Results of a GPS Zenith Total Delay data assimilation experiment over Italy

The Mediterranean area is often struck by severe weather events and deep convective events because of the presence of the warm sea, the complex orography of the area, and the specific synoptic scale environment. This scenario is worsened by climate change because, as climate change is affecting many weather and climate extremes, and the frequency and intensity of heavy precipitation events have increased in most of the world.

Over the past years, the use of Numerical Weather Prediction (NWP) models, along with an increasing availability of computing power, led to an improvement of the forecast accuracy. However, NWPs have well-known difficulties in capturing the physical processes at small spatial and temporal scales which are involved in convective or severe weather events. 

In this work we study the impact of assimilating GPS-ZTD (Global Positioning System-Zenith Total Delay) on the precipitation forecast over Italy for the month of October 2019, characterized by several moderate to intense precipitation events. The Weather Research and Forecasting (WRF, version 4.1.3) is used with its 3DVar data assimilation system. The horizontal resolution is 3km while the vertical domain spans the whole troposphere and lower stratosphere.

A dense network of about 500 GPS receivers was used for data assimilation and verification of the atmospheric water content. The dataset was built collecting data from all the major national and regional GNSS permanent networks, achieving dense coverage over the whole area.

Results show that WRF underestimates the atmospheric water content for the period, and GPS-ZTD data assimilation reduced this underestimation by increasing the water content of the atmosphere. The GPS-ZTD data assimilation increases the precipitation forecast amount, and the model performance are improved up to 6h.

Results for a case study show that the GPS-ZTD data assimilation can improve the precipitation forecast in different ways: predicting rainfall missed by the model without data assimilation or better focusing the precipitation already predicted by the model without GPS-ZTD data assimilation on the impacted area, the main drawback being the prediction of false alarms.