scholarly journals Retrieving H<sub>2</sub>O/HDO columns over cloudy and clear-sky scenes from the Tropospheric Monitoring Instrument (TROPOMI)

2021 ◽  
Author(s):  
Andreas Schneider ◽  
Tobias Borsdorff ◽  
Joost aan de Brugh ◽  
Alba Lorente ◽  
Franziska Aemisegger ◽  
...  
Keyword(s):  
Added Value ◽  
Total Carbon ◽  
A Posteriori ◽  
Data Set ◽  
Monitoring Instrument ◽  
Clear Sky ◽  
Cloud Parameters ◽  
Cold Air Outbreak ◽  
Low Sensitivity ◽  
Isotope System

Abstract. This paper presents an extension of the scientific HDO/H2O column data product from the Tropospheric Monitoring Instrument (TROPOMI) including clear-sky and cloudy scenes. The retrieval employs a forward model which accounts for scattering, and the algorithm infers the trace gas column information, surface properties and effective cloud parameters from the observations. The extension to cloudy scenes greatly enhances coverage, particularly enabling data over oceans. The data set is validated against co-located ground-based Fourier transform infrared (FTIR) observations by the Total Carbon Column Observing Network (TCCON). The median bias for clear-sky scenes is 1.4 × 1021 molec cm−2 (2.9 %) in H2O columns and 1.1 × 1017 molec cm−2 (−0.3 %) in HDO columns, which corresponds to −17 ‰ (9.9 %) in a posteriori δD. The bias for cloudy scenes is 4.9 × 1021 molec cm−2 (11 %) in H2O, 1.1 × 1017 molec cm−2 (7.9 %) in HDO, and −20 ‰ (9.7 %) in a posteriori δD. At low-altitude stations in low and middle latitudes the bias is small, and has a larger value at high latitude stations. At high altitude stations, an altitude correction is required to compensate for different partial columns seen by the station and the satellite. The bias in a posteriori δD after altitude correction depends on sensitivity due to shielding by clouds, and on realistic prior profile shapes for both isotopologues. Cloudy scenes generally involve low sensitivity below the clouds, and since the information is filled up by the prior, it plays an important role in these cases. Over oceans, aircraft measurements with the Water Isotope System for Precipitation and Entrainment Research (WISPER) instrument from a field campaign in 2018 are used for validation, yielding a bias of −3.9 % in H2O and −3 ‰ in δD over clouds. To demonstrate the added value of the new data set, a short case study of a cold air outbreak over the Atlantic Ocean in January 2020 is presented, showing the daily evolution of the event with single overpass results.

scholarly journals A full-mission data set of H<sub>2</sub>O and HDO columns from SCIAMACHY 2.3 µm reflectance measurements

2018 ◽  
Vol 11 (6) ◽  
pp. 3339-3350 ◽  
Author(s):  
Andreas Schneider ◽  
Tobias Borsdorff ◽  
Joost aan de Brugh ◽  
Haili Hu ◽  
Jochen Landgraf
Keyword(s):  
Light Scattering ◽  
A Posteriori ◽  
Vertical Column ◽  
Data Set ◽  
Average Bias ◽  
Cloud Parameters ◽  
Infrared Measurements ◽  
Mission Period ◽  
Scanning Imaging ◽  
Reflectance Measurements

Abstract. A new data set of vertical column densities of the water vapour isotopologues H2O and HDO from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument for the whole of the mission period from January 2003 to April 2012 is presented. The data are retrieved from reflectance measurements in the spectral range 2339 to 2383 nm with the Shortwave Infrared CO Retrieval (SICOR) algorithm, ignoring atmospheric light scattering in the measurement simulation. The retrievals are validated with ground-based Fourier transform infrared measurements obtained within the Multi-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water (MUSICA) project. A good agreement for low-altitude stations is found with an average bias of −3.6×1021 for H2O and −1.0×1018 molec cm−2 for HDO. The a posteriori computed δD shows an average bias of −8 ‰, even though polar stations have a larger negative bias. The latter is due to the large amount of sensor noise in SCIAMACHY in combination with low albedo and high solar zenith angles. To demonstrate the benefit of accounting for light scattering in the retrieval, the quality of the data product fitting effective cloud parameters simultaneously with trace gas columns is evaluated in a dedicated case study for measurements round high-altitude stations. Due to a large altitude difference between the satellite ground pixel and the mountain station, clear-sky scenes yield a large bias, resulting in a δD bias of 125 ‰. When selecting scenes with optically thick clouds within 1000 m above or below the station altitude, the bias in a posteriori δD is reduced from 125 to 44 ‰. The insights from the present study will also benefit the analysis of the data from the new Sentinel-5 Precursor mission.

