Articles | Volume 16, issue 5
https://doi.org/10.5194/essd-16-2425-2024
© 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-16-2425-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
22 May 2024
Data description paper |
| 22 May 2024
| 22 May 2024
LGHAP v2: a global gap-free aerosol optical depth and PM2.5 concentration dataset since 2000 derived via big Earth data analytics
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Institute of Eco-Chongming, 20 Cuiniao Rd., Chongming, Shanghai 202162, China
Ke Li
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Liuqing Shao
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Xinran Li
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Chaoshun Liu
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Zhengqiang Li
State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Mingliang Ma
School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
Di Han
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Yibing Sun
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Zhe Zheng
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Ruijie Li
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Ni-Bin Chang
Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, United States of America
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China
Related authors
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Jianping Guo, Boming Liu, Wei Gong, Lijuan Shi, Yong Zhang, Yingying Ma, Jian Zhang, Tianmeng Chen, Kaixu Bai, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Chem. Phys., 21, 2945–2958, https://doi.org/10.5194/acp-21-2945-2021, https://doi.org/10.5194/acp-21-2945-2021, 2021
Short summary
Short summary
Vertical wind profiles are crucial to a wide range of atmospheric disciplines. Aeolus is the first satellite mission to directly observe wind profile information on a global scale. However, Aeolus wind products over China have thus far not been evaluated by in situ comparison. This work is expected to let the public and science community better know the Aeolus wind products and to encourage use of these valuable data in future research and applications.
Kaixu Bai, Ke Li, Chengbo Wu, Ni-Bin Chang, and Jianping Guo
Earth Syst. Sci. Data, 12, 3067–3080, https://doi.org/10.5194/essd-12-3067-2020, https://doi.org/10.5194/essd-12-3067-2020, 2020
Short summary
Short summary
PM2.5 data from the national air quality monitoring network in China suffered from significant inconsistency and inhomogeneity issues. To create a coherent PM2.5 concentration dataset to advance our understanding of haze pollution and its impact on weather and climate, we homogenized this PM2.5 dataset between 2015 and 2019 after filling in the data gaps. The homogenized PM2.5 data is found to better characterize the variation of aerosol in space and time compared to the original dataset.
Kaixu Bai, Ke Li, Jianping Guo, Yuanjian Yang, and Ni-Bin Chang
Atmos. Meas. Tech., 13, 1213–1226, https://doi.org/10.5194/amt-13-1213-2020, https://doi.org/10.5194/amt-13-1213-2020, 2020
Short summary
Short summary
A novel gap-filling method called the diurnal-cycle-constrained empirical orthogonal function (DCCEOF) is proposed. Cross validation indicates that this method gives high accuracy in predicting missing values in daily PM2.5 time series by accounting for the local diurnal phases, especially by reconstructing daily extrema that cannot be accurately restored by other approaches. The DCCEOF method can be easily applied to other data sets because of its self-consistent capability.
Juan Zhao, Jianping Guo, and Xiaohui Zheng
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-194, https://doi.org/10.5194/gmd-2024-194, 2024
Preprint under review for GMD
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Zhiqi Xu, Jianping Guo, Guwei Zhang, Yuchen Ye, Haikun Zhao, and Haishan Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-329, https://doi.org/10.5194/essd-2024-329, 2024
Revised manuscript accepted for ESSD
Short summary
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-hour temporal resolution, using machine learning model. These can be valuable for filling observational data gaps, advancing our understanding of TC climatology, thereby facilitating risk assessments and defenses against TC-related disasters.
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
Short summary
Short summary
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.
Wenxin Zhao, Yu Zhao, Yu Zheng, Dong Chen, Jinyuan Xin, Kaitao Li, Huizheng Che, Zhengqiang Li, Mingrui Ma, and Yun Hang
Atmos. Chem. Phys., 24, 6593–6612, https://doi.org/10.5194/acp-24-6593-2024, https://doi.org/10.5194/acp-24-6593-2024, 2024
Short summary
Short summary
We evaluate the long-term (2000–2020) variabilities of aerosol absorption optical depth, black carbon emissions, and associated health risks in China with an integrated framework that combines multiple observations and modeling techniques. We demonstrate the remarkable emission abatement resulting from the implementation of national pollution controls and show how human activities affected the emissions with a spatiotemporal heterogeneity, thus supporting differentiated policy-making by region.
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
Short summary
Short summary
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.
Ouyang Liu, Zhengqiang Li, Yangyan Lin, Cheng Fan, Ying Zhang, Kaitao Li, Peng Zhang, Yuanyuan Wei, Tianzeng Chen, Jiantao Dong, and Gerrit de Leeuw
Atmos. Meas. Tech., 17, 377–395, https://doi.org/10.5194/amt-17-377-2024, https://doi.org/10.5194/amt-17-377-2024, 2024
Short summary
Short summary
Nitrogen dioxide (NO2) is a trace gas which is important for atmospheric chemistry and may affect human health. To understand processes leading to harmful concentrations, it is important to monitor NO2 concentrations near the surface and higher up. To this end, a Pandora instrument has been installed in Beijing. An overview of the first year of data shows the large variability on diurnal to seasonal timescales and how this is affected by wind speed and direction and chemistry.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Seoung Soo Lee, Chang-Hoon Jung, Young Jun Yoon, Junshik Um, Youtong Zheng, Jianping Guo, Manguttathil G. Manoj, and Sang-Keun Song
EGUsphere, https://doi.org/10.5194/egusphere-2023-862, https://doi.org/10.5194/egusphere-2023-862, 2023
Short summary
Short summary
This study is motivated by the fact that there are no general factors that represent the overall properties of mixed-phase clouds. The absence of these factors contributes to the high uncertainty in the prediction of climate change. Hence, this study finds a general factor that explains differences in the properties of different mixed-phase clouds, using a modeling tool. This factor is useful to develop a general way of using climate models to better predict climate change.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Jie Luo, Zhengqiang Li, Chenchong Zhang, Qixing Zhang, Yongming Zhang, Ying Zhang, Gabriele Curci, and Rajan K. Chakrabarty
Atmos. Chem. Phys., 22, 7647–7666, https://doi.org/10.5194/acp-22-7647-2022, https://doi.org/10.5194/acp-22-7647-2022, 2022
Short summary
Short summary
The fractal black carbon was applied to re-evaluate the regional impacts of morphologies on aerosol–radiation interactions (ARIs), and the effects were compared between the US and China. The regional-mean clear-sky ARI is significantly affected by the BC morphology, and relative differences of 17.1 % and 38.7 % between the fractal model with a Df of 1.8 and the spherical model were observed in eastern China and the northwest US, respectively.
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
Short summary
Short summary
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.
Jie Luo, Zhengqiang Li, Cheng Fan, Hua Xu, Ying Zhang, Weizhen Hou, Lili Qie, Haoran Gu, Mengyao Zhu, Yinna Li, and Kaitao Li
Atmos. Meas. Tech., 15, 2767–2789, https://doi.org/10.5194/amt-15-2767-2022, https://doi.org/10.5194/amt-15-2767-2022, 2022
Short summary
Short summary
A single model is difficult to represent various shapes of dust. We proposed a tunable model to represent dust with various shapes. Two tunable parameters were used to represent the effects of the erosion degree and binding forces from the mass center. Thus, the model can represent various dust shapes by adjusting the tunable parameters. Besides, the applicability of the spheroid model in calculating the optical properties and polarimetric characteristics is evaluated.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Ifeanyichukwu C. Nduka, Chi-Yung Tam, Jianping Guo, and Steve Hung Lam Yim
Atmos. Chem. Phys., 21, 13443–13454, https://doi.org/10.5194/acp-21-13443-2021, https://doi.org/10.5194/acp-21-13443-2021, 2021
Short summary
Short summary
This study analyzed the nature, mechanisms and drivers for hot-and-polluted episodes (HPEs) in the Pearl River Delta, China. A total of eight HPEs were identified and can be grouped into three clusters of HPEs that were respectively driven (1) by weak subsidence and convection induced by approaching tropical cyclones, (2) by calm conditions with low wind speed in the lower atmosphere and (3) by the combination of both aforementioned conditions.
Cheng Fan, Zhengqiang Li, Ying Li, Jiantao Dong, Ronald van der A, and Gerrit de Leeuw
Atmos. Chem. Phys., 21, 7723–7748, https://doi.org/10.5194/acp-21-7723-2021, https://doi.org/10.5194/acp-21-7723-2021, 2021
Short summary
Short summary
Emission control policy in China has resulted in the decrease of nitrogen dioxide concentrations, which however leveled off and stabilized in recent years, as shown from satellite data. The effects of the further emission reduction during the COVID-19 lockdown in 2020 resulted in an initial improvement of air quality, which, however, was offset by chemical and meteorological effects. The study shows the regional dependence over east China, and results have a wider application than China only.
Tianmeng Chen, Zhanqing Li, Ralph A. Kahn, Chuanfeng Zhao, Daniel Rosenfeld, Jianping Guo, Wenchao Han, and Dandan Chen
Atmos. Chem. Phys., 21, 6199–6220, https://doi.org/10.5194/acp-21-6199-2021, https://doi.org/10.5194/acp-21-6199-2021, 2021
Short summary
Short summary
A convective cloud identification process is developed using geostationary satellite data from Himawari-8.
Convective cloud fraction is generally larger before noon and smaller in the afternoon under polluted conditions, but megacities and complex topography can influence the pattern.
A robust relationship between convective cloud and aerosol loading is found. This pattern varies with terrain height and is modulated by varying thermodynamic, dynamical, and humidity conditions during the day.
Wenyuan Chang, Ying Zhang, Zhengqiang Li, Jie Chen, and Kaitao Li
Atmos. Chem. Phys., 21, 4403–4430, https://doi.org/10.5194/acp-21-4403-2021, https://doi.org/10.5194/acp-21-4403-2021, 2021
Short summary
Short summary
Aerosol simulation in WRF-Chem often uses the MOSAIC aerosol mechanism. Still, we need variational data assimilation (DA) for the MOSAIC aerosols to blend aerosol optical measurements. This study provides a developed GSI variational DA system, with a tangent linear operator designed for multi-source and multi-wavelength aerosol optical measurements. We successfully applied the DA system in an aerosol field campaign to assimilate aerosol optical data in northwestern China.
Yang Zhang, Zhengqiang Li, Zhihong Liu, Yongqian Wang, Lili Qie, Yisong Xie, Weizhen Hou, and Lu Leng
Atmos. Meas. Tech., 14, 1655–1672, https://doi.org/10.5194/amt-14-1655-2021, https://doi.org/10.5194/amt-14-1655-2021, 2021
Short summary
Short summary
The aerosol fine-mode fraction (FMF) is an important parameter reflecting the content of man-made aerosols. This study carried out the retrieval of FMF in China based on multi-angle polarization data and validated the results. The results of this study can contribute to the FMF retrieval algorithm of multi-angle polarization sensors. At the same time, a high-precision FMF dataset of China was obtained, which can provide basic data for atmospheric environment research.
Jianping Guo, Boming Liu, Wei Gong, Lijuan Shi, Yong Zhang, Yingying Ma, Jian Zhang, Tianmeng Chen, Kaixu Bai, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Chem. Phys., 21, 2945–2958, https://doi.org/10.5194/acp-21-2945-2021, https://doi.org/10.5194/acp-21-2945-2021, 2021
Short summary
Short summary
Vertical wind profiles are crucial to a wide range of atmospheric disciplines. Aeolus is the first satellite mission to directly observe wind profile information on a global scale. However, Aeolus wind products over China have thus far not been evaluated by in situ comparison. This work is expected to let the public and science community better know the Aeolus wind products and to encourage use of these valuable data in future research and applications.
Yuan Gao, Lili Yao, Ni-Bin Chang, and Dingbao Wang
Hydrol. Earth Syst. Sci., 25, 945–956, https://doi.org/10.5194/hess-25-945-2021, https://doi.org/10.5194/hess-25-945-2021, 2021
Short summary
Short summary
Mean annual runoff prediction is of great interest but still poses a challenge in ungauged basins. The purpose of this study is to diagnose the data requirement for predicting mean annual runoff in ungauged basins based on a water balance model, in which the effects of climate variability are explicitly represented. The performance of predicting mean annual runoff can be improved by employing better estimation of soil water storage capacity including the effects of soil, topography, and bedrock.
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. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-41, https://doi.org/10.5194/acp-2021-41, 2021
Revised manuscript not accepted
Short summary
Short summary
Vertical wind profiles are crucial to a wide range of atmospheric disciplines. 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 expected to let the public and science community better know the Aeolus wind products and to encourage use of these valuable data in future researches and applications.
Kaixu Bai, Ke Li, Chengbo Wu, Ni-Bin Chang, and Jianping Guo
Earth Syst. Sci. Data, 12, 3067–3080, https://doi.org/10.5194/essd-12-3067-2020, https://doi.org/10.5194/essd-12-3067-2020, 2020
Short summary
Short summary
PM2.5 data from the national air quality monitoring network in China suffered from significant inconsistency and inhomogeneity issues. To create a coherent PM2.5 concentration dataset to advance our understanding of haze pollution and its impact on weather and climate, we homogenized this PM2.5 dataset between 2015 and 2019 after filling in the data gaps. The homogenized PM2.5 data is found to better characterize the variation of aerosol in space and time compared to the original dataset.
