Comparisons between Sentinel-5P TROPOMI NO2 and the European ensemble air quality forecasts of CAMS

2020 ◽  
Author(s):  
John Douros ◽  
Henk Eskes ◽  
Pepijn Veefkind
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
A Priori ◽  
Regional Scale ◽  
Point Sources ◽  
Regional Air Quality ◽  
Priori Information ◽  
The Individual ◽  
Monitoring Service ◽  
Regional Forecasts

<pre class="moz-quote-pre">In our contribution we present comparisons between TROPOMI observations of NO2 (nitrogen dioxide) and the CAMS regional forecasts and analyses for Europe. The Sentinel-5P TROPOMI instrument, launched in October 2017, provides unique observations of atmospheric trace gases at a high resolution of about 5 km, resolving individual point sources, medium-scale towns, roads and shipping routes. The datasets have a global daily coverage, but these datasets are especially well suited to test high-resolution regional-scale air quality models and provide valuable input for emission inversion systems. In Europe, the Copernicus Atmosphere Monitoring Service (CAMS) has implemented a regional air quality forecasting capability for Europe based on an ensemble of 7-9 European models, available at a resolution of 0.1x0.1 degree. <br />We discuss the different ways of making these comparisons, and present the quantitative results for summer and winter months and individual days. The models generally capture the fine-scale daily and averaged features observed by TROPOMI in much detail. We show that replacing the global 1x1 degree a-priori information in the retrieval by the regional 0.1x0.1 degree profiles of CAMS leads to significant changes (increases at hotspot emission locations) in the TROPOMI retrieved tropospheric column. Apart from comparing with the ensemble model, we also present the results for the individual CAMS models. </pre>

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.

Estimating local CH4 emissions in the Upper Silesian Coal Basin using inverse modelling

2021 ◽  
Author(s):  
Sebastian Wolff ◽  
Friedemann Reum ◽  
Christoph Kiemle ◽  
Gerhard Ehret ◽  
Mathieu Quatrevalet ◽  
...  
Keyword(s):  
Remote Sensing ◽  
A Priori ◽  
Point Sources ◽  
Airborne Lidar ◽  
Inverse Modelling ◽  
Emission Rates ◽  
Coal Basin ◽  
Upper Silesian Coal Basin ◽  
The Individual ◽  
Modelling Approach

<p>Methane (CH<sub>4</sub>) is the second most important anthropogenic greenhouse gas (GHG) with respect to radiative forcing. Since pre-industrial times, the globally averaged CH<sub>4</sub> concentration in the atmosphere has risen by a factor of 2.5. A large fraction of global anthropogenic CH<sub>4</sub> emissions originates from localized point sources, e.g. coal mine ventilation shafts. International treaties foresee GHG emission reductions, entailing independent monitoring and verification support capacities. Considering the spatially widespread distribution of point sources, remote sensing approaches are favourable, in order to enable rapid survey of larger areas. In this respect, active remote sensing by airborne lidar is promising, such as provided by the integrated-path differential-absorption lidar CHARM-F operated by DLR. Installed onboard the German research aircraft HALO, CHARM-F serves as a demonstrator for future satellite missions, e.g. MERLIN. CHARM-F simultaneously measures weighted vertical column mixing ratios of CO<sub>2</sub> and CH<sub>4</sub> below the aircraft. In spring 2018, during the CoMet field campaign, measurements were taken in the Upper Silesian Coal Basin (USCB) in Poland. The USCB is considered to be a European hotspot of CH<sub>4</sub> emissions, covering an area of approximately 50 km × 50 km. Due to the high number of coal mines and density of ventilation shafts in the USCB, individual CH<sub>4</sub> exhaust plumes can overlap. This makes simple approaches to determine the emission rates of single shafts, i.e. the cross-sectional flux method, difficult. Therefore, we use an inverse modelling approach to obtain an estimate of the individual emission rates. Specifically, we employ the Weather Research and Forecast Model (WRF) coupled to the CarbonTracker Data Assimilation Shell (CTDAS), an Ensemble Kalman Filter. CTDAS-WRF propagates an ensemble realization of the a priori CH<sub>4</sub> emissions forward in space and time, samples the simulated CH<sub>4</sub> concentrations along the measurement’s flight path, and scales the a priori emission rates to optimally fit the measured values, while remaining tied to the prior. Hereby, we obtain a regularized a posteriori best emission estimate for the individual ventilation shafts. Here, we report on the results of this inverse modelling approach, including individual and aggregated emission estimates, their uncertainties, and to which extent the data are able to constrain individual emitters independently.</p>

