Evaluation of Regional Air Quality Models over Sydney and Australia: Part 1—Meteorological Model Comparison

The ability of meteorological models to accurately characterise regional meteorology plays a crucial role in the performance of photochemical simulations of air pollution. As part of the research funded by the Australian government’s Department of the Environment Clean Air and Urban Landscape hub, this study set out to complete an intercomparison of air quality models over the Sydney region. This intercomparison would test existing modelling capabilities, identify any problems and provide the necessary validation of models in the region. The first component of the intercomparison study was to assess the ability of the models to reproduce meteorological observations, since it is a significant driver of air quality. To evaluate the meteorological component of these air quality modelling systems, seven different simulations based on varying configurations of inputs, integrations and physical parameterizations of two meteorological models (the Weather Research and Forecasting (WRF) and Conformal Cubic Atmospheric Model (CCAM)) were examined. The modelling was conducted for three periods coinciding with comprehensive air quality measurement campaigns (the Sydney Particle Studies (SPS) 1 and 2 and the Measurement of Urban, Marine and Biogenic Air (MUMBA)). The analysis focuses on meteorological variables (temperature, mixing ratio of water, wind (via wind speed and zonal wind components), precipitation and planetary boundary layer height), that are relevant to air quality. The surface meteorology simulations were evaluated against observations from seven Bureau of Meteorology (BoM) Automatic Weather Stations through composite diurnal plots, Taylor plots and paired mean bias plots. Simulated vertical profiles of temperature, mixing ratio of water and wind (via wind speed and zonal wind components) were assessed through comparison with radiosonde data from the Sydney Airport BoM site. The statistical comparisons with observations identified systematic overestimations of wind speeds that were more pronounced overnight. The temperature was well simulated, with biases generally between ±2 °C and the largest biases seen overnight (up to 4 °C). The models tend to have a drier lower atmosphere than observed, implying that better representations of soil moisture and surface moisture fluxes would improve the subsequent air quality simulations. On average the models captured local-scale meteorological features, like the sea breeze, which is a critical feature driving ozone formation in the Sydney Basin. The overall performance and model biases were generally within the recommended benchmark values (e.g., ±1 °C mean bias in temperature, ±1 g/kg mean bias of water vapour mixing ratio and ±1.5 m s<sup<−1</sup< mean bias of wind speed) except at either end of the scale, where the bias tends to be larger. The model biases reported here are similar to those seen in other model intercomparisons. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Khalia Monk [verfasserIn] Elise-Andrée Guérette [verfasserIn] Clare Paton-Walsh [verfasserIn] Jeremy D. Silver [verfasserIn] Kathryn M. Emmerson [verfasserIn] Steven R. Utembe [verfasserIn] Yang Zhang [verfasserIn] Alan D. Griffiths [verfasserIn] Lisa T.-C. Chang [verfasserIn] Hiep N. Duc [verfasserIn] Toan Trieu [verfasserIn] Yvonne Scorgie [verfasserIn] Martin E. Cope [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	model evaluation meteorological modelling air quality modelling Clean Air and Urban Landscapes Hub NSW Australia

Übergeordnetes Werk:	In: Atmosphere - MDPI AG, 2011, 10(2019), 7, p 374
Übergeordnetes Werk:	volume:10 ; year:2019 ; number:7, p 374

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/atmos10070374

Katalog-ID:	DOAJ042408172