Qiaoyun Hu, Haofei Wang, Philippe Goloub, Zhengqiang Li, Igor Veselovskii, Thierry Podvin, Kaitao Li, and Mikhail Korenskiy
Atmos. Chem. Phys., 20, 13817–13834, https://doi.org/10.5194/acp-20-13817-2020, https://doi.org/10.5194/acp-20-13817-2020, 2020
Short summary
Short summary
This study presents the characteristics of Taklamakan dust particles derived from lidar measurements collected in the dust aerosol observation field campaign. It provides comprehensive parameters for Taklamakan dust properties and vertical distributions of Taklamakan dust. This paper also points out the importance of polluted dust which was frequently observed in the field campaign. The results contribute to improving knowledge about dust and reducing uncertainties in the climatic model.
Ying Zhang, Zhengqiang Li, Yu Chen, Gerrit de Leeuw, Chi Zhang, Yisong Xie, and Kaitao Li
Atmos. Chem. Phys., 20, 12795–12811, https://doi.org/10.5194/acp-20-12795-2020, https://doi.org/10.5194/acp-20-12795-2020, 2020
Short summary
Short summary
Observation of atmospheric aerosol components plays an important role in reducing uncertainty in climate assessment. In this study, an improved remote sensing method which can better distinguish scattering components is developed, and the aerosol components in the atmospheric column over China are retrieved based on the Sun–sky radiometer Observation NETwork (SONET). The component distribution shows there could be a sea salt component in northwest China from a paleomarine source in desert land.
Yang Yang, Min Chen, Xiujuan Zhao, Dan Chen, Shuiyong Fan, Jianping Guo, and Shaukat Ali
Atmos. Chem. Phys., 20, 12527–12547, https://doi.org/10.5194/acp-20-12527-2020, https://doi.org/10.5194/acp-20-12527-2020, 2020
Short summary
Short summary
This study analyzed the impacts of aerosol–radiation interaction on radiation and meteorological forecasts using the offline coupling of WRF and high-frequency updated AOD simulated by WRF-Chem. The results revealed that aerosol–radiation interaction had a positive influence on the improvement of predictive accuracy, including 2 m temperature (~ 73.9 %) and horizontal wind speed (~ 7.8 %), showing potential prospects for its application in regional numerical weather prediction in northern China.
Ruqian Miao, Qi Chen, Yan Zheng, Xi Cheng, Yele Sun, Paul I. Palmer, Manish Shrivastava, Jianping Guo, Qiang Zhang, Yuhan Liu, Zhaofeng Tan, Xuefei Ma, Shiyi Chen, Limin Zeng, Keding Lu, and Yuanhang Zhang
Atmos. Chem. Phys., 20, 12265–12284, https://doi.org/10.5194/acp-20-12265-2020, https://doi.org/10.5194/acp-20-12265-2020, 2020
Short summary
Short summary
In this study we evaluated the model performances for simulating secondary inorganic aerosol (SIA) and organic aerosol (OA) in PM2.5 in China against comprehensive datasets. The potential biases from factors related to meteorology, emission, chemistry, and atmospheric removal are systematically investigated. This study provides a comprehensive understanding of modeling PM2.5, which is important for studies on the effectiveness of emission control strategies.
Li Li, Zhengqiang Li, Wenyuan Chang, Yang Ou, Philippe Goloub, Chengzhe Li, Kaitao Li, Qiaoyun Hu, Jianping Wang, and Manfred Wendisch
Atmos. Chem. Phys., 20, 10845–10864, https://doi.org/10.5194/acp-20-10845-2020, https://doi.org/10.5194/acp-20-10845-2020, 2020
Short summary
Short summary
Dust Aerosol Observation-Kashi (DAO-K) campaign was conducted near the Taklimakan Desert in April 2019 to obtain comprehensive aerosol, atmosphere, and surface parameters. Estimations of aerosol solar radiative forcing by a radiative transfer (RT) model were improved based on the measured aerosol parameters, additionally considering atmospheric profiles and diurnal variations of surface albedo. RT simulations agree well with simultaneous irradiance observations, even in dust-polluted conditions.
Boming Liu, Jianping Guo, Wei Gong, Lijuan Shi, Yong Zhang, and Yingying Ma
Atmos. Meas. Tech., 13, 4589–4600, https://doi.org/10.5194/amt-13-4589-2020, https://doi.org/10.5194/amt-13-4589-2020, 2020
Short summary
Short summary
Vertical wind profiles are crucial to a wide range of atmospheric disciplines. However, the wind profile across China remains poorly understood. Here we reveal the salient features of winds from the radar wind profile of China, including the main instruments, spatial coverage and sampling frequency. This work is expected to allow the public and scientific community to be more familiar with the nationwide network and encourage the use of these valuable data in future research and applications.
Haofei Wang, Zhengqiang Li, Yang Lv, Ying Zhang, Hua Xu, Jianping Guo, and Philippe Goloub
Atmos. Chem. Phys., 20, 8839–8854, https://doi.org/10.5194/acp-20-8839-2020, https://doi.org/10.5194/acp-20-8839-2020, 2020
Short summary
Short summary
Lidar shows good performance in calculating the convective layer height in the daytime and the residual layer height at night, as well as having the potential to describe the stable layer height at night. The MLH seasonal change in Beijing indicates that it is low in winter and autumn and high in spring and summer. From 2014 to 2018, the magnitude of the diurnal cycle of MLH increased year by year. MLH from lidar shows better accuracy than a radiosonde when calculating surface pollution.
Haipeng Lin, Xu Feng, Tzung-May Fu, Heng Tian, Yaping Ma, Lijuan Zhang, Daniel J. Jacob, Robert M. Yantosca, Melissa P. Sulprizio, Elizabeth W. Lundgren, Jiawei Zhuang, Qiang Zhang, Xiao Lu, Lin Zhang, Lu Shen, Jianping Guo, Sebastian D. Eastham, and Christoph A. Keller
Geosci. Model Dev., 13, 3241–3265, https://doi.org/10.5194/gmd-13-3241-2020, https://doi.org/10.5194/gmd-13-3241-2020, 2020
Short summary
Short summary
Online coupling of meteorology and chemistry models often presents maintenance issues with hard-wired coding. We present WRF-GC, an one-way online coupling of the WRF meteorological model and GEOS-Chem atmospheric chemistry model for regional atmospheric chemistry and air quality modeling. Our coupling structure allows future versions of either parent model to be immediately integrated into WRF-GC. The WRF-GC model was able to well reproduce regional PM2.5 with greater computational efficiency.
Wenchao Han, Zhanqing Li, Fang Wu, Yuwei Zhang, Jianping Guo, Tianning Su, Maureen Cribb, Jiwen Fan, Tianmeng Chen, Jing Wei, and Seoung-Soo Lee
Atmos. Chem. Phys., 20, 6479–6493, https://doi.org/10.5194/acp-20-6479-2020, https://doi.org/10.5194/acp-20-6479-2020, 2020
Short summary
Short summary
Observational data and model simulation were used to analyze the daytime urban heat island intensity (UHII) under polluted and clean conditions in China. We found that aerosols reduce the UHII in summer but increase the UHII in winter. Two mechanisms, the aerosol radiative effect (ARE) and the aerosol dynamic effect (ADE), behave differently in summer and winter. In summer, the UHII is mainly affected by the ARE, and the ADE is weak, and the opposite is the case in winter.
Tianning Su, Zhanqing Li, Chengcai Li, Jing Li, Wenchao Han, Chuanyang Shen, Wangshu Tan, Jing Wei, and Jianping Guo
Atmos. Chem. Phys., 20, 3713–3724, https://doi.org/10.5194/acp-20-3713-2020, https://doi.org/10.5194/acp-20-3713-2020, 2020
Short summary
Short summary
We study the role of aerosol vertical distribution in thermodynamic stability and PBL development. Under different aerosol vertical structures, the diurnal cycles of PBLH and PM2.5 show distinct characteristics. Large differences in the heating rate affect atmospheric buoyancy and stability differently under different aerosol structures. As a result, the aerosol–PBL interaction can be strengthened by the inverse aerosol structure and potentially neutralized by the decreasing structure.
Jing Wei, Zhanqing Li, Maureen Cribb, Wei Huang, Wenhao Xue, Lin Sun, Jianping Guo, Yiran Peng, Jing Li, Alexei Lyapustin, Lei Liu, Hao Wu, and Yimeng Song
Atmos. Chem. Phys., 20, 3273–3289, https://doi.org/10.5194/acp-20-3273-2020, https://doi.org/10.5194/acp-20-3273-2020, 2020
Short summary
Short summary
This study introduced an enhanced space–time extremely randomized trees (STET) approach to improve the 1 km resolution ground-level PM2.5 estimates across China using the remote sensing technology. The STET model shows high accuracy and strong predictive power and appears to outperform most models reported by previous studies. Thus, it is of great importance for future air pollution studies at medium- or small-scale areas and will be applied to generate the historical PM2.5 dataset across China.
Kaixu Bai, Ke Li, Jianping Guo, Yuanjian Yang, and Ni-Bin Chang
Atmos. Meas. Tech., 13, 1213–1226, https://doi.org/10.5194/amt-13-1213-2020, https://doi.org/10.5194/amt-13-1213-2020, 2020
Short summary
Short summary
A novel gap-filling method called the diurnal-cycle-constrained empirical orthogonal function (DCCEOF) is proposed. Cross validation indicates that this method gives high accuracy in predicting missing values in daily PM2.5 time series by accounting for the local diurnal phases, especially by reconstructing daily extrema that cannot be accurately restored by other approaches. The DCCEOF method can be easily applied to other data sets because of its self-consistent capability.
Zhen Liu, Yi Ming, Chun Zhao, Ngar Cheung Lau, Jianping Guo, Massimo Bollasina, and Steve Hung Lam Yim
Atmos. Chem. Phys., 20, 223–241, https://doi.org/10.5194/acp-20-223-2020, https://doi.org/10.5194/acp-20-223-2020, 2020
Short summary
Short summary
OH and HO2 radicals are important trace constituents of the atmosphere that are closely coupled via several types of reaction. This paper describes a new laboratory method to simultaneously determine OH kinetics and HO2 yields from chemical processes. The instrument also provides some time resolution on HO2 detection allowing one to separate HO2 produced from the target reaction from HO2 arising from secondary chemistry. Examples of applications are presented.
Lei Li, Oleg Dubovik, Yevgeny Derimian, Gregory L. Schuster, Tatyana Lapyonok, Pavel Litvinov, Fabrice Ducos, David Fuertes, Cheng Chen, Zhengqiang Li, Anton Lopatin, Benjamin Torres, and Huizheng Che
Atmos. Chem. Phys., 19, 13409–13443, https://doi.org/10.5194/acp-19-13409-2019, https://doi.org/10.5194/acp-19-13409-2019, 2019
Short summary
Short summary
A novel methodology to monitor atmospheric aerosol components using remote sensing is presented. The concept is realized within the GRASP (Generalized Retrieval of Aerosol and Surface Properties) project. Application to POLDER/PARASOL and AERONET observations yielded the spatial and temporal variability of absorbing and non-absorbing insoluble and soluble aerosol species in the fine and coarse size fractions. This presents the global-scale aerosol component derived from satellite measurements.
B. Y. Ge, Z. Q. Li, W. Z. Hou, Y. Zhang, and K. T. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W9, 51–56, https://doi.org/10.5194/isprs-archives-XLII-3-W9-51-2019, https://doi.org/10.5194/isprs-archives-XLII-3-W9-51-2019, 2019
W. Z. Hou, H. F. Wang, Z. Q. Li, L. L. Qie, B. Y. Ge, C. Fan, and S. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W9, 63–69, https://doi.org/10.5194/isprs-archives-XLII-3-W9-63-2019, https://doi.org/10.5194/isprs-archives-XLII-3-W9-63-2019, 2019
Chun Zhao, Mingyue Xu, Yu Wang, Meixin Zhang, Jianping Guo, Zhiyuan Hu, L. Ruby Leung, Michael Duda, and William Skamarock
Geosci. Model Dev., 12, 2707–2726, https://doi.org/10.5194/gmd-12-2707-2019, https://doi.org/10.5194/gmd-12-2707-2019, 2019
Short summary
Short summary
Simulations at global uniform and variable resolutions share similar characteristics of precipitation and wind in the refined region. The experiments reveal the significant impacts of resolution on simulating the distribution and intensity of precipitation and updrafts. This study provides evidence supporting the use of convection-permitting global variable-resolution simulations to study extreme precipitation.
Jing Wei, Yiran Peng, Rashed Mahmood, Lin Sun, and Jianping Guo
Atmos. Chem. Phys., 19, 7183–7207, https://doi.org/10.5194/acp-19-7183-2019, https://doi.org/10.5194/acp-19-7183-2019, 2019
Short summary
Short summary
This study evaluates the suitability of 11 satellite-derived aerosol products in describing the spatio-temporal variations over the world. Our results show similar global patterns among these products but noticeable spatial heterogeneity and numerical differences over land regions. In general, MODIS products perform best at reflecting the spatial distributions and capturing the temporal trends of aerosol. This study help readers select a suitable aerosol dataset for their studies.
