Article
Geosciences, Multidisciplinary
Jie Jiang, Tianjun Zhou
Summary: In 2021, Central Asia experienced a severe agricultural drought, resulting in mass die-offs of crops and livestock. It has been unclear how much human activity has contributed to the decline in soil moisture in this region. Through analysis of simulation results, this study finds that the aggravation of agricultural droughts in southern Central Asia since 1992 can be attributed to both human-induced forcing and internal variability associated with the Interdecadal Pacific Oscillation (IPO). The findings emphasize the importance of considering the interplay between anthropogenic forcing and natural variability in policymaking in this climate-sensitive region.
Article
Meteorology & Atmospheric Sciences
Zhu Liu, Jingheng Huang, Xiong Xiao, Xiaolong Tong
Summary: The study evaluated the abilities of 22 GCMs of CMIP6 in simulating extreme precipitation over Central Asia and found that models have difficulties in capturing overall spatial patterns, with better performance in summer but still room for improvements in consistent bias and spatial distribution.
ATMOSPHERIC RESEARCH
(2022)
Article
Engineering, Civil
Junqiang Yao, Yaning Chen, Jing Chen, Yong Zhao, Dilinuer Tuoliewubieke, Jiangang Li, Lianmei Yang, Weiyi Mao
Summary: Studies on precipitation changes in Central Asia show an increasing trend in total and extreme precipitation indices, especially in the wetter sub-regions. Model simulations suggest a robust increase in total precipitation, extreme precipitation, and consecutive dry days in the region under different climate scenarios.
JOURNAL OF HYDROLOGY
(2021)
Article
Engineering, Civil
Wei Wei, Shan Zou, Weili Duan, Yaning Chen, Shuai Li, Yiqi Zhou
Summary: Increased sea surface temperature, evaporation, air temperature, and moisture holding capacity associated with climate change may enhance the transport of moisture to Central Asia, resulting in increased extreme precipitation events. The study analyzed 10 extreme precipitation indices in Central Asia from 1950 to 2019 and quantified the influence of large-scale climate factors on these events. The results showed an increasing trend in extreme precipitation events, with a significant change in 1986. The changes in climate indices and two-factor interactions were identified as the main reasons for the increase in extreme precipitation events. The study provides important insights for understanding the changes in extreme precipitation events and has implications for water resource management and disaster prevention in Central Asia.
JOURNAL OF HYDROLOGY
(2023)
Article
Meteorology & Atmospheric Sciences
Victor Nnamdi Dike, Zhaohui Lin, Kece Fei, Gaby S. Langendijk, Debashis Nath
Summary: This study investigates the representation of precipitation extremes and their future changes in Central Asia using CMIP6 models. The results suggest that spring precipitation will become more intense, while summer precipitation is projected to decrease, leading to potential water resource challenges in the region.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2022)
Article
Engineering, Civil
Peixi Li, Zhongbo Yu, Peng Jiang, Changxian Wu
Summary: The frequency of extreme precipitation is increasing globally, posing a significant threat to human and natural systems. Understanding the spatiotemporal evolution of extreme precipitation on a regional scale is critical. The study found that extreme precipitation events in the Yangtze River basin have been increasing in frequency and spatial coverage since 1970, with potential relationships with large-scale climate indices such as ENSO and PDO.
JOURNAL OF HYDROLOGY
(2021)
Article
Meteorology & Atmospheric Sciences
Yue Zhang, Wen Zhou, Xin Wang, Xuan Wang, Ruhua Zhang, Yana Li, Jianping Gan
Summary: This study investigates the influence of Indian Ocean Dipole (IOD) and El Nino-Southern Oscillation (ENSO) on seasonal precipitation variation over eastern China. The results show that IOD primarily affects precipitation in South China during autumn and in the region between the Yangtze River and Yellow River during summer, while ENSO primarily boosts precipitation over eastern China during winter and spring. The distinct effects of IOD on ensuing summer precipitation contrast with the weaker signals associated with ENSO. These precipitation responses are associated with anomalous anticyclonic circulation patterns and the direct and indirect heating effects of IOD.
ATMOSPHERIC RESEARCH
(2022)
Article
Meteorology & Atmospheric Sciences
Sining Ling, Riyu Lu
Summary: This study investigates the impact of El Nino-Southern Oscillation (ENSO) on the seasonal evolution of the Asian-Pacific summer monsoon. The results show that El Nino delays the seasonal march of the monsoon and Asian jet, while La Nina does not exhibit clear variations. These variations can be explained by the changes in tropical tropospheric temperatures and meridional temperature gradient.
JOURNAL OF CLIMATE
(2022)
Article
Multidisciplinary Sciences
Alexandre Tuel, Olivia Martius
Summary: The study shows that climate variability and geographical location are key factors influencing the temporal clustering of extreme precipitation events. ENSO, IOD and MJO are the major drivers of extreme precipitation clustering in tropical regions, while the North Atlantic Oscillation and Pacific North American pattern impact clustering in the Northern Hemisphere.
