The past half century has seen an unprecedented growth of the world's urban population. While urban areas proffer the highest quality of life, they also inflict environmental degradation that pervades a multitude of space-time scales. In the atmospheric context, stressors of human ͑anthropogenic͒ origin are mainly imparted on the lower urban atmosphere and communicated to regional, global, and smaller scales via transport and turbulence processes. Conversely, changes in all scales are transmitted to urban regions through the atmosphere. The fluid dynamics of the urban atmospheric boundary layer and its prediction is the theme of this overview paper, where it is advocated that decision and policymaking in urban atmospheric management must be based on integrated models that incorporate cumulative effects of anthropogenic forcing, atmospheric dynamics, and social implications ͑e.g., health outcomes͒. An integrated modeling system juxtaposes a suite of submodels, each covering a particular range of scales while communicating with models of neighboring scales. Unresolved scales of these models need to be parametrized based on flow physics, for which developments in fluid dynamics play an indispensible role. Illustrations of how controlled laboratory, outdoor ͑field͒, and numerical experiments can be used to understand and parametrize urban atmospheric processes are presented, and the utility of predictive models is exemplified. Field experiments in real urban areas are central to urban atmospheric research, as validation of predictive models requires data that encapsulate four-dimensional complexities of nature.

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Environmental contours describing extreme sea states are generated as the input for numerical or physical model simulations as a part of the standard current practice for designing marine structures to survive extreme sea states. Such environmental contours are characterized by combinations of significant wave height (H s ) and energy period (T e ) or peak period (T p ) values calculated for a given recurrence interval using a set of data based on hindcast simulations or buoy observations over a sufficient period of record. The use of the inverse first-order reliability method (I-FORM) is standard design practice for generating environmental contours. This paper develops enhanced methodologies for data analysis prior to the application of the I-FORM, including the use of principal component analysis (PCA) to create an uncorrelated representation of the variables under consideration as well as new distribution and parameter fitting techniques. These modifications better represent the measured data and, therefore, should contribute to the development of more realistic representations of environmental contours of extreme sea states for use in survivability analysis for marine structures.

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