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.
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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Tao Shi, Yuanjian Yang, Gaopeng Lu, Zuofang Zheng, Yucheng Zi, Ye Tian, Lei Liu, and Simone Lolli
Atmos. Chem. Phys., 25, 9219–9234, https://doi.org/10.5194/acp-25-9219-2025, https://doi.org/10.5194/acp-25-9219-2025, 2025
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The city significantly influences thunderstorm and lightning activity, yet the potential mechanisms remain largely unexplored. Our study has revealed that both city size and building density play pivotal roles in modulating thunderstorm and lightning activity. This research not only deepens our understanding of urban meteorology but also lays an important foundation for developing accurate and targeted urban thunderstorm risk prediction models.
Jialu Xu, Yingjie Zhang, Yuying Wang, Xing Yan, Bin Zhu, Chunsong Lu, Yuanjian Yang, Yele Sun, Junhui Zhang, Xiaofan Zuo, Zhanghanshu Han, and Rui Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-3184, https://doi.org/10.5194/egusphere-2025-3184, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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We conducted a year-long study in Nanjing to explore how the height of the atmospheric boundary layer affects fine particle pollution. We found that low boundary layers in winter trap pollutants like nitrate and primary particles, while higher layers in summer help form secondary pollutants like sulfate and organic aerosols. These findings show that boundary layer dynamics are key to understanding and managing seasonal air pollution.
Junhui Zhang, Yuying Wang, Jialu Xu, Xiaofan Zuo, Chunsong Lu, Bin Zhu, Yuanjian Yang, Xing Yan, and Yele Sun
EGUsphere, https://doi.org/10.5194/egusphere-2025-3186, https://doi.org/10.5194/egusphere-2025-3186, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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We conducted a year-long study in Nanjing to understand how tiny airborne particles take up water, which affects air quality and climate. We found that particle water uptake varies by season and size, with lower values in summer due to more organic materials. Local pollution mainly influences smaller particles, while larger ones are shaped by air mass transport. These findings help improve climate models and support better air pollution control in fast-growing cities.
Tao Shi, Yuanjian Yang, Ping Qi, and Simone Lolli
EGUsphere, https://doi.org/10.5194/egusphere-2025-2785, https://doi.org/10.5194/egusphere-2025-2785, 2025
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Using Beijing’s Fifth Ring Road, the team combined data and models. Heatwave results: canopy heat island was 91.3 % stronger day/52.7 % night. Day heat relied on building coverage, night on sky visibility. Tall buildings block sun by day, trap heat at night. Night ventilation cools, day winds spread heat. Urban design must consider day-night cycles to fight extreme heat, guiding risk reduction.
Tao Shi, Yuanjian Yang, Lian Zong, Min Guo, Ping Qi, and Simone Lolli
Atmos. Chem. Phys., 25, 4989–5007, https://doi.org/10.5194/acp-25-4989-2025, https://doi.org/10.5194/acp-25-4989-2025, 2025
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Our study explored the daily temperature patterns in urban areas of the Yangtze River Delta, focusing on how weather and human activities impact these patterns. We found that temperatures were higher at night, and weather patterns had a bigger impact during the day, while human activities mattered more at night. This helps us understand and address urban overheating.
Zeyuan Tian, Jiandong Wang, Jiaping Wang, Chao Liu, Jia Xing, Jinbo Wang, Zhouyang Zhang, Yuzhi Jin, Sunan Shen, Bin Wang, Wei Nie, Xin Huang, and Aijun Ding
Atmos. Meas. Tech., 18, 1149–1162, https://doi.org/10.5194/amt-18-1149-2025, https://doi.org/10.5194/amt-18-1149-2025, 2025
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The radiative effect of black carbon (BC) is substantially modulated by its mixing state, which is challenging to derive physically with a single-particle soot photometer. This study establishes a machine-learning-based inversion model which can accurately and efficiently acquire the BC mixing state. Compared to the widely used leading-edge-only method, our model utilizes a broader scattering signal coverage to more accurately capture diverse particle characteristics.
