scholarly journals Multiscale observations of NH3 around Toronto, Canada

2020 ◽  
Author(s):  
Shoma Yamanouchi ◽  
Camille Viatte ◽  
Kimberly Strong ◽  
Dylan B. A. Jones ◽  
Cathy Clerbaux ◽  
...  
Keyword(s):  
Case Studies ◽  
Biomass Burning ◽  
Grid Point ◽  
Fine Particulate Matter ◽  
Regional Scale ◽  
Model Performance ◽  
In Situ Measurements ◽  
Coefficient Of Determination ◽  
Predictive Capacity

<div> <div> <div> <p>Ammonia (NH<sub>3</sub>) is a major source of nitrates in the atmosphere, and a major source of fine particulate matter. As such, there have been increasing efforts to monitor NH<sub>3</sub>. This study examines long-term measurements of NH<sub>3</sub> around Toronto, Canada, derived from three multiscale datasets: 16 years of total column measurements using ground-based Fourier transform infrared (FTIR) spectroscopy, three years of surface in-situ measurements, and ten years of total columns from the Infrared Atmospheric Sounding Interferometer (IASI) sensor onboard the Metop satellites. These datasets were used to quantify NH<sub>3</sub> temporal variabilities (trends, inter-annual, seasonal) over Toronto to assess the observational footprint of the FTIR measurements, and two case studies of pollution events due to transport of biomass burning plumes.</p> <p>All three timeseries showed increasing trends in NH<sub>3</sub> over Toronto: 3.34 ± 0.44 %/year from 2002 to 2018 in the FTIR columns, 8.88 ± 2.49 %/year from 2013 to 2017 in the surface in-situ data, and 8.78 ± 0.84 %/year from 2008 to 2018 in the IASI columns. To assess the observational footprint of the FTIR NH<sub>3</sub> columns, correlations between the datasets were examined. The best correlation between FTIR and IASI was found for coincidence criterion of ≤ 50 km and ≤ 20 minutes, with r = 0.66 and a slope of 0.988 ± 0.058. The FTIR column and in-situ measurements were standardized and correlated, with 24-day averages and monthly averages yielding correlation coefficients of r = 0.72 and r = 0.75, respectively.<br>FTIR and IASI were also compared against the GEOS-Chem model, run at 2° by 2.5° resolution, to assess model performance and investigate correlation of the model output with local column measurements (FTIR) and measurements on a regional scale (IASI). Comparisons on a regional scale (domain spanning from 35°N to 53°N, and 93.75°W to 63.75°W) resulted in r = 0.62, and thus a coefficient of determination, which is indicative of the predictive capacity of the model, of r<sup>2</sup> = 0.38, but comparing a single model grid point against the FTIR resulted in a poorer correlation, with r<sup>2</sup> = 0.26, indicating that a finer spatial resolution is needed to adequately model the variability of NH<sub>3</sub>. This study also examines two case studies of NH<sub>3</sub> enhancements due to biomass burning plumes, in August 2014 and May 2016. In these events, enhancements in both the total columns and surface NH3, were observed.</p> </div> </div> </div>

scholarly journals Multiscale observations of NH<sub>3</sub> around Toronto, Canada

2021 ◽  
Vol 14 (2) ◽  
pp. 905-921
Author(s):  
Shoma Yamanouchi ◽  
Camille Viatte ◽  
Kimberly Strong ◽  
Erik Lutsch ◽  
Dylan B. A. Jones ◽  
...  
Keyword(s):  
Temporal Variability ◽  
Grid Point ◽  
Regional Scale ◽  
Model Performance ◽  
In Situ Measurements ◽  
Coefficient Of Determination ◽  
Spatial And Temporal Variability ◽  
Predictive Capacity ◽  
Total Column

