scholarly journals Influence of under-sampled a priori data on tropospheric NO<sub>2</sub> satellite retrievals

2011 ◽  
Vol 4 (2) ◽  
pp. 1893-1934 ◽  
Author(s):  
A. Heckel ◽  
S.-W. Kim ◽  
G. J. Frost ◽  
A. Richter ◽  
M. Trainer ◽  
...  
Keyword(s):  
High Resolution ◽  
San Francisco ◽  
Power Plants ◽  
Transport Model ◽  
A Priori ◽  
Satellite Observations ◽  
Chemical Transport Model ◽  
Under Sampling ◽  
Global Chemical Transport Model ◽  
Priori Information

Abstract. The retrieval of tropospheric columns of NO2 and other trace gases from satellite observations of backscattered solar radiation relies on the use of accurate a priori information. The spatial resolution of current space sensors is often significantly higher than that of the a priori datasets used, introducing uncertainties from spatial misrepresentation. In this study, the effect of spatial under-sampling of a priori data on the retrieval of NO2 columns was studied for a typical coastal area (around San Francisco). High-resolution (15 × 15 km2) NO2 a priori data from the WRF-Chem model in combination with high-resolution MODIS surface reflectance and aerosol data were used to investigate the uncertainty introduced by applying a priori data at typical global chemical transport model resolution. The results show that the relative uncertainties can be large (more than a factor of 2) for individual measurements, mainly due to spatial variations in NO2 profile and surface albedo, with smaller contributions from aerosols and surface height changes. Similar sensitivities are expected for other coastal regions and localised sources such as power plants, highlighting the need for high-resolution a priori data in quantitative analysis of the spatial patterns retrieved from satellite observations of tropospheric pollution.

scholarly journals Influence of low spatial resolution a priori data on tropospheric NO<sub>2</sub> satellite retrievals

2011 ◽  
Vol 4 (9) ◽  
pp. 1805-1820 ◽  
Author(s):  
A. Heckel ◽  
S.-W. Kim ◽  
G. J. Frost ◽  
A. Richter ◽  
M. Trainer ◽  
...  
Keyword(s):  
High Resolution ◽  
San Francisco ◽  
Spatial Resolution ◽  
Power Plants ◽  
Transport Model ◽  
A Priori ◽  
Satellite Observations ◽  
Chemical Transport Model ◽  
Under Sampling ◽  
Priori Information

Abstract. The retrieval of tropospheric columns of NO2 and other trace gases from satellite observations of backscattered solar radiation relies on the use of accurate a priori information. The spatial resolution of current space sensors is often significantly higher than that of the a priori datasets used, introducing uncertainties from spatial misrepresentation. In this study, the effect of spatial under-sampling of a priori data on the retrieval of NO2 columns was studied for a typical coastal area (around San Francisco). High-resolution (15 × 15 km2) NO2 a priori data from the WRF-Chem model in combination with high-resolution MODIS surface reflectance and aerosol data were used to investigate the uncertainty introduced by applying a priori data at typical global chemical transport model resolution. The results show that the relative uncertainties can be large (more than a factor of 2 if all a priori data used is at the coarsest resolution) for individual measurements, mainly due to spatial variations in NO2 profile and surface albedo, with smaller contributions from aerosols and surface height changes. Similar sensitivities are expected for other coastal regions and localised sources such as power plants, highlighting the need for high-resolution a priori data in quantitative analysis of the spatial patterns retrieved from satellite observations of tropospheric pollution.

scholarly journals Potential sources of a priori ozone profiles for TEMPO tropospheric ozone retrievals

2018 ◽  
Author(s):  
Matthew S. Johnson ◽  
Xiong Liu ◽  
Peter Zoogman ◽  
John Sullivan ◽  
Michael J. Newchurch ◽  
...  
Keyword(s):  
Tropospheric Ozone ◽  
Transport Model ◽  
A Priori ◽  
Chemical Transport Model ◽  
Diurnal Variability ◽  
Earth Observing System ◽  
Potential Sources ◽  
Tropospheric O3 ◽  
Priori Information ◽  
Lidar Observations

