Mean atmospheric temperature model estimation for GNSS meteorology using AIRS and AMSU data
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Abstract
In this paper, the problem of modeling the relationship between the mean atmospheric and air surface temperatures is addressed. Particularly, the major goal is to estimate the model parameters at a regional scale in Thailand. To formulate the relationship between the mean atmospheric and air surface temperatures, a triply modulated cosine function was adopted to model the surface temperature as a periodic function. The surface temperature was then converted to mean atmospheric temperature using a linear function. The parameters of the model were estimated using an extended Kalman filter. Traditionally, radiosonde data is used. In this paper, satellite data from an atmospheric infrared sounder, and advanced microwave sounding unit sensors was used because it is open source data and has global coverage with high temporal resolution. The performance of the proposed model was tested against that of a global model via an accuracy assessment of the computed GNSS-derived PWV.
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