scholarly journals Regional-scale modelling for the assessment of atmospheric particulate matter concentrations at rural background locations in Europe

2020 ◽  
Vol 20 (11) ◽  
pp. 6395-6415
Author(s):  
Goran Gašparac ◽  
Amela Jeričević ◽  
Prashant Kumar ◽  
Branko Grisogono
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Regional Scale ◽  
Atmospheric Particulate Matter ◽  
Spatial And Temporal Variability ◽  
Pollution Transport ◽  
Air Quality Modelling ◽  
Rural Background ◽  
Stable Conditions ◽  
Regional Air Quality

Abstract. The application of regional-scale air quality models is an important tool in air quality assessment and management. For this reason, the understanding of model abilities and performances is mandatory. The main objective of this research was to investigate the spatial and temporal variability of background particulate matter (PM) concentrations, to evaluate the regional air quality modelling performance in simulating PM concentrations during statically stable conditions and to investigate processes that contribute to regionally increased PM concentrations with a focus on eastern and central Europe. The temporal and spatial variability of observed PM was analysed at 310 rural background stations in Europe during 2011. Two different regional air quality modelling systems (offline coupled European Monitoring and Evaluation Programme, EMEP, and online coupled Weather Research and Forecasting with Chemistry) were applied to simulate the transport of pollutants and to further investigate the processes that contributed to increased concentrations during high-pollution episodes. Background PM measurements from rural background stations, wind speed, surface pressure and ambient temperature data from 920 meteorological stations across Europe, classified according to the elevation, were used for the evaluation of individual model performance. Among the sea-level stations (up to 200 m), the best modelling performance, in terms of meteorology and chemistry, was found for both models. The underestimated modelled PM concentrations in some cases indicated the importance of the accurate assessment of regional air pollution transport under statically stable atmospheric conditions and the necessity of further model improvements.

scholarly journals The BErkeley Atmospheric CO<sub>2</sub> Observation Network: field calibration and evaluation of low-cost air quality sensors

2018 ◽  
Vol 11 (4) ◽  
pp. 1937-1946 ◽  
Author(s):  
Jinsol Kim ◽  
Alexis A. Shusterman ◽  
Kaitlyn J. Lieschke ◽  
Catherine Newman ◽  
Ronald C. Cohen
Keyword(s):  
Air Quality ◽  
San Francisco ◽  
Low Cost ◽  
A Priori ◽  
Sensor Nodes ◽  
Bay Area ◽  
Attractive Option ◽  
Global Background ◽  
Zero Offset ◽  
The Individual

Abstract. The newest generation of air quality sensors is small, low cost, and easy to deploy. These sensors are an attractive option for developing dense observation networks in support of regulatory activities and scientific research. They are also of interest for use by individuals to characterize their home environment and for citizen science. However, these sensors are difficult to interpret. Although some have an approximately linear response to the target analyte, that response may vary with time, temperature, and/or humidity, and the cross-sensitivity to non-target analytes can be large enough to be confounding. Standard approaches to calibration that are sufficient to account for these variations require a quantity of equipment and labor that negates the attractiveness of the sensors' low cost. Here we describe a novel calibration strategy for a set of sensors, including CO, NO, NO2, and O3, that makes use of (1) multiple co-located sensors, (2) a priori knowledge about the chemistry of NO, NO2, and O3, (3) an estimate of mean emission factors for CO, and (4) the global background of CO. The strategy requires one or more well calibrated anchor points within the network domain, but it does not require direct calibration of any of the individual low-cost sensors. The procedure nonetheless accounts for temperature and drift, in both the sensitivity and zero offset. We demonstrate this calibration on a subset of the sensors comprising BEACO2N, a distributed network of approximately 50 sensor “nodes”, each measuring CO2, CO, NO, NO2, O3 and particulate matter at 10 s time resolution and approximately 2 km spacing within the San Francisco Bay Area.

