Risk assessment of atmospheric emissions using machine learning

Supervised and unsupervised machine learning algorithms are used to perform statistical and logical analysis of several transport and dispersion model runs which simulate emissions from a fixed source under different atmospheric conditions. <br><br> First, a clustering algorithm is used to automatically group the results of different transport and dispersion simulations according to specific cloud characteristics. Then, a symbolic classification algorithm is employed to find complex non-linear relationships between the meteorological input conditions and each cluster of clouds. The patterns discovered are provided in the form of probabilistic measures of contamination, thus suitable for result interpretation and dissemination. <br><br> The learned patterns can be used for quick assessment of the areas at risk and of the fate of potentially hazardous contaminants released in the atmosphere

Role of Anomalous Warm Gulf Waters in the Intensification of Hurricane Katrina

The year 2005 experienced several strong hurricanes intensifying in the Gulf of Mexico before making landfall that severely damaged the Gulf States, especially Hurricane Katrina. Remarkable similarities between sea surface temperature anomaly (SSTA) and major hurricane (categories 3 and higher) activity over the Gulf are identified. However, the intensification of individual hurricanes may not necessarily be temporally and spatially coincident with the distribution of warm waters or high sea surface temperature (SST). High SST values are found in advance of significant intensification of Hurricane Katrina. We emphasize that high SSTA which occurred at the right time and right place was conducive to the hurricane intensification. In particular, high SSTA in the northeastern quadrant of the storm track induced significant increases in surface latent heat fluxes (LHF) contributing to the rapid intensification of Katrina. We also compared and verified model simulations with buoy observations

Numerical Simulations of the Impacts of the Saharan Air Layer on Atlantic Tropical Cyclone Development

In this study, the role of the Saharan air layer (SAL) is investigated in the development and intensification of tropical cyclones (TCs) via modifying environmental stability and moisture, using multisensor satellite data, long-term TC track and intensity records, dust data, and numerical simulations with a state-of-the-art Weather Research and Forecasting model (WRF). The long-term relationship between dust and Atlantic TC activity shows that dust aerosols are negatively associated with hurricane activity in the Atlantic basin, especially with the major hurricanes in the western Atlantic region. Numerical simulations with the WRF for specific cases during the NASA African Monsoon Multidisciplinary Analyses (NAMMA) experiment show that, when vertical temperature and humidity profiles from the Atmospheric Infrared Sounder (AIRS) were assimilated into the model, detailed features of the warm and dry SAL, including the entrainment of dry air wrapping around the developing vortex, are well simulated. Active tropical disturbances are found along the southern edge of the SAL. The simulations show an example where the dry and warm air of the SAL intruded into the core of a developing cyclone, suppressing convection and causing a spin down of the vortical circulation. The cyclone eventually weakened. To separate the contributions from the warm temperature and dry air associated with the SAL, two additional simulations were performed, one assimilating only AIRS temperature information (AIRST) and one assimilating only AIRS humidity information (AIRSH) while keeping all other conditions the same. The AIRST experiments show almost the same simulations as the full AIRS assimilation experiments, whereas the AIRSH is close to the non-AIRS simulation. This is likely due to the thermal structure of the SAL leading to low-level temperature inversion and increased stability and vertical wind shear. These analyses suggest that dry air entrainment and the enhanced vertical wind shear may play the direct roles in leading to the TC suppression. On the other hand, the warm SAL temperature may play the indirect effects by enhancing vertical wind shear; increasing evaporative cooling; and initiating mesoscale downdrafts, which bring dry air from the upper troposphere to the lower levels

A non-hybrid method for the PDF equations of turbulent flows on unstructured grids

In probability density function (PDF) methods of turbulent flows, the joint PDF of several flow variables is computed by numerically integrating a system of stochastic differential equations for Lagrangian particles. A set of parallel algorithms is proposed to provide an efficient solution of the PDF transport equation, modeling the joint PDF of turbulent velocity, frequency and concentration of a passive scalar in geometrically complex configurations. An unstructured Eulerian grid is employed to extract Eulerian statistics, to solve for quantities represented at fixed locations of the domain (e.g. the mean pressure) and to track particles. All three aspects regarding the grid make use of the finite element method (FEM) employing the simplest linear FEM shape functions. To model the small-scale mixing of the transported scalar, the interaction by exchange with the conditional mean model is adopted. An adaptive algorithm that computes the velocity-conditioned scalar mean is proposed that homogenizes the statistical error over the sample space with no assumption on the shape of the underlying velocity PDF. Compared to other hybrid particle-in-cell approaches for the PDF equations, the current methodology is consistent without the need for consistency conditions. The algorithm is tested by computing the dispersion of passive scalars released from concentrated sources in two different turbulent flows: the fully developed turbulent channel flow and a street canyon (or cavity) flow. Algorithmic details on estimating conditional and unconditional statistics, particle tracking and particle-number control are presented in detail. Relevant aspects of performance and parallelism on cache-based shared memory machines are discussed.Comment: Accepted in Journal of Computational Physics, Feb. 20, 200

Joint PDF modelling of turbulent flow and dispersion in an urban street canyon

The joint probability density function (PDF) of turbulent velocity and concentration of a passive scalar in an urban street canyon is computed using a newly developed particle-in-cell Monte Carlo method. Compared to moment closures, the PDF methodology provides the full one-point one-time PDF of the underlying fields containing all higher moments and correlations. The small-scale mixing of the scalar released from a concentrated source at the street level is modelled by the interaction by exchange with the conditional mean (IECM) model, with a micro-mixing time scale designed for geometrically complex settings. The boundary layer along no-slip walls (building sides and tops) is fully resolved using an elliptic relaxation technique, which captures the high anisotropy and inhomogeneity of the Reynolds stress tensor in these regions. A less computationally intensive technique based on wall functions to represent boundary layers and its effect on the solution are also explored. The calculated statistics are compared to experimental data and large-eddy simulation. The present work can be considered as the first example of computation of the full joint PDF of velocity and a transported passive scalar in an urban setting. The methodology proves successful in providing high level statistical information on the turbulence and pollutant concentration fields in complex urban scenarios.Comment: Accepted in Boundary-Layer Meteorology, Feb. 19, 200

A Solution-Adaptive Grid Generation Scheme for Atmospheric Flow Simulations

Science Applications International Corporation (SAIC) has developed a novel hazardous dispersion model with support from the Defense Special Weapons Agency. The Operational Multiscale Environment model with Grid Adaptivity (OMEGA) is based on an unstructured prismatic grid. OMEGA has the capability to adapt its grid statically as well as dynamically. The grid can adapt to a set of user defined criteria such as shoreline, terrain, particle locations, and velocity deformation. A considerable reduction in CPU time is achieved by dynamically adapting the grid. This paper gives an overview of the OMEGA model, and describes the grid-generation scheme used by the model. To our knowledge, OMEGA is the only fully operational atmospheric model with spatial and dynamic grid adaptation capabilities