Abstract. Potential sources of a priori ozone (O3) profiles for use in Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite tropospheric O3 retrievals are evaluated with observations from multiple Tropospheric Ozone Lidar Network (TOLNet) systems in North America. An O3 profile climatology (tropopause-based O3 climatology (TB-Clim), currently proposed for use in TEMPO O3 retrieval algorithms) based on ozonesonde observations and O3 profiles from 3 separate models (operational Goddard Earth Observing System (GEOS-5) Forward Processing (FP) product, reanalysis product from Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), and the GEOS-Chem chemical transport model (CTM)) were: 1) evaluated with TOLNet measurements on various temporal scales (seasonally, daily, hourly) and 2) implemented as a priori information in theoretical TEMPO tropospheric O3 retrievals in order to determine how each a priori impacts the accuracy of retrieved tropospheric (0–10 km) and lowermost tropospheric (LMT, 0–2 km) O3 columns. We found that all potential sources of a priori profiles evaluated in this study generally reproduced the vertical structure of summer-averaged observations of O3 profiles. However, larger differences between the a priori profiles and lidar observations were observed when evaluating inter-daily and diurnal variability of tropospheric O3. The TB-Clim O3 profile climatology was unable to replicate observed inter-daily and diurnal variability of O3 while model products, in particular GEOS-Chem simulations, displayed more skill in reproducing these features. Due to the ability of models, primarily the CTM used in this study, on average to capture the inter-daily and diurnal variability of tropospheric and LMT O3 columns, using a priori profiles from these model simulations resulted in TEMPO retrievals with the best statistical comparison with lidar observations. Furthermore, important from an air quality perspective, when high LMT O3 values are observed, using GEOS-Chem a priori profiles resulted in TEMPO LMT O3 retrievals with the least bias.

scholarly journals Global estimates of CO sources with high resolution by adjoint inversion of multiple satellite datasets (MOPITT, AIRS, SCIAMACHY, TES)

2010 ◽  
Vol 10 (3) ◽  
pp. 855-876 ◽  
Author(s):  
M. Kopacz ◽  
D. J. Jacob ◽  
J. A. Fisher ◽  
J. A. Logan ◽  
L. Zhang ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Transport Model ◽  
Large Fraction ◽  
A Priori ◽  
A Posteriori ◽  
Chemical Transport Model ◽  
In Situ Data ◽  
Global Consistency ◽  
Priori Information ◽  
Cold Starts

Abstract. We combine CO column measurements from the MOPITT, AIRS, SCIAMACHY, and TES satellite instruments in a full-year (May 2004–April 2005) global inversion of CO sources at 4°×5° spatial resolution and monthly temporal resolution. The inversion uses the GEOS-Chem chemical transport model (CTM) and its adjoint applied to MOPITT, AIRS, and SCIAMACHY. Observations from TES, surface sites (NOAA/GMD), and aircraft (MOZAIC) are used for evaluation of the a posteriori solution. Using GEOS-Chem as a common intercomparison platform shows global consistency between the different satellite datasets and with the in situ data. Differences can be largely explained by different averaging kernels and a priori information. The global CO emission from combustion as constrained in the inversion is 1350 Tg a−1. This is much higher than current bottom-up emission inventories. A large fraction of the correction results from a seasonal underestimate of CO sources at northern mid-latitudes in winter and suggests a larger-than-expected CO source from vehicle cold starts and residential heating. Implementing this seasonal variation of emissions solves the long-standing problem of models underestimating CO in the northern extratropics in winter-spring. A posteriori emissions also indicate a general underestimation of biomass burning in the GFED2 inventory. However, the tropical biomass burning constraints are not quantitatively consistent across the different datasets.

scholarly journals Estimates of black carbon emissions in the western United States using the GEOS-Chem adjoint model

2015 ◽  
Vol 15 (13) ◽  
pp. 7685-7702 ◽  
Author(s):  
Y. H. Mao ◽  
Q. B. Li ◽  
D. K. Henze ◽  
Z. Jiang ◽  
D. B. A. Jones ◽  
...  
Keyword(s):  
United States ◽  
Black Carbon ◽  
Biomass Burning ◽  
Transport Model ◽  
A Priori ◽  
Western United States ◽  
Sufficient Information ◽  
Chemical Transport Model ◽  
Global Chemical Transport Model ◽  
Substantial Bias

