Assessing the performance of troposphere tomographic modeling using multi-source water vapor data during Hong Kong's rainy season from May to October 2013

Abstract

Abstract. Acquiring accurate atmospheric water vapor spatial information remains one of the most challenging tasks in meteorology. The tomographic technique is a powerful tool for modeling atmospheric water vapor and monitoring the water vapor spatial and temporal distribution/variation information. This paper presents a study on the monitoring of water vapor variations using tomographic techniques based on multi-source water vapor data, including GPS (Global Positioning System), radiosonde, WVR (water vapor radiometer), NWP (numerical weather prediction), AERONET (AErosol RObotic NETwork) sun photometer and synoptic station measurements. An extensive investigation has been carried out using multi-source data collected from May to October 2013 in Hong Kong. With the use of radiosonde observed profiles, five different vertical a priori information schemes were designed and examined. Analysis results revealed that the best vertical constraint is to employ the average radiosonde profiles over the 3 days prior to the tomographic time and that the assimilation of multi-source data can increase the tomography modeling accuracy. Based on the best vertical a priori information scheme, comparisons of slant wet delay (SWD) measurements between GPS data and multi-observational tomography showed that the root mean square error (RMSE) of their differences is 10.85 mm. Multi-observational tomography achieved an accuracy of 7.13 mm km −1 when compared with radiosonde wet refractivity observations. The vertical layer tomographic modeling accuracy was also assessed using radiosonde water vapor profiles. An accuracy of 11.44 mm km −1 at the lowest layer (0-0.4 km) and an RMSE of 3.30 mm km −1 at the uppermost layer (7.5-8.5 km) were yielded. At last, a test of the tomographic modeling in a torrential storm occurring on 21-22 May 2013 in Hong Kong demonstrated that the tomographic modeling is very robust, even during severe precipitation conditions.

3-D Water Vapor Tomography in Wuhan from GPS, BDS and GLONASS Observations

“…The average water vapor profile from three-day radiosonde data prior to the tomographic time is adopted as vertical prior information for the tomography [13], and the size of the tomography window is set to 0.5 h. The corresponding radiosonde data and the latest ERA5 reanalysis products provided by the European Center for Medium Range Weather Forecasts (ECMWF) are used for external validation for the tomographic fields. Here, only one radiosonde station is available in the tomography area.…”

“…A priori water vapor distribution information is used to establish the vertical constraints [13,18] to solve the alteration phenomenon of inversion water vapor field that upper water vapor density is smaller than water vapor density at the bottom. In addition, the top boundary constraint can also be added to force the water vapor density of voxels at the topper layer to zeros.…”

Real-Time Precise Point Positioning Using Tomographic Wet Refractivity Fields

“…This makes sure that the tomographic WR fields are independent on the testing stations. Based on this tomographic model, SWDs derived from the tomographic WR fields using multi-source data in Hong Kong have an accuracy of better than 12 mm [21]. The WR fields generated by the tomography have a temporal …”

“…The tomographic technique is a powerful tool for modeling atmospheric water vapor with high spatiotemporal resolutions [18,31]. A multi-source water vapor tomography system has been developed in Hong Kong as reported by Reference [21]. The tomographic products have been successfully used for detecting the water vapor variability during heavy precipitation events in Hong Kong [25].…”

An improved troposphere tomographic approach considering the signals coming from the side face of the tomographic area

“…Notarpietro et al (2011) propose a method to calculate the value of signal slant water vapour (SWV) outside the tomographic area with ECMWF data, while Benevides et al (2014) propose the geometric linear method using the empirically exponential negative function. Chen and Liu (2016) estimate the slant wet delay (SWD) outside the modelling area with the help of numerical weather prediction (NWP) profile data. proposed a method which also considers the signals penetrating from the side face of the research area by introducing the unit scale factor model, while the unit scale factor refers to the proportion between the value of signal SWV inside the tomographic area and the total value of this signal SWV.…”