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Liu, ZZ; Wong, MS; Nichol, J; Chan, PW (2013). A multi-sensor study of water vapour from radiosonde, MODIS and AERONET: a case study of Hong Kong. INTERNATIONAL JOURNAL OF CLIMATOLOGY, 33(1), 109-120.

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 of water vapour 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 this paper, water vapour observations over the last 6 years from an AErosol RObotic NETwork (AERONET) sunphotometer, 9 years from the MODerate resolution Imaging Spectroradiometer (MODIS) TERRA satellite images and 38 years from radiosonde were analysed and cross-validated. The operational MODIS water vapour products, namely MOD05 and MOD07, with a spatial resolution of 1 and 5 km, respectively, were compared with both radiosonde and AERONET data. The correlation coefficients between MODIS water vapour products and radiosonde data are r = 0.878 and 0.876 for MOD05 and MOD07 products, and the correlations of those with AERONET data are r = 0.822, r = 0.976, respectively. The results also indicate that radiosonde and AERONET water vapour observations have a good agreement, with a correlation of r = 0.988 and a small mean absolute difference (MAD), and root mean square error (RMS) of 0.197 and 0.289 cm, respectively. Although the satellite data (with a frequency of once per day) represent water vapour coverage of all the territories of Hong Kong, they do not meet short-term weather prediction demand due to their low temporal resolution. Radiosonde observations with a frequency of twice per day are also temporally inadequate. This study demonstrates that the AERONET sunphotometer can provide accurate and high temporal resolution water vapour data which can be used for short-term weather prediction and long-term climate change research. Copyright (C) 2011 Royal Meteorological Society

DOI:

ISSN:
0899-8418

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