Articles | Volume 16, issue 12
https://doi.org/10.5194/essd-16-5753-2024
© 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/essd-16-5753-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
18 Dec 2024
Data description article |
| 18 Dec 2024
| 18 Dec 2024
Global tropical cyclone size and intensity reconstruction dataset for 1959–2022 based on IBTrACS and ERA5 data
Zhiqi Xu
Institute of Urban Meteorology, China Metrological Administration, Beijing 100089, China
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Guwei Zhang
Institute of Urban Meteorology, China Metrological Administration, Beijing 100089, China
Yuchen Ye
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing, 210044, China
Haikun Zhao
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing, 210044, China
Haishan Chen
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing, 210044, China
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EGUsphere, https://doi.org/10.5194/egusphere-2026-5822, https://doi.org/10.5194/egusphere-2026-5822, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Turbulence in the planetary boundary layer (PBL) affects cloud and precipitation, yet little is known about it across China. With summertime 2022 data from 132 radar wind profilers, we map the turbulence dissipation rate (ε) within and above the PBL. Stability and wind shear jointly govern turbulence within the PBL, whereas shear dominates above it; clouds enhance turbulence aloft, and topography and land cover shape its regional pattern, offering new constraints for weather and climate models.
Hui Xu, Jianping Guo, Jianbo Deng, Rongfang Yang, Deli Meng, Zhen Zhang, Ning Li, Yuping Sun, Shuairu Jiang, Tianmeng Chen, Juan Chen, Liping Zeng, Yongshui Zhou, and Bing Tong
Atmos. Chem. Phys., 26, 14303–14327, https://doi.org/10.5194/acp-26-14303-2026, https://doi.org/10.5194/acp-26-14303-2026, 2026
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Using a new national network of 80 cloud radars, we mapped how clouds are layered and how high they reach across China. Clouds vary strongly by region, season, and time of day, with deeper clouds in humid conditions and shallower clouds under stable or strong shear conditions. Afternoon single‑layer low CBH is significantly higher than all‑hour, especially over evergreen broadleaf. These findings help improve weather and climate predictions.
Weilong Deng, Jianping Guo, Ning Li, Zhen Zhang, Yifei Wang, and Yi Yang
Atmos. Chem. Phys., 26, 14259–14275, https://doi.org/10.5194/acp-26-14259-2026, https://doi.org/10.5194/acp-26-14259-2026, 2026
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Heavy rain far ahead of weather fronts is easily overlooked and hard to forecast. Using five warm seasons of rain gauge and wind radar data from eastern China's plains, we found that these brief, intense events supplied nearly half of heavy rainfall at some sites. Before rain began, fast winds at low heights became more common and the strongest upward air movements intensified. These changes could help identify warning signs, but their ability to predict individual downpours still needs testing.
Qi Zhang, Tianmeng Chen, Jianping Guo, Bin Deng, Han Li, and Yu Wu
Geosci. Model Dev., 19, 9555–9580, https://doi.org/10.5194/gmd-19-9555-2026, https://doi.org/10.5194/gmd-19-9555-2026, 2026
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This study presents an adaptive observation-weighting scheme for thermodynamic profile retrievals from ground-based microwave radiometers and Mie–Raman lidars. The method dynamically estimates observational contributions during the retrieval process, replacing the commonly used static weighting assumption. Evaluation using 107 heavy-precipitation cases shows improved retrieval accuracy, particularly for atmospheric moisture profiles.
Weijie Fu, Chenguang Tian, Yuan Zhao, Yihan Hu, Jingchao Huang, Haishan Chen, and Xu Yue
Geosci. Model Dev., 19, 9395–9409, https://doi.org/10.5194/gmd-19-9395-2026, https://doi.org/10.5194/gmd-19-9395-2026, 2026
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Land-atmosphere interaction is an important process for the exchange of matter and energy between land and atmosphere. In this work, we incorporated the interactive Model for Air Pollution and Land Ecosystems into the European Centre Hamburg general circulation model. Compare with original configuration, the new coupling model demonstrates better performance in simulating terrestrial carbon and water fluxes. Our research provides a useful tool for studying land–atmosphere interactions.
Lingfeng Li, Bo Qiu, Siwen Zhao, Xin Miao, Chaorong Chen, Jiuyi Chen, Yueyang Ni, Xin Huang, Haishan Chen, and Weidong Guo
Geosci. Model Dev., 19, 8447–8467, https://doi.org/10.5194/gmd-19-8447-2026, https://doi.org/10.5194/gmd-19-8447-2026, 2026
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Ground-level ozone can reduce plant growth, but models represent this damage in different ways. We tested six approaches in the same vegetation model for China during the 2010s. They estimated that ozone reduced plant carbon uptake by 17 % to 27 %, averaging about 21 %. Approaches based on broader observations and leaf traits matched measurements more closely, while using leaf lifespan improved how the model represented the accumulation and decline of ozone damage.
Juan Zhao, Jianping Guo, Jidong Gao, and Honglong Yang
EGUsphere, https://doi.org/10.5194/egusphere-2026-3350, https://doi.org/10.5194/egusphere-2026-3350, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Expanding low-altitude activities demand better lower-atmosphere weather data. Using observing system simulation experiments (OSSEs) over the Beijing-Tianjin-Hebei region, we tested if meteorological drone observations of temperature, humidity, and wind improve short-term rainfall forecasts. Dense, multi-height networks with wind measurements yield best accuracy, especially over mountains where storms form. These findings guide low-altitude observing network design to support safe operations.
Rongfang Yang, Deli Meng, Guanglei Zhang, Lihui Liu, Guocui Li, Zhen Zhang, and Jianping Guo
EGUsphere, https://doi.org/10.5194/egusphere-2026-4218, https://doi.org/10.5194/egusphere-2026-4218, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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The air near the ground affects weather, pollution, and low-flying aircraft safety, but is hard to measure over hills. We flew a drone with weather sensors and compared it with a tall measurement tower. The drone accurately captured how wind, temperature, and humidity change with height. It shook most when strong shifting winds met weak stability, especially near cloud base. This helps make drone weather observations and low-altitude flight safer.
