Assimilation of GNSS Zenith Delays and Tropospheric Gradients: A Sensitivity Study utilizing sparse and dense station networks

Abstract. The assimilation of Global Navigation Satellite System (GNSS) zenith total delays (ZTDs) into numerical weather models improves weather forecasts. In addition, the GNSS tropospheric gradient (TG) estimates provide valuable insight into the moisture distribution in the lower troposphere. In this study, we utilize a newly developed forward operator for TGs to investigate the sensitivity effects of incorporating TGs into the Weather Research and Forecasting model at varying station network densities. We assimilated ZTD and TGs from sparse and dense station networks (0.5 and 1-degree). Through this study, we found that the improvement in the humidity field with the assimilation of ZTD and TGs from the sparse station network (1-degree resolution) is comparable to the improvement achieved by assimilating ZTD only from the dense station network (0.5-degree resolution). These results encourage the assimilation of TGs alongside ZTDs in operational weather forecasting agencies, especially in regions with few GNSS stations. Conversely, assimilating TGs alongside ZTDs from sparse GNSS networks can be a cost-effective way to enhance the accuracy of the model fields and subsequent forecast quality.