In comparison with the ITRF2000 model, the ITRF2005 model represents a significant improvement in solution generation, datum definition and realization. However, these improvements cause a frame difference between the...In comparison with the ITRF2000 model, the ITRF2005 model represents a significant improvement in solution generation, datum definition and realization. However, these improvements cause a frame difference between the ITRF2000 and ITRF2005 models, which may impact GNSS data processing. To quantify this im- pact, the differences of the GNSS results obtained using the two models, including station coordinates, base- line length and horizontal velocity field, were analyzed. After transformation, the differences in position were at the millimeter level, and the differences in baseline length were less than 1 ram. The differences in the hori- zontal velocity fields decreased with as the study area was reduced. For a large region, the differences in these value were less than 1 mm/a, with a systematic difference of approximately 2 degrees in direction, while for a medium-sized region, the differences in value and direction were not significant.展开更多
海平面变化是全球气候和环境变化的重要指标之一,本文基于中国首套全球海洋气候数据集(Climate Data Records,CDRs)中的海平面高度异常(Sea Level Anomaly,SLA)数据、长期验潮站观测数据以及GNSS数据对北欧海的海平面变化进行了详细的...海平面变化是全球气候和环境变化的重要指标之一,本文基于中国首套全球海洋气候数据集(Climate Data Records,CDRs)中的海平面高度异常(Sea Level Anomaly,SLA)数据、长期验潮站观测数据以及GNSS数据对北欧海的海平面变化进行了详细的分析。首先利用1993-2019年的SLA数据分析了北欧海海平面变化的空间分布、主要周期;然后联合长期验潮站观测数据、GNSS数据分析了北欧海部分站点的绝对海平面变化,并与SLA数据获得的绝对海平面变化进行比对。结果表明:1993-2019年北欧海海平面变化具有显著的区域性差异,挪威海和格陵兰海北部海平面变化振幅最大,整体变化速率约为2.47±0.54 mm/a,每年春季(2-4月)为季节性低海平面期,秋季(9-11月)为季节性高海平面期;联合验潮站观测数据和GNSS数据可以很好地校正验潮站地壳垂直运动的影响,其中NY-ALESUN和ANDENES站经GNSS数据校正后的海平面变化趋势与同期基于CDRs数据的绝对海平面变化差异最小。展开更多
针对未配备气象传感器的全球卫星导航系统(GNSS)站缺乏实测气象参数,通常利用再分析数据、经验模型替代反演大气可降水量(PWV)的问题,定量分析地面气压(P)和加权平均温度(Tm)在反演PWV中的适用性:利用欧洲中期天气预报中心(ECMWF)发布...针对未配备气象传感器的全球卫星导航系统(GNSS)站缺乏实测气象参数,通常利用再分析数据、经验模型替代反演大气可降水量(PWV)的问题,定量分析地面气压(P)和加权平均温度(Tm)在反演PWV中的适用性:利用欧洲中期天气预报中心(ECMWF)发布的第五代再分析数据集(ERA5)、美国国家环境预报中心(NCEP)/国家大气研究中心(NCAR)再分析数据、第二代和第三代全球气压和温度模型(GPT2和GPT3)、全球格网非线性加权平均温度模型(GGNTm)计算P和Tm;然后以探空站实测气象数据为参考,评估不同P和Tm的精度及其对PWV反演的影响。结果表明:1)ERA5、NCEP、GPT2、GPT3获取P的偏差(bias)绝对值小于1 h Pa,均方根误差(RMSE)小于5.5 h Pa;2)在获取Tm方面,ERA5、NCEP、GGNTm、GPT3的bias绝对值小于2.5 K,RMSE小于5.5 K;3)将ERA5、NCEP、GPT2+GGNTm、GPT3获取的气象参数应用到PWV反演中,PWV偏差绝对值小于0.5 mm,RMSE小于3 mm。分析得出应用非实测气象参数反演的GNSS大气可降水量也可获得较高精度的结论。展开更多
基金supported by the Special Earthquake Research Project Granted by the China Earthquake Administration(201308009)
文摘In comparison with the ITRF2000 model, the ITRF2005 model represents a significant improvement in solution generation, datum definition and realization. However, these improvements cause a frame difference between the ITRF2000 and ITRF2005 models, which may impact GNSS data processing. To quantify this im- pact, the differences of the GNSS results obtained using the two models, including station coordinates, base- line length and horizontal velocity field, were analyzed. After transformation, the differences in position were at the millimeter level, and the differences in baseline length were less than 1 ram. The differences in the hori- zontal velocity fields decreased with as the study area was reduced. For a large region, the differences in these value were less than 1 mm/a, with a systematic difference of approximately 2 degrees in direction, while for a medium-sized region, the differences in value and direction were not significant.
文摘基于全球卫星导航系统(global navigation satellite systems,GNSS)基准站建立的全球电离层模型是目前广泛使用的全球电离层产品,对全球电离层模型在磁暴期间可靠性和精度的分析和评价是合理使用该模型的必要前提。本研究采用靠近南海的基准站数据来验证船载GNSS数据解算的电离层天顶方向总电子含量(vertial total electron content,VTEC)的可靠性,并利用船载数据和基准站数据对磁暴期间全球电离层模型在我国南海区域的精度进行了初步的分析和评价。结果表明,船载数据与基准站数据解算的电离层VTEC有相同的变化趋势;磁暴期间,我国南海区域的全球电离层模型值与船载数据解算值及基准站(HKSL、PIMO)数据解算值之间的误差增大,其RMSE日均值分别为41.21、27.40和30.86 TECU,这表明磁暴活动对电离层的扰动导致了全球电离层模型精度明显下降。
文摘针对未配备气象传感器的全球卫星导航系统(GNSS)站缺乏实测气象参数,通常利用再分析数据、经验模型替代反演大气可降水量(PWV)的问题,定量分析地面气压(P)和加权平均温度(Tm)在反演PWV中的适用性:利用欧洲中期天气预报中心(ECMWF)发布的第五代再分析数据集(ERA5)、美国国家环境预报中心(NCEP)/国家大气研究中心(NCAR)再分析数据、第二代和第三代全球气压和温度模型(GPT2和GPT3)、全球格网非线性加权平均温度模型(GGNTm)计算P和Tm;然后以探空站实测气象数据为参考,评估不同P和Tm的精度及其对PWV反演的影响。结果表明:1)ERA5、NCEP、GPT2、GPT3获取P的偏差(bias)绝对值小于1 h Pa,均方根误差(RMSE)小于5.5 h Pa;2)在获取Tm方面,ERA5、NCEP、GGNTm、GPT3的bias绝对值小于2.5 K,RMSE小于5.5 K;3)将ERA5、NCEP、GPT2+GGNTm、GPT3获取的气象参数应用到PWV反演中,PWV偏差绝对值小于0.5 mm,RMSE小于3 mm。分析得出应用非实测气象参数反演的GNSS大气可降水量也可获得较高精度的结论。