Qi Zhang, Tianmeng Chen, Jianping Guo, and Xun Li
Atmos. Meas. Tech., 19, 4853–4873, https://doi.org/10.5194/amt-19-4853-2026, https://doi.org/10.5194/amt-19-4853-2026, 2026
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Accurate monitoring of boundary-layer water vapor prior to nocturnal heavy precipitation remains challenging. This study extends a physically constrained retrieval framework by integrating Raman lidar observations to improve water vapor profile estimation. The method shows improved accuracy compared to reanalysis data and captures coherent pre-precipitation moisture evolution, demonstrating its potential for studying and monitoring severe weather processes.
Yiming Wang, Yi Zhang, Yilun Han, Wei Xue, Tianru Chen, Yihui Zhou, Xiaohan Li, and Haishan Chen
Geosci. Model Dev., 19, 5553–5570, https://doi.org/10.5194/gmd-19-5553-2026, https://doi.org/10.5194/gmd-19-5553-2026, 2026
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This study demonstrates that short-period Global Storm Resolving Model (GSRM) simulations can inform long-term Global Climate Model (GCM) integrations through a machine-learning-based physics suite. With 80 d of GSRM-derived training data, the hybrid model achieves stable multiyear climate simulations and improved precipitation climatic characteristics.
Xiaozhong Cao, Qiyun Guo, Haowen Luo, Jincheng Wang, Rongkang Yang, Die Xiao, Yinfeng Liu, Zhongliang Sun, Shijun Liu, Sijie Chen, Anfan Huang, Guo Jianping, and Peng Zhang
Atmos. Meas. Tech., 19, 3231–3251, https://doi.org/10.5194/amt-19-3231-2026, https://doi.org/10.5194/amt-19-3231-2026, 2026
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The Ascent-Drift-Descent Radiosonde System (ADDRS) represents an emerging cost-effective technology for upper-air measurements, Its 'Internet cloud + instrument terminal' design enables targeted observations for extreme weather research. Initial data application shows high potential for numerical weather prediction, The pilot application of ADDRS is list in the World Meteorological Organization Region II Coordination 2025-2027 demonstration project (No.RA II-18-I-DP-2).
Rumo Wang, Tianyi Fan, Zhanqing Li, and Jianping Guo
EGUsphere, https://doi.org/10.5194/egusphere-2026-1730, https://doi.org/10.5194/egusphere-2026-1730, 2026
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This study investigated whether aerosols from anthropogenic emissions can make storm environment more favorable for producing tornados. We numerically modeled a real storm in the populated eastern China and found that aerosols can warm the low-level air and strengthen the updraft, allowing the storm to more effectively ingest environmental vorticity, and thus favoring the production of tornados. This suggests that air pollution may increase severe weather risk in densely populated regions.
Yonglin Fang, Hancheng Hu, Xiangdong Zheng, Jianping Guo, Xingbing Zhao, Fang Ma, and Hao Wu
Atmos. Chem. Phys., 26, 4089–4104, https://doi.org/10.5194/acp-26-4089-2026, https://doi.org/10.5194/acp-26-4089-2026, 2026
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This study shows how temperature inversions (warm air above cold) trap pollution in China. Using six years of national data, we find these "lids" frequently cause severe haze, especially in winter, by increasing pollution probability and intensity. Impacts differ by region and inversion type/strength. These findings help improve air quality forecasts and regional pollution control strategies.
Zhe Tong, Boming Liu, Xin Ma, Jianping Guo, Haowei Zhang, Haoyu Dong, Ge Han, Yingying Ma, and Wei Gong
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-73, https://doi.org/10.5194/essd-2026-73, 2026
Revised manuscript accepted for ESSD
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Near-surface wind is crucial for weather and wind energy studies. This work uses physically constrained machine learning combined with satellite wind observations to generate a wind speed profile dataset within the global atmospheric boundary layer. The dataset accurately depicts the spatial distribution and variation of global wind speeds with improved accuracy, finer vertical detail, and reduced data gaps, supporting boundary-layer meteorology, climate studies, and wind energy applications.
Ning Li, Jianping Guo, Xiaoran Guo, Tianmeng Chen, Zhen Zhang, Na Tang, Yifei Wang, Honglong Yang, Yongguang Zheng, and Yongshui Zhou
Atmos. Chem. Phys., 26, 3339–3356, https://doi.org/10.5194/acp-26-3339-2026, https://doi.org/10.5194/acp-26-3339-2026, 2026
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Nighttime rainfall often links to low-level jets (LLJs), but we lack clarity on nationwide LLJ features. We here used a nationwide radar wind profiler network to study LLJ changes 2 hours before rainfall, covering China’s 2023–2024 rainy seasons. 56% nighttime rainfall had LLJs. The LLJs-associated heavy rain needed a rapid adjustment of LLJs’ vertical structure, especially a significant intensification within 30 minutes preceding rain. This shows the importance of LLJ in nowcasting rainfall.
Xiaoran Guo, Jianping Guo, Ning Li, Zhen Zhang, Tianmeng Chen, Yu Shi, Pengzhan Yao, Shuairu Jiang, Lei Zhao, and Fei Hu
Atmos. Chem. Phys., 26, 2391–2409, https://doi.org/10.5194/acp-26-2391-2026, https://doi.org/10.5194/acp-26-2391-2026, 2026
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Wind gusts threaten safety and infrastructure but are hard to predict. To address this gap, we studied an extreme wind gust event in Beijing on 30 May 2024. We used seven radar wind profilers to track how this gust developed. It formed when cold northeasterly air clashed with warm southerly winds as the storm moved downhill. Evaporation of rain cooled the air, boosting downward air movement and wind strength. The turbulence transferring energy from small to large eddies intensify winds.
