Near-surface temperature and humidity observations over Germany, coming on the one hand from the citizen weather station's network Netatmo and on the other hand from synoptic weather stations, were assimilated into the limited are mode of the Icosahedral Nonhydrostatic Model with 2-km resolution (ICON-D2). For that we use the Kilometre-Scale Ensemble Data Assimilation (KENDA) system and a bias-correction approach that improves the assimilation of the observations by taking into account the diurnal cycle of temperature and humidity variables. Our results show that the assimilation of bias-corrected observations from Netatmo stations reduces the forecast error considerably; meanwhile, the assimilation of Netatmo observations without bias correction leads to a strong warm bias with a negative impact on forecast performance.In contrast, for the assimilation of synoptic observations the usage of our bias-correction approach does not lead to any significant decrease in the forecast error, yet reduces the bias for the diurnal cycle of synoptic stations. Overall, it can be concluded that the forecast quality can gain from assimilating Netatmo data, provided an effective bias-correction approach is applied.
<p>The growing availability of high resolved meteorological measurements coming from automobiles puts forward the possibility of developing real time weather forecast systems, which appears to be an essential key of autonomous driving enhancement. In this frame, the Fleet Weather Maps (Flotten-Wetter-Karte - FloWKar) project, a joint work of the German Meteorological Service (DWD) and the German car manufacturer AUDI AG, aims to explore how environmental data from sensors of vehicles on Germany&#8217;s roads, respecting data protection regulations, can be used in real time to improve weather forecast, nowcasting and warnings within DWD&#8217;s products. Regarding weather forecasting, an exceptionally fast data assimilation cycle with an update rate of the order of minutes is necessary. However, this cannot be achieved using standard assimilation systems. Hence, an ultra-rapid data assimilation (URDA) method has been developed. The URDA updates only a reduced version of the state variables in an existing model forecast, using different kind of observation data available, only after a standard assimilation cycle and a full model forecast. Moreover, the quality of the meteorological data collected by moving vehicles is vital and therefore a series of quality control and bias correction algorithms has been built for the correction of the raw observations, employing among others artificial intelligence techniques. The first preliminary results of both project partners are promising: the corrected measured variables of the mass-produced vehicle-based sensors match well with the &#8216;ground truth&#8217; and real time maps are produced after the assimilation of the high resolved project data. The improved and detailed model outputs for road weather forecasting are a first necessary step towards the safety on roads especially in the winter conditions and the future autonomous driving.</p>
<p>The accurate and precise weather information in polar region have proved to be essential for the global weather and climate research. However, even though we leave in the &#8216;golden age&#8217; of earth observations, there is a lack of in-situ observation coverage in the polar regions. Additionally, the use of radiances measured from polar-orbiting satellites is of limited use due to the difficulties using those kind of data over ice and snow. The major research expedition in Arctic, MOSAiC (Multidisciplinary drifting Observatory of Arctic Climate), managed to shed light on polar conditions with the collection of a huge variety of highly resolved data.</p><p>The SynopSys Project (Synoptic events during MOSAiC and their Forecast Reliability in the Troposhere-Stratosphere System) is a collaboration of the German Weather Service (DWD) with the Alfred-Wegener Institute (AWI) and the University of Bremen. The project aims to combine the state-of-the-art weather observations from the MOSAiC Expedition together with remote sensing products and meteorological forecast in order to identify and study synoptic events in the Arctic.</p><p>The current work focuses on the evaluation and improvement of the weather forecasting capabilities of ICON-NWP model in Arctic. In this frame, the latest version of the global model ICON is employed to assimilate the different kind MOSAiC data &#8211; from synoptic station data to ascending and descending radiosondes. On the one hand, a series of sensitivity studies has taken place to evaluate the different observation systems and identify the ones with the highest influence on the arctic model forecast. The improvement of the weather forecast itself and the weather analysis because of the assimilation of the project data is studied on the other hand, as well as their influence on the weather forecast of the mid-latitudes. The experiment period covers March and April 2020, which is of high meteorological interest, due to the observed day-to-day variability &#8211; a cold period at the beginning of the month was followed by strong warm air intrusion, challenging the model forecast and analysis performance.</p>
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