On the use of Mobile-DOAS measurements for air quality satellite validation

2021 ◽  
Author(s):  
Alexis Merlaud ◽  
Frederik Tack ◽  
Michel Van Roozendael ◽  
Henk Eskes ◽  
John Douros
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Spatial Resolution ◽  
Atmospheric Composition ◽  
Negative Bias ◽  
Clear Sky ◽  
Satellite Validation ◽  
Resolution Model ◽  
Local Measurements ◽  
Ozone Monitoring

<p>The TROPOMI/S5p instrument was launched in October 2017, aiming to measure from space the atmospheric composition for air quality and ozone monitoring. Since 30 April 2018, TROPOMI/S5p routinely delivers NO2 tropospheric VCDs in quasi-real-time. The first comparisons between this operational TROPOMI product and measurements from the ground and aircraft generally show good correlations but also a negative bias over polluted areas. Such a bias is expected from the low spatial resolution of the CTM used in the operational TROPOMI retrieval and several studies reported a better agreement with local measurements of NO2 VCDs when using a higher resolution model for the satellite AMFs, in practice, changing the original TM5-MP for the CAMS Ensemble. We compare mobile-DOAS measurements with the two aforementioned versions of the TROPOMI retrievals (TM5-MP and CAMS). Our Mobile-DOAS measurements were performed with the BIRA-IASB Mobile-DOAS during 19 clear sky days. We sampled polluted and clean areas during TROPOMI overpasses in Belgium and Germany between June 2018 and September 2020. Beside studying the effect of the CTM model on the comparisons, we investigate the general added-values of such mobile-DOAS measurements for the validation of TROPOMI/S5p and forthcoming missions.</p>

scholarly journals Near real-time air quality forecasts using the NASA GEOS model

2021 ◽  
Author(s):  
K. Emma Knowland ◽  
Christoph Keller ◽  
Krzysztof Wargan ◽  
Brad Weir ◽  
Pamela Wales ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Real Time ◽  
Weather Forecasting ◽  
Atmospheric Composition ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Wide Range ◽  
Reactive Trace Gases ◽  
Assimilation System

<p>NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution global forecasts for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke. The assimilation system is being expanded to include chemically reactive trace gases. We discuss current capabilities of the GEOS Constituent Data Assimilation System (CoDAS) to improve atmospheric composition modeling and possible future directions, notably incorporating new observations (TROPOMI, geostationary satellites) and machine learning techniques. We show how machine learning techniques can be used to correct for sub-grid-scale variability, which further improves model estimates at a given observation site.</p>

scholarly journals Air quality modelling using the Met Office Unified Model (AQUM OS24-26): model description and initial evaluation

2013 ◽  
Vol 6 (2) ◽  
pp. 353-372 ◽  
Author(s):  
N. H. Savage ◽  
P. Agnew ◽  
L. S. Davis ◽  
C. Ordóñez ◽  
R. Thorpe ◽  
...  
Keyword(s):  
Air Quality ◽  
Evaluation Studies ◽  
Unified Model ◽  
Atmospheric Composition ◽  
Model Description ◽  
Limited Area ◽  
Negative Bias ◽  
Air Quality Model ◽  
Air Quality Modelling ◽  
Forecast Lead Time

Abstract. The on-line air quality model AQUM (Air Quality in the Unified Model) is a limited-area forecast configuration of the Met Office Unified Model which uses the UKCA (UK Chemistry and Aerosols) sub-model. AQUM has been developed with two aims: as an operational system to deliver regional air quality forecasts and as a modelling system to conduct air quality studies to inform policy decisions on emissions controls. This paper presents a description of the model and the methods used to evaluate the performance of the forecast system against the automated UK surface network of air quality monitors. Results are presented of evaluation studies conducted for a year-long period of operational forecast trials and several past cases of poor air quality episodes. The results demonstrate that AQUM tends to over-predict ozone (~8 μg m−3 mean bias for the year-long forecast), but has a good level of responsiveness to elevated ozone episode conditions – a characteristic which is essential for forecasting poor air quality episodes. AQUM is shown to have a negative bias for PM10, while for PM2.5 the negative bias is much smaller in magnitude. An analysis of speciated PM2.5 data during an episode of elevated particulate matter (PM) suggests that the PM bias occurs mainly in the coarse component. The sensitivity of model predictions to lateral boundary conditions (LBCs) has been assessed by using LBCs from two different global reanalyses and by comparing the standard, single-nested configuration with a configuration having an intermediate European nest. We conclude that, even with a much larger regional domain, the LBCs remain an important source of model error for relatively long-lived pollutants such as ozone. To place the model performance in context we compare AQUM ozone forecasts with those of another forecasting system, the MACC (Monitoring Atmospheric Composition and Climate) ensemble, for a 5-month period. An analysis of the variation of model skill with forecast lead time is presented and the insights this provides to the relative sources of error in air quality modelling are discussed.