Qi Zhang, Tianmeng Chen, Jianping Guo, Yu Wu, Bin Deng, and Junjie Yan
Geosci. Model Dev., 19, 505–522, https://doi.org/10.5194/gmd-19-505-2026, https://doi.org/10.5194/gmd-19-505-2026, 2026
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We propose TCKF1D-Var, a thermodynamic-constrained variational framework for ground-based microwave radiometer retrievals. Using virtual potential temperature, a ratio-based cost function, and a microphysics closure, it reduces biases relative to ERA5 and 1D-Var, improves cloud liquid water representation, and enhances heavy rainfall precursors, extending lead times. This approach strengthens continuous profiling and supports high-impact weather nowcasting.
Kyaw Than Oo, Chen Haishan, Kazora Jonah, and Du Xinguan
Weather Clim. Dynam., 6, 1399–1417, https://doi.org/10.5194/wcd-6-1399-2025, https://doi.org/10.5194/wcd-6-1399-2025, 2025
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The study examines the delayed withdrawal of the Mainland Indochina Southwest Monsoon by exploring spatial trends. The new Cumulative Change-Point Monsoon index effectively describes seasonal shifts. Results indicate stronger subtropical westerly jets and weaker tropical easterly jets in recent years, impacting wind patterns and delaying monsoon withdrawal.
Deli Meng, Jianping Guo, Juan Chen, Xiaoran Guo, Ning Li, Yuping Sun, Zhen Zhang, Na Tang, Hui Xu, Tianmeng Chen, Rongfang Yang, and Jiajia Hua
Earth Syst. Sci. Data, 17, 4023–4037, https://doi.org/10.5194/essd-17-4023-2025, https://doi.org/10.5194/essd-17-4023-2025, 2025
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This study provides a high-resolution dataset of low-level atmospheric turbulence across China, using radar and weather balloon observations. It reveals regional and seasonal variations in turbulence, with stronger activity in spring and summer. The dataset supports weather forecasting, aviation safety, and low-altitude flight planning, aiding China's growing low-altitude economy, and is accessible at https://doi.org/10.5281/zenodo.14959025.
Xiaoran Guo, Jianping Guo, Deli Meng, Yuping Sun, Zhen Zhang, Hui Xu, Liping Zeng, Juan Chen, Ning Li, and Tianmeng Chen
Earth Syst. Sci. Data, 17, 3541–3552, https://doi.org/10.5194/essd-17-3541-2025, https://doi.org/10.5194/essd-17-3541-2025, 2025
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Optimal atmospheric dynamic conditions are essential for convective storms. This study generates a dataset of high-resolution divergence and vorticity profiles using the measurements of a radar wind profiler mesonet in Beijing. The negative divergence and positive vorticity are present ahead of rainfall events. This suggests that this dataset can help improve our understanding of the pre-storm environment and has the potential to be applied in weather forecasting.
Juan Zhao, Jianping Guo, and Xiaohui Zheng
Geosci. Model Dev., 18, 4075–4101, https://doi.org/10.5194/gmd-18-4075-2025, https://doi.org/10.5194/gmd-18-4075-2025, 2025
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A series of observing system simulation experiments are conducted to assess the impact of multiple radar wind profiler (RWP) networks on convective-scale numerical weather prediction. Results from three southwest-type heavy rainfall cases in the Beijing–Tianjin–Hebei region suggest the added forecast skill of ridge and foothill networks associated with the Taihang Mountains over the existing RWP network. This research provides valuable guidance for designing optimal RWP networks in the region.
Seoung Soo Lee, Chang Hoon Jung, Jinho Choi, Young Jun Yoon, Junshik Um, Youtong Zheng, Jianping Guo, Manguttathil G. Manoj, Sang-Keun Song, and Kyung-Ja Ha
Atmos. Chem. Phys., 25, 705–726, https://doi.org/10.5194/acp-25-705-2025, https://doi.org/10.5194/acp-25-705-2025, 2025
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This study attempts to test a general factor that explains differences in the properties of different mixed-phase clouds using a modeling tool. Although this attempt is not to identify a factor that can perfectly explain and represent the properties of different mixed-phase clouds, we believe that this attempt acts as a valuable stepping stone towards a more complete, general way of using climate models to better predict climate change.
Deli Meng, Jianping Guo, Xiaoran Guo, Yinjun Wang, Ning Li, Yuping Sun, Zhen Zhang, Na Tang, Haoran Li, Fan Zhang, Bing Tong, Hui Xu, and Tianmeng Chen
Atmos. Chem. Phys., 24, 8703–8720, https://doi.org/10.5194/acp-24-8703-2024, https://doi.org/10.5194/acp-24-8703-2024, 2024
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The turbulence in the planetary boundary layer (PBL) over the Tibetan Plateau (TP) remains unclear. Here we elucidate the vertical profile of and temporal variation in the turbulence dissipation rate in the PBL over the TP based on a radar wind profiler (RWP) network. To the best of our knowledge, this is the first time that the turbulence profile over the whole TP has been revealed. Furthermore, the possible mechanisms of clouds acting on the PBL turbulence structure are investigated.
Lijuan Chen, Ren Wang, Ying Fei, Peng Fang, Yong Zha, and Haishan Chen
Atmos. Meas. Tech., 17, 4411–4424, https://doi.org/10.5194/amt-17-4411-2024, https://doi.org/10.5194/amt-17-4411-2024, 2024
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This study explores the problems of surface reflectance estimation from previous MISR satellite remote sensing images and develops an error correction model to obtain a higher-precision aerosol optical depth (AOD) product. High-accuracy AOD is important not only for the daily monitoring of air pollution but also for the study of energy exchange between land and atmosphere. This will help further improve the retrieval accuracy of multi-angle AOD on large spatial scales and for long time series.