Abstract. Ammonia (NH3) is a major source of nitrates in the atmosphere and a major source of fine particulate matter. As such, there have been increasing efforts to measure the atmospheric abundance of NH3 and its spatial and temporal variability. In this study, long-term measurements of NH3 derived from multiscale datasets are examined. These NH3 datasets include 16 years of total column measurements using Fourier transform infrared (FTIR) spectroscopy, 3 years of surface in situ measurements, and 10 years of total column measurements from the Infrared Atmospheric Sounding Interferometer (IASI). The datasets were used to quantify NH3 temporal variability over Toronto, Canada. The multiscale datasets were also compared to assess the representativeness of the FTIR measurements. All three time series showed positive trends in NH3 over Toronto: 3.34 ± 0.89 %/yr from 2002 to 2018 in the FTIR columns, 8.88 ± 5.08 %/yr from 2013 to 2017 in the surface in situ data, and 8.38 ± 1.54 %/yr from 2008 to 2018 in the IASI columns. To assess the representative scale of the FTIR NH3 columns, correlations between the datasets were examined. The best correlation between FTIR and IASI was obtained with coincidence criteria of ≤25 km and ≤20 min, with r=0.73 and a slope of 1.14 ± 0.06. Additionally, FTIR column and in situ measurements were standardized and correlated. Comparison of 24 d averages and monthly averages resulted in correlation coefficients of r=0.72 and r=0.75, respectively, although correlation without averaging to reduce high-frequency variability led to a poorer correlation, with r=0.39. The GEOS-Chem model, run at 2∘ × 2.5∘ resolution, was compared to FTIR and IASI to assess model performance and investigate the correlation of observational data and model output, both with local column measurements (FTIR) and measurements on a regional scale (IASI). Comparisons on a regional scale (a domain spanning 35 to 53∘ N and 93.75 to 63.75∘ W) resulted in r=0.57 and thus a coefficient of determination, which is indicative of the predictive capacity of the model, of r2=0.33, but comparing a single model grid point against the FTIR resulted in a poorer correlation, with r2=0.13, indicating that a finer spatial resolution is needed for modeling NH3.

scholarly journals Multiscale observations of NH<sub>3</sub> around Toronto, Canada

2020 ◽  
Author(s):  
Shoma Yamanouchi ◽  
Camille Viatte ◽  
Kimberly Strong ◽  
Erik Lutsch ◽  
Dylan B. A. Jones ◽  
...  
Keyword(s):  
Temporal Variability ◽  
Grid Point ◽  
Regional Scale ◽  
Model Performance ◽  
In Situ Measurements ◽  
Coefficient Of Determination ◽  
Spatial And Temporal Variability ◽  
Predictive Capacity ◽  
Total Column

Abstract. Ammonia (NH3) is a major source of nitrates in the atmosphere, and a major source of fine particulate matter. As such, there have been increasing efforts to measure the atmospheric abundance of NH3 and its spatial and temporal variability. In this study, long-term measurements of NH3 derived from multiscale datasets are examined. These NH3 datasets include 16 years of total column measurements using Fourier transform infrared (FTIR) spectroscopy, three years of surface in-situ measurements, and 10 years of total column measurements from the Infrared Atmospheric Sounding Interferometer (IASI). The datasets were used to quantify NH3 temporal variability over Toronto, Canada. The multiscale datasets were also compared to assess the observational footprint of the FTIR measurements. All three time series showed positive trends in NH3 over Toronto: 3.34 ± 0.46 %/year from 2002 to 2018 in the FTIR columns, 8.88 ± 2.83 %/year from 2013 to 2017 in the surface in-situ data, and 8.38 ± 0.77 %/year from 2008 to 2018 in the IASI columns. To assess the observational footprint of the FTIR NH3 columns, correlations between the datasets were examined. The best correlation between FTIR and IASI was obtained with coincidence criteria of ≤ 25 km and ≤ 20 minutes, with r = 0.73 and a slope of 1.14 ± 0.06. Additionally, FTIR column and in-situ measurements were standardized and correlated. Comparison of 24-day averages and monthly averages resulted in correlation coefficients of r = 0.72 and r = 0.75, respectively, although correlation without resampling to reduce high-frequency variability led to a poorer correlation, with r = 0.39. The GEOS-Chem model, run at 2° × 2.5° resolution, was compared against FTIR and IASI to assess model performance and investigate correlation of observational data and model output, both with local column measurements (FTIR) and measurements on a regional scale (IASI). Comparisons on a regional scale (a domain spanning 35° N to 53° N, and 93.75° W to 63.75° W) resulted in r = 0.57, and thus a coefficient of determination, which is indicative of the predictive capacity of the model, of r2 = 0.33, but comparing a single model grid point against the FTIR resulted in a poorer correlation, with r2 = 0.13, indicating that a finer spatial resolution is needed for modeling NH3.