scholarly journals A description and evaluation of an air quality model nested within global and regional composition-climate models using MetUM

2017 ◽  
Author(s):  
Lucy S. Neal ◽  
Mohit Dalvi ◽  
Gerd Folberth ◽  
Rachel N. McInnes ◽  
Paul Agnew ◽  
...  
Keyword(s):  
Air Quality ◽  
Spatial Resolution ◽  
Climate Models ◽  
Climate Model ◽  
Air Quality Model ◽  
Nested Models ◽  
Modelling Framework ◽  
Resolution Model ◽  
The Uk ◽  
The Impact

Abstract. There is a clear need for the development of modelling frameworks for both climate change and air quality to help inform policies for addressing these issues. This paper presents an initial attempt to develop a single modelling framework, by introducing a greater degree of consistency in the modelling framework by using a two-step, one-way nested configuration of models, from a global composition-climate model (GCCM) (140 km resolution) to a regional composition-climate model covering Europe (RCCM) (50 km resolution) and finally to a high (12 km) resolution model over the UK (AQUM). The latter model is used to produce routine air quality forecasts for the UK. All three models are based on the Met Office's Unified Model (MetUM). In order to better understand the impact of resolution on the downscaling of projections of future climate and air quality, we have used this nest of models to simulate a five year period using present-day emissions and under present-day climate conditions. We also consider the impact of running the higher resolution model with higher spatial resolution emissions, rather than simply regridding emissions from the RCCM. We present an evaluation of the models compared to in situ air quality observations over the UK, plus a comparison against an independent 1 km resolution gridded dataset, derived from a combination of modelling and observations. We show that using a high resolution model over the UK has some benefits in improving air quality modelling, but that the use of higher spatial resolution emissions is important to capture local variations in concentrations, particularly for primary pollutants such as nitrogen dioxide and sulphur dioxide. For secondary pollutants such as ozone and the secondary component of PM10, the benefits of a higher resolution nested model are more limited and reasons for this are discussed. This study confirms that the resolution of models is not the only factor in determining model performance - consistency between nested models is also important.

scholarly journals Monitoring and assimilation tests with TROPOMI data in the CAMS system: near-real-time total column ozone

2019 ◽  
Vol 19 (6) ◽  
pp. 3939-3962 ◽  
Author(s):  
Antje Inness ◽  
Johannes Flemming ◽  
Klaus-Peter Heue ◽  
Christophe Lerot ◽  
Diego Loyola ◽  
...  
Keyword(s):  
Real Time ◽  
Positive Impact ◽  
Atmospheric Composition ◽  
Ozone Monitoring Instrument ◽  
Total Column Ozone ◽  
Monitoring Instrument ◽  
Ozone Monitoring ◽  
Column Ozone ◽  
The Impact ◽  
Total Column

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor (S5P) satellite launched in October 2017 yields a wealth of atmospheric composition data, including retrievals of total column ozone (TCO3) that are provided in near-real-time (NRT) and off-line. The NRT TCO3 retrievals (v1.0.0–v1.1.2) have been included in the data assimilation system of the Copernicus Atmosphere Monitoring Service (CAMS), and tests to monitor the data and to carry out first assimilation experiments with them have been performed for the period 26 November 2017 to 30 November 2018. The TROPOMI TCO3 data agree to within 2 % with the CAMS analysis over large parts of the globe between 60∘ N and 60∘ S and also with TCO3 retrievals from the Ozone Monitoring Instrument (OMI) and the Global Ozone Monitoring Experiment-2 (GOME-2) that are routinely assimilated by CAMS. However, the TCO3 NRT data from TROPOMI show some retrieval anomalies at high latitudes, at low solar elevations and over snow/ice (e.g. Antarctica and snow-covered land areas in the Northern Hemisphere), where the differences with the CAMS analysis and the other data sets are larger. These differences are particularly pronounced over land in the NH during winter and spring (when they can reach up to 40 DU) and come mainly from the surface albedo climatology that is used in the NRT TROPOMI TCO3 retrieval. This climatology has a coarser horizontal resolution than the TROPOMI TCO3 data, which leads to problems in areas where there are large changes in reflectivity from pixel to pixel, e.g. pixels covered by snow/ice or not. The differences between TROPOMI and the CAMS analysis also show some dependency on scan position. The assimilation of TROPOMI TCO3 has been tested in the CAMS system for data between 60∘ N and 60∘ S and for solar elevations greater than 10∘ and is found to have a small positive impact on the ozone analysis compared to Brewer TCO3 data and an improved fit to ozone sondes in the tropical troposphere and to IAGOS aircraft profiles at West African airports. The impact of the TROPOMI data is relatively small because the CAMS analysis is already well constrained by several other ozone retrievals that are routinely assimilated. When averaged over the periods February–April and September–October 2018, differences between experiments with and without assimilation of TROPOMI data are less than 2 % for TCO3 and less than 3 % in the vertical for seasonal mean zonal mean O3 mixing ratios, with the largest relative differences found in the troposphere.

