scholarly journals What Can We Expect from Data Assimilation for Air Quality Forecast? Part I: Quantification with Academic Test Cases

2019 ◽  
Vol 36 (2) ◽  
pp. 269-279 ◽  
Author(s):  
Laurent Menut ◽  
Bertrand Bessagnet
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Nitrogen Dioxide ◽  
Atmospheric Composition ◽  
Quality Analysis ◽  
Test Cases ◽  
Ozone Formation ◽  
Air Quality Forecast ◽  
Maximum Benefit ◽  
Academic Test

Abstract Data assimilation has been successfully used for meteorology for many years and is now used more and more for atmospheric composition issues (air quality analysis and forecast). The data assimilation of pollutants remains difficult and its deployment is currently in progress. It is thus difficult to have quantitative knowledge of what we can expect as the maximum benefit. In this study we propose a simple framework to make this quantification. In this first part, the gain of data assimilation is quantified using academic but realistic test cases over an urbanized polluted area and during a summertime period favorable to ozone formation. Different data assimilation configurations are tested, corresponding to different amounts of data available for assimilation. For ozone (O3) and nitrogen dioxide (NO2), it is shown that the benefit resulting from data assimilation lasts from a few hours to a possible maximum of 60 and 21 h, respectively. Maps of the number of hours are presented, spatializing the benefit of data assimilation.

scholarly journals A complex aerosol transport event over Europe during the 2017 Storm Ophelia in CAMS forecast systems: analysis and evaluation

2020 ◽  
Author(s):  
Dimitris Akritidis ◽  
Eleni Katragkou ◽  
Aristeidis K. Georgoulias ◽  
Prodromos Zanis ◽  
Stergios Kartsios ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Data Assimilation ◽  
Qualitative Evaluation ◽  
Monitoring And Evaluation ◽  
Saharan Dust ◽  
Atmospheric Composition ◽  
Orthogonal Polarization ◽  
Aerosol Transport ◽  
Air Quality Forecast

Abstract. In mid-October 2017 Storm Ophelia crossed over western coastal Europe, inducing the combined transport of Saharan dust and Iberian biomass burning aerosols over several European areas. In this study we assess the performance of the Copernicus Atmosphere Monitoring Service (CAMS) forecast systems during this complex aerosol transport event, and the potential benefits that data assimilation and regional models could bring. To this end, CAMS global and regional day-1 forecast data are analyzed and compared against observations from passive (MODIS: Moderate resolution Imaging Spectroradiometer aboard Terra and Aqua) and active (CALIOP/CALIPSO: Cloud-Aerosol Lidar with Orthogonal Polarization aboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellites, and ground-based measurements (EMEP: European Monitoring and Evaluation Programme). The analysis of CAMS global forecast indicates that dust and smoke aerosols, discretely located on the warm and cold front of Ophelia, respectively, are affecting the aerosol atmospheric composition over Europe during the passage of the Storm. The observed MODIS Aerosol Optical Depth (AOD) values are satisfactorily reproduced by CAMS global forecast system, with a shared variance of 60 % and a fractional gross error (fge) of 0.4. The comparison with a CAMS global control simulation not including data assimilation, indicates a significant improvement in the bias due to data assimilation implementation, as the fge decreases by 32 %. The qualitative evaluation of the IFS dominant aerosol type and location against the CALIPSO observations reveals a good agreement. Regarding the footprint on air quality, both CAMS global and regional forecast systems are generally able to reproduce the observed signal of increase in surface particulate matter concentrations, with the regional component performing better in terms of bias and temporal variability. Yet, both products exhibit inconsistencies on the quantitative and temporal representation of the observed surface particulate matter enhancements, stressing the need for further development of the air quality forecast systems, for even more accurate and timely support of citizens and policy-makers.

scholarly journals What Can We Expect from Data Assimilation for Air Quality Forecast? Part II: Analysis with a Semi-Real Case

2019 ◽  
Vol 36 (7) ◽  
pp. 1433-1448 ◽  
Author(s):  
Bertrand Bessagnet ◽  
Laurent Menut ◽  
Florian Couvidat ◽  
Frédérik Meleux ◽  
Guillaume Siour ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Observational Data ◽  
Vertical Direction ◽  
Pollutant Emissions ◽  
Air Quality Model ◽  
Quality Model ◽  
Initial State ◽  
Air Quality Forecast ◽  
Maximum Benefit

