Articles | Volume 17, issue 14
https://doi.org/10.5194/gmd-17-5657-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-5657-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
26 Jul 2024
Development and technical paper |
| 26 Jul 2024
| 26 Jul 2024
ZJU-AERO V0.5: an Accurate and Efficient Radar Operator designed for CMA-GFS/MESO with the capability to simulate non-spherical hydrometeors
Hejun Xie
Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Hangzhou, 310027, China
Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Hangzhou, 310027, China
CMA Center for Earth System Modeling and Prediction (CEMC), China Meteorological Administration, Beijing, 100081, China
State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing, 100081, China
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Atmos. Meas. Tech., 18, 4249–4269, https://doi.org/10.5194/amt-18-4249-2025, https://doi.org/10.5194/amt-18-4249-2025, 2025
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Yongzhu Liu, Xiaoye Zhang, Wei Han, Chao Wang, Wenxing Jia, Deying Wang, Zhaorong Zhuang, and Xueshun Shen
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Geosci. Model Dev., 18, 3781–3798, https://doi.org/10.5194/gmd-18-3781-2025, https://doi.org/10.5194/gmd-18-3781-2025, 2025
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Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025, https://doi.org/10.5194/gmd-18-1947-2025, 2025
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Atmos. Meas. Tech., 18, 1339–1353, https://doi.org/10.5194/amt-18-1339-2025, https://doi.org/10.5194/amt-18-1339-2025, 2025
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EGUsphere, https://doi.org/10.5194/egusphere-2025-680, https://doi.org/10.5194/egusphere-2025-680, 2025
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Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025, https://doi.org/10.5194/gmd-18-117-2025, 2025
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Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
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Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Yongbo Zhou, Yubao Liu, Wei Han, Yuefei Zeng, Haofei Sun, Peilong Yu, and Lijian Zhu
Atmos. Meas. Tech., 17, 6659–6675, https://doi.org/10.5194/amt-17-6659-2024, https://doi.org/10.5194/amt-17-6659-2024, 2024
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Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
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Senyi Kong, Zheng Wang, and Lei Bi
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The retrieval of refractive indices of dust aerosols from laboratory optical measurements is commonly done assuming spherical particles. This paper aims to investigate the uncertainties in the shortwave refractive indices and corresponding optical properties by considering non-spherical and inhomogeneous models for dust samples. The study emphasizes the significance of using non-spherical models for simulating dust aerosols.
Li Fang, Jianbing Jin, Arjo Segers, Hong Liao, Ke Li, Bufan Xu, Wei Han, Mijie Pang, and Hai Xiang Lin
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Machine learning models have gained great popularity in air quality prediction. However, they are only available at air quality monitoring stations. In contrast, chemical transport models (CTM) provide predictions that are continuous in the 3D field. Owing to complex error sources, they are typically biased. In this study, we proposed a gridded prediction with high accuracy by fusing predictions from our regional feature selection machine learning prediction (RFSML v1.0) and a CTM prediction.
Jianbing Jin, Mijie Pang, Arjo Segers, Wei Han, Li Fang, Baojie Li, Haochuan Feng, Hai Xiang Lin, and Hong Liao
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Super dust storms reappeared in East Asia last spring after being absent for one and a half decades. Accurate simulation of such super sandstorms is valuable, but challenging due to imperfect emissions. In this study, the emissions of these dust storms are estimated by assimilating multiple observations. The results reveal that emissions originated from both China and Mongolia. However, for northern China, long-distance transport from Mongolia contributes much more dust than Chinese deserts.
Liang Xu, Xiaohuan Liu, Huiwang Gao, Xiaohong Yao, Daizhou Zhang, Lei Bi, Lei Liu, Jian Zhang, Yinxiao Zhang, Yuanyuan Wang, Qi Yuan, and Weijun Li
Atmos. Chem. Phys., 21, 17715–17726, https://doi.org/10.5194/acp-21-17715-2021, https://doi.org/10.5194/acp-21-17715-2021, 2021
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We quantified different types of marine aerosols and explored the Cl depletion of sea salt aerosol (SSA) in the eastern China seas and the northwestern Pacific Ocean. We found that anthropogenic acidic gases in the troposphere were transported longer distances compared to the anthropogenic aerosols and could significantly impact remote marine aerosols. Meanwhile, variations of chloride depletion in SSA can serve as a potential indicator for anthropogenic gaseous pollutants in remote marine air.
Lin Tian, Lin Chen, Peng Zhang, and Lei Bi
Atmos. Chem. Phys., 21, 11669–11687, https://doi.org/10.5194/acp-21-11669-2021, https://doi.org/10.5194/acp-21-11669-2021, 2021
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The result shows dust aerosols from the Taklimakan Desert have higher aerosol scattering during dust storm cases of this paper, and this caused higher negative direct radiative forcing efficiency (DRFEdust) than aerosols from the Sahara.
The microphysical properties and particle shapes of dust aerosol significantly influence DRFEdust. The satellite-based equi-albedo method has a unique advantage in DRFEdust estimation: it could validate the results derived from the numerical model directly.
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Short summary
A radar operator plays a crucial role in utilizing radar observations to enhance numerical weather forecasts. However, developing an advanced radar operator is challenging due to various complexities associated with the wave scattering by non-spherical hydrometeors, radar beam propagation, and multiple platforms. In this study, we introduce a novel radar operator named the Accurate and Efficient Radar Operator developed by ZheJiang University (ZJU-AERO) which boasts several unique features.
A radar operator plays a crucial role in utilizing radar observations to enhance numerical...
