scholarly journals Systematic detection of local CH<sub>4</sub> anomalies by combining satellite measurements with high-resolution forecasts

2021 ◽  
Vol 21 (6) ◽  
pp. 5117-5136
Author(s):  
Jérôme Barré ◽  
Ilse Aben ◽  
Anna Agustí-Panareda ◽  
Gianpaolo Balsamo ◽  
Nicolas Bousserez ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Observations ◽  
Anthropogenic Emissions ◽  
Synoptic Scale ◽  
Satellite Measurements ◽  
Satellite Retrieval ◽  
Quantitative Manner ◽  
Ch4 Concentration ◽  
Monitoring Service ◽  
Total Column

Abstract. In this study, we present a novel monitoring methodology that combines satellite retrievals and forecasts to detect local CH4 concentration anomalies worldwide. These anomalies are caused by rapidly changing anthropogenic emissions that significantly contribute to the CH4 atmospheric budget and by biases in the satellite retrieval data. The method uses high-resolution (7 km × 7 km) retrievals of total column CH4 from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite. Observations are combined with high-resolution CH4 forecasts (∼ 9 km) produced by the Copernicus Atmosphere Monitoring Service (CAMS) to provide departures (observations minus forecasts) at close to the satellite's native resolution at appropriate time. Investigating these departures is an effective way to link satellite measurements and emission inventory data in a quantitative manner. We perform filtering on the departures to remove the synoptic-scale and meso-alpha-scale biases in both forecasts and satellite observations. We then apply a simple classification scheme to the filtered departures to detect anomalies and plumes that are missing (e.g. pipeline or facility leaks), underreported or overreported (e.g. depleted drilling fields) in the CAMS emissions. The classification method also shows some limitations to detect emission anomalies only due to local satellite retrieval biases linked to albedo and scattering issues.

scholarly journals Systematic detection of local CH<sub>4</sub> emissions anomalies combining satellite measurements and high-resolution forecasts

2020 ◽  
Author(s):  
Jérôme Barré ◽  
Ilse Aben ◽  
Anna Agustí-Panareda ◽  
Gianpaolo Balsamo ◽  
Nicolas Bousserez ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Satellite Observations ◽  
Satellite Measurements ◽  
Satellite Retrieval ◽  
Quantitative Manner ◽  
Ch4 Concentration ◽  
Monitoring Service ◽  
Total Column

Abstract. In this study we present a novel monitoring methodology to detect local CH4 concentration anomalies worldwide that are related to rapidly changing anthropogenic emissions that significantly contribute to the CH4 atmospheric budget. The method uses high resolution (7 km × 7 km) retrievals of total column CH4 from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel 5 Precursor satellite. Observations are combined with high resolution CH4 forecasts (~ 9 km) produced by the Copernicus Atmosphere Monitoring Service (CAMS) to provide departures (observations minus forecasts) close to the native satellite resolution at appropriate time. Investigating the departures is an effective way to link satellite measurements and emission inventory data in a quantitative manner. We perform filtering on the departures to remove the large-scale biases on both forecasts and satellite observations. We then use a simple classification on the filtered departures to detect anomalies and plumes coming from CAMS emissions that are missing (e.g. pipeline or facility leaks), under-reported or over-reported (e.g. depleted drilling fields). Additionally, the classification helps to detect local satellite retrieval errors due to land surface albedo issues.

scholarly journals The STRatospheric Estimation Algorithm from Mainz (STREAM): estimating stratospheric NO<sub>2</sub> from nadir-viewing satellites by weighted convolution

2016 ◽  
Vol 9 (7) ◽  
pp. 2753-2779 ◽  
Author(s):  
Steffen Beirle ◽  
Christoph Hörmann ◽  
Patrick Jöckel ◽  
Song Liu ◽  
Marloes Penning de Vries ◽  
...  
Keyword(s):  
Input Data ◽  
Polar Vortex ◽  
Chemical Transport ◽  
Estimation Algorithm ◽  
Satellite Observations ◽  
Satellite Measurements ◽  
Transport Models ◽  
Verification Algorithm ◽  
Chemical Transport Models ◽  
Total Column

