54 research outputs found
Global Mapping of Atmospheric Composition from Space: Retrieving Aerosol Height and Tropospheric NO2 from OMI
No full textThe main objective of this thesis is to design a new aerosol layer height retrieval in order to improve the operational NO2 retrieval, both in the troposphere, from space-borne instruments for highly polluted events and under cloud-free conditions. This thesis focuses on the exploitation of the OMI satellite measurements acquired in the visible wavelength range (405-490 nm). In addition, we develop numerical methods and tools (e.g. machine learning) in order to support the operational processing of big data amounts from the forthcoming new-generation satellite instruments for air quality and climate research.Atmospheric Remote Sensin
Aerosol Absorption from Global Satellite Measurements in the Ultra-Violet: From Qualitative Aerosol Index to Quantitative Aerosol Absorptive Properties
No full textAtmospheric aerosols are solid or liquid particles suspended in the air. The majority of them are produced by natural processes, including sea salt from oceans, mineral dust from (semi-)arid regions, carbon containing particles from wildfires, and sulfates and ash from volcanic activities. Anthropogenic aerosols are produced by industrial activities, power generation, transportation, agriculture, and human-induced biomass burning events. Depending on the meteorological conditions, aerosol particles can stay in the atmosphere for several hours to several months and can be transported over long distances, causing adverse effects on human health, visibility and climate.This thesis focuses on the aerosol optical properties, particularly the light absorption of the aerosol particles that has significant effects on the Earth’s climate system. This thesis starts with a general introduction of atmospheric aerosols, including its sources, categories, physical properties and measurement techniques (Chapter 1). Next, the Ultra-Violet Aerosol Index (UVAI) is introduced, which is calculated from satellite measurements of the radiance at two wavelengths in the UV. UVAI contains information of aerosol absorption, and it has a very long andalmost continuous data record starting in 1978. Direct use of UVAI is challenging because it is not a geophysical quantity, but a numerical index. The objective of this thesis is to derive quantitative properties on aerosol absorption from the UVAI (e.g. single scattering albedo, absorption aerosol optical depth) that can be directly used in aerosol radiative transfer assessments. Two types of methods have been developed, i.e. physically-based methods and statistically-based methods. The first compares the observed UVAI to the one simulated by radiative transfer models. The second uses Machine Learning algorithms trained by existing data sets.The physically-based methods have been applied to quantify aerosol absorption of several large scale wildfires (Chapter 2 and 3). An important challenge of these method is that assumptions have to be made on the aerosol micro-physical properties, leading to significant uncertainties in the results, whereas theMachine Learning-based methods can avoid this kind of assumptions. Chapter 3 investigates the feasibility to quantify aerosol absorption from UVAI using a Machine Learning algorithm. Despite the higher computational efficiency and better results, the application of such data-driven methods is still restricted by the limited data on the aerosol vertical distribution. Therefore, in Chapter4, a database of aerosol height is created from a chemistry transport model. This database is applied in Chapter 5, where a Deep Neural Network method is used to derive the quantitative aerosol absorptive properties from the OMI/Aura UVAI for the period from 2006 to 2019. In comparison to ground-based observations, the results of the Deep Neural Network agree better than satellite retrievals and also better than chemistry transport model simulations.This thesis demonstrates the feasibility of deriving quantitative aerosol absorptive properties from the satellite retrieved UVAI.We use traditional radiative transfer simulations meanwhile investigating the new possibilities of data-driven methods in aerosol remote sensing. Although the retrieval results are encouraging, there remain limitations and challenges which need to be addressed. These are discussed in Chapter 6 with corresponding suggestions and prospects. Despite the challenges, it is expected that a synthetic database of global aerosol absorption can be derived fromUVAI observations provided by multiple satellite products. Such a data set will make great contributions to quantify the effect of absorbing aerosols on the climate system.Atmospheric Remote Sensin
Satellite-derived NO<sub>x</sub> emissions over East Asia