scholarly journals A full-mission data set of H<sub>2</sub>O and HDO columns from SCIAMACHY 2.3 μm reflectance measurements

2018 ◽  
Author(s):  
Andreas Schneider ◽  
Tobias Borsdorff ◽  
Joost aan de Brugh ◽  
Haili Hu ◽  
Jochen Landgraf
Keyword(s):  
Light Scattering ◽  
A Posteriori ◽  
Vertical Column ◽  
Data Set ◽  
Average Bias ◽  
Cloud Parameters ◽  
Infrared Measurements ◽  
Mission Period ◽  
Scanning Imaging ◽  
Reflectance Measurements

Abstract. A new data set of vertical column densities of the water vapour isotopologues H₂O and HDO from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) instrument for the whole mission period from January 2003 to April 2012 is presented. The data are retrieved from reflectance measurements in the spectral range 2339 nm to 2383 nm with the Shortwave Infrared CO Retrieval (SICOR) algorithm, ignoring atmospheric light scattering in the measurement simulation. The retrievals are validated with ground-based Fourier transform infrared measurements obtained within the Multi-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water (MUSICA) project. A good agreement for low-altitude stations is found with an average bias of −3.6·1021 molec cm−2 for HO and −1.0·1018molec cm−2 for HDO. The a posteriori computed δD shows an average bias of −8 ‰, even though polar stations have a larger negative bias. The latter is due to large sensor noise of SCIAMACHY in combination with low albedo and high solar zenith angles. To demonstrate the benefit of accounting for light scattering in the retrieval, the quality of the data product fitting effective cloud parameters simultaneously with trace gas columns is evaluated in a dedicated case study for measurements round high altitude stations. Due to a large altitude difference between the satellite ground pixel and the mountain station, clear sky scenes yield a large bias, resulting in a δD bias of 125 ‰. When selecting scenes with optically thick clouds within 1000 m above or below the station altitude, the bias in a posteriori δD is reduced from 125 ‰ to 44 ‰. The insights from the present study will also benefit the analysis of the data from the new Sentinel 5 Precursor mission.

scholarly journals First data set of H<sub>2</sub>O/HDO columns from the Tropospheric Monitoring Instrument (TROPOMI)

2020 ◽  
Vol 13 (1) ◽  
pp. 85-100 ◽  
Author(s):  
Andreas Schneider ◽  
Tobias Borsdorff ◽  
Joost aan de Brugh ◽  
Franziska Aemisegger ◽  
Dietrich G. Feist ◽  
...  
Keyword(s):  
Water Vapour ◽  
Hydrological Cycle ◽  
Short Wave ◽  
Total Carbon ◽  
Atmospheric Water ◽  
Vertical Column ◽  
Data Set ◽  
Monitoring Instrument ◽  
Global Circulation Models ◽  
Blocking Anticyclone

Abstract. Global measurements of atmospheric water vapour isotopologues aid to better understand the hydrological cycle and improve global circulation models. This paper presents a new data set of vertical column densities of H2O and HDO retrieved from short-wave infrared (2.3 µm) reflectance measurements by the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite. TROPOMI features daily global coverage with a spatial resolution of up to 7 km×7 km. The retrieval utilises a profile-scaling approach. The forward model neglects scattering, and strict cloud filtering is therefore necessary. For validation, recent ground-based water vapour isotopologue measurements by the Total Carbon Column Observing Network (TCCON) are employed. A comparison of TCCON δD with ground-based measurements by the Multi-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water (MUSICA) project for data prior to 2014 (where MUSICA data are available) shows a bias in TCCON δD estimates. As TCCON HDO is currently not validated, an overall correction of recent TCCON HDO data is derived based on this finding. The agreement between the corrected TCCON measurements and co-located TROPOMI observations is good with an average bias of (-0.2±3)×1021 molec cm−2 ((1.1±7.2) %) in H2O and (-2±7)×1017 molec cm−2 ((-1.1±7.3) %) in HDO, which corresponds to a mean bias of (-14±17) ‰ in a posteriori δD. The bias is lower at low- and mid-latitude stations and higher at high-latitude stations. The use of the data set is demonstrated with a case study of a blocking anticyclone in northwestern Europe in July 2018 using single-overpass data.

