A real-time eulerian photochemical model forecast system - Overview and initial ozone forecast performance in the Northeast US corridor

This article reports on the first implementation of a real-time Eulerian photochemical model forecast system in the United States. The forecast system consists of a tripartite set of one-way coupled models that run routinely on a parallel microprocessor supercomputer. The component models are the fifth-generation Pennsylvania State University (PSU)-NCAR Mesoscale Model (MM5), the Sparse-Matrix Opera Kernel for Emissions (SMOKE) model, and the Multiscale Air Quality Simulation Platform-Real Time (MAQSIP-RT) photochemical model. Though the system has been run in real time since the summer of 1998, forecast results obtained during August of 2001 at 15-km grid spacing over New England and the northern mid-Atlantic-conducted as part of an "early start" NOAA air quality forecasting initiative-are described in this article. The development and deployment of a real-time numerical air quality prediction (NAQP) system is technically challenging. MAQSIP-RT contains a full photochemical oxidant gas-phase chemical mechanism together with transport, dry deposition, and sophisticated cloud treatment. To enable the NAQP system to run fast enough to meet operational forecast deadlines, significant work was devoted to data flow design and software engineering of the models and control codes. The result is a turnkey system now in use by a number of agencies concerned with operational ozone forecasting. Results of the chosen episode are compared against three other models/modeling techniques: a traditional statistical model used routinely in the metropolitan Philadelphia, Pennsylvania, area, a set of publicly issued forecasts in the northeastern United States, and the operational Canadian Hemispheric and Regional Ozone and NOx System (CHRONOS) model. For the test period it is shown that the NAQP system performs as well or better than all of these operational approaches. implications for the impending development of an operational U.S. ozone forecasting capability are discussed in light of these results.