Paper Info
Title
Application of Gaussian Mixture Model and Estimator to Radar-Based Weather Parameter Estimations
Abstract
The estimation of weather parameters such as attenuation and rainfall rates from remotely sensed weather radar data has been based mainly on deterministic regression models. This letter introduces a new Gaussian mixture parameter estimator (GMPE)-based framework to incorporate prior knowledge into this process. The GMPE makes possible a versatile model for parameter estimation under all conditions without compromising accuracy. Observations from dual-polarized and dual-frequency radar sensors can be utilized in the GMPE in a very flexible manner. Simulation examples have demonstrated that the GMPE has better estimation error performance than traditional methods for parameter estimation applications, particularly for noisy observations. The impacts of mixture number and state vector selections in the GMPE are also discussed.
Year
DOI
Venue
2011
10.1109/LGRS.2011.2151250
IEEE Geosci. Remote Sensing Lett.
Keywords
Field
DocType
gmpe-based framework,dual-frequency radar,gaussian mixture estimator,rain-rate retrieval/estimation,radar-based weather parameter estimations,dual-frequency radar sensor,regression analysis,estimation error performance,dual-polarized radar sensor,attenuation correction,dual-polarization radar,rain,rainfall rate,remote sensing by radar,deterministic regression models,gaussian processes,attenuation rate,bayesian approach,weather forecasting,gaussian mixture model,remote sensing,weather radar,parameter estimation,attenuation,regression model,dual polarization radar,estimation
Radar,Weather radar,Remote sensing,Gaussian,Gaussian process,Estimation theory,Weather forecasting,Mathematics,Mixture model,Estimator
Journal
Volume
Issue
ISSN
8
6
1545-598X
Citations 
PageRank 
References 
0
0.34
4
Authors
2
Name
Order
Citations
PageRank
Zhengzheng Li182.16
Yan Zhang201.01