• Improving air-quality forecasts: Model output statistics or data assimilation?

    Numerical forecasts based on atmospheric chemistry and transport models are used widely by government departments as part of air-quality forecast operations. However, in spite of increasingly advanced dynamic, physical and chemical processes within such models, forecast errors are always found during real forecast applications.  

    Without changing the model itself, two technologies can be applied to improve the forecast skill by using available observations of atmospheric constituents. The first is data assimilation (DA), which focuses on the model’s input. By mixing the model’s initial chemical condition with corresponding observations, DA can generate more accurate input data for the model to yield a better forecast. The second is model output statistics (MOS), which applies to the model’s output. It corrects the model’s input according to the statistic relation found by comparing past model outputs and their corresponding observations.


    In order to investigate the performance of these two technologies in improving model forecasts, Chaoqun MA, a Ph.D. student from the research group led by Professor Tijian WANG in the School of Atmospheric Sciences, Nanjing University, implemented three-dimensional variational (3DVar) DA and MOS based on one-dimensional Kalman filtering, separately and simultaneously, during a forecast experiment in Hebei Province, China. The results, published in Advances in Atmospheric Sciences, showed that MOS is more effective and durable than DA. 


    The authors explain their results as follows: 

     “Atmospheric chemistry models are not usually sensitive to their initial chemical conditions, and therefore the better input generated by DA will soon lose its effect during the model run. However, unlike DA, MOS retains its effect during the forecast, which is why MOS fairs better than DA in the comparison.”


    Dr. Chaoqun MA goes on to explain that “Considering the combined use of DA and MOS shows little improvement, and DA is usually more computationally complex, it is recommended to try MOS as the first option when seeking to improve air-quality forecasts. However, given the 3Dvar DA tested is a rather simple DA approach, it is possible a different conclusion might be reached after applying more advanced DA algorithms.”


    Reference 

    Ma, C. Q., T. J. Wang, Z. L. Zang, and Z. J. Li, 2018: Comparisons of three-dimensional variational data assimilation and model output statistics in improving atmospheric chemistry forecasts. Adv. Atmos. Sci., 35(7), https://doi.org/10.1007/s00376-017-7179-y.


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