Xiaoran Guo, Jianping Guo, Tianmeng Chen, Ning Li, Fan Zhang, and Yuping Sun
Atmos. Chem. Phys., 24, 8067–8083, https://doi.org/10.5194/acp-24-8067-2024, https://doi.org/10.5194/acp-24-8067-2024, 2024
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The prediction of downhill thunderstorms (DSs) remains elusive. We propose an objective method to identify DSs, based on which enhanced and dissipated DSs are discriminated. A radar wind profiler (RWP) mesonet is used to derive divergence and vertical velocity. The mid-troposphere divergence and prevailing westerlies enhance the intensity of DSs, whereas low-level divergence is observed when the DS dissipates. The findings highlight the key role that an RWP mesonet plays in the evolution of DSs.
Kaixu Bai, Ke Li, Liuqing Shao, Xinran Li, Chaoshun Liu, Zhengqiang Li, Mingliang Ma, Di Han, Yibing Sun, Zhe Zheng, Ruijie Li, Ni-Bin Chang, and Jianping Guo
Earth Syst. Sci. Data, 16, 2425–2448, https://doi.org/10.5194/essd-16-2425-2024, https://doi.org/10.5194/essd-16-2425-2024, 2024
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A global gap-free high-resolution air pollutant dataset (LGHAP v2) was generated to provide spatially contiguous AOD and PM2.5 concentration maps with daily 1 km resolution from 2000 to 2021. This gap-free dataset has good data accuracies compared to ground-based AOD and PM2.5 concentration observations, which is a reliable database to advance aerosol-related studies and trigger multidisciplinary applications for environmental management, health risk assessment, and climate change analysis.
Boming Liu, Xin Ma, Jianping Guo, Renqiang Wen, Hui Li, Shikuan Jin, Yingying Ma, Xiaoran Guo, and Wei Gong
Atmos. Chem. Phys., 24, 4047–4063, https://doi.org/10.5194/acp-24-4047-2024, https://doi.org/10.5194/acp-24-4047-2024, 2024
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Accurate wind profile estimation, especially for the lowest few hundred meters of the atmosphere, is of great significance for the weather, climate, and renewable energy sector. We propose a novel method that combines the power-law method with the random forest algorithm to extend wind profiles beyond the surface layer. Compared with the traditional algorithm, this method has better stability and spatial applicability and can be used to obtain the wind profiles on different land cover types.
Jianping Guo, Jian Zhang, Jia Shao, Tianmeng Chen, Kaixu Bai, Yuping Sun, Ning Li, Jingyan Wu, Rui Li, Jian Li, Qiyun Guo, Jason B. Cohen, Panmao Zhai, Xiaofeng Xu, and Fei Hu
Earth Syst. Sci. Data, 16, 1–14, https://doi.org/10.5194/essd-16-1-2024, https://doi.org/10.5194/essd-16-1-2024, 2024
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A global continental merged high-resolution (PBLH) dataset with good accuracy compared to radiosonde is generated via machine learning algorithms, covering the period from 2011 to 2021 with 3-hour and 0.25º resolution in space and time. The machine learning model takes parameters derived from the ERA5 reanalysis and GLDAS product as input, with PBLH biases between radiosonde and ERA5 as the learning targets. The merged PBLH is the sum of the predicted PBLH bias and the PBLH from ERA5.
Hui Xu, Jianping Guo, Bing Tong, Jinqiang Zhang, Tianmeng Chen, Xiaoran Guo, Jian Zhang, and Wenqing Chen
Atmos. Chem. Phys., 23, 15011–15038, https://doi.org/10.5194/acp-23-15011-2023, https://doi.org/10.5194/acp-23-15011-2023, 2023
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The radiative effect of cloud remains one of the largest uncertain factors in climate change, largely due to the lack of cloud vertical structure (CVS) observations. The study presents the first near-global CVS climatology using high-vertical-resolution soundings. Single-layer cloud mainly occurs over arid regions. As the number of cloud layers increases, clouds tend to have lower bases and thinner layer thicknesses. The occurrence frequency of cloud exhibits a pronounced seasonal diurnal cycle.
Shanlei Sun, Zaoying Bi, Jingfeng Xiao, Yi Liu, Ge Sun, Weimin Ju, Chunwei Liu, Mengyuan Mu, Jinjian Li, Yang Zhou, Xiaoyuan Li, Yibo Liu, and Haishan Chen
Earth Syst. Sci. Data, 15, 4849–4876, https://doi.org/10.5194/essd-15-4849-2023, https://doi.org/10.5194/essd-15-4849-2023, 2023
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Based on various existing datasets, we comprehensively considered spatiotemporal differences in land surfaces and CO2 effects on plant stomatal resistance to parameterize the Shuttleworth–Wallace model, and we generated a global 5 km ensemble mean monthly potential evapotranspiration (PET) dataset (including potential transpiration PT and soil evaporation PE) during 1982–2015. The new dataset may be used by academic communities and various agencies to conduct various studies.
Boming Liu, Xin Ma, Jianping Guo, Hui Li, Shikuan Jin, Yingying Ma, and Wei Gong
Atmos. Chem. Phys., 23, 3181–3193, https://doi.org/10.5194/acp-23-3181-2023, https://doi.org/10.5194/acp-23-3181-2023, 2023
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Wind energy is one of the most essential clean and renewable forms of energy in today’s world. However, the traditional power law method generally estimates the hub-height wind speed by assuming a constant exponent between surface and hub-height wind speeds. This inevitably leads to significant uncertainties in estimating the wind speed profile. To minimize the uncertainties, we here use a machine learning algorithm known as random forest to estimate the wind speed at hub height.