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allfields	10.3390/atmos10070374 doi (DE-627)DOAJ042408172 (DE-599)DOAJe83c75dfba534d53bf5c6004f9ad40d0 DE-627 ger DE-627 rakwb eng QC851-999 Khalia Monk verfasserin aut Evaluation of Regional Air Quality Models over Sydney and Australia: Part 1—Meteorological Model Comparison 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The ability of meteorological models to accurately characterise regional meteorology plays a crucial role in the performance of photochemical simulations of air pollution. As part of the research funded by the Australian government’s Department of the Environment Clean Air and Urban Landscape hub, this study set out to complete an intercomparison of air quality models over the Sydney region. This intercomparison would test existing modelling capabilities, identify any problems and provide the necessary validation of models in the region. The first component of the intercomparison study was to assess the ability of the models to reproduce meteorological observations, since it is a significant driver of air quality. To evaluate the meteorological component of these air quality modelling systems, seven different simulations based on varying configurations of inputs, integrations and physical parameterizations of two meteorological models (the Weather Research and Forecasting (WRF) and Conformal Cubic Atmospheric Model (CCAM)) were examined. The modelling was conducted for three periods coinciding with comprehensive air quality measurement campaigns (the Sydney Particle Studies (SPS) 1 and 2 and the Measurement of Urban, Marine and Biogenic Air (MUMBA)). The analysis focuses on meteorological variables (temperature, mixing ratio of water, wind (via wind speed and zonal wind components), precipitation and planetary boundary layer height), that are relevant to air quality. The surface meteorology simulations were evaluated against observations from seven Bureau of Meteorology (BoM) Automatic Weather Stations through composite diurnal plots, Taylor plots and paired mean bias plots. Simulated vertical profiles of temperature, mixing ratio of water and wind (via wind speed and zonal wind components) were assessed through comparison with radiosonde data from the Sydney Airport BoM site. The statistical comparisons with observations identified systematic overestimations of wind speeds that were more pronounced overnight. The temperature was well simulated, with biases generally between ±2 °C and the largest biases seen overnight (up to 4 °C). The models tend to have a drier lower atmosphere than observed, implying that better representations of soil moisture and surface moisture fluxes would improve the subsequent air quality simulations. On average the models captured local-scale meteorological features, like the sea breeze, which is a critical feature driving ozone formation in the Sydney Basin. The overall performance and model biases were generally within the recommended benchmark values (e.g., ±1 °C mean bias in temperature, ±1 g/kg mean bias of water vapour mixing ratio and ±1.5 m s<sup<−1</sup< mean bias of wind speed) except at either end of the scale, where the bias tends to be larger. The model biases reported here are similar to those seen in other model intercomparisons. model evaluation meteorological modelling air quality modelling Clean Air and Urban Landscapes Hub NSW Australia Meteorology. Climatology Elise-Andrée Guérette verfasserin aut Clare Paton-Walsh verfasserin aut Jeremy D. Silver verfasserin aut Kathryn M. Emmerson verfasserin aut Steven R. Utembe verfasserin aut Yang Zhang verfasserin aut Alan D. Griffiths verfasserin aut Lisa T.-C. Chang verfasserin aut Hiep N. Duc verfasserin aut Toan Trieu verfasserin aut Yvonne Scorgie verfasserin aut Martin E. Cope verfasserin aut In Atmosphere MDPI AG, 2011 10(2019), 7, p 374 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:10 year:2019 number:7, p 374 https://doi.org/10.3390/atmos10070374 kostenfrei https://doaj.org/article/e83c75dfba534d53bf5c6004f9ad40d0 kostenfrei https://www.mdpi.com/2073-4433/10/7/374 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2019 7, p 374
spelling	10.3390/atmos10070374 doi (DE-627)DOAJ042408172 (DE-599)DOAJe83c75dfba534d53bf5c6004f9ad40d0 DE-627 ger DE-627 rakwb eng QC851-999 Khalia Monk verfasserin aut Evaluation of Regional Air Quality Models over Sydney and Australia: Part 1—Meteorological Model Comparison 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The ability of meteorological models to accurately characterise regional meteorology plays a crucial role in the performance of photochemical simulations of air pollution. As part of the research funded by the Australian government’s Department of the Environment Clean Air and Urban Landscape hub, this study set out to complete an intercomparison of air quality models over the Sydney region. This intercomparison would test existing modelling capabilities, identify