Yuzhi Jin, Jiandong Wang, Chao Liu, David C. Wong, Golam Sarwar, Kathleen M. Fahey, Shang Wu, Jiaping Wang, Jing Cai, Zeyuan Tian, Zhouyang Zhang, Jia Xing, Aijun Ding, and Shuxiao Wang
Atmos. Chem. Phys., 25, 2613–2630, https://doi.org/10.5194/acp-25-2613-2025, https://doi.org/10.5194/acp-25-2613-2025, 2025
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Black carbon (BC) affects climate and the environment, and its aging process alters its properties. Current models, like WRF-CMAQ, lack full accounting for it. We developed the WRF-CMAQ-BCG model to better represent BC aging by introducing bare and coated BC species and their conversion. The WRF-CMAQ-BCG model introduces the capability to simulate BC mixing states and bare and coated BC wet deposition, and it improves the accuracy of BC mass concentration and aerosol optics.
Zhouyang Zhang, Jiandong Wang, Jiaping Wang, Nicole Riemer, Chao Liu, Yuzhi Jin, Zeyuan Tian, Jing Cai, Yueyue Cheng, Ganzhen Chen, Bin Wang, Shuxiao Wang, and Aijun Ding
Atmos. Chem. Phys., 25, 1869–1881, https://doi.org/10.5194/acp-25-1869-2025, https://doi.org/10.5194/acp-25-1869-2025, 2025
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Black carbon (BC) exerts notable warming effects. We use a particle-resolved model to investigate the long-term behavior of the BC mixing state, revealing its compositions, coating thickness distribution, and optical properties all stabilize with a characteristic time of less than 1 d. This study can effectively simplify the description of the BC mixing state, which facilitates the precise assessment of the optical properties of BC aerosols in global and chemical transport models.
Fengjiao Chen, Yuanjian Yang, Lu Yu, Yang Li, Weiguang Liu, Yan Liu, and Simone Lolli
Atmos. Chem. Phys., 25, 1587–1601, https://doi.org/10.5194/acp-25-1587-2025, https://doi.org/10.5194/acp-25-1587-2025, 2025
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The microphysical mechanisms of precipitation responsible for the varied impacts of aerosol particles on shallow precipitation remain unclear. This study reveals that coarse aerosol particles invigorate shallow rainfall through enhanced coalescence processes, whereas fine aerosol particles suppress shallow rainfall through intensified microphysical breaks. These impacts are independent of thermodynamic environments but are more significant in low-humidity conditions.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech., 17, 7129–7141, https://doi.org/10.5194/amt-17-7129-2024, https://doi.org/10.5194/amt-17-7129-2024, 2024
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This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine learning method. Retrievals from a machine learning algorithm are used to provide a priori states, and a radiative transfer model is used to create lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and it is applicable to both daytime and nighttime conditions.
Eui-Jong Kang, Byung-Ju Sohn, Sang-Woo Kim, Wonho Kim, Young-Cheol Kwon, Seung-Bum Kim, Hyoung-Wook Chun, and Chao Liu
Geosci. Model Dev., 17, 8553–8568, https://doi.org/10.5194/gmd-17-8553-2024, https://doi.org/10.5194/gmd-17-8553-2024, 2024
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Sea surface temperature (SST) is vital in climate, weather, and ocean sciences because it influences air–sea interactions. Errors in the ECMWF model's scheme for predicting ocean skin temperature prompted a revision of the ocean mixed layer model. Validation against infrared measurements and buoys showed a good correlation with minimal deviations. The revised model accurately simulates SST variations and aligns with solar radiation distributions, showing promise for weather and climate models.
Tao Shi, Yuanjian Yang, Ping Qi, and Simone Lolli
Atmos. Chem. Phys., 24, 12807–12822, https://doi.org/10.5194/acp-24-12807-2024, https://doi.org/10.5194/acp-24-12807-2024, 2024
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This paper explored the formation mechanisms of the amplified canopy urban heat island intensity (ΔCUHII) during heat wave (HW) periods in the megacity of Beijing from the perspectives of mountain–valley breeze and urban morphology. During the mountain breeze phase, high-rise buildings with lower sky view factors (SVFs) had a pronounced effect on the ΔCUHII. During the valley breeze phase, high-rise buildings exerted a dual influence on the ΔCUHII.
Chaman Gul, Shichang Kang, Yuanjian Yang, Xinlei Ge, and Dong Guo
EGUsphere, https://doi.org/10.5194/egusphere-2024-1144, https://doi.org/10.5194/egusphere-2024-1144, 2024
Preprint archived
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Long-term variations in upper atmospheric temperature and water vapor in the selected domains of time and space are presented. The temperature during the past two decades showed a cooling trend and water vapor showed an increasing trend and had an inverse relation with temperature in selected domains of space and time. Seasonal temperature variations are distinct, with a summer minimum and a winter maximum. Our results can be an early warning indication for future climate change.
