An Observing System Simulation Experiment Framework for Air Quality Forecasts in Northeast Asia: A Case Study Utilizing Virtual Geostationary Environment Monitoring Spectrometer and Surface Monitored Aerosol Data
Prior knowledge of the effectiveness of new observation instruments or new data streams for air quality can contribute significantly to shaping the policy and budget planning related to those instruments and data. In view of this, one of the main purposes of the development and application of the Ob...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/2/389 |
| _version_ | 1827662975366856704 |
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| author | Hyeon-Kook Kim Seunghee Lee Kang-Ho Bae Kwonho Jeon Myong-In Lee Chang-Keun Song |
| author_facet | Hyeon-Kook Kim Seunghee Lee Kang-Ho Bae Kwonho Jeon Myong-In Lee Chang-Keun Song |
| author_sort | Hyeon-Kook Kim |
| collection | DOAJ |
| description | Prior knowledge of the effectiveness of new observation instruments or new data streams for air quality can contribute significantly to shaping the policy and budget planning related to those instruments and data. In view of this, one of the main purposes of the development and application of the Observing System Simulation Experiments (OSSE) is to assess the potential impact of new observations on the quality of the current monitoring or forecasting systems, thereby making this framework valuable. This study introduces the overall OSSE framework established to support air quality forecasting and the details of its individual components. Furthermore, it shows case study results from Northeast Asia and the potential benefits of the new observation data scenarios on the PM<sub>2.5</sub> forecasting skills, including the PM data from 200 virtual monitoring sites in the Gobi Desert and North Korean non-forest areas (NEWPM) and the aerosol optical depths (AOD) data from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS AOD). Performance statistics suggest that the concurrent assimilation of the NEWPM and the PM data from current monitoring sites in China and South Korea can improve the PM<sub>2.5</sub> concentration forecasts in South Korea by 66.4% on average for October 2017 and 95.1% on average for February 2018. Assimilating the GEMS AOD improved the performance of the PM<sub>2.5</sub> forecasts in South Korea for October 2017 by approximately 68.4% (~78.9% for February 2018). This OSSE framework is expected to be continuously implemented to verify its utilization potential for various air quality observation systems and data scenarios. Hopefully, this kind of application result will aid environmental researchers and decision-makers in performing additional in-depth studies for the improvement of PM air quality forecasts. |
| first_indexed | 2024-03-10T00:36:24Z |
| format | Article |
| id | doaj.art-700cf7cc06e2458e945126c36f89c6a6 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T00:36:24Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-700cf7cc06e2458e945126c36f89c6a62023-11-23T15:16:57ZengMDPI AGRemote Sensing2072-42922022-01-0114238910.3390/rs14020389An Observing System Simulation Experiment Framework for Air Quality Forecasts in Northeast Asia: A Case Study Utilizing Virtual Geostationary Environment Monitoring Spectrometer and Surface Monitored Aerosol DataHyeon-Kook Kim0Seunghee Lee1Kang-Ho Bae2Kwonho Jeon3Myong-In Lee4Chang-Keun Song5Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, KoreaDepartment of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, KoreaDepartment of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, KoreaGlobal Environment Research Division, National Institute of Environmental Research, Incheon 22689, KoreaDepartment of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, KoreaDepartment of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, KoreaPrior knowledge of the effectiveness of new observation instruments or new data streams for air quality can contribute significantly to shaping the policy and budget planning related to those instruments and data. In view of this, one of the main purposes of the development and application of the Observing System Simulation Experiments (OSSE) is to assess the potential impact of new observations on the quality of the current monitoring or forecasting systems, thereby making this framework valuable. This study introduces the overall OSSE framework established to support air quality forecasting and the details of its individual components. Furthermore, it shows case study results from Northeast Asia and the potential benefits of the new observation data scenarios on the PM<sub>2.5</sub> forecasting skills, including the PM data from 200 virtual monitoring sites in the Gobi Desert and North Korean non-forest areas (NEWPM) and the aerosol optical depths (AOD) data from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS AOD). Performance statistics suggest that the concurrent assimilation of the NEWPM and the PM data from current monitoring sites in China and South Korea can improve the PM<sub>2.5</sub> concentration forecasts in South Korea by 66.4% on average for October 2017 and 95.1% on average for February 2018. Assimilating the GEMS AOD improved the performance of the PM<sub>2.5</sub> forecasts in South Korea for October 2017 by approximately 68.4% (~78.9% for February 2018). This OSSE framework is expected to be continuously implemented to verify its utilization potential for various air quality observation systems and data scenarios. Hopefully, this kind of application result will aid environmental researchers and decision-makers in performing additional in-depth studies for the improvement of PM air quality forecasts.https://www.mdpi.com/2072-4292/14/2/389Observing System Simulation Experimentssatellite observationsurface observationdata assimilationair quality forecasting |
| spellingShingle | Hyeon-Kook Kim Seunghee Lee Kang-Ho Bae Kwonho Jeon Myong-In Lee Chang-Keun Song An Observing System Simulation Experiment Framework for Air Quality Forecasts in Northeast Asia: A Case Study Utilizing Virtual Geostationary Environment Monitoring Spectrometer and Surface Monitored Aerosol Data Remote Sensing Observing System Simulation Experiments satellite observation surface observation data assimilation air quality forecasting |
| title | An Observing System Simulation Experiment Framework for Air Quality Forecasts in Northeast Asia: A Case Study Utilizing Virtual Geostationary Environment Monitoring Spectrometer and Surface Monitored Aerosol Data |
| title_full | An Observing System Simulation Experiment Framework for Air Quality Forecasts in Northeast Asia: A Case Study Utilizing Virtual Geostationary Environment Monitoring Spectrometer and Surface Monitored Aerosol Data |
| title_fullStr | An Observing System Simulation Experiment Framework for Air Quality Forecasts in Northeast Asia: A Case Study Utilizing Virtual Geostationary Environment Monitoring Spectrometer and Surface Monitored Aerosol Data |
| title_full_unstemmed | An Observing System Simulation Experiment Framework for Air Quality Forecasts in Northeast Asia: A Case Study Utilizing Virtual Geostationary Environment Monitoring Spectrometer and Surface Monitored Aerosol Data |
| title_short | An Observing System Simulation Experiment Framework for Air Quality Forecasts in Northeast Asia: A Case Study Utilizing Virtual Geostationary Environment Monitoring Spectrometer and Surface Monitored Aerosol Data |
| title_sort | observing system simulation experiment framework for air quality forecasts in northeast asia a case study utilizing virtual geostationary environment monitoring spectrometer and surface monitored aerosol data |
| topic | Observing System Simulation Experiments satellite observation surface observation data assimilation air quality forecasting |
| url | https://www.mdpi.com/2072-4292/14/2/389 |
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