A geostatistical framework for quantifying the imprint of mesoscale atmospheric transport on satellite trace gas retrievals

National Aeronautics and Space Administration's Orbiting Carbon Observatory‐2 (OCO‐2) satellite provides observations of total column‐averaged CO2 mole fractions (X_(CO₂)) at high spatial resolution that may enable novel constraints on surface‐atmosphere carbon fluxes. Atmospheric inverse modeling provides an approach to optimize surface fluxes at regional scales, but the accuracy of the fluxes from inversion frameworks depends on key inputs, including spatially and temporally dense CO₂ observations and reliable representations of atmospheric transport. Since X_(CO₂) observations are sensitive to both synoptic and mesoscale variations within the free troposphere, horizontal atmospheric transport imparts substantial variations in these data and must be either resolved explicitly by the atmospheric transport model or accounted for within the error covariance budget provided to inverse frameworks. Here, we used geostatistical techniques to quantify the imprint of atmospheric transport in along‐track OCO‐2 soundings. We compare high‐pass‐filtered (<250 km, spatial scales that primarily isolate mesoscale or finer‐scale variations) along‐track spatial variability in X_(CO₂) and X_(H₂O) from OCO‐2 tracks to temporal synoptic and mesoscale variability from ground‐based X_(CO₂) and X_(H₂O) observed by nearby Total Carbon Column Observing Network sites. Mesoscale atmospheric transport is found to be the primary driver of along‐track, high‐frequency variability for OCO‐2 X_(H₂O). For X_(CO₂), both mesoscale transport variability and spatially coherent bias associated with other elements of the OCO‐2 retrieval state vector are important drivers of the along‐track variance budget

A Geostatistical Framework for Quantifying the Imprint of Mesoscale Atmospheric Transport on Satellite Trace Gas Retrievals

National Aeronautics and Space Administration’s Orbiting Carbon Observatory-2 (OCO-2) satellite provides observations of total column-averaged CO2 mole fractions (XCO2) at high spatial resolution that may enable novel constraints on surface-atmosphere carbon fluxes. Atmospheric inverse modeling provides an approach to optimize surface fluxes at regional scales, but the accuracy of the fluxes from inversion frameworks depends on key inputs, including spatially and temporally dense CO2 observations and reliable representations of atmospheric transport. Since XCO2 observations are sensitive to both synoptic and mesoscale variations within the free troposphere, horizontal atmospheric transport imparts substantial variations in these data and must be either resolved explicitly by the atmospheric transport model or accounted for within the error covariance budget provided to inverse frameworks. Here, we used geostatistical techniques to quantify the imprint of atmospheric transport in along-track OCO-2 soundings. We compare high-pass-filtered (<250 km, spatial scales that primarily isolate mesoscale or finer-scale variations) along-track spatial variability in XCO2 and XH2O from OCO-2 tracks to temporal synoptic and mesoscale variability from ground-based XCO2 and XH2O observed by nearby Total Carbon Column Observing Network sites. Mesoscale atmospheric transport is found to be the primary driver of along-track, high-frequency variability for OCO-2 XH2O. For XCO2, both mesoscale transport variability and spatially coherent bias associated with other elements of the OCO-2 retrieval state vector are important drivers of the along-track variance budget.Plain Language SummaryNumerous efforts have been made to quantify sources and sinks of atmospheric CO2 at regional spatial scales. A common approach to infer these sources and sinks requires accurate representation of variability of CO2 observations attributed to transport by weather systems. While numerical weather prediction models have a fairly reasonable representation of larger-scale weather systems, such as frontal systems, representation of smaller-scale features (<250 km), is less reliable. In this study, we find that the variability of total column-averaged CO2 observations attributed to these fine-scale weather systems accounts for up to half of the variability attributed to local sources and sinks. Here, we provide a framework for quantifying the drivers of spatial variability of atmospheric trace gases rather than simply relying on numerical weather prediction models. We use this framework to quantify potential sources of errors in measurements of total column-averaged CO2 and water vapor from National Aeronautics and Space Administration’s Orbiting Carbon Observatory-2 satellite.Key PointsWe developed a framework to relate high-frequency spatial variations to transport-induced temporal fluctuations in atmospheric tracersWe use geostatistical analysis to quantify the variance budget for XCO2 and XH2O retrieved from NASA’s OCO-2 satelliteAccounting for random errors, systematic errors, and real geophysical coherence in remotely sensed trace gas observations may yield improved flux constraintsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151988/1/jgrd55658.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151988/2/jgrd55658_am.pd