AbstractAssimilation of observational data from ground stations and satellites has been identified as a technique to improve air quality model results. This study is an evaluation of the maximum benefit expected from data assimilation in chemical transport models. Various tests are performed under real meteorological conditions; the injection of various subsets of “simulated observational data” at the initial state of a forecasting period is analyzed in terms of benefit on selected criteria. This observation dataset is generated by a simulation with perturbed input data. Several criteria are defined to analyze the simulations leading to the definition of a “tipping time” to compare the behavior of simulations. Assimilating three-dimensional data instead of ground observations clearly adds value to the forecast. For the studied period and considering the expected best favorable data assimilation experiment, the maximum benefit is higher for particulate matter (PM) with tipping times exceeding 80 h; for ozone (O3) the gain is on average around 30 h. Assimilating O3 concentrations with a delta calculated on the first level and propagated over the vertical direction provides better results on O3 mean concentrations when compared with the expected best experiment corresponding to the injection of the O3 “observations” 3D dataset, but for maximum O3 concentrations the opposite behavior is observed. If data assimilation of secondary pollutant concentrations provides an improvement, assimilation of primary pollutant emissions can have beneficial impacts when compared with an assimilation of concentrations, after several days on secondary pollutants like O3 or nitrate concentrations and more quickly for the emitted primary pollutants. An assimilation of ammonia concentrations has slightly better performances on nitrate, ammonium, and PM concentrations relative to the assimilation of nitrogen or sulfur dioxides.

scholarly journals A complex aerosol transport event over Europe during the 2017 Storm Ophelia in CAMS forecast systems: analysis and evaluation

2020 ◽  
Vol 20 (21) ◽  
pp. 13557-13578 ◽  
Author(s):  
Dimitris Akritidis ◽  
Eleni Katragkou ◽  
Aristeidis K. Georgoulias ◽  
Prodromos Zanis ◽  
Stergios Kartsios ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Data Assimilation ◽  
Saharan Dust ◽  
Atmospheric Composition ◽  
Orthogonal Polarization ◽  
Aerosol Transport ◽  
Forecast System ◽  
Air Quality Forecast ◽  
Regional Forecast

Abstract. In mid-October 2017 Storm Ophelia crossed over western coastal Europe, inducing the combined transport of Saharan dust and Iberian biomass burning aerosols over several European areas. In this study we assess the performance of the Copernicus Atmosphere Monitoring Service (CAMS) forecast systems during this complex aerosol transport event and the potential benefits that data assimilation and regional models could bring. To this end, CAMS global and regional forecast data are analysed and compared against observations from passive (MODIS: Moderate Resolution Imaging Spectroradiometer aboard Terra and Aqua) and active (CALIOP/CALIPSO: Cloud-Aerosol LIdar with Orthogonal Polarization aboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite sensors and ground-based measurements (EMEP: European Monitoring and Evaluation Programme). The analysis of the CAMS global forecast indicates that dust and smoke aerosols, discretely located on the warm and cold fronts of Ophelia, respectively, were affecting the aerosol atmospheric composition over Europe during the passage of the Storm. The observed MODIS aerosol optical depth (AOD) values are satisfactorily reproduced by the CAMS global forecast system, with a correlation coefficient of 0.77 and a fractional gross error (FGE) of 0.4. The comparison with a CAMS global control simulation not including data assimilation indicates a significant improvement in the bias due to data assimilation implementation, as the FGE decreases by 32 %. The qualitative evaluation of the IFS (Integrated Forecast System) dominant-aerosol type and location against the CALIPSO observations overall reveals a good agreement. Regarding the footprint on air quality, both CAMS global and regional forecast systems are generally able to reproduce the observed signal of increase in surface particulate matter concentrations. The regional component performs better in terms of bias and temporal variability, with the correlation deteriorating over forecast time. Yet, both products exhibit inconsistencies on the quantitative and temporal representation of the observed surface particulate matter enhancements, stressing the need for further development of the air quality forecast systems for even more accurate and timely support of citizens and policy-makers.

