Impact of albedo and cloud retrievals on the NO2 tropospheric column derived from Sentinel-5P TROPOMI observations

2020 ◽  
Author(s):  
Henk Eskes ◽  
Maarten Sneep ◽  
Jos van Geffen ◽  
Folkert Boersma ◽  
Ping Wang ◽  
...  
Keyword(s):  
Near Infrared ◽  
Cloud Fraction ◽  
Trace Gas ◽  
Retrieval Algorithm ◽  
Absorption Feature ◽  
Total Uncertainty ◽  
Retrieval Algorithms ◽  
Global Coverage ◽  
Consistent Treatment ◽  
The Impact

<p>Sentinel-5P, with the TROPOMI instrument, was launched in October 2017 and is providing unique high-quality and high-resolution (5 km) observations of trace gas pollutants with a daily global coverage. In our contribution we will discuss the retrieval of nitrogen dioxide (NO2). A major contributions to the total uncertainty of these measurements are the TROPOMI retrievals of cloud fraction and effective cloud pressure (or altitude). Several cloud retrieval algorithms have been implemented, deriving cloud height information from the near-infrared O2-A band, O2-B band or the O2-O2 absorption feature near 477nm. In our presentation we will show the importance of a consistent treatment of clouds and albedo as input for the retrieval radiative transfer calculations. The impact of the different cloud products on the retrieved NO2 is demonstrated for a new implementation of the FRESCO O2-A band cloud retrieval algorithm and an implementation of the O2-O2 retrievals for TROPOMI. A recipe to make optimal use of the available cloud information is presented.</p>

scholarly journals The impact of improved satellite retrievals on estimates of biospheric carbon balance

2020 ◽  
Vol 20 (1) ◽  
pp. 323-331 ◽  
Author(s):  
Scot M. Miller ◽  
Anna M. Michalak
Keyword(s):  
Carbon Balance ◽  
Near Infrared ◽  
Co2 Flux ◽  
Retrieval Algorithm ◽  
Co2 Uptake ◽  
Near Infrared Radiation ◽  
Sources And Sinks ◽  
Satellite Retrievals ◽  
The Impact ◽  
Insight Into

Abstract. The Orbiting Carbon Observatory 2 (OCO-2) is NASA's first satellite dedicated to monitoring CO2 from space and could provide novel insight into CO2 fluxes across the globe. However, one continuing challenge is the development of a robust retrieval algorithm: an estimate of atmospheric CO2 from satellite observations of near-infrared radiation. The OCO-2 retrievals have undergone multiple updates since the satellite's launch, and the retrieval algorithm is now on its ninth version. Some of these retrieval updates, particularly version 8, led to marked changes in the CO2 observations, changes of 0.5 ppm or more. In this study, we evaluate the extent to which current OCO-2 observations can constrain monthly CO2 sources and sinks from the biosphere, and we particularly focus on how this constraint has evolved with improvements to the OCO-2 retrieval algorithm. We find that improvements in the CO2 retrieval are having a potentially transformative effect on satellite-based estimates of the global biospheric carbon balance. The version 7 OCO-2 retrievals formed the basis of early inverse modeling studies using OCO-2 data; these observations are best equipped to constrain the biospheric carbon balance across only continental or hemispheric regions. By contrast, newer versions of the retrieval algorithm yield a far more detailed constraint, and we are able to constrain CO2 budgets for seven global biome-based regions, particularly during the Northern Hemisphere summer when biospheric CO2 uptake is greatest. Improvements to the OCO-2 observations have had the largest impact on glint-mode observations, and we also find the largest improvements in the terrestrial CO2 flux constraint when we include both nadir and glint data.

scholarly journals Nitrogen dioxide observations from the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument: Retrieval algorithm and measurements during DISCOVER-AQ Texas 2013

2016 ◽  
Vol 9 (6) ◽  
pp. 2647-2668 ◽  
Author(s):  
Caroline R. Nowlan ◽  
Xiong Liu ◽  
James W. Leitch ◽  
Kelly Chance ◽  
Gonzalo González Abad ◽  
...  
Keyword(s):  
Air Quality ◽  
Near Infrared ◽  
Infrared Light ◽  
Trace Gas ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Test Bed ◽  
Mixing Ratios ◽  
Sensor Optimization

