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Regional inversion of CO<sub>2</sub> ecosystem fluxes from atmospheric measurements: reliability of the uncertainty estimates Abstract. The Bayesian framework of CO2 flux inversions permits estimates of the retrieved flux uncertainties. Here, the reliability of these theoretical estimates is studied through a comparison against the misfits between the inverted fluxes and independent measurements of the CO2 Net Ecosystem Exchange (NEE) made by the eddy covariance technique at local (few hectares) scale. Regional inversions at 0.5° resolution are applied for the western European domain where ~ 50 eddy covariance sites are operated. These inversions are conducted for the period 2002–2007. They use a mesoscale atmospheric transport model, a prior estimate of the NEE from a terrestrial ecosystem model and rely on the variational assimilation of in situ continuous measurements of CO2 atmospheric mole fractions. Averaged over monthly periods and over the whole domain, the misfits are in good agreement with the theoretical uncertainties for prior and inverted NEE, and pass the chi-square test for the variance at the 30% and 5% significance levels respectively, despite the scale mismatch and the independence between the prior (respectively inverted) NEE and the flux measurements. The theoretical uncertainty reduction for the monthly NEE at the measurement sites is 53% while the inversion decreases the standard deviation of the misfits by 38%. These results build confidence in the NEE estimates at the European/monthly scales and in their theoretical uncertainty from the regional inverse modelling system. However, the uncertainties at the monthly (respectively annual) scale remain larger than the amplitude of the inter-annual variability of monthly (respectively annual) fluxes, so that this study does not engender confidence in the inter-annual variations. The uncertainties at the monthly scale are significantly smaller than the seasonal variations. The seasonal cycle of the inverted fluxes is thus reliable. In particular, the CO2 sink period over the European continent likely ends later than represented by the prior ecosystem model.
Tập 13 Số 17 - Trang 9039-9056
Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models Abstract. Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM.
Tập 15 Số 10 - Trang 5325-5358
Top-down estimates of benzene and toluene emissions in the Pearl River Delta and Hong Kong, China Abstract. Benzene (C6H6) and toluene (C7H8) are toxic to humans and the environment. They are also important precursors of ground-level ozone and secondary organic aerosols and contribute substantially to severe air pollution in urban areas in China. Discrepancies exist between different bottom-up inventories for benzene and toluene emissions in the Pearl River Delta (PRD) and Hong Kong (HK), which are emission hot spots in China. This study provides top-down estimates of benzene and toluene emissions in the PRD and HK using atmospheric measurement data from a rural site in the area, Heshan, an atmospheric transport model, and an inverse modeling method. The model simulations captured the measured mixing ratios during most pollution episodes. For the PRD and HK, the benzene emissions estimated in this study for 2010 were 44 (12–75) and 5 (2–7) Gg yr−1 for the PRD and HK, respectively, and the toluene emissions were 131 (44–218) and 6 (2–9) Gg yr−1, respectively. Temporal and spatial differences between the inversion estimate and four different bottom-up emission estimates are discussed, and it is proposed that more observations at different sites are urgently needed to better constrain benzene and toluene (and other air pollutant) emissions in the PRD and HK in the future.
Tập 16 Số 5 - Trang 3369-3382
Improvement of ozone forecast over Beijing based on ensemble Kalman filter with simultaneous adjustment of initial conditions and emissions Abstract. In order to improve the surface ozone forecast over Beijing and surrounding regions, data assimilation method integrated into a high-resolution regional air quality model and a regional air quality monitoring network are employed. Several advanced data assimilation strategies based on ensemble Kalman filter are designed to adjust O3 initial conditions, NOx initial conditions and emissions, VOCs initial conditions and emissions separately or jointly through assimilating ozone observations. As a result, adjusting precursor initial conditions demonstrates potential improvement of the 1-h ozone forecast almost as great as shown by adjusting precursor emissions. Nevertheless, either adjusting precursor initial conditions or emissions show deficiency in improving the short-term ozone forecast at suburban areas. Adjusting ozone initial values brings significant improvement to the 1-h ozone forecast, and its limitations lie in the difficulty in improving the 1-h forecast at some urban site. A simultaneous adjustment of the above five variables is found to be able to reduce these limitations and display an overall better performance in improving both the 1-h and 24-h ozone forecast over these areas. The root mean square errors of 1-h ozone forecast at urban sites and suburban sites decrease by 51% and 58% respectively compared with those in free run. Through these experiments, we found that assimilating local ozone observations is determinant for ozone forecast over the observational area, while assimilating remote ozone observations could reduce the uncertainty in regional transport ozone.
