Calibrating GPM IMERG Late-Run product using ground-based CPC daily precipitation data: a case study in the Beijing-Tianjin-Hebei urban agglomeration-Reference-Cited by-同舟云学术

Calibrating GPM IMERG Late-Run product using ground-based CPC daily precipitation data: a case study in the Beijing-Tianjin-Hebei urban agglomeration

Published:2021-06-30 Issue:9 Volume:12 Page:848-858
ISSN:2150-704X
Container-title:Remote Sensing Letters
language:en
Short-container-title:Remote Sensing Letters

Author:

Xu Jintao¹,Zhu Siyu¹,Ma Ziqiang¹,Liu Hui¹,Shangguan Yulin²,Yang Yuanjian³

Affiliation:

1. Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China

2. Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China

3. School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China

Funder

National Natural Science Foundation of China

Key R&D Program of the Ministry of Science and Technology, China

Second Tibetan Plateau Scientiﬁc Expedition and Research

Research on Key Technologies of Early Warning and Forecast using Satellite-based and Ground-based Spatio-temporal data

Open Fund of the State Key Laboratory of Remote Sensing Science, China

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Earth and Planetary Sciences (miscellaneous)

Link

https://www.tandfonline.com/doi/pdf/10.1080/2150704X.2021.1942576

Reference26 articles.

1. PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies

2. Assessing objective techniques for gauge-based analyses of global daily precipitation

3. Comparison of Near-Real-Time Precipitation Estimates from Satellite Observations and Numerical Models

4. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling

5. Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System

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