基于山东省2021年3月—2022年2月1519个气象观测站2 m气温观测数据,对中国气象局高分辨率陆面数据同化系统(High Resolution China Meteorological Administration Land Data Assimilation System,HRCLDAS)和欧洲中期天气预报中心第五...基于山东省2021年3月—2022年2月1519个气象观测站2 m气温观测数据,对中国气象局高分辨率陆面数据同化系统(High Resolution China Meteorological Administration Land Data Assimilation System,HRCLDAS)和欧洲中期天气预报中心第五代陆面再分析数据集(ERA5-Land)逐小时2 m气温分析的日统计数据(平均气温、最高气温、最低气温)进行对比评估。结果显示:(1)HRCLDAS/ERA5-Land日统计平均气温、最高气温、最低气温的均方根误差分别为0.1/1.2℃、0.6/1.9℃、0.4/1.7℃,表明HRCLDAS具有更高的精度,且在不同地理区域、不同海拔高度的表现均优于ERA5-Land,大部地区的偏差(-0.5~0.5℃)远低于ERA5-Land(-2.0~2.0℃)。(2)两套数据对高温及寒潮过程的监测能力对比评估表明,HRCLDAS能够捕捉到大部分的高温以及寒潮过程,其与观测的高温日数及寒潮日数空间分布较为相似,但对影响范围存在一定的低估;ERA5-Land则只能监测到部分高温及寒潮过程,并对高温日数与寒潮日数存在严重的低估。展开更多
Aerosol optical depth （AOD） is the most basic paxalneter that describes the optical properties of atmospheric aerosols, and it can be used to indicate aerosol content. In this study, we assimilated AOD data from the...Aerosol optical depth （AOD） is the most basic paxalneter that describes the optical properties of atmospheric aerosols, and it can be used to indicate aerosol content. In this study, we assimilated AOD data from the Fengyun-3A （FY-3A） and MODIS meteorological satellite using the Gridpoint Statistical Interpolation three-dimensional variational data assimilation system. Experiments were conducted for a dust storm over East Asia in April 2011. Each 0600 UTC analysis initialized a 24-h Weather Research and Forecasting with Chemistry model forecast. The results generally showed that the assimilation of satellite AOD observational data can significantly improve model aerosol mass prediction skills. The AOD distribution of the analysis field was closer to the observations of the satellite after assimilation of satellite AOD data. In addition, the analysis resulting from the experiment assimilating both FY-3A/MERSI （Medium-resolution Spectral Imager） AOD data and MODIS AOD data had closer agreement with the ground-based values than the individual assimilation of the two datasets for the dust storm over East Asia. These results suggest that the Chinese FY-3A satellite aerosol products can be effectively applied to numerical models and dust weather analysis.展开更多
The wave Coriolis-Stokes-Force-modified ocean momentum equations are reviewed in this paper and the wave Stokes transport is pointed out to be part of the ocean circulations. Using the European Centre for Medium-Range...The wave Coriolis-Stokes-Force-modified ocean momentum equations are reviewed in this paper and the wave Stokes transport is pointed out to be part of the ocean circulations. Using the European Centre for Medium-Range Weather Forecasts 40-year reanalysis data(ERA-40 data) and the Simple Ocean Data Assimilation(SODA) version 2.2.4 data, the magnitude of this transport is compared with that of wind-driven Sverdrup transport and a 5-to-10-precent contribution by the wave Stokes transport is found. Both transports are stronger in boreal winter than in summers. The wave effect can be either contribution or cancellation in different seasons. Examination with Kuroshio transport verifies similar seasonal variations. The clarification of the efficient wave boundary condition helps to understand the role of waves in mass transport. It acts as surface wind stress and can be functional down to the bottom of the ageostrophic layer. The pumping velocities resulting from wave-induced stress are zonally distributed and are significant in relatively high latitudes. Further work will focus on the model performance of the wave-stress-changed-boundary and the role of swells in the eastern part of the oceans.展开更多
