scholarly journals Development of a custom OMI NO<sub>2</sub> data product for evaluating biases in a regional chemistry transport model

2015 ◽  
Vol 15 (10) ◽  
pp. 5627-5644 ◽  
Author(s):  
G. Kuhlmann ◽  
Y. F. Lam ◽  
H. M. Cheung ◽  
A. Hartl ◽  
J. C. H. Fung ◽  
...  
Keyword(s):  
Hong Kong ◽  
Air Quality ◽  
High Resolution ◽  
Correlation Coefficient ◽  
Spatial Resolution ◽  
Transport Model ◽  
Satellite Observations ◽  
Surface Reflectance ◽  
Data Set ◽  
Chemistry Transport Model

Abstract. In this paper, we present the custom Hong Kong NO2 retrieval (HKOMI) for the Ozone Monitoring Instrument (OMI) on board the Aura satellite which was used to evaluate a high-resolution chemistry transport model (CTM) (3 km × 3 km spatial resolution). The atmospheric chemistry transport was modelled in the Pearl River Delta (PRD) region in southern China by the Models-3 Community Multiscale Air Quality (CMAQ) modelling system from October 2006 to January 2007. In the HKOMI NO2 retrieval, tropospheric air mass factors (AMFs) were recalculated using high-resolution ancillary parameters of surface reflectance, a priori NO2 and aerosol profiles, of which the latter two were taken from the CMAQ simulation. We tested the influence of the ancillary parameters on the data product using four different aerosol parametrizations. Ground-level measurements by the PRD Regional Air Quality Monitoring (RAQM) network were used as additional independent measurements. The HKOMI retrieval increases estimated tropospheric NO2 vertical column densities (VCD) by (+31 ± 38)%, when compared to NASA's standard product (OMNO2-SP), and improves the normalized mean bias (NMB) between satellite and ground observations by 26 percentage points from −41 to −15%. The individual influences of the parameters are (+11.4 ± 13.4)% for NO2 profiles, (+11.0 ± 20.9)% for surface reflectance and (+6.0 ± 8.4)% for the best aerosol parametrization. The correlation coefficient r is low between ground and satellite observations (r = 0.35). The low r and the remaining NMB can be explained by the low model performance and the expected differences when comparing point measurements with area-averaged satellite observations. The correlation between CMAQ and the RAQM network is low (r ≈ 0.3) and the model underestimates the NO2 concentrations in the northwestern model domain (Foshan and Guangzhou). We compared the CMAQ NO2 time series of the two main plumes with our best OMI NO2 data set (HKOMI-4). The model overestimates the NO2 VCDs by about 15% in Hong Kong and Shenzhen, while the correlation coefficient is satisfactory (r = 0.56). In Foshan and Guangzhou, the correlation is low (r = 0.37) and the model underestimates the VCDs strongly (NMB = −40%). In addition, we estimated that the OMI VCDs are also underestimated by about 10 to 20% in Foshan and Guangzhou because of the influence of the model parameters on the AMFs. In this study, we demonstrate that the HKOMI NO2 retrieval reduces the bias of the satellite observations and how the data set can be used to study the magnitude of NO2 concentrations in a regional model at high spatial resolution of 3 × 3 km2. The low bias was achieved with recalculated AMFs using updated surface reflectance, aerosol profiles and NO2 profiles. Since unbiased concentrations are important, for example, in air pollution studies, the results of this paper can be very helpful in future model evaluation studies.

scholarly journals Evaluation of a regional chemistry transport model using a newly developed regional OMI NO<sub>2</sub> retrieval

2014 ◽  
Vol 14 (22) ◽  
pp. 31039-31090
Author(s):  
G. Kuhlmann ◽  
Y. F. Lam ◽  
H. M. Cheung ◽  
A. Hartl ◽  
J. C. H. Fung ◽  
...  
Keyword(s):  
Hong Kong ◽  
Air Quality ◽  
High Resolution ◽  
Correlation Coefficient ◽  
Atmospheric Chemistry ◽  
Transport Model ◽  
Southern China ◽  
Model Parameters ◽  
Surface Reflectance ◽  
Chemistry Transport Model

