The cities weather forecasting by crowdsourced atmospheric data

Abstract

The problem of the weather forecasting still exists in urban agglomerations, in part because of local factors on a city scale. It is important to take into acount urban processes, to make a high-resolution weather forecast. In this study, we suggest an approach to use crowdsourced meteorological data from citizen weather stations (CWS) to improve weather forecasting. The Weather Research and Forecasting model was used to simulate forecasting for two cities Saint Petersburg and Moscow. Using processed CWS data we propose a data-driven model to correct numeric modeling for short-time forecasting. We propose a procedure for statistical validation of citizen stations using observations from professional meteostations. Experiments have shown that in quality they can be compared with the government ones, the numerical model WRF need to be corrected for representing winter heat island. We considered the possibility of using text data from social media posts to predict weather conditions in the case of air temperature.