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|Title:||Tomographic modeling using multiple-sensor water vapor observations and improving the MODIS water vapor retrieval using ground-based GPS water vapor data|
|Source:||International Workshop on Advanced Geospatial Technology and Urban Applications, The Chinese University of Hong Kong, Hong Kong , 26-27 May 2016 How to cite?|
|Abstract:||Water vapour is one of green house gases (GHG) and a key parameter affecting weather forecasting. Situated on the edge of the South China Sea and in the path of the wet Asian monsoon, Hong Kong experiences more rainstorms than most other cities. An accurate observation and modeling of water vapor has a special significance in severe weather prediction for Hong Kong, one of the cities with the highest density of population in the world.|
In the part one of this talk, the 3D tomographic modeling method using precipitable water vapor (PWV) observed by multiple sensors is developed. The main PWV observations are derived from the Global Positioning System (GPS) network in Hong Kong, i.e. the Hong Kong Satellite Positioning Reference Station Network (SatRef). In addition, the PWV from radiosonde, numerical weather prediction (NWP) system, AERONET’s sunphotometers, and microwave radiometer are also assimilated in the tomographic modeling. The PWV modeling results are compared with independent radiosonde and microwave radiometer PWV data and good agreements have been achieved.
In the part two of this talk, a new algorithm has been developed to enhance the accuracy of MODIS remote sensing PWV data by calibrating the MODIS PWV data using ground PWV data derived from the Hong Kong GPS network “SatRef”. After the calibration, the accuracy of MODIS water vapor products can be significantly improved by 21% to 38%, depending on the land cover property. This provides a new way to obtain accurate water vapor data with large coverage and high spatial resolution.
|Appears in Collections:||Conference Paper|
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