Dongxu Yang

Analysis of XCO 2 retrieval sensitivity using simulated Chinese Carbon Satellite (TanSat) measurements

We present a study on the retrieval sensitivity of the column-averaged dry-air mole fraction of CO2 (XCO2) for the Chinese carbon dioxide observation satellite (TanSat) with a full physical forward model and the optimal estimation technique. The forward model is based on the vector linearized discrete ordinate radiative transfer model (VLIDORT) and considers surface reflectance, gas absorption, and the scattering of air molecules, aerosol particles, and cloud particles. XCO2 retrieval errors from synthetic TanSat measurements show solar zenith angle (SZA), albedo dependence with values varying from 0.3 to 1 ppm for bright land surface in nadir mode and 2 to 8 ppm for dark surfaces like snow. The use of glint mode over dark oceans significantly improves the CO2 information retrieved. The aerosol type and profile are more important than the aerosol optical depth, and underestimation of aerosol plume height will introduce a bias of 1.5 ppm in XCO2. The systematic errors due to radiometric calibration are also estimated using a forward model simulation approach.

Optimization of the instrument configuration for TanSat CO_2 spectrometer

The spectral resolution and spectral sampling rate are critical for the design of hyperspectral spectrometer for CO2 observation satellite. From the original configuration of TanSat spectrometer, it has been found that the spectral sampling rate was too low to keep the high precision of spectral observation in CO2 1.61 μm absorption band, for there were undersampling problems in the CO2 1.61 μm-band. The CO2 dry-air column (XCO2) error due to spectral undersampling could be up to ~1 ppm (1 ppm=1 μL L-1). Reduction of spectral resolution could improve spectral sampling rate with little changes in CO2 retrieval sensitivity. It was also helpful to increase the signal-to-noise ratio of the instrument.

Aerosol Retrieval Sensitivity and Error Analysis for the Cloud and Aerosol Polarimetric Imager on Board TanSat: The Effect of Multi-Angle Measurement

Aerosol scattering is an important source of error in CO2 retrievals from satellite. This paper presents an analysis of aerosol information content from the Cloud and Aerosol Polarimetric Imager (CAPI) onboard the Chinese Carbon Dioxide Observation Satellite (TanSat) to be launched in 2016. Based on optimal estimation theory, aerosol information content is quantified from radiance and polarization observed by CAPI in terms of the degrees of freedom for the signal (DFS). A linearized vector radiative transfer model is used with a linearized Mie code to simulate observation and sensitivity (or Jacobians) with respect to aerosol parameters. In satellite nadir mode, the DFS for aerosol optical depth is the largest, but for mode radius, it is only 0.55. Observation geometry is found to affect aerosol DFS based on the aerosol scattering phase function from the comparison between different viewing zenith angles or solar zenith angles. When TanSat is operated in target mode, we note that multi-angle retrieval represented by three along-track measurements provides additional 0.31 DFS on average, mainly from mode radius. When adding another two measurements, the a posteriori error decreases by another 2%–6%. The correlation coefficients between retrieved parameters show that aerosol is strongly correlated with surface reflectance, but multi-angle retrieval can weaken this correlation.

Monitoring carbon dioxide from space: Retrieval algorithm and flux inversion based on GOSAT data and using CarbonTracker-China

Monitoring atmospheric carbon dioxide (CO2) from space-borne state-of-the-art hyperspectral instruments can provide a high precision global dataset to improve carbon flux estimation and reduce the uncertainty of climate projection. Here, we introduce a carbon flux inversion system for estimating carbon flux with satellite measurements under the support of “The Strategic Priority Research Program of the Chinese Academy of Sciences—Climate Change: Carbon Budget and Relevant Issues”. The carbon flux inversion system is composed of two separate parts: the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing (IAPCAS), and CarbonTracker-China (CT-China), developed at the Chinese Academy of Sciences. The Greenhouse gases Observing SATellite (GOSAT) measurements are used in the carbon flux inversion experiment. To improve the quality of the IAPCAS-GOSAT retrieval, we have developed a post-screening and bias correction method, resulting in 25%–30% of the data remaining after quality control. Based on these data, the seasonal variation of XCO2 (column-averaged CO2 dry-air mole fraction) is studied, and a strong relation with vegetation cover and population is identified. Then, the IAPCAS-GOSAT XCO2 product is used in carbon flux estimation by CT-China. The net ecosystem CO2 exchange is −0.34 Pg C yr−1 (±0.08 Pg C yr−1), with a large error reduction of 84%, which is a significant improvement on the error reduction when compared with in situ-only inversion.摘要基于高光谱分辨率短波红外卫星观测可以获取高精度的全球大气CO2浓度资料, 有效提高碳通量的计算精度, 从而降低温室气体排放在气候变化研究中的不确定性. 本文介绍在中国科学院“应对气候变化的碳收支认证及相关问题”战略性科技先导专项的支持下建立的碳通量计算系统, 实现了从卫星观测到碳通量计算. 该系统由两部分构成: 中国科学院大气物理研究所研发的基于卫星观测的大气CO2浓度反演算法(The Institute of Atmospheric PhysicsCarbon Dioxide Retrieval Algorithm for Satellite Remote Sensing, IAPCAS)和中国科学院地理科学与资源研究所研发的CarbonTracker-China(CT-China)碳同化系统. 本研究使用日本Greenhouse gases Observing SATellite(GOSAT)卫星观测资料进行大气CO2浓度反演和碳通量计算. 为提高IAPCAS反演产品的质量, 研发了一种基于观测参数和反演参数的质量控制方法, 用于数据筛选和偏差订正等反演产品的优化, 最终25%–30%被认为是高质量产品, 可供数据分析和碳通量反演使用. 结合地表覆盖类型和人口密度, 本研究分析了大气CO2浓度的季节变化特征. 使用IAPCAS的反演产品, 应用CT-China开展了中国地区碳通量的计算实验, 结果表明净生态系统CO2交换量为−0.34 Pg C yr−1 (±0.08 Pg C yr−1). 理论上讲, 与仅使用地基观测相比, 使用卫星资料的碳通量计算可以有效降低85%的不确定性.

