A Reliability Assessment of the NCEP/FNL Reanalysis Data in Depicting Key Meteorological Factors on Clean Days and Polluted Days in Beijing

• Main Authors: Chao Liu, Jianping Guo, Bihui Zhang, Hengde Zhang, Panbo Guan, Ran Xu
• Format: Article
• Language: English
• Published: MDPI AG 2021-04-01
• Series: Atmosphere
• Subjects: NCEP/FNL; conventional meteorological factor; diagnostic physical quantity; clean day; polluted day
• Online Access: https://www.mdpi.com/2073-4433/12/4/481

In this study, based on the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) data, the reliability and performances of their application on clean days and polluted days (based on the PM_2.5 mass concentrations) in Beijing were assessed. Conventional meteorological factors and diagnostic physical quantities from the NCEP/FNL data were compared with the L-band radar observations in Beijing in the autumns and winters of 2017–2019. The results indicate that the prediction reliability of the temperature was the best compared with those of the relative humidity and wind speed. It is worth noting that the relative humidity was lower and the near-surface wind speed was higher on polluted days from the NCEP/FNL data than from the observations. As far as diagnostic physical quantity is concerned, it was revealed that the temperature inversion intensity depicted by the NCEP/FNL data was significantly lower than that from the observations, especially on polluted days. For example, the difference in the temperature inversion intensity between the NCEP/FNL data and the observation ranged from −0.56 to −0.77 °C on polluted days. In addition, the difference in the wind shears between the NCEP/FNL reanalysis data and the observations increased to 0.40 m/s in the lower boundary layer on polluted days compared with that on clean days. Therefore, it is suggested that the underestimation of the relative humidity and temperature inversion intensity, and the overestimation of the near-surface wind speed should be seriously considered in simulating the air quality in the model, particularly on polluted days, which should be focused on more in future model developments.