Abstract. In this paper, we evaluate a high-resolution chemistry transport model (CTM) (3 km x 3 km spatial resolution) with the new Hong Kong (HK) NO2 retrieval developed for the Ozone Monitoring Instrument (OMI) on-board the Aura satellite. The three-dimensional atmospheric chemistry was modelled in the Pearl River Delta (PRD) region in southern China by the Models-3 Community Multiscale Air Quality (CMAQ) modelling system from October 2006 to January 2007. In the HK NO2 retrieval, tropospheric air mass factors (AMF) were recalculated using high-resolution ancillary parameters of surface reflectance, NO2 profile shapes and aerosol profiles of which the latter two were taken from the CMAQ simulation. We also tested four different aerosol parametrizations. Ground level measurements by the PRD Regional Air Quality Monitoring (RAQM) network were used as additional independent measurements. The HK NO2 retrieval increases the NO2 vertical column densities (VCD) by (+31 ± 38) %, when compared to NASA's standard product (SP2), and reduces the mean bias (MB) between satellite and ground measurements by 26 percentage points from −41 to −15 %. The correlation coefficient r is low for both satellite datasets (r = 0.35) due to the high spatial variability of NO2 concentrations. The correlation between CMAQ and the RAQM network is low (r &amp;approx; 0.3) and the model underestimates the NO2 concentrations in the north-western model domain (Foshan and Guangzhou). We compared the CMAQ NO2 time series of the two main plumes with our regional OMI NO2 product. The model overestimates the NO2 VCDs by about 15 % in Hong Kong and Shenzhen, while the correlation coefficient is satisfactory (r = 0.56). In Foshan and Guangzhou, the correlation is low (r = 0.37) and the model underestimates the VCDs strongly (MB = −40 %). In addition, we estimated that the OMI VCDs are also underestimated by about 10 to 20 % in Foshan and Guangzhou because of the influence of the model parameters on the AMF. In this study, we demonstrate that the HK OMI NO2 retrieval reduces the bias of the satellite measurements and thus the dataset can be used to study the magnitude of NO2 concentrations in a regional model. The low bias can be achieved if AMFs are recalculated with more accurate surface reflectance, aerosol profiles and NO2 profiles; only NO2 profiles have been replaced in earlier studies. Since unbiased concentrations are important, for example, in air pollution studies, the results of this paper can be very helpful in future model evaluation studies.

scholarly journals Influence of low spatial resolution a priori data on tropospheric NO<sub>2</sub> satellite retrievals

2011 ◽  
Vol 4 (9) ◽  
pp. 1805-1820 ◽  
Author(s):  
A. Heckel ◽  
S.-W. Kim ◽  
G. J. Frost ◽  
A. Richter ◽  
M. Trainer ◽  
...  
Keyword(s):  
High Resolution ◽  
San Francisco ◽  
Spatial Resolution ◽  
Power Plants ◽  
Transport Model ◽  
A Priori ◽  
Satellite Observations ◽  
Chemical Transport Model ◽  
Under Sampling ◽  
Priori Information

Abstract. The retrieval of tropospheric columns of NO2 and other trace gases from satellite observations of backscattered solar radiation relies on the use of accurate a priori information. The spatial resolution of current space sensors is often significantly higher than that of the a priori datasets used, introducing uncertainties from spatial misrepresentation. In this study, the effect of spatial under-sampling of a priori data on the retrieval of NO2 columns was studied for a typical coastal area (around San Francisco). High-resolution (15 × 15 km2) NO2 a priori data from the WRF-Chem model in combination with high-resolution MODIS surface reflectance and aerosol data were used to investigate the uncertainty introduced by applying a priori data at typical global chemical transport model resolution. The results show that the relative uncertainties can be large (more than a factor of 2 if all a priori data used is at the coarsest resolution) for individual measurements, mainly due to spatial variations in NO2 profile and surface albedo, with smaller contributions from aerosols and surface height changes. Similar sensitivities are expected for other coastal regions and localised sources such as power plants, highlighting the need for high-resolution a priori data in quantitative analysis of the spatial patterns retrieved from satellite observations of tropospheric pollution.