Tập 11 Số 24 - Trang 12901-12916
High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets Abstract. On-road vehicle emissions are a major contributor to
elevated air pollution levels in populous metropolitan areas. We developed a
link-level emissions inventory of vehicular pollutants, called EMBEV-Link (Link-level Emission factor Model for the BEijing Vehicle fleet),
based on multiple datasets extracted from the extensive road traffic
monitoring network that covers the entire municipality of Beijing, China
(16 400 km2). We employed the EMBEV-Link model under various traffic
scenarios to capture the significant variability in vehicle emissions,
temporally and spatially, due to the real-world traffic dynamics and the
traffic restrictions implemented by the local government. The results
revealed high carbon monoxide (CO) and total hydrocarbon (THC) emissions in
the urban area (i.e., within the Fifth Ring Road) and during rush hours,
both associated with the passenger vehicle traffic. By contrast,
considerable fractions of nitrogen oxides (NOx), fine particulate
matter (PM2.5) and black carbon (BC) emissions were present beyond the
urban area, as heavy-duty trucks (HDTs) were not allowed to drive through
the urban area during daytime. The EMBEV-Link model indicates that nonlocal
HDTs could account for 29 % and 38 % of estimated total on-road emissions of
NOx and PM2.5, which were ignored in previous conventional
emission inventories. We further combined the EMBEV-Link emission inventory
and a computationally efficient dispersion model, RapidAir®,
to simulate vehicular NOx concentrations at fine resolutions (10 m × 10 m in the entire municipality and 1 m × 1 m in the
hotspots). The simulated results indicated a close agreement with ground
observations and captured sharp concentration gradients from line sources to
ambient areas. During the nighttime when the HDT traffic restrictions are
lifted, HDTs could be responsible for approximately 10 µg m−3 of
NOx in the urban area. The uncertainties of conventional top-down
allocation methods, which were widely used to enhance the spatial resolution
of vehicle emissions, are also discussed by comparison with the EMBEV-Link
emission inventory.
Tập 19 Số 13 - Trang 8831-8843
Inverse modelling of CH<sub>4</sub> emissions for 2010–2011 using different satellite retrieval products from GOSAT and SCIAMACHY Abstract. At the beginning of 2009 new space-borne observations of dry-air column-averaged mole fractions of atmospheric methane (XCH4) became available from the Thermal And Near infrared Sensor for carbon Observations–Fourier Transform Spectrometer (TANSO-FTS) instrument on board the Greenhouse Gases Observing SATellite (GOSAT). Until April 2012 concurrent {methane (CH4) retrievals} were provided by the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) instrument on board the ENVironmental SATellite (ENVISAT). The GOSAT and SCIAMACHY XCH4 retrievals can be compared during the period of overlap. We estimate monthly average CH4 emissions between January 2010 and December 2011, using the TM5-4DVAR inverse modelling system. In addition to satellite data, high-accuracy measurements from the Cooperative Air Sampling Network of the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA ESRL) are used, providing strong constraints on the remote surface atmosphere. We discuss five inversion scenarios that make use of different GOSAT and SCIAMACHY XCH4 retrieval products, including two sets of GOSAT proxy retrievals processed independently by the Netherlands Institute for Space Research (SRON)/Karlsruhe Institute of Technology (KIT), and the University of Leicester (UL), and the RemoTeC "Full-Physics" (FP) XCH4 retrievals available from SRON/KIT. The GOSAT-based inversions show significant reductions in the root mean square (rms) difference between retrieved and modelled XCH4, and require much smaller bias corrections compared to the inversion using SCIAMACHY retrievals, reflecting the higher precision and relative accuracy of the GOSAT XCH4. Despite the large differences between the GOSAT and SCIAMACHY retrievals, 2-year average emission maps show overall good agreement among all satellite-based inversions, with consistent flux adjustment patterns, particularly across equatorial Africa and North America. Over North America, the satellite inversions result in a significant redistribution of CH4 emissions from North-East to South-Central United States. This result is consistent with recent independent studies suggesting a systematic underestimation of CH4 emissions from North American fossil fuel sources in bottom-up inventories, likely related to natural gas production facilities. Furthermore, all four satellite inversions yield lower CH4 fluxes across the Congo basin compared to the NOAA-only scenario, but higher emissions across tropical East Africa. The GOSAT and SCIAMACHY inversions show similar performance when validated against independent shipboard and aircraft observations, and XCH4 retrievals available from the Total Carbon Column Observing Network (TCCON).
