makes satellite-based observations lucrative. At present, there are several aerosol observing missions that deliver aerosol data products in a consistent and operational manner; these missions report several aerosol properties that are important for reducing the contribution of uncertainties to our climate models. What is missing, however, is an operational data product that measures the height of these aerosols at a global scale. Earlier attempts at this use data derived from lidar instruments in space; an example being the Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP) instrument, which uses lasers to measure atmospheric composition. In the case of aerosols, the amount of backscattered electromagnetic radiation at each atmospheric layer gives an idea of the amount and height of aerosols. The mobility afforded by space-based instruments gives space lidars a leg up over ground-based lidars. However, the coverage of such lidar instruments is merely near-global. This has to do with the fact that while lidars in space can circle the entire globe, their footprint on the ground is very narrow, in the order of several hundred meters to a few kilometers: this is an inherent limitation of the measurement principle. Consequently, a specific patch on Earth is revisited in periods that can range several days. An alternative to space based atmospheric lidars are space based spectral imagers. These are essentially cameras that take snapshots of the Earth, capturing the light and splitting its different electromagnetic frequencies into the scale of nanometers using very precise prisms and detection techniques. The advantage of these instruments over lidars is that they have a very large footprint, covering several thousand kilometers of area in a single _y-by. This allows for daily to even sub-daily coverage of the Earth, as each snapshot covers larger and sometimes overlapping areas. The challenge is to estimate aerosol height using spectral signatures of the Earth’s atmosphere in an operational environment that can handle data coming in from the satellite at a rate of several million pixels every few minutes. This dissertation focuses on delivering the aerosol height data product operationally using computer algorithms. The logic of aerosol height estimation using these so-called spectral snapshots of the atmosphere differ from that using lidars; the instrument does not provide data for different atmospheric layers. This has to be inferred using the chemistry of the oxygen molecule. O2, the second most abundant gas in our atmosphere, has a unique spectral signature in the near-infrared region, comprising of electromagnetic radiation around 765 nm. The chemical structure of the oxygen molecule allows it to absorb some of these radiations, creating a structure of absorption bands. This spectral signature deepens as more light is absorbed by the oxygen: this happens as photons penetrate deeper and deeper into the earth’s atmosphere, unless they hit a barrier. If the photons bounce back from an aerosol layer at a very high altitude, the amount of absorption by oxygen would be low. This ‘depth’ of absorption gives clues on how high an aerosol layer might be present. Computer models can reconstruct this oxygen absorption structure onto a simulated spectrum. One of the control parameters within the model is the height of an aerosol layer. The generated spectral signature of a simulated atmosphere resembling the atmosphere of a pixel in the snapshot from space-based hyperspectral imagers is then compared to the measured spectral signature. This usually results in a non-zero difference, which is caused by errors in the model. These errors can be minimised by using computer algorithms and mathematical information retrieval techniques, resulting in a modeled atmosphere closer to the measurement by changing the height of the aerosol layer, resulting in an aerosol height estimate. In this dissertation, computer algorithms inspired from mathematical models of brain neural networks as well as information retrieval techniques such as least squares are used…