Abstract. The STRatospheric Estimation Algorithm from Mainz (STREAM) determines stratospheric columns of NO2 which are needed for the retrieval of tropospheric columns from satellite observations. It is based on the total column measurements over clean, remote regions as well as over clouded scenes where the tropospheric column is effectively shielded. The contribution of individual satellite measurements to the stratospheric estimate is controlled by various weighting factors. STREAM is a flexible and robust algorithm and does not require input from chemical transport models. It was developed as a verification algorithm for the upcoming satellite instrument TROPOMI, as a complement to the operational stratospheric correction based on data assimilation. STREAM was successfully applied to the UV/vis satellite instruments GOME 1/2, SCIAMACHY, and OMI. It overcomes some of the artifacts of previous algorithms, as it is capable of reproducing gradients of stratospheric NO2, e.g., related to the polar vortex, and reduces interpolation errors over continents. Based on synthetic input data, the uncertainty of STREAM was quantified as about 0.1–0.2 × 1015 molecules cm−2, in accordance with the typical deviations between stratospheric estimates from different algorithms compared in this study.

scholarly journals A method for colocating satellite <i>X</i><sub>CO<sub>2</sub></sub> data to ground-based data and its application to ACOS-GOSAT and TCCON

2014 ◽  
Vol 7 (8) ◽  
pp. 2631-2644 ◽  
Author(s):  
H. Nguyen ◽  
G. Osterman ◽  
D. Wunch ◽  
C. O'Dell ◽  
L. Mandrake ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Weighted Average ◽  
Satellite Observations ◽  
Total Carbon ◽  
Satellite Measurements ◽  
Temporal Window ◽  
Squared Error ◽  
Scanning Imaging ◽  
Total Column ◽  
Absorption Spectrometer

Abstract. Satellite measurements are often compared with higher-precision ground-based measurements as part of validation efforts. The satellite soundings are rarely perfectly coincident in space and time with the ground-based measurements, so a colocation methodology is needed to aggregate "nearby" soundings into what the instrument would have seen at the location and time of interest. We are particularly interested in validation efforts for satellite-retrieved total column carbon dioxide (XCO2), where XCO2 data from Greenhouse Gas Observing Satellite (GOSAT) retrievals (ACOS, NIES, RemoteC, PPDF, etc.) or SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) are often colocated and compared to ground-based column XCO2 measurement from Total Carbon Column Observing Network (TCCON). Current colocation methodologies for comparing satellite measurements of total column dry-air mole fractions of CO2 (XCO2) with ground-based measurements typically involve locating and averaging the satellite measurements within a latitudinal, longitudinal, and temporal window. We examine a geostatistical colocation methodology that takes a weighted average of satellite observations depending on the "distance" of each observation from a ground-based location of interest. The "distance" function that we use is a modified Euclidian distance with respect to latitude, longitude, time, and midtropospheric temperature at 700 hPa. We apply this methodology to XCO2 retrieved from GOSAT spectra by the ACOS team, cross-validate the results to TCCON XCO2 ground-based data, and present some comparisons between our methodology and standard existing colocation methods showing that, in general, geostatistical colocation produces smaller mean-squared error.

scholarly journals Decadal record of satellite carbon monoxide observations

2013 ◽  
Vol 13 (2) ◽  
pp. 837-850 ◽  
Author(s):  
H. M. Worden ◽  
M. N. Deeter ◽  
C. Frankenberg ◽  
M. George ◽  
F. Nichitiu ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Eastern China ◽  
The United States ◽  
Satellite Observations ◽  
Anthropogenic Emissions ◽  
Fire Emissions ◽  
Time Period ◽  
Eastern Usa ◽  
Atmospheric Carbon Monoxide ◽  
Total Column

