Assessment of Availability and Utilization Potential of Integrated Observation Framework for Air Quality Study over Northeast Asia Region: An OSSE Case Study
Abstract
The integrated use of various atmospheric environmental data sets together can provide the potential to develop and derive a better understanding and explanation of air pollution phenomena. The Observation System Simulation Experiments (OSSE) framework is a useful tool to assess the availability and utilization potential of such system that integrates various sources of observations. As part of the National Strategic Project of Korea for the Fine Particulate Matter (PM), an integrated observation framework (IOF) has been designed to collectively utilize various sources of data sets in Northeast Asia including currently available and or upcoming surface air quality monitoring networks, remote sensing networks, and so forth. Similarly, an OSSE framework has also been established and can be implemented in Northeast Asia. Here, we present the potential contribution of IOF to the PM modeling (or forecasting) system in Northeast Asia which were quantitatively explored by using the OSSE framework. Couple of scenarios including the expansion of surface monitoring network and the utilization of Geostationary Environment Spectrometer (GEMS) were applied to perform the OSSE. Emphasis was given to investigation of the potential benefit of the IOF in terms of providing an integrated data set for the improvement of PM forecasting in Northeast Asia.
<Acknowledgements> This research was supported by the National Strategic Project-Fine Particle of the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT(MSIT), the Ministry of Environment(ME), and the Ministry of Health and Welfare(MOHW).(NRF-2017M3D8A1092021).- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.A31M2877K
- Keywords:
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- 3315 Data assimilation;
- ATMOSPHERIC PROCESSES;
- 3360 Remote sensing;
- ATMOSPHERIC PROCESSES;
- 0520 Data analysis: algorithms and implementation;
- COMPUTATIONAL GEOPHYSICS;
- 1855 Remote sensing;
- HYDROLOGY