Michael D. Moran

TL;DR: In this paper, the authors compare the results of ten state-of-the-science regional air quality (AQ) modeling systems for full-year simulations of 2006 in the context of AQMEII, whose main goals are model inter-comparison and evaluation.

TL;DR: In this paper, the authors investigated the optimal ensemble size and quality of the members based on a clustering methodology to build a skilful ensemble based on model association and data clustering, which makes no use of priori knowledge of model skill.

TL;DR: In this paper, low-and high- spatial frequency filtration was applied to OMI data to reduce the noise and bias to enhance and reveal weak SO2 signals that are otherwise not readily apparent.

TL;DR: In this article, the authors highlight the similarities and differences in how emission inventories and datasets were developed and processed across North America and Europe for the Air Quality Model Evaluation International Initiative (AQMEII) project and then characterize the emissions for the two domains.

TL;DR: A satellite-based, global emission inventory for SO2 that is derived through simultaneous detection, mapping and emission-quantifying procedure, and thereby independent of conventional information sources is presented in this article.