Yang Wang, Steffen Dörner, Sebastian Donner, Sebastian Böhnke, Isabelle De Smedt, Russell R. Dickerson, Zipeng Dong, Hao He, Zhanqing Li, Zhengqiang Li, Donghui Li, Dong Liu, Xinrong Ren, Nicolas Theys, Yuying Wang, Yang Wang, Zhenzhu Wang, Hua Xu, Jiwei Xu, and Thomas Wagner
Atmos. Chem. Phys., 19, 5417–5449, https://doi.org/10.5194/acp-19-5417-2019, https://doi.org/10.5194/acp-19-5417-2019, 2019
Short summary
Short summary
A MAX-DOAS instrument was operated to derive tropospheric vertical profiles of NO2, SO2, HONO, HCHO, CHOCHO and aerosols in the central western North China Plain in May and June 2016. The MAX-DOAS results are verified by comparisons with a collocated Raman lidar, overpass aircraft measurements, a sun photometer and in situ measurements. The contributions of regional transports and local emissions to the pollutants are evaluated based on case studies and statistic analysis.
Jianping Guo, Huan Liu, Zhanqing Li, Daniel Rosenfeld, Mengjiao Jiang, Weixin Xu, Jonathan H. Jiang, Jing He, Dandan Chen, Min Min, and Panmao Zhai
Atmos. Chem. Phys., 18, 13329–13343, https://doi.org/10.5194/acp-18-13329-2018, https://doi.org/10.5194/acp-18-13329-2018, 2018
Short summary
Short summary
Objective analysis has been used to discriminate between the local- and synoptic-scale precipitations based on wind and pressure fields at 500 hPa. Aerosol is found to be linked with changes in the vertical structure of precipitation, depending on precipitation regimes. There has been some success in separating aerosol and meteorological influences on precipitation.
Qianqian Wang, Zhanqing Li, Jianping Guo, Chuanfeng Zhao, and Maureen Cribb
Atmos. Chem. Phys., 18, 12797–12816, https://doi.org/10.5194/acp-18-12797-2018, https://doi.org/10.5194/acp-18-12797-2018, 2018
Short summary
Short summary
Based on 11-year data of lightning flashes, aerosol optical depth (AOD) and composion, and meteorological variables, we investigated the roles of aerosol and meteorological variables in lightning. Pronounced differences in lightning were found between clean and polluted conditions. Systematic changes of boomerang shape were found in lightning frequency with AOD, with a turning point around AOD = 0.3, beyond which lightning activity is saturated for smoke aerosols but always suppressed by dust.
W. Z. Hou, Z. Q. Li, F. X. Zheng, and L. L. Qie
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 533–537, https://doi.org/10.5194/isprs-archives-XLII-3-533-2018, https://doi.org/10.5194/isprs-archives-XLII-3-533-2018, 2018
K. Li, Z. Li, D. Li, Y. Xie, and H. Xu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 849–852, https://doi.org/10.5194/isprs-archives-XLII-3-849-2018, https://doi.org/10.5194/isprs-archives-XLII-3-849-2018, 2018
L. Li, L. L. Qie, H. Xu, and Z. Q. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 857–863, https://doi.org/10.5194/isprs-archives-XLII-3-857-2018, https://doi.org/10.5194/isprs-archives-XLII-3-857-2018, 2018
Z. Li, Y. Zhang, and J. Hong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 981–984, https://doi.org/10.5194/isprs-archives-XLII-3-981-2018, https://doi.org/10.5194/isprs-archives-XLII-3-981-2018, 2018
L. Qie, Z. Li, L. Li, K. Li, D. Li, and H. Xu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1421–1426, https://doi.org/10.5194/isprs-archives-XLII-3-1421-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1421-2018, 2018
Xiaowan Zhu, Guiqian Tang, Jianping Guo, Bo Hu, Tao Song, Lili Wang, Jinyuan Xin, Wenkang Gao, Christoph Münkel, Klaus Schäfer, Xin Li, and Yuesi Wang
Atmos. Chem. Phys., 18, 4897–4910, https://doi.org/10.5194/acp-18-4897-2018, https://doi.org/10.5194/acp-18-4897-2018, 2018
Short summary
Short summary
Our study first conducted a long-term observation of mixing layer height (MLH) with high resolution on the North China Plain (NCP), analyzed the spatiotemporal variations of regional MLH, investigated the reasons for MLH differences in the NCP and revealed the meteorological reasons for heavy haze pollution in southern Hebei. The study results provide scientific suggestions for regional industrial structure readjustment and have great importance for achieving the integrated development goals.
Myungje Choi, Jhoon Kim, Jaehwa Lee, Mijin Kim, Young-Je Park, Brent Holben, Thomas F. Eck, Zhengqiang Li, and Chul H. Song
Atmos. Meas. Tech., 11, 385–408, https://doi.org/10.5194/amt-11-385-2018, https://doi.org/10.5194/amt-11-385-2018, 2018
Short summary
Short summary
This study is a major version upgrade of the aerosol product from GOCI, the first and unique ocean color imager in geostationary earth orbit. It describes the improvement of version 2 of the GOCI Yonsei aerosol retrieval algorithm for near-real-time processing with improved accuracy from the modification of cloud masking, surface reflectance, etc. The product is validated against AERONET/SONET over East Asia with analyses of various errors features, and a pixel-level uncertainty is calculated.
Naifang Bei, Jiarui Wu, Miriam Elser, Tian Feng, Junji Cao, Imad El-Haddad, Xia Li, Rujin Huang, Zhengqiang Li, Xin Long, Li Xing, Shuyu Zhao, Xuexi Tie, André S. H. Prévôt, and Guohui Li
Atmos. Chem. Phys., 17, 14579–14591, https://doi.org/10.5194/acp-17-14579-2017, https://doi.org/10.5194/acp-17-14579-2017, 2017
Mengjiao Jiang, Jinqin Feng, Zhanqing Li, Ruiyu Sun, Yu-Tai Hou, Yuejian Zhu, Bingcheng Wan, Jianping Guo, and Maureen Cribb
Atmos. Chem. Phys., 17, 13967–13982, https://doi.org/10.5194/acp-17-13967-2017, https://doi.org/10.5194/acp-17-13967-2017, 2017
Short summary
Short summary
Aerosol–cloud interactions have been recognized as playing an important role in precipitation. As a benchmark evaluation of model results that exclude aerosol effects, the operational precipitation forecast (before any aerosol effects included) is evaluated using multiple datasets with the goal of determining if there is any link between the model bias and aerosol loading. The forecast model overestimates light and underestimates heavy rain. Aerosols suppress light rain and enhance heavy rain.
Ying Zhang, Zhengqiang Li, Yuhuan Zhang, Donghui Li, Lili Qie, Huizheng Che, and Hua Xu
Atmos. Meas. Tech., 10, 3203–3213, https://doi.org/10.5194/amt-10-3203-2017, https://doi.org/10.5194/amt-10-3203-2017, 2017
Yucong Miao, Jianping Guo, Shuhua Liu, Huan Liu, Zhanqing Li, Wanchun Zhang, and Panmao Zhai
Atmos. Chem. Phys., 17, 3097–3110, https://doi.org/10.5194/acp-17-3097-2017, https://doi.org/10.5194/acp-17-3097-2017, 2017
Short summary
Short summary
Three synoptic patterns associated with heavy aerosol pollution in Beijing were identified using an objective classification approach. Relationships between synoptic patterns, aerosol pollution, and boundary layer height in Beijing during summer were revealed as well. Further, factors/mechanisms leading to the low BLHs in Beijing were unraveled. The key findings have implications for understanding the crucial roles that meteorological factors play in forecasting aerosol pollution in Beijing.
Jianping Guo, Yucong Miao, Yong Zhang, Huan Liu, Zhanqing Li, Wanchun Zhang, Jing He, Mengyun Lou, Yan Yan, Lingen Bian, and Panmao Zhai
Atmos. Chem. Phys., 16, 13309–13319, https://doi.org/10.5194/acp-16-13309-2016, https://doi.org/10.5194/acp-16-13309-2016, 2016
Short summary
Short summary
The large-scale PBL climatology from sounding observations is still lacking in China. This work investigated the BLH characterization at diurnal, monthly and seasonal timescales throughout China, showing large geographic and meteorological dependences. BLH is, on average, negatively (positively) associated with the surface pressure and lower tropospheric stability (wind speed and temperature). Cloud tends to suppress the development of the PBL, which has implications for air quality forecasts.
Wanchun Zhang, Jianping Guo, Yucong Miao, Huan Liu, Yong Zhang, Zhengqiang Li, and Panmao Zhai
Atmos. Chem. Phys., 16, 9951–9963, https://doi.org/10.5194/acp-16-9951-2016, https://doi.org/10.5194/acp-16-9951-2016, 2016
Short summary
Short summary
The PBL height retrieval from CALIOP aboard CALIPSO can significantly complement the traditional ground-based methods, which is only for one site. Our study, to our current knowledge, is the first intercomparison study of PBLH on a large scale using long-term radiosonde observations in China. Three matchup schemes were proposed based on the position of radiosondes relative to CALIPSO ground tracks in China. Results indicate that CALIOP is promising for reliable PBLH retrievals.
Yahui Che, Yong Xue, Linlu Mei, Jie Guang, Lu She, Jianping Guo, Yincui Hu, Hui Xu, Xingwei He, Aojie Di, and Cheng Fan
Atmos. Chem. Phys., 16, 9655–9674, https://doi.org/10.5194/acp-16-9655-2016, https://doi.org/10.5194/acp-16-9655-2016, 2016
Short summary
Short summary
Remotely sensed data could provide continuous spatial coverage of aerosol property over the pan-Eurasian area for PEEX program. The AATSR data can be used to retrieve aerosol optical depth (AOD). The Aerosol_cci project provides users with three AOD retrieval algorithms for AATSR data. Because China is vast in territory and has great differences in terms of land surfaces, the combination of the AERONET and CARSNET data can validate the Level 2 AOD products from AATSR data more comprehensively.
Miriam Elser, Ru-Jin Huang, Robert Wolf, Jay G. Slowik, Qiyuan Wang, Francesco Canonaco, Guohui Li, Carlo Bozzetti, Kaspar R. Daellenbach, Yu Huang, Renjian Zhang, Zhengqiang Li, Junji Cao, Urs Baltensperger, Imad El-Haddad, and André S. H. Prévôt
Atmos. Chem. Phys., 16, 3207–3225, https://doi.org/10.5194/acp-16-3207-2016, https://doi.org/10.5194/acp-16-3207-2016, 2016
Short summary
Short summary
This work represents the first online chemical characterization of the PM2.5 using a high-resolution time-of flight aerosol mass spectrometer during extreme haze events China. The application of novel source apportionment techniques allowed for an improved identification and quantification of the sources of organic aerosols. The main sources and processes driving the extreme haze events are assessed.
Y. Q. Yang, J. Z. Wang, S. L. Gong, X. Y. Zhang, H. Wang, Y. Q. Wang, J. Wang, D. Li, and J. P. Guo
Atmos. Chem. Phys., 16, 1353–1364, https://doi.org/10.5194/acp-16-1353-2016, https://doi.org/10.5194/acp-16-1353-2016, 2016
Short summary
Short summary
A new model, PLAM/h, has been developed and used in near-real-time air quality forecasts by considering both meteorology and pollutant emissions, based on the two-dimensional probability density function diagnosis model for emissions. The results show that combining the influence of regular meteorological conditions and emission factors together in the PLAM/h parameterization scheme is very effective in improving the forecasting ability for fog-haze weather in North China.