scholarly journals First data set of H<sub>2</sub>O/HDO columns from TROPOMI

2019 ◽  
Author(s):  
Andreas Schneider ◽  
Tobias Borsdorff ◽  
Joost aan de Brugh ◽  
Franziska Aemisegger ◽  
Dietrich G. Feist ◽  
...  
Keyword(s):  
Water Vapour ◽  
Short Wave ◽  
Total Carbon ◽  
A Posteriori ◽  
Vertical Column ◽  
Data Set ◽  
Blocking Anticyclone ◽  
Global Coverage ◽  
Reflectance Measurements

Abstract. This paper presents a new data set of vertical column densities of the water vapour isotopologues H2O and HDO retrieved from short-wave infrared (2.3 μm) reflectance measurements by the Tropospheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite. TROPOMI features daily global coverage with a spatial resolution of up to 7 km × 7 km. The retrieval utilises a profile-scaling approach. The forward model neglects scattering, thus strict cloud filtering is necessary. For validation, recent ground-based water vapour isotopologue measurements by the Total Carbon Column Observing Network (TCCON) are employed. A comparison of TCCON δD with measurements by the project Multi-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water (MUSICA) for data prior to 2014 (where MUSICA data is available) shows a bias in TCCON δD estimates. As TCCON HDO is currently not validated, an overall correction of recent TCCON HDO data is derived based on this finding. The agreement between the corrected TCCON measurements and collocated TROPOMI observations is good with an average bias of (0.02 ± 2) · 1021 molec cm−2 in H2O and (−0.3 ± 7) · 1017 molec cm−2 in HDO, which corresponds to a bias of (−12 ± 17) ‰ in a posteriori δD. The use of the data set is demonstrated with a case study of a blocking anticyclone in northwestern Europe in July 2018 using single overpass data.

scholarly journals Detection of carbon monoxide pollution from cities and wildfires on regional and urban scales: the benefit of CO column retrievals from SCIAMACHY 2.3 µm measurements under cloudy conditions

2018 ◽  
Vol 11 (5) ◽  
pp. 2553-2565 ◽  
Author(s):  
Tobias Borsdorff ◽  
Josip Andrasec ◽  
Joost aan de Brugh ◽  
Haili Hu ◽  
Ilse Aben ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Los Angeles ◽  
Data Set ◽  
Clear Sky ◽  
Data Product ◽  
Cloud Parameters ◽  
Vertical Extension ◽  
Spatial Coverage ◽  
Low Cloud ◽  
High Clouds

Abstract. In the perspective of the upcoming TROPOMI Sentinel-5 Precursor carbon monoxide data product, we discuss the benefit of using CO total column retrievals from cloud-contaminated SCIAMACHY 2.3 µm shortwave infrared spectra to detect atmospheric CO enhancements on regional and urban scales due to emissions from cities and wildfires. The study uses the operational Sentinel-5 Precursor algorithm SICOR, which infers the vertically integrated CO column together with effective cloud parameters. We investigate its capability to detect localized CO enhancements distinguishing between clear-sky observations and observations with low (<  1.5 km) and medium–high clouds (1.5–5 km). As an example, we analyse CO enhancements over the cities Paris, Los Angeles and Tehran as well as the wildfire events in Mexico–Guatemala 2005 and Alaska–Canada 2004. The CO average of the SCIAMACHY full-mission data set of clear-sky observations can detect weak CO enhancements of less than 10 ppb due to air pollution in these cities. For low-cloud conditions, the CO data product performs similarly well. For medium–high clouds, the observations show a reduced CO signal both over Tehran and Los Angeles, while for Paris no significant CO enhancement can be detected. This indicates that information about the vertical distribution of CO can be obtained from the SCIAMACHY measurements. Moreover, for the Mexico–Guatemala fires, the low-cloud CO data captures a strong outflow of CO over the Gulf of Mexico and the Pacific Ocean and so provides complementary information to clear-sky retrievals, which can only be obtained over land. For both burning events, enhanced CO values are even detectable with medium–high-cloud retrievals, confirming a distinct vertical extension of the pollution. The larger number of additional measurements, and hence the better spatial coverage, significantly improve the detection of wildfire pollution using both the clear-sky and cloudy CO retrievals. Due to the improved instrument performance of the TROPOMI instrument with respect to its precursor SCIAMACHY, the upcoming Sentinel-5 Precursor CO data product will allow improved detection of CO emissions and their vertical extension over cities and fires, making new research applications possible.

