238 research outputs found
A geostatistical framework for quantifying the imprint of mesoscale atmospheric transport on satellite trace gas retrievals
National Aeronautics and Space Administration's Orbiting Carbon Observatoryâ2 (OCOâ2) satellite provides observations of total columnâaveraged CO2 mole fractions (X_(COâ)) at high spatial resolution that may enable novel constraints on surfaceâatmosphere 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 COâ observations and reliable representations of atmospheric transport. Since X_(COâ) 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 alongâtrack OCOâ2 soundings. We compare highâpassâfiltered (<250 km, spatial scales that primarily isolate mesoscale or finerâscale variations) alongâtrack spatial variability in X_(COâ) and X_(HâO) from OCOâ2 tracks to temporal synoptic and mesoscale variability from groundâbased X_(COâ) and X_(HâO) observed by nearby Total Carbon Column Observing Network sites. Mesoscale atmospheric transport is found to be the primary driver of alongâtrack, highâfrequency variability for OCOâ2 X_(HâO). For X_(COâ), both mesoscale transport variability and spatially coherent bias associated with other elements of the OCOâ2 retrieval state vector are important drivers of the alongâtrack variance budget
A Geostatistical Framework for Quantifying the Imprint of Mesoscale Atmospheric Transport on Satellite Trace Gas Retrievals
National Aeronautics and Space Administrationâs 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 Administrationâs 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 NASAâs OCO-2 satelliteAccounting for random errors, systematic errors, and real geophysical coherence in remotely sensed traceĂÂ 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 Ă© a prevenção e a promoção da saĂșde. A população escolar
foi escolhida como pĂșblico-alvo para aumentar a literacia para a saĂșde
e promover prĂĄticas saudĂĄveis e seguras relacionadas com os alimentos,
atravĂ©s do projeto âEducar para Prevenirâ. Foram produzidos e publicados
materiais educativos para o pĂșblico escolar e professores. Estes materiais,
que compreendem trĂȘs 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 SaĂșde Doutor Ricardo Jorge (INSA). O risco de ocorrĂȘncia
e os fatores contributivos, bem como as boas prĂĄticas, foram identificados
e usados como base para a elaboração dos materiais educativos. Adicionalmente,
foram usados materiais da Organização Mundial da SaĂșde como
o programa âCinco Chaves para uma Alimentação Mais Seguraâ. Nas prĂłximas
etapas deste projeto serĂŁo produzidos novos materiais para estudantes
contendo informação sobre a composição nutricional dos alimentos e a
compreensĂŁo da rotulagem alimentar.An important strategy to reduce food borne diseases burden is prevention
and health promotion. The studentâs population was chosen as
the target audience for improving health literacy and promoting healthy
and safe practices relating to food trough the Project âEducar para
Prevenirâ (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 âFive Keys to Safer
Foodâ 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 (CHâ) measurements with high accuracy and exceptional temporal and spatial resolution and sampling. TROPOMI CHâ 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 CHâ 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 â Improved Atmospheric Spectroscopy Databases SEOM-IAS) and found that SEOM-IAS provides the best fitting results. The most relevant update in the TROPOMI XCHâ 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 â GOSAT) and TROPOMI versus ground-based observations (Total Carbon Column Observing Network â TCCON) comparison, which is an independent verification of the correction scheme. We validate 2 years of TROPOMI CHâ data that show the good agreement of the updated TROPOMI CHâ with TCCON (â3.4â±â5.6âppb) and GOSAT (â10.3â±â16.8âppb) (mean bias and standard deviation). Low- and high-albedo scenes as well as snow-covered scenes are the most challenging for the CHâ 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 CHâ to COâ Retrieved from GOSAT Spectra
The proxy method, using the ratio of total column CHâ to COâ to reduce the effects of common biases, has been used to retrieve column-averaged dry-air mole fraction of CHâ from satellite data. The present study characterizes the remaining scattering effects in the CHâ/COâ 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 correctionânamely, airmass, 2 ÎŒm band radiance normalized with its noise level, the ratio between the partial column-averaged dry-air mole fraction of CHâ for the lower atmosphere and that for the upper atmosphere, and the difference in surface albedo between the CHâ and COâ bands. The ratio of partial column CHâ 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
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