Search

Your search keyword '"Warneke T."' showing total 575 results
Start Over Author "Warneke T."
575 results on '"Warneke T."'

2. Bias corrections of GOSAT SWIR XCO2 and XCH4 with TCCON data and their evaluation using aircraft measurement data

Author

Inoue, M, Morino, I, Uchino, O, Nakatsuru, T, Yoshida, Y, Yokota, T, Wunch, D, Wennberg, PO, Roehl, CM, Griffith, DWT, Velazco, VA, Deutscher, NM, Warneke, T, Notholt, J, Robinson, J, Sherlock, V, Hase, F, Blumenstock, T, Rettinger, M, Sussmann, R, Kyrö, E, Kivi, R, Shiomi, K, Kawakami, S, De Mazière, M, Arnold, SG, Feist, DG, Barrow, EA, Barney, J, Dubey, M, Schneider, M, Iraci, LT, Podolske, JR, Hillyard, PW, Machida, T, Sawa, Y, Tsuboi, K, Matsueda, H, Sweeney, C, Tans, PP, Andrews, AE, Biraud, SC, Fukuyama, Y, Pittman, JV, Kort, EA, and Tanaka, T

Subjects
Meteorology & Atmospheric Sciences, Atmospheric Sciences
Abstract

We describe a method for removing systematic biases of column-averaged dry air mole fractions of CO2 (XCO2) and CH4 (XCH4) derived from short-wavelength infrared (SWIR) spectra of the Greenhouse gases Observing SATellite (GOSAT). We conduct correlation analyses between the GOSAT biases and simultaneously retrieved auxiliary parameters. We use these correlations to bias correct the GOSAT data, removing these spurious correlations. Data from the Total Carbon Column Observing Network (TCCON) were used as reference values for this regression analysis. To evaluate the effectiveness of this correction method, the tnzuncorrected/corrected GOSAT data were compared to independent XCO2 and XCH4 data derived from aircraft measurements taken for the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project, the National Oceanic and Atmospheric Administration (NOAA), the US Department of Energy (DOE), the National Institute for Environmental Studies (NIES), the Japan Meteorological Agency (JMA), the HIAPER Pole-to-Pole observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. These comparisons demonstrate that the empirically derived bias correction improves the agreement between GOSAT XCO2/XCH4 and the aircraft data. Finally, we present spatial distributions and temporal variations of the derived GOSAT biases.

Published
2016

3. The covariation of Northern Hemisphere summertime CO2 with surface temperature in boreal regions

Author

Wunch, D., Wennberg, P. O, Messerschmidt, J., Parazoo, N. C, Toon, G. C, Deutscher, N. M, Keppel-Aleks, G., Roehl, C. M, Randerson, J. T, Warneke, T., and Notholt, J.

Subjects
annual variation, atmospheric transport, biosphere, carbon dioxide, Northern Hemisphere, summer, surface temperature
Abstract

We observe significant interannual variability in the strength of the seasonal cycle drawdown in northern midlatitudes from measurements of CO2 made by the Total Carbon Column Observing Network (TCCON) and the Greenhouse Gases Observing Satellite (GOSAT). This variability correlates with surface temperature in the boreal regions. Using TCCON measurements, we find that the slope of the relationship between the XCO2 seasonal cycle minima and boreal surface temperature is 1.2 ± 0.7 ppm K−1. Assimilations from CarbonTracker 2011 and CO2 simulations using the Simple Biosphere exchange Model (SiB) transported by GEOS-Chem underestimate this covariation. Both atmospheric transport and biospheric activity contribute to the observed covariation.

Published
2013

5. Global satellite observations of column-averaged carbon dioxide and methane: The GHG-CCI XCO2 and XCH4 CRDP3 data set

Author

Buchwitz, M., Reuter, M., Schneising, O., Hewson, W., Detmers, R.G., Boesch, H., Hasekamp, O.P., Aben, I., Bovensmann, H., Burrows, J.P., Butz, A., Chevallier, F., Dils, B., Frankenberg, C., Heymann, J., Lichtenberg, G., De Mazière, M., Notholt, J., Parker, R., Warneke, T., Zehner, C., Griffith, D.W.T., Deutscher, N.M., Kuze, A., Suto, H., and Wunch, D.

