인버스 모델링을 이용한 지표면 이산화탄소 플럭스 추정 향상을 위한항공기 관측 이산화탄소 자료동화 체계 개발
Development of the Aircraft CO2 Measurement Data Assimilation System to Improve the Estimation of Surface CO2 Fluxes Using an Inverse Modeling System
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초록
In order to monitor greenhouse gases including CO2, various types of surface-, aircraft-, and satellite-based measurement projects have been conducted. These data help understand the variations of greenhouse gases and are used in atmospheric inverse modeling systems to simulate surface fluxes for greenhouse gases. CarbonTracker is a system for estimating surface CO2 flux, using an atmospheric inverse modeling method, based on only surface observation data. Because of the insufficient surface observation data available for accurate estimation of the surface CO2 flux, additional observations would be required. In this study, a system that assimilates aircraft CO2 measurement data in CarbonTracker (CT2013B) is developed, and the estimated results from this data assimilation system are evaluated. The aircraft CO2 measurement data used are obtained from the Comprehensive Observation Network for Trace gases by the Airliner (CONTRAIL) project. The developed system includes the preprocessor of the raw observation data, the observation operator, and the ensemble Kalman filter (EnKF) data assimilation process. After preprocessing the raw data, the modeled value corresponding spatially and temporally to each observation is calculated using the observation operator. These modeled values and observations are then averaged in space and time, and used in the EnKF data assimilation process. The modeled values are much closer to the observations and show smaller biases and root-mean-square errors, after the assimilation of the aircraft CO2 measurement data. This system could also be used to assimilate other aircraft CO2 measurement data in CarbonTracker.
Abstract
In order to monitor greenhouse gases including CO2, various types of surface-, aircraft-, and satellite-based measurement projects have been conducted. These data help understand the variations of greenhouse gases and are used in atmospheric inverse modeling systems to simulate surface fluxes for greenhouse gases. CarbonTracker is a system for estimating surface CO2 flux, using an atmospheric inverse modeling method, based on only surface observation data. Because of the insufficient surface observation data available for accurate estimation of the surface CO2 flux, additional observations would be required. In this study, a system that assimilates aircraft CO2 measurement data in CarbonTracker (CT2013B) is developed, and the estimated results from this data assimilation system are evaluated. The aircraft CO2 measurement data used are obtained from the Comprehensive Observation Network for Trace gases by the Airliner (CONTRAIL) project. The developed system includes the preprocessor of the raw observation data, the observation operator, and the ensemble Kalman filter (EnKF) data assimilation process. After preprocessing the raw data, the modeled value corresponding spatially and temporally to each observation is calculated using the observation operator. These modeled values and observations are then averaged in space and time, and used in the EnKF data assimilation process. The modeled values are much closer to the observations and show smaller biases and root-mean-square errors, after the assimilation of the aircraft CO2 measurement data. This system could also be used to assimilate other aircraft CO2 measurement data in CarbonTracker.
참고문헌 (33)
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