K. Bai, C. Liu, R. Shi, and W. Gao
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amtd-6-4577-2013, https://doi.org/10.5194/amtd-6-4577-2013, 2013
Revised manuscript not accepted
Related subject area
Domain: ESSD – Atmosphere | Subject: Meteorology
SARAH-3 – satellite-based climate data records of surface solar radiation
A database of deep convective systems derived from the intercalibrated meteorological geostationary satellite fleet and the TOOCAN algorithm (2012–2020)
Generation of global 1 km all-weather instantaneous and daily mean land surface temperatures from MODIS data
Special Observing Period (SOP) data for the Year of Polar Prediction site Model Intercomparison Project (YOPPsiteMIP)
Dataset of spatially extensive long-term quality-assured land–atmosphere interactions over the Tibetan Plateau
Multifrequency radar observations of marine clouds during the EPCAPE campaign
The PAZ Polarimetric Radio Occultation Research Dataset for Scientific Applications
Data collected using small uncrewed aircraft systems during the TRacking Aerosol Convection interactions ExpeRiment (TRACER)
Water vapor Raman-lidar observations from multiple sites in the framework of WaLiNeAs
Reanalysis of multi-year high-resolution X-band weather radar observations in Hamburg
The 2023 National Offshore Wind data set (NOW-23)
Dataset of stable isotopes of precipitation in the Eurasian continent
A 7-year record of vertical profiles of radar measurements and precipitation estimates at Dumont d'Urville, Adélie Land, East Antarctica
Long-term monthly 0.05° terrestrial evapotranspiration dataset (1982–2018) for the Tibetan Plateau
High-resolution (1 km) all-sky net radiation over Europe enabled by the merging of land surface temperature retrievals from geostationary and polar-orbiting satellites
Atmospheric and surface observations during the Saint John River Experiment on Cold Season Storms (SAJESS)
Year-long buoy-based observations of the air–sea transition zone off the US west coast
The historical Greenland Climate Network (GC-Net) curated and augmented level-1 dataset
Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: a comprehensive long-term dataset of remote sensing observations
CHESS-SCAPE: high-resolution future projections of multiple climate scenarios for the United Kingdom derived from downscaled United Kingdom Climate Projections 2018 regional climate model output
Quality-controlled meteorological datasets from SIGMA automatic weather stations in northwest Greenland, 2012–2020
A dataset of energy, water vapor, and carbon exchange observations in oasis–desert areas from 2012 to 2021 in a typical endorheic basin
Derivation and compilation of lower-atmospheric properties relating to temperature, wind, stability, moisture, and surface radiation budget over the central Arctic sea ice during MOSAiC
CLARA-A3: The third edition of the AVHRR-based CM SAF climate data record on clouds, radiation and surface albedo covering the period 1979 to 2023
An integrated and homogenized global surface solar radiation dataset and its reconstruction based on a convolutional neural network approach
IWIN: the Isfjorden Weather Information Network
A new daily gridded precipitation dataset for the Chinese mainland based on gauge observations
A 16-year global climate data record of total column water vapour generated from OMI observations in the visible blue spectral range
The EUPPBench postprocessing benchmark dataset v1.0
CHELSA-W5E5: daily 1 km meteorological forcing data for climate impact studies
Database of the Italian disdrometer network
East Asia Reanalysis System (EARS)
Data rescue of historical wind observations in Sweden since the 1920s
LegacyClimate 1.0: a dataset of pollen-based climate reconstructions from 2594 Northern Hemisphere sites covering the last 30 kyr and beyond
EURADCLIM: the European climatological high-resolution gauge-adjusted radar precipitation dataset
Radar and ground-level measurements of clouds and precipitation collected during the POPE 2020 campaign at Princess Elisabeth Antarctica
Combined wind lidar and cloud radar for high-resolution wind profiling
An enhanced integrated water vapour dataset from more than 10 000 global ground-based GPS stations in 2020
TPHiPr: a long-term (1979–2020) high-accuracy precipitation dataset (1∕30°, daily) for the Third Pole region based on high-resolution atmospheric modeling and dense observations
The AntAWS dataset: a compilation of Antarctic automatic weather station observations
HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020
A long-term 1 km monthly near-surface air temperature dataset over the Tibetan glaciers by fusion of station and satellite observations
A global dataset of daily maximum and minimum near-surface air temperature at 1 km resolution over land (2003–2020)
Tropospheric water vapor: a comprehensive high-resolution data collection for the transnational Upper Rhine Graben region
The hourly wind-bias-adjusted precipitation data set from the Environment and Climate Change Canada automated surface observation network (2001–2019)
Enhanced automated meteorological observations at the Canadian Arctic Weather Science (CAWS) supersites
Quality control and correction method for air temperature data from a citizen science weather station network in Leuven, Belgium
Combined high-resolution rainfall and wind data collected for 3 months on a wind farm 110 km southeast of Paris (France)
Sub-mesoscale observations of convective cold pools with a dense station network in Hamburg, Germany
Observational data from uncrewed systems over Southern Great Plains
Uwe Pfeifroth, Jaqueline Drücke, Steffen Kothe, Jörg Trentmann, Marc Schröder, and Rainer Hollmann
Earth Syst. Sci. Data, 16, 5243–5265, https://doi.org/10.5194/essd-16-5243-2024, https://doi.org/10.5194/essd-16-5243-2024, 2024
Short summary
Short summary
The energy reaching Earth's surface from the Sun is a quantity of great importance for the climate system and for many applications. SARAH-3 is a satellite-based climate data record of surface solar radiation parameters. It is generated and distributed by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF). SARAH-3 covers more than 4 decades and provides a high spatial and temporal resolution, and its validation shows good accuracy and stability.
Thomas Fiolleau and Rémy Roca
Earth Syst. Sci. Data, 16, 4021–4050, https://doi.org/10.5194/essd-16-4021-2024, https://doi.org/10.5194/essd-16-4021-2024, 2024
Short summary
Short summary
This paper presents a database of tropical deep convective systems over the 2012–2020 period, built from a cloud-tracking algorithm called TOOCAN, which has been applied to homogenized infrared observations from a fleet of geostationary satellites. This database aims to analyze the tropical deep convective systems, the evolution of their associated characteristics over their life cycle, their organization, and their importance in the hydrological and energy cycle.
Bing Li, Shunlin Liang, Han Ma, Guanpeng Dong, Xiaobang Liu, Tao He, and Yufang Zhang
Earth Syst. Sci. Data, 16, 3795–3819, https://doi.org/10.5194/essd-16-3795-2024, https://doi.org/10.5194/essd-16-3795-2024, 2024
Short summary
Short summary
This study describes 1 km all-weather instantaneous and daily mean land surface temperature (LST) datasets on the global scale during 2000–2020. It is the first attempt to synergistically estimate all-weather instantaneous and daily mean LST data on a long global-scale time series. The generated datasets were evaluated by the observations from in situ stations and other LST datasets, and the evaluation indicated that the dataset is sufficiently reliable.
Zen Mariani, Sara M. Morris, Taneil Uttal, Elena Akish, Robert Crawford, Laura Huang, Jonathan Day, Johanna Tjernström, Øystein Godøy, Lara Ferrighi, Leslie M. Hartten, Jareth Holt, Christopher J. Cox, Ewan O'Connor, Roberta Pirazzini, Marion Maturilli, Giri Prakash, James Mather, Kimberly Strong, Pierre Fogal, Vasily Kustov, Gunilla Svensson, Michael Gallagher, and Brian Vasel
Earth Syst. Sci. Data, 16, 3083–3124, https://doi.org/10.5194/essd-16-3083-2024, https://doi.org/10.5194/essd-16-3083-2024, 2024
Short summary
Short summary
During the Year of Polar Prediction (YOPP), we increased measurements in the polar regions and have made dedicated efforts to centralize and standardize all of the different types of datasets that have been collected to facilitate user uptake and model–observation comparisons. This paper is an overview of those efforts and a description of the novel standardized Merged Observation Data Files (MODFs), including a description of the sites, data format, and instruments.
Yaoming Ma, Zhipeng Xie, Yingying Chen, Shaomin Liu, Tao Che, Ziwei Xu, Lunyu Shang, Xiaobo He, Xianhong Meng, Weiqiang Ma, Baiqing Xu, Huabiao Zhao, Junbo Wang, Guangjian Wu, and Xin Li
Earth Syst. Sci. Data, 16, 3017–3043, https://doi.org/10.5194/essd-16-3017-2024, https://doi.org/10.5194/essd-16-3017-2024, 2024
Short summary
Short summary
Current models and satellites struggle to accurately represent the land–atmosphere (L–A) interactions over the Tibetan Plateau. We present the most extensive compilation of in situ observations to date, comprising 17 years of data on L–A interactions across 12 sites. This quality-assured benchmark dataset provides independent validation to improve models and remote sensing for the region, and it enables new investigations of fine-scale L–A processes and their mechanistic drivers.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, Robert M. Beauchamp, and Arturo Umeyama
Earth Syst. Sci. Data, 16, 2701–2715, https://doi.org/10.5194/essd-16-2701-2024, https://doi.org/10.5194/essd-16-2701-2024, 2024
Short summary
Short summary
This paper describes multifrequency radar observations of clouds and precipitation during the EPCAPE campaign. The data sets were obtained from CloudCube, a Ka-, W-, and G-band atmospheric profiling radar, to demonstrate synergies between multifrequency retrievals. This data collection provides a unique opportunity to study hydrometeors with diameters in the millimeter and submillimeter size range that can be used to better understand the drop size distribution within clouds and precipitation.
Ramon Padullés, Estel Cardellach, Antía Paz, Santi Oliveras, Douglas C. Hunt, Sergey Sokolovskiy, Jan P. Weiss, Kuo-Nung Wang, F. Joe Turk, Chi O. Ao, and Manuel de la Torre Juárez
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-150, https://doi.org/10.5194/essd-2024-150, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
This dataset provides, for the first time, combined observations of clouds and precipitation with coincident retrievals of atmospheric thermodynamics obtained from the same space based instrument. Furthermore, it provides the locations of the ray-trajectories of the observations, along various precipitation-related products interpolated into them, with the aim to foster the use of such dataset in scientific and operational applications.
Francesca Lappin, Gijs de Boer, Petra Klein, Jonathan Hamilton, Michelle Spencer, Radiance Calmer, Antonio R. Segales, Michael Rhodes, Tyler M. Bell, Justin Buchli, Kelsey Britt, Elizabeth Asher, Isaac Medina, Brian Butterworth, Leia Otterstatter, Madison Ritsch, Bryony Puxley, Angelina Miller, Arianna Jordan, Ceu Gomez-Faulk, Elizabeth Smith, Steven Borenstein, Troy Thornberry, Brian Argrow, and Elizabeth Pillar-Little
Earth Syst. Sci. Data, 16, 2525–2541, https://doi.org/10.5194/essd-16-2525-2024, https://doi.org/10.5194/essd-16-2525-2024, 2024
Short summary
Short summary
This article provides an overview of the lower-atmospheric dataset collected by two uncrewed aerial systems near the Gulf of Mexico coastline south of Houston, TX, USA, as part of the TRacking Aerosol Convection interactions ExpeRiment (TRACER) campaign. The data were collected through boundary layer transitions, through sea breeze circulations, and in the pre- and near-storm environment to understand how these processes influence the coastal environment.
Frédéric Laly, Patrick Chazette, Julien Totems, Jérémy Lagarrigue, Laurent Forges, and Cyrille Flamant
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-73, https://doi.org/10.5194/essd-2024-73, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
We present a dataset of water vapor mixing ratio profiles acquired during the WaLiNeAs campaign in fall and winter 2022 and summer 2023, using 3 lidar systems deployed on the Western Mediterranean coastline. This innovative campaign gives access to low tropospheric water vapor variability to constrain meteorological forecasting models. The scientific objective is to improve forecasting of heavy precipation events that lead to severe flash floods.
Finn Burgemeister, Marco Clemens, and Felix Ament
Earth Syst. Sci. Data, 16, 2317–2332, https://doi.org/10.5194/essd-16-2317-2024, https://doi.org/10.5194/essd-16-2317-2024, 2024
Short summary
Short summary
Knowledge of small-scale rainfall variability is needed for hydro-meteorological applications in urban areas. Therefore, we present an open-access data set covering reanalyzed radar reflectivities and rainfall estimates measured by a weather radar at high spatio-temporal resolution in the urban environment of Hamburg between 2013 and 2021. We describe the data reanalysis, outline the measurement’s performance for long time periods, and discuss open issues and limitations of the data set.
Nicola Bodini, Mike Optis, Stephanie Redfern, David Rosencrans, Alex Rybchuk, Julie K. Lundquist, Vincent Pronk, Simon Castagneri, Avi Purkayastha, Caroline Draxl, Raghavendra Krishnamurthy, Ethan Young, Billy Roberts, Evan Rosenlieb, and Walter Musial
Earth Syst. Sci. Data, 16, 1965–2006, https://doi.org/10.5194/essd-16-1965-2024, https://doi.org/10.5194/essd-16-1965-2024, 2024
Short summary
Short summary
This article presents the 2023 National Offshore Wind data set (NOW-23), an updated resource for offshore wind information in the US. It replaces the Wind Integration National Dataset (WIND) Toolkit, offering improved accuracy through advanced weather prediction models. The data underwent regional tuning and validation and can be accessed at no cost.
Longhu Chen, Qinqin Wang, Guofeng Zhu, Xinrui Lin, Dongdong Qiu, Yinying Jiao, Siyu Lu, Rui Li, Gaojia Meng, and Yuhao Wang
Earth Syst. Sci. Data, 16, 1543–1557, https://doi.org/10.5194/essd-16-1543-2024, https://doi.org/10.5194/essd-16-1543-2024, 2024
Short summary
Short summary
We have compiled data regarding stable precipitation isotopes from 842 sampling points throughout the Eurasian continent since 1961, accumulating a total of 51 753 data records. The collected data have undergone pre-processing and statistical analysis. We also analysed the spatiotemporal distribution of stable precipitation isotopes across the Eurasian continent and their interrelationships with meteorological elements.
Valentin Wiener, Marie-Laure Roussel, Christophe Genthon, Étienne Vignon, Jacopo Grazioli, and Alexis Berne
Earth Syst. Sci. Data, 16, 821–836, https://doi.org/10.5194/essd-16-821-2024, https://doi.org/10.5194/essd-16-821-2024, 2024
Short summary
Short summary
This paper presents 7 years of data from a precipitation radar deployed at the Dumont d'Urville station in East Antarctica. The main characteristics of the dataset are outlined in a short statistical study. Interannual and seasonal variability are also investigated. Then, we extensively describe the processing method to retrieve snowfall profiles from the radar data. Lastly, a brief comparison is made with two climate models as an application example of the dataset.
