Abstract
Accurate water vapor density (WVD) measurement is critical for weather models, health risk management, and industrial management among many other applications. A number of machine-learning based algorithms (e.g. support vector machine) for estimating water vapor density at a reference weather station using the received signal level values measured at a commercial microwave link has been proposed in the past, and also was expanded to include a combination of three commercial microwave links with temperature measurements to achieve a higher estimation accuracy (with respect to the root mean square error at a given location). In this paper, we leverage on the preliminary potential presented, and propose enhanced machine learning models that utilize a larger number of CMLs combined with temperature data inside a given area to estimate a reference weather station humidity measurements. We then show how the presented approach can be expanded to estimate the water vapor density field - taking into consideration the elevation via the humidity-elevation profile. The models were evaluated using data from 32 weather stations and 505 CMLs in Germany, with performance assessed through root mean square error (RMSE) and correlation coefficients (CC). The enhanced models achieved a mean RMSE of 0.587 g/m³ for WVD field estimation, outperforming prior approaches as well as can be used as "virtual weather stations" - to estimate the water vapor density values in locations where no actual weather stations exist.
Published Version
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