Educate to prevent: science-based materials on food hygiene and safety

Uma importante estratégia para a redução do impacto das doenças de origem alimentar é a prevenção e a promoção da saúde. A população escolar foi escolhida como público-alvo para aumentar a literacia para a saúde e promover práticas saudáveis e seguras relacionadas com os alimentos, através do projeto “Educar para Prevenir”. Foram produzidos e publicados materiais educativos para o público escolar e professores. Estes materiais, que compreendem três diferentes tipos de ferramentas, foram publicados como um kit. O desenvolvimento destes materiais baseou-se na recolha de dados de surtos de doenças de origem alimentar, de 2009 a 2013, do Instituto Nacional de Saúde Doutor Ricardo Jorge (INSA). O risco de ocorrência e os fatores contributivos, bem como as boas práticas, foram identificados e usados como base para a elaboração dos materiais educativos. Adicionalmente, foram usados materiais da Organização Mundial da Saúde como o programa “Cinco Chaves para uma Alimentação Mais Segura”. Nas próximas etapas deste projeto serão produzidos novos materiais para estudantes contendo informação sobre a composição nutricional dos alimentos e a compreensão da rotulagem alimentar.An important strategy to reduce food borne diseases burden is prevention and health promotion. The student’s population was chosen as the target audience for improving health literacy and promoting healthy and safe practices relating to food trough the Project “Educar para Prevenir” (Education for Prevention). School educational materials on food safety, on teacher level, were developed and published, aiming the different school levels. These materials comprised 3 different kinds of tools were published as a kit. The development of these materials was based on data collected foodborne outbreaks from 2009 to 2013, at the National Institute of Health (INSA). The occurrence risk and contributing factors were identified as well as the good practices and were the basis for the elaboration of the educational materials. In addition, some World Health Organization materials, such as “Five Keys to Safer Food” programme, were used. On the next steps of the project include new materials for students will be produced, including information about nutritional composition of the food and understanding of the food labelling.info:eu-repo/semantics/publishedVersio

Methane retrieved from TROPOMI: improvement of the data product and validation of the first 2 years of measurements

The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor (S5-P) satellite provides methane (CH₄) measurements with high accuracy and exceptional temporal and spatial resolution and sampling. TROPOMI CH₄ measurements are highly valuable to constrain emissions inventories and for trend analysis, with strict requirements on the data quality. This study describes the improvements that we have implemented to retrieve CH₄ from TROPOMI using the RemoTeC full-physics algorithm. The updated retrieval algorithm features a constant regularization scheme of the inversion that stabilizes the retrieval and yields less scatter in the data and includes a higher resolution surface altitude database. We have tested the impact of three state-of-the-art molecular spectroscopic databases (HITRAN 2008, HITRAN 2016 and Scientific Exploitation of Operational Missions – Improved Atmospheric Spectroscopy Databases SEOM-IAS) and found that SEOM-IAS provides the best fitting results. The most relevant update in the TROPOMI XCH₄ data product is the implementation of an a posteriori correction fully independent of any reference data that is more accurate and corrects for the underestimation at low surface albedo scenes and the overestimation at high surface albedo scenes. After applying the correction, the albedo dependence is removed to a large extent in the TROPOMI versus satellite (Greenhouse gases Observing SATellite – GOSAT) and TROPOMI versus ground-based observations (Total Carbon Column Observing Network – TCCON) comparison, which is an independent verification of the correction scheme. We validate 2 years of TROPOMI CH₄ data that show the good agreement of the updated TROPOMI CH₄ with TCCON (−3.4 ± 5.6 ppb) and GOSAT (−10.3 ± 16.8 ppb) (mean bias and standard deviation). Low- and high-albedo scenes as well as snow-covered scenes are the most challenging for the CH₄ retrieval algorithm, and although the a posteriori correction accounts for most of the bias, there is a need to further investigate the underlying cause

The Orbiting Carbon Observatory (OCO-2) Tracks 2-3 Peta-Gram Increase in Carbon Release to the Atmosphere During the 2014-2016 El Nino

