Abstract
Global pressure and temperature (GPT) series models can provide the underlying meteorological parameters for tropospheric corrections without any other meteorological observations, which allows them to be widely used for a series of geodetic as well as meteorological and climatological purposes. Due to the height difference between the empirical model height and user location, a vertical correction of meteorological parameters is inevitable, particularly for airborne users. Unfortunately, the GPT series models have limitations on the vertical correction. We explored the temperature lapse rate for the vertical adjustment using 10 years of reanalysis data provided by the National Centers for Environmental Prediction (NCEP), and extended the GPT models to improved global pressure and temperature (IGPT) series models by introducing a new temperature lapse rate model and a new formulation of pressure reduction. An evaluation of the IGPT models expression determines that the IGPT models have better accuracy than the GPT models, particularly under large height differences, which is attributed to their ability to consider the real behavior of temperature in the atmosphere and adiabatic effects on air pressure. The performance of the IGPT models in zenith tropospheric delay (ZTD) estimations was also evaluated by comparison with the fifth-generation European Centers for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5) data and International GNSS Service (IGS) data. The results confirm that our new models can effectively improve the accuracy of ZTDs, particularly at larger altitude differences between the target height and the corresponding four grid points of the model, not only enhancing the performance of the model in complex terrain but also extending the feasibility of the IGPT models from the Earth's surface to higher altitudes.
Highlights
The incorrect modeling of troposphere delays is one of the major error sources for space geodetic techniques such as global navigation satellite systems (GNSS) or very long baseline interferometry (VLBI) [1]
Many accurate tropospheric delay models have been proposed based on numerical weather prediction (NWP) data, e.g., TropGrid series models [2,3], global pressure and temperature (GPT) series models [1,4,5,6], and IGGtrop series models [7,8]
The meteorological parameters are derived as the mean values and annual amplitudes from 3 years of global monthly mean profiles for pressure and temperature provided by the European Centre for Medium-Range Weather Forecasts (ECMWF)
Summary
The incorrect modeling of troposphere delays is one of the major error sources for space geodetic techniques such as global navigation satellite systems (GNSS) or very long baseline interferometry (VLBI) [1]. Where k2′ and k3 represent empirically determined refractivity constants, Rd is the specific gas constant of dry air, λ is the vapor pressure decrease factor, and Tm is the weighted mean temperature These two additional parameters are provided as average values as well as amplitudes of annual and semiannual variations on a global. It can be concluded that the mean dT fields of both models clearly show odd values with respect to the average value of -6.5 K ∙ km−1 in the troposphere This is mainly caused by the fact that the dT in GPT series models is derived from the lowest two pressure levels of the ECMWF data, which limits their applications to the lower atmosphere
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