首页|大气环境监测卫星星载IPDA激光雷达的大气二氧化碳柱浓度反演及多源数据对比验证

大气环境监测卫星星载IPDA激光雷达的大气二氧化碳柱浓度反演及多源数据对比验证

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基于星载积分路径差分吸收(IPDA)激光雷达探测数据对全球二氧化碳柱浓度(XCO2)进行反演并利用全球CO2柱浓度观测网(TCCON)、轨道碳观测者2号(OCO-2)卫星以及碳追踪器三者的CO2数据产品验证了反演结果的准确性。结果表明:IPDA激光雷达的反演结果与TCCON的观测结果一致性较好,两者的平均偏差为0。3×10-6,决定系数(R2)为0。952,均方根误差(RMSE)为0。584×10-6。与OCO-2和CT的XCO2数据产品的对比结果也显示出IPDA激光雷达反演结果与TCCON数据产品的吻合程度均高于OCO-2卫星与碳追踪器,进一步表明IPDA激光雷达能够获得更准确的全球XCO2观测数据。IPDA激光雷达对于不同地表类型上空的XCO2探测精度存在一定差异,在空间分辨率为50 km(陆地)和100 km(海洋)下,其探测精度主要分布在(0。80~0。82)× 10-6与(0。76~0。78)× 10-6,均满足1× 10-6的探测精度要求。星载IPDA激光雷达的全天时、高精度探测技术将在今后全球碳源碳汇研究中发挥重要作用。
Inversion and Validation of Atmospheric CO2 Column Concentration Inversion of Spaceborne IPDA Lidar Based on Atmospheric Environment Monitoring Satellite
Objective Carbon dioxide(CO2),a prevalent greenhouse gas,affects the radiation and energy budget of the earth-atmospheric system.The continuous increase in CO2 concentration has intensified the greenhouse effect globally.Accurate monitoring of CO2 concentration is crucial for studying the carbon cycle and greenhouse effect.Traditional ground-based atmospheric CO2 detection approaches,although highly accurate and reliable,lack consistent monitoring approach and the capacity to detect on a large-scale,worldwide,or regional basis.Remote sensing retrieval methods based on satellite platforms can provide long-term CO2 observation data globally.Nevertheless,passive remote sensing satellites were used for greenhouse gas space-based observation platforms in the past.Because of solar light source limitations,passive satellites can not be used to detect during night time as well as in polar regions.On 16 April,2022,the spaceborne Integrated Path Differential Absorption(IPDA)lidar was successfully launched with the Atmospheric Environment Monitoring Satellite(DQ-1).A year ago,the IPDA lidar has been operating to achieve full day carbon dioxide column concentration(XCO2)observations globally with high precision and accuracy.As clouds and aerosols cause potential errors using satellite detection of near surface CO2,it is essential to verify the accuracy of XCO2 data products acquired by the satellites.We conduct the verification and application of data based on IPDA lidar observations.The analysis results demonstrate crucial data support for researchers to track carbon sources and sinks by fully describing that IPDA lidar can track variations in anthropogenic CO2 emissions over time and space and meets the 1× 10-6 precision design criterion.Methods In combination with the European Center for Medium-Range Weather Forecasts(ECMWFs)atmospheric reanalysis dataset(ERA5)and the latest version(2020 version)of HITRAN data,XCO2 was obtained through inversion of IPDA lidar observation data.We validate the inversion results using data products from the Total Carbon Column Observing Network(TCCON),the Orbiting Carbon Observatory-2(OCO-2)satellite,and the CarbonTracker.Additionally,we demonstrate the detection accuracy of IPDA lidar at different resolutions.Results and Discussions The comparison between XCO2 and TCCON data,obtained through the inversion of IPDA lidar observation data,shows a high level of fitness described in terms of an R2 value of 0.952,and the root mean square error(RMSE)value of 0.584X10-6(Fig.3).When compared to OCO-2 satellite and CarbonTracker mode,the data of IPDA lidar and TCCON exhibit a higher degree of consistency(Figs.4 and 7),indicating that IPDA lidar provides more precise and accurate global XCO2 observations.Furthermore,analysis of detection accuracy at a spatial resolution of 50 km over land and 100 km over the ocean reveals that IPDA lidar meets the 1×10-6(Fig.9)accuracy requirement.Thus,IPDA lidar can support research on carbon sources and sinks with high accuracy.Conclusions To verify the observation performance of the spaceborne IPDA lidar,we use data products from TCCON sites passed by the IPDA lidar to validate its inversion results.The results show that the inversion data align well with TCCON data,exhibiting an average deviation of 0.3X 10-6,a strong correlation with an R2 value of 0.952,and a root mean square error(RMSE)of 0.584× 10-6.To comprehensively assess accuracy of IPDA lidar data,the XCO2 inversion results were compared with data products from the Orbiting Carbon Observatory-2(OCO-2)satellite and CarbonTracker.The results indicate that the findings of IPDA lidar correspond more closely with the TCCON data products than those from OCO-2 and CarbonTracker,demonstrating the ability of the IPDA lidar to deliver more accurate global XCO2 data.Additionally,we analyze global XCO2 observations from June to September 2022 at different spatial resolutions.The data indicate a clear seasonal and latitudinal variation,with global XCO2 values gradually decreasing from June to August,reaching a minimum in August,and then increasing in September.This trend is closely related to changes in global vegetation cover,population density distribution,and other factors.There are also significant differences between land and ocean areas and in regions of intense emissions.In the analysis of detection accuracy of IPDA lidar,a single-pass averaging method was employed,with spatial resolutions of 50 km over land and 100 km over the ocean.The detection accuracy for land ranged from 0.80 × 10-6 to 0.82× 10-6,and for the ocean,it ranged from 0.76× 10-6 to 0.78× 10-6,both meeting the required 1×10-6 detection accuracy.The spaceborne IPDA lidar possesses unique advantages of high spatial and temporal resolution with high detection accuracy,enabling precise monitoring of ground carbon sources.In conclusion,the spaceborne IPDA lidar provides significant data support for the study of carbon sources and sinks.

atmospheric carbon dioxideatmospheric environment monitoring satellitespaceborne lidarintegrated path differential absorptionvalidation

赖锴婕、卜令兵、王勤、毛志华、Khalid Muhammad Burhan、樊纯璨、刘继桥、陈卫标、赵少华

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南京信息工程大学大气物理学院,江苏 南京 210044

天津气象雷达研究试验中心,天津 300061

中国科学院上海光学精密机械研究所空间激光信息传输与探测技术重点实验室,上海 201800

生态环境部卫星环境应用中心,北京 100094

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大气二氧化碳 大气环境监测卫星 星载激光雷达 积分路径差分吸收 验证

国家自然科学基金上海航天科技创新基金

42175145SAST2022-039

2024

光学学报
中国光学学会 中国科学院上海光学精密机械研究所

光学学报

CSTPCD北大核心
影响因子:1.931
ISSN:0253-2239
年,卷(期):2024.44(12)