Published
2017
Full Text
View/download PDF

6. The Greenhouse Gas Climate Change Initiative (GHG-CCI): Comparison and quality assessment of near-surface-sensitive satellite-derived CO2 and CH4 global data sets

Author

Buchwitz, M., Reuter, M., Schneising, O., Boesch, H., Guerlet, S., Dils, B., Aben, I., Armante, R., Bergamaschi, P., Blumenstock, T., Bovensmann, H., Brunner, D., Buchmann, B., Burrows, J.P., Butz, A., Chédin, A., Chevallier, F., Crevoisier, C.D., Deutscher, N.M., Frankenberg, C., Hase, F., Hasekamp, O.P., Heymann, J., Kaminski, T., Laeng, A., Lichtenberg, G., De Mazière, M., Noël, S., Notholt, J., Orphal, J., Popp, C., Parker, R., Scholze, M., Sussmann, R., Stiller, G.P., Warneke, T., Zehner, C., Bril, A., Crisp, D., Griffith, D.W.T., Kuze, A., O'Dell, C., Oshchepkov, S., Sherlock, V., Suto, H., Wennberg, P., Wunch, D., Yokota, T., and Yoshida, Y.

Published
2015
Full Text
View/download PDF

10. Upgrade verification note for the CAMS near-real time global atmospheric composition service: Evaluation of the e-suite for the CAMS CY48R1 upgrade of 27 June 2023

Author

Eskes, H.J., Tsikerdekis, A., Benedictow, A., Bennouna, Y., Blake, L., Bouarar, I., Errera, Q., Griesfeller, J., Ilic, L., Kapsomenakis, J., Langerock, B., Mortier, A., Pison, I., Pitkänen, M.R.A., Richter, A., Schoenhardt, A., Schulz, M., Thouret, V., Warneke, T., and Zerefos, C.

Published
2023
Full Text
View/download PDF

11. Investigation of spaceborne trace gas products over St Petersburg and Yekaterinburg, Russia, by using COllaborative Column Carbon Observing Network (COCCON) observations

Author

Alberti, C., Tu, Q., Hase, F., Makarova, M. V., Gribanov, K., Foka, S. C., Zakharov, V., Blumenstock, T., Buchwitz, M., Diekmann, C., Ertl, B., Frey, M. M., Imhasin, H. K., Ionov, D. V., Khosrawi, F., Osipov, S. I., Reuter, M., Schneider, M., Warneke, T., Alberti, C., Tu, Q., Hase, F., Makarova, M. V., Gribanov, K., Foka, S. C., Zakharov, V., Blumenstock, T., Buchwitz, M., Diekmann, C., Ertl, B., Frey, M. M., Imhasin, H. K., Ionov, D. V., Khosrawi, F., Osipov, S. I., Reuter, M., Schneider, M., and Warneke, T.

Abstract

This work employs ground- and space-based observations, together with model data, to study columnar abundances of atmospheric trace gases (XH2O, XCO2, XCH4 and XCO) in two high-latitude Russian cities, St. Petersburg and Yekaterinburg. Two portable COllaborative Column Carbon Observing Network (COCCON) spectrometers were used for continuous measurements at these locations during 2019 and 2020. Additionally, a subset of data of special interest (a strong gradient in XCH4 and XCO was detected) collected in the framework of a mobile city campaign performed in 2019 using both instruments is investigated. All studied satellite products (TROPOMI, OCO-2, GOSAT, MUSICA IASI) show generally good agreement with COCCON observations. Satellite and ground-based observations at high latitudes are much sparser than at low or mid latitudes, which makes direct coincident comparisons between remote-sensing observations more difficult. Therefore, a method of scaling continuous Copernicus Atmosphere Monitoring Service (CAMS) model data to the ground-based observations is developed and used for creating virtual COCCON observations. These adjusted CAMS data are then used for satellite validation, showing good agreement in both Peterhof and Yekaterinburg. The gradients between the two study sites ( "Xgas) are similar between CAMS and CAMS-COCCON datasets, indicating that the model gradients are in agreement with the gradients observed by COCCON. This is further supported by a few simultaneous COCCON and satellite "Xgas measurements, which also agree with the model gradient. With respect to the city campaign observations recorded in St Petersburg, the downwind COCCON station measured obvious enhancements for both XCH4 (10.6ĝ€¯ppb) and XCO (9.5ĝ€¯ppb), which is nicely reflected by TROPOMI observations, which detect city-scale gradients of the order 9.4ĝ€¯ppb for XCH4 and 12.5ĝ€¯ppb for XCO. Copyright: © 2022 Carlos Alberti et al.