Abstract. Atmospheric carbon monoxide (CO) distributions are controlled by anthropogenic emissions, biomass burning, transport and oxidation by reaction with the hydroxyl radical (OH). Quantifying trends in CO is therefore important for understanding changes related to all of these contributions. Here we present a comprehensive record of satellite observations from 2000 through 2011 of total column CO using the available measurements from nadir-viewing thermal infrared instruments: MOPITT, AIRS, TES and IASI. We examine trends for CO in the Northern and Southern Hemispheres along with regional trends for Eastern China, Eastern USA, Europe and India. We find that all the satellite observations are consistent with a modest decreasing trend ~ −1 % yr−1 in total column CO over the Northern Hemisphere for this time period and a less significant, but still decreasing trend in the Southern Hemisphere. Although decreasing trends in the United States and Europe have been observed from surface CO measurements, we also find a decrease in CO over E. China that, to our knowledge, has not been reported previously. Some of the interannual variability in the observations can be explained by global fire emissions, but the overall decrease needs further study to understand the implications for changes in anthropogenic emissions.

scholarly journals Decadal record of satellite carbon monoxide observations

2012 ◽  
Vol 12 (9) ◽  
pp. 25703-25741
Author(s):  
H. M. Worden ◽  
M. N. Deeter ◽  
C. Frankenberg ◽  
M. George ◽  
F. Nichitiu ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Eastern China ◽  
The United States ◽  
Satellite Observations ◽  
Anthropogenic Emissions ◽  
Fire Emissions ◽  
Time Period ◽  
Eastern Usa ◽  
Atmospheric Carbon Monoxide ◽  
Total Column

Abstract. Atmospheric carbon monoxide (CO) distributions are controlled by anthropogenic emissions, biomass burning, transport and oxidation by reaction with the hydroxyl radical (OH). Quantifying trends in CO is therefore important for understanding changes related to all of these contributions. Here we present a comprehensive record of satellite observations from 2000 through 2011 of total column CO using the available measurements from nadir-viewing thermal infrared instruments: MOPITT, AIRS, TES and IASI. We examine trends for CO in the Northern and Southern Hemispheres along with regional trends for Eastern China, Eastern USA, Europe and India. We find that all the satellite observations are consistent with a modest decreasing trend ∼−1% yr−1 in total column CO over the Northern Hemisphere for this time period and a less significant, but still decreasing trend in the Southern Hemisphere. Although decreasing trends in the United States and Europe have been observed from surface CO measurements, we also find a decrease in CO over E. China that, to our knowledge, has not been reported previously. Some of the interannual variability in the observations can be explained by global fire emissions, but the overall decrease needs further study to understand the implications for changes in anthropogenic emissions.

Validating the Validation: The Influence of Liquid Water Distribution in Clouds on the Intercomparison of Satellite and Surface Observations

2009 ◽  
Vol 26 (8) ◽  
pp. 1457-1474 ◽  
Author(s):  
N. A. J. Schutgens ◽  
R. A. Roebeling
Keyword(s):  
High Resolution ◽  
Total Error ◽  
Satellite Observations ◽  
Field Of View ◽  
Track Length ◽  
Large Field ◽  
Northern Europe ◽  
Random Errors ◽  
Plane Parallel ◽  
Satellite Retrieval

