The British landscape painter John Constable is considered foundational for the Realist movement in 19th-century European painting. Constable's painted skies, in particular, were seen as remarkably accurate by his contemporaries, an impression shared by many viewers today. Yet, assessing the accuracy of realist paintings like Constable's is subjective or intuitive, even for professional art historians, making it difficult to say with certainty what set Constable's skies apart from those of his contemporaries. Our goal is to contribute to a more objective understanding of Constable's realism. We propose a new machine-learning-based paradigm for studying pictorial realism in an explainable way. Our framework assesses realism by measuring the similarity between clouds painted by artists noted for their skies, like Constable, and photographs of clouds. The experimental results of cloud classification show that Constable approximates more consistently than his contemporaries the formal features of actual clouds in his paintings. The study, as a novel interdisciplinary approach that combines computer vision and machine learning, meteorology, and art history, is a springboard for broader and deeper analyses of pictorial realism.

A genetic algorithm is used to couple a dispersion and transport model with a pollution receptor model for the purpose of assimilating sensor data to characterize emission sources. This coupling allows the use of the backward (receptor) model to calibrate the forward (dispersion) model, potentially across a wide range of meteorological conditions. The genetic algorithm optimizes the source calibration factors that connect the two models. This methodology is demonstrated for a basic Gaussian plume dispersion model, then progresses to incorporating an operational transport and dispersion model. It is verified in the context of both synthetic data and actual monitored data from field tests with known release amounts. Its error bounds are set using Monte Carlo techniques and robustness assessed through the addition of white noise. The impact of varying the genetic algorithm parameters is assessed.

Documents the typical variations in quasi-two dimensional marine atmospheric boundary layer SAR signature orientation with respect to the near-surface mean wind direction, making it of practical interest to those researchers using such techniques to infer wind speed from SAR imagery.