Abstract. We estimate black carbon (BC) emissions in the western United States for July–September 2006 by inverting surface BC concentrations from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network using a global chemical transport model (GEOS-Chem) and its adjoint. Our best estimate of the BC emissions is 49.9 Gg at 2° × 2.5° (a factor of 2.1 increase) and 47.3 Gg at 0.5° × 0.667° (1.9 times increase). Model results now capture the observed major fire episodes with substantial bias reductions (~ 35 % at 2° × 2.5° and ~ 15 % at 0.5° × 0.667°). The emissions are ~ 20–50 % larger than those from our earlier analytical inversions (Mao et al., 2014). The discrepancy is especially drastic in the partitioning of anthropogenic versus biomass burning emissions. The August biomass burning BC emissions are 4.6–6.5 Gg and anthropogenic BC emissions 8.6–12.8 Gg, varying with the model resolution, error specifications, and subsets of observations used. On average both anthropogenic and biomass burning emissions in the adjoint inversions increase 2-fold relative to the respective {a priori} emissions, in distinct contrast to the halving of the anthropogenic and tripling of the biomass burning emissions in the analytical inversions. We attribute these discrepancies to the inability of the adjoint inversion system, with limited spatiotemporal coverage of the IMPROVE observations, to effectively distinguish collocated anthropogenic and biomass burning emissions on model grid scales. This calls for concurrent measurements of other tracers of biomass burning and fossil fuel combustion (e.g., carbon monoxide and carbon isotopes). We find that the adjoint inversion system as is has sufficient information content to constrain the total emissions of BC on the model grid scales.

scholarly journals Satellite observations of peroxyacetyl nitrate from the Aura Tropospheric Emission Spectrometer

2014 ◽  
Vol 7 (6) ◽  
pp. 5347-5379 ◽  
Author(s):  
V. H. Payne ◽  
M. J. Alvarado ◽  
K. E. Cady-Pereira ◽  
J. R. Worden ◽  
S. S. Kulawik ◽  
...  
Keyword(s):  
Detection Limit ◽  
Transport Model ◽  
A Priori ◽  
Initial Guess ◽  
Chemical Transport Model ◽  
Peroxyacetyl Nitrate ◽  
Cloud Optical Depth ◽  
Spatial Features ◽  
Instrument Noise ◽  
Global Chemical Transport Model

Abstract. We present a description of the algorithm used to retrieve peroxyacetyl nitrate (PAN) concentrations from the Aura Tropospheric Emission Spectrometer (TES). We describe the spectral microwindows, error analysis and the utilization of a priori and initial guess information provided by the GEOS-Chem global chemical transport model. The TES PAN retrievals contain up to one degree of freedom for signal. Estimated single-measurement uncertainties are 30 to 50%. The detection limit for a single TES measurement is dependent on the atmospheric and surface conditions as well as on the instrument noise. For observations where the cloud optical depth is less than 0.5, we find that the TES detection limit for PAN is in the region of 200 to 300 pptv. We show that PAN retrievals over the Northern Hemisphere Pacific in springtime show spatial features that are qualitatively consistent with the expected distribution of PAN in outflow of Asian pollution.

scholarly journals Satellite observations of peroxyacetyl nitrate from the Aura Tropospheric Emission Spectrometer

2014 ◽  
Vol 7 (11) ◽  
pp. 3737-3749 ◽  
Author(s):  
V. H. Payne ◽  
M. J. Alvarado ◽  
K. E. Cady-Pereira ◽  
J. R. Worden ◽  
S. S. Kulawik ◽  
...  
Keyword(s):  
Detection Limit ◽  
Transport Model ◽  
A Priori ◽  
Initial Guess ◽  
Chemical Transport Model ◽  
Peroxyacetyl Nitrate ◽  
Cloud Optical Depth ◽  
Spatial Features ◽  
Instrument Noise ◽  
Global Chemical Transport Model

