Articles | Volume 14, issue 11
https://doi.org/10.5194/amt-14-7007-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-7007-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Nov 2021
Research article |
| 05 Nov 2021
| 05 Nov 2021
Leveraging machine learning for quantitative precipitation estimation from Fengyun-4 geostationary observations and ground meteorological measurements
Xinyan Li
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University
of Information Science and Technology, Nanjing, 210044, China
Yuanjian Yang
CORRESPONDING AUTHOR
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University
of Information Science and Technology, Nanjing, 210044, China
Jiaqin Mi
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University
of Information Science and Technology, Nanjing, 210044, China
Xueyan Bi
Institute of Tropical and Marine Meteorology, China Meteorological
Administration, Guangzhou, 510080, China
You Zhao
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University
of Information Science and Technology, Nanjing, 210044, China
Zehao Huang
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University
of Information Science and Technology, Nanjing, 210044, China
Chao Liu
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University
of Information Science and Technology, Nanjing, 210044, China
Lian Zong
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University
of Information Science and Technology, Nanjing, 210044, China
Wanju Li
Institute of Tropical and Marine Meteorology, China Meteorological
Administration, Guangzhou, 510080, China
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- Near real-time estimation of high spatiotemporal resolution rainfall from cloud top properties of the MSG satellite and commercial microwave link rainfall intensities K. Kumah et al. 10.1016/j.atmosres.2022.106357
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- Exploring impacts of aerosol on convective clouds using satellite remote sensing and machine learning J. Mi et al. 10.1117/1.JRS.18.012007
21 citations as recorded by crossref.
- Near Real-Time Estimation of High Spatiotemporal Resolution Rainfall from Cloud Top Properties of the Msg Satellite and Commercial Microwave Link Rainfall Intensities K. Kumah et al. 10.2139/ssrn.4098667
- Estimating spatio-temporal variability of aerosol pollution in Yunnan Province, China F. Zhou et al. 10.1016/j.apr.2022.101450
- PECA-FY4A: Precipitation Estimation using Chromatographic Analysis methodology for full-disc multispectral observations from FengYun-4A/AGRI S. Zhu & Z. Ma 10.1016/j.rse.2022.113234
- Enhancing precipitation estimation accuracy: An evaluation of traditional and machine learning approaches in rainfall predictions Y. Yin et al. 10.1016/j.jastp.2024.106175
- High-resolution typhoon precipitation integrations using satellite infrared observations and multisource data Y. Zhao et al. 10.5194/amt-15-2791-2022
- Spatiotemporal estimation of 6-hour high-resolution precipitation across China based on Himawari-8 using a stacking ensemble machine learning model S. Zhou et al. 10.1016/j.jhydrol.2022.127718
- Typhoon-associated air quality over the Guangdong–Hong Kong–Macao Greater Bay Area, China: machine-learning-based prediction and assessment Y. Chen et al. 10.5194/amt-16-1279-2023
- Long lead-time radar rainfall nowcasting method incorporating atmospheric conditions using long short-term memory networks K. Zhu et al. 10.3389/fenvs.2022.1054235
- Technology for Position Correction of Satellite Precipitation and Contributions to Error Reduction—A Case of the ‘720’ Rainstorm in Henan, China W. Tian et al. 10.3390/s22155583
- Severe Precipitation Recognition Using Attention-UNet of Multichannel Doppler Radar W. Chen et al. 10.3390/rs15041111
- A First Step towards Meteosat Third Generation Day-2 Precipitation Rate Product: Deep Learning for Precipitation Rate Retrieval from Geostationary Infrared Measurements L. D’Adderio et al. 10.3390/rs15245662
- Near real-time estimation of high spatiotemporal resolution rainfall from cloud top properties of the MSG satellite and commercial microwave link rainfall intensities K. Kumah et al. 10.1016/j.atmosres.2022.106357
- Fine vertical structures of cloud from a ground‐based cloud radar over the western Tianshan mountains P. Chen et al. 10.1002/met.2105
- Modern methods to explore the dynamics between aerosols and convective precipitation: A critical review S. Metangley et al. 10.1016/j.dynatmoce.2024.101465
- Integration of shapley additive explanations with random forest model for quantitative precipitation estimation of mesoscale convective systems Z. He et al. 10.3389/fenvs.2022.1057081
- Contribution from the Western Pacific Subtropical High Index to a Deep Learning Typhoon Rainfall Forecast Model Z. Fang et al. 10.3390/rs16122207
- Multi-source precipitation estimation using machine learning: Clarification and benchmarking Y. Xu et al. 10.1016/j.jhydrol.2024.131195
- Integrated Evaluation and error decomposition of four gridded precipitation products using dense rain gauge observations over the Yunnan-Kweichow Plateau, China T. Lu et al. 10.1080/22797254.2024.2322742
- Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives B. Bochenek & Z. Ustrnul 10.3390/atmos13020180
- Dual-Frequency Radar Retrievals of Snowfall Using Random Forest T. Yu et al. 10.3390/rs14112685
- Exploring impacts of aerosol on convective clouds using satellite remote sensing and machine learning J. Mi et al. 10.1117/1.JRS.18.012007
Latest update: 02 Nov 2024
Short summary
A random forest (RF) model framework for Fengyun-4A (FY-4A) daytime and nighttime quantitative precipitation estimation (QPE) is established using FY-4A multi-band spectral information, cloud parameters, high-density precipitation observations and physical quantities from reanalysis data. The RF model of FY-4A QPE has a high accuracy in estimating precipitation at the heavy-rain level or below, which has advantages for quantitative estimation of summer precipitation over East Asia in future.
A random forest (RF) model framework for Fengyun-4A (FY-4A) daytime and nighttime quantitative...