The atmospheric reanalysis datasets have been widely used to understand the variability of atmospheric water va- por on various temporal and spatial scales for climate change research. The difference among a variety o...The atmospheric reanalysis datasets have been widely used to understand the variability of atmospheric water va- por on various temporal and spatial scales for climate change research. The difference among a variety of reanalysis datasets, however, causes the uncertainty of corresponding results. In this study, the climatology of atmospheric column-integrated wa- ter vapor for the period from 2000 to 2012 was compared among three latest third-generation atmospheric reanalyses including European Centre for Medium-range Weather Forecasts Interim Re-Analysis （ERA-Interim）, Modem-Era Retrospective Analy- sis for Research and Applications （MERRA）, and Climate Forecast System Reanalysis （CFSR）, while possible explanation on the difference between them was given. The results show that there are significant differences among three datasets in the mul- ti-year global distribution, variation of interannual cycle, long-term trend and so on, though high similarity for principal mode describing the variability of water vapor. Over oceans, the characteristics of long-term CWV variability are similar, whereas the main discrepancy among three datasets is located around the equatorial regions of the Intertropical Convergence Zone, the South Pacific Convergence Zone and warm cloud area, which is related with the difference between reanalysis models for the scheme of convective parameterization, the treatment of warm clouds, and the assimilation of satellite-based observations. Moreover, these CWV products are fairly consistent with observations （satellite-based retrievals） for oceans. On the other hand there are systematic underestimations about 2.5 kg/m2 over lands for all three CWV datasets, compared with radiosonde ob- servations. The difference between models to solve land-atmosphere interaction in complex environment, as well as the pauci- ty in radiosonde observations, leads to significant water vapor gaps in the Amazon Basin of South America, central parts of Africa and some mountainous regions. These results would help better understand the climatology difference among various reanalysis datasets better, and more properly choose water vapor datasets for different research requirements.展开更多
文摘基于山东省2021年3月—2022年2月1519个气象观测站2 m气温观测数据,对中国气象局高分辨率陆面数据同化系统(High Resolution China Meteorological Administration Land Data Assimilation System,HRCLDAS)和欧洲中期天气预报中心第五代陆面再分析数据集(ERA5-Land)逐小时2 m气温分析的日统计数据(平均气温、最高气温、最低气温)进行对比评估。结果显示:(1)HRCLDAS/ERA5-Land日统计平均气温、最高气温、最低气温的均方根误差分别为0.1/1.2℃、0.6/1.9℃、0.4/1.7℃,表明HRCLDAS具有更高的精度,且在不同地理区域、不同海拔高度的表现均优于ERA5-Land,大部地区的偏差(-0.5~0.5℃)远低于ERA5-Land(-2.0~2.0℃)。(2)两套数据对高温及寒潮过程的监测能力对比评估表明,HRCLDAS能够捕捉到大部分的高温以及寒潮过程,其与观测的高温日数及寒潮日数空间分布较为相似,但对影响范围存在一定的低估;ERA5-Land则只能监测到部分高温及寒潮过程,并对高温日数与寒潮日数存在严重的低估。
基金supported by the National Key Research and Development Program of China (Grant Nos.2017YFC1502100 and 2016YFA0602302)the Natural Science Foundation of Jiangsu Province (Grant Nos.BK20160954 and BK20170940)+3 种基金the Beijige Funding from Jiangsu Research Institute of Meteorological Science (Grant Nos.BJG201510 and BJG201604)the Startup Foundation for Introducing Talent of NUIST (Grant Nos.2016r27,2016r043 and 2017r058)a project for data application of Fengyun3 meteorological satellite [FY-3(02)UDS-1.1.2]the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘Aerosol optical depth （AOD） is the most basic paxalneter that describes the optical properties of atmospheric aerosols, and it can be used to indicate aerosol content. In this study, we assimilated AOD data from the Fengyun-3A （FY-3A） and MODIS meteorological satellite using the Gridpoint Statistical Interpolation three-dimensional variational data assimilation system. Experiments were conducted for a dust storm over East Asia in April 2011. Each 0600 UTC analysis initialized a 24-h Weather Research and Forecasting with Chemistry model forecast. The results generally showed that the assimilation of satellite AOD observational data can significantly improve model aerosol mass prediction skills. The AOD distribution of the analysis field was closer to the observations of the satellite after assimilation of satellite AOD data. In addition, the analysis resulting from the experiment assimilating both FY-3A/MERSI （Medium-resolution Spectral Imager） AOD data and MODIS AOD data had closer agreement with the ground-based values than the individual assimilation of the two datasets for the dust storm over East Asia. These results suggest that the Chinese FY-3A satellite aerosol products can be effectively applied to numerical models and dust weather analysis.
