Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/639957
Title: Spatiotemporal Modeling and Implementation for Radar-Based Rainfall Estimation
Authors: Qiuming Kuang;Xuebing Yang;Wensheng Zhang;Guoping Zhang
subject: rain processes|Radar reflectivity|rainfall estimation|spatiotemporal model
Year: 2016
Publisher: IEEE
Abstract: Radar-based rainfall estimation is one of the most important inputs for various meteorological applications. Although exciting progresses have been made in this area, accurate real-time rainfall estimation is still a significant opening topic that requires practical modeling. The research study presented in this letter improves rainfall estimation accuracy by proposing a random forest and linear chain conditional random-field-based spatiotemporal model (RANLIST). To apply this model for rainfall estimation, the implementing approach is presented. The advantages are listed as follows: 1) RANLIST improves rainfall estimation accuracy by exploiting both underlying local spatial structure of multiple radar reflectivity factors and time-series information of rain processes. 2) The time-series information of rain processes can be utilized in virtue of the presented implementation method. Experiments have been carried out over the radar-covered area of Quanzhou, China, in June and July 2014. Results show that RANLIST is superior to previous works.
URI: http://localhost/handle/Hannan/183998
http://localhost/handle/Hannan/639957
ISSN: 1545-598X
1558-0571
volume: 13
issue: 11
Appears in Collections:2016

Files in This Item:
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7549028.pdf8.78 MBAdobe PDFThumbnail
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Title: Spatiotemporal Modeling and Implementation for Radar-Based Rainfall Estimation
Authors: Qiuming Kuang;Xuebing Yang;Wensheng Zhang;Guoping Zhang
subject: rain processes|Radar reflectivity|rainfall estimation|spatiotemporal model
Year: 2016
Publisher: IEEE
Abstract: Radar-based rainfall estimation is one of the most important inputs for various meteorological applications. Although exciting progresses have been made in this area, accurate real-time rainfall estimation is still a significant opening topic that requires practical modeling. The research study presented in this letter improves rainfall estimation accuracy by proposing a random forest and linear chain conditional random-field-based spatiotemporal model (RANLIST). To apply this model for rainfall estimation, the implementing approach is presented. The advantages are listed as follows: 1) RANLIST improves rainfall estimation accuracy by exploiting both underlying local spatial structure of multiple radar reflectivity factors and time-series information of rain processes. 2) The time-series information of rain processes can be utilized in virtue of the presented implementation method. Experiments have been carried out over the radar-covered area of Quanzhou, China, in June and July 2014. Results show that RANLIST is superior to previous works.
URI: http://localhost/handle/Hannan/183998
http://localhost/handle/Hannan/639957
ISSN: 1545-598X
1558-0571
volume: 13
issue: 11
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7549028.pdf8.78 MBAdobe PDFThumbnail
Preview File
Title: Spatiotemporal Modeling and Implementation for Radar-Based Rainfall Estimation
Authors: Qiuming Kuang;Xuebing Yang;Wensheng Zhang;Guoping Zhang
subject: rain processes|Radar reflectivity|rainfall estimation|spatiotemporal model
Year: 2016
Publisher: IEEE
Abstract: Radar-based rainfall estimation is one of the most important inputs for various meteorological applications. Although exciting progresses have been made in this area, accurate real-time rainfall estimation is still a significant opening topic that requires practical modeling. The research study presented in this letter improves rainfall estimation accuracy by proposing a random forest and linear chain conditional random-field-based spatiotemporal model (RANLIST). To apply this model for rainfall estimation, the implementing approach is presented. The advantages are listed as follows: 1) RANLIST improves rainfall estimation accuracy by exploiting both underlying local spatial structure of multiple radar reflectivity factors and time-series information of rain processes. 2) The time-series information of rain processes can be utilized in virtue of the presented implementation method. Experiments have been carried out over the radar-covered area of Quanzhou, China, in June and July 2014. Results show that RANLIST is superior to previous works.
URI: http://localhost/handle/Hannan/183998
http://localhost/handle/Hannan/639957
ISSN: 1545-598X
1558-0571
volume: 13
issue: 11
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7549028.pdf8.78 MBAdobe PDFThumbnail
Preview File