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The microwave radiances are key observations especially over data sparse regions for operational data assimilation in numerical weather prediction (NWP). An often applied simplification is that these observations are used as point measurements, however, the satellite field-of-view may cover many grid points of high-resolution models. Therefore, we examine a solution in high-resolution data assimilation to better account for the spatial representation of the radiance observations. This solution is based on a footprint operator implemented and tested in the variational assimilation scheme of the AROME-Arctic (Application of Research to Operations at Mesoscale – Arctic) limited-area model. In this paper, the design and technical challenges of the microwave radiance footprint operator are presented. In particular, implementation strategies, the representation of satellite field-of-view ellipses, and the emissivity retrieval inside the footprint area are discussed. Furthermore, the simulated brightness temperatures and the sub-footprint variability are analysed in a case study indicating particular areas where the use of the footprint operator is expected to provide significant added value. For radiances measured by the Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS) sensors, the standard deviation of the observation minus background (OmB) departures are computed on a short period in order to compare the statistics of the default and the implemented footprint observation operator. For all operationally used AMSU-A and MHS channels, it is shown that the standard deviation of OmB departures is reduced when the footprint operator is applied. [ABSTRACT FROM AUTHOR]

The delay of satellite signals broadcasted by Global Navigation Satellite System (GNSS) provides unique atmospheric observation which endorses numerial weather prediction from global to limited-area models. Due to the possibility of its frequent and near real-time estimation, the zenith total delays (ZTD) are valuable information for any state-of-the-art data assimilation systems. This article introduces the data assimilation of ZTDs in a Hungarian numerical weather prediction system which was carried out taking into account observations from Central-European GNSS analysis and processing centres. The importance of ZTD observations is described and showed by a diagnostic tool in the three hourly updated 3D-Var variational assimilation scheme. Furthermore, observing system experiments are done to evaluate the impact of GNSS ZTDs on mesoscale limited-area forecasts. The results of the use of GNSS ZTDs showed a clear added value to improve screen-level temperature and humidity forecasts when bias is accurately estimated and corrected in the data assimilation scheme. The importance of variational i.e. adaptive bias correction is highlighted by verification scores compared to static bias correction. Moreover, this paper reviews the quality control of GNSS ground-based stations inside the Central-European domain, the calculation of optimal thinning distance and the preparation of two above mentioned bias correction methods. At the end of this article, the conclusion is drawn about different settings of the forecast and analysis experiments with a brief future outlook. [ABSTRACT FROM AUTHOR]