Tianfeng Chai
University of Maryland, College Park
77 Papers
472 Citations
Tianfeng Chai is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Data assimilation & CMAQ. The author has an hindex of 27, co-authored 71 publications. Previous affiliations of Tianfeng Chai include Air Resources Laboratory & University of Iowa.
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Papers
Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature
TL;DR: In this article, the root mean square error (RMSE) and the mean absolute error (MAE) are used to evaluate model performance and it is shown that the RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian.
Root mean square error (RMSE) or mean absolute error (MAE)
Tianfeng Chai,Roland R. Draxler +1 more
TL;DR: It is demonstrated that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009), and is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian.
Predicting air quality: Improvements through advanced methods to integrate models and measurements
Gregory R. Carmichael,Adrian Sandu,Tianfeng Chai,Dacian N. Daescu,Emil M. Constantinescu,Youhua Tang +5 more
TL;DR: Advances in air quality forecasting are discussed with an emphasis on data assimilation, and applications of the four-dimensional variational method (4D-Var) and the ensemble Kalman filter (EnKF) approach are presented and the computation challenges are discussed.
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Long-term NOx trends over large cities in the United States during the great recession: Comparison of satellite retrievals, ground observations, and emission inventories
Daniel Tong,Daniel Tong,Daniel Tong,Lok N. Lamsal,Lok N. Lamsal,Li Pan,Li Pan,Charles Ding,Charles Ding,Hyun-cheol Kim,Hyun-cheol Kim,Pius Lee,Tianfeng Chai,Tianfeng Chai,Kenneth E. Pickering,Ivanka Stajner +15 more
TL;DR: In this paper, the authors compare multi-year NO x trends derived from satellite and ground observations and uses these data to evaluate the updates of NO x emission data by the US National Air Quality Forecast Capability (NAQFC) for next-day ozone prediction during the 2008 Global Economic Recession.
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Ensemble-based chemical data assimilation. I: General approach
TL;DR: In this article, the performance of the ensemble Kalman filter (EnKF) and compare it with a state-of-the-art 4D-Var approach is analyzed. But the results also point to several issues on which further research is necessary.
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