Seoung Soo Lee, Junshik Um, Won Jun Choi, Kyung-Ja Ha, Chang Hoon Jung, Jianping Guo, and Youtong Zheng
Atmos. Chem. Phys., 23, 273–286, https://doi.org/10.5194/acp-23-273-2023, https://doi.org/10.5194/acp-23-273-2023, 2023
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This paper elaborates on process-level mechanisms regarding how the interception of radiation by aerosols interacts with the surface heat fluxes and atmospheric instability in warm cumulus clouds. This paper elucidates how these mechanisms vary with the location or altitude of an aerosol layer. This elucidation indicates that the location of aerosol layers should be taken into account for parameterizations of aerosol–cloud interactions.
Seoung Soo Lee, Jinho Choi, Goun Kim, Kyung-Ja Ha, Kyong-Hwan Seo, Chang Hoon Jung, Junshik Um, Youtong Zheng, Jianping Guo, Sang-Keun Song, Yun Gon Lee, and Nobuyuki Utsumi
Atmos. Chem. Phys., 22, 9059–9081, https://doi.org/10.5194/acp-22-9059-2022, https://doi.org/10.5194/acp-22-9059-2022, 2022
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This study investigates how aerosols affect clouds and precipitation and how the aerosol effects vary with varying types of clouds that are characterized by cloud depth in two metropolitan areas in East Asia. As cloud depth increases, the enhancement of precipitation amount transitions to no changes in precipitation amount with increasing aerosol concentrations. This indicates that cloud depth needs to be considered for a comprehensive understanding of aerosol-cloud interactions.
Peilin Song, Yongqiang Zhang, Jianping Guo, Jiancheng Shi, Tianjie Zhao, and Bing Tong
Earth Syst. Sci. Data, 14, 2613–2637, https://doi.org/10.5194/essd-14-2613-2022, https://doi.org/10.5194/essd-14-2613-2022, 2022
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Soil moisture information is crucial for understanding the earth surface, but currently available satellite-based soil moisture datasets are imperfect either in their spatiotemporal resolutions or in ensuring image completeness from cloudy weather. In this study, therefore, we developed one soil moisture data product over China that has tackled most of the above problems. This data product has the potential to promote the investigation of earth hydrology and be extended to the global scale.
Kaixu Bai, Ke Li, Mingliang Ma, Kaitao Li, Zhengqiang Li, Jianping Guo, Ni-Bin Chang, Zhuo Tan, and Di Han
Earth Syst. Sci. Data, 14, 907–927, https://doi.org/10.5194/essd-14-907-2022, https://doi.org/10.5194/essd-14-907-2022, 2022
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The Long-term Gap-free High-resolution Air Pollutant concentration dataset, providing gap-free aerosol optical depth (AOD) and PM2.5 and PM10 concentration with a daily 1 km resolution for 2000–2020 in China, is generated and made publicly available. This is the first long-term gap-free high-resolution aerosol dataset in China and has great potential to trigger multidisciplinary applications in Earth observations, climate change, public health, ecosystem assessment, and environment management.
Linye Song, Shangfeng Chen, Wen Chen, Jianping Guo, Conglan Cheng, and Yong Wang
Atmos. Chem. Phys., 22, 1669–1688, https://doi.org/10.5194/acp-22-1669-2022, https://doi.org/10.5194/acp-22-1669-2022, 2022
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This study shows that in most years when haze pollution (HP) over the North China Plain (NCP) is more (less) serious in winter, air conditions in the following spring are also worse (better) than normal. Conversely, there are some years when HP in the following spring is opposed to that in winter. It is found that North Atlantic sea surface temperature (SST) anomalies play important roles in HP evolution over the NCP. Thus North Atlantic SST is an important preceding signal for NCP HP evolution.
Boming Liu, Jianping Guo, Wei Gong, Yong Zhang, Lijuan Shi, Yingying Ma, Jian Li, Xiaoran Guo, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-26, https://doi.org/10.5194/amt-2022-26, 2022
Publication in AMT not foreseen
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Aeolus is the first satellite mission to directly observe wind profile information on a global scale. However, Aeolus wind products over China were thus far not evaluated by in-situ comparison. This work is the comparison of wind speed on a large scale between the Aeolus, ERA5 and RS , shedding important light on the data application of Aeolus wind products.
Jianping Guo, Jian Zhang, Kun Yang, Hong Liao, Shaodong Zhang, Kaiming Huang, Yanmin Lv, Jia Shao, Tao Yu, Bing Tong, Jian Li, Tianning Su, Steve H. L. Yim, Ad Stoffelen, Panmao Zhai, and Xiaofeng Xu
Atmos. Chem. Phys., 21, 17079–17097, https://doi.org/10.5194/acp-21-17079-2021, https://doi.org/10.5194/acp-21-17079-2021, 2021
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The planetary boundary layer (PBL) is the lowest part of the troposphere, and boundary layer height (BLH) is the depth of the PBL and is of critical importance to the dispersion of air pollution. The study presents the first near-global BLH climatology by using high-resolution (5-10 m) radiosonde measurements. The variations in BLH exhibit large spatial and temporal dependence, with a peak at 17:00 local solar time. The most promising reanalysis product is ERA-5 in terms of modeling BLH.
Seoung Soo Lee, Kyung-Ja Ha, Manguttathil Gopalakrishnan Manoj, Mohammad Kamruzzaman, Hyungjun Kim, Nobuyuki Utsumi, Youtong Zheng, Byung-Gon Kim, Chang Hoon Jung, Junshik Um, Jianping Guo, Kyoung Ock Choi, and Go-Un Kim
Atmos. Chem. Phys., 21, 16843–16868, https://doi.org/10.5194/acp-21-16843-2021, https://doi.org/10.5194/acp-21-16843-2021, 2021
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Using a modeling framework, a midlatitude stratocumulus cloud system is simulated. It is found that cloud mass in the system becomes very low due to interactions between ice and liquid particles compared to that in the absence of ice particles. It is also found that interactions between cloud mass and aerosols lead to a reduction in cloud mass in the system, and this is contrary to an aerosol-induced increase in cloud mass in the absence of ice particles.
