Atmospheric Releases Uncertainty Assessment using Remote Sensing, Mesoscale Modeling, and Data Mining

Guido Cervone⁽¹⁾, Pasquale Franzese⁽¹⁾, Yasmin Ezber⁽¹⁾, Zafer Boybeyi⁽¹⁾, Menas Kafatos⁽¹⁾ and Ramesh P. Singh⁽¹⁾

⁽¹⁾ George Mason University, 4400 University Drive, Fairfax, VA 22030, United States

Abstract

In applications such as homeland security and hazards response, it is necessary to know in real time which areas are most at risks from a potentially harmful atmospheric release. Using high resolution remote sensing measurements and atmospheric mesoscale numerical models, it is possible to detect and study the transport and dispersion of particles with great accuracy, and to determine the ground concentrations which might pose a threat to people and property. Satellite observations from different sensors can be fused together to compensate for different spatial, temporal and spectral resolutions and data availability. Such observations are used to initialize and validate atmospheric mesoscale models, which can provide accurate estimates of ground concentrations. Running a numerical model is, however, usually slow due to the complex nature of the computations, and such running of a model does not provide real time answers

We will define probality maps of risks by running several atmospheric mesoscale and T&D simulations spanning the climatological input conditions of an entire year, observed using high resolution remote sensing instruments. Such maps provide an immediate risk assessment area associated with a given source location. If a release indeed occurs, the computed risk maps can be used for first assesment and rapid response.

We analyze the output of the mesoscale model runs using machine learning algorithms to find characteristic patterns which relate potential risk areas with atmospheric parameters which can be observed using remote sensing instruments and ground measurements. Therefore, when a release occurs, it is possible to give a quick hazard assessment without running a time consuming model, but by comparing the current atmospheric conditions with those associated with each identified risk area. The offline learning provides knowledge that can later be used to protect people and properties.

Workshop presentation

Keywords: ESA European Space Agency - Agence spatiale europeenne, observation de la terre, earth observation, satellite remote sensing, teledetection, geophysique, altimetrie, radar, chimique atmospherique, geophysics, altimetry, radar, atmospheric chemistry