Published
2022

15. Validation report of the CAMS near-real-time global atmospheric composition service: Period December 2021 – February 2022

Author

Arola, A., Basart, S., Benedictow, A., Bennouna, Y., Blechschmidt, A.-M., Bouarar, I., Cuevas, E., Errera, Q., Eskes, H.J., Griesfeller, J., Kapsomenakis, J., Langerock, B., Mortier, A., Pison, I., Pitkänen, M., Ramonet, M., Richter, A., Schulz, M., Tarniewicz, J., Thouret, V., Tsikerdekis, A., Warneke, T., and Zerefos, C.

Published
2022
Full Text
View/download PDF

16. Validation report of the CAMS near-real-time global atmospheric composition service: Period June – August 2021

Author

Basart, S., Benedictow, Anna M.K., Bennouna, Y., Blechschmidt, Anne M., Chabrillat, S., Christophe, Y., Cuevas, E., El-Yazidi, A., Flentje, H., Fritzsche, P., Hansen, Kaj Mantzius, Im, Ulas, Kapsomenakis, J., Langerock, B., Richter, A., Thouret, V., Wagner, Annette, Warneke, T., Zerefos, C., Ramonet, Michel, Sudarchikova, N., Schulz, M, Errera, Q., and Eskes, H.

Published
2021

17. Validation report of the CAMS near-real-time global atmospheric composition service:Period March – May 2021

Author

Basart, S., Benedictow, Anna M.K., Bennouna, Y., Blechschmidt, Anne M., Chabrillat, S., Christophe, Y., Cuevas, E., El-Yazidi, A., Flentje, H., Fritzsche, P., Hansen, Kaj Mantzius, Im, Ulas, Kapsomenakis, J., Langerock, B., Richter, A., Thouret, V., Wagner, Annette, Warneke, T., Zerefos, C., Errera, Q., Ramonet, Michel, Sudarchikova, N., Schulz, M, and Eskes, H.

Published
2021

19. Validation report of the CAMS near-real-time global atmospheric composition service:Period December 2020 – February 2021

Author

Schulz, M., Errera, Q., Ramonet, M., Sudarchikova, N., Eskes, H.J., Basart, S., Benedictow, A., Bennouna, Y., Blechschmidt, A.-M., Chabrillat, S., Christophe, Y., Cuevas, E., El-Yazidi, A., Flentje, H., Fritzsche, P., Hansen, K.M., Im, U., Kapsomenakis, J., Langerock, B., Richter, A., Thouret, V., Wagner, A., Warneke, T., Zerefos, C., Schulz, M, Errera, Q., Ramonet, Michel, Sudarchikova, N., and Eskes, H.

Published
2021

22. Validation report of the CAMS near-real-time global atmospheric composition service:Period September - November 2020

Author

Basart, S., Benedictow, Anna M.K., Bennouna, Y., Blechschmidt, Anne M., Chabrillat, S., Christophe, Y., Cuevas, E., El-Yazidi, A., Flentje, H., Fritzsche, P., Hansen, Kaj Mantzius, Im, Ulas, Kapsomenakis, J., Langerock, B., Richter, A., Thouret, V., Wagner, Annette, Warneke, T., Zerefos, C., Sudarchikova, N., Schulz, M., Errera, Q., Ramonet, Michel, and Eskes, H.

Published
2021

24. Validation report of the CAMS near-real-time global atmospheric composition service: Period June – August 2021

Author

Ramonet, M., Sudarchikova, N., Schulz, M., Errera, Q., Eskes, H.J., Basart, S., Benedictow, A., Bennouna, Y., Blechschmidt, A.-M., Chabrillat, S., Christophe, Y., Cuevas, E., El-Yazidi, A., Flentje, H., Fritzsche, P., Hansen, K.M., Im, U., Kapsomenakis, J., Langerock, B., Richter, A., Thouret, V., Wagner, A., Warneke, T., and Zerefos, C.

Published
2021
Full Text
View/download PDF

26. Observation characterisation and validation methods document

Author

Eskes, H.J., Basart, S., Benedictow, A., Bennouna, Y., Blechschmidt, A.-M., Chabrillat, S., Christophe, Y., Cuevas, E., Flentje, H., Hansen, K.M., Kapsomenakis, J., Langerock, B., Ramonet, M., Richter, A., Schulz, M., Sudarchikova, N., Wagner, A., Warneke, T., Zerefos, C., and Eskes, H.