Abstract The intercomparison of LWP retrievals from observations by a geostationary satellite imager [Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG)] and a ground-based microwave (MW) radiometer is examined in the context of the inhomogeneity of overcast cloudy skies. Although the influence of cloud inhomogeneity on satellite observations has received much attention, relatively little is known about its impact on validation studies. Given SEVIRI’s large field of view (3 km × 6 km for northern Europe), especially when compared to the narrow width of the radiometer tracks (100–200 m), cloud inhomogeneity may be expected to significantly affect the satellite retrieval validation. This paper quantifies the various validation uncertainties resulting from cloud inhomogeneities and proposes an approach to minimize these uncertainties. The study is performed by simulating both satellite and ground-based observations through resampling a set of high-resolution (100 m) cloud fields that are derived from 1 km × 1 km Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The authors’ technique for generating realistic high-resolution LWP fields preserves the information present in the original observations and creates extra LWP variation at smaller-length scales by considering clouds as simple fractals. The authors believe that this is a new technique for creating high-resolution LWP fields. Validation errors resulting from cloud inhomogeneity can be classified in two groups. The first group relates entirely to the retrieval process for satellite observations and includes the well-known plane-parallel bias as well as field-of-view mismatches between different channels used in the retrieval. The second group relates to differences in the scene observed by satellite and ground-based sensors. This includes systematic shifts in the observed scene resulting from viewing conditions (parallax effect), offsets between satellite images and ground sites, and different fields of view. Results indicate that the plane-parallel bias for the authors’ sample of 604 clouds has a median value of −3.3 g m−2. All other error contributions appear to be random and have no biases. For individual observations, the parallax effect easily dominates the total error budget for sites that are observed under large viewing angles (e.g., northern Europe). The authors show that this error may be partly compensated by using information about cloud-top heights and by spatially interpolating among an array of SEVIRI pixels to obtain the best estimate of the satellite-retrieved LWP value over the ground site. Optimal intercomparison of satellite and ground-based observations is also possible by matching the track length of the ground observations to the imager’s pixel size in the wind direction. Thus, one surprising conclusion is that the LWP errors resulting from the second group (scene differences) are significantly larger than those resulting from the first group (satellite retrieval), even after corrections have been applied. Smaller satellite pixels do not alleviate the problem but rather aggravate it, unless the parallax error is corrected. Temporal or spatial averages of observations may be used to reduce the random errors, but the statistical properties of such aggregates are, at the moment, not obvious for reasons that will be discussed. Calibration errors are not considered in the present study.

scholarly journals 2010–2015 North American methane emissions, sectoral contributions, and trends: a high-resolution inversion of GOSAT satellite observations of atmospheric methane

2020 ◽  
Author(s):  
Joannes D. Maasakkers ◽  
Daniel J. Jacob ◽  
Melissa P. Sulprizio ◽  
Tia R. Scarpelli ◽  
Hannah Nesser ◽  
...  
Keyword(s):  
High Resolution ◽  
Analytical Solution ◽  
Oil Production ◽  
Methane Emissions ◽  
Gas Production ◽  
Satellite Observations ◽  
Anthropogenic Emissions ◽  
Atmospheric Methane ◽  
Oil Gas ◽  
Gosat Satellite

Abstract. We use 2010–2015 GOSAT satellite observations of atmospheric methane columns over North America in a high- resolution inversion of methane emissions, including contributions from different sectors and long-term trends. The inversion involves analytical solution to the Bayesian optimization problem for a Gaussian mixture model (GMM) of the emission field with up to 0.5° × 0.625° resolution in concentrated source regions. Analytical solution provides a closed-form characterization of the information content from the inversion and facilitates the construction of a large ensemble of solutions exploring the effect of different uncertainties and assumptions. Prior estimates for the inversion include a gridded version of the EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks (GHGI) and the WetCHARTS model ensemble for wetlands. Our best estimate for mean 2010–2015 US anthropogenic emissions is 30.6 (range: 29.4–31.3) Tg a-1, slightly higher than the gridded EPA inventory (28.7 (26.4–36.2) Tg a-1). The main discrepancy is for the oil and gas production sectors where we find higher emissions than the GHGI by 35 % and 22 % respectively. The most recent version of the EPA GHGI revises downward its estimate of emissions from oil production and we find that these are a factor 2 lower than our estimate. Our best estimate of US wetland emissions is 10.2 (5.6–11.1) Tg a-1, on the low end of the prior WetCHARTS inventory uncertainty range (14.2 (3.3–32.4) Tg a-1) and calling for better understanding of these emissions. We find an increasing trend in US anthropogenic emissions over 2010–2015 of 0.4 % a-1, lower than previous GOSAT-based estimates but opposite to the decrease reported by the EPA GHGI. Most of this increase appears driven by unconventional oil/gas production in the eastern US. We also find that oil/gas production emissions in Mexico are higher than in the nationally reported inventory, though there is evidence for a 2010–2015 decrease in emissions from offshore oil production.