High-Resolution Air Quality Forecasts with MOCAGE Chemistry Transport Model

2016 ◽  
pp. 563-565
Author(s):  
Mathieu Joly ◽  
Béatrice Josse ◽  
Matthieu Plu ◽  
Joaquim Arteta ◽  
Jonathan Guth ◽  
...  
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Transport Model ◽  
Chemistry Transport Model

scholarly journals The On-Line Integrated Mesoscale Chemistry Model BOLCHEM

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 192
Author(s):  
Rita Cesari ◽  
Tony Christian Landi ◽  
Massimo D’Isidoro ◽  
Mihaela Mircea ◽  
Felicita Russo ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Transport Model ◽  
Horizontal Resolution ◽  
Integration Step ◽  
Chemistry Transport Model ◽  
European Environmental Agency ◽  
On Line ◽  
One Year ◽  
Pm2.5 And Pm10 ◽  
Monitoring Service

This work presents the on-line coupled meteorology–chemistry transport model BOLCHEM, based on the hydrostatic meteorological BOLAM model, the gas chemistry module SAPRC90, and the aerosol dynamic module AERO3. It includes parameterizations to describe natural source emissions, dry and wet removal processes, as well as the transport and dispersion of air pollutants. The equations for different processes are solved on the same grid during the same integration step, by means of a time-split scheme. This paper describes the model and its performance at horizontal resolution of 0.2∘× 0.2∘ over Europe and 0.1∘× 0.1∘ in a nested configuration over Italy, for one year run (December 2009–November 2010). The model has been evaluated against the AIRBASE data of the European Environmental Agency. The basic statistics for higher resolution simulations of O3, NO2 and particulate matter concentrations (PM2.5 and PM10) have been compared with those from Copernicus Atmosphere Monitoring Service (CAMS) ensemble median. In summer, for O3 we found a correlation coefficient R of 0.72 and mean bias of 2.15 over European domain and a correlation coefficient R of 0.67 and mean bias of 2.36 over Italian domain. PM10 and PM2.5 are better reproduced in the winter, the latter with a correlation coefficient R of 0.66 and the mean bias MB of 0.35 over Italian domain.

scholarly journals TNO-MACC_II emission inventory; a multi-year (2003–2009) consistent high-resolution European emission inventory for air quality modelling

2014 ◽  
Vol 14 (20) ◽  
pp. 10963-10976 ◽  
Author(s):  
J. J. P. Kuenen ◽  
A. J. H. Visschedijk ◽  
M. Jozwicka ◽  
H. A. C. Denier van der Gon
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
High Resolution ◽  
Emission Inventory ◽  
Point Sources ◽  
Air Quality Modelling ◽  
Emission Data ◽  
Data Set ◽  
Spatially Distributed ◽  
Reported Data

Abstract. Emissions to air are reported by countries to EMEP. The emissions data are used for country compliance checking with EU emission ceilings and associated emission reductions. The emissions data are also necessary as input for air quality modelling. The quality of these "official" emissions varies across Europe. As alternative to these official emissions, a spatially explicit high-resolution emission inventory (7 × 7 km) for UNECE-Europe for all years between 2003 and 2009 for the main air pollutants was made. The primary goal was to supply air quality modellers with the input they need. The inventory was constructed by using the reported emission national totals by sector where the quality is sufficient. The reported data were analysed by sector in detail, and completed with alternative emission estimates as needed. This resulted in a complete emission inventory for all countries. For particulate matter, for each source emissions have been split in coarse and fine particulate matter, and further disaggregated to EC, OC, SO4, Na and other minerals using fractions based on the literature. Doing this at the most detailed sectoral level in the database implies that a consistent set was obtained across Europe. This allows better comparisons with observational data which can, through feedback, help to further identify uncertain sources and/or support emission inventory improvements for this highly uncertain pollutant. The resulting emission data set was spatially distributed consistently across all countries by using proxy parameters. Point sources were spatially distributed using the specific location of the point source. The spatial distribution for the point sources was made year-specific. The TNO-MACC_II is an update of the TNO-MACC emission data set. Major updates included the time extension towards 2009, use of the latest available reported data (including updates and corrections made until early 2012) and updates in distribution maps.

scholarly journals Observation and modeling of a historic African dust intrusion into the Caribbean Basin and the southern U.S. in June 2020

2021 ◽  
Author(s):  
Hongbin Yu ◽  
Qian Tan ◽  
Lillian Zhou ◽  
Yaping Zhou ◽  
Huisheng Bian ◽  
...  
Keyword(s):  
Air Quality ◽  
West Africa ◽  
Transport Model ◽  
Satellite Observations ◽  
Moist Convection ◽  
Caribbean Basin ◽  
Dust Emissions ◽  
African Dust ◽  
The Caribbean ◽  
The U.S