Abstract. We present a description of the algorithm used to retrieve peroxyacetyl nitrate (PAN) concentrations from the Aura Tropospheric Emission Spectrometer (TES). We describe the spectral microwindows, error analysis, and the utilization of a priori and initial guess information provided by the GEOS-Chem global chemical transport model. The TES PAN retrievals contain up to one degree of freedom for signal. In general, the retrievals are most sensitive to PAN in the mid-troposphere. Estimated single-measurement uncertainties are on the order of 30 to 50%. The detection limit for a single TES measurement is dependent on the atmospheric and surface conditions as well as on the instrument noise. For observations where the cloud optical depth is less than 0.5, we find that the TES detection limit for PAN is in the region of 200 to 300 pptv. We show that PAN retrievals capture plumes associated with boreal burning. Retrievals over the Northern Hemisphere Pacific in springtime show spatial features that are qualitatively consistent with the expected distribution of PAN in outflow of Asian pollution.

scholarly journals Variational estimates of black carbon emissions in the western United States

2014 ◽  
Vol 14 (15) ◽  
pp. 21865-21916 ◽  
Author(s):  
Y. H. Mao ◽  
Q. B. Li ◽  
D. K. Henze ◽  
Z. Jiang ◽  
D. B. A. Jones ◽  
...  
Keyword(s):  
United States ◽  
Black Carbon ◽  
Biomass Burning ◽  
Transport Model ◽  
A Priori ◽  
Western United States ◽  
Sufficient Information ◽  
Chemical Transport Model ◽  
Global Chemical Transport Model ◽  
Substantial Bias

Abstract. We estimate black carbon (BC) emissions in the Western United States (WUS) for July–September 2006 by inverting surface BC concentrations from the Interagency Monitoring of PROtected Visual Environment (IMPROVE) network using a global chemical transport model (GEOS-Chem) and its adjoint. Our best estimate of the BC emissions is 49.9 Gg at 2° × 2.5° (a factor of 2.1 increase) and 47.3 Gg at 0.5° × 0.667° (1.9 times increase). Model results now capture the observed major fire episodes with substantial bias reductions (∼35% at 2° × 2.5° and ∼15% at 0.5° × 0.667°). The emissions are ∼20–50% larger than those from our earlier analytical inversions (Mao et al., 2014). The discrepancy is especially drastic in the partitioning of anthropogenic vs. biomass burning emissions. The August biomass burning BC emissions are 4.6–6.5 Gg and anthropogenic BC emissions 8.6–12.8 Gg, varying with the model resolution, error specifications, and subsets of observations used. On average both increase twofold relative to the respective a priori emissions, in distinct contrast to the halving of the anthropogenic and tripling of the biomass burning emissions in the analytical inversions. We attribute these discrepancies to the inability of the inversion system, with limited spatiotemporal coverage of the IMPROVE observations, to effectively distinguish collocated anthropogenic and biomass burning emissions on model grid scales. This calls for concurrent measurements of other tracers of biomass burning and fossil fuel combustion (e.g., carbon monoxide and carbon isotopes). We find that the inversion system as is has sufficient information content to constrain the total emissions of BC on the model grid scales.

scholarly journals Changes in Power Plant NOx Emissions over Northwest Greece Using a Data Assimilation Technique

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 900
Author(s):  
Ioanna Skoulidou ◽  
Maria-Elissavet Koukouli ◽  
Arjo Segers ◽  
Astrid Manders ◽  
Dimitris Balis ◽  
...  
Keyword(s):  
Power Plant ◽  
Data Assimilation ◽  
Energy Production ◽  
Power Plants ◽  
Transport Model ◽  
A Priori ◽  
Filter Method ◽  
Nox Emissions ◽  
A Posteriori ◽  
Data Assimilation Technique

