GPT2w模型检验以及对流层模型的参数互融

发布时间：2018-07-03 08:38

本文选题：GPTw模型 + 天顶对流层延迟　；参考：《武汉大学学报(信息科学版)》2017年10期

【摘要】：各种对流层经验模型中,GPT2w模型是目前标称精度最高的对流层经验模型,其在模型化对流层延迟的同时,也提供具体的模型化气象元素。以USNO的ZTD产品检验模型ZTD精度;以IGRA发布的大气廓线数据,对模型加权平均温度T_m、水汽直减率λ的精度进行验证。计算发现,模型加权平均温度T_m具有-2.56K的系统偏差,改正该偏差后,模型ZTD对比USNO偏差从-1.38 mm提升至-0.3 mm;还验证了模型水汽直减率λ的两种获取方式具有很好的一致性。提出以测站气压P、测站温度t、测站相对湿度hr为实测气象元素,以校正后的T_m、高精度的λ为经验气象元素,作为对流层延迟模型输入参数的互融方法。该互融方法计算ZHD、ZWD经验模型分别采用目前最优的Saast静力学延迟模型和AskneNordius湿延迟模型。以USNO发布的340个IGS跟踪站的对流层延迟数据作为参考,该互融方法较直接气象元素法、校正后的GPT2w模型均有显著精度提升。在不可获取气象数据的前提下,校正后的GPT2w模型具有很高的先验精度;若可获取近实时气象数据(如自动气象站),则推荐采用新的参数互融模型。
[Abstract]:Among the various tropospheric empirical models, the GPT2w model is currently the most accurate one in the troposphere. It not only models the tropospheric delay, but also provides the specific meteorological elements for modeling the troposphere. The accuracy of the model is verified by using the ZTD product test model of USNO and the atmospheric profile data published by IGRA to verify the accuracy of the weighted mean temperature T _ m and the water vapor direct reduction rate 位. It is found that the weighted average temperature of model T stack has a systematic deviation of -2.56 K. after correcting the deviation, the model ZTD increases the USNO deviation from -1.38 mm to -0.3 mm, and also verifies that the two methods of obtaining the model water vapor direct reduction rate 位 have good consistency. This paper presents a method of mutual fusion of the input parameters of the tropospheric delay model with the measured station pressure (P), the station temperature (t), the station relative humidity (hr) as the measured meteorological elements, the corrected T _ (m) and the high precision 位 as the empirical meteorological elements. The empirical model of ZHDN ZWD is calculated by using the current optimal Saast statics delay model and Askne Nordius wet delay model. Based on the tropospheric delay data of 340 IGS tracking stations released by USNO, the accuracy of the modified GPT2w model is improved significantly compared with the direct meteorological element method. The corrected GPT2w model has a high prior precision on the premise that meteorological data can not be obtained. If the near real time meteorological data (such as automatic weather station) can be obtained, a new model of parameter fusion is recommended.
【作者单位】：中国科学院大学;中国科学院测量与地球物理研究所大地测量与地球动力学国家重点实验室;
【基金】：国家重大科学仪器设备开发专项(2011YQ120045) 国家自然科学基金(41304023)~~
【分类号】：P228

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