Exploitation of GNSS tropospheric gradients for severe weather Monitoring And Prediction (EGMAP): Project status and Initial results

Data assimilation (DA) is a tool that is capable of combining observations and numerical weather models (NWMs) in an optimal manner. Current DA systems used by operational forecasting centres are constantly evolving and getting better than before. High-quality observations are very important for the accurate representation of variables in a weather model. In this study, we are incorporating Global Navigation Satellite System (GNSS) tropospheric gradients and Zenith Total Delays (ZTDs) into the Weather Research and Forecasting (WRF) model. WRF model has its operator already developed for the ZTDs and in this research, we are developing a new operator for the assimilation of tropospheric gradients. The assimilation of ZTDs, which are closely related to Integrated Water Vapor (IWV) above the GNSS station, has a positive impact on weather forecasts. On the other hand, tropospheric gradients are not yet assimilated by the weather agencies. Our research is based on a project titled “Exploitation of GNSS tropospheric gradients for severe weather Monitoring And Prediction” (EGMAP) focusing on the impact of GNSS tropospheric gradients and how it can be effectively used for operational forecasting of severe weather. EGMAP is funded by the German Research Foundation (DFG).

The observation operator currently in use for tropospheric gradients is based on a linear combination of ray-traced tropospheric delays (Zus et al., 2022). This observation operator is challenging to be implemented into an NWM DA system. We will thus rely on a more simple and fast observation operator which is based on the closed-form expression depending on the north–south and east–west horizontal gradients of atmospheric refractivity (Davis et al., 1993).

Initial testing of the operator is done on a 0.1 x 0.1-degree mesh configured over Central Europe in the WRF model with 50 vertical levels up to 50 hPa. The model configuration will be later upgraded to a convective-scale resolution after initial testing of the tropospheric gradient operator. Model forcing observations are derived from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) data at 0.25-degree resolution. Conventional observations are obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) and are the base dataset for the assimilation studies. The conventional datasets used for assimilation are restricted to surface stations (SYNOP observations) and radiosondes. Additionally, observations from roughly 100 GNSS stations are assimilated at each DA cycle. Three experiments are conducted: 1) Control run with only conventional data; 2) ZTD assimilation on top of the control run, and; 3) ZTD and tropospheric gradient assimilation on top of the control run. Initial DA tests are being performed with an automated rapid update cycle DA framework with 6 hourly intervals based on a deterministic three-Dimensional Variational (3DVar) DA system for the testing of ZTDs and tropospheric gradients. The DA system will be later upgraded to a probabilistic one based on the Hybrid 3DVar-Ensemble Transform Kalman Filter (-ETKF, Thundathil et al., 2021) and 4DEnVar. The EGMAP project status and initial results from the impact of GNSS-ZTDs and tropospheric gradients will be presented.