scholarly journals Nudging technique for scale bridging in air quality/climate atmospheric composition modelling

2012 ◽  
Vol 12 (8) ◽  
pp. 3677-3685 ◽  
Author(s):  
A. Maurizi ◽  
F. Russo ◽  
M. D'Isidoro ◽  
F. Tampieri
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Hot Spot ◽  
Atmospheric Composition ◽  
Po Valley ◽  
Model Response ◽  
Wide Range ◽  
Resolution Model ◽  
High Resolution Model ◽  
One Year

Abstract. The interaction between air quality and climate involves dynamical scales that cover a very wide range. Bridging these scales in numerical simulations is fundamental in studies devoted to megacity/hot-spot impacts on larger scales. A technique based on nudging is proposed as a bridging method that can couple different models at different scales. Here, nudging is used to force low resolution chemical composition models with a run of a high resolution model on a critical area. A one-year numerical experiment focused on the Po Valley hot spot is performed using the BOLCHEM model to asses the method. The results show that the model response is stable to perturbation induced by the nudging and that, taking the high resolution run as a reference, performances of the nudged run increase with respect to the non-forced run. The effect outside the forcing area depends on transport and is significant in a relevant number of events although it becomes weak on seasonal or yearly basis.

scholarly journals Monitoring and assimilation tests with TROPOMI data in the CAMS system. Part 1: Near-real time total column ozone

2018 ◽  
Author(s):  
Antje Inness ◽  
Johannes Flemming ◽  
Klaus-Peter Heue ◽  
Christophe Lerot ◽  
Diego Loyola ◽  
...  
Keyword(s):  
Real Time ◽  
Early Stage ◽  
The Other ◽  
Atmospheric Composition ◽  
Observation System ◽  
Total Column Ozone ◽  
Monitoring Instrument ◽  
Ozone Monitoring ◽  
Column Ozone ◽  
Total Column

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor (S5P) satellite launched in October 2017 yields a wealth of atmospheric composition data, including retrievals of total column ozone (TCO3) that are provided in near-real time (NRT) and off-line. These NRT TCO3 retrievals (V1.0.0) have been included in the data assimilation system of the Copernicus Atmosphere Monitoring Service (CAMS), and tests to monitor the data and to carry out first assimilation experiments with them have been performed for the period 26 November 2017 to 3 May 2018. TROPOMI was still in its commissioning phase until 24 April 2018. Nevertheless, the results show that, even at this early stage, the TROPOMI TCO3 data generally agree well with the CAMS analysis over large parts of the Globe and also with TCO3 retrievals from the Ozone Monitoring Instrument (OMI) and the Global Ozone Monitoring Experiment-2 (GOME-2) that are routinely assimilated by CAMS. However, the TCO3 NRT data from TROPOMI show some retrieval anomalies at high latitudes, at low solar elevations and over snow/ice (e.g. Antarctica) where the differences with the CAMS analysis and the other datasets are larger. These differences come mainly from the surface albedo climatology that is used in the NRT TROPOMI TCO3 retrieval. This climatology has a coarser horizontal resolution than the TROPOMI TCO3 data which leads to problems in areas where there are large changes in reflectivity from pixel to pixel, e.g. pixels covered by snow/ice or not. The assimilation of TROPOMI TCO3 has been tested in the CAMS system for data between 60° N and 60° S and for solar elevations less than 10° and is found to have only little impact on the ozone analysis, because the CAMS analysis is already well constrained by several other ozone retrievals that are routinely assimilated. Variational bias correction is applied to the TROPOMI NRT TCO3 data and successfully corrects for the biases seen in the data. Averaged over the period 26 November 2017 to 3 May 2018, difference between experiments with and without assimilation of TROPOMI data are less than 2 % for TCO3 and less than 1 % in the vertical for averaged zonal mean O3 mixing ratios. Compared to independent observation (Brewer spectrometers, ozone sondes, IAGOS ozone profiles and GAW surface measurements) the differences between the assimilation run and a run without TROPOMI assimilation are also small. The only noteworthy differences between the experiment with and without assimilation of TROPOMI data are seen compared to IAGOS profiles at West African airports where the assimilation of TROPOMI improves the fit of the CAMS analysis to the independent data. Despite the small impact of TROPOMI TCO3 in the CAMS analysis it will be beneficial to include the TROPOMI TCO3 NRT data actively in the operational NRT CAMS analysis after more tests. This will add some redundancy and resilience in the system and will allow us to use a more robust observation system in case some of the other older instruments, whose retrievals are currently assimilated by CAMS, stop working.