No full textNitrogen oxides (NOx) are important air pollutants and play a crucial role in climate change. NOx emissions are important for chemical transport models to simulate and forecast air quality. Up-to-date emission information also helps policymakers to mitigate air pollution. In this thesis, we have focused on providing better NOx emission estimates with the DECSO (Daily Emission estimates Constrained by Satellite Observations) inversion algorithm applied to satellite observations. DECSO is a fast algorithm, which enables daily emissions estimates as soon as the satellite observations are available. Satellite-derived emissions reveal more specific information on the location and strength of sources than concentration observations. The monthly and yearly variability in emissions are well captured. This is demonstrated by our monitoring of the effect of air quality regulations on emissions during events like the 2014 Youth Olympic Games. Near the Chinese coast ship tracks, which are otherwise hidden under the outflow of air pollution from the mainland, are revealed in our NOx emissions derived with DECSO applied to OMI satellite observations. Trends of shipping emissions for a 10-year period (2007 to 2016) over Chinese seas are presented for the first time.Atmospheric Remote Sensin
Three-dimensional ozone distribution based on assimilation of nadir-sounding UV-VIS satellite observations
No full textOzone (O3) directly and indirectly affects human health (depending on the altitude it is sometimes referred to as “good” or “bad” ozone) and has an important role in the temperature structure of the atmosphere. Because of the impact of ozone on air quality and climate change, the objective of this thesis is to improve our understanding of the global distribution of atmospheric ozone in space and time, not just in the stratosphere, but also in the troposphere, where it directly affects living organisms. In this thesis, ozone is measured with satellite-based instruments that measure reflected solar light in the Ultra Violet - VISible (UV-VIS) wavelength range (280 < λ< 330 nm). In the UV-VIS, the absorption crosss-ection of ozone varies by several orders of magnitude, providing the altitude information for the ozone distribution. The ozone profiles are retrieved from the measured radiation with the optimal estimation technique. To make optimal use of the advantages of both observations and atmospheric models, they are combined using the Kalman filter data assimilation technique.The assimilation output consists of regular gridded 3D ozone fields without missing data at regular time intervals.Atmospheric Remote Sensin
Developing an Aerosol Layer Height Retrieval Algorithm for Passive Space-Based Sensors
No full textAerosols are the source of the largest uncertainties in our climate models, blurring our outlook of the future. This has been attributed to the complexity of measuring their properties, which vary over time and space. Atmospheric circulation spreads aerosols across the globe from a point source, which makes satellite-based observations lucrative. At present, there are several aerosol observing missions that deliver aerosol data products in a consistent and operational manner; these missions report several aerosol properties that are important for reducing the contribution of uncertainties to our climate models. What is missing, however, is an operational data product that measures the height of these aerosols at a global scale. Earlier attempts at this use data derived from lidar instruments in space; an example being the Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP) instrument, which uses lasers to measure atmospheric composition. In the case of aerosols, the amount of backscattered electromagnetic radiation at each atmospheric layer gives an idea of the amount and height of aerosols. The mobility afforded by space-based instruments gives space lidars a leg up over ground-based lidars. However, the coverage of such lidar instruments is merely near-global. This has to do with the fact that while lidars in space can circle the entire globe, their footprint on the ground is very narrow, in the order of several hundred meters to a few kilometers: this is an inherent limitation of the measurement principle. Consequently, a specific patch on Earth is revisited in periods that can range several days. An alternative to space based atmospheric lidars are space based spectral imagers. These are essentially cameras that take snapshots of the Earth, capturing the light and splitting its different electromagnetic frequencies into the scale of nanometers using very precise prisms and detection techniques. The advantage of these instruments over lidars is that they have a very large footprint, covering several thousand kilometers of area in a single _y-by. This allows for daily to even sub-daily coverage of the Earth, as each snapshot covers larger and sometimes overlapping areas. The challenge is to estimate aerosol height using spectral signatures of the Earth’s atmosphere in an operational environment that can handle data coming in from the satellite at a rate of several million pixels every few minutes. This dissertation focuses on delivering the aerosol height data product operationally using computer algorithms. The logic of aerosol height estimation using these so-called spectral snapshots of the atmosphere differ from that using lidars; the instrument does not provide data for different atmospheric layers. This has to be inferred using the chemistry of the oxygen molecule. O2, the second most abundant gas in our atmosphere, has a unique spectral signature in the near-infrared region, comprising of electromagnetic radiation around 765 nm. The chemical structure of the oxygen molecule allows it