Abstract. The Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument is a test bed for upcoming air quality satellite instruments that will measure backscattered ultraviolet, visible and near-infrared light from geostationary orbit. GeoTASO flew on the NASA Falcon aircraft in its first intensive field measurement campaign during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) Earth Venture Mission over Houston, Texas, in September 2013. Measurements of backscattered solar radiation between 420 and 465 nm collected on 4 days during the campaign are used to determine slant column amounts of NO2 at 250 m  ×  250 m spatial resolution with a fitting precision of 2.2 × 1015 moleculescm−2. These slant columns are converted to tropospheric NO2 vertical columns using a radiative transfer model and trace gas profiles from the Community Multiscale Air Quality (CMAQ) model. Total column NO2 from GeoTASO is well correlated with ground-based Pandora observations (r = 0.90 on the most polluted and cloud-free day of measurements and r = 0.74 overall), with GeoTASO NO2 slightly higher for the most polluted observations. Surface NO2 mixing ratios inferred from GeoTASO using the CMAQ model show good correlation with NO2 measured in situ at the surface during the campaign (r = 0.85). NO2 slant columns from GeoTASO also agree well with preliminary retrievals from the GEO-CAPE Airborne Simulator (GCAS) which flew on the NASA King Air B200 (r = 0.81, slope = 0.91). Enhanced NO2 is resolvable over areas of traffic NOx emissions and near individual petrochemical facilities.

scholarly journals The Impact of Improved Aerosol Priors on Near-Infrared Measurements of Carbon Dioxide

2018 ◽  
Author(s):  
Robert R. Nelson ◽  
Christopher W. O'Dell
Keyword(s):  
Carbon Dioxide ◽  
Near Infrared ◽  
Carbon Sources ◽  
Total Carbon ◽  
Primary Sources ◽  
Retrieval Algorithm ◽  
Sources And Sinks ◽  
Earth Observing System ◽  
Infrared Measurements ◽  
The Impact

Abstract. The Orbiting Carbon Observatory-2 (OCO-2) was launched in 2014 with the goal of measuring the column-averaged dry-air mole fraction of carbon dioxide (XCO2) with sufficient precision and accuracy to infer regional carbon sources and sinks. One of the primary sources of error in near-infrared measurements of XCO2 is the scattering effects of cloud and aerosol layers. In this work, we study the impact of ingesting intelligent aerosol priors from the Goddard Earth Observing System Model, Version 5 (GEOS-5) into the OCO-2 ACOS V8 retrieval algorithm with the objective of reducing the error in XCO2 from real measurements. Multiple levels of both aerosol setup complexity and uncertainty on the aerosol priors were tested, ranging from a mostly unconstrained aerosol optical depth (AOD) setup to ingesting full aerosol profiles with high confidence. We find that using co-located GEOS-5 aerosol types and AODs with low uncertainty results in a small improvement in the retrieved XCO2 against the Total Carbon Column Observing Network relative to V8. In contrast, attempting to use modeled vertical information in the aerosol prior to improve the XCO2 retrieval generally gives poor results, as aerosol models struggle with the vertical placement of aerosol layers. To assess regional differences in XCO2, we compare our results to a global CO2 model validation suite. We find that the GEOS-5 setup performs better than V8 over Northern Africa and Central Asia, with the standard deviation of the XCO2 error reduced from 2.12 ppm to 1.83 ppm, due to a combination of smaller prior AODs and lower prior uncertainty. In general, the use of more intelligent aerosol priors shows promise but is currently restricted by the accuracy of aerosol models.

scholarly journals High-resolution tropospheric carbon monoxide profiles retrieved from CrIS and TROPOMI

2016 ◽  
Vol 9 (6) ◽  
pp. 2567-2579 ◽  
Author(s):  
Dejian Fu ◽  
Kevin W. Bowman ◽  
Helen M. Worden ◽  
Vijay Natraj ◽  
John R. Worden ◽  
...  
Keyword(s):  
Measurement Uncertainty ◽  
Degrees Of Freedom ◽  
Near Infrared ◽  
Retrieval Algorithm ◽  
Hyper Spectral ◽  
Increased Sensitivity ◽  
Global Coverage ◽  
Cross Track ◽  
Co Observations ◽  
Unique Capability