scholarly journals Intercomparison of biomass burning aerosol optical properties from in situ and remote-sensing instruments in ORACLES-2016

2019 ◽  
Vol 19 (14) ◽  
pp. 9181-9208 ◽  
Author(s):  
Kristina Pistone ◽  
Jens Redemann ◽  
Sarah Doherty ◽  
Paquita Zuidema ◽  
Sharon Burton ◽  
...  
Keyword(s):  
Optical Properties ◽  
Case Studies ◽  
Biomass Burning ◽  
Radiative Forcing ◽  
Measurement Techniques ◽  
Single Scattering ◽  
Single Scattering Albedo ◽  
Aerosol Optical Properties ◽  
Biomass Burning Aerosol

Abstract. The total effect of aerosols, both directly and on cloud properties, remains the biggest source of uncertainty in anthropogenic radiative forcing on the climate. Correct characterization of intensive aerosol optical properties, particularly in conditions where absorbing aerosol is present, is a crucial factor in quantifying these effects. The southeast Atlantic Ocean (SEA), with seasonal biomass burning smoke plumes overlying and mixing with a persistent stratocumulus cloud deck, offers an excellent natural laboratory to make the observations necessary to understand the complexities of aerosol–cloud–radiation interactions. The first field deployment of the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) campaign was conducted in September of 2016 out of Walvis Bay, Namibia. Data collected during ORACLES-2016 are used to derive aerosol properties from an unprecedented number of simultaneous measurement techniques over this region. Here, we present results from six of the eight independent instruments or instrument combinations, all applied to measure or retrieve aerosol absorption and single-scattering albedo. Most but not all of the biomass burning aerosol was located in the free troposphere, in relative humidities typically ranging up to 60 %. We present the single-scattering albedo (SSA), absorbing and total aerosol optical depth (AAOD and AOD), and absorption, scattering, and extinction Ångström exponents (AAE, SAE, and EAE, respectively) for specific case studies looking at near-coincident and near-colocated measurements from multiple instruments, and SSAs for the broader campaign average over the month-long deployment. For the case studies, we find that SSA agrees within the measurement uncertainties between multiple instruments, though, over all cases, there is no strong correlation between values reported by one instrument and another. We also find that agreement between the instruments is more robust at higher aerosol loading (AOD400>0.4). The campaign-wide average and range shows differences in the values measured by each instrument. We find the ORACLES-2016 campaign-average SSA at 500 nm (SSA500) to be between 0.85 and 0.88, depending on the instrument considered (4STAR, AirMSPI, or in situ measurements), with the interquartile ranges for all instruments between 0.83 and 0.89. This is consistent with previous September values reported over the region (between 0.84 and 0.90 for SSA at 550nm). The results suggest that the differences observed in the campaign-average values may be dominated by instrument-specific spatial sampling differences and the natural physical variability in aerosol conditions over the SEA, rather than fundamental methodological differences.

scholarly journals Leveraging Very-High Spatial Resolution Hyperspectral and Thermal UAV Imageries for Characterizing Diurnal Indicators of Grapevine Physiology

2020 ◽  
Vol 12 (19) ◽  
pp. 3216 ◽  
Author(s):  
Matthew Maimaitiyiming ◽  
Vasit Sagan ◽  
Paheding Sidike ◽  
Maitiniyazi Maimaitijiang ◽  
Allison J. Miller ◽  
...  
Keyword(s):  
Canopy Temperature ◽  
Crop Productivity ◽  
In Situ Measurements ◽  
Sensor Technology ◽  
Stress Index ◽  
Coefficient Of Determination ◽  
Neural Net ◽  
Water Stress Index ◽  
Very High