Ling Yuan, Xuelong Chen, Yaoming Ma, Cunbo Han, Binbin Wang, and Weiqiang Ma
Earth Syst. Sci. Data, 16, 775–801, https://doi.org/10.5194/essd-16-775-2024, https://doi.org/10.5194/essd-16-775-2024, 2024
Short summary
Short summary
Accurately monitoring and understanding the spatial–temporal variability of evapotranspiration (ET) components over the Tibetan Plateau (TP) remains difficult. Here, 37 years (1982–2018) of monthly ET component data for the TP was produced, and the data are consistent with measurements. The annual average ET for the TP was about 0.93 (± 0.037) × 103 Gt yr−1. The rate of increase of the ET was around 0.96 mm yr−1. The increase in the ET can be explained by warming and wetting of the climate.
Dominik Rains, Isabel Trigo, Emanuel Dutra, Sofia Ermida, Darren Ghent, Petra Hulsman, Jose Gómez-Dans, and Diego G. Miralles
Earth Syst. Sci. Data, 16, 567–593, https://doi.org/10.5194/essd-16-567-2024, https://doi.org/10.5194/essd-16-567-2024, 2024
Short summary
Short summary
Land surface temperature and surface net radiation are vital inputs for many land surface and hydrological models. However, current remote sensing datasets of these variables come mostly at coarse resolutions, and the few high-resolution datasets available have large gaps due to cloud cover. Here, we present a continuous daily product for both variables across Europe for 2018–2019 obtained by combining observations from geostationary as well as polar-orbiting satellites.
Hadleigh D. Thompson, Julie M. Thériault, Stephen J. Déry, Ronald E. Stewart, Dominique Boisvert, Lisa Rickard, Nicolas R. Leroux, Matteo Colli, and Vincent Vionnet
Earth Syst. Sci. Data, 15, 5785–5806, https://doi.org/10.5194/essd-15-5785-2023, https://doi.org/10.5194/essd-15-5785-2023, 2023
Short summary
Short summary
The Saint John River experiment on Cold Season Storms was conducted in northwest New Brunswick, Canada, to investigate the types of precipitation that can lead to ice jams and flooding along the river. We deployed meteorological instruments, took precipitation measurements and photographs of snowflakes, and launched weather balloons. These data will help us to better understand the atmospheric conditions that can affect local communities and townships downstream during the spring melt season.
Raghavendra Krishnamurthy, Gabriel García Medina, Brian Gaudet, William I. Gustafson Jr., Evgueni I. Kassianov, Jinliang Liu, Rob K. Newsom, Lindsay M. Sheridan, and Alicia M. Mahon
Earth Syst. Sci. Data, 15, 5667–5699, https://doi.org/10.5194/essd-15-5667-2023, https://doi.org/10.5194/essd-15-5667-2023, 2023
Short summary
Short summary
Our understanding and ability to observe and model air–sea processes has been identified as a principal limitation to our ability to predict future weather. Few observations exist offshore along the coast of California. To improve our understanding of the air–sea transition zone and support the wind energy industry, two buoys with state-of-the-art equipment were deployed for 1 year. In this article, we present details of the post-processing, algorithms, and analyses.
Baptiste Vandecrux, Jason E. Box, Andreas P. Ahlstrøm, Signe B. Andersen, Nicolas Bayou, William T. Colgan, Nicolas J. Cullen, Robert S. Fausto, Dominik Haas-Artho, Achim Heilig, Derek A. Houtz, Penelope How, Ionut Iosifescu Enescu, Nanna B. Karlsson, Rebecca Kurup Buchholz, Kenneth D. Mankoff, Daniel McGrath, Noah P. Molotch, Bianca Perren, Maiken K. Revheim, Anja Rutishauser, Kevin Sampson, Martin Schneebeli, Sandy Starkweather, Simon Steffen, Jeff Weber, Patrick J. Wright, Henry Jay Zwally, and Konrad Steffen
Earth Syst. Sci. Data, 15, 5467–5489, https://doi.org/10.5194/essd-15-5467-2023, https://doi.org/10.5194/essd-15-5467-2023, 2023
Short summary
Short summary
The Greenland Climate Network (GC-Net) comprises stations that have been monitoring the weather on the Greenland Ice Sheet for over 30 years. These stations are being replaced by newer ones maintained by the Geological Survey of Denmark and Greenland (GEUS). The historical data were reprocessed to improve their quality, and key information about the weather stations has been compiled. This augmented dataset is available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al., 2022).
Giovanni Chellini, Rosa Gierens, Kerstin Ebell, Theresa Kiszler, Pavel Krobot, Alexander Myagkov, Vera Schemann, and Stefan Kneifel
Earth Syst. Sci. Data, 15, 5427–5448, https://doi.org/10.5194/essd-15-5427-2023, https://doi.org/10.5194/essd-15-5427-2023, 2023
Short summary
Short summary
We present a comprehensive quality-controlled dataset of remote sensing observations of low-level mixed-phase clouds (LLMPCs) taken at the high Arctic site of Ny-Ålesund, Svalbard, Norway. LLMPCs occur frequently in the Arctic region, and substantially warm the surface. However, our understanding of microphysical processes in these clouds is incomplete. This dataset includes a comprehensive set of variables which allow for extensive investigation of such processes in LLMPCs at the site.
Emma L. Robinson, Chris Huntingford, Valyaveetil Shamsudheen Semeena, and James M. Bullock
Earth Syst. Sci. Data, 15, 5371–5401, https://doi.org/10.5194/essd-15-5371-2023, https://doi.org/10.5194/essd-15-5371-2023, 2023
Short summary
Short summary
CHESS-SCAPE is a suite of high-resolution climate projections for the UK to 2080, derived from United Kingdom Climate Projections 2018 (UKCP18), designed to support climate impact modelling. It contains four realisations of four scenarios of future greenhouse gas levels (RCP2.6, 4.5, 6.0 and 8.5), with and without bias correction to historical data. The variables are available at 1 km resolution and a daily time step, with monthly, seasonal and annual means and 20-year mean-monthly time slices.
Motoshi Nishimura, Teruo Aoki, Masashi Niwano, Sumito Matoba, Tomonori Tanikawa, Tetsuhide Yamasaki, Satoru Yamaguchi, and Koji Fujita
Earth Syst. Sci. Data, 15, 5207–5226, https://doi.org/10.5194/essd-15-5207-2023, https://doi.org/10.5194/essd-15-5207-2023, 2023
Short summary
Short summary
We presented the method of data quality checks and the dataset for two ground weather observations in northwest Greenland. We found that the warm and clear weather conditions in the 2015, 2019, and 2020 summers caused the snowmelt and the decline in surface reflectance of solar radiation at a low-elevated site (SIGMA-B; 944 m), but those were not seen at the high-elevated site (SIGMA-A; 1490 m). We hope that our data management method and findings will help climate scientists.
Shaomin Liu, Ziwei Xu, Tao Che, Xin Li, Tongren Xu, Zhiguo Ren, Yang Zhang, Junlei Tan, Lisheng Song, Ji Zhou, Zhongli Zhu, Xiaofan Yang, Rui Liu, and Yanfei Ma
Earth Syst. Sci. Data, 15, 4959–4981, https://doi.org/10.5194/essd-15-4959-2023, https://doi.org/10.5194/essd-15-4959-2023, 2023
Short summary
Short summary
We present a suite of observational datasets from artificial and natural oases–desert systems that consist of long-term turbulent flux and auxiliary data, including hydrometeorological, vegetation, and soil parameters, from 2012 to 2021. We confirm that the 10-year, long-term dataset presented in this study is of high quality with few missing data, and we believe that the data will support ecological security and sustainable development in oasis–desert areas.
Gina C. Jozef, Robert Klingel, John J. Cassano, Björn Maronga, Gijs de Boer, Sandro Dahlke, and Christopher J. Cox
Earth Syst. Sci. Data, 15, 4983–4995, https://doi.org/10.5194/essd-15-4983-2023, https://doi.org/10.5194/essd-15-4983-2023, 2023
Short summary
Short summary
Observations from the MOSAiC expedition relating to lower-atmospheric temperature, wind, stability, moisture, and surface radiation budget from radiosondes, a meteorological tower, radiation station, and ceilometer were compiled to create a dataset which describes the thermodynamic and kinematic state of the central Arctic lower atmosphere between October 2019 and September 2020. This paper describes the methods used to develop this lower-atmospheric properties dataset.
Karl-Göran Karlsson, Martin Stengel, Jan Fokke Meirink, Aku Riihelä, Jörg Trentmann, Tom Akkermans, Diana Stein, Abhay Devasthale, Salomon Eliasson, Erik Johansson, Nina Håkansson, Irina Solodovnik, Nikos Benas, Nicolas Clerbaux, Nathalie Selbach, Marc Schröder, and Rainer Hollmann
Earth Syst. Sci. Data, 15, 4901–4926, https://doi.org/10.5194/essd-15-4901-2023, https://doi.org/10.5194/essd-15-4901-2023, 2023
Short summary
Short summary
This paper presents a global climate data record on cloud parameters, radiation at the surface and at the top of atmosphere, and surface albedo. The temporal coverage is 1979–2020 (42 years) and the data record is also continuously updated until present time. Thus, more than four decades of climate parameters are provided. Based on CLARA-A3, studies on distribution of clouds and radiation parameters can be made and, especially, investigations of climate trends and evaluation of climate models.
Boyang Jiao, Yucheng Su, Qingxiang Li, Veronica Manara, and Martin Wild
Earth Syst. Sci. Data, 15, 4519–4535, https://doi.org/10.5194/essd-15-4519-2023, https://doi.org/10.5194/essd-15-4519-2023, 2023
Short summary
Short summary
This paper develops an observational integrated and homogenized global-terrestrial (except for Antarctica) SSRIH station. This is interpolated into a 5° × 5° SSRIH grid and reconstructed into a long-term (1955–2018) global land (except for Antarctica) 5° × 2.5° SSR anomaly dataset (SSRIH20CR) by an improved partial convolutional neural network deep-learning method. SSRIH20CR yields trends of −1.276 W m−2 per decade over the dimming period and 0.697 W m−2 per decade over the brightening period.
Lukas Frank, Marius Opsanger Jonassen, Teresa Remes, Florina Roana Schalamon, and Agnes Stenlund
Earth Syst. Sci. Data, 15, 4219–4234, https://doi.org/10.5194/essd-15-4219-2023, https://doi.org/10.5194/essd-15-4219-2023, 2023
Short summary
Short summary
The Isfjorden Weather Information Network (IWIN) provides continuous meteorological near-surface observations from Isfjorden in Svalbard. The network combines permanent automatic weather stations on lighthouses along the coast line with mobile stations on board small tourist cruise ships regularly trafficking the fjord during spring to autumn. All data are available online in near-real time. Besides their scientific value, IWIN data crucially enhance the safety of field activities in the region.
Jingya Han, Chiyuan Miao, Jiaojiao Gou, Haiyan Zheng, Qi Zhang, and Xiaoying Guo
Earth Syst. Sci. Data, 15, 3147–3161, https://doi.org/10.5194/essd-15-3147-2023, https://doi.org/10.5194/essd-15-3147-2023, 2023
Short summary
Short summary
Constructing a high-quality, long-term daily precipitation dataset is essential to current hydrometeorology research. This study aims to construct a long-term daily precipitation dataset with different spatial resolutions based on 2839 gauge observations. The constructed precipitation dataset shows reliable quality compared with the other available precipitation products and is expected to facilitate the advancement of drought monitoring, flood forecasting, and hydrological modeling.
Christian Borger, Steffen Beirle, and Thomas Wagner
Earth Syst. Sci. Data, 15, 3023–3049, https://doi.org/10.5194/essd-15-3023-2023, https://doi.org/10.5194/essd-15-3023-2023, 2023
Short summary
Short summary
This study presents a long-term data set of monthly mean total column water vapour (TCWV) based on measurements of the Ozone Monitoring Instrument (OMI) covering the time range from January 2005 to December 2020. We describe how the TCWV values are retrieved from UV–Vis satellite spectra and demonstrate that the OMI TCWV data set is in good agreement with various different reference data sets. Moreover, we also show that it fulfills typical stability requirements for climate data records.
Jonathan Demaeyer, Jonas Bhend, Sebastian Lerch, Cristina Primo, Bert Van Schaeybroeck, Aitor Atencia, Zied Ben Bouallègue, Jieyu Chen, Markus Dabernig, Gavin Evans, Jana Faganeli Pucer, Ben Hooper, Nina Horat, David Jobst, Janko Merše, Peter Mlakar, Annette Möller, Olivier Mestre, Maxime Taillardat, and Stéphane Vannitsem
Earth Syst. Sci. Data, 15, 2635–2653, https://doi.org/10.5194/essd-15-2635-2023, https://doi.org/10.5194/essd-15-2635-2023, 2023
Short summary
Short summary
A benchmark dataset is proposed to compare different statistical postprocessing methods used in forecasting centers to properly calibrate ensemble weather forecasts. This dataset is based on ensemble forecasts covering a portion of central Europe and includes the corresponding observations. Examples on how to download and use the data are provided, a set of evaluation methods is proposed, and a first benchmark of several methods for the correction of 2 m temperature forecasts is performed.