any problems and provide the necessary validation of models in the region. The first component of the intercomparison study was to assess the ability of the models to reproduce meteorological observations, since it is a significant driver of air quality. To evaluate the meteorological component of these air quality modelling systems, seven different simulations based on varying configurations of inputs, integrations and physical parameterizations of two meteorological models (the Weather Research and Forecasting (WRF) and Conformal Cubic Atmospheric Model (CCAM)) were examined. The modelling was conducted for three periods coinciding with comprehensive air quality measurement campaigns (the Sydney Particle Studies (SPS) 1 and 2 and the Measurement of Urban, Marine and Biogenic Air (MUMBA)). The analysis focuses on meteorological variables (temperature, mixing ratio of water, wind (via wind speed and zonal wind components), precipitation and planetary boundary layer height), that are relevant to air quality. The surface meteorology simulations were evaluated against observations from seven Bureau of Meteorology (BoM) Automatic Weather Stations through composite diurnal plots, Taylor plots and paired mean bias plots. Simulated vertical profiles of temperature, mixing ratio of water and wind (via wind speed and zonal wind components) were assessed through comparison with radiosonde data from the Sydney Airport BoM site. The statistical comparisons with observations identified systematic overestimations of wind speeds that were more pronounced overnight. The temperature was well simulated, with biases generally between ±2 °C and the largest biases seen overnight (up to 4 °C). The models tend to have a drier lower atmosphere than observed, implying that better representations of soil moisture and surface moisture fluxes would improve the subsequent air quality simulations. On average the models captured local-scale meteorological features, like the sea breeze, which is a critical feature driving ozone formation in the Sydney Basin. The overall performance and model biases were generally within the recommended benchmark values (e.g., ±1 °C mean bias in temperature, ±1 g/kg mean bias of water vapour mixing ratio and ±1.5 m s<sup<−1</sup< mean bias of wind speed) except at either end of the scale, where the bias tends to be larger. The model biases reported here are similar to those seen in other model intercomparisons. model evaluation meteorological modelling air quality modelling Clean Air and Urban Landscapes Hub NSW Australia Meteorology. Climatology Elise-Andrée Guérette verfasserin aut Clare Paton-Walsh verfasserin aut Jeremy D. Silver verfasserin aut Kathryn M. Emmerson verfasserin aut Steven R. Utembe verfasserin aut Yang Zhang verfasserin aut Alan D. Griffiths verfasserin aut Lisa T.-C. Chang verfasserin aut Hiep N. Duc verfasserin aut Toan Trieu verfasserin aut Yvonne Scorgie verfasserin aut Martin E. Cope verfasserin aut In Atmosphere MDPI AG, 2011 10(2019), 7, p 374 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:10 year:2019 number:7, p 374 https://doi.org/10.3390/atmos10070374 kostenfrei https://doaj.org/article/e83c75dfba534d53bf5c6004f9ad40d0 kostenfrei https://www.mdpi.com/2073-4433/10/7/374 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2019 7, p 374
allfields_unstemmed	10.3390/atmos10070374 doi (DE-627)DOAJ042408172 (DE-599)DOAJe83c75dfba534d53bf5c6004f9ad40d0 DE-627 ger DE-627 rakwb eng QC851-999 Khalia Monk verfasserin aut Evaluation of Regional Air Quality Models over Sydney and Australia: Part 1—Meteorological Model Comparison 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The ability of meteorological models to accurately characterise regional meteorology plays a crucial role in the performance of photochemical simulations of air pollution. As part of the research funded by the Australian government’s Department of the Environment Clean Air and Urban Landscape hub, this study set out to complete an intercomparison of air quality models over the Sydney region. This intercomparison would test existing modelling capabilities, identify any problems and provide the necessary validation of models in the region. The first component of the intercomparison study was to assess the ability of the models to reproduce meteorological observations, since it is a significant driver of air quality. To evaluate