Impacts of A Priori Databases Using Six WRF Microphysics Schemes on Passive Microwave Rainfall Retrievals

2013 ◽  
Vol 30 (10) ◽  
pp. 2367-2381 ◽  
Author(s):  
Ju-Hye Kim ◽  
Dong-Bin Shin ◽  
Christian Kummerow
Keyword(s):  
A Priori ◽  
Wrf Model ◽  
Tropical Rainfall Measuring Mission ◽  
Cloud Microphysics ◽  
Passive Microwave ◽  
A Priori Information ◽  
Physically Based ◽  
Double Moment ◽  
Priori Information ◽  
The Individual

Abstract Physically based rainfall retrievals from passive microwave sensors often make use of cloud-resolving models (CRMs) to build a priori databases of potential rain structures. Each CRM, however, has its own cloud microphysics assumptions. Hence, approximated microphysics may cause uncertainties in the a priori information resulting in inaccurate rainfall estimates. This study first builds a priori databases by combining the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) observations and simulations from the Weather Research and Forecasting (WRF) model with six different cloud microphysics schemes. The microphysics schemes include the Purdue–Lin (LIN), WRF Single-Moment 6 (WSM6), Goddard Cumulus Ensemble (GCE), Thompson (THOM), WRF Double-Moment 6 (WDM6), and Morrison (MORR) schemes. As expected, the characteristics of the a priori databases are inherited from the individual cloud microphysics schemes. There are several distinct differences in the databases. Particularly, excessive graupel and snow exist with the LIN and THOM schemes, while more rainwater is incorporated into the a priori information with WDM6 than with any of the other schemes. Major results show that convective rainfall regions are not well captured by the LIN and THOM schemes-based retrievals. Rainfall distributions and their quantities retrieved from the WSM6 and WDM6 schemes-based estimations, however, show relatively better agreement with the PR observations. Based on the comparisons of the various microphysics schemes in the retrievals, it appears that differences in the a priori databases considerably affect the properties of rainfall estimations.

Assessment of the quality of TROPOMI high-spatial-resolution NO2 data products

2020 ◽  
Author(s):  
Xiaoyi Zhao ◽  
Debora Griffin ◽  
Vitali Fioletov ◽  
Chris McLinden ◽  
Alexander Cede ◽  
...  
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Near Infrared ◽  
Forecast Model ◽  
Research Data ◽  
Data Product ◽  
Air Quality Forecast ◽  
Data Products ◽  
Regional Air Quality ◽  
Quality Forecast