Efficient and accurate methods to monitor crop physiological responses help growers better understand crop physiology and improve crop productivity. In recent years, developments in unmanned aerial vehicles (UAV) and sensor technology have enabled image acquisition at very-high spectral, spatial, and temporal resolutions. However, potential applications and limitations of very-high-resolution (VHR) hyperspectral and thermal UAV imaging for characterization of plant diurnal physiology remain largely unknown, due to issues related to shadow and canopy heterogeneity. In this study, we propose a canopy zone-weighting (CZW) method to leverage the potential of VHR (≤9 cm) hyperspectral and thermal UAV imageries in estimating physiological indicators, such as stomatal conductance (Gs) and steady-state fluorescence (Fs). Diurnal flights and concurrent in-situ measurements were conducted during grapevine growing seasons in 2017 and 2018 in a vineyard in Missouri, USA. We used neural net classifier and the Canny edge detection method to extract pure vine canopy from the hyperspectral and thermal images, respectively. Then, the vine canopy was segmented into three canopy zones (sunlit, nadir, and shaded) using K-means clustering based on the canopy shadow fraction and canopy temperature. Common reflectance-based spectral indices, sun-induced chlorophyll fluorescence (SIF), and simplified canopy water stress index (siCWSI) were computed as image retrievals. Using the coefficient of determination (R2) established between the image retrievals from three canopy zones and the in-situ measurements as a weight factor, weighted image retrievals were calculated and their correlation with in-situ measurements was explored. The results showed that the most frequent and the highest correlations were found for Gs and Fs, with CZW-based Photochemical reflectance index (PRI), SIF, and siCWSI (PRICZW, SIFCZW, and siCWSICZW), respectively. When all flights combined for the given field campaign date, PRICZW, SIFCZW, and siCWSICZW significantly improved the relationship with Gs and Fs. The proposed approach takes full advantage of VHR hyperspectral and thermal UAV imageries, and suggests that the CZW method is simple yet effective in estimating Gs and Fs.

scholarly journals Wildfire Smoke Particle Properties and Evolution, From Space-Based Multi-Angle Imaging II: The Williams Flats Fire during the FIREX-AQ Campaign

2020 ◽  
Vol 12 (22) ◽  
pp. 3823
Author(s):  
Katherine T. Junghenn Noyes ◽  
Ralph A. Kahn ◽  
James A. Limbacher ◽  
Zhanqing Li ◽  
Marta A. Fenn ◽  
...  
Keyword(s):  
Particle Size ◽  
Biomass Burning ◽  
In Situ Measurements ◽  
Statistical Characterization ◽  
Smoke Particle ◽  
Particle Properties ◽  
In Situ Data ◽  
Particle Property ◽  
Aircraft Data

Although the characteristics of biomass burning events and the ambient ecosystem determine emitted smoke composition, the conditions that modulate the partitioning of black carbon (BC) and brown carbon (BrC) formation are not well understood, nor are the spatial or temporal frequency of factors driving smoke particle evolution, such as hydration, coagulation, and oxidation, all of which impact smoke radiative forcing. In situ data from surface observation sites and aircraft field campaigns offer deep insight into the optical, chemical, and microphysical traits of biomass burning (BB) smoke aerosols, such as single scattering albedo (SSA) and size distribution, but cannot by themselves provide robust statistical characterization of both emitted and evolved particles. Data from the NASA Earth Observing System’s Multi-Angle Imaging SpectroRadiometer (MISR) instrument can provide at least a partial picture of BB particle properties and their evolution downwind, once properly validated. Here we use in situ data from the joint NOAA/NASA 2019 Fire Influence on Regional to Global Environments Experiment-Air Quality (FIREX-AQ) field campaign to assess the strengths and limitations of MISR-derived constraints on particle size, shape, light-absorption, and its spectral slope, as well as plume height and associated wind vectors. Based on the satellite observations, we also offer inferences about aging mechanisms effecting downwind particle evolution, such as gravitational settling, oxidation, secondary particle formation, and the combination of particle aggregation and condensational growth. This work builds upon our previous study, adding confidence to our interpretation of the remote-sensing data based on an expanded suite of in situ measurements for validation. The satellite and in situ measurements offer similar characterizations of particle property evolution as a function of smoke age for the 06 August Williams Flats Fire, and most of the key differences in particle size and absorption can be attributed to differences in sampling and changes in the plume geometry between sampling times. Whereas the aircraft data provide validation for the MISR retrievals, the satellite data offer a spatially continuous mapping of particle properties over the plume, which helps identify trends in particle property downwind evolution that are ambiguous in the sparsely sampled aircraft transects. The MISR data record is more than two decades long, offering future opportunities to study regional wildfire plume behavior statistically, where aircraft data are limited or entirely lacking.

