Constructing a carbon flux estimation system with originally bias corrected satellite data
Abstract
In recent years, many greenhouse gas observation satellites have been launched and operated. The satellite observation has advantages such as its wide observable area and spatial representation close to the model horizontal resolution. On the other hand, satellite observation has a critical issue of bias. This bias varies spatio-temporally. We need to properly evaluate and correct this bias in carbon cycle analysis. Many researchers have attempted to verify the bias of satellite observation data by direct observations. However, numbers of ground observation sites are limited and it is insufficient to evaluate the bias of satellite observation with a vast observable area in detail. We constructed a method to evaluate bias of satellite observation data with independent inverse analysis data (JMA CO2 distributions) which uses no satellite observation data. As we can obtain long-term observation data from 2009 to 2017 from GOSAT, we calculated average bias data of satellite observation data by averaging differences between monthly satellite observation data and independent analysis values in order to extract signal and remove noise. The global average bias of GOSAT observation data (NIES SWIR L2 Ver. 2.8) throughout the whole period was -1.2 ppm which is almost consistent with verification results by ground observations. Looking at the geographical distribution, the bias of the GOSAT observation data showed relatively large seasonal fluctuation at the land area. By using this satellite observation data after this bias correction method for inverse analysis, we can obtain CO2 flux analysis consistent with the existing inverse analysis. Our inversion results suggested that we could strongly constrain tropical and temperate land area by introducing bias corrected satellite data. We should make use of satellite data globally in sub-continental scale carbon cycle analysis.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.A11O2859M
- Keywords:
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- 0322 Constituent sources and sinks;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 0365 Troposphere: composition and chemistry;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 1910 Data assimilation;
- integration and fusion;
- INFORMATICS;
- 3260 Inverse theory;
- MATHEMATICAL GEOPHYSICS