HR: 1340h
AN: H33A-0970    [Abstracts]
TI: Radar Image and Rain-gauge Alignment using the Multi-resolution Viscous Alignment (MVA) Algorithm
AU: * Chatdarong, V
EM: cvirat@gmail.com
AF: Department of Water Resources Engineering, Chulalongkorn University, 254 Phayatai Rd, Pathumwan, Bangkok, 10330, Thailand
AB: Rainfall is a complex environmental variable that is difficult to describe either deterministically or statistically. To understand rainfall behaviors, many types of instruments are employed to detect and collect rainfall information. Among them, radar seems to provide the most comprehensive rainfall measurement at fine spatial and temporal resolution and over a relatively wide area. Nevertheless, it does not detects surface rainfall directly like what rain-gauge does. The accuracy radar rainfall, therefore, depends greatly on the Z-R relationship which convert radar reflectivity (Z) to surface rainrate (R). This calibration is usually done by fitting the rain-gauge data with the corresponding radar reflectivity using the regression analysis. To best fit the data, the radar reflectivity at neighbor pixels are usually used to best match the rain-gauge data. However, when applying the Z-R relationship to the radar image, there is no position adjustment despite the calibration technique. Hence, it is desirable to adjust the position of the radar reflectivity images prior to applying the Z-R relationship to improve the accuracy of the rainfall estimation. In this research, the Multi-resolution Viscous Alignment (MVA) algorithm is applied to best align radar reflectivity images to rain-gauge data in order to improve rainfall estimation from the Z-R relationship. The MVA algorithm solves the motion estimation problems using a Bayesian formulation to minimize misfits between two data sets. In general, the problem are ill-posed; therefore, some regularizations and constraints based on smoothness and non-divergence assumptions are employed. This algorithm is superior to the conventional techniques and correlation based techniques. It is fast, robust, easy to implement, and does not require data training. In addition, it can handle higher-order, missing data, and small-scale deformations. The algorithm provides spatially dense, consistency, and smooth transition vector. The MVA algorithm is employed to align more than 30 pairs of radar and rain-gauge data during the 2005 rainy season in Bangkok. The results show that the rainfall estimates after the alignment with MVA algorithm is superior than those without the alignment. This concept will lead to more efficient use of all available rainfall data, provide more accurate estimation of spatial rainfall in Thailand, as well as improve the confidence of using radar images to represent surface rainfall in many region of the world.
DE: 1853 Precipitation-radar
DE: 1855 Remote sensing (1640)
DE: 1894 Instruments and techniques: modeling
DE: 1895 Instruments and techniques: monitoring
DE: 3354 Precipitation (1854)
SC: Hydrology [H]
MN: 2007 Fall Meeting