scholarly journals A description and evaluation of an air quality model nested within global and regional composition-climate models using MetUM

2017 ◽  
Vol 10 (11) ◽  
pp. 3941-3962 ◽  
Author(s):  
Lucy S. Neal ◽  
Mohit Dalvi ◽  
Gerd Folberth ◽  
Rachel N. McInnes ◽  
Paul Agnew ◽  
...  
Keyword(s):  
Air Quality ◽  
Spatial Resolution ◽  
Climate Models ◽  
Climate Model ◽  
Air Quality Model ◽  
Nested Models ◽  
Modelling Framework ◽  
Resolution Model ◽  
The Uk ◽  
The Impact

Abstract. There is a clear need for the development of modelling frameworks for both climate change and air quality to help inform policies for addressing these issues simultaneously. This paper presents an initial attempt to develop a single modelling framework, by introducing a greater degree of consistency in the meteorological modelling framework by using a two-step, one-way nested configuration of models, from a global composition-climate model (GCCM) (140 km resolution) to a regional composition-climate model covering Europe (RCCM) (50 km resolution) and finally to a high (12 km) resolution model over the UK (AQUM). The latter model is used to produce routine air quality forecasts for the UK. All three models are based on the Met Office's Unified Model (MetUM). In order to better understand the impact of resolution on the downscaling of projections of future climate and air quality, we have used this nest of models to simulate a 5-year period using present-day emissions and under present-day climate conditions. We also consider the impact of running the higher-resolution model with higher spatial resolution emissions, rather than simply regridding emissions from the RCCM. We present an evaluation of the models compared to in situ air quality observations over the UK, plus a comparison against an independent 1 km resolution gridded dataset, derived from a combination of modelling and observations, effectively producing an analysis of annual mean surface pollutant concentrations. We show that using a high-resolution model over the UK has some benefits in improving air quality modelling, but that the use of higher spatial resolution emissions is important to capture local variations in concentrations, particularly for primary pollutants such as nitrogen dioxide and sulfur dioxide. For secondary pollutants such as ozone and the secondary component of PM10, the benefits of a higher-resolution nested model are more limited and reasons for this are discussed. This study highlights the point that the resolution of models is not the only factor in determining model performance – consistency between nested models is also important.

scholarly journals Assessment of Tropospheric Concentrations of NO2 from the TROPOMI/Sentinel-5 Precursor for the Estimation of Long-Term Exposure to Surface NO2 over South Korea

2021 ◽  
Vol 13 (10) ◽  
pp. 1877
Author(s):  
Ukkyo Jeong ◽  
Hyunkee Hong
Keyword(s):  
South Korea ◽  
Spatial Resolution ◽  
Atmospheric Composition ◽  
Mixing Ratio ◽  
Mean Values ◽  
Mixing Ratios ◽  
Tropospheric No2 ◽  
Korean Ministry ◽  
Surface Mixing ◽  
Rural Sites

Since April 2018, the TROPOspheric Monitoring Instrument (TROPOMI) has provided data on tropospheric NO2 column concentrations (CTROPOMI) with unprecedented spatial resolution. This study aims to assess the capability of TROPOMI to acquire high spatial resolution data regarding surface NO2 mixing ratios. In general, the instrument effectively detected major and moderate sources of NO2 over South Korea with a clear weekday–weekend distinction. We compared the CTROPOMI with surface NO2 mixing ratio measurements from an extensive ground-based network over South Korea operated by the Korean Ministry of Environment (SKME; more than 570 sites), for 2019. Spatiotemporally collocated CTROPOMI and SKME showed a moderate correlation (correlation coefficient, r = 0.67), whereas their annual mean values at each site showed a higher correlation (r = 0.84). The CTROPOMI and SKME were well correlated around the Seoul metropolitan area, where significant amounts of NO2 prevailed throughout the year, whereas they showed lower correlation at rural sites. We converted the tropospheric NO2 from TROPOMI to the surface mixing ratio (STROPOMI) using the EAC4 (ECMWF Atmospheric Composition Reanalysis 4) profile shape, for quantitative comparison with the SKME. The estimated STROPOMI generally underestimated the in-situ value obtained, SKME (slope = 0.64), as reported in previous studies.

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