Satellite data assimilation for air quality forecast

2008 ◽  
Vol 16 (10) ◽  
pp. 1541-1545 ◽  
Author(s):  
H. Boisgontier ◽  
V. Mallet ◽  
J.P. Berroir ◽  
M. Bocquet ◽  
I. Herlin ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Satellite Data ◽  
Air Quality Forecast ◽  
Quality Forecast

scholarly journals Data assimilation of satellite-retrieved ozone, carbon monoxide and nitrogen dioxide with ECMWF's Composition-IFS

2015 ◽  
Vol 15 (9) ◽  
pp. 5275-5303 ◽  
Author(s):  
A. Inness ◽  
A.-M. Blechschmidt ◽  
I. Bouarar ◽  
S. Chabrillat ◽  
M. Crepulja ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Data Assimilation ◽  
Nitrogen Dioxide ◽  
Initial Conditions ◽  
Stratospheric Ozone ◽  
Lower Troposphere ◽  
Atmospheric Composition ◽  
Reactive Gases ◽  
The Impact ◽  
Total Column

Abstract. Daily global analyses and 5-day forecasts are generated in the context of the European Monitoring Atmospheric Composition and Climate (MACC) project using an extended version of the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The IFS now includes modules for chemistry, deposition and emission of reactive gases, aerosols, and greenhouse gases, and the 4-dimensional variational data assimilation scheme makes use of multiple satellite observations of atmospheric composition in addition to meteorological observations. This paper describes the data assimilation setup of the new Composition-IFS (C-IFS) with respect to reactive gases and validates analysis fields of ozone (O3), carbon monoxide (CO), and nitrogen dioxide (NO2) for the year 2008 against independent observations and a control run without data assimilation. The largest improvement in CO by assimilation of Measurements of Pollution in the Troposphere (MOPITT) CO columns is seen in the lower troposphere of the Northern Hemisphere (NH) extratropics during winter, and during the South African biomass-burning season. The assimilation of several O3 total column and stratospheric profile retrievals greatly improves the total column, stratospheric and upper tropospheric O3 analysis fields relative to the control run. The impact on lower tropospheric ozone, which comes from the residual of the total column and stratospheric profile O3 data, is smaller, but nevertheless there is some improvement particularly in the NH during winter and spring. The impact of the assimilation of tropospheric NO2 columns from the Ozone Monitoring Instrument (OMI) is small because of the short lifetime of NO2, suggesting that NO2 observations would be better used to adjust emissions instead of initial conditions. The results further indicate that the quality of the tropospheric analyses and of the stratospheric ozone analysis obtained with the C-IFS system has improved compared to the previous "coupled" model system of MACC.

scholarly journals Accounting for model error in air-quality forecasts: an application of 4DEnVar to the assimilation of atmospheric composition using QG-Chem 1.0

2016 ◽  
Author(s):  
Emanuele Emili ◽  
Selime Gürol ◽  
Daniel Cariolle
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Atmospheric Chemistry ◽  
Transport Model ◽  
Model Error ◽  
Tropospheric Chemistry ◽  
Atmospheric Composition ◽  
Forecast Errors ◽  
Model Errors ◽  
Ensemble Size

Abstract. Model errors play a significant role in air-quality forecasts. Accounting for them in the data assimilation (DA) procedures is decisive to obtain improved forecasts. We address this issue using a reduced-order chemical transport model based on quasi-geostrophic dynamics and a detailed tropospheric chemistry mechanism, which we name QG-Chem. This model has been coupled to a generic software library for data assimilation and used to assess the potential of the 4DEnVar algorithm for air-quality analyses and forecasts. Among the assets of 4DEnVar, we reckon the possibility to deal with multivariate aspects of atmospheric chemistry and to account for model errors of generic type. A simple diagnostic procedure for detecting model errors is proposed, based on the 4DEnVar analysis and one additional model forecast. A large number of idealized data assimilation experiments are shown for several chemical species of relevance for air-quality forecasts (O3, NOx, CO and CO2), with very different atmospheric life-times and chemical couplings. Experiments are done both under a perfect model hypothesis and including model error through perturbation of surface chemical emissions, for two meteorological and chemical regimes. Some key elements of the 4DEnVar algorithm such as the ensemble size and localization are also discussed. A comparison with results of 3D-Var, widely used in operational centers, shows that, for some species, analyses and next day forecast errors can be halved when model error is taken in account. This result was obtained using a small ensemble size, which remain affordable for most operational centers. We conclude that 4DEnVar has a promising potential for operational air-quality models. We finally highlight areas that deserve further research for applying 4DEnVar to large scale chemistry models, i.e. localization techniques, propagation of analysis covariance between DA cycles and treatment for chemical non-linearities. QG-Chem provides a useful tool in this regard.