Dirk Nikolaus Karger, Stefan Lange, Chantal Hari, Christopher P. O. Reyer, Olaf Conrad, Niklaus E. Zimmermann, and Katja Frieler
Earth Syst. Sci. Data, 15, 2445–2464, https://doi.org/10.5194/essd-15-2445-2023, https://doi.org/10.5194/essd-15-2445-2023, 2023
Short summary
Short summary
We present the first 1 km, daily, global climate dataset for climate impact studies. We show that the high-resolution data have a decreased bias and higher correlation with measurements from meteorological stations than coarser data. The dataset will be of value for a wide range of climate change impact studies both at global and regional level that benefit from using a consistent global dataset.
Elisa Adirosi, Federico Porcù, Mario Montopoli, Luca Baldini, Alessandro Bracci, Vincenzo Capozzi, Clizia Annella, Giorgio Budillon, Edoardo Bucchignani, Alessandra Lucia Zollo, Orietta Cazzuli, Giulio Camisani, Renzo Bechini, Roberto Cremonini, Andrea Antonini, Alberto Ortolani, Samantha Melani, Paolo Valisa, and Simone Scapin
Earth Syst. Sci. Data, 15, 2417–2429, https://doi.org/10.5194/essd-15-2417-2023, https://doi.org/10.5194/essd-15-2417-2023, 2023
Short summary
Short summary
The paper describes the database of 1 min drop size distribution (DSD) of atmospheric precipitation collected by the Italian disdrometer network over the last 10 years. These data are useful for several applications that range from climatological, meteorological and hydrological uses to telecommunications, agriculture and conservation of cultural heritage exposed to precipitation. Descriptions of the processing and of the database organization, along with some examples, are provided.
Jinfang Yin, Xudong Liang, Yanxin Xie, Feng Li, Kaixi Hu, Lijuan Cao, Feng Chen, Haibo Zou, Feng Zhu, Xin Sun, Jianjun Xu, Geli Wang, Ying Zhao, and Juanjuan Liu
Earth Syst. Sci. Data, 15, 2329–2346, https://doi.org/10.5194/essd-15-2329-2023, https://doi.org/10.5194/essd-15-2329-2023, 2023
Short summary
Short summary
A collection of regional reanalysis datasets has been produced. However, little attention has been paid to East Asia, and there are no long-term, physically consistent regional reanalysis data available. The East Asia Reanalysis System was developed using the WRF model and GSI data assimilation system. A 39-year (1980–2018) reanalysis dataset is available for the East Asia region, at a high temporal (of 3 h) and spatial resolution (of 12 km), for mesoscale weather and regional climate studies.
John Erik Engström, Lennart Wern, Sverker Hellström, Erik Kjellström, Chunlüe Zhou, Deliang Chen, and Cesar Azorin-Molina
Earth Syst. Sci. Data, 15, 2259–2277, https://doi.org/10.5194/essd-15-2259-2023, https://doi.org/10.5194/essd-15-2259-2023, 2023
Short summary
Short summary
Newly digitized wind speed observations provide data from the time period from around 1920 to the present, enveloping one full century of wind measurements. The results of this work enable the investigation of the historical variability and trends in surface wind speed in Sweden for
the last century.
Ulrike Herzschuh, Thomas Böhmer, Chenzhi Li, Manuel Chevalier, Raphaël Hébert, Anne Dallmeyer, Xianyong Cao, Nancy H. Bigelow, Larisa Nazarova, Elena Y. Novenko, Jungjae Park, Odile Peyron, Natalia A. Rudaya, Frank Schlütz, Lyudmila S. Shumilovskikh, Pavel E. Tarasov, Yongbo Wang, Ruilin Wen, Qinghai Xu, and Zhuo Zheng
Earth Syst. Sci. Data, 15, 2235–2258, https://doi.org/10.5194/essd-15-2235-2023, https://doi.org/10.5194/essd-15-2235-2023, 2023
Short summary
Short summary
Climate reconstruction from proxy data can help evaluate climate models. We present pollen-based reconstructions of mean July temperature, mean annual temperature, and annual precipitation from 2594 pollen records from the Northern Hemisphere, using three reconstruction methods (WA-PLS, WA-PLS_tailored, and MAT). Since no global or hemispheric synthesis of quantitative precipitation changes are available for the Holocene so far, this dataset will be of great value to the geoscientific community.
Aart Overeem, Else van den Besselaar, Gerard van der Schrier, Jan Fokke Meirink, Emiel van der Plas, and Hidde Leijnse
Earth Syst. Sci. Data, 15, 1441–1464, https://doi.org/10.5194/essd-15-1441-2023, https://doi.org/10.5194/essd-15-1441-2023, 2023
Short summary
Short summary
EURADCLIM is a new precipitation dataset covering a large part of Europe. It is based on weather radar data to provide local precipitation information every hour and combined with rain gauge data to obtain good precipitation estimates. EURADCLIM provides a much better reference for validation of weather model output and satellite precipitation datasets. It also allows for climate monitoring and better evaluation of extreme precipitation events and their impact (landslides, flooding).
Alfonso Ferrone and Alexis Berne
Earth Syst. Sci. Data, 15, 1115–1132, https://doi.org/10.5194/essd-15-1115-2023, https://doi.org/10.5194/essd-15-1115-2023, 2023
Short summary
Short summary
This article presents the datasets collected between November 2019 and February 2020 in the vicinity of the Belgian research base Princess Elisabeth Antarctica. Five meteorological radars, a multi-angle snowflake camera, three weather stations, and two radiometers have been deployed at five sites, up to a maximum distance of 30 km from the base. Their varied locations allow the study of spatial variability in snowfall and its interaction with the complex terrain in the region.
José Dias Neto, Louise Nuijens, Christine Unal, and Steven Knoop
Earth Syst. Sci. Data, 15, 769–789, https://doi.org/10.5194/essd-15-769-2023, https://doi.org/10.5194/essd-15-769-2023, 2023
Short summary
Short summary
This paper describes a dataset from a novel experimental setup to retrieve wind speed and direction profiles, combining cloud radars and wind lidar. This setup allows retrieving profiles from near the surface to the top of clouds. The field campaign occurred in Cabauw, the Netherlands, between September 13th and October 3rd 2021. This paper also provides examples of applications of this dataset (e.g. studying atmospheric turbulence, validating numerical atmospheric models).
Peng Yuan, Geoffrey Blewitt, Corné Kreemer, William C. Hammond, Donald Argus, Xungang Yin, Roeland Van Malderen, Michael Mayer, Weiping Jiang, Joseph Awange, and Hansjörg Kutterer
Earth Syst. Sci. Data, 15, 723–743, https://doi.org/10.5194/essd-15-723-2023, https://doi.org/10.5194/essd-15-723-2023, 2023
Short summary
Short summary
We developed a 5 min global integrated water vapour (IWV) product from 12 552 ground-based GPS stations in 2020. It contains more than 1 billion IWV estimates. The dataset is an enhanced version of the existing operational GPS IWV dataset from the Nevada Geodetic Laboratory. The enhancement is reached by using accurate meteorological information from ERA5 for the GPS IWV retrieval with a significantly higher spatiotemporal resolution. The dataset is recommended for high-accuracy applications.
Yaozhi Jiang, Kun Yang, Youcun Qi, Xu Zhou, Jie He, Hui Lu, Xin Li, Yingying Chen, Xiaodong Li, Bingrong Zhou, Ali Mamtimin, Changkun Shao, Xiaogang Ma, Jiaxin Tian, and Jianhong Zhou
Earth Syst. Sci. Data, 15, 621–638, https://doi.org/10.5194/essd-15-621-2023, https://doi.org/10.5194/essd-15-621-2023, 2023
Short summary
Short summary
Our work produces a long-term (1979–2020) high-resolution (1/30°, daily) precipitation dataset for the Third Pole (TP) region by merging an advanced atmospheric simulation with high-density rain gauge (more than 9000) observations. Validation shows that the produced dataset performs better than the currently widely used precipitation datasets in the TP. This dataset can be used for hydrological, meteorological and ecological studies in the TP.
Yetang Wang, Xueying Zhang, Wentao Ning, Matthew A. Lazzara, Minghu Ding, Carleen H. Reijmer, Paul C. J. P. Smeets, Paolo Grigioni, Petra Heil, Elizabeth R. Thomas, David Mikolajczyk, Lee J. Welhouse, Linda M. Keller, Zhaosheng Zhai, Yuqi Sun, and Shugui Hou
Earth Syst. Sci. Data, 15, 411–429, https://doi.org/10.5194/essd-15-411-2023, https://doi.org/10.5194/essd-15-411-2023, 2023
Short summary
Short summary
Here we construct a new database of Antarctic automatic weather station (AWS) meteorological records, which is quality-controlled by restrictive criteria. This dataset compiled all available Antarctic AWS observations, and its resolutions are 3-hourly, daily and monthly, which is very useful for quantifying spatiotemporal variability in weather conditions. Furthermore, this compilation will be used to estimate the performance of the regional climate models or meteorological reanalysis products.
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
Short summary
Short summary
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.
Jun Qin, Weihao Pan, Min He, Ning Lu, Ling Yao, Hou Jiang, and Chenghu Zhou
Earth Syst. Sci. Data, 15, 331–344, https://doi.org/10.5194/essd-15-331-2023, https://doi.org/10.5194/essd-15-331-2023, 2023
Short summary
Short summary
To enrich a glacial surface air temperature (SAT) product of a long time series, an ensemble learning model is constructed to estimate monthly SATs from satellite land surface temperatures at a spatial resolution of 1 km, and long-term glacial SATs from 1961 to 2020 are reconstructed using a Bayesian linear regression. This product reveals the overall warming trend and the spatial heterogeneity of warming on TP glaciers and helps to monitor glacier warming, analyze glacier evolution, etc.
Tao Zhang, Yuyu Zhou, Kaiguang Zhao, Zhengyuan Zhu, Gang Chen, Jia Hu, and Li Wang
Earth Syst. Sci. Data, 14, 5637–5649, https://doi.org/10.5194/essd-14-5637-2022, https://doi.org/10.5194/essd-14-5637-2022, 2022
Short summary
Short summary
We generated a global 1 km daily maximum and minimum near-surface air temperature (Tmax and Tmin) dataset (2003–2020) using a novel statistical model. The average root mean square errors ranged from 1.20 to 2.44 °C for Tmax and 1.69 to 2.39 °C for Tmin. The gridded global air temperature dataset is of great use in a variety of studies such as the urban heat island phenomenon, hydrological modeling, and epidemic forecasting.
Benjamin Fersch, Andreas Wagner, Bettina Kamm, Endrit Shehaj, Andreas Schenk, Peng Yuan, Alain Geiger, Gregor Moeller, Bernhard Heck, Stefan Hinz, Hansjörg Kutterer, and Harald Kunstmann
Earth Syst. Sci. Data, 14, 5287–5307, https://doi.org/10.5194/essd-14-5287-2022, https://doi.org/10.5194/essd-14-5287-2022, 2022
Short summary
Short summary
In this study, a comprehensive multi-disciplinary dataset for tropospheric water vapor was developed. Geodetic, photogrammetric, and atmospheric modeling and data fusion techniques were used to obtain maps of water vapor in a high spatial and temporal resolution. It could be shown that regional weather simulations for different seasons benefit from assimilating these maps and that the combination of the different observation techniques led to positive synergies.
Craig D. Smith, Eva Mekis, Megan Hartwell, and Amber Ross
Earth Syst. Sci. Data, 14, 5253–5265, https://doi.org/10.5194/essd-14-5253-2022, https://doi.org/10.5194/essd-14-5253-2022, 2022
Short summary
Short summary
It is well understood that precipitation gauges underestimate the measurement of solid precipitation (snow) as a result of systematic bias caused by wind. Relationships between the wind speed and gauge catch efficiency of solid precipitation have been previously established and are applied to the hourly precipitation measurements made between 2001 and 2019 in the automated Environment and Climate Change Canada observation network. The adjusted data are available for download and use.
Zen Mariani, Laura Huang, Robert Crawford, Jean-Pierre Blanchet, Shannon Hicks-Jalali, Eva Mekis, Ludovick Pelletier, Peter Rodriguez, and Kevin Strawbridge
Earth Syst. Sci. Data, 14, 4995–5017, https://doi.org/10.5194/essd-14-4995-2022, https://doi.org/10.5194/essd-14-4995-2022, 2022
Short summary
Short summary
Environment and Climate Change Canada (ECCC) commissioned two supersites in Iqaluit (64°N, 69°W) and Whitehorse (61°N, 135°W) to provide new and enhanced automated and continuous altitude-resolved meteorological observations as part of the Canadian Arctic Weather Science (CAWS) project. These observations are being used to test new technologies, provide recommendations to the optimal Arctic observing system, and evaluate and improve the performance of numerical weather forecast systems.