scholarly journals Atmospheric inversion of SO<sub>2</sub> and primary aerosol emissions for the year 2010

2013 ◽  
Vol 13 (13) ◽  
pp. 6555-6573 ◽  
Author(s):  
N. Huneeus ◽  
O. Boucher ◽  
F. Chevallier
Keyword(s):  
Biomass Burning ◽  
Fossil Fuel ◽  
A Priori ◽  
Regional Scale ◽  
Model Performance ◽  
So2 Emissions ◽  
Aerosol Model ◽  
Skill Scores ◽  
Emission Fluxes ◽  
Aerosol Emissions

Abstract. Natural and anthropogenic emissions of primary aerosols and sulphur dioxide (SO2) are estimated for the year 2010 by assimilating daily total and fine mode aerosol optical depth (AOD) at 550 nm from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument into a global aerosol model of intermediate complexity. The system adjusts monthly emission fluxes over a set of predefined regions tiling the globe. The resulting aerosol emissions improve the model performance, as measured from usual skill scores, both against the assimilated observations and a set of independent ground-based measurements. The estimated emission fluxes are 67 Tg S yr−1 for SO2, 12 Tg yr−1 for black carbon (BC), 87 Tg yr−1 for particulate organic matter (POM), 17 000 Tg yr−1 for sea salt (SS, estimated at 80 % relative humidity) and 1206 Tg yr−1 for desert dust (DD). They represent a difference of +53, +73, +72, +1 and −8%, respectively, with respect to the first guess (FG) values. Constant errors throughout the regions and the year were assigned to the a priori emissions. The analysis errors are reduced with respect to the a priori ones for all species and throughout the year, they vary between 3 and 18% for SO2, 1 and 130% for biomass burning, 21 and 90 % for fossil fuel, 1 and 200% for DD and 1 and 5% for SS. The maximum errors on the global-yearly scale for the estimated fluxes (considering temporal error dependence) are 3% for SO2, 14% for BC, 11% for POM, 14% for DD and 2% for SS. These values represent a decrease as compared to the global-yearly errors from the FG of 7% for SO2, 40% for BC, 55% for POM, 81% for DD and 300% for SS. The largest error reduction, both monthly and yearly, is observed for SS and the smallest one for SO2. The sensitivity and robustness of the inversion system to the choice of the first guess emission inventory is investigated by using different combinations of inventories for industrial, fossil fuel and biomass burning sources. The initial difference in the emissions between the various set-ups is reduced after the inversion. Furthermore, at the global scale, the inversion is sensitive to the choice of the BB (biomass burning) inventory and not so much to the industrial and fossil fuel inventory. At the regional scale, however, the choice of the industrial and fossil fuel inventory can make a difference. The estimated baseline emission fluxes for SO2, BC and POM are within the estimated uncertainties of the four experiments. The resulting emissions were compared against projected emissions for the year 2010 for SO2, BC and POM. The new estimate presents larger emissions than the projections for all three species, with larger differences for SO2 than POM and BC. These projected SO2 emissions are outside the uncertainties of the estimated emission inventories.