Tập 15 Số 1 - Trang 113-133
High-resolution inversion of OMI formaldehyde columns to quantify isoprene emission on ecosystem-relevant scales: application to the southeast US Abstract. Isoprene emissions from vegetation have a large effect on
atmospheric chemistry and air quality. “Bottom-up” isoprene emission
inventories used in atmospheric models are based on limited vegetation
information and uncertain land cover data, leading to potentially large
errors. Satellite observations of atmospheric formaldehyde (HCHO), a
high-yield isoprene oxidation product, provide “top-down” information to
evaluate isoprene emission inventories through inverse analyses. Past inverse
analyses have however been hampered by uncertainty in the HCHO satellite
data, uncertainty in the time- and NOx-dependent yield of HCHO from
isoprene oxidation, and coarse resolution of the atmospheric models used for
the inversion. Here we demonstrate the ability to use HCHO satellite data
from OMI in a high-resolution inversion to constrain isoprene emissions on
ecosystem-relevant scales. The inversion uses the adjoint of the GEOS-Chem
chemical transport model at 0.25∘ × 0.3125∘
horizontal resolution to interpret observations over the southeast US in
August–September 2013. It takes advantage of concurrent NASA SEAC4RS
aircraft observations of isoprene and its oxidation products including HCHO
to validate the OMI HCHO data over the region, test the GEOS-Chem isoprene
oxidation mechanism and NOx environment, and independently evaluate the
inversion. This evaluation shows in particular that local model errors in
NOx concentrations propagate to biases in inferring isoprene emissions
from HCHO data. It is thus essential to correct model NOx biases, which
was done here using SEAC4RS observations but can be done more generally
using satellite NO2 data concurrently with HCHO. We find in our
inversion that isoprene emissions from the widely used MEGAN v2.1 inventory
are biased high over the southeast US by 40 % on average, although the
broad-scale distributions are correct including maximum emissions in
Arkansas/Louisiana and high base emission factors in the oak-covered Ozarks
of southeast Missouri. A particularly large discrepancy is in the Edwards
Plateau of central Texas where MEGAN v2.1 is too high by a factor of 3,
possibly reflecting errors in land cover. The lower isoprene emissions
inferred from our inversion, when implemented into GEOS-Chem, decrease
surface ozone over the southeast US by 1–3 ppb and decrease the isoprene
contribution to organic aerosol from 40 to 20 %.
Tập 18 Số 8 - Trang 5483-5497
Long-term particulate matter modeling for health effect studies in California – Part 1: Model performance on temporal and spatial variations Abstract. For the first time, a ~ decadal (9 years from 2000 to 2008) air quality model simulation with 4 km horizontal resolution over populated regions and daily time resolution has been conducted for California to provide air quality data for health effect studies. Model predictions are compared to measurements to evaluate the accuracy of the simulation with an emphasis on spatial and temporal variations that could be used in epidemiology studies. Better model performance is found at longer averaging times, suggesting that model results with averaging times ≥ 1 month should be the first to be considered in epidemiological studies. The UCD/CIT model predicts spatial and temporal variations in the concentrations of O3, PM2.5, elemental carbon (EC), organic carbon (OC), nitrate, and ammonium that meet standard modeling performance criteria when compared to monthly-averaged measurements. Predicted sulfate concentrations do not meet target performance metrics due to missing sulfur sources in the emissions. Predicted seasonal and annual variations of PM2.5, EC, OC, nitrate, and ammonium have mean fractional biases that meet the model performance criteria in 95, 100, 71, 73, and 92% of the simulated months, respectively. The base data set provides an improvement for predicted population exposure to PM concentrations in California compared to exposures estimated by central site monitors operated 1 day out of every 3 days at a few urban locations. Uncertainties in the model predictions arise from several issues. Incomplete understanding of secondary organic aerosol formation mechanisms leads to OC bias in the model results in summertime but does not affect OC predictions in winter when concentrations are typically highest. The CO and NO (species dominated by mobile emissions) results reveal temporal and spatial uncertainties associated with the mobile emissions generated by the EMFAC 2007 model. The WRF model tends to overpredict wind speed during stagnation events, leading to underpredictions of high PM concentrations, usually in winter months. The WRF model also generally underpredicts relative humidity, resulting in less particulate nitrate formation, especially during winter months. These limitations must be recognized when using data in health studies. All model results included in the current manuscript can be downloaded free of charge at http://faculty.engineering.ucdavis.edu/kleeman/ .