&lt;p&gt;The TROPOspheric Monitoring Instrument (TROPOMI) on-board the Sentinel-5 Precursor satellite (launched on 13 October 2017) is a nadir-viewing spectrometer measuring reflected sunlight in the ultraviolet, visible, near-infrared, and shortwave infrared spectral ranges. The measured spectra are used to retrieve total columns of trace gases, including nitrogen dioxide (NO&lt;sub&gt;2&lt;/sub&gt;). In this study, Pandora NO&lt;sub&gt;2&lt;/sub&gt; measurements made at three sites located in or north of the Greater Toronto Area (GTA) are used to evaluate the TROPOMI NO&lt;sub&gt;2&lt;/sub&gt; data products, including the standard Royal Netherlands Meteorological Institute (KNMI) NO&lt;sub&gt;2&lt;/sub&gt; data product and a research data product developed by Environment and Climate Change Canada (ECCC) using a high-resolution regional air quality forecast model (used in the airmass factor calculation).&lt;/p&gt;&lt;p&gt;TROPOMI pixels located upwind and downwind from the Pandora sites were analyzed using a new wind-based validation method that increases the number of coincident measurements by about a factor of five compared to standard techniques. Using this larger number of coincident measurements, this work shows that both TROPOMI and Pandora instruments can reveal detailed spatial patterns (i.e., horizontal distributions) of local and transported NO&lt;sub&gt;2&lt;/sub&gt; emissions, which can be used to evaluate regional air quality changes. The TROPOMI ECCC NO&lt;sub&gt;2&lt;/sub&gt; research data product shows improved agreement with Pandora measurements compared to the TROPOMI standard tropospheric NO&lt;sub&gt;2&lt;/sub&gt; data product, demonstrating the benefit of using the high-resolution regional air quality forecast model to derive NO&lt;sub&gt;2&lt;/sub&gt; airmass factors.&lt;/p&gt;

scholarly journals Retrieval of Canopy component temperatures through Bayesian inversion of directional thermal measurements

2009 ◽  
Vol 6 (2) ◽  
pp. 3007-3040 ◽  
Author(s):  
J. Timmermans ◽  
W. Verhoef ◽  
C. van der Tol ◽  
Z. Su
Keyword(s):  
Sensitivity Analysis ◽  
Surface Temperature ◽  
A Priori ◽  
Inversion Method ◽  
Success Rates ◽  
Brightness Temperatures ◽  
Ill Posed ◽  
Priori Information ◽  
The Individual ◽  
High Level

Abstract. In remote sensing evapotranspiration is estimated using a single surface temperature. This surface temperature is an aggregate over multiple canopy components. The temperature of the individual components can differ significantly, introducing errors in the evapotranspiration estimations. The temperature aggregate has a high level of directionality. An inversion method is presented in this paper to retrieve four canopy component temperatures from directional brightness temperatures. The Bayesian method uses both a priori information and sensor characteristics to solve the ill-posed inversion problem. The method is tested using two case studies: 1) a sensitivity analysis, using a large forward simulated dataset, and 2) in a reality study, using two datasets of two field campaigns. The results of the sensitivity analysis show that the Bayesian approach is able to retrieve the four component temperatures from directional brightness temperatures with good success rates using multi-directional sensors (ℜspectra≈0.3, ℜgonio≈0.3, and ℜAATSR≈0.5), and no improvement using mono-angular sensors (ℜ≈1). The results of the experimental study show that the approach gives good results for high LAI values (RMSEgrass=0.50 K, RMSEwheat=0.29 K, RMSEsugar beet=0.75 K, RMSEbarley=0.67 K), but for low LAI values the measurement setup provides extra disturbances in the directional brightness temperatures, RMSEyoung maize=2.85 K, RMSEmature maize=2.85 K. As these disturbances, were only present for two crops and can be eliminated using masked thermal images the method is considered successful.

scholarly journals Comparison of OMI NO<sub>2</sub> tropospheric columns with an ensemble of global and European regional air quality models

2009 ◽  
Vol 9 (5) ◽  
pp. 22271-22330 ◽  
Author(s):  
V. Huijnen ◽  
H. J. Eskes ◽  
B. Amstrup ◽  
R. Bergstrom ◽  
K. F. Boersma ◽  
...  
Keyword(s):  
Air Quality ◽  
A Priori ◽  
Chemical Mechanism ◽  
Regional Models ◽  
Surface Layers ◽  
Global Models ◽  
Tropospheric No2 ◽  
Regional Air Quality ◽  
The Mean ◽  
European Regional