Monitoring air pollution in China from geostationary satellite: A synthetic study using simulated observations

We simulated geostationary satellite observations to assess the potential for high spatial- and temporal-resolution monitoring of air pollution in China with a focus on tropospheric ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and formaldehyde (HCHO). Based on the capabilities and parameters of the payloads onboard sun-synchronous satellites, we simulated the observed spectrum based on a radiative transfer model using a geostationary satellite model. According to optimal estimation theory, we analyzed the sensitivities and retrieval uncertainties of the main parameters of the instrument for the target trace gases. Considering the retrieval error requirements of each trace gas, we determined the major instrument parameter values (e.g., observation channel, spectral resolution, and signal-to-noise ratio). To evaluate these values, retrieval simulation was performed based on the three-dimensional distribution of the atmospheric components over China using an atmospheric chemical transportation model. As many as 90% of the experiments met the retrieval requirements for all target gases. The retrieval precision of total-column and stratospheric O3 was 2%. In addition, effective retrieval of all trace gases could be achieved at solar zenith angles larger than 70°. Therefore, the geostationary satellite observation and instrument parameters provided herein can be used in air pollution monitoring in China. This study offers a theoretical basis and simulation tool for improving the design of instruments onboard geostationary satellites.

Exploration of the potential for using Spark-I observations to derive atmospheric parameters

ABSTRACT As part of a low-cost nanosatellite constellation, Spark-I, a hyperspectral resolution instrument with 149 channels covering blue to near-infrared, has been successfully launched on 22 December 2016. In this study, we try to explore its potential to derive atmospheric parameters according to optimal estimation theory. From simulated measurements, we estimated the information content described as degrees of freedom for signal (DFS) and error reduction of typical aerosols, including dust, soot, sea salt, and sulphate, as well as water vapour (H2O), nitrogen dioxide (NO2), surface pressure, and Sun-induced fluorescence (SIF). Based on the results, only the H2O column, aerosol optical depth (AOD), and surface albedo could be derived with error reduction of 20%, 40%, and near 100%, respectively. The retrieval of aerosol was strongly correlated with surface albedo, especially dust, with a DFS of 0.3–0.9 due to surface variations. After investigating the impact of the oxygen and H2O absorption bands on the aerosol information content, we recommend retrieving aerosol characteristics using full channels, rather than sub-band channels. The more fraction one type of aerosol is, the larger information about it we can get. Different AOD results in 0.3–0.8 aerosol DFS change and solar zenith angle influences less. The information of atmospheric gases was sensitive to both signal-to-noise ratio (SNR) and spectral resolution due to their particular absorption patterns. However, improving only the SNR by double or more could allow for the derivation of SIF emissions, assuming the a priori estimation is accurate.

First Global Carbon Dioxide Maps Produced from TanSat Measurements

Global warming is a major problem, for which carbon dioxide (CO2) is the main greenhouse gas involved in heating the troposphere. However, the poor availability of global CO2 measurements makes it difficult to estimate CO2 emissions accurately. Satellite measurements would be very helpful for understanding the global CO2 flux distribution if the CO2 column-averaged dry-air mole fraction (XCO2) could be measured with a precision of 1–2 ppm (Baker et al., 2010). The Greenhouse Gases Observing Satellite (GOSAT) (Yokota et al., 2009; Yoshida et al., 2013; Kuze et al., 2014) was launched in 2009, followed by the Orbiting Carbon Observatory 2 (OCO-2) (Eldering et al., 2016; Crisp et al., 2017; Bösch et al., 2011) in 2014. Tansat, a Chinese Earth observation satellite dedicated to monitoring CO2, was launched in December 2016 and is the third satellite capable of monitoring greenhouse gases by hyper-spectral nearinfrared/shortwave infrared (NIR/ SWIR) measurement. The TanSat mission was supported by the Ministry of Science and Technology of China, the Chinese Academy of Sciences, and the China Meteorological Administration. TanSat is an agile, sun-synchronous satellite that operates in three observation modes—namely, the nadir, sun-glint, and target modes. The line of sight tracks the principal plain in nadir mode and the glint in sun-glint mode, which increases the incident signal level and guarantees high performance of the charge-coupled device (Liu et al., 2013a; Cai et al., 2014). The Atmospheric Carbon dioxide Grating Spectroradiometer (ACGS) was designed to measure near-infrared/shortwave infrared backscattered sunlight in the molecular oxygen A-band (0.76 μm) and two CO2 bands (1.61 and 2.06 μm) (Wang et al., 2014; Li et al., 2017; Zhang et al., 2017). The Cloud and Aerosol Polarization Imager (CAPI) measures in ultraviolet, visible, and NIR regions to improve the information on aerosol optical properties and the cloud mask for the CDS measurements (Chen et al., 2017a, 2017b; Wang et al., 2017).