scholarly journals Simulating Secondary Inorganic Aerosols using the chemistry transport model MOCAGE version R2.15.0

2015 ◽  
Vol 8 (4) ◽  
pp. 3593-3651 ◽  
Author(s):  
J. Guth ◽  
B. Josse ◽  
V. Marécal ◽  
M. Joly
Keyword(s):  
North America ◽  
Transport Model ◽  
Regional Scale ◽  
In Situ Measurements ◽  
Aerosol Composition ◽  
Chemistry Transport Model ◽  
Inorganic Aerosols ◽  
Modis Aod ◽  
Inorganic Aerosol

Abstract. In this study we develop a Secondary Inorganic Aerosol (SIA) module for the chemistry transport model MOCAGE developed at CNRM. Based on the thermodynamic equilibrium module ISORROPIA II, the new version of the model is evaluated both at the global scale and at the regional scale. The results show high concentrations of secondary inorganic aerosols in the most polluted regions being Europe, Asia and the eastern part of North America. Asia shows higher sulfate concentrations than other regions thanks to emissions reduction in Europe and North America. Using two simulations, one with and the other without secondary inorganic aerosol formation, the model global outputs are compared to previous studies, to MODIS AOD retrievals, and also to in situ measurements from the HTAP database. The model shows a better agreement in all geographical regions with MODIS AOD retrievals when introducing SIA. It also provides a good statistical agreement with in situ measurements of secondary inorganic aerosol composition: sulfate, nitrate and ammonium. In addition, the simulation with SIA gives generally a better agreement for secondary inorganic aerosols precursors (nitric acid, sulfur dioxide, ammonia) in particular with a reduction of the Modified Normalised Mean Bias (MNMB). At the regional scale, over Europe, the model simulation with SIA are compared to the in situ measurements from the EMEP database and shows a good agreement with secondary inorganic aerosol composition. The results at the regional scale are consistent with those obtained with the global simulations. The AIRBASE database was used to compare the model to regulated air quality pollutants being particulate matter, ozone and nitrogen dioxide concentrations. The introduction of the SIA in MOCAGE provides a reduction of the PM2.5 MNMB of 0.44 on a yearly basis and even 0.52 on a three spring months period (March, April, May) when SIA are maximum.

scholarly journals Aerosol classification by airborne high spectral resolution lidar observations

2013 ◽  
Vol 13 (5) ◽  
pp. 2487-2505 ◽  
Author(s):  
S. Groß ◽  
M. Esselborn ◽  
B. Weinzierl ◽  
M. Wirth ◽  
A. Fix ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Spectral Resolution ◽  
Classification Scheme ◽  
In Situ Measurements ◽  
High Spectral Resolution ◽  
Data Set ◽  
Aerosol Classification ◽  
High Spectral Resolution Lidar ◽  
Aerosol Types

Abstract. During four aircraft field experiments with the DLR research aircraft Falcon in 1998 (LACE), 2006 (SAMUM-1) and 2008 (SAMUM-2 and EUCAARI), airborne High Spectral Resolution Lidar (HSRL) and in situ measurements of aerosol microphysical and optical properties were performed. Altogether, the properties of six different aerosol types and aerosol mixtures – Saharan mineral dust, Saharan dust mixtures, Canadian biomass burning aerosol, African biomass burning mixture, anthropogenic pollution aerosol, and marine aerosol have been studied. On the basis of this extensive HSRL data set, we present an aerosol classification scheme which is also capable to identify mixtures of different aerosol types. We calculated mixing lines that allowed us to determine the contributing aerosol types. The aerosol classification scheme was supported by backward trajectory analysis and validated with in-situ measurements. Our results demonstrate that the developed aerosol mask is capable to identify complex stratifications with different aerosol types throughout the atmosphere.

scholarly journals Remote sensed and in situ constraints on processes affecting tropical tropospheric ozone