Eva Beele, Maarten Reyniers, Raf Aerts, and Ben Somers
Earth Syst. Sci. Data, 14, 4681–4717, https://doi.org/10.5194/essd-14-4681-2022, https://doi.org/10.5194/essd-14-4681-2022, 2022
Short summary
Short summary
This paper presents crowdsourced data from the Leuven.cool network, a citizen science network of around 100 low-cost weather stations distributed across Leuven, Belgium. The temperature data have undergone a quality control (QC) and correction procedure. The procedure consists of three levels that remove implausible measurements while also correcting for between-station and station-specific temperature biases.
Auguste Gires, Jerry Jose, Ioulia Tchiguirinskaia, and Daniel Schertzer
Earth Syst. Sci. Data, 14, 3807–3819, https://doi.org/10.5194/essd-14-3807-2022, https://doi.org/10.5194/essd-14-3807-2022, 2022
Short summary
Short summary
The Hydrology Meteorology and Complexity laboratory of École des Ponts ParisTech (https://hmco.enpc.fr) has made a data set of high-resolution atmospheric measurements (rainfall, wind, temperature, pressure, and humidity) available. It comes from a campaign carried out on a meteorological mast located on a wind farm in the framework of the Rainfall Wind Turbine or Turbulence project (RW-Turb; supported by the French National Research Agency – ANR-19-CE05-0022).
Bastian Kirsch, Cathy Hohenegger, Daniel Klocke, Rainer Senke, Michael Offermann, and Felix Ament
Earth Syst. Sci. Data, 14, 3531–3548, https://doi.org/10.5194/essd-14-3531-2022, https://doi.org/10.5194/essd-14-3531-2022, 2022
Short summary
Short summary
Conventional observation networks are too coarse to resolve the horizontal structure of kilometer-scale atmospheric processes. We present the FESST@HH field experiment that took place in Hamburg (Germany) during summer 2020 and featured a dense network of 103 custom-built, low-cost weather stations. The data set is capable of providing new insights into the structure of convective cold pools and the nocturnal urban heat island and variations of local temperature fluctuations.
Fan Mei, Mikhail S. Pekour, Darielle Dexheimer, Gijs de Boer, RaeAnn Cook, Jason Tomlinson, Beat Schmid, Lexie A. Goldberger, Rob Newsom, and Jerome D. Fast
Earth Syst. Sci. Data, 14, 3423–3438, https://doi.org/10.5194/essd-14-3423-2022, https://doi.org/10.5194/essd-14-3423-2022, 2022
Short summary
Short summary
This work focuses on an expanding number of data sets observed using ARM TBS (133 flights) and UAS (seven flights) platforms by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility. These data streams provide new perspectives on spatial variability of atmospheric and surface parameters, helping to address critical science questions in Earth system science research, such as the aerosol–cloud interaction in the boundary layer.
Cited articles
Bai, K. and Li, K.: LGHAP: Long-term Gap-free High-resolution Air Pollutants concentration dataset, Zenodo [data set], https://zenodo.org/communities/ecnu_lghap (last access: 3 April 2024), 2023a.
Bai, K. and Li, K.: LGHAP air pollution data user guide version 2, Zenodo [code], https://doi.org/10.5281/zenodo.10216396, 2023b.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2000), Zenodo [data set], https://doi.org/10.5281/zenodo.8307595, 2023c.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2000), Zenodo [data set], https://doi.org/10.5281/zenodo.8281206, 2023d.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2001), Zenodo [data set], https://doi.org/10.5281/zenodo.8281216, 2023e.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2001), Zenodo [data set], https://doi.org/10.5281/zenodo.8307597, 2023f.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2002), Zenodo [data set], https://doi.org/10.5281/zenodo.8281218, 2023g.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2002), Zenodo [data set], https://doi.org/10.5281/zenodo.8307599, 2023h.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2003), Zenodo [data set], https://doi.org/10.5281/zenodo.8281222, 2023i.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2003), Zenodo [data set], https://doi.org/10.5281/zenodo.8307601, 2023j.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2004), Zenodo [data set], https://doi.org/10.5281/zenodo.8281226, 2023k.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2004), Zenodo [data set], https://doi.org/10.5281/zenodo.8307605, 2023l.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2005), Zenodo [data set], https://doi.org/10.5281/zenodo.8281228, 2023m.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2005), Zenodo [data set], https://doi.org/10.5281/zenodo.8307607, 2023n.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2006), Zenodo [data set], https://doi.org/10.5281/zenodo.8287125, 2023o.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2006), Zenodo [data set], https://doi.org/10.5281/zenodo.8308225, 2023p.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2007), Zenodo [data set], https://doi.org/10.5281/zenodo.8287129, 2023q.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2007), Zenodo [data set], https://doi.org/10.5281/zenodo.8308227, 2023r.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2008), Zenodo [data set], https://doi.org/10.5281/zenodo.8287133, 2023s.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2008), Zenodo [data set], https://doi.org/10.5281/zenodo.8308231, 2023t.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2009), Zenodo [data set], https://doi.org/10.5281/zenodo.8287995, 2023u.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2009), Zenodo [data set], https://doi.org/10.5281/zenodo.8308233, 2023v.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2010), Zenodo [data set], https://doi.org/10.5281/zenodo.8288389, 2023w.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2010), Zenodo [data set], https://doi.org/10.5281/zenodo.8308237, 2023x.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2011), Zenodo [data set], https://doi.org/10.5281/zenodo.8288395, 2023y.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2011), Zenodo [data set], https://doi.org/10.5281/zenodo.8310586, 2023z.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2012), Zenodo [data set], https://doi.org/10.5281/zenodo.8288397, 2023aa.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2012), Zenodo [data set], https://doi.org/10.5281/zenodo.8310590, 2023ab.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2013), Zenodo [data set], https://doi.org/10.5281/zenodo.8287207, 2023ac.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2013), Zenodo [data set], https://doi.org/10.5281/zenodo.8310702, 2023ad.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2014), Zenodo [data set], https://doi.org/10.5281/zenodo.8288387, 2023ae.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2014), Zenodo [data set], https://doi.org/10.5281/zenodo.8310704, 2023ef.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2015), Zenodo [data set], https://doi.org/10.5281/zenodo.8289613, 2023ag.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2015), Zenodo [data set], https://doi.org/10.5281/zenodo.8310706, 2023ah.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2016), Zenodo [data set], https://doi.org/10.5281/zenodo.8289615, 2023ai.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2016), Zenodo [data set], https://doi.org/10.5281/zenodo.8310708, 2023aj.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2017), Zenodo [data set], https://doi.org/10.5281/zenodo.8294100, 2023ak.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2017), Zenodo [data set], https://doi.org/10.5281/zenodo.8310711, 2023al.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2018), Zenodo [data set], https://doi.org/10.5281/zenodo.8301364, 2023am.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2018), Zenodo [data set], https://doi.org/10.5281/zenodo.8313603, 2023an.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2019), Zenodo [data set], https://doi.org/10.5281/zenodo.8301367, 2023ao.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2019), Zenodo [data set], https://doi.org/10.5281/zenodo.8313611, 2023ap.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2020), Zenodo [data set], https://doi.org/10.5281/zenodo.8301375, 2023aq.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2020), Zenodo [data set], https://doi.org/10.5281/zenodo.8313613, 2023ar.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free AOD grids (2021), Zenodo [data set], https://doi.org/10.5281/zenodo.8301379, 2023as.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2021), Zenodo [data set], https://doi.org/10.5281/zenodo.8313615, 2023at.
Bai, K., Chang, N.-B., and Chen, C.-F.: Spectral Information Adaptation and Synthesis Scheme for Merging Cross-Mission Ocean Color Reflectance Observations from MODIS and VIIRS, IEEE T. Geosci. Remote, 54, 311–329, https://doi.org/10.1109/TGRS.2015.2456906, 2016a.
Bai, K., Chang, N.-B., Yu, H., and Gao, W.: Statistical bias correction for creating coherent total ozone record from OMI and OMPS observations, Remote Sens. Environ., 182, 150–168, https://doi.org/10.1016/j.rse.2016.05.007, 2016b.
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, 2019.
Bai, K., Li, K., Guo, J., Yang, Y., and Chang, N.-B.: Filling the gaps of in situ hourly PM2.5 concentration data with the aid of empirical orthogonal function analysis constrained by diurnal cycles, Atmos. Meas. Tech., 13, 1213–1226, https://doi.org/10.5194/amt-13-1213-2020, 2020.
Bai, K., Li, K., Ma, M., Li, K., Li, Z., Guo, J., Chang, N.-B., Tan, Z., and Han, D.: LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion, Earth Syst. Sci. Data, 14, 907–927, https://doi.org/10.5194/essd-14-907-2022, 2022a.
Bai, K., Li, K., Guo, J., and Chang, N.-B.: Multiscale and multisource data fusion for full-coverage PM2.5 concentration mapping: Can spatial pattern recognition come with modeling accuracy?, ISPRS J. Photogramm., 184, 31–44, https://doi.org/10.1016/j.isprsjprs.2021.12.002, 2022b.
Bai, K., Li, K., Sun, Y., Wu, L., Zhang, Y., Chang, N.-B., and Li, Z.: Global synthesis of two decades of research on improving PM2.5 estimation models from remote sensing and data science perspectives, Earth-Sci. Rev., 241, 104461, https://doi.org/10.1016/j.earscirev.2023.104461, 2023.
Beckers, J. M. and Rixen, M.: EOF calculations and data filling from incomplete oceanographic datasets, J. Atmos. Ocean. Technol., 20, 1839–1856, https://doi.org/10.1175/1520-0426(2003)020<1839:ECADFF>2.0.CO;2, 2003.
Bi, J., Belle, J. H., Wang, Y., Lyapustin, A. I., Wildani, A., and Liu, Y.: Impacts of snow and cloud covers on satellite-derived PM2.5 levels, Remote Sens. Environ., 221, 665–674, https://doi.org/10.1016/j.rse.2018.12.002, 2019.
Buchard, V., Randles, C. A., da Silva, A. M., Darmenov, A., Colarco, P. R., Govindaraju, R., Ferrare, R., Hair, J., Beyersdorf, A. J., Ziemba, L. D., and Yu, H.: The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part II: Evaluation and Case Studies, J. Climate, 30, 6851–6872, https://doi.org/10.1175/JCLI-D-16-0613.1, 2017.
Burke, M., Childs, M. L., de la Cuesta, B., Qiu, M., Li, J., Gould, C. F., Heft-Neal, S., and Wara, M.: The contribution of wildfire to PM2.5 trends in the USA, Nature, 622, 761–766, https://doi.org/10.1038/s41586-023-06522-6, 2023.
Che, H., Zhang, X.-Y., Xia, X., Goloub, P., Holben, B., Zhao, H., Wang, Y., Zhang, X.-C., Wang, H., Blarel, L., Damiri, B., Zhang, R., Deng, X., Ma, Y., Wang, T., Geng, F., Qi, B., Zhu, J., Yu, J., Chen, Q., and Shi, G.: Ground-based aerosol climatology of China: aerosol optical depths from the China Aerosol Remote Sensing Network (CARSNET) 2002–2013, Atmos. Chem. Phys., 15, 7619–7652, https://doi.org/10.5194/acp-15-7619-2015, 2015.
Chen, X., Ding, J., Liu, J., Wang, J., Ge, X., Wang, R., and Zuo, H.: Validation and comparison of high-resolution MAIAC aerosol products over Central Asia, Atmos. Environ., 251, 118273, https://doi.org/10.1016/j.atmosenv.2021.118273, 2021.
Giles, D. M., Sinyuk, A., Sorokin, M. G., Schafer, J. S., Smirnov, A., Slutsker, I., Eck, T. F., Holben, B. N., Lewis, J. R., Campbell, J. R., Welton, E. J., Korkin, S. V., and Lyapustin, A. I.: Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements, Atmos. Meas. Tech., 12, 169–209, https://doi.org/10.5194/amt-12-169-2019, 2019.
Guo, B., Wang, Z., Pei, L., Zhu, X., Chen, Q., Wu, H., Zhang, W., and Zhang, D.: Reconstructing MODIS aerosol optical depth and exploring dynamic and influential factors of AOD via random forest at the global scale, Atmos. Environ., 315, 120159, https://doi.org/10.1016/j.atmosenv.2023.120159, 2023.
Guo, J., Deng, M., Lee, S. S., Wang, F., Li, Z., Zhai, P., Liu, H., Lv, W., Yao, W., and Li, X.: Delaying precipitation and lightning by air pollution over the Pearl River Delta. Part I: Observational analyses, J. Geophys. Res.-Atmos., 121, 6472–6488, https://doi.org/10.1002/2015JD023257, 2016.
Guo, J., Su, T., Chen, D., Wang, J., Li, Z., Lv, Y., Guo, X., Liu, H., Cribb, M., and Zhai, P.: Declining summertime local-scale precipitation frequency over China and the United States, 1981–2012: The disparate roles of aerosols, Geophys. Res. Lett., 46, 13281–13289. https://doi.org/10.1029/2019GL085442, 2019.