Abstract. The Measurements of Pollution in the Troposphere (MOPITT) instrument is the only satellite-borne sensor in operation that uses both thermal (TIR) and near-infrared (NIR) channels to estimate CO profiles. With more than 15 years (2000 to present) of validated multispectral observations, MOPITT provides the unique capability to separate CO in the lowermost troposphere (LMT, surface to 3 km (∼ 700 hPa)) from the free-tropospheric abundance. To extend this record, a new, hyper-spectral approach is presented here that will provide CO data products exceeding the capabilities of MOPITT by combining the short-wavelength infrared (SWIR, equivalent to the MOPITT NIR) channels from the TROPOspheric Monitoring Instrument (TROPOMI) to be launched aboard the European Sentinel 5 Precursor (S5p) satellite in 2016 and the TIR channels from the Cross-track Infrared Sounder (CrIS) aboard the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. We apply the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES) retrieval algorithm to quantify the potential of this joint CO product. CO profiles are retrieved from a single-footprint, full-spectral-resolution CrIS transect over Africa on 27–28 August 2013 coincident with significant biomass burning. Comparisons of collocated CrIS and MOPITT CO observations for the LMT show a mean difference of 2.8 ± 24.9 ppb, which is well within the estimated measurement uncertainty of both sensors. The estimated degrees of freedom (DOF) for CO signals from synergistic CrIS–TROPOMI retrievals are approximately 0.9 in the LMT and 1.3 above the LMT, which indicates that the LMT CO can be distinguished from the free troposphere, similar to MOPITT multispectral observations (0.8 in the LMT, and 1.1 above the LMT). In addition to increased sensitivity, the combined retrievals reduce measurement uncertainty, with ∼ 15 % error reduction in the LMT. With a daily global coverage and a combined spatial footprint of 14 km, the joint CrIS–TROPOMI measurements have the potential to extend and improve upon the MOPITT multispectral CO data records for the coming decade.

Impact of 3D cloud structures on tropospheric NO2 column measurements from UV-VIS sounders

2021 ◽  
Author(s):  
Huan Yu ◽  
Arve Kylling ◽  
Claudia Emde ◽  
Bernhard Mayer ◽  
Michel Van Roozendael ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Synthetic Data ◽  
Sensitivity Study ◽  
Trace Gas ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Boundary Layer Clouds ◽  
Cloud Parameters ◽  
The Impact

<p>Operational retrievals of tropospheric trace gases from space-borne spectrometers are made using 1D radiative transfer models. To minimize cloud effects generally only partially cloudy pixels are analysed using simplified cloud contamination treatments based on radiometric cloud fraction estimates and photon path length corrections based on oxygen collision pair (O2-O2) or O2A-absorption band measurements. In reality, however, the impact of clouds can be much more complex, involving unresolved sub-pixel clouds, scattering of clouds in neighbouring pixels, and cloud shadow effects, such that 3D radiation scattering from unresolved boundary layer clouds may give significant biases in the trace gas retrievals. In order to quantify this impact, we use the MYSTIC 3D radiative transfer model to generate synthetic data. The realistic 3D cloud fields, needed for MYSTIC input, are generated by the ICOsahedral Non-hydrostatic (ICON) atmosphere model for a region including Germany, the Netherlands and parts of other surrounding countries. The retrieval algorithm is applied to the synthetic data and comparison to the known input trace gas concentrations yields the retrieval error due to 3D cloud effects. <br>In this study, we study NO2, which is a key tropospheric trace gas measured by TROPOMI and the future atmospheric Sentinels (S4 and S5). The work starts with a sensitivity study for the simulations with a simple 2D box cloud. The influence of cloud parameters (e.g., cloud top height, cloud optical thickness), observation geometry, and spatial resolution are studied, and the most significant dependences of retrieval biases are identified and investigated. Several approaches to correct the NO2 retrieval in the cloud shadow are explored and ultimately applied to both synthetic data with realistic 3D clouds and real observations.</p>

scholarly journals The impact of improved aerosol priors on near-infrared measurements of carbon dioxide

2019 ◽  
Vol 12 (3) ◽  
pp. 1495-1512 ◽  
Author(s):  
Robert R. Nelson ◽  
Christopher W. O'Dell
Keyword(s):  
Carbon Dioxide ◽  
Near Infrared ◽  
Carbon Sources ◽  
Total Carbon ◽  
Primary Sources ◽  
Retrieval Algorithm ◽  
Sources And Sinks ◽  
Earth Observing System ◽  
Infrared Measurements ◽  
The Impact