to absorb some of these radiations, creating a structure of absorption bands. This spectral signature deepens as more light is absorbed by the oxygen: this happens as photons penetrate deeper and deeper into the earth’s atmosphere, unless they hit a barrier. If the photons bounce back from an aerosol layer at a very high altitude, the amount of absorption by oxygen would be low. This ‘depth’ of absorption gives clues on how high an aerosol layer might be present. Computer models can reconstruct this oxygen absorption structure onto a simulated spectrum. One of the control parameters within the model is the height of an aerosol layer. The generated spectral signature of a simulated atmosphere resembling the atmosphere of a pixel in the snapshot from space-based hyperspectral imagers is then compared to the measured spectral signature. This usually results in a non-zero difference, which is caused by errors in the model. These errors can be minimised by using computer algorithms and mathematical information retrieval techniques, resulting in a modeled atmosphere closer to the measurement by changing the height of the aerosol layer, resulting in an aerosol height estimate. In this dissertation, computer algorithms inspired from mathematical models of brain neural networks as well as information retrieval techniques such as least squares are used…Atmospheric Remote Sensin
Improvements to the OMI O<sub>2</sub>-O<sub>2</sub> operational cloud algorithm and comparisons with ground-based radar-lidar observations
No full textThe OMI (Ozone Monitoring Instrument on board NASA's Earth Observing System (EOS) Aura satellite) OMCLDO2 cloud product supports trace gas retrievals of for example ozone and nitrogen dioxide. The OMCLDO2 algorithm derives the effective cloud fraction and effective cloud pressure using a DOAS (differential optical absorption spectroscopy) fit of the O2-O2 absorption feature around 477m. A new version of the OMI OMCLDO2 cloud product is presented that contains several improvements, of which the introduction of a temperature correction on the O2-O2 slant columns and the updated look-up tables have the largest impact. Whereas the differences in the effective cloud fraction are on average limited to 0.01, the differences of the effective cloud pressure can be up to 200hPa, especially at cloud fractions below 0.3. As expected, the temperature correction depends on latitude and season. The updated look-up tables have a systematic effect on the cloud pressure at low cloud fractions. The improvements at low cloud fractions are very important for the retrieval of trace gases in the lower troposphere, for example for nitrogen dioxide and formaldehyde. The cloud pressure retrievals of the improved algorithm are compared with ground-based radar-lidar observations for three sites at mid-latitudes. For low clouds that have a limited vertical extent the comparison yields good agreement. For higher clouds, which are vertically extensive and often contain several layers, the satellite retrievals give a lower cloud height. For high clouds, mixed results are obtained.</p
Spatial distribution analysis of the OMI aerosol layer height: A pixel-by-pixel comparison to CALIOP observations
No full textA global picture of atmospheric aerosol vertical distribution with a high temporal resolution is of key importance not only for climate, cloud formation, and air quality research studies but also for correcting scattered radiation induced by aerosols in absorbing trace gas retrievals from passive satellite sensors. Aerosol layer height (ALH) was retrieved from the OMI 477 nm O2 − O2 band and its spatial pattern evaluated over selected cloud-free scenes. Such retrievals benefit from a synergy with MODIS data to provide complementary information on aerosols and cloudy pixels. We used a neural network approach previously trained and developed. Comparison with CALIOP aerosol level 2 products over urban and industrial pollution in eastern China shows consistent spatial patterns with an uncertainty in the range of 462–648 m. In addition, we show the possibility to determine the height of thick aerosol layers released by intensive biomass burning events in South America and Russia from OMI visible measurements. A Saharan dust outbreak over sea is finally discussed. Complementary detailed analyses show that the assumed aerosol properties in the forward modelling are the key factors affecting the accuracy of the results, together with potential cloud residuals in the observation pixels. Furthermore, we demonstrate that the physical meaning of the retrieved ALH scalar corresponds to the weighted average of the vertical aerosol extinction profile. These encouraging findings strongly suggest the potential of the OMI ALH product, and in more general the use of the 477 nm O2 − O2 band from present and future similar satellite sensors, for climate studies as well as for future aerosol correction in air quality trace gas retrievals
Quantifying the single-scattering albedo for the January 2017 Chile wildfires from simulations of the OMI absorbing aerosol index