Tập 15 Số 6 - Trang 3445-3461
Limitations of ozone data assimilation with adjustment of NO<sub><i>x</i></sub> emissions: mixed effects on NO<sub>2</sub> forecasts over Beijing and surrounding areas Abstract. This study investigates a cross-variable ozone data assimilation (DA) method based on an ensemble Kalman filter (EnKF) that has been used in the companion study to improve ozone forecasts over Beijing and surrounding areas. The main purpose is to delve into the impacts of the cross-variable adjustment of nitrogen oxide (NOx) emissions on the nitrogen dioxide (NO2) forecasts over this region during the 2008 Beijing Olympic Games. A mixed effect on the NO2 forecasts was observed through application of the cross-variable assimilation approach in the real-data assimilation (RDA) experiments. The method improved the NO2 forecasts over almost half of the urban sites with reductions of the root mean square errors (RMSEs) by 15–36 % in contrast to big increases of the RMSEs over other urban stations by 56–239 %. Over the urban stations with negative DA impacts, improvement of the NO2 forecasts (with 7 % reduction of the RMSEs) was noticed at night and in the morning versus significant deterioration during daytime (with 190 % increase of the RMSEs), suggesting that the negative data assimilation impacts mainly occurred during daytime. Ideal-data assimilation (IDA) experiments with a box model and the same cross-variable assimilation method confirmed the mixed effects found in the RDA experiments. In the same way, NOx emission estimation was improved at night and in the morning even under large biases in the prior emission, while it deteriorated during daytime (except for the case of minor errors in the prior emission). The mixed effects observed in the cross-variable data assimilation, i.e., positive data assimilation impacts on NO2 forecasts over some urban sites, negative data assimilation impacts over the other urban sites, and weak data assimilation impacts over suburban sites, highlighted the limitations of the EnKF under strong nonlinear relationships between chemical variables. Under strong nonlinearity between daytime ozone concentrations and NOx emissions uncertainties (with large biases in the a priori emission), the EnKF may come up with inefficient or wrong adjustments to NOx emissions. The present findings reveal that bias correction is essential for the application of the EnKF in dealing with the data assimilation problem over strong nonlinear system.
Tập 16 Số 10 - Trang 6395-6405
Satellite observations of atmospheric methane and their value for quantifying methane emissions Abstract. Methane is a greenhouse gas emitted by a range of natural and anthropogenic sources. Atmospheric methane has been measured continuously from space since 2003, and new instruments are planned for launch in the near future that will greatly expand the capabilities of space-based observations. We review the value of current, future, and proposed satellite observations to better quantify and understand methane emissions through inverse analyses, from the global scale down to the scale of point sources and in combination with suborbital (surface and aircraft) data. Current global observations from Greenhouse Gases Observing Satellite (GOSAT) are of high quality but have sparse spatial coverage. They can quantify methane emissions on a regional scale (100–1000 km) through multiyear averaging. The Tropospheric Monitoring Instrument (TROPOMI), to be launched in 2017, is expected to quantify daily emissions on the regional scale and will also effectively detect large point sources. A different observing strategy by GHGSat (launched in June 2016) is to target limited viewing domains with very fine pixel resolution in order to detect a wide range of methane point sources. Geostationary observation of methane, still in the proposal stage, will have the unique capability of mapping source regions with high resolution, detecting transient "super-emitter" point sources and resolving diurnal variation of emissions from sources such as wetlands and manure. Exploiting these rapidly expanding satellite measurement capabilities to quantify methane emissions requires a parallel effort to construct high-quality spatially and sectorally resolved emission inventories. Partnership between top-down inverse analyses of atmospheric data and bottom-up construction of emission inventories is crucial to better understanding methane emission processes and subsequently informing climate policy.
Tập 16 Số 22 - Trang 14371-14396