A high-accuracy surface modeling （HASM） method based on the fimdamental theorem of surfaces, is developed to simulate XCO2 surfaces using the GOSAT retrieval XCO2 data. Two tests are designed to investigate the simu...A high-accuracy surface modeling （HASM） method based on the fimdamental theorem of surfaces, is developed to simulate XCO2 surfaces using the GOSAT retrieval XCO2 data. Two tests are designed to investigate the simulation accuracy. The first test divides the existing satellite retrieval XCO2 data into training points and testing points, and simulates the XCO2 surface using the training points while computing the simulation error using the testing points. The absolute mean error （MAE） of the testing points is 1.189 ppmv, and the corresponding values of the comparison methods, Ordinary Kriging, IDW, and Spline are 1.203, 1.301, and 1.355 ppmv, respectively. The second test simulates the XCO2 surface using all the satellite retrieval points and uses the TCCON （Total Carbon Column Observing Network） site observation values as the ture values. For the six typical TCCON sites, the HASM simulation MAE is 1.688 ppmv, and the satellite retrieval MAE at the same sites is 2.147 ppmv. These results indicate that HASM can successfully simulate XCO2 surfaces based on satellite retrieval data.展开更多
A method to tighten the cloud screening thresholds based on local conditions is used to provide more stringent schemes for Orbiting Carbon Observatory-2(OCO-2)cloud screening algorithms.Cloud screening strategies are ...A method to tighten the cloud screening thresholds based on local conditions is used to provide more stringent schemes for Orbiting Carbon Observatory-2(OCO-2)cloud screening algorithms.Cloud screening strategies are essential to remove scenes with significant cloud and/or aerosol contamination from OCO-2 observations,which helps to save on the data processing cost and ensure high quality retrievals of the column-averaged CO2 dry air mole fraction(XCO2).Based on the radiance measurements in the 0.76μm O2A band,1.61μm(weak),and 2.06μm(strong)CO2 bands,the current combination of the A-Band Preprocessor(ABP)algorithm and Iterative Maximum A Posteriori(IMAP)Differential Optical Absorption Spectroscopy(DOAS)Preprocessor(IDP)algorithm passes around 20%-25%of all soundings,which means that some contaminated scenes also pass the screening process.In this work,three independent pairs of threshold parameters used in the ABP and IDP algorithms are sufficiently tuned until the overall pass rate is close to the monthly clear-sky fraction from the MODIS cloud mask.The tightened thresholds are applied to observations over land surfaces in Europe and Japan in 2016.The results show improvement of agreement and positive predictive value compared to the collocated MODIS cloud mask,especially in summer and fall.In addition,analysis indicates that XCO2 retrievals with more stringent thresholds are in closer agreement with measurements from collocated Total Carbon Column Observing Network(TCCON)sites.展开更多
Atmospheric CO2 concentrations from January 2010 to December 2010 were simulated using the GEOS-Chem(Goddard Earth Observing System-Chemistry) model and the results were compared to satellite Gases Observing Satellite...Atmospheric CO2 concentrations from January 2010 to December 2010 were simulated using the GEOS-Chem(Goddard Earth Observing System-Chemistry) model and the results were compared to satellite Gases Observing Satellite(GOSAT) and ground-based the Total Carbon Column Observing Network(TCCON) data. It was found that CO2 concentrations based on GOSAT satellite retrievals were generally higher than those simulated by GEOS-Chem. The differences over the land area in January and April ranged from 1 to 2 ppm, and there were major differences in June and August. At high latitudes in the Northern Hemisphere in June, as well as south of the Sahara, the difference was greater than 5 ppm. In the high latitudes of the Northern Hemisphere the model results were higher than the GOSAT retrievals, while in South America the satellite data were higher. The trend of the difference in the high latitudes of the Northern Hemisphere and the Saharan region in August was opposite to June. Maximum correlation coefficients were found in April, reaching 0.72, but were smaller in June and August. In January, the correlation coefficient was only 0.36. The comparisons between GEOS-Chem data and TCCON observations showed better results than the comparison between GEOS and GOSAT. The correlation coefficients ranged between 0.42(Darwin) and 0.92(Izana). Analysis of the results indicated that the inconsistency between satellite observations and model simulations depended on inversion errors caused by data inaccuracies of the model simulation's inputs, as well as the mismatch of satellite retrieval model input parameters.展开更多
基金the members of the GOSAT Project (JAXA,NIES and Ministry of the Environment,Japan) for providing GOSAT Level 2 data productssupported by the National High-Tech R&D Program of the Ministry of Science and Technology of China(Grant Nos.2013AA122003,2011AA12A104-3)+5 种基金the Research Projects of Chuzhou University(Grant No.2015QD08)the Key Program of National Natural Science Foundation of China(Grant No. 91325204)the National Fundamental R&D Program of the Ministry of Science and Technology of China(Grant No.2013FY111600-4)the European Commission's Seventh Framework Programme "PANDA "(Grant No. FP7-SPACE-2013-1)the Public Industry-Specific Fund for Meteorology (Grant No.GYHY201106045)the 4th and 5th GOSAT/TANSO Joint Research Project
文摘A high-accuracy surface modeling （HASM） method based on the fimdamental theorem of surfaces, is developed to simulate XCO2 surfaces using the GOSAT retrieval XCO2 data. Two tests are designed to investigate the simulation accuracy. The first test divides the existing satellite retrieval XCO2 data into training points and testing points, and simulates the XCO2 surface using the training points while computing the simulation error using the testing points. The absolute mean error （MAE） of the testing points is 1.189 ppmv, and the corresponding values of the comparison methods, Ordinary Kriging, IDW, and Spline are 1.203, 1.301, and 1.355 ppmv, respectively. The second test simulates the XCO2 surface using all the satellite retrieval points and uses the TCCON （Total Carbon Column Observing Network） site observation values as the ture values. For the six typical TCCON sites, the HASM simulation MAE is 1.688 ppmv, and the satellite retrieval MAE at the same sites is 2.147 ppmv. These results indicate that HASM can successfully simulate XCO2 surfaces based on satellite retrieval data.
