Exploring Deep Neural Networks to Retrieve Rain and Snow in High Latitudes Using Multisensor and Reanalysis Data-Reference-Cited by-同舟云学术

Exploring Deep Neural Networks to Retrieve Rain and Snow in High Latitudes Using Multisensor and Reanalysis Data

Published:2018-10 Issue:10 Volume:54 Page:8253-8278
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Tang Guoqiang¹²^ORCID,Long Di¹^ORCID,Behrangi Ali²³⁴^ORCID,Wang Cunguang¹,Hong Yang¹⁵^ORCID

Affiliation:

1. State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering Tsinghua University Beijing China

2. Joint Institute for Regional Earth System Science and Engineering and Department of Atmospheric and Oceanic Sciences University of California Los Angeles CA USA

3. Department of Hydrology and Atmospheric sciences University of Arizona Tucson AZ USA

4. Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA

5. School of Civil Engineering and Environmental Sciences University of Oklahoma Norman OK USA

Funder

National Natural Science Foundation of China

China Scholarship Council

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2018WR023830

Reference79 articles.

1. Characterization of precipitation product errors across the United States using multiplicative triple collocation

2. Evaluation of Integrated Multisatellite Retrievals for GPM (IMERG) over Southern Canada against Ground Precipitation Observations: A Preliminary Assessment

3. GOES Multispectral Rainfall Algorithm (GMSRA)

4. Mountain hydrology of the western United States

5. MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data

Cited by 56 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Let It Snow: Intercomparison of Various Total and Snow Precipitation Data over the Tibetan Plateau;Atmosphere;2024-09-05

2. Uncertainty Assessment of Surface Water Salinity Using Standalone, Ensemble, and Deep Machine Learning Methods: A Case Study of Lake Urmia;Iranian Journal of Science and Technology, Transactions of Civil Engineering;2024-03-02

3. A comprehensive investigation of three long‐term precipitation datasets: Which performs better in the Yellow River basin?;International Journal of Climatology;2024-02-07

4. StarNet: A Deep Learning Model for Enhancing Polarimetric Radar Quantitative Precipitation Estimation;IEEE Transactions on Geoscience and Remote Sensing;2024

5. Estimation of the deep drainage for irrigated cropland based on satellite observations and deep neural networks;Remote Sensing of Environment;2023-12