the meteorological component of these air quality modelling systems, seven different simulations based on varying configurations of inputs, integrations and physical parameterizations of two meteorological models (the Weather Research and Forecasting (WRF) and Conformal Cubic Atmospheric Model (CCAM)) were examined. The modelling was conducted for three periods coinciding with comprehensive air quality measurement campaigns (the Sydney Particle Studies (SPS) 1 and 2 and the Measurement of Urban, Marine and Biogenic Air (MUMBA)). The analysis focuses on meteorological variables (temperature, mixing ratio of water, wind (via wind speed and zonal wind components), precipitation and planetary boundary layer height), that are relevant to air quality. The surface meteorology simulations were evaluated against observations from seven Bureau of Meteorology (BoM) Automatic Weather Stations through composite diurnal plots, Taylor plots and paired mean bias plots. Simulated vertical profiles of temperature, mixing ratio of water and wind (via wind speed and zonal wind components) were assessed through comparison with radiosonde data from the Sydney Airport BoM site. The statistical comparisons with observations identified systematic overestimations of wind speeds that were more pronounced overnight. The temperature was well simulated, with biases generally between ±2 °C and the largest biases seen overnight (up to 4 °C). The models tend to have a drier lower atmosphere than observed, implying that better representations of soil moisture and surface moisture fluxes would improve the subsequent air quality simulations. On average the models captured local-scale meteorological features, like the sea breeze, which is a critical feature driving ozone formation in the Sydney Basin. The overall performance and model biases were generally within the recommended benchmark values (e.g., ±1 °C mean bias in temperature, ±1 g/kg mean bias of water vapour mixing ratio and ±1.5 m s<sup<−1</sup< mean bias of wind speed) except at either end of the scale, where the bias tends to be larger. The model biases reported here are similar to those seen in other model intercomparisons. model evaluation meteorological modelling air quality modelling Clean Air and Urban Landscapes Hub NSW Australia Meteorology. Climatology Elise-Andrée Guérette verfasserin aut Clare Paton-Walsh verfasserin aut Jeremy D. Silver verfasserin aut Kathryn M. Emmerson verfasserin aut Steven R. Utembe verfasserin aut Yang Zhang verfasserin aut Alan D. Griffiths verfasserin aut Lisa T.-C. Chang verfasserin aut Hiep N. Duc verfasserin aut Toan Trieu verfasserin aut Yvonne Scorgie verfasserin aut Martin E. Cope verfasserin aut In Atmosphere MDPI AG, 2011 10(2019), 7, p 374 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:10 year:2019 number:7, p 374 https://doi.org/10.3390/atmos10070374 kostenfrei https://doaj.org/article/e83c75dfba534d53bf5c6004f9ad40d0 kostenfrei https://www.mdpi.com/2073-4433/10/7/374 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2019 7, p 374
allfieldsGer	10.3390/atmos10070374 doi (DE-627)DOAJ042408172 (DE-599)DOAJe83c75dfba534d53bf5c6004f9ad40d0 DE-627 ger DE-627 rakwb eng QC851-999 Khalia Monk verfasserin aut Evaluation of Regional Air Quality Models over Sydney and Australia: Part 1—Meteorological Model Comparison 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The ability of meteorological models to accurately characterise regional meteorology plays a crucial role in the performance of photochemical simulations of air pollution. As part of the research funded by the Australian government’s Department of the Environment Clean Air and Urban Landscape hub, this study set out to complete an intercomparison of air quality models over the Sydney region. This intercomparison would test existing modelling capabilities, identify any problems and provide the necessary validation of models in the region. The first component of the intercomparison study was to assess the ability of the models to reproduce meteorological observations, since it is a significant driver of air quality. To evaluate the meteorological component of these air quality modelling systems, seven different simulations based on varying configurations of inputs, integrations and physical parameterizations of two meteorological models (the Weather Research and Forecasting (WRF) and Conformal Cubic