Valuable dropsonde data were obtained from multiple field campaigns targeting tropical cyclones,namely Higos,Nangka,Saudel,and Atsani,over the western North Pacific by the Hong Kong Observatory and Taiwan Central Weat...Valuable dropsonde data were obtained from multiple field campaigns targeting tropical cyclones,namely Higos,Nangka,Saudel,and Atsani,over the western North Pacific by the Hong Kong Observatory and Taiwan Central Weather Bureau in 2020.The conditional nonlinear optimal perturbation(CNOP)method has been utilized in real-time to identify the sensitive regions for targeting observations adhering to the procedure of real-time field campaigns for the first time.The observing system experiments were conducted to evaluate the effect of dropsonde data and CNOP sensitivity on TC forecasts in terms of track and intensity,using the Weather Research and Forecasting model.It is shown that the impact of assimilating all dropsonde data on both track and intensity forecasts is case-dependent.However,assimilation using only the dropsonde data inside the sensitive regions displays unanimously positive effects on both the track and intensity forecast,either of which obtains comparable benefits to or greatly reduces deterioration of the skill when assimilating all dropsonde data.Therefore,these results encourage us to further carry out targeting observations for the forecast of tropical cyclones according to CNOP sensitivity.展开更多
This paper reports the use of a specialized, mesoscale, numerical weather prediction （NWP） system and a satellite imaging and prediction system that were set up to support the CLAMS （Chesapeake Lighthouse and Aircr...This paper reports the use of a specialized, mesoscale, numerical weather prediction （NWP） system and a satellite imaging and prediction system that were set up to support the CLAMS （Chesapeake Lighthouse and Aircraft Measurements for Satellites） field campaign during the summer of 2001. The primary objective of CLAMS was to validate satellite-based retrievals of aerosol properties and vertical profiles of the radiative flux, temperature and water vapor. Six research aircraft were deployed to make detailed coincident measurements of the atmosphere and ocean surface with the research satellites that orbited overhead. The mesoscale weather modeling system runs in real-time to provide high spatial and temporal resolution for forecasts that are delivered via the World Wide Web along with a variety of satellite imagery and satellite location predictions. This system is a multi-purpose modeling system capable of both data analysis/assimilation and multi-scale NWP ranging from cloud-scale to larger than regional scale. This is a three-dimensional, non-hydrostatic compressible model in a terrain-following coordinate. The model employs advanced numerical techniques and contains detailed interactive physical processes. The utility of the forecasting system is illustrated throughout the discussion on the impact of the surface-wind forecast on BRDF （Bidirectional Reflectance Distribution Function） and the description of the cloud/moisture forecast versus the aircraft measurement.展开更多
A better knowledge of aerosol properties is of great significance for elucidating the complex mechanisms behind frequently occurring haze pollution events.In this study,we examine the temporal and spatial variations i...A better knowledge of aerosol properties is of great significance for elucidating the complex mechanisms behind frequently occurring haze pollution events.In this study,we examine the temporal and spatial variations in both PM_(1)and its major chemical constituents using three-year field measurements that were collected in six representative regions in China between 2012 and 2014.Our results show that both PM_(1)and its chemical compositions varied significantly in space and time,with high PM_(1)loadings mainly observed in the winter.By comparing chemical constituents between clean and polluted episodes,we find that the elevated PM_(1)mass concentration during pollution events should be largely attributable to significant increases in organic matter(OM)and inorganic aerosols like sulfate,nitrate,and ammonium(SNA),indicative of the critical role of primary emissions and secondary aerosols in elevating PM_(1)pollution levels.The ratios of PM_(1)/PM2.5 are found to be generally high in Shanghai and Guangzhou,while relatively low ratios are seen in Xi’an and Chengdu,indicating anthropogenic emissions were more likely to accumulate in forms of finer particles.With respect to the relative importance of chemical components and meteorological factors quantified via statistical modeling practices,we find that primary emissions and secondary aerosols were the two leading factors contributing to PM_(1)variations,though meteorological factors also played important roles in regulating the dispersion of atmospheric PM.展开更多
基金jointly sponsored by the National Nature Scientific Foundation of China(Grant.Nos.41930971 and 41775061)the National Key Research and Development Program of China(Grant No.2018YFC1506402)。
文摘Valuable dropsonde data were obtained from multiple field campaigns targeting tropical cyclones,namely Higos,Nangka,Saudel,and Atsani,over the western North Pacific by the Hong Kong Observatory and Taiwan Central Weather Bureau in 2020.The conditional nonlinear optimal perturbation(CNOP)method has been utilized in real-time to identify the sensitive regions for targeting observations adhering to the procedure of real-time field campaigns for the first time.The observing system experiments were conducted to evaluate the effect of dropsonde data and CNOP sensitivity on TC forecasts in terms of track and intensity,using the Weather Research and Forecasting model.It is shown that the impact of assimilating all dropsonde data on both track and intensity forecasts is case-dependent.However,assimilation using only the dropsonde data inside the sensitive regions displays unanimously positive effects on both the track and intensity forecast,either of which obtains comparable benefits to or greatly reduces deterioration of the skill when assimilating all dropsonde data.Therefore,these results encourage us to further carry out targeting observations for the forecast of tropical cyclones according to CNOP sensitivity.