Cited articles
Atkinson, G. D. and Holliday, C. R.: Tropical cyclone minimum sea level pressure/maximum sustained wind relationship for the western North Pacific, Mon. Weather Rev., 105, 421–427, https://doi.org/10.1175/1520-0493(1977)105<0421:TCMSLP>2.0.CO;2, 1977.
Bell, B., Hersbach, H., Simmons, A., Berrisford, P., Dahlgren, P., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Radu, R., Schepers, D., and Soci, C.: The ERA5 global reanalysis: Preliminary extension to 1950, Q. J. Roy. Meteor. Soc., 147, 4186–4227, https://doi.org/10.1002/qj.4174, 2021.
Bian, G. F., Nie, G. Z., and Qiu, X.: How well is outer tropical cyclone size represented in the ERA5 reanalysis dataset?, Atmos. Res., 249, 105339, https://doi.org/10.1016/j.atmosres.2020.105339, 2021.
Bloemendaal, N., Haigh, I. D., de Moel, H., Muis, S., Haarsma, R. J., and Aerts, J. C.: Generation of a global synthetic tropical cyclone hazard dataset using STORM, Sci. Data, 7, 40, https://doi.org/10.1038/s41597-020-0381-2, 2020.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Casas, E. G., Tao, D., and Bell, M. M.: An intensity and size phase space for tropical cyclone structure and evolution, J. Geophys. Res.-Atmos., 128, e2022JD037089, https://doi.org/10.1029/2022JD037089, 2023.
Chavas, D. R. and Vigh, J.: QSCAT-R: The QuikSCAT tropical cyclone radial structure dataset, NCAR Tech. Note TN-513+STR, https://doi.org/10.5065/d65b00j3, 2014.
Chavas, D. R., Lin, N., and Emanuel, K.: A model for the complete radial structure of the tropical cyclone wind field. Part I: Comparison with observed structure, J. Atmos. Sci., 72, 3647–3662, https://doi.org/10.1175/JAS-D-15-0014.1, 2015.
Chavas, D. R., Reed, K. A., and Knaff, J. A.: Physical understanding of the tropical cyclone wind-pressure relationship, Nat. Commun., 8, 1360, https://doi.org/10.1038/s41467-017-01546-9, 2017.
Chu, P. S.: ENSO and tropical cyclone activity, in: Hurricanes and typhoons: Past, present, and potential, 297–332, https://www.soest.hawaii.edu/MET/Hsco/publications/2004.2.pdf (last access: 16 December 2024), 2004.
CRED: 2023 Disasters in Numbers: A Significant Year of Disaster Impact, Université catholique de Louvain (UCL) – CRED, Brussels, Belgium, https://files.emdat.be/reports/2023_EMDAT_report.pdf (last access: 16 December 2024), 2023.
DeMaria, M.: Tropical cyclone track prediction with a barotropic spectral model, Mon. Weather Rev., 115, 2346–2357, https://doi.org/10.1175/1520-0493(1987)115<2346:TCTPWA>2.0.CO;2, 1987.
Demuth, J. L., DeMaria, M., and Knaff, J. A.: Improvement of Advanced Microwave Sounding Unit tropical cyclone intensity and size estimation algorithms, J. Appl. Meteorol. Clim., 45, 1573–1581, https://doi.org/10.1175/JAM2429.1, 2006.
Dulac, W., Cattiaux, J., Chauvin, F., Bourdin, S., and Fromang, S.: Assessing the representation of tropical cyclones in ERA5 with the CNRM tracker, Clim. Dynam., 62, 223–238, https://doi.org/10.1007/s00382-023-06902-8, 2024.
Emanuel, K. and Rotunno, R.: Self-stratification of tropical cyclone outflow. Part I: Implications for storm structure, J. Atmos. Sci., 68, 2236–2249, https://doi.org/10.1175/JAS-D-10-05024.1, 2011.
Eusebi, R., Vecchi, G. A., Lai, C. Y., and Tong, M.: Realistic tropical cyclone wind and pressure fields can be reconstructed from sparse data using deep learning, Commun. Earth Environ., 5, 8, https://doi.org/10.1038/s43247-023-01144-2, 2024.
Frisius, T., Schönemann, D., and Vigh, J.: The impact of gradient wind imbalance on potential intensity of tropical cyclones in an unbalanced slab boundary layer model, J. Atmos. Sci., 70, 1874–1890, https://doi.org/10.1175/JAS-D-12-0160.1, 2013.
Gahtan, J., Knapp, K. R., Schreck, C. J., Diamond, H. J., Kossin, J. P., and Kruk, M. C.: International Best Track Archive for Climate Stewardship (IBTrACS) Project, Version 4r01, NOAA National Centers for Environmental Information, https://doi.org/10.25921/82ty-9e16, 2024.
Geiger, T., Frieler, K., and Bresch, D. N.: A global historical data set of tropical cyclone exposure (TCE-DAT), Earth Syst. Sci. Data, 10, 185–194, https://doi.org/10.5194/essd-10-185-2018, 2018.
Gori, A., Lin, N., Schenkel, B., and Chavas, D.: North Atlantic Tropical Cyclone Size and Storm Surge Reconstructions From 1950–Present, J. Geophys. Res.-Atmos., 128, e2022JD037312, https://doi.org/10.1029/2022JD037312, 2023.
Gray, W. M.: Global view of the origin of tropical disturbances and storms, Mon. Weather Rev., 96, 669–700, https://doi.org/10.1175/1520-0493(1968)096<0669:GVOTOO>2.0.CO;2, 1968.
Gualdi, S., Scoccimarro, E., and Navarra, A.: Changes in tropical cyclone activity due to global warming: Results from a high-resolution coupled general circulation model, J. Climate, 21, 5204–5228, https://doi.org/10.1175/2008JCLI1921.1, 2008.
