From photon paths to pollution plumes: better radiative transfer calculations to monitor NOx emissions with OMI and TROPOMI

Nitrogen oxides (NOx = NO + NO2) play an important role in atmospheric chemistry, therefore affecting air quality and Earth's radiative forcing, which impact public health, ecosystems and climate. Remote sensing from satellites in the ultraviolet and visible (UV-Vis) spectral range results in measurements of tropospheric NO2 column densities with high spatial and temporal resolution that allow, among many applications, to monitor NO2 concentrations and to estimate NOx emissions. NO2 satellite retrievals have improved extensively in the last decade, together with the increased need of having traceable characterization of the uncertainties associated with the NO2 satellite measurements. The spatial resolution of the satellite instruments is improving such that the observed NO2 pollution can now be traced back to emissions from individual cities, power plants, and transportation sectors. However, the uncertainty of satellite NO2 retrievals is still considerable and mainly related to the adequacy of the assumptions made on the state of the atmosphere. In this thesis we have improved the critical assumptions and our understanding in the radiative transfer modelling for NO2 satellite measurements, and we use the new TROPOMI NO2 measurements to quantify daily NOx emissions from a single urban hot spot. The work presented in this thesis contributes to the satellite remote sensing community (1) because of the improvement of the satellite retrieval and the knowledge of its main uncertainty sources (Chapter 2, 3 and 4), and (2) because of the application of TROPOMI NO2 measurements for the first time to infer daily NOx emissions at urban scales (Chapter 5). </p

Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm

Since September 2014, NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite has been taking measurements of reflected solar spectra and using them to infer atmospheric carbon dioxide levels. This work provides details of the OCO-2 retrieval algorithm, versions 7 and 8, used to derive the column-averaged dry air mole fraction of atmospheric CO2 (XCO2) for the roughly 100&thinsp;000 cloud-free measurements recorded by OCO-2 each day. The algorithm is based on the Atmospheric Carbon Observations from Space (ACOS) algorithm which has been applied to observations from the Greenhouse Gases Observing SATellite (GOSAT) since 2009, with modifications necessary for OCO-2. Because high accuracy, better than 0.25&thinsp;%, is required in order to accurately infer carbon sources and sinks from XCO2, significant errors and regional-scale biases in the measurements must be minimized. We discuss efforts to filter out poor-quality measurements, and correct the remaining good-quality measurements to minimize regional-scale biases. Updates to the radiance calibration and retrieval forward model in version 8 have improved many aspects of the retrieved data products. The version 8 data appear to have reduced regional-scale biases overall, and demonstrate a clear improvement over the version 7 data. In particular, error variance with respect to TCCON was reduced by 20&thinsp;% over land and 40&thinsp;% over ocean between versions 7 and 8, and nadir and glint observations over land are now more consistent. While this paper documents the significant improvements in the ACOS algorithm, it will continue to evolve and improve as the CO2 data record continues to expand.</p

Sea ice-atmospheric interaction: Application of multispectral satellite data in polar surface energy flux estimates

This is the third annual report on: Sea Ice-Atmosphere Interaction - Application of Multispectral Satellite Data in Polar Surface Energy Flux Estimates. The main emphasis during the past year was on: radiative flux estimates from satellite data; intercomparison of satellite and ground-based cloud amounts; radiative cloud forcing; calibration of the Advanced Very High Resolution Radiometer (AVHRR) visible channels and comparison of two satellite derived albedo data sets; and on flux modeling for leads. Major topics covered are arctic clouds and radiation; snow and ice albedo, and leads and modeling

Understanding the aerosol information content in multi-spectral reflectance measurements using a synergetic retrieval algorithm

An information content analysis for multi-wavelength SYNergetic AErosol Retrieval algorithm SYNAER was performed to quantify the number of independent pieces of information that can be retrieved. In particular, the capability of SYNAER to discern various aerosol types is assessed. This information content depends on the aerosol optical depth, the surface albedo spectrum and the observation geometry. The theoretical analysis is performed for a large number of scenarios with various geometries and surface albedo spectra for ocean, soil and vegetation. When the surface albedo spectrum and its accuracy is known under cloud-free conditions, reflectance measurements used in SYNAER is able to provide for 2–4° of freedom that can be attributed to retrieval parameters: aerosol optical depth, aerosol type and surface albedo. &lt;br&gt;&lt;br&gt; The focus of this work is placed on an information content analysis with emphasis to the aerosol type classification. This analysis is applied to synthetic reflectance measurements for 40 predefined aerosol mixtures of different basic components, given by sea salt, mineral dust, biomass burning and diesel aerosols, water soluble and water insoluble aerosols. The range of aerosol parameters considered through the 40 mixtures covers the natural variability of tropospheric aerosols. After the information content analysis performed in Holzer-Popp et al. (2008) there was a necessity to compare derived degrees of freedom with retrieved aerosol optical depth for different aerosol types, which is the main focus of this paper. &lt;br&gt;&lt;br&gt; The principle component analysis was used to determine the correspondence between degrees of freedom for signal in the retrieval and derived aerosol types. The main results of the analysis indicate correspondence between the major groups of the aerosol types, which are: water soluble aerosol, soot, mineral dust and sea salt and degrees of freedom in the algorithm and show the ability of the SYNAER to discern between this aerosol types. &lt;br&gt;&lt;br&gt; The results of the work will be further used for the development of the promising methodology of the construction error covariance matrices in the assimilation system