文摘This paper reports the use of a specialized, mesoscale, numerical weather prediction （NWP） system and a satellite imaging and prediction system that were set up to support the CLAMS （Chesapeake Lighthouse and Aircraft Measurements for Satellites） field campaign during the summer of 2001. The primary objective of CLAMS was to validate satellite-based retrievals of aerosol properties and vertical profiles of the radiative flux, temperature and water vapor. Six research aircraft were deployed to make detailed coincident measurements of the atmosphere and ocean surface with the research satellites that orbited overhead. The mesoscale weather modeling system runs in real-time to provide high spatial and temporal resolution for forecasts that are delivered via the World Wide Web along with a variety of satellite imagery and satellite location predictions. This system is a multi-purpose modeling system capable of both data analysis/assimilation and multi-scale NWP ranging from cloud-scale to larger than regional scale. This is a three-dimensional, non-hydrostatic compressible model in a terrain-following coordinate. The model employs advanced numerical techniques and contains detailed interactive physical processes. The utility of the forecasting system is illustrated throughout the discussion on the impact of the surface-wind forecast on BRDF （Bidirectional Reflectance Distribution Function） and the description of the cloud/moisture forecast versus the aircraft measurement.
基金This work was financially supported by National Key R&D Plan(Grant No.2017YFC0210000)National Natural Science Foundation of China(Grant No.41701413)+1 种基金National Key R&D Plan(Grant No.2017YFC0212703)Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB05020401).Meteorological data were acquired from the Meteorological Information Comprehensive Analysis and Process System(air temperature,relative humidity,and wind speed),and ERA-Interim reanalysis(boundary layer height)that was provided by the European Centre for Medium-Range Weather Forecasts.
文摘A better knowledge of aerosol properties is of great significance for elucidating the complex mechanisms behind frequently occurring haze pollution events.In this study,we examine the temporal and spatial variations in both PM_(1)and its major chemical constituents using three-year field measurements that were collected in six representative regions in China between 2012 and 2014.Our results show that both PM_(1)and its chemical compositions varied significantly in space and time,with high PM_(1)loadings mainly observed in the winter.By comparing chemical constituents between clean and polluted episodes,we find that the elevated PM_(1)mass concentration during pollution events should be largely attributable to significant increases in organic matter(OM)and inorganic aerosols like sulfate,nitrate,and ammonium(SNA),indicative of the critical role of primary emissions and secondary aerosols in elevating PM_(1)pollution levels.The ratios of PM_(1)/PM2.5 are found to be generally high in Shanghai and Guangzhou,while relatively low ratios are seen in Xi’an and Chengdu,indicating anthropogenic emissions were more likely to accumulate in forms of finer particles.With respect to the relative importance of chemical components and meteorological factors quantified via statistical modeling practices,we find that primary emissions and secondary aerosols were the two leading factors contributing to PM_(1)variations,though meteorological factors also played important roles in regulating the dispersion of atmospheric PM.
