| 书目名称 | Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery |
| 编辑 | Nasrin Nasrollahi |
| 视频video | http://file.papertrans.cn/463/462794/462794.mp4 |
| 概述 | Nominated by the University of California, Irvine, USA, as an outstanding Ph.D. thesis.Presents data sets that reduce false rain signals in satellite precipitation measurements.Provides advances in th |
| 丛书名称 | Springer Theses |
| 图书封面 |  |
| 描述 | .This thesis transforms satellite precipitation estimation through the integration of a multi-sensor, multi-channel approach to current precipitation estimation algorithms, and provides more accurate readings of precipitation data from space..Using satellite data to estimate precipitation from space overcomes the limitation of ground-based observations in terms of availability over remote areas and oceans as well as spatial coverage. However, the accuracy of satellite-based estimates still need to be improved..The approach introduced in this thesis takes advantage of the recent NASA satellites in observing clouds and precipitation. In addition, machine-learning techniques are also employed to make the best use of remotely-sensed "big data." The results provide a significant improvement in detecting non-precipitating areas and reducing false identification of precipitation.. |
| 出版日期 | Book 2015 |
| 关键词 | CloudSat precipitation data; CloudSat texts; MODIS satellite observations; award-winning thesis; current |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-12081-2 |
| isbn_softcover | 978-3-319-36332-5 |
| isbn_ebook | 978-3-319-12081-2Series ISSN 2190-5053 Series E-ISSN 2190-5061 |
| issn_series | 2190-5053 |
| copyright | Springer International Publishing Switzerland 2015 |