Abstract
Abstract. Data fusion aims at integrating multiple data sources that can be redundant or complementary to produce complete, accurate information of the parameter of interest. In this work, data fusion of precipitable water vapor (PWV) estimated from remote sensing observations and data from the Weather Research and Forecasting (WRF) modeling system are applied to provide complete grids of PWV with high quality. Our goal is to correctly infer PWV at spatially continuous, highly resolved grids from heterogeneous data sets. This is done by a geostatistical data fusion approach based on the method of fixed-rank kriging. The first data set contains absolute maps of atmospheric PWV produced by combining observations from the Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). These PWV maps have a high spatial density and a millimeter accuracy; however, the data are missing in regions of low coherence (e.g., forests and vegetated areas). The PWV maps simulated by the WRF model represent the second data set. The model maps are available for wide areas, but they have a coarse spatial resolution and a still limited accuracy. The PWV maps inferred by the data fusion at any spatial resolution show better qualities than those inferred from single data sets. In addition, by using the fixed-rank kriging method, the computational burden is significantly lower than that for ordinary kriging.
Highlights
Water vapor is a vital constituent of the Earth’s electrically neutral atmosphere
We compare the interpolations obtained by applying fixed-rank kriging (FRK) to single data sets with those obtained by statistical data fusion (SSDF), and we compare both with the MERIS data
The results show that the map obtained by data fusion correlates more consistently with the map predicted only from Persistent scatterer InSAR (PSI) + Global Navigation Satellite Systems (GNSS) (Table 2)
Summary
Water vapor is a vital constituent of the Earth’s electrically neutral atmosphere (neutrosphere). The ratio of water vapor partial to total atmospheric pressure is typically below 4 %, it is an important constituent in many respects. The neutrospheric water vapor contributes less than 10 % of the signal path delay; this error source is not eliminated. Accurate information about the water vapor concentration along the signal path is required, which is not always obtainable. Various studies suggested the assimilation of atmospheric parameters, such as water vapor, estimated from the Global Positioning System (GPS) or Interferometric Synthetic Aperture Radar (InSAR), into these models to improve the quality of Published by Copernicus Publications on behalf of the European Geosciences Union
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