Retrieving H2O/HDO above clouds using TROPOMI SWIR measurements

2020 ◽  
Author(s):  
Andreas Schneider ◽  
Tobias Borsdorff ◽  
Joost aan de Brugh ◽  
Alba Lorente Delgado ◽  
Jochen Landgraf
Keyword(s):  
Infrared Imaging ◽  
Cloud Model ◽  
Short Wave ◽  
Forward Model ◽  
Total Carbon ◽  
Clear Sky ◽  
Cloud Parameters ◽  
Short Wave Infrared ◽  
The Impact ◽  
Reflectance Measurements

&lt;p&gt;The current scientific H&lt;sub&gt;2&lt;/sub&gt;O/HDO data product from short-wave infrared reflectance measurements by the Tropospheric Monitoring Instrument (TROPOMI) is retrieved using a profile-scaling approach with a forward model which ignores scattering. Since water is too dark in the short-wave infrared, the coverage is limited to clear-sky scenes over land. Clouds are relatively bright in this spectral region, thus retrievals over low clouds will greatly enlarge the coverage. To do so, retrievals using a forward model which accounts for scattering and fitting effective cloud parameters additionally to the trace gases are examined. Inferred effective cloud parameters are compared with measurements by the Visible Infrared Imaging Radiometer Suite (VIIRS) to optimise the cloud model. Furthermore, the impact on the validation of the retrieved H&lt;sub&gt;2&lt;/sub&gt;O/HDO columns with collocated measurements by the Total Carbon Column Observing Network (TCCON) is discussed.&lt;/p&gt;

scholarly journals Detection of carbon monoxide pollution from megacities and wildfires on regional and urban scale: The benefit of CO column retrievals from SCIAMACHY 2.3 μm measurements under cloudy conditions

2017 ◽  
Author(s):  
Tobias Borsdorff ◽  
Josip Andrasec ◽  
Joost aan de Brugh ◽  
Haili Hu ◽  
Ilse Aben ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Los Angeles ◽  
Data Set ◽  
Clear Sky ◽  
Data Product ◽  
Cloud Parameters ◽  
Vertical Extension ◽  
Spatial Coverage ◽  
Low Cloud ◽  
High Clouds

Abstract. In the perspective of the upcoming Sentinel-5 Precursor carbon monoxide data product, we discuss the benefit of CO total column retrievals from cloud contaminated SCIAMACHY 2.3 micron shortwave infrared spectra to detect atmospheric CO enhancements on regional and urban scales due to emissions from megacities and wildfires. The study uses the operational Sentinel-5 Precursor algorithm SICOR, which infers the vertically integrated CO column together with effective cloud parameters. We investigate the capability to detect localized CO enhancements distinguishing between clear-sky observations and observations with low and medium-high clouds. Exemplary, we analyze CO enhancements over the megacities Paris, Los Angeles, and Tehran as well as the wildfire events in Mexico/Guatemala 2005 and Alaska/Canada 2004. The CO average of the SCIAMACHY full mission data set of clear-sky observations can detect weak CO enhancements of less than 10 ppb due to air pollution in these cities. For low cloud conditions, the CO data product performs similarly well. For medium-high clouds, the observations show a reduced CO signal both over Tehran and Los Angeles, while for Paris no significant CO enhancement can be detected. This indicates that information about the vertical distribution of CO can be obtained from the SCIAMACHY measurements. Moreover, for the Mexico/Guatemala fires, the low-cloud CO data captures a strong outflow of CO over the Gulf of Mexico and the Pacific Ocean and so provides complementary information to clear-sky retrievals. For both burning events, enhanced CO values are even detectable with medium-high cloud retrievals, confirming a distinct vertical extension of the pollution. The larger number of additional measurements and hence the better spatial coverage, improves significantly the detection of wild fire pollution using both the clear-sky and cloudy CO retrievals. Due to the improved instrument performance of the TROPOMI instrument with respect to its precursor SCIAMACHY, the upcoming Sentinel-5 Precursor CO data product will allow to detect CO emission and its vertical extension of many more cities and wildfires and so opens new research opportunities.

scholarly journals Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm

2015 ◽  
Vol 8 (2) ◽  
pp. 1787-1832 ◽  
Author(s):  
J. Heymann ◽  
M. Reuter ◽  
M. Hilker ◽  
M. Buchwitz ◽  
O. Schneising ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Detailed Comparison ◽  
Mean Difference ◽  
Total Carbon ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Time Period ◽  
Comparison Results ◽  
The Future

