Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Data assimilation experiments over Europe with the Chemical Transport Model FARM
https://orcid.org/0000-0002-9229-5218
Abstract Chemical Transport Models (CTM) and air quality measurements provide complementary information on atmospheric composition. Their melding improves accuracy, important for air quality studies and to support policy makers. An assimilation scheme based on 3D-Var / OI has been developed and implemented for the CTM FARM, core of the MINNI modelling system, which is operational within the regional Copernicus Atmospheric Monitoring Service (CAMS). The scheme assimilates O3, NO2, CO, SO2, PM10 and PM2.5 concentration observations from ground stations and it can be adapted to other pollutants. Two assimilation experiments are validated with independent observations, and compared with a simulation and with CAMS Validated ReAnalysis (VRA) for the year 2018 over the European domain. Both experiments expectedly improve on the free simulation control run, and their scores are consistent with those of the VRA ensemble. In the second experiment the Spatial Consistency Test (SCT) is employed to precisely pinpoint observations affected by large errors and to prevent their assimilation. This significantly improves results that compare very well with the reanalysis ensemble median.
Highlights An assimilation scheme based on 3Dvar/O.I. is implemented on CTM FARM model. Results are consistent with CAMS ensemble reanalysis. Spatial Consistency Test improves model skill scores. Advance quality control of assimilated observations improves model estimates.
Data assimilation experiments over Europe with the Chemical Transport Model FARM
https://orcid.org/0000-0002-9229-5218
Abstract Chemical Transport Models (CTM) and air quality measurements provide complementary information on atmospheric composition. Their melding improves accuracy, important for air quality studies and to support policy makers. An assimilation scheme based on 3D-Var / OI has been developed and implemented for the CTM FARM, core of the MINNI modelling system, which is operational within the regional Copernicus Atmospheric Monitoring Service (CAMS). The scheme assimilates O3, NO2, CO, SO2, PM10 and PM2.5 concentration observations from ground stations and it can be adapted to other pollutants. Two assimilation experiments are validated with independent observations, and compared with a simulation and with CAMS Validated ReAnalysis (VRA) for the year 2018 over the European domain. Both experiments expectedly improve on the free simulation control run, and their scores are consistent with those of the VRA ensemble. In the second experiment the Spatial Consistency Test (SCT) is employed to precisely pinpoint observations affected by large errors and to prevent their assimilation. This significantly improves results that compare very well with the reanalysis ensemble median.
Highlights An assimilation scheme based on 3Dvar/O.I. is implemented on CTM FARM model. Results are consistent with CAMS ensemble reanalysis. Spatial Consistency Test improves model skill scores. Advance quality control of assimilated observations improves model estimates.
Data assimilation experiments over Europe with the Chemical Transport Model FARM
https://orcid.org/0000-0002-9229-5218
Abstract Chemical Transport Models (CTM) and air quality measurements provide complementary information on atmospheric composition. Their melding improves accuracy, important for air quality studies and to support policy makers. An assimilation scheme based on 3D-Var / OI has been developed and implemented for the CTM FARM, core of the MINNI modelling system, which is operational within the regional Copernicus Atmospheric Monitoring Service (CAMS). The scheme assimilates O3, NO2, CO, SO2, PM10 and PM2.5 concentration observations from ground stations and it can be adapted to other pollutants. Two assimilation experiments are validated with independent observations, and compared with a simulation and with CAMS Validated ReAnalysis (VRA) for the year 2018 over the European domain. Both experiments expectedly improve on the free simulation control run, and their scores are consistent with those of the VRA ensemble. In the second experiment the Spatial Consistency Test (SCT) is employed to precisely pinpoint observations affected by large errors and to prevent their assimilation. This significantly improves results that compare very well with the reanalysis ensemble median.
Highlights An assimilation scheme based on 3Dvar/O.I. is implemented on CTM FARM model. Results are consistent with CAMS ensemble reanalysis. Spatial Consistency Test improves model skill scores. Advance quality control of assimilated observations improves model estimates.
Data assimilation experiments over Europe with the Chemical Transport Model FARM
Adani, Mario (Autor:in) / Uboldi, Francesco (Autor:in)
Atmospheric Environment ; 306
23.04.2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Data Assimilation Experiments with the Navy Layered Ocean Model
| British Library Conference Proceedings | 1998
Forecasting smoke transport during compartment fires using a data assimilation model
| SAGE Publications | 2015
Chemical Data Assimilation: Computational Tools and Applications
| HENRY – Bundesanstalt für Wasserbau (BAW) | 2008
Forecasting smoke transport during compartment fires using a data assimilation model
| Online Contents | 2015