In this work, we investigate the ability of a data assimilation technique and space-borne observations to quantify and monitor changes in nitrogen oxides (NOx) emissions over Northwestern Greece for the summers of 2018 and 2019. In this region, four lignite-burning power plants are located. The data assimilation technique, based on the Ensemble Kalman Filter method, is employed to combine space-borne atmospheric observations from the high spatial resolution Sentinel-5 Precursor (S5P) Tropospheric Monitoring Instrument (TROPOMI) and simulations using the LOTOS-EUROS Chemical Transport model. The Copernicus Atmosphere Monitoring Service-Regional European emissions (CAMS-REG, version 4.2) inventory based on the year 2015 is used as the a priori emissions in the simulations. Surface measurements of nitrogen dioxide (NO2) from air quality stations operating in the region are compared with the model surface NO2 output using either the a priori (base run) or the a posteriori (assimilated run) NOx emissions. Relative to the a priori emissions, the assimilation suggests a strong decrease in concentrations for the station located near the largest power plant, by 80% in 2019 and by 67% in 2018. Concerning the estimated annual a posteriori NOx emissions, it was found that, for the pixels hosting the two largest power plants, the assimilated run results in emissions decreased by ~40–50% for 2018 compared to 2015, whereas a larger decrease, of ~70% for both power plants, was found for 2019, after assimilating the space-born observations. For the same power plants, the European Pollutant Release and Transfer Register (E-PRTR) reports decreased emissions in 2018 and 2019 compared to 2015 (−35% and −38% in 2018, −62% and −72% in 2019), in good agreement with the estimated emissions. We further compare the a posteriori emissions to the reported energy production of the power plants during the summer of 2018 and 2019. Mean decreases of about −35% and−63% in NOx emissions are estimated for the two larger power plants in summer of 2018 and 2019, respectively, which are supported by similar decreases in the reported energy production of the power plants (~−30% and −70%, respectively).

scholarly journals Extending methane profiles from aircraft into the stratosphere for satellite total column validation using the ECMWF C-IFS and TOMCAT/SLIMCAT 3-D model

2017 ◽  
Vol 17 (11) ◽  
pp. 6663-6678 ◽  
Author(s):  
Shreeya Verma ◽  
Julia Marshall ◽  
Mark Parrington ◽  
Anna Agustí-Panareda ◽  
Sebastien Massart ◽  
...  
Keyword(s):  
Greenhouse Gases ◽  
Transport Model ◽  
A Priori ◽  
Atmospheric Composition ◽  
Random Errors ◽  
Chemical Transport Model ◽  
Spatial Coverage ◽  
Forecasting System ◽  
The Impact ◽  
Total Column

Abstract. Airborne observations of greenhouse gases are a very useful reference for validation of satellite-based column-averaged dry air mole fraction data. However, since the aircraft data are available only up to about 9–13 km altitude, these profiles do not fully represent the depth of the atmosphere observed by satellites and therefore need to be extended synthetically into the stratosphere. In the near future, observations of CO2 and CH4 made from passenger aircraft are expected to be available through the In-Service Aircraft for a Global Observing System (IAGOS) project. In this study, we analyse three different data sources that are available for the stratospheric extension of aircraft profiles by comparing the error introduced by each of them into the total column and provide recommendations regarding the best approach. First, we analyse CH4 fields from two different models of atmospheric composition – the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System for Composition (C-IFS) and the TOMCAT/SLIMCAT 3-D chemical transport model. Secondly, we consider scenarios that simulate the effect of using CH4 climatologies such as those based on balloons or satellite limb soundings. Thirdly, we assess the impact of using a priori profiles used in the satellite retrievals for the stratospheric part of the total column. We find that the models considered in this study have a better estimation of the stratospheric CH4 as compared to the climatology-based data and the satellite a priori profiles. Both the C-IFS and TOMCAT models have a bias of about −9 ppb at the locations where tropospheric vertical profiles will be measured by IAGOS. The C-IFS model, however, has a lower random error (6.5 ppb) than TOMCAT (12.8 ppb). These values are well within the minimum desired accuracy and precision of satellite total column XCH4 retrievals (10 and 34 ppb, respectively). In comparison, the a priori profile from the University of Leicester Greenhouse Gases Observing Satellite (GOSAT) Proxy XCH4 retrieval and climatology-based data introduce larger random errors in the total column, being limited in spatial coverage and temporal variability. Furthermore, we find that the bias in the models varies with latitude and season. Therefore, applying appropriate bias correction to the model fields before using them for profile extension is expected to further decrease the error contributed by the stratospheric part of the profile to the total column.

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