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Fusing Observations and Model Results for Creation of Enhanced Ozone Spatial Fields: Comparison of Three Techniques
Citation:
Gego, E., P. S. Porter, V. GARCIA, C. Hogrefe, AND S.T. RAO. Fusing Observations and Model Results for Creation of Enhanced Ozone Spatial Fields: Comparison of Three Techniques. Chapter 3.0, Carlos Borrego and Ana Isabel Miranda (ed.), Air Pollution Modeling and Its Application XIX. Springer, New York, NY, (Series C):339-346, (2008).
Impact/Purpose:
National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling Division (AMD) conducts research in support of EPA′s mission to protect human health and the environment. AMD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Description:
This paper presents three simple techniques for fusing observations and numerical model predictions. The techniques rely on model/observation bias being considered either as error free, or containing some uncertainty, the latter mitigated with a Kalman filter approach or a spatial smoothing method. The fusion techniques are applied to the daily maximum 8-hour average ozone concentrations observed in the New York state area during summer of 2001. Classical evaluation metrics (mean absolute bias, mean squared error, correlation, etc.) show that fused predictions are not better than a simple interpolation of observations. However, fused maps better reproduce the spatial texture of the model predictions.