Guo, J., Zhang, J., Shao, J., Chen, T., Bai, K., Sun, Y., Li, N., Wu, J., Li, R., Li, J., Guo, Q., Cohen, J. B., Zhai, P., Xu, X., and Hu, F.: A merged continental planetary boundary layer height dataset based on high-resolution radiosonde measurements, ERA5 reanalysis, and GLDAS, Earth Syst. Sci. Data, 16, 1–14, https://doi.org/10.5194/essd-16-1-2024, 2024.
Harper, B.: Tropical Cyclone Parameter Estimation in the Australian Region: Wind-Pressure Relationships and Related Issues for Engineering Planning and Design – A Discussion Paper, Systems Engineering Australia Pty Ltd for Woodside Energy Ltd, Perth, https://doi.org/10.13140/RG.2.2.13057.04961, 2002.
Hatsushika, H., Tsutsui, J., Fiorino, M., and Onogi, K.: Impact of wind profile retrievals on the analysis of tropical cyclones in the JRA-25 reanalysis, J. Meteorol. Soc. Jpn. Ser. II, 84, 891–905, https://doi.org/10.2151/jmsj.84.891, 2006.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Gebhardt, C., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., and Dee, D.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023a.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023b.
Hill, K. A. and Lackmann, G. M.: Influence of environmental humidity on tropical cyclone size, Mon. Weather Rev., 137, 3294–3315, https://doi.org/10.1175/2009MWR2679.1, 2009.
Holland, G. J.: An analytic model of the wind and pressure profiles in hurricanes, Mon. Weather Rev., 108, 1212–1218, https://doi.org/10.1175/1520-0493(1980)108<1212:AAMOTW>2.0.CO;2, 1980.
Kistler, R., Kalnay, E., Collins, W., Saha, S., White, G., Woollen, J., Chelliah, M., Ebisuzaki, W., Kanamitsu, M., Kousky, V., van den Dool, H., Jenne, R., and Fiorino, M.: The NCEP–NCAR 50 year reanalysis: monthly means CD-ROM and documentation, B. Am. Meteorol. Soc., 82, 247–268, https://doi.org/10.1175/1520-0477(2001)082<0247:TNNYRM>2.3.CO;2, 2001.
Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., and Neumann, C. J.: The international best track archive for climate stewardship (IBTrACS) unifying tropical cyclone data, B. Am. Meteorol. Soc., 91, 363–376, https://doi.org/10.1175/2009BAMS2755.1, 2010.
Knutson, T., Camargo, S. J., Chan, J. C., Emanuel, K., Ho, C. H., Kossin, J., Mohapatra, M., Satoh, M., Sugi, M., Walsh, K., and Wu, L.: Tropical cyclones and climate change assessment: Part I: Detection and attribution, B. Am. Meteorol. Soc., 100, 1987–2007, https://doi.org/10.1175/BAMS-D-18-0189.1, 2019.
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.: The JRA-55 reanalysis: General specifications and basic characteristics, J. Meteorol. Soc. Jpn. Ser. II, 93, 5–48, https://doi.org/10.2151/jmsj.2015-001, 2015.
Li, X., Han, X., Yang, J., Wang, J., and Han, G.: Transfer learning-based generative adversarial network model for tropical cyclone wind speed reconstruction from SAR images, IEEE T. Geosci. Remote, 62, 1–16, https://doi.org/10.1109/TGRS.2024.3390392, 2024.
Lin, N. and Chavas, D.: On hurricane parametric wind and applications in storm surge modeling, J. Geophys. Res.-Atmos., 117, D09120, https://doi.org/10.1029/2011JD017126, 2012.
Liu, K. S. and Chan, J. C. L.: Size of tropical cyclones as inferred from ERS-1 and ERS-2 data, Mon. Weather Rev., 127, 2992–3001, https://doi.org/10.1175/1520-0493(1999)127<2992:SOTCAI>2.0.CO;2, 1999.
Magnusson, L., Majumdar, S., Emerton, R., Richardson, D., Alonso-Balmaseda, M., Baugh, C., Bechtold, P., Bidlot, J., Bonanni, A., Bonavita, M., Bormann, N., Brown, A., Browne, P., Carr, H., Dahoui, M., De Chiara, G., Diamantakis, M., Duncan, D., English, S., Forbes, R., Geer, A., Haiden, T., Healy, S., Hewson, T., Ingleby, B., Janousek, M., Kuehnlein, C., Lang, S., Lock, S.-J., McNally, T., Mogensen, K., Pappenberger, F., Polichtchouk, I., Prates, F., Prudhomme, C., Rabier, F., de Rosnay, P., Quintino, T., Rennie, M., Titley, H., Vana, F., Vitart, F., Warrick, F., Wedi, N., and Zsoter, E.: Tropical cyclone activities at ECMWF, ECMWF Tech. Memo., ECMWF, University of Miami, https://doi.org/10.21957/zzxzzygwv, 2021.
Mei, W. and Xie, S. P.: Intensification of landfalling typhoons over the northwest Pacific since the late 1970s, Nat. Geosci., 9, 753–757, https://doi.org/10.1038/ngeo2792, 2016.
Mo, Y., Simard, M., and Hall, J. W.: Tropical cyclone risk to global mangrove ecosystems: potential future regional shifts, Front. Ecol. Environ., 21, 269–274, https://doi.org/10.1002/fee.2650, 2023.
Pérez-Alarcón, A., Sorí, R., Fernández-Alvarez, J. C., Nieto, R., and Gimeno, L.: Comparative climatology of outer tropical cyclone size using radial wind profiles, Weather Clim. Extremes, 33, 100366, https://doi.org/10.1016/j.wace.2021.100366, 2021.
Radu, R., Toumi, R., and Phau, J.: Influence of atmospheric and sea surface temperature on the size of hurricane Catarina, Q. J. Roy. Meteor. Soc., 140, 1778–1784, https://doi.org/10.1002/qj.2232, 2014.
Ren, H., Dudhia, J., and Li, H.: The size characteristics and physical explanation for the radius of maximum wind of hurricanes, Atmos. Res., 277, 106313, https://doi.org/10.1016/j.atmosres.2022.106313, 2022.