Yueyue Cheng, Chao Liu, Jiandong Wang, Jiaping Wang, Zhouyang Zhang, Li Chen, Dafeng Ge, Caijun Zhu, Jinbo Wang, and Aijun Ding
Atmos. Chem. Phys., 24, 3065–3078, https://doi.org/10.5194/acp-24-3065-2024, https://doi.org/10.5194/acp-24-3065-2024, 2024
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Brown carbon (BrC), a light-absorbing aerosol, plays a pivotal role in influencing global climate. However, assessing BrC radiative effects remains challenging because the required observational data are hardly accessible. Here we develop a new BrC radiative effect estimation method combining conventional observations and numerical models. Our findings reveal that BrC absorbs up to a third of the sunlight at 370 nm that black carbon does, highlighting its importance in aerosol radiative effects.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-36, https://doi.org/10.5194/amt-2024-36, 2024
Preprint withdrawn
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This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine-learning method. Retrievals from a machine learning algorithm are used to provide initial guesses, and a radiative transfer model is used to create radiance lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and is applicable both daytime and nighttime conditions.
Yuan Wang, Qiangqiang Yuan, Tongwen Li, Yuanjian Yang, Siqin Zhou, and Liangpei Zhang
Earth Syst. Sci. Data, 15, 3597–3622, https://doi.org/10.5194/essd-15-3597-2023, https://doi.org/10.5194/essd-15-3597-2023, 2023
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We propose a novel spatiotemporally self-supervised fusion method to establish long-term daily seamless global XCO2 and XCH4 products. Results show that the proposed method achieves a satisfactory accuracy that distinctly exceeds that of CAMS-EGG4 and is superior or close to those of GOSAT and OCO-2. In particular, our fusion method can effectively correct the large biases in CAMS-EGG4 due to the issues from assimilation data, such as the unadjusted anthropogenic emission for COVID-19.
Yilin Chen, Yuanjian Yang, and Meng Gao
Atmos. Meas. Tech., 16, 1279–1294, https://doi.org/10.5194/amt-16-1279-2023, https://doi.org/10.5194/amt-16-1279-2023, 2023
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The Guangdong–Hong Kong–Macao Greater Bay Area suffers from summertime air pollution events related to typhoons. The present study leverages machine learning to predict typhoon-associated air quality over the area. The model evaluation shows that the model performs excellently. Moreover, the change in meteorological drivers of air quality on typhoon days and non-typhoon days suggests that air pollution control strategies should have different focuses on typhoon days and non-typhoon days.
Hui Zhang, Ming Luo, Yongquan Zhao, Lijie Lin, Erjia Ge, Yuanjian Yang, Guicai Ning, Jing Cong, Zhaoliang Zeng, Ke Gui, Jing Li, Ting On Chan, Xiang Li, Sijia Wu, Peng Wang, and Xiaoyu Wang
Earth Syst. Sci. Data, 15, 359–381, https://doi.org/10.5194/essd-15-359-2023, https://doi.org/10.5194/essd-15-359-2023, 2023
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We generate the first monthly high-resolution (1 km) human thermal index collection (HiTIC-Monthly) in China over 2003–2020, in which 12 human-perceived temperature indices are generated by LightGBM. The HiTIC-Monthly dataset has a high accuracy (R2 = 0.996, RMSE = 0.693 °C, MAE = 0.512 °C) and describes explicit spatial variations for fine-scale studies. It is freely available at https://zenodo.org/record/6895533 and https://data.tpdc.ac.cn/disallow/036e67b7-7a3a-4229-956f-40b8cd11871d.
Fan Wang, Gregory R. Carmichael, Jing Wang, Bin Chen, Bo Huang, Yuguo Li, Yuanjian Yang, and Meng Gao
Atmos. Chem. Phys., 22, 13341–13353, https://doi.org/10.5194/acp-22-13341-2022, https://doi.org/10.5194/acp-22-13341-2022, 2022
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Unprecedented urbanization in China has led to serious urban heat island (UHI) issues, exerting intense heat stress on urban residents. We find diverse influences of aerosol pollution on urban heat island intensity (UHII) under different circulations. Our results also highlight the role of black carbon in aggravating UHI, especially during nighttime. It could thus be targeted for cooperative management of heat islands and aerosol pollution.