Atmospheric Model (CCAM)) were examined. The modelling was conducted for three periods coinciding with comprehensive air quality measurement campaigns (the Sydney Particle Studies (SPS) 1 and 2 and the Measurement of Urban, Marine and Biogenic Air (MUMBA)). The analysis focuses on meteorological variables (temperature, mixing ratio of water, wind (via wind speed and zonal wind components), precipitation and planetary boundary layer height), that are relevant to air quality. The surface meteorology simulations were evaluated against observations from seven Bureau of Meteorology (BoM) Automatic Weather Stations through composite diurnal plots, Taylor plots and paired mean bias plots. Simulated vertical profiles of temperature, mixing ratio of water and wind (via wind speed and zonal wind components) were assessed through comparison with radiosonde data from the Sydney Airport BoM site. The statistical comparisons with observations identified systematic overestimations of wind speeds that were more pronounced overnight. The temperature was well simulated, with biases generally between ±2 °C and the largest biases seen overnight (up to 4 °C). The models tend to have a drier lower atmosphere than observed, implying that better representations of soil moisture and surface moisture fluxes would improve the subsequent air quality simulations. On average the models captured local-scale meteorological features, like the sea breeze, which is a critical feature driving ozone formation in the Sydney Basin. The overall performance and model biases were generally within the recommended benchmark values (e.g., ±1 °C mean bias in temperature, ±1 g/kg mean bias of water vapour mixing ratio and ±1.5 m s<sup<−1</sup< mean bias of wind speed) except at either end of the scale, where the bias tends to be larger. The model biases reported here are similar to those seen in other model intercomparisons. model evaluation meteorological modelling air quality modelling Clean Air and Urban Landscapes Hub NSW Australia Meteorology. Climatology Elise-Andrée Guérette verfasserin aut Clare Paton-Walsh verfasserin aut Jeremy D. Silver verfasserin aut Kathryn M. Emmerson verfasserin aut Steven R. Utembe verfasserin aut Yang Zhang verfasserin aut Alan D. Griffiths verfasserin aut Lisa T.-C. Chang verfasserin aut Hiep N. Duc verfasserin aut Toan Trieu verfasserin aut Yvonne Scorgie verfasserin aut Martin E. Cope verfasserin aut In Atmosphere MDPI AG, 2011 10(2019), 7, p 374 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:10 year:2019 number:7, p 374 https://doi.org/10.3390/atmos10070374 kostenfrei https://doaj.org/article/e83c75dfba534d53bf5c6004f9ad40d0 kostenfrei https://www.mdpi.com/2073-4433/10/7/374 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2019 7, p 374
allfieldsSound	10.3390/atmos10070374 doi (DE-627)DOAJ042408172 (DE-599)DOAJe83c75dfba534d53bf5c6004f9ad40d0 DE-627 ger DE-627 rakwb eng QC851-999 Khalia Monk verfasserin aut Evaluation of Regional Air Quality Models over Sydney and Australia: Part 1—Meteorological Model Comparison 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The ability of meteorological models to accurately characterise regional meteorology plays a crucial role in the performance of photochemical simulations of air pollution. As part of the research funded by the Australian government’s Department of the Environment Clean Air and Urban Landscape hub, this study set out to complete an intercomparison of air quality models over the Sydney region. This intercomparison would test existing modelling capabilities, identify any problems and provide the necessary validation of models in the region. The first component of the intercomparison study was to assess the ability of the models to reproduce meteorological observations, since it is a significant driver of air quality. To evaluate the meteorological component of these air quality modelling systems, seven different simulations based on varying configurations of inputs, integrations and physical parameterizations of two meteorological models (the Weather Research and Forecasting (WRF) and Conformal Cubic Atmospheric Model (CCAM)) were examined. The modelling was conducted for three periods coinciding with comprehensive air quality measurement campaigns (the Sydney Particle Studies (SPS) 1 and 2 and the Measurement of Urban, Marine and Biogenic Air (MUMBA)). The analysis focuses on