Published
2021
Full Text
View/download PDF

27. Validation report of the CAMS near-real-time global atmospheric composition service: Period March – May 2021

Author

Errera, Q., Ramonet, M., Sudarchikova, N., Schulz, M., Eskes, H.J., Basart, S., Benedictow, A., Bennouna, Y., Blechschmidt, A.-M., Chabrillat, S., Christophe, Y., Cuevas, E., El-Yazidi, A., Flentje, H., Fritzsche, P., Hansen, K.M., Im, U., Kapsomenakis, J., Langerock, B., Richter, A., Thouret, V., Wagner, A., Warneke, T., and Zerefos, C.

Published
2021
Full Text
View/download PDF

29. Validation report of the CAMS near-real-time global atmospheric composition service: Period September – November 2020

Author

Sudarchikova, N., Schulz, M., Errera, Q., Ramonet, M., Eskes, H.J., Basart, S., Benedictow, A., Bennouna, Y., Blechschmidt, A.-M., Chabrillat, S., Christophe, Y., Cuevas, E., El-Yazidi, A., Flentje, H., Fritzsche, P., Hansen, K.M., Im, U., Kapsomenakis, J., Langerock, B., Richter, A., Thouret, V., Wagner, A., Warneke, T., and Zerefos, C.

Published
2021
Full Text
View/download PDF

37. Toward High Precision XCO��� Retrievals From TanSat Observations: Retrieval Improvement and Validation Against TCCON Measurements

Author

Yang, D., Boesch, H., Liu, Y., Somkuti, P., Cai, Z., Chen, X., Di Noia, A., Lin, C., Lu, N., Lyu, D., Parker, R. J., Tian, L., Wang, M., Webb, A., Yao, L., Yin, Z., Zheng, Y., Deutscher, N. M., Griffith, D. W. T., Hase, F., Kivi, R., Morino, I., Notholt, J., Ohyama, H., Pollard, D. F., Shiomi, K., Sussmann, R., Té, Y., Velazco, V. A., Warneke, T., and Wunch, D.

Subjects
Earth sciences, ddc:550
Abstract

TanSat is the 1st Chinese carbon dioxide (CO$_{2}$) measurement satellite, launched in 2016. In this study, the University of Leicester Full Physics (UoL-FP) algorithm is implemented for TanSat nadir mode XCO$_{2}$ retrievals. We develop a spectrum correction method to reduce the retrieval errors by the online fitting of an 8th order Fourier series. The spectrum-correction model and its a priori parameters are developed by analyzing the solar calibration measurement. This correction provides a significant improvement to the O$_{2}$ A band retrieval. Accordingly, we extend the previous TanSat single CO$_{2}$ weak band retrieval to a combined O$_{2}$ A and CO$_{2}$ weak band retrieval. A Genetic Algorithm (GA) has been applied to determine the threshold values of post-screening filters. In total, 18.3% of the retrieved data is identified as high quality compared to the original measurements. The same quality control parameters have been used in a footprint independent multiple linear regression bias correction due to the strong correlation with the XCO$_{2}$ retrieval error. Twenty sites of the Total Column Carbon Observing Network (TCCON) have been selected to validate our new approach for the TanSat XCO$_{2}$ retrieval. We show that our new approach produces a significant improvement on the XCO$_{2}$ retrieval accuracy and precision when compared to TCCON with an average bias and RMSE of ���0.08 ppm and 1.47 ppm, respectively. The methods used in this study can help to improve the XCO$_{2}$ retrieval from TanSat and subsequently the Level-2 data production, and hence will be applied in the TanSat operational XCO$_{2}$ processing.

