Quality control and bias adjustment of crowdsourced wind speed observations

Wind observations collected at citizen weather stations (CWSs) could be an invaluable resource in climate and meteorology studies, yet these are underutilised because scientists do not have confidence their quality. These wind speed systematic biases, likely caused by improper instrumentation station sitings. Such biases introduce spatial inconsistencies that prevent comparison of spatially limit the possible usage data. In this paper, we address issues improving developing new methods for identifying suspect adjusting biases. Our complete quality control bias adjustment procedure consists four steps: (a) performing within-station tests to check plausible range temporal consistency observations, (b) using empirical quantile mapping, (c) implementing between-station compare from neighbouring identify inconsistent (d) providing estimates true when CWSs falsely report zero speeds, as a complement adjustment. We apply Weather Observation Website (WOW) Netherlands, comparing crowdsourced data with official data, statistically assessing improvements after each step. The results demonstrate more comparable checks steps. assessment therefore give CWSs, converting into usable product applications need additional observations.