.png){kind=icon}
返回
Spatiotemporal Modeling and Implementation for Radar-Based Rainfall Estimation
.png){kind=icon}
PRE
AI
DOI：10.1109/LGRS.2016.2597170.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Radar reflectivity
rain processes
rainfall estimation
spatiotemporal model
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
机构
引用论文
分享收藏The quantitative precipitation estimation system for Dallas-Fort Worth (DFW) urban remote sensing network.png)
JOURNAL OF HYDROLOGY
OAAI
JOURNAL OF HYDROLOGYIF6.3
OAAI分享收藏分享收藏Compromised Mitochondrial Fatty Acid Synthesis in Transgenic Mice Results in Defective Protein Lipoylation and Energy DisequilibriumPLoS ONE
OAAI
PLoS ONEIF0
OAAI分享收藏
查看更多内容