Abstract. This study characterizes a massive African dust intrusion into the Caribbean Basin and southern U.S. in June 2020, which is nicknamed the Godzilla dust plume, using a comprehensive set of satellite and ground-based observations (including MODIS, CALIOP, SEVIRI, AERONET, and EPA Air Quality network) and the NASA GEOS global aerosol transport model. The MODIS data record registered this massive dust intrusion event as the most intense episode over the past two decades. During this event, the aerosol optical depth observed by AERONET and MODIS peaked at 3.5 off the coast of West Africa and 1.8 in the Caribbean Basin. CALIOP observations show that the top of dust plume reached altitudes of 6–8 km in West Africa and descended to about 4 km altitude over the Caribbean Basin and 2 km over the U.S. Gulf coast. The dust plume degraded the air quality in Puerto Rico to the hazardous level, with maximum daily PM10 concentration of 453 μg m−3 recorded on June 23. The dust intrusion into the U.S. raised the PM2.5 concentration on June 27 to a level exceeding the EPA air quality standard in about 40 % of the stations in the southern U.S. Satellite observations reveal that dust emissions from convection-generated haboobs and other sources in West Africa were large albeit not extreme on a daily basis. However, the anomalous strength and northern shift of the North Atlantic Subtropical High (NASH) together with the Azores low formed a closed circulation pattern that allowed for accumulation of the dust near the African coast for about four days. When the NASH was weakened and wandered back to south, the dust outflow region was dominated by a strong African Easterly Jet that rapidly transported the accumulated dust from the coastal region toward the Caribbean Basin, resulting in the record-breaking African dust intrusion. In comparison to satellite observations, the GEOS model well reproduced the MODIS observed tracks of the meandering dust plume as it was carried by the wind systems. However, the model substantially underestimated dust emissions from haboobs and did not lift up enough dust to the middle troposphere for ensuing long-range transport. Consequently, the model largely missed the satellite-observed elevated dust plume along the cross-ocean track and underestimated the dust intrusion into the Caribbean Basin by a factor of more than 4. Modeling improvements need to focus on developing more realistic representations of moist convection, haboobs, and the vertical transport of dust.

scholarly journals Assimilation of surface NO<sub>2</sub> and O<sub>3</sub> observations into the SILAM chemistry transport model

2015 ◽  
Vol 8 (2) ◽  
pp. 191-203 ◽  
Author(s):  
J. Vira ◽  
M. Sofiev
Keyword(s):  
Transport Model ◽  
Time Of Day ◽  
Covariance Matrices ◽  
Atmospheric Composition ◽  
Integrated Modelling ◽  
Daily Maximum ◽  
Observation Error ◽  
Data Set ◽  
Chemistry Transport Model ◽  
Error Covariance

Abstract. This paper describes the assimilation of trace gas observations into the chemistry transport model SILAM (System for Integrated modeLling of Atmospheric coMposition) using the 3D-Var method. Assimilation results for the year 2012 are presented for the prominent photochemical pollutants ozone (O3) and nitrogen dioxide (NO2). Both species are covered by the AirBase observation database, which provides the observational data set used in this study. Attention was paid to the background and observation error covariance matrices, which were obtained primarily by the iterative application of a posteriori diagnostics. The diagnostics were computed separately for 2 months representing summer and winter conditions, and further disaggregated by time of day. This enabled the derivation of background and observation error covariance definitions, which included both seasonal and diurnal variation. The consistency of the obtained covariance matrices was verified using χ2 diagnostics. The analysis scores were computed for a control set of observation stations withheld from assimilation. Compared to a free-running model simulation, the correlation coefficient for daily maximum values was improved from 0.8 to 0.9 for O3 and from 0.53 to 0.63 for NO2.

scholarly journals Long-term MAX-DOAS network observations of NO<sub>2</sub> in Russia and Asia (MADRAS) during the period 2007–2012: instrumentation, elucidation of climatology, and comparisons with OMI satellite observations and global model simulations

