Search

GNSS data collected at a single station allow the estimation of the Zenith Total Delay (ZTD) and tropospheric gradients. In order to make use of such data in numerical weather prediction the observation operators must be developed. The development of a cost efficient observation operator for ZTDs is a straightforward task. On the other hand the development of a cost efficient observation operator for tropospheric gradients is not an easy task. It is also important to bear in mind that for variational data assimilation the corresponding tangent-linear and adjoint operators must be coded.Our current observation operator for tropospheric gradients is based on dozens of tropospheric delays (Zus et al., 2019). Thereby each tropospheric delay is computed with high precision utilizing a technique called ray-tracing. Clearly, this makes the current observation operator for tropospheric gradients for practical applications too expensive. In this contribution we show how to reduce the computational cost. For example, as expected the high precision with which the tropospheric delays are computed is not too crucial. In addition, the number of tropospheric delays that are involved in the computation of the tropospheric gradients can be reduced. The tropospheric gradients can be understood as a specific linear combination of tropospheric delays. Hence, the difficulty in the derivation of the tangent-linear (adjoint) code for tropospheric gradients lies in the difficulty in the derivation of the tangent-linear (adjoint) code for tropospheric delays. However, this does actually not pose a problem as these codes are available from our previous work.The output of this study is a cost efficient observation operator (a piece of Fortran code), which, together with its tangent-linear and adjoint operator, is ready to be implemented into existing assimilation systems. One of them is our experimental assimilation system (Zus et al., 2019). Another one will be the assimilation system of the Weather Research and Forecasting (WRF) model in support of the research project EGMAP (Exploitation of GNSS tropospheric gradients for severe weather Monitoring And Prediction) funded by the German Research Foundation (DFG).Zus, F.; Douša, J.; Kačmařík, M.; Václavovic, P.; Dick, G.; Wickert, J. Estimating the Impact of Global Navigation Satellite System Horizontal Delay Gradients in Variational Data Assimilation. Remote Sens. 2019, 11, 41. https://doi.org/10.3390/rs11010041

Ponencia expuesta online en el 8th International Radio Occultation Working Group Meeting (2021) celebrado del 7 al 9 de abril, Ground and satellite based GNSS Remote Sensing (GNSS-RS) developed during the recent two decades into a very powerful and versatile tool for Earth System Research. A highlight of these developments is the operational use of spaceborne GNSS Radio Occultation (RO) data from several satellite missions to improve day-by-day global weather predictions. GNSS Remote Sensing is briefly introduced with selected applications. One prominent example is the improvement of regional and global weather forecasts. GNSS signals, reflected from water, ice and land surfaces (GNSS-Reflectometry, GNSS-R) can usefully complement the observation capabilities of GNSS-RO mission and enable versatile additional geophysical applications such as observation of wind speed and precipitation over oceans, which are illustrated. Finally, selected aspects for a comprehensive GNSS based Earth Observation with small satellite constellations are presented.