Zexia Duan, Zhiqiu Gao, Qing Xu, Shaohui Zhou, Kai Qin, and Yuanjian Yang
Earth Syst. Sci. Data, 14, 4153–4169, https://doi.org/10.5194/essd-14-4153-2022, https://doi.org/10.5194/essd-14-4153-2022, 2022
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Land–atmosphere interactions over the Yangtze River Delta (YRD) in China are becoming more varied and complex, as the area is experiencing rapid land use changes. In this paper, we describe a dataset of microclimate and eddy covariance variables at four sites in the YRD. This dataset has potential use cases in multiple research fields, such as boundary layer parametrization schemes, evaluation of remote sensing algorithms, and development of climate models in typical East Asian monsoon regions.
Lian Zong, Yuanjian Yang, Haiyun Xia, Meng Gao, Zhaobin Sun, Zuofang Zheng, Xianxiang Li, Guicai Ning, Yubin Li, and Simone Lolli
Atmos. Chem. Phys., 22, 6523–6538, https://doi.org/10.5194/acp-22-6523-2022, https://doi.org/10.5194/acp-22-6523-2022, 2022
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Heatwaves (HWs) paired with higher ozone (O3) concentration at surface level pose a serious threat to human health. Taking Beijing as an example, three unfavorable synoptic weather patterns were identified to dominate the compound HW and O3 pollution events. Under the synergistic stress of HWs and O3 pollution, public mortality risk increased, and synoptic patterns and urbanization enhanced the compound risk of events in Beijing by 33.09 % and 18.95 %, respectively.
You Zhao, Chao Liu, Di Di, Ziqiang Ma, and Shihao Tang
Atmos. Meas. Tech., 15, 2791–2805, https://doi.org/10.5194/amt-15-2791-2022, https://doi.org/10.5194/amt-15-2791-2022, 2022
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A typhoon is a high-impact atmospheric phenomenon that causes most significant socioeconomic damage, and its precipitation observation is always needed for typhoon characteristics and disaster prevention. This study developed a typhoon precipitation fusion method to combine observations from satellite radiometers, rain gauges and reanalysis to provide much improved typhoon precipitation datasets.
Jiandong Wang, Jia Xing, Shuxiao Wang, Rohit Mathur, Jiaping Wang, Yuqiang Zhang, Chao Liu, Jonathan Pleim, Dian Ding, Xing Chang, Jingkun Jiang, Peng Zhao, Shovan Kumar Sahu, Yuzhi Jin, David C. Wong, and Jiming Hao
Atmos. Chem. Phys., 22, 5147–5156, https://doi.org/10.5194/acp-22-5147-2022, https://doi.org/10.5194/acp-22-5147-2022, 2022
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Aerosols reduce surface solar radiation and change the photolysis rate and planetary boundary layer stability. In this study, the online coupled meteorological and chemistry model was used to explore the detailed pathway of how aerosol direct effects affect secondary inorganic aerosol. The effects through the dynamics pathway act as an equally or even more important route compared with the photolysis pathway in affecting secondary aerosol concentration in both summer and winter.
Shaohui Zhou, Yuanjian Yang, Zhiqiu Gao, Xingya Xi, Zexia Duan, and Yubin Li
Atmos. Meas. Tech., 15, 757–773, https://doi.org/10.5194/amt-15-757-2022, https://doi.org/10.5194/amt-15-757-2022, 2022
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Our research has determined the possible relationship between Weibull natural wind mesoscale parameter c and shape factor k with height under the conditions of a desert steppe terrain in northern China, which has great potential in wind power generation. We have gained an enhanced understanding of the seasonal changes in the surface roughness of the desert grassland and the changes in the incoming wind direction.
Shihan Chen, Yuanjian Yang, Fei Deng, Yanhao Zhang, Duanyang Liu, Chao Liu, and Zhiqiu Gao
Atmos. Meas. Tech., 15, 735–756, https://doi.org/10.5194/amt-15-735-2022, https://doi.org/10.5194/amt-15-735-2022, 2022
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This paper proposes a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a random forest (RF) model. The spatial distribution of CUHII was evaluated at 30 m resolution based on the output of the RF model. The present RF model framework for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII.