2007 ◽  
Vol 7 (3) ◽  
pp. 815-838 ◽  
Author(s):  
B. Sauvage ◽  
R. V. Martin ◽  
A. van Donkelaar ◽  
X. Liu ◽  
K. Chance ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Tropospheric Ozone ◽  
In Situ Measurements ◽  
Model Bias ◽  
Top Down ◽  
Upper Troposphere ◽  
In Situ Observations ◽  
Tropospheric O3 ◽  
Bias Versus

Abstract. We use a global chemical transport model (GEOS-Chem) to evaluate the consistency of satellite measurements of lightning flashes and ozone precursors with in situ measurements of tropical tropospheric ozone. The measurements are tropospheric O3, NO2, and HCHO columns from the GOME satellite instrument, lightning flashes from the OTD and LIS satellite instruments, profiles of O3, CO, and relative humidity from the MOZAIC aircraft program, and profiles of O3 from the SHADOZ ozonesonde network. We interpret these multiple data sources with our model to better understand what controls tropical tropospheric ozone. Tropical tropospheric ozone is mainly affected by lightning NOx and convection in the upper troposphere and by surface emissions in the lower troposphere. Scaling the spatial distribution of lightning in the model to the observed flashes improves the simulation of O3 in the upper troposphere by 5–20 ppbv versus in situ observations and by 1–4 Dobson Units versus GOME retrievals of tropospheric O3 columns. A lightning source strength of 6±2 Tg N/yr best represents in situ observations from aircraft and ozonesonde. Tropospheric NO2 and HCHO columns from GOME are applied to provide top-down constraints on emission inventories of NOx (biomass burning and soils) and VOCs (biomass burning). The top-down biomass burning inventory is larger than the bottom-up inventory by a factor of 2 for HCHO and alkenes, and by a factor of 2.6 for NOx over northern equatorial Africa. These emissions increase lower tropospheric O3 by 5–20 ppbv, improving the simulation versus aircraft observations, and by 4 Dobson Units versus GOME observations of tropospheric O3 columns. Emission factors in the a posteriori inventory are more consistent with a recent compilation from in situ measurements. The ozone simulation using two different dynamical schemes (GEOS-3 and GEOS-4) is evaluated versus observations; GEOS-4 better represents O3 observations by 5–15 ppbv, reflecting enhanced convective detrainment in the upper troposphere. Heterogeneous uptake of HNO3 on aerosols reduces simulated O3 by 5–7 ppbv, reducing a model bias versus in situ observations over and downwind of deserts. Exclusion of HO2 uptake on aerosols increases O3 by 5 ppbv in biomass burning regions, reducing a model bias versus MOZAIC aircraft measurements.

scholarly journals Using GOME NO<sub>2</sub> satellite data to examine regional differences in TOMCAT model performance

2004 ◽  
Vol 4 (3) ◽  
pp. 2569-2613
Author(s):  
N. H. Savage ◽  
K. S. Law ◽  
J. A. Pyle ◽  
A. Richter ◽  
H. Nüß ◽  
...  
Keyword(s):  
North America ◽  
Case Studies ◽  
Biomass Burning ◽  
Satellite Data ◽  
Atmospheric Chemistry ◽  
Regional Differences ◽  
Model Development ◽  
Vertical Mixing ◽  
Model Performance ◽  
Model Treatment

Abstract. This paper compares column measurements of NO2 made by the GOME instrument on ERS-2 to model results from the TOMCAT global CTM. The overall correlation between the model and observations is good (0.79 for the whole world, and 0.89 for north America) but the modelled columns are too large over polluted areas (gradient of 1.4 for North America and 1.9 for Europe). NO2 columns in the region of outflow from North America into the Atlantic seem too high in winter in the model compared to the GOME results, whereas the modelled columns are too small off the coast of Africa where there appear to be biomass burning plumes in the satellite data. Several hypotheses are presented to explain these discrepancies. Weaknesses in the model treatment of vertical mixing and chemistry appear to be the most likely explanations. It is shown that GOME and other satellite data will be of great value in furthering our understanding of atmospheric chemistry and in targeting and testing future model development and case studies.

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