meteorological variables (temperature, mixing ratio of water, wind (via wind speed and zonal wind components), precipitation and planetary boundary layer height), that are relevant to air quality. The surface meteorology simulations were evaluated against observations from seven Bureau of Meteorology (BoM) Automatic Weather Stations through composite diurnal plots, Taylor plots and paired mean bias plots. Simulated vertical profiles of temperature, mixing ratio of water and wind (via wind speed and zonal wind components) were assessed through comparison with radiosonde data from the Sydney Airport BoM site. The statistical comparisons with observations identified systematic overestimations of wind speeds that were more pronounced overnight. The temperature was well simulated, with biases generally between ±2 °C and the largest biases seen overnight (up to 4 °C). The models tend to have a drier lower atmosphere than observed, implying that better representations of soil moisture and surface moisture fluxes would improve the subsequent air quality simulations. On average the models captured local-scale meteorological features, like the sea breeze, which is a critical feature driving ozone formation in the Sydney Basin. The overall performance and model biases were generally within the recommended benchmark values (e.g., ±1 °C mean bias in temperature, ±1 g/kg mean bias of water vapour mixing ratio and ±1.5 m s<sup<−1</sup< mean bias of wind speed) except at either end of the scale, where the bias tends to be larger. The model biases reported here are similar to those seen in other model intercomparisons. model evaluation meteorological modelling air quality modelling Clean Air and Urban Landscapes Hub NSW Australia Meteorology. Climatology Elise-Andrée Guérette verfasserin aut Clare Paton-Walsh verfasserin aut Jeremy D. Silver verfasserin aut Kathryn M. Emmerson verfasserin aut Steven R. Utembe verfasserin aut Yang Zhang verfasserin aut Alan D. Griffiths verfasserin aut Lisa T.-C. Chang verfasserin aut Hiep N. Duc verfasserin aut Toan Trieu verfasserin aut Yvonne Scorgie verfasserin aut Martin E. Cope verfasserin aut In Atmosphere MDPI AG, 2011 10(2019), 7, p 374 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:10 year:2019 number:7, p 374 https://doi.org/10.3390/atmos10070374 kostenfrei https://doaj.org/article/e83c75dfba534d53bf5c6004f9ad40d0 kostenfrei https://www.mdpi.com/2073-4433/10/7/374 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2019 7, p 374
language	English
source	In Atmosphere 10(2019), 7, p 374 volume:10 year:2019 number:7, p 374
sourceStr	In Atmosphere 10(2019), 7, p 374 volume:10 year:2019 number:7, p 374
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	model evaluation meteorological modelling air quality modelling Clean Air and Urban Landscapes Hub NSW Australia Meteorology. Climatology
isfreeaccess_bool	true
container_title	Atmosphere
authorswithroles_txt_mv	Khalia Monk @@aut@@ Elise-Andrée Guérette @@aut@@ Clare Paton-Walsh @@aut@@ Jeremy D. Silver @@aut@@ Kathryn M. Emmerson @@aut@@ Steven R. Utembe @@aut@@ Yang Zhang @@aut@@ Alan D. Griffiths @@aut@@ Lisa T.-C. Chang @@aut@@ Hiep N. Duc @@aut@@ Toan Trieu @@aut@@ Yvonne Scorgie @@aut@@ Martin E. Cope @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	657584010
id	DOAJ042408172
language_de	englisch
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The modelling was conducted for three periods coinciding with comprehensive air quality measurement campaigns (the Sydney Particle Studies (SPS) 1 and 2 and the Measurement of Urban, Marine and Biogenic Air (MUMBA)). The analysis focuses on meteorological variables (temperature, mixing ratio of water, wind (via wind speed and zonal wind components), precipitation and planetary boundary layer height), that are relevant to air quality. The surface meteorology simulations were evaluated against observations from seven Bureau of Meteorology (BoM) Automatic Weather Stations through composite diurnal plots, Taylor plots and paired mean bias plots. Simulated vertical profiles of temperature, mixing ratio of water and wind (via wind speed and zonal wind components) were assessed through comparison with radiosonde data from the Sydney Airport BoM site. The statistical comparisons with observations identified systematic overestimations of wind speeds that were more pronounced overnight. 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callnumber-first	Q - Science