Published
2020
Full Text
View/download PDF

40. Toward High Precision XCO2Retrievals From TanSat Observations: Retrieval Improvement and Validation Against TCCON Measurements

Author

Yang, D., primary, Boesch, H., additional, Liu, Y., additional, Somkuti, P., additional, Cai, Z., additional, Chen, X., additional, Di Noia, A., additional, Lin, C., additional, Lu, N., additional, Lyu, D., additional, Parker, R. J., additional, Tian, L., additional, Wang, M., additional, Webb, A., additional, Yao, L., additional, Yin, Z., additional, Zheng, Y., additional, Deutscher, N. M., additional, Griffith, D. W. T., additional, Hase, F., additional, Kivi, R., additional, Morino, I., additional, Notholt, J., additional, Ohyama, H., additional, Pollard, D. F., additional, Shiomi, K., additional, Sussmann, R., additional, Té, Y., additional, Velazco, V. A., additional, Warneke, T., additional, and Wunch, D., additional

Published
2020
Full Text
View/download PDF

41. Evaluation of MOPITT version 7 joint TIR-NIR XCO retrievals with TCCON

Author

Hedelius, J., He, T., Jones, D., Buchholz, R., Mazière, M., Deutscher, N., Dubey, M., Feist, D., Griffith, D., Hase, F., Iraci, L., Jeseck, P., Kiel, M., Kivi, R., Liu, C., Morino, I., Notholt, J., Oh, Y., Ohyama, H., Pollard, D., Rettinger, M., Roche, S., Roehl, C., Schneider, M., Shiomi, K., Strong, K., Sussmann, R., Sweeney, C., Té, Y., Uchino, O., Velazco, V., Wang, W., Warneke, T., Wennberg, P., Worden, H., and Wunch, D.

Abstract

Observations of carbon monoxide (CO) from the Measurements Of Pollution In The Troposphere (MOPITT) instrument onboard the Terra spacecraft were expected to have an accuracy of 10 % prior to launch in 1999. Here we evaluate MOPITT version 7 joint TIR-NIR (V7J) accuracy and precision, and suggest ways to further improve the accuracy of the observations. We take five steps involving filtering or bias corrections to reduce scatter and bias in the data relative to other MOPITT soundings, and ground based measurements. 1) We apply a preliminary filtering scheme in which measurements over snow and ice are removed. 2) We find a systematic pairwise bias among the four MOPITT along-track detectors (pixels) on the order of 3–4 ppb with a small temporal trend, which we remove on a global scale using a temporally trended bias correction. 3) Using a small region approximation (SRA) a new filtering scheme is developed and applied based on additional quality indicators such as signal-to-noise. After applying these new filters, the root mean squared error computed using the local median from the SRA over 16 years of global observations decreases from 3.84 ppb to 2.55 ppb. 4) We also use the SRA to find variability in MOPITT retrieval anomalies that relates to retrieval parameters. We apply a bias correction to one parameter from this analysis. 5) After applying the previous bias corrections and filtering, we compare the MOPITT results with the GGG2014 ground-based Total Carbon Column Observing Network (TCCON) observations to obtain an overall global bias correction. These comparisons show that MOPITT V7J is biased high by about 6–8 %, which is similar to past studies using independent validation datasets on V6J. When using TCCON spectrometric column retrievals without the standard airmass correction or scaling to aircraft (WMO scale), the ground and satellite based observations overall agree to better than 0.5 %. GEOS-Chem data assimilations are used to estimate the influence of filtering and scaling to TCCON on global CO, and tend to pull concentrations away from the prior, and closer to the truth. We conclude with suggestions for further improving the MOPITT data products.

Published
2019

48. Emissions of methane in Europe inferred by total column measurements

Author

Wunch, D., Jones, D.B.A., Toon, G.C., Deutscher, N.M., Hase, F., Notholt, J., Sussmann, R., Warneke, T., Kuenen, J.J.P., Denier van der Gon, H.A.C., Fisher, J.A., and Maasakkers, J.D.

Subjects
Urbanisation, Environment, Environment & Sustainability
Abstract

Using five long-running ground-based atmospheric observatories in Europe, we demonstrate the utility of long-term, stationary, ground-based measurements of atmospheric total columns for verifying annual methane emission inventories. Our results indicate that the methane emissions for the region in Europe between Orléans, Bremen, Białystok, and Garmisch-Partenkirchen are overestimated by the state-of-the-art inventories of the Emissions Database for Global Atmospheric Research (EDGAR) v4.2 FT2010 and the high-resolution emissions database developed by the Netherlands Organisation for Applied Scientific Research (TNO) as part of the Monitoring Atmospheric Composition and Climate project (TNO-MACC_III), possibly due to the disaggregation of emissions onto a spatial grid. Uncertainties in the carbon monoxide inventories used to compute the methaneemissionscontributetothediscrepancybetweenour inferred emissions and those from the inventories.

Published
2019

Catalog

Books, media, physical & digital resources
See catalog results