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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 9, issue 10
Atmos. Meas. Tech., 9, 5249–5263, 2016
https://doi.org/10.5194/amt-9-5249-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Meas. Tech., 9, 5249–5263, 2016
https://doi.org/10.5194/amt-9-5249-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 28 Oct 2016

Research article | 28 Oct 2016

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

Biyan Chen and Zhizhao Liu Biyan Chen and Zhizhao Liu
  • Department of Land Surveying & Geo-Informatics, Hong Kong Polytechnic University, Hong Kong, China

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.

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A multi-source water vapor tomography model is developed using GPS (Global Positioning System), radiosonde, WVR (water vapor radiometer), NWP (numerical weather prediction), AERONET (AErosol RObotic NETwork) sunphotometer and synoptic stations' data. Results show that the assimilation of multi-source data can increase the quality of the tomographic solution. Evaluation shows that the tomography model is robust during heavy rain conditions, and it can contribute to severe weather forecasting.
A multi-source water vapor tomography model is developed using GPS (Global Positioning System),...
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