A Study on the Influence of Air Pollution on Temperature Forecast Skill Based on Operational Weather Forecast in BTH Region
Surface air temperature is a comprehensive function of aerosols in the atmosphere and various weather factors. However, there is no real-time aerosol concentration feedback in most operational numerical weather prediction (NWP) models. This raises a scientific question of how abnormal changes in air...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/14/8/1229 |
| _version_ | 1797585547805327360 |
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| author | Ziyin Zhang Yangna Lei Siyu Cheng |
| author_facet | Ziyin Zhang Yangna Lei Siyu Cheng |
| author_sort | Ziyin Zhang |
| collection | DOAJ |
| description | Surface air temperature is a comprehensive function of aerosols in the atmosphere and various weather factors. However, there is no real-time aerosol concentration feedback in most operational numerical weather prediction (NWP) models. This raises a scientific question of how abnormal changes in air pollutants in a short period of time will affect the temperature prediction skill of NWP models. Thus, the study was carried out to investigate the possible influence of air pollution on the temperature forecast skill based on the operational NWP model over the Beijing–Tianjin–Hebei (BTH) region during January–February 2020. The results show that the average concentrations of PM<sub>2.5</sub>, SO<sub>2</sub>, NO<sub>2</sub> and CO over the BTH region in February were smaller than those in January by 38.5%, 35.1%, 48.0% and 33.1%, respectively. Simultaneously, the forecast skills for surface temperature in February from both regional (RMAPS, Rapid-refresh Multi-scale Analysis and Prediction System) and global (ECMWF, European Centre for Medium-Range Weather Forecasts) operational NWP models improved markedly compared with that in January. In both models, the underestimation of maximum temperature and the overestimation of minimum temperature in most cities over the BTH region in February were significantly reduced. With the 24 h (24 h) forecast lead time, the RMSE (root mean square error) of BTH daily mean, maximum and minimum temperature prediction in February based on RMAPS were 17.3%, 9.8% and 21.6% lower than that in January, respectively. These are generally consistent with the other statistical indices such as deviation and regression coefficient. As the forecast lead time extended to 48 h and 72 h forecast, the phenomena still existed and were also evident in the ECMWF model. The improvement of temperature forecast skill of NWP models may be attributed to the unexpected dramatical reduction of air pollutants. Less aerosols during the daytime allow more solar radiation reaching the surface and cause a warming in the near-surface temperature, while less aerosols during the nighttime favor the outgoing long-wave radiation and then lead to a cooling near the ground. |
| first_indexed | 2024-03-11T00:08:43Z |
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| id | doaj.art-a45d828b7ac9495a9a18e5b2f445008f |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-11T00:08:43Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-a45d828b7ac9495a9a18e5b2f445008f2023-11-19T00:12:17ZengMDPI AGAtmosphere2073-44332023-07-01148122910.3390/atmos14081229A Study on the Influence of Air Pollution on Temperature Forecast Skill Based on Operational Weather Forecast in BTH RegionZiyin Zhang0Yangna Lei1Siyu Cheng2Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, ChinaShaanxi Climate Center, Xi’an 710014, ChinaInstitute of Urban Meteorology, China Meteorological Administration, Beijing 100089, ChinaSurface air temperature is a comprehensive function of aerosols in the atmosphere and various weather factors. However, there is no real-time aerosol concentration feedback in most operational numerical weather prediction (NWP) models. This raises a scientific question of how abnormal changes in air pollutants in a short period of time will affect the temperature prediction skill of NWP models. Thus, the study was carried out to investigate the possible influence of air pollution on the temperature forecast skill based on the operational NWP model over the Beijing–Tianjin–Hebei (BTH) region during January–February 2020. The results show that the average concentrations of PM<sub>2.5</sub>, SO<sub>2</sub>, NO<sub>2</sub> and CO over the BTH region in February were smaller than those in January by 38.5%, 35.1%, 48.0% and 33.1%, respectively. Simultaneously, the forecast skills for surface temperature in February from both regional (RMAPS, Rapid-refresh Multi-scale Analysis and Prediction System) and global (ECMWF, European Centre for Medium-Range Weather Forecasts) operational NWP models improved markedly compared with that in January. In both models, the underestimation of maximum temperature and the overestimation of minimum temperature in most cities over the BTH region in February were significantly reduced. With the 24 h (24 h) forecast lead time, the RMSE (root mean square error) of BTH daily mean, maximum and minimum temperature prediction in February based on RMAPS were 17.3%, 9.8% and 21.6% lower than that in January, respectively. These are generally consistent with the other statistical indices such as deviation and regression coefficient. As the forecast lead time extended to 48 h and 72 h forecast, the phenomena still existed and were also evident in the ECMWF model. The improvement of temperature forecast skill of NWP models may be attributed to the unexpected dramatical reduction of air pollutants. Less aerosols during the daytime allow more solar radiation reaching the surface and cause a warming in the near-surface temperature, while less aerosols during the nighttime favor the outgoing long-wave radiation and then lead to a cooling near the ground.https://www.mdpi.com/2073-4433/14/8/1229air pollutionaerosoltemperature predictionNWPBeijing–Tianjin–Hebei |
| spellingShingle | Ziyin Zhang Yangna Lei Siyu Cheng A Study on the Influence of Air Pollution on Temperature Forecast Skill Based on Operational Weather Forecast in BTH Region Atmosphere air pollution aerosol temperature prediction NWP Beijing–Tianjin–Hebei |
| title | A Study on the Influence of Air Pollution on Temperature Forecast Skill Based on Operational Weather Forecast in BTH Region |
| title_full | A Study on the Influence of Air Pollution on Temperature Forecast Skill Based on Operational Weather Forecast in BTH Region |
| title_fullStr | A Study on the Influence of Air Pollution on Temperature Forecast Skill Based on Operational Weather Forecast in BTH Region |
| title_full_unstemmed | A Study on the Influence of Air Pollution on Temperature Forecast Skill Based on Operational Weather Forecast in BTH Region |
| title_short | A Study on the Influence of Air Pollution on Temperature Forecast Skill Based on Operational Weather Forecast in BTH Region |
| title_sort | study on the influence of air pollution on temperature forecast skill based on operational weather forecast in bth region |
| topic | air pollution aerosol temperature prediction NWP Beijing–Tianjin–Hebei |
| url | https://www.mdpi.com/2073-4433/14/8/1229 |
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