scholarly journals Development of the Global to Mesoscale Air Quality Forecast and Analysis System (GMAF) and Its Application to PM2.5 Forecast in Korea

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 411
Author(s):  
SeogYeon Cho ◽  
HyeonYeong Park ◽  
JeongSeok Son ◽  
LimSeok Chang
Keyword(s):  
Kalman Filter ◽  
Air Quality ◽  
Data Assimilation ◽  
Bias Correction ◽  
Bias Adjustment ◽  
Air Quality Forecast ◽  
Correction Technique ◽  
Analysis System ◽  
Mesoscale Air Quality ◽  
Quality Forecast

This paper presents the development of the global to mesoscale air quality forecast and analysis system (GMAF) and its application to particulate matter under 2.5 μm (PM2.5) forecast in Korea. The GMAF combined a mesoscale model with a global data assimilation system by the grid nudging based four-dimensional data assimilation (FDDA). The grid nudging based FDDA developed for weather forecast and analysis was extended to air quality forecast and analysis for the first time as an alternative to data assimilation of surface monitoring data. The below cloud scavenging module and the secondary organic formation module of the community multiscale air quality model (CMAQ) were modified and subsequently verified by comparing with the PM speciation observation from the PM supersite. The observation data collected from the criteria air pollutant monitoring networks in Korea were used to evaluate forecast performance of GMAF for the year of 2016. The GMAF showed good performance in forecasting the daily mean PM2.5 concentrations at Seoul; the correlation coefficient between the observed and forecasted PM2.5 concentrations was 0.78; the normalized mean error was 25%; the probability of detection for the events exceeding the national PM2.5 standard was 0.81 whereas the false alarm rate was only 0.38. Both the hybrid bias correction technique and the Kalman filter bias adjustment technique were implemented into the GMAF as postprocessors. For the continuous and the categorical performance metrics examined, the Kalman filter bias adjustment technique performed better than the hybrid bias correction technique.

scholarly journals Accounting for model error in air quality forecasts: an application of 4DEnVar to the assimilation of atmospheric composition using QG-Chem 1.0

2016 ◽  
Vol 9 (11) ◽  
pp. 3933-3959 ◽  
Author(s):  
Emanuele Emili ◽  
Selime Gürol ◽  
Daniel Cariolle
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Atmospheric Chemistry ◽  
Large Scale ◽  
Model Error ◽  
Tropospheric Chemistry ◽  
Atmospheric Composition ◽  
Forecast Errors ◽  
Model Errors ◽  
Ensemble Size

Abstract. Model errors play a significant role in air quality forecasts. Accounting for them in the data assimilation (DA) procedures is decisive to obtain improved forecasts. We address this issue using a reduced-order coupled chemistry–meteorology model based on quasi-geostrophic dynamics and a detailed tropospheric chemistry mechanism, which we name QG-Chem. This model has been coupled to the software library for the data assimilation Object Oriented Prediction System (OOPS) and used to assess the potential of the 4DEnVar algorithm for air quality analyses and forecasts. The assets of 4DEnVar include the possibility to deal with multivariate aspects of atmospheric chemistry and to account for model errors of a generic type. A simple diagnostic procedure for detecting model errors is proposed, based on the 4DEnVar analysis and one additional model forecast. A large number of idealized data assimilation experiments are shown for several chemical species of relevance for air quality forecasts (O3, NOx, CO and CO2) with very different atmospheric lifetimes and chemical couplings. Experiments are done both under a perfect model hypothesis and including model error through perturbation of surface chemical emissions. Some key elements of the 4DEnVar algorithm such as the ensemble size and localization are also discussed. A comparison with results of 3D-Var, widely used in operational centers, shows that, for some species, analysis and next-day forecast errors can be halved when model error is taken into account. This result was obtained using a small ensemble size, which remains affordable for most operational centers. We conclude that 4DEnVar has a promising potential for operational air quality models. We finally highlight areas that deserve further research for applying 4DEnVar to large-scale chemistry models, i.e., localization techniques, propagation of analysis covariance between DA cycles and treatment for chemical nonlinearities. QG-Chem can provide a useful tool in this regard.

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