Lian Zong, Yuanjian Yang, Meng Gao, Hong Wang, Peng Wang, Hongliang Zhang, Linlin Wang, Guicai Ning, Chao Liu, Yubin Li, and Zhiqiu Gao
Atmos. Chem. Phys., 21, 9105–9124, https://doi.org/10.5194/acp-21-9105-2021, https://doi.org/10.5194/acp-21-9105-2021, 2021
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In recent years, summer O3 pollution over eastern China has become more serious, and it is even the case that surface O3 and PM2.5 pollution can co-occur. However, the synoptic weather pattern (SWP) related to this compound pollution remains unclear. Regional PM2.5 and O3 compound pollution is characterized by various SWPs with different dominant factors. Our findings provide insights into the regional co-occurring high PM2.5 and O3 levels via the effects of certain meteorological factors.
Cited articles
Arkin, P. A. and Meisner, B. N.: The Relationship between
Large-Scale Convective Rainfall and Cold Cloud over the Western Hemisphere
during 1982–84, Mon. Weather Rev., 115, 51–74, https://doi.org/10.1175/1520-0493(1987)115<0051:TRBLSC>2.0.CO;2, 2009.
Atkinson, P. M. and Tatnall, A. R. L.: Introduction Neural networks in remote sensing, Int. J. Remote Sens., 18, 699–709, https://doi.org/10.1080/014311697218700, 1997.
Ba, M. B. and Gruber, A.: GOES Multispectral Rainfall Algorithm (GMSRA), J. Appl. Meteorol., 40, 1500–1514, https://doi.org/10.1175/1520-0450(2001)040<1500:GMRAG>2.0.CO;2, 2001.
Bai, K., Li, K., Chang, N.-B., and Gao, W.: Advancing the prediction accuracy
of satellite-based PM2.5 concentration mapping: A perspective of
data mining through in situ PM2.5 measurements, Environ. Pollut.,
254, 113047, https://doi.org/10.1016/j.envpol.2019.113047, 2019a.
Bai, K., Chang, N.-B., Zhou, J., Gao, W., and Guo, J.: Diagnosing atmospheric stability effects on the modeling accuracy of PM2.5/AOD relationship in eastern China using radiosonde data, Environ. Pollut., 251, 380–389, https://doi.org/10.1016/j.envpol.2019.04.104, 2019b.
Bauer, P., Schanz, L., Bennartz, R., and Schlüssel, P.: Outlook for combined TMI-VIRS algorithms for TRMM: Lessons from the PIP and AIP projects, J. Atmos. Sci., 55, 1714–1729, https://doi.org/10.1175/1520-0469(1998)055{<}1714:OFCTVA{>}2.0.CO;2, 1995.
Behrangi, A., Andreadis, K., Fisher, J. B.,, Turk, F. J., Granger, S.,
Painter, T., and Das, N.: Satellite-Based Precipitation Estimation and Its Application for Streamflow Prediction over Mountainous Western U.S. Basins, J. Appl. Meteorol. Clim., 53, 2823–2842, https://doi.org/10.1175/JAMC-D-14-0056.1, 2014.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/a:1010933404324, 2001.
Chen, H., Chandrasekar, V., Cifelli, R., and Xie, P.: A Machine Learning
System for Precipitation Estimation Using Satellite and Ground Radar Network
Observations, IEEE T. Geosci. Remote, 58, 982–994,
https://doi.org/10.1109/TGRS.2019.2942280, 2019.
Copernicus Climate Change Service: ERA5 hourly data on single levels from 1979 to present, available at: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form, last access: 30 October 2021.
Ebert, E. E. and Manton, M. J.: Performance of Satellite Rainfall Estimation Algorithms during TOGA COARE, J. Atmos. Sci., 55, 1537–1557, https://doi.org/10.1175/1520-0469(1998)055{<}1537:POSREA{>}2.0.CO;2, 1996.
FENGYUN Satellite Data Center (under National Satellite Meteorological Center of China Meteorological Administration): AGRI L1 Full Disk, 4KM, Cloud Top Temperature(CTT), Cloud Top Height(CTH), Cloud Type(CLT), Cloud Phase(CLP), FENGYUN Satellite Data Center [data set], available at: http://satellite.nsmc.org.cn/PortalSite/Data/Satellite.aspx, last access: 30 October 2021.
Ferraro, R. R.: Special sensor microwave imager derived global rainfall estimates for climatological applications, J. Geophys. Res.-Atmos., 102, 16715–16735, https://doi.org/10.1029/97JD01210, 1997.
Fu, Y.: Cloud Parameters Retrieved by the Bispectral Reflectance Algorithm and Associated Applications, J. Meteorol. Res., 28, 965–982, https://doi.org/10.1007/s13351-014-3292-3, 2014.