Article
Meteorology & Atmospheric Sciences
Joseph A. Jonaitis, L. Baker Perry, Peter T. Soule, Christopher Thaxton, Marcos F. Andrade-Flores, Tania Ita Vargas, Laura Ticona
Summary: Precipitation in the outer tropical Andes is highly seasonal and influenced by ENSO, with significant spatiotemporal differences. Analysis of high-elevation meteorological station data revealed distinct precipitation variability characteristics, aiding in improved seasonal climate prediction and water resource management.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2021)
Article
Multidisciplinary Sciences
Jinah Kim, Minho Kwon, Sung-Dae Kim, Jong-Seong Kug, Joon-Gyu Ryu, Jaeil Kim
Summary: This study proposes a neural network model called STANet, which is used to learn spatiotemporal representations and anomaly predictions from geophysical data. The model improves upon existing architectures by learning spatial features globally and encoding long-term sequential features using a visual attention mechanism. Experimental results show that STANet outperforms state-of-the-art models in predicting El Nino events and provides a good understanding of the spatiotemporal behavior of global sea surface temperature and oceanic heat content.
SCIENTIFIC REPORTS
(2022)
Article
Geosciences, Multidisciplinary
Xiaodan Guan, Kaiwei Zhu, Xiaoqian Huang, Xinrui Zeng, Yongli He
Summary: The semi-arid regions of East Asia are influenced by both the monsoon and westerly winds, with wet years seeing an enhancement in both systems leading to more rainfall, while dry years experience a weakening of both resulting in less precipitation. The interaction between the monsoon and westerlies is dynamic, with the monsoon shifting westward in wet years and impacting rainfall distribution.
FRONTIERS IN EARTH SCIENCE
(2021)
Article
Geosciences, Multidisciplinary
Gen Li, Lin Chen, Bo Lu
Summary: July is the rainy peak month in central China, with significant variation in local precipitation leading to droughts and floods. Predicting the central China July precipitation (CCJP) is important but challenging. We propose robust seasonal predictors and develop a physics-based empirical model for CCJP prediction, achieving a high seasonal prediction skill of 0.81 during 1993-2021. Our findings have potential benefits for local society and economy.
GEOPHYSICAL RESEARCH LETTERS
(2023)
Article
Meteorology & Atmospheric Sciences
Sunyong Kim, Jong-Seong Kug
Summary: The study investigates the seasonal transition of surface temperature in East Asia based on warm/cold ENSO developing phases, finding that the different seasonal transitions in East Asia according to phases of ENSO are mostly explained by atmospheric responses to seasonally-dependent tropical/subtropical precipitation forcings. Additionally, the Coupled Model Intercomparison Project Phase 5 models reasonably simulate the relatively rapid temperature transition in East Asia during La Nina years.
Article
Meteorology & Atmospheric Sciences
Junaid Dar, Abdul Qayoom Dar, Munir Ahmad Nayak
Summary: Mid-latitude storms play a crucial role in connecting global teleconnection patterns and regional precipitation distribution in South Asia during boreal winter. This study examines the spatio-temporal variability of seasonal precipitation over South Asia in the past four decades, revealing distinct regional precipitation patterns across seasons. The results show that various climate indices influence winter precipitation in the region, and the relationship between ENSO and winter precipitation is modulated by PDO and AMO. The atmospheric patterns identified in this study significantly improve winter precipitation forecasting compared to traditional climate indices, especially during positive AMO winters.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2023)
Article
Chemistry, Analytical
Bo Sun, Yang Zhang, Qiming Zhou, Duo Gao
Article
Meteorology & Atmospheric Sciences
Jianfeng Li, Thian Yew Gan, Yongqin David Chen, Xihui Gu, Zengyun Hu, Qiming Zhou, Yangchen Lai
ATMOSPHERIC RESEARCH
(2020)
Editorial Material
Computer Science, Information Systems
Qiming Zhou, Jianfeng Li
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2020)
Article
Environmental Sciences
Jing Qian, Qiming Zhou, Xi Chen, Bo Sun
Article
Environmental Sciences
Qiming Zhou, Ali Ismaeel
Summary: This study analyzed the seasonal cropland trends in the Indus River Plain using multi-year remote sensing data and found that over 50% of cropland significantly improved from 2003 to 2018. The research also revealed correlations between different agrometeorological parameters and the enhanced vegetation index (EVI), showing different relationships during water-stressed and high soil moisture crop seasons.