Abstract. The Orbiting Carbon Observatory-2 (OCO-2) was launched in 2014 with the goal of measuring the column-averaged dry-air mole fraction of carbon dioxide (XCO2) with sufficient precision and accuracy to infer regional carbon sources and sinks. One of the primary sources of error in near-infrared measurements of XCO2 is the scattering effects of cloud and aerosol layers. In this work, we study the impact of ingesting better informed aerosol priors from the Goddard Earth Observing System Model, Version 5 (GEOS-5) into the OCO-2 ACOS V8 retrieval algorithm with the objective of reducing the error in XCO2 from real measurements. Multiple levels of both aerosol setup complexity and uncertainty on the aerosol priors were tested, ranging from a mostly unconstrained aerosol optical depth (AOD) setup to ingesting full aerosol profiles with high confidence. We find that using co-located GEOS-5 aerosol types and AODs with low uncertainty results in a small improvement in the retrieved XCO2 against the Total Carbon Column Observing Network relative to V8. In contrast, attempting to use modeled vertical information in the aerosol prior to improve the XCO2 retrieval generally gives poor results, as aerosol models struggle with the vertical placement of aerosol layers. To assess regional differences in XCO2, we compare our results to a global CO2 model validation suite. We find that the GEOS-5 setup performs better than V8 over northern Africa and central Asia, with the standard deviation of the XCO2 error reduced from 2.12 to 1.83 ppm, due to a combination of smaller prior AODs and lower prior uncertainty. In general, the use of better informed aerosol priors shows promise but may be restricted by the current accuracy of aerosol models.

scholarly journals Impact of 3D Cloud Structures on the Atmospheric Trace Gas Products from UV-VIS Sounders – Part I: Synthetic dataset for validation of trace gas retrieval algorithms

2021 ◽  
Author(s):  
Claudia Emde ◽  
Huan Yu ◽  
Arve Kylling ◽  
Michel van Roozendael ◽  
Kerstin Stebel ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
3D Structure ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Synthetic Dataset ◽  
Trace Gas ◽  
Transfer Model ◽  
Retrieval Error ◽  
Retrieval Algorithms ◽  
The Impact

Abstract. Retrievals of trace gas concentrations from satellite observations are mostly performed for clear regions or regions with low cloud coverage. However, even fully clear pixels can be affected by clouds in the vicinity, either by shadowing or by scattering of radiation from clouds in the clear region. Quantifying the error of retrieved trace gas concentrations due to cloud scattering is a difficult task. One possibility is to generate synthetic data by three-dimensional (3D) radiative transfer simulations using realistic 3D atmospheric input data, including 3D cloud structures. Retrieval algorithms may be applied on the synthetic data and comparison to the known input trace gas concentrations yields the retrieval error due to cloud scattering. In this paper we present a comprehensive synthetic dataset which has been generated using the Monte Carlo radiative transfer model MYSTIC. The dataset includes simulated spectra in two spectral ranges (400–500 nm and the O2A-band from 755–775 nm). Moreover it includes layer air mass factors (layer-AMF) calculated at 460 nm. All simulations are performed for a fixed background atmosphere for various sun positions, viewing directions and surface albedos. Two cloud setups are considered: The first includes simple box-clouds with various geometrical and optical thicknesses. This can be used to systematically investigate the sensitivity of the retrieval error on solar zenith angle, surface albedo and cloud parameters. Corresponding 1D simulations are also provided. The second includes realistic three-dimensional clouds from an ICON large eddy simulation (LES) for a region covering Germany and parts of surrounding countries. The scene includes cloud types typical for central Europe such as shallow cumulus, convective cloud cells, cirrus, and stratocumulus. This large dataset can be used to quantify the trace gas concentration retrieval error statistically. Along with the dataset the impact of horizontal photon transport on reflectance spectra and layer-AMFs is analyzed for the box-cloud scenarios. Moreover, the impact of 3D cloud scattering on the NO2 vertical column density (VCD) retrieval is presented for a specific LES case. We find that the retrieval error is largest in cloud shadow regions, where the NO2 VCD is underestimated by more than 20 %. The dataset is available for the scientific community to assess the behavior of trace gas retrieval algorithms and cloud correction schemes in cloud conditions with 3D structure.

scholarly journals First results from a rotational Raman scattering cloud algorithm applied to the Suomi National Polar-orbiting Partnership (NPP) Ozone Mapping Profiler Spectrometer (OMPS) nadir mapper

2014 ◽  
Vol 7 (3) ◽  
pp. 2689-2714 ◽  
Author(s):  
A. Vasilkov ◽  
J. Joiner ◽  
C. Seftor
Keyword(s):  
Raman Scattering ◽  
Ultra Violet ◽  
Cloud Fraction ◽  
Trace Gas ◽  
Retrieval Algorithm ◽  
Ozone Monitoring Instrument ◽  
Total Column Ozone ◽  
First Results ◽  
Cloud Optical Thickness ◽  
Column Ozone

