Using global navigation satellite system data for real-time moisture analysis and forecasting over the Australian region I. The system

Abstract

The use of high spatial and temporal resolution data assimilation and forecasting around Australia’s capital cities and rural land provided an opportunity to improve moisture analysis and forecasting. To support this endeavour, RMIT University and Geoscience Australia worked with the Bureau of Meteorology (BoM) to provide real-time GNSS (global navigation satellite system) zenith total delay (ZTD) data over the Australian region, from which a high-resolution total water vapour field for SE Australia could be determined. The ZTD data could play an important role in high-resolution data assimilation by providing mesoscale moisture data coverage from existing GNSS surface stations over significant areas of the Australian continent. The data were used by the BoM’s high-resolution ACCESS-C3 capital city numerical weather prediction (NWP) systems, the ACCESS-G3 Global system and had been used by the ACCESS-R2-Regional NWP model. A description of the data collection and analysis system is provided. An example of the application of these local GNSS data for a heavy rainfall event over SE Australia/Victoria is shown using the 1.5-km resolution ACCESS-C3 model, which was being prepared for operational use. The results from the test were assessed qualitatively, synoptically and also examined quantitatively using the Fractions Skills Score which showed the reasonableness of the forecasts and demonstrated the potential for improving rainfall forecasts over south-eastern Australia by the inclusion of ZTD data in constructing the moisture field. These data have been accepted for operational use in NWP.

Development of an Improved Model for Prediction of Short-Term Heavy Precipitation Based on GNSS-Derived PWV

“…where ϕ and H are the latitude ( • ) and height (km) above the reference ellipsoid of the site, respectively. The zenith wet delay (ZWD) over the GNSS station is then obtained from subtracting the ZHD from the ZTD, then the ZWD is converted to GNSS-PWV (mm) using the following formula [32]:…”

“…Nowadays, many regional and global GNSS networks, which consist of various numbers of tracking stations, are available. This makes the best use of GNSS data for studies and applications on short-term weather predictions feasible and meaningful [17,[30][31][32], in addition to facilitating climate change research [33][34][35].…”

Testing Variational Bias Correction of Satellite Radiance Data in the ACCESS-C: Australian Convective-Scale NWP System

“…over seven domains: Perth (PH), Adelaide (AD), Victoria-Tasmania (VT), Sydney (SY), Brisbane (BN), Darwin (DN), and North Queensland (NQ), as shown in Figure 1 . It has a variable horizontal resolution grid with fixed 1.5 km resolution inner domain, stretching to 4 km at the outer boundary, and 80 vertical levels up to 40 km, with the lowest model level at 2.5 m. In this study, we focus on the Sydney domain, shown in blue (outer) and green (inner) in Figure 1 [ 14 , 15 , 16 ]. The variational assimilation system uses the observations and the background model short-range forecast from the previous cycle to generate an analysis increment.…”

Investigating the Optimal Spatial Resolution for Assimilating GNSS PWV into an NWP System to Improve the Accuracy of Humidity Field

“…In this study, Victoria, located in southeastern Australia, is selected as the study region. Although it is the second smallest state in Australia, it has a varied climate with frequent occurrences of severe weather events, e.g., rainstorms, tropical cyclones, and extreme floods [40], [41], [42]. According to statistics, severe weather events happened in the state of Victoria alone have caused over $1.9 billion economic loss and have also greatly affected more than 2.5 million people over the past 20 years [43], [44].…”