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Peterson, David A.

Publications and source records attributed to Peterson, David A..

Contrasting Stratospheric Smoke Mass and Lifetime From 2017 Canadian and 2019/2020 Australian Megafires: Global Simulations and Satellite Observations

Stratospheric injections of carbonaceous aerosols and combustion gases by extreme wildfires have become increasingly common. Recent “megafires,” particularly large and intense fires, delivered particulate burdens to the lower stratosphere comparable to those of moderate volcanic eruptions. The 2017 Canadian megafire generated four large Pyrocumulonimbi (pyroCbs), injecting up to ≈0.3 Tg of smoke in the lower stratosphere. Even more extreme, the 2019/2020 Australian event produced a pyroCb activity resulting in stratospheric smoke intrusions of ≈1 Tg. To understand their contrasting behavior, we present global climate simulations of the atmospheric response to these events, applying smoke burdens informed by remote observations. Model outcomes, compared to satellite data of smoke transport, reproduce reasonably well the initial plume rise, at 0.2–0.3 km/day, attaining heights of ≈20 km in Canada and above 30 km in Australia. Global dispersal of the plume occurs within about 3 weeks in both cases, consistent with observations. Smoke removal timescales, ≈5 months for the Canadian megafire, agree with remote measurements. During the Australian megafire, observations indicate stratospheric injections three times as large, and models predict comparatively longer smoke lifetimes, ≈16 months. After the latter event, atmospheric optical depths and radiative cooling achieved values close to those measured following the Pinatubo eruption. Sensitivity tests of model assumptions indicate, in accord with prior studies, that smoke burden, injection heights, and black carbon content can determine plume evolution and possible climate impacts. An empirical relation between peak heights of stratospheric plumes and lifetimes is derived that can help assess megafire impacts on the stratosphere, climate and the Earth system

54 ENVIRONMENTAL SCIENCES↗

Device and method for constructing and displaying high quality images from imaging data by transforming a data structure utilizing machine learning techniques

Constructing a computer image from raw imaging data or encoded imaging data by transforming a first data structure in which the raw imaging data or the encoded imaging data is stored into a second data structure storing reorganized imaging data. The raw imaging data or the encoded imaging data is received, stored in the first data structure. The computer reorganizes the raw imaging data or the encoded imaging data into the reorganized data and stores the reorganized data in the second data structure, which is a multi-dimensional array having subarrays containing local information needed by a convolutional neural network for processing the reorganized data. Other portions of the multi-dimensional array store other portions of the raw imaging data or the encoded imaging data. The computer also processes the reorganized data using the convolutional neural network to construct the image, whereby a constructed image is formed.

Korbin, John P.↗