Article
Meteorology & Atmospheric Sciences
Qiming Zhou, Deliang Chen, Zengyun Hu, Xi Chen
Summary: The study introduces a new method for evaluating climate or hydrology models or data, which is more flexible compared to the traditional Taylor diagram approach. By extending the statistical metrics of DISO and conducting comparative analysis, the study finds that DISO can better express the overall quality of a model or dataset.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2021)
Article
Environmental Sciences
Huizeng Liu, Qingquan Li, Yan Bai, Chao Yang, Junjie Wang, Qiming Zhou, Shuibo Hu, Tiezhu Shi, Xiaomei Liao, Guofeng Wu
Summary: This study explored machine learning methods for satellite retrieval of particulate organic carbon (POC) concentrations in global oceans. Results showed that machine learning methods outperformed the traditional algorithm, with XGBoost being the most robust and ANN being more effective in optically complex waters with high POC.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Environmental Sciences
Huizeng Liu, Xianqiang He, Qingquan Li, Susanne Kratzer, Junjie Wang, Tiezhu Shi, Zhongwen Hu, Chao Yang, Shuibo Hu, Qiming Zhou, Guofeng Wu
Summary: The study proposes a hybrid approach for estimating UV Rrs from visible bands and evaluates its performance using in situ and satellite data, showing high accuracy in both clear open ocean and optically complex waters. The model-estimated UV Rrs may improve the accuracy of absorption coefficients in semi-analytical IOPs algorithm, indicating great potential for reconstructing UV Rrs data and enhancing IOPs retrieval for historical satellite sensors.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Environmental Sciences
Xia Wang, Gang Yin, Zengyun Hu, Daihai He, Qianqian Cui, Xiaomei Feng, Zhidong Teng, Qi Hu, Jiansen Li, Qiming Zhou
Summary: This study used global COVID-19 data to analyze the dynamic variations of 84 countries in different climate regions, proposing a new method to obtain transmission rates in different climate zones. The findings suggest that the COVID-19 pandemic may persist for a long time or enter into regular circulation, with higher infection rates in temperate and cold climate regions.
Article
Remote Sensing
Qiming Zhou, Ali Ismaeel
Summary: This study successfully predicted wheat and rice yields in Pakistan's Punjab province using satellite remote sensing data and Machine Learning Regression models, with the Gaussian process regression model performing the best among the five models compared.
GEO-SPATIAL INFORMATION SCIENCE
(2021)
Article
Water Resources
Qiming Zhou, Junyi Huang, Zengyun Hu, Gang Yin
Summary: This study investigates the changes in terrestrial water storage (TWS) in Xinjiang, China, in response to climate change. The results show an overall decreasing trend in TWS, with regional disparities in the change rates.
HYDROLOGICAL SCIENCES JOURNAL
(2022)
Article
Environmental Sciences
Bo Sun, Yang Zhang, Qiming Zhou, Xinchang Zhang
Summary: This study explores the effectiveness of a semi-supervised classification framework with multi-source data for detailed urban landuse classification with a few labeled samples. The results show that the classification accuracy of the semi-supervised method is generally on par with that of traditional supervised methods, and less labeled samples are needed to achieve a comparable result. The study also found that the classification accuracy using multi-source data is significantly higher than that with any single data source being applied.
Article
Geosciences, Multidisciplinary
Zengyun Hu, Deliang Chen, Xi Chen, Qiming Zhou, Yuzhou Peng, Jianfeng Li, Yanfang Sang
Summary: With the rapid development of big data, the assessment of data quality and model performance has become increasingly important. However, existing assessment systems often focus on specific aspects and lack comprehensive evaluation. To address this issue, a new assessment system named CCHZ-DISO has been developed, based on the contributions of Xi Chen, Deliang Chen, Zengyun Hu, and Qiming Zhou. CCHZ-DISO builds on the principles of Euclidean Distance and flexible determination of statistical metrics, making it suitable for various scientific subjects.
GEOPHYSICAL RESEARCH LETTERS
(2022)
Article
Environmental Sciences
Honglin Zhu, Huizeng Liu, Qiming Zhou, Aihong Cui
Summary: This study presents a framework combining machine learning-based downscaling algorithm, residual correction, and precipitation calibration for accurate high-resolution precipitation estimation. The results show that the machine learning-based methods outperform conventional approaches, with spatial random forest and eXtreme gradient boosting performing the best in generating high-resolution precipitation. The geographical difference analysis calibration process significantly improves the downscaled results.
Article
Environmental Sciences
Meng Gao, Yang Yang, Hong Liao, Bin Zhu, Yuxuan Zhang, Zirui Liu, Xiao Lu, Chen Wang, Qiming Zhou, Yuesi Wang, Qiang Zhang, Gregory R. Carmichael, Jianlin Hu
Summary: The study found that emission control measures during the APEC summit reduced both the mass concentration of BC and the BC coating materials, resulting in decreased light absorption and enhancement. This reduction in BC light absorption led to an increase in planetary boundary layer height and a decrease in near-surface PM2.5 concentrations, but an increase in near-surface O-3 concentrations in the North China Plain.
ATMOSPHERIC CHEMISTRY AND PHYSICS
(2021)
