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Remote Sens. 2017, 9(9), 886; doi:10.3390/rs9090886

An Improved Tomography Approach Based on Adaptive Smoothing and Ground Meteorological Observations

1
School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
2
Earth System Science Programme, Faculty of Science, The Chinese University of Hong Kong, Hong Kong, China
*
Author to whom correspondence should be addressed.
Received: 10 June 2017 / Revised: 20 July 2017 / Accepted: 23 August 2017 / Published: 25 August 2017
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Abstract

Using the Global Navigation Satellite System (GNSS) to sense three-dimensional water vapor (WV) has been intensively investigated. However, this technique still heavily relies on the a priori information. In this study, we propose an improved tomography approach based on adaptive Laplacian smoothing (ALS) and ground meteorological observations. By using the proposed approach, the troposphere tomography is less dependent on a priori information and the ALS constraints match better with the actual situation than the constant constraints. Tomography experiments in Hong Kong during a heavy rainy period and a rainless period show that the ALS method gets superior results compared with the constant Laplacian smoothing (CLS) method. By validation with radiosonde and European Centre for Medium-Range Weather Forecasts (ECMWF) data, we found that the introduction of ground meteorological observations into tomography can solve the perennial problem of resolving the wet refractivity in the lower troposphere and thus significantly improve the tomography results. However, bad data quality and incompatibility of the ground meteorological observations may introduce errors into tomography results. View Full-Text
Keywords: troposphere tomography; wet refractivity; adaptive Laplacian smoothing; a priori information troposphere tomography; wet refractivity; adaptive Laplacian smoothing; a priori information
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Zhang, B.; Fan, Q.; Yao, Y.; Xu, C.; Li, X. An Improved Tomography Approach Based on Adaptive Smoothing and Ground Meteorological Observations. Remote Sens. 2017, 9, 886.

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