基金the National Key Research Program of China(Grant No.2016YFC0200900)the National Natural Science Foundation of China(NSFC)(Grant No.41775023)+1 种基金the Excellent Young Scientists Program of the Zhejiang Provincial Natural Science Foundation of China(Grant No.LR19D050001)the Fundamental Research Funds for the Central Universities,and the State Key Laboratory of Modern Optical Instrumentation Innovation Program.
文摘A method to tighten the cloud screening thresholds based on local conditions is used to provide more stringent schemes for Orbiting Carbon Observatory-2(OCO-2)cloud screening algorithms.Cloud screening strategies are essential to remove scenes with significant cloud and/or aerosol contamination from OCO-2 observations,which helps to save on the data processing cost and ensure high quality retrievals of the column-averaged CO2 dry air mole fraction(XCO2).Based on the radiance measurements in the 0.76μm O2A band,1.61μm(weak),and 2.06μm(strong)CO2 bands,the current combination of the A-Band Preprocessor(ABP)algorithm and Iterative Maximum A Posteriori(IMAP)Differential Optical Absorption Spectroscopy(DOAS)Preprocessor(IDP)algorithm passes around 20%-25%of all soundings,which means that some contaminated scenes also pass the screening process.In this work,three independent pairs of threshold parameters used in the ABP and IDP algorithms are sufficiently tuned until the overall pass rate is close to the monthly clear-sky fraction from the MODIS cloud mask.The tightened thresholds are applied to observations over land surfaces in Europe and Japan in 2016.The results show improvement of agreement and positive predictive value compared to the collocated MODIS cloud mask,especially in summer and fall.In addition,analysis indicates that XCO2 retrievals with more stringent thresholds are in closer agreement with measurements from collocated Total Carbon Column Observing Network(TCCON)sites.
基金supported by the National High Technology Research and Development Program of China (Grant No. 2013AA122002)the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCX2-EW-QN207)the National Basic Research Program of China (Grant Nos. 2010CB428403 and 2009CB421407)
文摘Atmospheric CO2 concentrations from January 2010 to December 2010 were simulated using the GEOS-Chem(Goddard Earth Observing System-Chemistry) model and the results were compared to satellite Gases Observing Satellite(GOSAT) and ground-based the Total Carbon Column Observing Network(TCCON) data. It was found that CO2 concentrations based on GOSAT satellite retrievals were generally higher than those simulated by GEOS-Chem. The differences over the land area in January and April ranged from 1 to 2 ppm, and there were major differences in June and August. At high latitudes in the Northern Hemisphere in June, as well as south of the Sahara, the difference was greater than 5 ppm. In the high latitudes of the Northern Hemisphere the model results were higher than the GOSAT retrievals, while in South America the satellite data were higher. The trend of the difference in the high latitudes of the Northern Hemisphere and the Saharan region in August was opposite to June. Maximum correlation coefficients were found in April, reaching 0.72, but were smaller in June and August. In January, the correlation coefficient was only 0.36. The comparisons between GEOS-Chem data and TCCON observations showed better results than the comparison between GEOS and GOSAT. The correlation coefficients ranged between 0.42(Darwin) and 0.92(Izana). Analysis of the results indicated that the inconsistency between satellite observations and model simulations depended on inversion errors caused by data inaccuracies of the model simulation's inputs, as well as the mismatch of satellite retrieval model input parameters.
