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Fig.9. 24-h rainfall totals. Part (a) totals between 0000 UTC on 1 and 2 December from the operational AWAP system, (b) forecast totals for the control system for the same period (no ZTD data) and (c) forecast rainfall totals for the experimental system (including ZTD data). Colour scale vs rainfall shown in Fig. 11a.

Figure 9 24-h rainfall totals. Part (a) totals between 0000 UTC on 1 and 2 December from the operational AWAP system, (b) forecast totals for the control system for the same period (no ZTD data) and (c) forecast rainfall totals for the experimental system (including ZTD data). Colour scale vs rainfall shown in Fig. 11a.

Related Figures (13)

Fig. 2. Zenith wet delay (ZWD) in metres over Victoria, calculated from ZTD at 0000 UTC 29 May 2018.
Fig. 2. Zenith wet delay (ZWD) in metres over Victoria, calculated from ZTD at 0000 UTC 29 May 2018.
Fig. 1. (a) Map ofstations around Victoria used for near real or real-time processing as sources of GNSS data. (b) Map of stations around Australia used for near real or real-time processing as sources of GNSS data. ARGN, Australia Reference Geodetic Network; IGS, International GNSS Service.
Fig. 1. (a) Map ofstations around Victoria used for near real or real-time processing as sources of GNSS data. (b) Map of stations around Australia used for near real or real-time processing as sources of GNSS data. ARGN, Australia Reference Geodetic Network; IGS, International GNSS Service.
Fig. 3. The distribution of GNSS stations processed by Geoscience Australia. window of 24h of raw observations obtained from real-time streams sourced from the ARGN, Auscope, South Pacific and LINZ GPS networks. To help validate the ZTD estimates, GA also produces a rapid (R) solution, which relies upon the rapid IGS orbit and clock products. For the rapid solution, GA pro- cesses over 600 stations from a global network dominated by stations in the Asia Pacific region. The system has been estab- lished in the Amazon Web Services cloud to help aid in reli- ability and scalability.
Fig. 3. The distribution of GNSS stations processed by Geoscience Australia. window of 24h of raw observations obtained from real-time streams sourced from the ARGN, Auscope, South Pacific and LINZ GPS networks. To help validate the ZTD estimates, GA also produces a rapid (R) solution, which relies upon the rapid IGS orbit and clock products. For the rapid solution, GA pro- cesses over 600 stations from a global network dominated by stations in the Asia Pacific region. The system has been estab- lished in the Amazon Web Services cloud to help aid in reli- ability and scalability.
2015.54800228 2015.56170091 2015.57539954 2015.58909817 2015.60279680 2015.61649543 2015.6300799° Year
2015.54800228 2015.56170091 2015.57539954 2015.58909817 2015.60279680 2015.61649543 2015.6300799° Year
Fig. 5. The distribution of RMS differences from the comparison of processing methodologies GA relies upon the IGS Real Time clock products and the IGS UR orbits. ZTD estimates derived from the sliding window of 24h of raw observations are provided every hour with a latency of ~40 min from ~ 160 stations. Figure 6 shows a typical product from the GA system, namely ZTD in millimetres over Mount Stromlo and Parkes, compared to the current operational nondata assimilating ACCESS-C2 and GA Rapid and UR Data.
Fig. 5. The distribution of RMS differences from the comparison of processing methodologies GA relies upon the IGS Real Time clock products and the IGS UR orbits. ZTD estimates derived from the sliding window of 24h of raw observations are provided every hour with a latency of ~40 min from ~ 160 stations. Figure 6 shows a typical product from the GA system, namely ZTD in millimetres over Mount Stromlo and Parkes, compared to the current operational nondata assimilating ACCESS-C2 and GA Rapid and UR Data.
Fig. 6. Zenith total delay (ZTD) in mm (millimetres) over Mount Stromlo and Parkes, compared to ACCESS-C2 for GA Rapid and UR Data processed by GA. GA, Geoscience Australia; RMSE, root-mean- square error.
Fig. 6. Zenith total delay (ZTD) in mm (millimetres) over Mount Stromlo and Parkes, compared to ACCESS-C2 for GA Rapid and UR Data processed by GA. GA, Geoscience Australia; RMSE, root-mean- square error.
Table 1. Bias and Standard Deviation (m) of GNSS ZTD observations compared to ACCESS-R at selected GNSS Sites, 19 November-19 December 2017
Table 1. Bias and Standard Deviation (m) of GNSS ZTD observations compared to ACCESS-R at selected GNSS Sites, 19 November-19 December 2017
AIRS, advanced infrared sounder; CrIS, cross-track infrared sounder; GPSRO, GPS radio occultation satellites; ATMS, advanced technology microwave sounder; AMSU, advanced microwave sounding unit; ATOVS, advanced TIROS operational vertical sounders
AIRS, advanced infrared sounder; CrIS, cross-track infrared sounder; GPSRO, GPS radio occultation satellites; ATMS, advanced technology microwave sounder; AMSU, advanced microwave sounding unit; ATOVS, advanced TIROS operational vertical sounders
Fig. 8. | 24-h rainfall totals. Part (a) totals between 0000 UTC 30 November and 0000 UTC 1 December from the operational AWAP system, (5) forecast totals for the control system for the same period (no ZTD data) and (c) forecast totals for the experimental system (including ZTD data). Colour scale vs rainfall shown in Fig. 11a.
Fig. 8. | 24-h rainfall totals. Part (a) totals between 0000 UTC 30 November and 0000 UTC 1 December from the operational AWAP system, (5) forecast totals for the control system for the same period (no ZTD data) and (c) forecast totals for the experimental system (including ZTD data). Colour scale vs rainfall shown in Fig. 11a.
Fig. 10. 24-h rainfall totals as in Fig. 9a—c. Colour scale vs rainfall shown in Fig. 11b. Rain totals ranging from 0.4 to =40 mm.
Fig. 10. 24-h rainfall totals as in Fig. 9a—c. Colour scale vs rainfall shown in Fig. 11b. Rain totals ranging from 0.4 to =40 mm.
Fig. 12. (qa) Fractions Skill Score (FSS) vs Scale (in grid units) for 12-h forecasts during the period 30 November to 4 December 2017, for forecasts where GPS data were and were not included in the forecast database. (b) FSS vs Scale for 18-h forecasts during the same period for forecasts where GPS data were and were not included in the forecast database. Fig. 11. (a) 24-h rainfall totals by colour for Figs 8 and 9, (6) 24-h rainfall totals by colour for Fig. 10.
Fig. 12. (qa) Fractions Skill Score (FSS) vs Scale (in grid units) for 12-h forecasts during the period 30 November to 4 December 2017, for forecasts where GPS data were and were not included in the forecast database. (b) FSS vs Scale for 18-h forecasts during the same period for forecasts where GPS data were and were not included in the forecast database. Fig. 11. (a) 24-h rainfall totals by colour for Figs 8 and 9, (6) 24-h rainfall totals by colour for Fig. 10.
Fig. 13. Zenith total delay (ZTD) data impact by grid scale shown through number of improved forecasts when GPS is or is not used in the forecast process. 5 Conclusions
Fig. 13. Zenith total delay (ZTD) data impact by grid scale shown through number of improved forecasts when GPS is or is not used in the forecast process. 5 Conclusions
Related topics:
Environmental ScienceData AssimilationNumerical Weather PredictionMesoscale MeteorologyGNSS applications
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