author	Khalia Monk
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topic_title	QC851-999 Evaluation of Regional Air Quality Models over Sydney and Australia: Part 1—Meteorological Model Comparison model evaluation meteorological modelling air quality modelling Clean Air and Urban Landscapes Hub NSW Australia
topic	misc QC851-999 misc model evaluation misc meteorological modelling misc air quality modelling misc Clean Air and Urban Landscapes Hub misc NSW Australia misc Meteorology. Climatology
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title	Evaluation of Regional Air Quality Models over Sydney and Australia: Part 1—Meteorological Model Comparison
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title_full	Evaluation of Regional Air Quality Models over Sydney and Australia: Part 1—Meteorological Model Comparison
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author_browse	Khalia Monk Elise-Andrée Guérette Clare Paton-Walsh Jeremy D. Silver Kathryn M. Emmerson Steven R. Utembe Yang Zhang Alan D. Griffiths Lisa T.-C. Chang Hiep N. Duc Toan Trieu Yvonne Scorgie Martin E. Cope
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title_sort	evaluation of regional air quality models over sydney and australia: part 1—meteorological model comparison
callnumber	QC851-999
title_auth	Evaluation of Regional Air Quality Models over Sydney and Australia: Part 1—Meteorological Model Comparison
abstract	The ability of meteorological models to accurately characterise regional meteorology plays a crucial role in the performance of photochemical simulations of air pollution. As part of the research funded by the Australian government’s Department of the Environment Clean Air and Urban Landscape hub, this study set out to complete an intercomparison of air quality models over the Sydney region. This intercomparison would test existing modelling capabilities, identify any problems and provide the necessary validation of models in the region. The first component of the intercomparison study was to assess the ability of the models to reproduce meteorological observations, since it is a significant driver of air quality. To evaluate the meteorological component of these air quality modelling systems, seven different simulations based on varying configurations of inputs, integrations and physical parameterizations of two meteorological models (the Weather Research and Forecasting (WRF) and Conformal Cubic Atmospheric Model (CCAM)) were examined. The modelling was conducted for three periods coinciding with comprehensive air quality measurement campaigns (the Sydney Particle Studies (SPS) 1 and 2 and the Measurement of Urban, Marine and Biogenic Air (MUMBA)). The analysis focuses on meteorological variables (temperature, mixing ratio of water, wind (via wind speed and zonal wind components), precipitation and planetary boundary layer height), that are relevant to air quality. The surface meteorology simulations were evaluated against observations from seven Bureau of Meteorology (BoM) Automatic Weather Stations through composite diurnal plots, Taylor plots and paired mean bias plots. Simulated vertical profiles of temperature, mixing ratio of water and wind (via wind speed and zonal wind components) were assessed through comparison with radiosonde data from the Sydney Airport BoM site. The statistical comparisons with observations identified systematic overestimations of wind speeds that were more pronounced overnight. The temperature was well simulated, with biases generally between ±2 °C and the largest biases seen overnight (up to 4 °C). The models tend to have a drier lower atmosphere than observed, implying that better representations of soil moisture and surface moisture fluxes would improve the subsequent air quality simulations. On average the models captured local-scale meteorological features, like the sea breeze, which is a critical feature driving ozone formation in the Sydney Basin. The overall performance and model biases were generally within the recommended benchmark values (e.g., ±1 °C mean bias in temperature, ±1 g/kg mean bias of water vapour mixing ratio and ±1.5 m s<sup<−1</sup< mean bias of wind speed) except at either end of the scale, where the bias tends to be larger. The model biases reported here are similar to those seen in other model intercomparisons.
