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Comparison of GPS-based precipitable water vapor using various reanalysis datasets for the coastal regions of China

机译:利用各种再分析数据集比较中国沿海地区基于GPS的可降水量水汽

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摘要

This paper investigated the quality of site-specific surface temperature and surface pressure data in the coastal regions of China, which were interpolated from the European Centre for Medium-Range Weather Forecast Interim reanalysis (ERA-Interim), Japanese 55-year Reanalysis Project (JRA-55), and the National Centers for Environmental Prediction Final (NCEP FNL) reanalysis surface meteorological datasets as well as from the new Global Pressure and Temperature (GPT2) model data. The measured temperature and pressure along with the collocated GPS data from 2014 were collected from 25 observation stations evenly located in the region. Compared with the actual meteorological observations, the performances of the interpolated data from three reanalysis datasets differ marginally, with the root mean square errors (RMSEs) of the interpolated surface temperature and pressure less than 2.4K and 1.6hPa, respectively; however, the RMSEs of the surface temperature and pressure interpolated from the GPT2 model were 3.0K and 4.2hPa, respectively. Data based on GPS PWV products that used the meteorological parameters interpolated from three reanalysis data were very close to those of meteorological observations, with biases within +/- 0.4mm and RMSEs below 0.5mm in most areas, and the RMSE of PWV using the GPT2 model interpolation data was superior by 2mm. The measurement of GPS PWV using the interpolated reanalysis meteorological data also compared well with radiosonde observations, with RMSE between them tending to increase with a decrease of the GPS station's latitude. However, the GPS PWV based on the interpolated data could not reflect the true change in water vapor during typhoon events.
机译:本文调查了中国沿海地区特定地点的地表温度和表面压力数据的质量,这些数据是根据日本55年再分析项目欧洲中期气象预报中期再分析(ERA-Interim)( JRA-55)和美国国家环境最终评估中心(NCEP FNL)重新分析地表气象数据集以及新的全球压力和温度(GPT2)模型数据。从2014年均匀分布在该地区的25个观测站收集了测得的温度和压力以及2014年并置的GPS数据。与实际的气象观测相比,来自三个再分析数据集的插值数据的性能略有不同,插值表面温度和压力的均方根误差(RMSE)分别小于2.4K和1.6hPa;然而,从GPT2模型内插得到的表面温度和压力的均方根误差(RMSE)分别为3.0K和4.2hPa。基于GPS PWV产品的数据使用了从三个再分析数据中插入的气象参数,这些数据与气象观测值非常接近,在大多数地区,偏差在+/- 0.4mm之内,RMSE在0.5mm以下,而使用GPT2的PWV的RMSE模型插补数据优于2mm。使用内插的再分析气象数据对GPS PWV的测量也与无线电探空仪观测结果进行了很好的比较,随着GPS站纬度的减小,它们之间的RMSE趋于增加。但是,基于插值数据的GPS PWV无法反映台风事件期间水蒸气的真实变化。

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  • 来源
    《Theoretical and applied climatology》 |2019年第2期|1541-1553|共13页
  • 作者单位

    Natl Marine Data & Informat Serv, Tianjin 300171, Peoples R China;

    State Ocean Adm, Inst Oceanog 1, Qingdao 266061, Shandong, Peoples R China;

    Natl Marine Data & Informat Serv, Tianjin 300171, Peoples R China;

    State Ocean Adm, Inst Oceanog 1, Qingdao 266061, Shandong, Peoples R China;

    Natl Marine Data & Informat Serv, Tianjin 300171, Peoples R China;

    Jari Automat Co Ltd, Qingdao 266071, Shandong, Peoples R China;

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