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., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Huang, X., Song, Y., Yang, J., Wang, W., Ren, H., Dong, M., Feng, Y., Yin, H., and Li, J.: Toward accurate mapping of 30-m time-series global impervious surface area (GISA), Int. J. Appl. Earth Obs., 109, 102787, https://doi.org/10.1016/j.jag.2022.102787, 2022.
Jiang, J., Liu, J., Jiao, D., Zha, Y., and Cao, S.: Evaluation of MODIS DT, DB, and MAIAC Aerosol Products over Different Land Cover Types in the Yangtze River Delta of China, Remote Sens.-Basel, 15, 275, https://doi.org/10.3390/rs15010275, 2023.
Johnson, J. M. and Khoshgoftaar, T. M.: Survey on deep learning with class imbalance, J. Big Data, 6, 27, https://doi.org/10.1186/s40537-019-0192-5, 2019.
Li, K., Bai, K., Li, Z., Guo, J., and Chang, N.-B.: Synergistic data fusion of multimodal AOD and air quality data for near real-time full coverage air pollution assessment, J. Environ. Manage., 302, 114121, https://doi.org/10.1016/j.jenvman.2021.114121, 2022a.
Li, K., Bai, K., Ma, M., Guo, J., Li, Z., Wang, G., and Chang, N.-B.: Spatially gap free analysis of aerosol type grids in China: First retrieval via satellite remote sensing and big data analytics, ISPRS J. Photogramm., 193, 45–59, https://doi.org/10.1016/j.isprsjprs.2022.09.001, 2022b.
Li, K., Bai, K., Jiao, P., Sun, Y., Shao, L., Li, X., Liu, C., Ma, M., Qiu, S., Zheng, Z., Han, D., Li, R., Li, Z., Guo, J., Chang, N.: SCAGAT: A scene-aware ensemble learning graph attention network for global PM2.5 pollution mapping, in preparation, 2024.
Li, L., Franklin, M., Girguis, M., Lurmann, F., Wu, J., Pavlovic, N., Breton, C., Gilliland, F., and Habre, R.: Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling, Remote Sens. Environ., 237, 111584, https://doi.org/10.1016/j.rse.2019.111584, 2020.
Li, Z., Guo, J., Ding, A., Liao, H., Liu, J., Sun, Y., Wang, T., Xue, H., Zhang, H., and Zhu, B.: Aerosol and boundary-layer interactions and impact on air quality, Natl. Sci. Rev., 4, 810–833, https://doi.org/10.1093/nsr/nwx117, 2017.
Li, Z., Wang, Y., Guo, J., Zhao, C., Cribb, M. C., Dong, X., Fan, J., Gong, D., Huang, J., Jiang, M., Jiang, Y., Lee, S. S., Li, H., Li, J., Liu, J., Qian, Y., Rosenfeld, D., Shan, S., Sun, Y., Wang, H., Xin, J., Yan, X., Yang, X., Yang, X., Zhang, F., and Zheng, Y.: East Asian Study of Tropospheric Aerosols and their Impact on Regional Clouds, Precipitation, and Climate (EAST-AIRCPC), J. Geophys. Res.-Atmos., 124, 13026–13054, https://doi.org/10.1029/2019JD030758, 2019.
Li, Z. Q., Xu, H., Li, K. T., Li, D. H., Xie, Y. S., Li, L., Zhang, Y., Gu, X. F., Zhao, W., Tian, Q. J., Deng, R. R., Su, X. L., Huang, B., Qiao, Y. L., Cui, W. Y., Hu, Y., Gong, C. L., Wang, Y. Q., Wang, X. F., Wang, J. P., Du, W. B., Pan, Z. Q., Li, Z. Z., and Bu, D.: Comprehensive study of optical, physical, chemical, and radiative properties of total columnar atmospheric aerosols over China: An overview of sun–Sky radiometer observation network (SONET) measurements, B. Am. Meteorol. Soc., 99, 739–755, https://doi.org/10.1175/BAMS-D-17-0133.1, 2018.
Liang, F., Xiao, Q., Huang, K., Yang, X., Liu, F., Li, J., Lu, X., Liu, Y., and Gu, D.: The 17-y spatiotemporal trend of PM2.5 and its mortality burden in China, P. Natl. Acad. Sci. USA, 117, 25601–25608, https://doi.org/10.1073/pnas.1919641117, 2020.
Liu, J., Ren, C., Huang, X., Nie, W., Wang, J., Sun, P., Chi, X., and Ding, A.: Increased Aerosol Extinction Efficiency Hinders Visibility Improvement in Eastern China, Geophys. Res. Lett., 47, e2020GL090167, https://doi.org/10.1029/2020GL090167, 2020.
Liu, N., Zou, B., Feng, H., Wang, W., Tang, Y., and Liang, Y.: Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China, Atmos. Chem. Phys., 19, 8243–8268, https://doi.org/10.5194/acp-19-8243-2019, 2019.
Liu, X. and Wang, M.: Filling the gaps of missing data in the merged VIIRS SNPP/NOAA-20 ocean color product using the DINEOF method, Remote Sens., 11, 178, https://doi.org/10.3390/rs11020178, 2019.
Lyapustin, A., Wang, Y., Laszlo, I., Kahn, R., Korkin, S., Remer, L., Levy, R., and Reid, J. S.: Multiangle implementation of atmospheric correction (MAIAC): 2. Aerosol algorithm, J. Geophys. Res.-Atmos., 116, 1–15, https://doi.org/10.1029/2010JD014986, 2011.
Lyapustin, A., Wang, Y., Korkin, S., and Huang, D.: MODIS Collection 6 MAIAC algorithm, Atmos. Meas. Tech., 11, 5741–5765, https://doi.org/10.5194/amt-11-5741-2018, 2018.
Ma, Z., Liu, Y., Zhao, Q., Liu, M., Zhou, Y., and Bi, J.: Satellite-derived high resolution PM2.5 concentrations in Yangtze River Delta Region of China using improved linear mixed effects model, Atmos. Environ., 133, 156–164, https://doi.org/10.1016/j.atmosenv.2016.03.040, 2016.
Martins, V. S., Lyapustin, A., Carvalho, L. A. S., Barbosa, C. C. F., and Novo, E. M. L. M.: Validation of high-resolution MAIAC aerosol product over South America, J. Geophys. Res.-Atmos., 122, 7537–7559, https://doi.org/10.1002/2016JD026301, 2017.
Mhawish, A., Banerjee, T., Sorek-Hamer, M., Lyapustin, A., Broday, D. M., and Chatfield, R.: Comparison and evaluation of MODIS Multi-angle Implementation of Atmospheric Correction (MAIAC) aerosol product over South Asia, Remote Sens. Environ., 224, 12–28, https://doi.org/10.1016/j.rse.2019.01.033, 2019.
Qin, W., Fang, H., Wang, L., Wei, J., Zhang, M., Su, X., Bilal, M., and Liang, X.: MODIS high-resolution MAIAC aerosol product: Global validation and analysis, Atmos. Environ., 264, 118684, https://doi.org/10.1016/j.atmosenv.2021.118684, 2021.
Randles, C. A., da Silva, A. M., Buchard, V., Colarco, P. R., Darmenov, A., Govindaraju, R., Smirnov, A., Holben, B., Ferrare, R., Hair, J., Shinozuka, Y., and Flynn, C. J.: The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation, J. Climate, 30, 6823–6850, https://doi.org/10.1175/JCLI-D-16-0609.1, 2017.
Shannon, C. E.: A Mathematical Theory of Communication, Bell Syst. Tech. J., 27, 379–423, 1948.
Shi, H., Zhang, Y., Chen, Y., Ji, S., Dong, Y.: Resampling algorithms based on sample concatenation for imbalance learning, Knowl.-Based Syst., 245, 108592, https://doi.org/10.1016/j.knosys.2022.108592, 2022.
Sogacheva, L., Popp, T., Sayer, A. M., Dubovik, O., Garay, M. J., Heckel, A., Hsu, N. C., Jethva, H., Kahn, R. A., Kolmonen, P., Kosmale, M., de Leeuw, G., Levy, R. C., Litvinov, P., Lyapustin, A., North, P., Torres, O., and Arola, A.: Merging regional and global aerosol optical depth records from major available satellite products, Atmos. Chem. Phys., 20, 2031–2056, https://doi.org/10.5194/acp-20-2031-2020, 2020.
Tang, Q., Bo, Y., and Zhu, Y.: Spatiotemporal fusion of multiple-satellite aerosol optical depth (AOD) products using Bayesian maximum entropy method, J. Geophys. Res.-Atmos., 121, 4034–4048, https://doi.org/10.1002/2015JD024571, 2016.
Up in the aerosol, Nat. Geosci., 15, 157, https://doi.org/10.1038/s41561-022-00915-4, 2022.
Wang, Y. W. and Yang, Y. H.: China's dimming and brightening: evidence, causes and hydrological implications, Ann. Geophys., 32, 41–55, https://doi.org/10.5194/angeo-32-41-2014, 2014.
Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M.: Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications, Remote Sens. Environ., 252, 112136, https://doi.org/10.1016/j.rse.2020.112136, 2021.
Wei, X., Chang, N.-B., Bai, K., and Gao, W.: Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives, Crit. Rev. Env. Sci. Tec., 50, 1640–1725, https://doi.org/10.1080/10643389.2019.1665944, 2020.
Wei, X., Bai, K., Chang, N.-B., and Gao, W.: Multisource hierarchical data fusion for high-resolution AOD mapping in a forest fire event, International J. Appl. Earth Obs. Geoinf., 102, 102366, https://doi.org/10.1016/j.jag.2021.102366, 2021.
WHO: Ambient air pollution, https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health (last access: 3 April 2024), 2022.
Wild, M., Wacker, S., Yang, S., and Sanchez-Lorenzo, A.: Evidence for Clear-Sky Dimming and Brightening in Central Europe, Geophys. Res. Lett., 48, e2020GL092216, https://doi.org/10.1029/2020GL092216, 2021.
Xiao, Q., Wang, Y., Chang, H. H., Meng, X., Geng, G., Lyapustin, A., and Liu, Y.: Full-coverage high-resolution daily PM2.5 estimation using MAIAC AOD in the Yangtze River Delta of China, Remote Sens. Environ., 199, 437–446, https://doi.org/10.1016/j.rse.2017.07.023, 2017.
Xiao, Q., Geng, G., Cheng, J., Liang, F., Li, R., Meng, X., Xue, T., Huang, X., Kan, H., Zhang, Q., and He, K.: Evaluation of gap-filling approaches in satellite-based daily PM2.5 prediction models, Atmos. Environ., 244, 117921, https://doi.org/10.1016/j.atmosenv.2020.117921, 2021.
Xu, H., Guang, J., Xue, Y., de Leeuw, G., Che, Y. H., Guo, J., He, X. W., and Wang, T. K.: A consistent aerosol optical depth (AOD) dataset over mainland China by integration of several AOD products, Atmos. Environ., 114, 48–56, https://doi.org/10.1016/j.atmosenv.2015.05.023, 2015.
Yang, X., Zhao, C., Zhou, L., Wang, Y., and Liu, X.: Distinct impact of different types of aerosols on surface solar radiation in China, J. Geophys. Res.-Atmos., 121, 6459–6471, https://doi.org/10.1002/2016JD024938, 2016.
Yang, Y., Ren, L., Li, H., Wang, H., Wang, P., Chen, L., Yue, X., and Liao, H.: Fast Climate Responses to Aerosol Emission Reductions During the COVID-19 Pandemic, Geophys. Res. Lett., 47, e2020GL089788, https://doi.org/10.1029/2020GL089788, 2020.
Zhang, Q., Zheng, Y., Tong, D., Shao, M., Wang, S., Zhang, Y., Xu, X., Wang, J., He, H., Liu, W., Ding, Y., Lei, Y., Li, J., Wang, Z., Zhang, X., Wang, Y., Cheng, J., Liu, Y., Shi, Q., Yan, L., Geng, G., Hong, C., Li, M., Liu, F., Zheng, B., Cao, J., Ding, A., Gao, J., Fu, Q., Huo, J., Liu, B., Liu, Z., Yang, F., He, K., and Hao, J.: Drivers of improved PM2.5 air quality in China from 2013 to 2017, P. Natl. Acad. Sci. USA, 116, 24463–24469, https://doi.org/10.1073/pnas.1907956116, 2019.
Zhang, T., Zhou, Y., Zhao, K., Zhu, Z., Asrar, G. R., and Zhao, X.: Gap-filling MODIS daily aerosol optical depth products by developing a spatiotemporal fitting algorithm, Geosci. Remote Sens., 59, 762–781, https://doi.org/10.1080/15481603.2022.2060596, 2022.
Zhao, C., Yang, Y., Fan, H., Huang, J., Fu, Y., Zhang, X., Kang, S., Cong, Z., Letu, H., and Menenti, M.: Aerosol characteristics and impacts on weather and climate over the Tibetan Plateau, Natl. Sci. Rev., 7, 492–495, https://doi.org/10.1093/nsr/nwz184, 2020.
Short summary
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.
A global gap-free high-resolution air pollutant dataset (LGHAP v2) was generated to provide...
Altmetrics
Final-revised paper
Preprint