基金funded by the National Science Foundation of China (40976005 and 40930844)
文摘The wave Coriolis-Stokes-Force-modified ocean momentum equations are reviewed in this paper and the wave Stokes transport is pointed out to be part of the ocean circulations. Using the European Centre for Medium-Range Weather Forecasts 40-year reanalysis data(ERA-40 data) and the Simple Ocean Data Assimilation(SODA) version 2.2.4 data, the magnitude of this transport is compared with that of wind-driven Sverdrup transport and a 5-to-10-precent contribution by the wave Stokes transport is found. Both transports are stronger in boreal winter than in summers. The wave effect can be either contribution or cancellation in different seasons. Examination with Kuroshio transport verifies similar seasonal variations. The clarification of the efficient wave boundary condition helps to understand the role of waves in mass transport. It acts as surface wind stress and can be functional down to the bottom of the ageostrophic layer. The pumping velocities resulting from wave-induced stress are zonally distributed and are significant in relatively high latitudes. Further work will focus on the model performance of the wave-stress-changed-boundary and the role of swells in the eastern part of the oceans.
基金supported by the National Natural Science Foundation of China(Grant Nos.9133721341230419+5 种基金4137503041375148 and 41205126)the Special Funds for Public Welfare of China(Grant No.GYHY201306077)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA05100303)the Knowledge Innovation Program of the Chinese Academy of Sciences(Grant No.KZCX2-EW-QN507)sponsored by the Jiangsu Provincial 2011 Program(Collaborative Innovation Center of Climate Change)
文摘The atmospheric reanalysis datasets have been widely used to understand the variability of atmospheric water va- por on various temporal and spatial scales for climate change research. The difference among a variety of reanalysis datasets, however, causes the uncertainty of corresponding results. In this study, the climatology of atmospheric column-integrated wa- ter vapor for the period from 2000 to 2012 was compared among three latest third-generation atmospheric reanalyses including European Centre for Medium-range Weather Forecasts Interim Re-Analysis （ERA-Interim）, Modem-Era Retrospective Analy- sis for Research and Applications （MERRA）, and Climate Forecast System Reanalysis （CFSR）, while possible explanation on the difference between them was given. The results show that there are significant differences among three datasets in the mul- ti-year global distribution, variation of interannual cycle, long-term trend and so on, though high similarity for principal mode describing the variability of water vapor. Over oceans, the characteristics of long-term CWV variability are similar, whereas the main discrepancy among three datasets is located around the equatorial regions of the Intertropical Convergence Zone, the South Pacific Convergence Zone and warm cloud area, which is related with the difference between reanalysis models for the scheme of convective parameterization, the treatment of warm clouds, and the assimilation of satellite-based observations. Moreover, these CWV products are fairly consistent with observations （satellite-based retrievals） for oceans. On the other hand there are systematic underestimations about 2.5 kg/m2 over lands for all three CWV datasets, compared with radiosonde ob- servations. The difference between models to solve land-atmosphere interaction in complex environment, as well as the pauci- ty in radiosonde observations, leads to significant water vapor gaps in the Amazon Basin of South America, central parts of Africa and some mountainous regions. These results would help better understand the climatology difference among various reanalysis datasets better, and more properly choose water vapor datasets for different research requirements.