The powerful El Nio event of 2015-2016 - the third most intense since the 1950s - has exerted a large impact on the Earth's natural climate system. The column-averaged CO2 dry-air mole fraction (XCO2) observations from satellites and ground based networks are analyzed together with in situ observations for the period of September 2014 to October 2016. From the differences between satellite (OCO-2) observations and simulations using an atmospheric chemistry-transport model, we estimate that, relative to the mean annual fluxes for 2014, the most recent El Nio has contributed to an excess CO2 emission from the Earth's surface (land+ocean) to the atmosphere in the range of 2.4+/-0.2 PgC (1 Pg = 10(exp 15) g) over the period of July 2015 to June 2016. The excess CO2 flux is resulted primarily from reduction in vegetation uptake due to drought, and to a lesser degree from increased biomass burning. It is about the half of the CO2 flux anomaly (range: 4.4-6.7 PgC) estimated for the 1997/1998 El Nio. The annual total sink is estimated to be 3.9+/-0.2 PgC for the assumed fossil fuel emission of 10.1 PgC. The major uncertainty in attribution arise from error in anthropogenic emission trends, satellite data and atmospheric transport

Bias Correction of the Ratio of Total Column CH₄ to CO₂ Retrieved from GOSAT Spectra

The proxy method, using the ratio of total column CH₄ to CO₂ to reduce the effects of common biases, has been used to retrieve column-averaged dry-air mole fraction of CH₄ from satellite data. The present study characterizes the remaining scattering effects in the CH₄/CO₄ ratio component of the Greenhouse gases Observing SATellite (GOSAT) retrieval and uses them for bias correction. The variation of bias between the GOSAT and Total Carbon Column Observing Network (TCCON) ratio component with GOSAT data-derived variables was investigated. Then, it was revealed that the variability of the bias could be reduced by using four variables for the bias correction—namely, airmass, 2 μm band radiance normalized with its noise level, the ratio between the partial column-averaged dry-air mole fraction of CH₄ for the lower atmosphere and that for the upper atmosphere, and the difference in surface albedo between the CH₄ and CO₄ bands. The ratio of partial column CH₄ reduced the dependence of bias on the cloud fraction and the difference between hemispheres. In addition to the reduction of bias (from 0.43% to 0%), the precision (standard deviation of the difference between GOSAT and TCCON) was reduced from 0.61% to 0.55% by the correction. The bias and its temporal variation were reduced for each site: the mean and standard deviation of the mean bias for individual seasons were within 0.2% for most of the sites

A scientific algorithm to simultaneously retrieve carbon monoxide and methane from TROPOMI onboard Sentinel-5 Precursor

Carbon monoxide (CO) is an important atmospheric constituent affecting air quality, and methane (CH$_{4}$) is the second most important greenhouse gas contributing to human-induced climate change. Detailed and continuous observations of these gases are necessary to better assess their impact on climate and atmospheric pollution. While surface and airborne measurements are able to accurately determine atmospheric abundances on local scales, global coverage can only be achieved using satellite instruments. The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite, which was successfully launched in October 2017, is a spaceborne nadirviewing imaging spectrometer measuring solar radiation reflected by the Earth in a push-broom configuration. It has a wide swath on the terrestrial surface and covers wavelength bands between the ultraviolet (UV) and the shortwave infrared (SWIR), combining a high spatial resolution with daily global coverage. These characteristics enable the determination of both gases with an unprecedented level of detail on a global scale, introducing new areas of application. Abundances of the atmospheric column-averaged dry air mole fractions XCO and XCH$_{4}$ are simultaneously retrieved from TROPOMI’s radiance measurements in the 2:3 μm spectral range of the SWIR part of the solar spectrum using the scientific retrieval algorithm Weighting Function Modified Differential Optical Absorption Spectroscopy (WFMDOAS). This algorithm is intended to be used with the operational algorithms for mutual verification and to provide new geophysical insights. We introduce the algorithm in detail, including expected error characteristics based on synthetic data, a machine-learning-based quality filter, and a shallow learning calibration procedure applied in the post-processing of the XCH$_{4}$ data. The quality of the results based on real TROPOMI data is assessed by validation with ground-based Fourier transform spectrometer (FTS) measurements providing realistic error estimates of the satellite data: the XCO data set is characterised by a random error of 5:1 ppb (5:8 %) and a systematic error of 1:9 ppb (2:1 %); the XCH$_{4}$ data set exhibits a random error of 14:0 ppb (0:8 %) and a systematic error of 4:3 ppb (0:2 %). The natural XCO and XCH$_{4}$ variations are well-captured by the satellite retrievals, which is demonstrated by a high correlation with the validation data (R = 0:97 for XCO and R D 0:91 for XCH$_{4}$ based on daily averages). We also present selected results from the mission start until the end of 2018, including a first comparison to the operational products and examples of the detection of emission sources in a single satellite overpass, such as CO emissions from the steel industry and CH$_{4}$ emissions from the energy sector, which potentially allows for the advance of emission monitoring and air quality assessments to an entirely new level