In this paper, the concept of Virtual Weather Stations (VWS) is introduced. A VWS is an integration of algorithms to download meteorological data, process and use them with the main objective of estimate data in nearby locations with no meteorological stations. To develop the VWS, the performances of different interpolation methods were evaluated to test the accuracy. Daily data from an automatic weather station network, such as precipitation (Precip), air temperature (Temp), air relative humidity, mean wind speed, total solar irradiation, and reference evapotranspiration were interpolated using artificial neural networks (ANNs) with the hardlim, sigmoid, hyperbolic tangent (tanh), softsign, and rectified linear unit (relu) activations functions were employed. To contrast the ANNs interpolations, alternatives methods such as inverse distance weighting, inverse-squared distance weighting, multilinear regression, and random forest regression were used. To validate the models, a randomly selected weather station was removed from the daily datasets, and the interpolated values were compared with the actual station records. Additionally, interpolations in the summer and winter months were performed to check the capability of the models during periods with more extreme phenomena. The results showed that the interpolation methods have an R2 up to 0.98 for variables such as temperatures for the period of 1 year. Meanwhile, during the summer and winter, the models presented lower accuracy. From a practical perspective, the methods here described could be useful to produce meteorological data with the VWS to record temperatures and dose the irrigation in crops.