abstractGer	The ability of meteorological models to accurately characterise regional meteorology plays a crucial role in the performance of photochemical simulations of air pollution. As part of the research funded by the Australian government’s Department of the Environment Clean Air and Urban Landscape hub, this study set out to complete an intercomparison of air quality models over the Sydney region. This intercomparison would test existing modelling capabilities, identify any problems and provide the necessary validation of models in the region. The first component of the intercomparison study was to assess the ability of the models to reproduce meteorological observations, since it is a significant driver of air quality. To evaluate the meteorological component of these air quality modelling systems, seven different simulations based on varying configurations of inputs, integrations and physical parameterizations of two meteorological models (the Weather Research and Forecasting (WRF) and Conformal Cubic Atmospheric Model (CCAM)) were examined. The modelling was conducted for three periods coinciding with comprehensive air quality measurement campaigns (the Sydney Particle Studies (SPS) 1 and 2 and the Measurement of Urban, Marine and Biogenic Air (MUMBA)). The analysis focuses on meteorological variables (temperature, mixing ratio of water, wind (via wind speed and zonal wind components), precipitation and planetary boundary layer height), that are relevant to air quality. The surface meteorology simulations were evaluated against observations from seven Bureau of Meteorology (BoM) Automatic Weather Stations through composite diurnal plots, Taylor plots and paired mean bias plots. Simulated vertical profiles of temperature, mixing ratio of water and wind (via wind speed and zonal wind components) were assessed through comparison with radiosonde data from the Sydney Airport BoM site. The statistical comparisons with observations identified systematic overestimations of wind speeds that were more pronounced overnight. The temperature was well simulated, with biases generally between ±2 °C and the largest biases seen overnight (up to 4 °C). The models tend to have a drier lower atmosphere than observed, implying that better representations of soil moisture and surface moisture fluxes would improve the subsequent air quality simulations. On average the models captured local-scale meteorological features, like the sea breeze, which is a critical feature driving ozone formation in the Sydney Basin. The overall performance and model biases were generally within the recommended benchmark values (e.g., ±1 °C mean bias in temperature, ±1 g/kg mean bias of water vapour mixing ratio and ±1.5 m s<sup<−1</sup< mean bias of wind speed) except at either end of the scale, where the bias tends to be larger. The model biases reported here are similar to those seen in other model intercomparisons.
abstract_unstemmed	The ability of meteorological models to accurately characterise regional meteorology plays a crucial role in the performance of photochemical simulations of air pollution. As part of the research funded by the Australian government’s Department of the Environment Clean Air and Urban Landscape hub, this study set out to complete an intercomparison of air quality models over the Sydney region. This intercomparison would test existing modelling capabilities, identify any problems and provide the necessary validation of models in the region. The first component of the intercomparison study was to assess the ability of the models to reproduce meteorological observations, since it is a significant driver of air quality. To evaluate the meteorological component of these air quality modelling systems, seven different simulations based on varying configurations of inputs, integrations and physical parameterizations of two meteorological models (the Weather Research and Forecasting (WRF) and Conformal Cubic Atmospheric Model (CCAM)) were examined. The modelling was conducted for three periods coinciding with comprehensive air quality measurement campaigns (the Sydney Particle Studies (SPS) 1 and 2 and the Measurement of Urban, Marine and Biogenic Air (MUMBA)). The analysis focuses on meteorological variables (temperature, mixing ratio of water, wind (via wind speed and zonal wind components), precipitation and planetary boundary layer height), that are relevant to air quality. The surface meteorology simulations were evaluated against observations from seven Bureau of Meteorology (BoM) Automatic Weather Stations through composite diurnal plots, Taylor plots and paired mean bias plots. Simulated vertical profiles of temperature, mixing ratio of water and wind (via wind speed and zonal wind components) were assessed through comparison with radiosonde data from the Sydney Airport BoM site. The statistical comparisons with observations identified systematic overestimations of wind speeds that were more pronounced overnight. The temperature was well simulated, with biases generally between ±2 °C and the largest biases seen overnight (up to 4 °C). The models tend to have a drier lower atmosphere than observed, implying that better representations of soil moisture and surface moisture fluxes would improve the subsequent air quality simulations. On average the models captured local-scale meteorological features, like the sea breeze, which is a critical feature driving ozone formation in the Sydney Basin. The overall performance and model biases were generally within the recommended benchmark values (e.g., ±1 °C mean bias in temperature, ±1 g/kg mean bias of water vapour mixing ratio and ±1.5 m s<sup<−1</sup< mean bias of wind speed) except at either end of the scale, where the bias tends to be larger. The model biases reported here are similar to those seen in other model intercomparisons.
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title_short	Evaluation of Regional Air Quality Models over Sydney and Australia: Part 1—Meteorological Model Comparison
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