scholarly journals Estimation of NO<sub>x</sub> emissions from Delhi using Car MAX-DOAS observations and comparison with OMI satellite data

2011 ◽  
Vol 11 (21) ◽  
pp. 10871-10887 ◽  
Author(s):  
R. Shaiganfar ◽  
S. Beirle ◽  
M. Sharma ◽  
A. Chauhan ◽  
R. P. Singh ◽  
...  
Keyword(s):  
Emission Inventory ◽  
A Priori ◽  
Satellite Observations ◽  
Emission Sources ◽  
Nox Emissions ◽  
Satellite Measurements ◽  
Strong Emission ◽  
Satellite Retrieval ◽  
Tropospheric No2 ◽  
Good Agreement

Abstract. We present the first Multi-Axis-(MAX-) DOAS observations in India performed during April 2010 and January 2011 in Delhi and nearby regions. The MAX-DOAS instrument was mounted on a car roof, which allowed us to perform measurements along individual driving routes. From car MAX-DOAS observations along closed circles around Delhi, together with information on wind speed and direction, the NOx emissions from the greater Delhi area were determined: our estimate of 4.4 × 1025 molecules s−1 is found to be slightly lower than the corresponding emission estimates using the EDGAR emission inventory and substantially smaller compared to a recent study by Gurjar et al. (2004). We also determined NOx emissions from Delhi using OMI satellite observations on the same days. These emissions are slightly smaller than those from the car MAX-DOAS measurements. Finally the car MAX-DOAS observations were also used for the validation of simultaneous OMI satellite measurements of the tropospheric NO2 VCD and found a good agreement of the spatial patterns. Concerning the absolute values, OMI data are, on average, higher than the car MAX-DOAS observations close to strong emission sources, and vice versa over less polluted regions. Our results indicate that OMI NO2 VCDs are biased low over strongly polluted regions, probably caused by inadequate a-priori profiles used in the OMI satellite retrieval.

scholarly journals The STRatospheric Estimation Algorithm from Mainz (STREAM): Estimating stratospheric NO<sub>2</sub> from nadir-viewing satellites by weighted convolution

2016 ◽  
Author(s):  
S. Beirle ◽  
C. Hörmann ◽  
P. Jöckel ◽  
M. Penning de Vries ◽  
A. Pozzer ◽  
...  
Keyword(s):  
Input Data ◽  
Polar Vortex ◽  
Chemical Transport ◽  
Estimation Algorithm ◽  
Satellite Observations ◽  
Satellite Measurements ◽  
Transport Models ◽  
Verification Algorithm ◽  
Chemical Transport Models ◽  
Total Column

Abstract. Abstract. The STRatospheric Estimation Algorithm from Mainz (STREAM) determines stratospheric columns of NO2 which are needed for the retrieval of tropospheric columns from satellite observations. It is based on the total column measurements over clean, remote regions as well as over clouded scenes where the tropospheric column is effectively shielded. The contribution of individual satellite measurements to the stratospheric estimate is controlled by various weighting factors. STREAM is a flexible and robust algorithm and does not require input from chemical transport models. It was developed as verification algorithm for the upcoming satellite instrument TROPOMI, as complement to the operational stratospheric correction based on data assimilation. STREAM was successfully applied to the UV/vis satellite instruments GOME 1/2, SCIAMACHY, and OMI. It overcomes some of the artefacts of previous algorithms, as it is capable of reproducing gradients of stratospheric NO2, e.g. related to the polar vortex, and reduces interpolation errors over continents. Based on synthetic input data, the uncertainty of STREAM was quantified as about 0.1–0.2 × 1015 molecules cm−2, in accordance to the typical deviations between stratospheric estimates from different algorithms compared in this study.

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