Fu, Y., Pan, X., Yang, Y., Chen, F., and Liu, P.: Climatological characteristics of summer precipitation over East Asia measured by TRMM PR: A review, J. Meteorol. Res., 31, 142–159, https://doi.org/10.1007/s13351-017-6156-9, 2017.
Gan, T. Y., Ito, M., Hülsmann, S., Qin, X., Lu, X. X., Liong, S.-Y., Rutschman, P., Disse, M., and Koivusalo, H.: Possible climate change/variability and human impacts, vulnerability of drought-prone regions, water resources and capacity building for Africa, Hydrolog. Sci. J., 61, 1209–1226, https://doi.org/10.1080/02626667.2015.1057143, 2016.
Geospatial Data Cloud (under Computer Network Information Centre Chinese Academy of Sciences):
Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model 30M resolution digital elevation data, Geospatial Data Cloud [data set], available at: http://www.gscloud.cn/sources/?cdataid=302&pdataid=10, last access: 30 October 2021.
Griffith, C. G., Woodley, W. L., Grube, P. G., Martin, D. W., and Sikdar, D. N.: Rain Estimation from Geosynchronous Satellite Imagery – Visible and Infrared Studies, Mon. Weather Rev., 106, 1153, https://doi.org/10.1175/1520-0493(1978)106{<}1153:REFGSI{>}2.0.CO;2, 1978.
Hobbs, P. V.: Research on Clouds and Precipitation: Past, Present, and Future, Part I, B. Am. Meteorol. Soc., 70, 282–285, https://doi.org/10.1175/1520-0477-70.3.282, 1989.
Holl, G., Buehler, S. A., Rydberg, B., and Jiménez, C.: Collocating satellite-based radar and radiometer measurements – methodology and usage examples, Atmos. Meas. Tech., 3, 693–708, https://doi.org/10.5194/amt-3-693-2010, 2010.
Hou, A. Y., Kakar, R. K., Neeck, S., Azarbarzin, A. A., Kummerow, C. D., Kojima, M., Oki, R., Nakamura, K., and Iguchi, T.: The global precipitation measurement mission, B. Am. Meteorol. Soc., 95, 701–722, https://doi.org/10.1175/BAMS-D-13-00164.1, 2014.
Iguchi, T., Kozu, T., Meneghini, R., and Okamoto, K.: Rain Profiling Algorithm
for the TRMM Precipitation Radar, J. Appl. Meteorol., 87-A, 1–30,
https://doi.org/10.2151/jmsj.87A.1, 2000.
Iguchi, T., Kozu, T., Kwiatkowski, J., Meneghini, R., Awaka, J., and
Okamoto, K. I.: Uncertainties in the Rain Profiling Algorithm for the TRMM
Precipitation Radar, J. Meteorol. Soc. Jpn. Ser. II, 87, 1–30, https://doi.org/10.2151/jmsj.87A.1, 2009.
Kanamitsu, M.: Description of the NMC Global Data Assimilation and Forecast System, Weather Forecast., 4, 335–342, https://doi.org/10.1175/1520-0434(1989)004{<}0335:DOTNGD{>}2.0.CO;2, 1989.
Kidd, C.: Satellite rainfall climatology: a review, Int. J. Climatol., 21, 1041–1066, https://doi.org/10.1002/joc.635, 2010.
Kühnlein, M., Appelhans, T., Thies, B., and Nauss, T.: Precipitation Estimates from MSG SEVIRI Daytime, Nighttime, and Twilight Data with Random Forests, J. Appl. Meteorol. Clim., 53, 2457–2480, https://doi.org/10.1175/JAMC-D-14-0082.1, 2014.
Kummerow, C., Hong, Y., Olson, W. S., Yang, S., Adler, R. F., McCollum, J., Ferraro, R., Petty, G., Shin, D. B., and Wilheit, T. T.: The Evolution of the Goddard Profiling Algorithm (GPROF) for Rainfall Estimation from Passive Microwave Sensors, J. Appl. Meteorol., 40, 1801–1820, https://doi.org/10.1175/1520-0450(2001)040{<}1801:TEOTGP{>}2.0.CO;2, 2001.
Lao, P., Liu, Q., Ding, Y., Wang, Y., Li, Y., and Li, M.: Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using Machine Learning Algorithm, Remote Sens.-Basel, 13, 3273, https://doi.org/10.3390/rs13163273, 2021.
