Characterisation of residual ionospheric errors in bending angles using GNSS RO end-to-end simulations

Global Navigation Satellite System (GNSS) radio occultation (RO) is an innovative meteorological remote sensing technique for measuring atmospheric parameters such as refractivity, temperature, water vapour and pressure for the improvement of numerical weather prediction (NWP) and global climate monitoring (GCM). GNSS RO has many unique characteristics including global coverage, long-term stability of observations, as well as high accuracy and high vertical resolution of the derived atmospheric profiles. One of the main error sources in GNSS RO observations that significantly affect the accuracy of the derived atmospheric parameters in the stratosphere is the ionospheric error. In order to mitigate the effect of this error, the linear ionospheric correction approach for dual-frequency GNSS RO observations is commonly used. However, the residual ionospheric errors (RIEs) can be still significant, especially when large ionospheric disturbances occur and prevail such as during the periods of active space weather. In this study, the RIEs were investigated under different local time, propagation direction and solar activity conditions and their effects on RO bending angles are characterised using end-to-end simulations. A three-step simulation study was designed to investigate the characteristics of the RIEs through comparing the bending angles with and without the effects of the RIEs. This research forms an important step forward in improving the accuracy of the atmospheric profiles derived from the GNSS RO technique.     

GPS sensing of precipitable water vapour during the March 2010 Melbourne storm

The March 2010 Melbourne storm is used as a case study to examine the potential of using Global Positioning System (GPS) observations for studying the precipitable water vapour (PWV) field. The Victorian statewide GPS infrastructure network, i.e. GPSnet, was used in this study. GPSnet is currently the only statewide and densest GPS infrastructure network in Australia, which provides an excellent opportunity to examine the distribution of water vapour as the severe weather system passed over the state. Data from 15 GPSnet stations were processed over a one-week period, i.e. a few days prior to and after the storm passage, during which the course of the storm extended from the west to the southeast corner of the state. In addition, data from two radiosonde sites of the Australian Bureau of Meteorology Upper Air Network were used to compare and validate the GPS derived PWV measurements. The findings demonstrate that there is strong spatial and temporal correlation between variations of the ground-based GPS-PWV estimates and the passage of the storm over the state. This is encouraging as the ground-based GPS water vapour sensing technique can be considered as a supplemental meteorological sensor in studying severe weather events. The advantage of using ground-based GPS-PWV technique is that it is capable of providing continuous observation of the storm passage with high temporal resolution. The spatial resolution of the distribution of water vapour is dependent on the geographical location and density of the GPS stations.     

Determining the precipitable water vapor thresholds under different rainfall strengths in Taiwan

Precipitable Water Vapor (PWV) plays an important role for weather forecasting. It is helpful in evaluating the changes of the weather system via observing the distribution of water vapor. The ability of calculating PWV from Global Positioning System (GPS) signals is useful to understand the special weather phenomenon. In this study, 95 ground-based GPS and rainfall stations in Taiwan were utilized from 2006 to 2012 to analyze the relationship between PWV and rainfall. The PWV data were classified into four classes (no, light, moderate and heavy rainfall), and the vertical gradients of the PWV were obtained and the variations of the PWV were analyzed. The results indicated that as the GPS elevation increased every 100?m, the PWV values decreased by 9.5?mm, 11.0?mm, 12.2?mm and 12.3?mm during the no, light, moderate and heavy rainfall conditions, respectively. After applying correction using the vertical gradients mentioned above, the average PWV thresholds were 41.8?mm, 52.9?mm, 62.5?mm and 64.4?mm under the no, light, moderate and heavy rainfall conditions, respectively. This study offers another type of empirical threshold to assist the rainfall prediction and can be used to distinguish the rainfall features between different areas in Taiwan.