Bias Correction of the Ratio of Total Column CH₄ to CO₂ Retrieved from GOSAT Spectra

The proxy method, using the ratio of total column CH₄ to CO₂ to reduce the effects of common biases, has been used to retrieve column-averaged dry-air mole fraction of CH₄ from satellite data. The present study characterizes the remaining scattering effects in the CH₄/CO₄ ratio component of the Greenhouse gases Observing SATellite (GOSAT) retrieval and uses them for bias correction. The variation of bias between the GOSAT and Total Carbon Column Observing Network (TCCON) ratio component with GOSAT data-derived variables was investigated. Then, it was revealed that the variability of the bias could be reduced by using four variables for the bias correction—namely, airmass, 2 μm band radiance normalized with its noise level, the ratio between the partial column-averaged dry-air mole fraction of CH₄ for the lower atmosphere and that for the upper atmosphere, and the difference in surface albedo between the CH₄ and CO₄ bands. The ratio of partial column CH₄ reduced the dependence of bias on the cloud fraction and the difference between hemispheres. In addition to the reduction of bias (from 0.43% to 0%), the precision (standard deviation of the difference between GOSAT and TCCON) was reduced from 0.61% to 0.55% by the correction. The bias and its temporal variation were reduced for each site: the mean and standard deviation of the mean bias for individual seasons were within 0.2% for most of the sites

Bias Correction of the Ratio of Total Column CH4 to CO2 Retrieved from GOSAT Spectra

The proxy method, using the ratio of total column CH4 to CO2 to reduce the effects of common biases, has been used to retrieve column-averaged dry-air mole fraction of CH4 from satellite data. The present study characterizes the remaining scattering effects in the CH4/CO2 ratio component of the Greenhouse gases Observing SATellite (GOSAT) retrieval and uses them for bias correction. The variation of bias between the GOSAT and Total Carbon Column Observing Network (TCCON) ratio component with GOSAT data-derived variables was investigated. Then, it was revealed that the variability of the bias could be reduced by using four variables for the bias correction—namely, airmass, 2 μm band radiance normalized with its noise level, the ratio between the partial column-averaged dry-air mole fraction of CH4 for the lower atmosphere and that for the upper atmosphere, and the difference in surface albedo between the CH4 and CO2 bands. The ratio of partial column CH4 reduced the dependence of bias on the cloud fraction and the difference between hemispheres. In addition to the reduction of bias (from 0.43% to 0%), the precision (standard deviation of the difference between GOSAT and TCCON) was reduced from 0.61% to 0.55% by the correction. The bias and its temporal variation were reduced for each site: the mean and standard deviation of the mean bias for individual seasons were within 0.2% for most of the sites

Experimental Study on the Inverse Estimation of Horizontal Air Temperature Distribution in Built Spaces Using a Ground-Based Thermal Infrared Spectroradiometer

Air temperature is an important physical indicator for urban and architectural environments; however, it is difficult to obtain its distributive characteristics by field measurements owing to the limitations of current measuring instruments. In this context, this study was conducted to demonstrate whether a small and portable ground-based thermal infrared spectroradiometer can be used to estimate the horizontal air temperature distribution in built spaces. For this estimation, we first calculated a forward model using radiative transfer simulations, and the air temperature distribution was inversely estimated from the observed radiance using the model. To regularize the estimated air temperature, we used the maximum a posteriori method, which uses prior information. To verify this estimation method, we conducted measurement experiments in two types of built spaces that had different air temperature distributions within spaces that were approximately 20 m long. Moreover, we conducted a parametric case study on the prior information. As a result, we were able to estimate the air temperature distribution with an average root mean square error (RMSE) of 1.3 °C for all cases when the average RMSE of the prior information for all cases was 2.1 °C. This improvement in the RMSE indicates that this method is able to remotely estimate the horizontal air temperature distribution in built spaces

On correcting the effect of sun-sensor geometry on solar-induced chlorophyll fluorescence measurements: a study using numerical simulation and satellite observation data

identifier:oai:t2r2.star.titech.ac.jp:5068590

Comparison of simplified and Monte Carlo models for simulating the absorbed shortwave radiation by individual trees using LiDAR-derived leaf area density

identifier:oai:t2r2.star.titech.ac.jp:5068591

Experimental Study on the Inverse Estimation of Horizontal Air Temperature Distribution in Built Spaces Using a Ground-Based Thermal Infrared Spectroradiometer

Air temperature is an important physical indicator for urban and architectural environments; however, it is difficult to obtain its distributive characteristics by field measurements owing to the limitations of current measuring instruments. In this context, this study was conducted to demonstrate whether a small and portable ground-based thermal infrared spectroradiometer can be used to estimate the horizontal air temperature distribution in built spaces. For this estimation, we first calculated a forward model using radiative transfer simulations, and the air temperature distribution was inversely estimated from the observed radiance using the model. To regularize the estimated air temperature, we used the maximum a posteriori method, which uses prior information. To verify this estimation method, we conducted measurement experiments in two types of built spaces that had different air temperature distributions within spaces that were approximately 20 m long. Moreover, we conducted a parametric case study on the prior information. As a result, we were able to estimate the air temperature distribution with an average root mean square error (RMSE) of 1.3 °C for all cases when the average RMSE of the prior information for all cases was 2.1 °C. This improvement in the RMSE indicates that this method is able to remotely estimate the horizontal air temperature distribution in built spaces