Lee, Y.-R., Shin, D.-B., Kim, J.-H., and Park, H.-S.: Precipitation estimation over radar gap areas based on satellite and adjacent radar observations, Atmos. Meas. Tech., 8, 719–728, https://doi.org/10.5194/amt-8-719-2015, 2015.
Lensky, I. M. and Rosenfeld, D.: Estimation of Precipitation Area and Rain Intensity Based on the Microphysical Properties Retrieved from NOAA AVHRR Data, J. Appl. Meteorol. Clim., 36, 234–242, https://doi.org/10.1175/1520-0450(1997)036{<}0234:EOPAAR{>}2.0.CO;2, 1995.
Levizzani, V., Bauer, P., and Turk, F. J.: Measuring Precipitation from Space,
Measuring Precipitation from Space, Springer, Dordrecht, Switzerland, https://doi.org/10.1007/978-1-4020-5835-6,
2007.
Li, Z., Yang, D., and Hong, Y.: Multi-scale evaluation of high-resolution multi-sensor blended global precipitation products over the Yangtze River, J. Hydrol., 500, 157–169, https://doi.org/10.1016/j.jhydrol.2013.07.023, 2013.
Liu, D., Zhao, Q., Fu, D., Guo, S., and Zeng, Y.: Comparison of spatial interpolation methods for the estimation of precipitation patterns at different time scales to improve the accuracy of discharge simulations, Hydrology Research, 51, 583–601, https://doi.org/10.2166/nh.2020.146, 2020.
Liu, X. C., Gao, T. C., and Liu, L.: A comparison of rainfall measurements from multiple instruments, Atmos. Meas. Tech., 6, 1585–1595, https://doi.org/10.5194/amt-6-1585-2013, 2013.
Lolli, S., D'Adderio, L. P., Campbell, J. R., Sicard, M., Welton, E. J.,
Binci, A., Rea, A., Tokay, A., Comerón, A., Barragan, R.,
Baldasano, J. M., Gonzalez, S., Bech, J., Afflitto, N., Lewis, J. R., and Madonna, F.: Vertically Resolved Precipitation Intensity Retrieved through a Synergy between the Ground-Based NASA MPLNET Lidar Network Measurements, Surface Disdrometer Datasets and an Analytical Model Solution, Remote Sens.-Basel, 10, 1102, https://doi.org/10.3390/rs10071102, 2018.
Lolli, S., Vivone, G., Lewis, J. R., Sicard, M., Welton, E. J., Campbell, J. R., Comerón, A., D'Adderio, L. P., Tokay, A., and Giunta, A.: Overview of the New Version 3 NASA Micro-Pulse Lidar Network (MPLNET) Automatic Precipitation Detection Algorithm, Remote Sens.-Basel, 12, 71, https://doi.org/10.3390/rs12010071, 2020.
Michaelides, S., Levizzani, V., Anagnostou, E., Bauer, P., Kasparis, T., and Lane, J. E.: Precipitation: Measurement, remote sensing, climatology and modeling, Atmos. Res., 94, 512–533, https://doi.org/10.1016/j.atmosres.2009.08.017, 2009.
Min, M., Bai, C., Guo, J., Sun, F., Liu, C., Wang, F., Xu, H., Tang, S.,
Li, B., and Di, D.: Estimating Summertime Precipitation from Himawari-8 and
Global Forecast System Based on Machine Learning, IEEE T. Geosci. Remote,
57, 2557–2570, https://doi.org/10.1109/TGRS.2018.2874950, 2019.
Mugnai, A., Casella, D., Cattani, E., Dietrich, S., Laviola, S., Levizzani, V., Panegrossi, G., Petracca, M., Sanò, P., Di Paola, F., Biron, D., De Leonibus, L., Melfi, D., Rosci, P., Vocino, A., Zauli, F., Pagliara, P., Puca, S., Rinollo, A., Milani, L., Porcù., F., and Gattari, F.: Precipitation products from the hydrology SAF, Nat. Hazards Earth Syst. Sci., 13, 1959–1981, https://doi.org/10.5194/nhess-13-1959-2013, 2013.
Nauss, T., Thies, B., Turek, A., Bendix, J., and Kokhanovsky, A.: Operational
discrimination of raining from non-raining clouds in mid-latitudes using
multispectral satellite data, in: Precipitation: Advances in Measurement,
Estimation and Prediction, Springer, Berlin, Heidelberg, Germany, 171–194, https://doi.org/10.1007/978-3-540-77655-0_7,
2008.
