Itay Bragin ^* Yoav Rubin Pinhas Alpert Jonatan Ostrometzky

• Tel Aviv University, Tel Aviv, Israel

Accurate water vapor density measurement is crucial for weather models, health risk management, and industrial management among many other applications. A number of machinelearning 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 commercial microwave links with temperature measurements inside a given area to estimate a reference weather stations 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. Specifically, we show that the estimation achieved by the proposed approach is both more accurate when compared with previously presented approaches, as well as can be used to as "virtual weather stations" -and to estimate the water vapor density values in locations where no actual weather stations exist.