2014 ◽  
Vol 14 (15) ◽  
pp. 7909-7927 ◽  
Author(s):  
Y. Kanaya ◽  
H. Irie ◽  
H. Takashima ◽  
H. Iwabuchi ◽  
H. Akimoto ◽  
...  
Keyword(s):  
Satellite Data ◽  
Transport Model ◽  
Satellite Observations ◽  
Global Model ◽  
Optical Absorption Spectroscopy ◽  
Ozone Monitoring Instrument ◽  
Vertical Column ◽  
Data Set ◽  
Model Simulations

Abstract. We conducted long-term network observations using standardized Multi-Axis Differential optical absorption spectroscopy (MAX-DOAS) instruments in Russia and ASia (MADRAS) from 2007 onwards and made the first synthetic data analysis. At seven locations (Cape Hedo, Fukue and Yokosuka in Japan, Hefei in China, Gwangju in Korea, and Tomsk and Zvenigorod in Russia) with different levels of pollution, we obtained 80 927 retrievals of tropospheric NO2 vertical column density (TropoNO2VCD) and aerosol optical depth (AOD). In the technique, the optimal estimation of the TropoNO2VCD and its profile was performed using aerosol information derived from O4 absorbances simultaneously observed at 460–490 nm. This large data set was used to analyze NO2 climatology systematically, including temporal variations from the seasonal to the diurnal scale. The results were compared with Ozone Monitoring Instrument (OMI) satellite observations and global model simulations. Two NO2 retrievals of OMI satellite data (NASA ver. 2.1 and Dutch OMI NO2 (DOMINO) ver. 2.0) generally showed close correlations with those derived from MAX-DOAS observations, but had low biases of up to ~50%. The bias was distinct when NO2 was abundantly present near the surface and when the AOD was high, suggesting a possibility of incomplete accounting of NO2 near the surface under relatively high aerosol conditions for the satellite observations. Except for constant biases, the satellite observations showed nearly perfect seasonal agreement with MAX-DOAS observations, suggesting that the analysis of seasonal features of the satellite data were robust. Weekend reduction in the TropoNO2VCD found at Yokosuka and Gwangju was absent at Hefei, implying that the major sources had different weekly variation patterns. While the TropoNO2VCD generally decreased during the midday hours, it increased exceptionally at urban/suburban locations (Yokosuka, Gwangju, and Hefei) during winter. A global chemical transport model, MIROC-ESM-CHEM (Model for Interdisciplinary Research on Climate–Earth System Model–Chemistry), was validated for the first time with respect to background NO2 column densities during summer at Cape Hedo and Fukue in the clean marine atmosphere.

scholarly journals A large-scale, high-resolution hydrological model parameter data set for climate change impact assessment for the conterminous US

2014 ◽  
Vol 18 (1) ◽  
pp. 67-84 ◽  
Author(s):  
A. A. Oubeidillah ◽  
S.-C. Kao ◽  
M. Ashfaq ◽  
B. S. Naz ◽  
G. Tootle
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Impact Assessment ◽  
Spatial Resolution ◽  
Climate Impact ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Data Set ◽  
Model Parameter ◽  
Climate Impact Assessment

Abstract. To extend geographical coverage, refine spatial resolution, and improve modeling efficiency, a computation- and data-intensive effort was conducted to organize a comprehensive hydrologic data set with post-calibrated model parameters for hydro-climate impact assessment. Several key inputs for hydrologic simulation – including meteorologic forcings, soil, land class, vegetation, and elevation – were collected from multiple best-available data sources and organized for 2107 hydrologic subbasins (8-digit hydrologic units, HUC8s) in the conterminous US at refined 1/24° (~4 km) spatial resolution. Using high-performance computing for intensive model calibration, a high-resolution parameter data set was prepared for the macro-scale variable infiltration capacity (VIC) hydrologic model. The VIC simulation was driven by Daymet daily meteorological forcing and was calibrated against US Geological Survey (USGS) WaterWatch monthly runoff observations for each HUC8. The results showed that this new parameter data set may help reasonably simulate runoff at most US HUC8 subbasins. Based on this exhaustive calibration effort, it is now possible to accurately estimate the resources required for further model improvement across the entire conterminous US. We anticipate that through this hydrologic parameter data set, the repeated effort of fundamental data processing can be lessened, so that research efforts can emphasize the more challenging task of assessing climate change impacts. The pre-organized model parameter data set will be provided to interested parties to support further hydro-climate impact assessment.

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