Roman, J., Knuteson, R., Ackerman, S., and Revercomb, H.: Estimating Minimum Detection Times for Satellite Remote Sensing of Trends in Mean and Extreme Precipitable Water Vapor, J. Climate, 29, 8211–8230, https://doi.org/10.1175/JCLI-D-16-0303.1, 2016.
Rosenfeld, D. and Gutman, G.: Retrieving microphysical properties near the tops of potential rain clouds by multispectral analysis of AVHRR data, Atmos. Res., 34, 259–283, https://doi.org/10.1016/0169-8095(94)90096-5, 1994.
Rosenfeld, D., Wang, H., and Rasch, P. J.: The roles of cloud drop effective radius and LWP in determining rain properties in marine stratocumulus, Geophys. Res. Lett., 39, L13801, https://doi.org/10.1029/2012GL052028, 2012.
Sanò, P., Panegrossi, G., Casella, D., Di Paola, F., Milani, L., Mugnai, A., Petracca, M., and Dietrich, S.: The Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for AMSU/MHS observations: description and application to European case studies, Atmos. Meas. Tech., 8, 837–857, https://doi.org/10.5194/amt-8-837-2015, 2015.
Sharifi, E., Steinacker, R., and Saghafian, B.: Assessment of GPM-IMERG and other precipitation products against gauge data under different topographic and climatic conditions in Iran: Preliminary results, Remote Sens.-Basel, 8, 135, https://doi.org/10.3390/rs8020135, 2016.
Tan, M. L. and Duan, Z.: Assessment of GPM and TRMM precipitation products over Singapore, Remote Sens.-Basel, 9, 720, https://doi.org/10.3390/rs9070720, 2017.
Tang, G., Ma, Y., Long, D., Zhong, L., and Hong, Y.: Evaluation of GPM Day-1 IMERG and TMPA Version-7 legacy products over Mainland China at multiple spatiotemporal scales, J. Hydrol., 533, 152–167, https://doi.org/10.1016/j.jhydrol.2015.12.008, 2016.
Thies, B., Nauß, T., and Bendix, J.: Precipitation process and rainfall intensity differentiation using Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager data, J. Geophys. Res., 113, D23206, https://doi.org/10.1029/2008JD010464, 2008.
Xie, P. and Arkin, P. A.: Global precipitation: A 17-year monthly analysis based on gauge observations, B. Am. Meteorol. Soc., 78, 2539–2558, https://doi.org/10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2, 2001.
Yang, Y., Zhang, M., Li, Q., Chen, B., and Luo, M.: Modulations of surface thermal environment and agricultural activity on intraseasonal variations of summer diurnal temperature range in the Yangtze River Delta of China, Sci. Total Environ., 736, 139445, https://doi.org/10.1007/s00024-018-1940-8, 2020.
Yang, Y., Wang, R., Chen, F., Liu, C., Bi, X., and Huang, M.: Synoptic weather patterns modulate the frequency, type and vertical structure of summer precipitation over Eastern China: A perspective from GPM observations, Atmos. Res., 249, 105342, https://doi.org/10.1016/j.atmosres.2020.105342, 2021.
Yang, Y. J., Wang, H., Chen, F., Zheng, X., and Zhou, S.: TRMM-Based Optical and Microphysical Features of Precipitating Clouds in Summer Over the Yangtze–Huaihe River Valley, China, Pure Appl. Geophys., 176, 357–370, https://doi.org/10.1007/s00024-018-1940-8, 2018.
Zeng, Z., Wang, Z., Gui, K., Yan, X., Gao, M., Luo, M., Geng, H., Liao, T., Li, X., and An, J.: Daily Global Solar Radiation in China Estimated From High-Density Meteorological Observations: A Random Forest Model Framework, Earth and Space Science, 7, e2019EA001058, https://doi.org/10.1029/2019EA001058, 2020.
Zhang, C., Yang, X., and Zhang, W.: Accurate Precipitation Nowcasting with Meteorological Big Data: Machine Learning Method and Application, Journal of Agricultural Big Data, 1, 78–87, https://doi.org/10.19788/j.issn.2096-6369.190108, 2019.
Zhang, G. J.: Roles of tropospheric and boundary layer forcing in the diurnal cycle of convection in the U.S. southern great plains, Geophys. Res. Lett., 30, 2281, https://doi.org/10.1029/2003GL018554, 2003.
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...