Abstract. Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY onboard ENVISAT (March 2002–April 2012) and TANSO-FTS onboard GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System (IFS). We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between the SCIAMACHY and the GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT-SCIAMACHY (linear correlation coefficient r = 0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT-TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY-TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (±2 h, 10° × 10° around TCCON sites), i.e., the observed air masses are not exactly identical, but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by using also data from other missions (e.g., OCO-2, GOSAT-2, CarbonSat) in the future.

scholarly journals Using airborne HIAPER Pole-to-Pole Observations (HIPPO) to evaluate model and remote sensing estimates of atmospheric carbon dioxide

2016 ◽  
Author(s):  
C. Frankenberg ◽  
S. S. Kulawik ◽  
S. Wofsy ◽  
F. Chevallier ◽  
B. Daube ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Atmospheric Carbon Dioxide ◽  
Correlation Coefficients ◽  
Added Value ◽  
Total Carbon ◽  
Accuracy And Precision ◽  
Atmospheric Carbon ◽  
Carbon Dioxide Co2 ◽  
Global Carbon ◽  
Comparable Quality

Abstract. In recent years, space-borne observations of atmospheric carbon-dioxide (CO2) have become increasingly used in global carbon-cycle studies. In order to obtain added value from space-borne measurements, they have to suffice stringent accuracy and precision requirements, with the latter being less crucial as it can be reduced by just enhanced sample size. Validation of CO2 column averaged dry air mole fractions (XCO2) heavily relies on measurements of the Total Carbon Column Observing Network TCCON. Owing to the sparseness of the network and the requirements imposed on space-based measurements, independent additional validation is highly valuable. Here, we use observations from the HIAPER Pole-to-Pole Observations (HIPPO) flights from January 2009 through September 2011 to validate CO2 measurements from satellites (GOSAT, TES, AIRS) and atmospheric inversion models (CarbonTracker CT2013B, MACC v13r1). We find that the atmospheric models capture the XCO2 variability observed in HIPPO flights very well, with correlation coefficients (r2) of 0.93 and 0.95 for CT2013B and MACC, respectively. Some larger discrepancies can be observed in profile comparisons at higher latitudes, esp. at 300 hPa during the peaks of either carbon uptake or release. These deviations can be up to 4 ppm and hint at misrepresentation of vertical transport. Comparisons with the GOSAT satellite are of comparable quality, with an r2 of 0.85, a mean bias μ of −0.06 ppm and a standard deviation σ of 0.45 ppm. TES exhibits an r2 of 0.75, μ of 0.34 ppm and σ of 1.13 ppm. For AIRS, we find an r2 of 0.37, μ of 1.11 ppm and σ of 1.46 ppm, with latitude-dependent biases. For these comparisons at least 6, 20 and 50 atmospheric soundings have been averaged for GOSAT, TES and AIRS, respectively. Overall, we find that GOSAT soundings over the remote pacific ocean mostly meet the stringent accuracy requirements of about 0.5 ppm for space-based CO2 observations.

scholarly journals VALIDATION OF LIDAR SURVEY DATA BY COMPARISON OF SEVERAL UNCERTAINTY MODELS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1385-1390
Author(s):  
N. Seube
Keyword(s):  
Point Cloud ◽  
A Priori ◽  
A Posteriori ◽  
Data Set ◽  
Timing Errors ◽  
Beam Incidence ◽  
A Priori Uncertainty ◽  
Uncertainty Models ◽  
Attitude Uncertainty ◽  
The Relationship

Abstract. This paper introduce a new method for validating the precision of an airborne or a mobile LiDAR data set. The proposed method is based on the knowledge of an a Combined Standard Measurement Uncertainty (CSMU) model which describes LiDAR point covariance matrix and thus uncertainty ellipsoid. The model we consider includes timing errors and most importantly the incidence of the LiDAR beam. After describing the relationship between the beam incidence and other variable uncertainty (especially attitude uncertainty), we show that we can construct a CSMU model giving the covariance of each oint as a function of the relative geometry between the LiDAR beam and the point normal. The validation method we propose consist in comparing the CSMU model (predictive a priori uncertainty) t the Standard Deviation Alog the Surface Normal (SDASN), for all set of quasi planr segments of the point cloud. Whenever the a posteriori (i.e; observed by the SDASN) level of uncertainty is greater than a priori (i.e; expected) level of uncertainty, the point fails the validation test. We illustrate this approach on a dataset acquired by a Microdrones mdLiDAR1000 system.

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