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

Chen, Biyan; Liu, Zhizhao

doi:https://doi.org/10.5194/amt-9-5249-2016

Articles | Volume 9, issue 10

https://doi.org/10.5194/amt-9-5249-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/amt-9-5249-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 9, issue 10

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

Data sets

GNSS observation data Lands Department of the Government of Hong Kong Special Administrative Region (HKSAR) http://www.geodetic.gov.hk/smo/gsi/programs/en/GSS/satref/satref.htm

Radiosonde data Department of Atmospheric Science of the University of Wyoming http://weather.uwyo.edu/upperair/sounding.html

AERONET data National Aeronautics and Space Administration (NASA） http://aeronet.gsfc.nasa.gov

Short summary

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.