Abstract. We present model results for tropospheric NO2 from 9 regional models and 2 global models that are part of the GEMS-RAQ forecast system, for July 2008 to June 2009 over Europe. These modeled NO2 columns are compared with OMI NO2 satellite retrievals and surface observations from the Dutch Air Quality Network. The participating models apply principally the same emission inventory, but vary in model resolution (0.15 to 0.5°), chemical mechanism, meteorology and transport scheme. For area-averaged columns only a small bias is found when the averaging kernel is neglected in the comparison to OMI NO2 columns. The reason for this is that TM4 a priori profiles have higher NOx concentrations in the free troposphere (where sensitivity to NO2 is high) and higher NOx concentrations in the surface layers (where sensitivity to NO2 is low) than RAQ models, effectively cancelling the effect of applying the averaging kernel. We attribute these low NO2 concentrations in the RAQ models to missing emissions from aircraft and lightning. It is also shown that the NO2 concentrations from the upper part of the troposphere (higher than 500 hPa) contribute up to 20% of the total tropospheric NO2 signal observed by OMI. Compared to the global models the RAQ models show a better correlation to the OMI NO2 observations, which are characterized by high spatial variation due to the short lifetime for NO2. The spread in the modeled tropospheric NO2 column is on average 20–40%. In summer the mean of all models is on average 46% below the OMI observations, whereas in winter the models are more in line with OMI. On the other hand the models on average under-predict surface concentrations in winter by 24% and are more in line with observations in summer. These findings suggest that OMI tropospheric columns in summer over polluted regions are biased high by about 40%. The diurnal cycle and profiles in the regional models are well in line, and the profile shapes correspond well to results from the global models. The analyses against OMI observations have proven to be very useful to initiate model improvements, and to quantify uncertainties in the retrieval product.

scholarly journals The BErkeley Atmospheric CO<sub>2</sub> Observation Network: Field Calibration and Evaluation of Low-cost Air Quality Sensors

2017 ◽  
Author(s):  
Jinsol Kim ◽  
Alexis A. Shusterman ◽  
Kaitlyn J. Lieschke ◽  
Catherine Newman ◽  
Ronald C. Cohen
Keyword(s):  
Air Quality ◽  
San Francisco ◽  
Low Cost ◽  
A Priori ◽  
Sensor Nodes ◽  
Bay Area ◽  
Attractive Option ◽  
Global Background ◽  
Zero Offset ◽  
The Individual

Abstract. The newest generation of air quality sensors is small, low cost, and easy to deploy. These sensors are an attractive option for developing dense observation networks in support of regulatory activities and scientific research. They are also of interest for use by individuals to characterize their home environment and for citizen science. However, these sensors are difficult to interpret. Although some have an approximately linear response to the target analyte, that response may vary with time, temperature, and/or humidity, and the cross-sensitivity to non-target analytes can be large enough to be confounding. Standard approaches to calibration that are sufficient to account for these variations require a quantity of equipment and labor that negates the attractiveness of the sensors’ low cost. Here we describe a novel calibration strategy for a set of sensors including CO, NO, NO2, and O3 that makes use of multiple co-located sensors, a priori knowledge about the chemistry of NO, NO2, and O3, as well as an estimate of mean emission factors for CO and the global background of CO. The strategy requires one or more well calibrated anchor points within the network domain, but it does not require direct calibration of any of the individual low-cost sensors. The procedure nonetheless accounts for temperature and drift, in both the sensitivity and zero offset. We demonstrate this calibration on a subset of the sensors comprising BEACO2N, a distributed network of approximately 50 sensor “nodes,” each measuring CO2, CO, NO, NO2, O3 and particle matter at 10 second time resolution at approximately 2 km spacing in locations surrounding the San Francisco Bay Area.

scholarly journals Reduced Middle East Air Pollution Linked to Societal Disruption

Eos ◽  
2015 ◽  
Vol 96 ◽  
Author(s):  
JoAnna Wendel
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
High Resolution ◽  
Middle East ◽  
Satellite Data ◽  
Armed Conflict ◽  
Economic Distress ◽  
The Individual

Invasions, armed conflict, sanctions, and economic distress correlate with cleaner air in high-resolution satellite data that reveal air quality at the individual city level.

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