Abstract. This paper reports initial results from an Ozone Mapping Profiler Suite (OMPS) nadir mapper cloud pressure and cloud fraction algorithm. The OMPS cloud products are intended for use in OMPS ozone or other trace-gas algorithms. We developed the OMPS cloud products using a heritage algorithm developed for the Ozone Monitoring Instrument (OMI) on NASA's Aura satellite. The cloud pressure algorithm utilizes the filling-in of ultra-violet solar Fraunhofer lines by rotational Raman scattering. The OMPS cloud products are evaluated by comparison with OMI cloud products that have been compared in turn with other collocated satellite data including cloud optical thickness profiles derived from a combination of measurements from the CloudSat radar and the MODIS imaging radiometer. We find that the probability density functions (PDFs) of effective cloud fraction retrieved from OMPS and OMI measurements are very similar. The PDFs of the OMPS and OMI cloud pressures are comparable. However, OMPS retrieves somewhat higher pressures on average. The current NASA total ozone retrieval algorithm makes use of a monthly gridded cloud pressure climatology developed from OMI. This climatology captures much of the variability associated with the relevant cloud pressures. However, the use of actual cloud pressures retrieved with OMPS in place of the OMI climatology appears to improve OMPS total column ozone estimates slightly.

scholarly journals The impact of improved satellite retrievals on estimates of biospheric carbon balance

2019 ◽  
Author(s):  
Scot M. Miller ◽  
Anna M. Michalak
Keyword(s):  
Carbon Balance ◽  
Near Infrared ◽  
Co2 Flux ◽  
Retrieval Algorithm ◽  
Co2 Uptake ◽  
Near Infrared Radiation ◽  
Sources And Sinks ◽  
Satellite Retrievals ◽  
The Impact ◽  
Insight Into

Abstract. The Orbiting Carbon Observatory 2 (OCO-2) is NASA's first satellite dedicated to monitoring CO2 from space and could provide novel insight into CO2 fluxes across the globe. However, one continuing challenge is the development of a robust retrieval algorithm: an estimate of atmospheric CO2 from satellite observations of near infrared radiation. The OCO-2 retrievals have undergone multiple updates since the satellite's launch, and the retrieval algorithm is now on its ninth version. Some of these retrieval updates, particularly version 8, led to marked changes in the CO2 observations, changes of 0.5 ppm or more. In this study, we evaluate the extent to which current OCO-2 observations can constrain monthly CO2 sources and sinks from the biosphere, and we particularly focus on how this constraint has evolved with improvements to the OCO-2 retrieval algorithm. We find that improvements in the CO2 retrieval are having a potentially transformative effect on satellite-based estimates of the global biospheric carbon balance. The version 7 OCO-2 retrievals formed the basis of early inverse modeling studies using OCO-2 data; these observations are best-equipped to constrain the biospheric carbon balance across only continental or hemispheric regions. By contrast, newer versions of the retrieval algorithm yield a far more detailed constraint, and we are able to constrain CO2 budgets for seven global biome-based regions, particularly during the Northern Hemisphere summer when biospheric CO2 uptake is greatest. Improvements to the OCO-2 observations have had the largest impact on glint mode observations, and we also find the largest improvements in the terrestrial CO2 flux constraint when we include both nadir and glint data.

OClO as observed by TROPOMI on Sentinel 5P

2020 ◽  
Author(s):  
Janis Pukite ◽  
Christian Borger ◽  
Steffen Dörner ◽  
Thomas Wagner
Keyword(s):  
Chlorine Dioxide ◽  
Meteorological Data ◽  
Trace Gas ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Polar Regions ◽  
Arctic Regions ◽  
Halogen Chemistry ◽  
Global Coverage ◽  
Spectrally Resolved

<p>The TROPOspheric Monitoring Instrument (TROPOMI) is an UV-VIS-NIR-SWIR instrument on board of Sentinel-5P satellite developed for monitoring the Earth’s atmosphere. It was launched on 13 October 2017 in a near polar orbit. It measures spectrally resolved earthshine radiances at an unprecedented spatial resolution of around 3.5x7.2 km<sup>2</sup> (3.5x5.6 km<sup>2 </sup>starting from 6 Aug 2019) (near nadir) with a total swath width of ~2600 km on the Earth's surface providing daily global coverage. From the measured spectra high resolved trace gas distributions can be retrieved by means of differential optical absorption spectroscopy (DOAS).</p><p>Chlorine dioxide (OClO) is a by-product of the ozone depleting halogen chemistry in the stratosphere. Although being rapidly photolysed at low solar zenith angles (SZAs) it plays an important role as an indicator of the chlorine activation in polar regions during polar winter and spring at twilight conditions because of the nearly linear relation of its formation to chlorine oxide (ClO).</p><p>Here we present a new DOAS retrieval algorithm of the slant column densities (SCDs) of chlorine dioxide (OClO) and correlate this TROPOMI OClO signal with meteorological data for both Antarctic and Arctic regions.</p>

Sign in / Sign up

Export Citation Format

Share Document