Multitask Learning for Precipitation Estimation Using Satellite Observations From the GOES-R Series-Reference-Cited by-同舟云学术

Multitask Learning for Precipitation Estimation Using Satellite Observations From the GOES-R Series

Published:2023 Issue: Volume:20 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Yang Wensheng¹^ORCID,Chen Haonan²^ORCID,Han Lei¹^ORCID,Ge Yurong¹^ORCID

Affiliation:

1. Faculty of Information Science and Engineering, Ocean University of China, Qingdao, China

2. Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA

Funder

National Natural Science Foundation of China

National Oceanic and Atmospheric Administration

U.S. National Science Foundation (NSF) Faculty Early Career Development Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

https://ieeexplore.ieee.org/ielam/8859/10034981/10244061-aam.pdf

Reference19 articles.

1. Precipitation identification and quantification using ABI and GLM observations aboard the GOES-R series: A machine learning perspective;chen;Proc Fall Meeting Abstr (AGU),2021

2. Improving GOES-R/ABI rainfall estimates with ground-based radar observations using machine learning;chen;Proc Fall Meeting Abstr (AGU),2020

3. PECA-FY4A: Precipitation Estimation using Chromatographic Analysis methodology for full-disc multispectral observations from FengYun-4A/AGRI

4. Impacts of the GLM measurements aboard the GOES-R series satellites on severe convective precipitation detection and retrievals;chen;Proc Fall Meeting Abstr (AGU),2022

5. FY4QPE-MSA: An All-Day Near-Real-Time Quantitative Precipitation Estimation Framework Based on Multispectral Analysis From AGRI Onboard Chinese FY-4 Series Satellites

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