GPT2w模型检验以及对流层模型的参数互融

An Assessment of GPT2w Model and Fusion of a Troposphere Model with in Situ Data

  • 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Tm、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Tm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Tm、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Askne & Nordius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: GPT2w is used to estimate slant tropospheric delay, and considered the best empirical model based on its nominal accuracy. Besides the model value of ZHD, this model delivers a blind value for meteorological parameters. We used the database from USNO to validate the marked accuracy of the model, and data from IGRA to access the accuracy of the blind meteorological elements. A system bias from Tm was detected. After the correction of this bias, the bias of model ZTD against the USNO ZTD rose from-1.38 mm to-0.3 mm. This paper presents a fusion of blind model and the in situ data. The input parameters of this new method are in situ P, t and hr, the corrected blind Tm and λ. This method performs better than the modified GPT2w model because of the in situ data, better than the Saast model as it profits from a improved ZWD model. Without the in situ data, the modified GPT2w is a good choice. If with available in situ data, the fusion method is recommended.

     

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