Schenkel, B. A. and Hart, R. E.: An examination of tropical cyclone position, intensity, and intensity life cycle within atmospheric reanalysis datasets, J. Climate, 25, 3453–3475, https://doi.org/10.1175/2011JCLI4208.1, 2012.
Schenkel, B. A., Lin, N., and Chavas, D.: Evaluating outer tropical cyclone size in reanalysis datasets using QuikSCAT data, J. Climate, 30, 8745–8762, https://doi.org/10.1175/JCLI-D-17-0122.1, 2017.
Simpson, R. H.: The hurricane disaster – Potential scale, Weatherwise, 27, 169–186, https://doi.org/10.1080/00431672.1974.9931702, 1974.
Sun, Y., Zhong, Z., Ha, Y., Wang, Y., and Wang, X.: The dynamic and thermodynamic effects of relative and absolute sea surface temperature on tropical cyclone intensity, Acta Meteorol. Sin., 27, 40–49, https://doi.org/10.1007/s13351-013-0105-z, 2013.
Sun, Y., Zhong, Z., Yi, L., Ha, Y., and Sun, Y.: The opposite effects of inner and outer sea surface temperature on tropical cyclone intensity, J. Geophys. Res.-Atmos., 119, 2193–2208, https://doi.org/10.1002/2013jd021354, 2014.
Thompson, D. T., Keim, B. D., and Brown, V. M.: Construction of a tropical cyclone size dataset using reanalysis data, Int. J. Climatol., 44, 3028–3053, https://doi.org/10.1002/joc.8511, 2024.
Vincent, E. M., Emanuel, K. A., Lengaigne, M., Vialard, J., and Madec, G.: Influence of upper ocean stratification interannual variability on tropical cyclones, J. Adv. Model. Earth Sy., 6, 680–699, https://doi.org/10.1002/2014MS000327, 2014.
Walsh, K. J. E., McBride, J. L., Klotzbach, P. J., Balachandran, S., Camargo, S. J., Holland, G., Knutson, T. R., Kossin, J. P., Lee, T.-C., Sobel, A., and Sugi, M.: Tropical cyclones and climate change, WIRes Clim. Change, 7, 65–89, https://doi.org/10.1002/wcc.371, 2016.
Weber, H. C., Lok, C. C. F., Davidson, N. E., and Xiao, Y.: Objective estimation of the radius of the outermost closed isobar in tropical cyclones, Trop. Cyclone Res. Rev., 3, 1–21, https://doi.org/10.6057/2014TCRR01.01, 2014.
Webster, P. J., Holland, G. J., Curry, J. A., and Chang, H. R.: Changes in tropical cyclone number, duration, and intensity in a warming environment, Science, 309, 1844–1846, https://doi.org/10.1126/science.1116448, 2005.
Willoughby, H. E., Darling, R. W. R., and Rahn, M. E.: Parametric representation of the primary hurricane vortex. Part II: A new family of sectionally continuous profiles, Mon. Weather Rev., 134, 1102–1120, https://doi.org/10.1175/MWR3106.1, 2006.
Wright, C. J.: Quantifying the global impact of tropical cyclone-associated gravity waves using HIRDLS, MLS, SABER and IBTrACS data, Q. J. Roy. Meteor. Soc., 145, 3023–3039, https://doi.org/10.1002/qj.3602, 2019.
Wu, L., Zhao, H., Wang, C., Cao, J., and Liang, J.: Understanding of the effect of climate change on tropical cyclone intensity: A Review, Adv. Atmos. Sci., 39, 205–221, https://doi.org/10.1007/s00376-021-1026-x, 2022.
Xu, Z., Sun, Y., Li, T., Zhong, Z., Liu, J., and Ma, C.: Tropical cyclone size change under ocean warming and associated responses of tropical cyclone destructiveness: idealized experiments, J. Meteorol. Res.-PRC, 34, 163–175, https://doi.org/10.1007/s13351-020-8164-4, 2020.
Xu, Z., Guo, J., Zhang, G., Ye, Y., Zhao, H., and Chen, H.: Global tropical cyclone size and intensity reconstruction dataset for 1959–2022 based on IBTrACS and ERA5 data, Zenodo [data set], https://doi.org/10.5281/zenodo.13919874, 2024.
Yang, Q., Lee, C. Y., Tippett, M. K., Chavas, D. R., and Knutson, T. R.: Machine learning–based hurricane wind reconstruction, Weather Forecast., 37, 477–493, https://doi.org/10.1175/WAF-D-21-0077.1, 2022.
Yeasmin, A., Chand, S., and Sultanova, N.: Reconstruction of tropical cyclone and depression proxies for the South Pacific since the 1850s, Weather Clim. Extremes, 39, 100543, https://doi.org/10.1016/j.wace.2022.100543, 2023.
Zhuo, J. Y. and Tan, Z. M.: A Deep-Learning Reconstruction of Tropical Cyclone Size Metrics 1981–2017: Examining Trends, J. Climate, 36, 5103–5123, https://doi.org/10.1175/JCLI-D-22-0714.1, 2023.
Zick, S. E. and Matyas, C. J.: Tropical cyclones in the North American Regional Reanalysis: The impact of satellite-derived precipitation over ocean, J. Geophys. Res.-Atmos., 120, 8724–8742, https://doi.org/10.1002/2015JD023722, 2015.
Short summary
Tropical cyclones (TCs) are powerful weather systems that can cause extreme disasters. Here we generate a global long-term TC size and intensity reconstruction dataset, covering a time period from 1959 to 2022, with a 3 h temporal resolution, using machine learning models. These can be valuable for filling observational data gaps and advancing our understanding of TC climatology, thereby facilitating risk assessments and defenses against TC-related disasters.
Tropical cyclones (TCs) are powerful weather systems that can cause extreme disasters. Here we...
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