No full textThe absorbing aerosol index (AAI) is a qualitative parameter directly calculated from satellite-measured reflectance. Its sensitivity to absorbing aerosols in combination with a long-term data record since 1978 makes it an important parameter for climate research. In this study, we attempt to quantify aerosol absorption by retrieving the single-scattering albedo (ω0) at 550 nm from the satellite-measured AAI. In the first part of this study, AAI sensitivity studies are presented exclusively for biomass-burning aerosols. Later on, we employ a radiative transfer model (DISAMAR) to simulate the AAI measured by the Ozone Monitoring Instrument (OMI) in order to derive ω0 at 550 nm. Inputs for the radiative transfer calculations include satellite measurement geometry and surface conditions from OMI, aerosol optical thickness (τ) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and aerosol microphysical parameters from the AErosol RObotic NETwork (AERONET), respectively. This approach is applied to the Chile wildfires for the period from 26 to 30 January 2017, when the OMI-observed AAI of this event reached its peak. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) overpasses missed the evolution of the smoke plume over the research region; therefore the aerosol profile is parameterized. The simulated plume is at an altitude of 4.5-4.9 km, which is in good agreement with available CALIOP backscatter coefficient measurements. The data may contain pixels outside the plume, so an outlier detection criterion is applied. The results show that the AAI simulated by DISAMAR is consistent with satellite observations. The correlation coefficients fall into the range between 0.85 and 0.95. The retrieved mean ω0 at 550 nm for the entire plume over the research period from 26 to 30 January 2017 varies from 0.81 to 0.87, whereas the nearest AERONET station reported ω0 between 0.89 and 0.92. The difference in geolocation between the AERONET site and the plume, the assumption of homogeneous plume properties, the lack of the aerosol profile information and the uncertainties in the inputs for radiative transfer calculation are primarily responsible for this discrepancy in ω0.Atmospheric Remote Sensin
Aerosol Absorption over Land Derived from the Ultra-Violet Aerosol Index by Deep Learning
No full textQuantitative measurements of aerosol absorptive properties, e.g., the absorbing aerosol optical depth (AAOD) and the single scattering albedo (SSA), are important to reduce uncertainties of aerosol climate radiative forcing assessments. Currently, global retrievals of AAOD and SSA are mainly provided by the ground-based aerosol robotic network (AERONET), whereas it is still challenging to retrieve them from space. However, we found the AERONET AAOD has a relatively strong correlation with the satellite retrieved ultra-violet aerosol index (UVAI). Based on this, a numerical relation is built by a deep neural network (DNN) to predict global AAOD and SSA over land from the long-term UVAI record (2006-2019) provided by the ozone monitoring instrument (OMI) onboard Aura. The DNN predicted aerosol absorption is satisfying for samples with AOD at 550 nm larger than 0.1, and the DNN model performance is better for smaller absorbing aerosols (e.g., smoke) than larger ones (e.g., mineral dust). The comparison of the DNN predictions with AERONET shows a high correlation coefficient of 0.90 and a root mean square of 0.005 for the AAOD, and over 80% of samples are within the expected uncertainty of AERONET SSA (pm0.03).Atmospheric Remote Sensin
Separation of NOx emissions from Drilling, and Oil and Gas Extraction in the U.S. using Monthly Data from the Ozone Monitoring Instrument
No full textHorizontal drilling and hydraulic fracturing have increased unconventional oil and gas extraction from shale reserves in the U.S. in the last decade, making up more than half of total U.S. oil and gas production at present. This activity results in NOx emissions in the extraction regions that are measurable from space using the Ozone Monitoring Instrument (OMI) on the NASA Aura satellite. The NOx emissions are a result of two different activities: (1) the drilling and hydraulic fracturing of new wells, and (2) the extraction of oil and gas after the well is completed. To separate the NOx emissions from drilling and extraction, a multiple linear regression to the NO2 columns as a function of time is calculated for 9 extraction regions using the number of drilling rigs and the oil and gas extraction data from 2007 until 2018. In 3 regions (Permian, Bakken, Eagle Ford) a significant correlation between measured and modeled NO2 columns is found, of which the Permian region shows the highest correlation. The analysis shows that half of the total NOx emissions in the Permian region can be attributed to emissions from oil and gas activities, and that both the drilling and extraction activities have an equal share in the emissions. A fuel-based oil and gas emission inventory shows a different split for NOx emissions from drilling and extraction in the Permian region, indicating drilling as the larger source. In other extraction regions, NO2 columns show poor correlation with the oil and gas activities due to the proximity of urban areas (Barnett, Denver-Julesburg, Haynesville regions), power plants (San Juan) or variations in the drilling and extraction activity over time that are too small (Uintah, Upper Green River)
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