Contribution of personal weather station data in regional data assimilation

Abstract

In this thesis, the effect of temperature and relative humidity observations from a personal weather station network in a regional Numerical Weather Prediction (NWP) Model is investigated. The goal is to investigate whether these observations can improve the short-range weather forecast at high-resolution (~1 km)typical of regional NWP. To be able to use observations from personal weather stations, a simple bias correction method and a thinning approach were applied to data in order to reduce observation bias, and observation error correlations. Next, the observations are used in the regional data assimilation system used for weather prediction in the Netherlands. The analysis of the observation minus the background values show positive to neutral effect after application of preprocessing methods. Forecast verification show similar neutral results. Overall, it is shown that the forecast quality at high resolution can benefit from the assimilation of observations from personal weather stations provided that an effective bias correction approach is applied.