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Your search keyword '"Xin, Jinyuan"' showing total 298 results
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298 results on '"Xin, Jinyuan"'

42. An Optimal Weighted Ensemble Machine Learning Approach to Accurate Estimate the Coastal Boundary Layer Height Using ERA5 Multi‐Variables.

Author

Peng, Kecheng, Xin, Jinyuan, Zhu, Xiaoqian, Xu, Qiang, Wang, Xiaoyuan, Wang, Weifeng, Tan, Yulong, Zhao, Dandan, Jia, Danjie, Cao, Xiaoqun, Ren, Xinbing, Ma, Yongjing, Wang, Guangjie, and Wang, Zifa

Subjects
MACHINE learning, MARINE meteorology, HUMIDITY, WIND speed, WEATHER
Abstract

The coastal boundary layer height (CBLH/Coastal‐BLH) is critical in determining the exchange of heat, momentum, and materials between the land and ocean, thereby regulating the local climate and weather change. However, due to the complexity of geographical characteristics and meteorological conditions, accurate estimation of the CBLH remains challenging. Herein, based on continuous high‐resolution measurements of CBL performed from November 2019 to April 2020 in coastal Ningbo city in eastern China, an optimal weighted ensemble model (OWEM) integrating multi‐meteorological variables of the ERA5 reanalysis data sets is constructed and validated to estimate the CBLH. The mean absolute percentage error of the derived CBLH by OWEM is as low as 3%–5%, significantly lower than that of 36%–65% of the ERA5 CBLH products. Furthermore, three categories of different weather scenarios, that is, sunny, cloudy, and rainy, are separately discussed, and OWEM shows greater performance and higher accuracies in comparison with the traditional Least Absolute Shrinkage and Selection Operator, Random Forest, Adaboost, LightGBM, and ensemble model, among which, OWEM under fair weather days behave best, with a robust R2 of 0.97 and a minimum mean absolute error (MAE) of 23 m. Further training results based on wind flow classification, that is, land breeze, sea breeze, and parallel wind, also indicate the outperformance of OWEM than other models, with a relatively large error in parallel wind of 50 m. Subsequent analysis of the Shapley Additive Explanations method strongly correlated with model feature importance, both reveal that thermodynamic factors such as temperature (T2m) and wind velocity (10 m U) are the major factors positively related to estimation accuracy during sunny days. Nevertheless, Relative Humidity dominates on rainy and cloudy days, TP on land breeze days, and dynamic variables like 10 m U and 10 m V on entire types of wind flow weather. In conclusion, the accurate estimation of CBLH from OWEM serves as a feasible and innovative approach, providing technical support for marine meteorology and related engineering applications, for example, onshore wind power, coastal ecological protection, etc. Plain Language Summary: The coastal boundary layer height (CBLH) is fast becoming a key parameter in atmosphere‐ocean interaction studies, and machine learning has been thought of as an effective way to accurate estimate it. In this work, we present a new model called optimal weighted ensemble model (OWEM), which integrates several meteorological variables from reanalysis data sets to estimate the CBLH and make a comparison with the ERA5 BLH under different weather conditions. We also analyze the feature importance that affects estimation accuracy using the Shapley Additive Explanations method and identify several variables as essential factors, to better understand the applicability and characterization of the OWEM for sunny, cloudy, rainy, and different wind flow days. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Long-term variability in black carbon emissions constrained by gap-filled absorption aerosol optical depth and associated premature mortality in China.

Author

Zhao, Wenxin, Zhao, Yu, Zheng, Yu, Chen, Dong, Xin, Jinyuan, Li, Kaitao, Che, Huizheng, Li, Zhengqiang, Ma, Mingrui, and Hang, Yun

Subjects
EARLY death, CARBON-black, CARBON emissions, SOOT, LIGHT absorption, AIR quality
Abstract

Black carbon (BC) plays an important role in air quality, public health, and climate, but its long-term variations in emissions and health effects have been insufficiently understood for China. Here, we present the spatiotemporal evolution of BC emissions and the associated premature mortality in China during 2000–2020 based on an integrated framework combining satellite observations from the Ozone Monitoring Instrument (OMI), an extreme gradient boosting (XGBoost) algorithm, a "top-down" inversion approach, and an exposure–response model. We found that the "bottom-up" approach most likely underestimated BC emissions, particularly in less developed western and remote areas. Pollution controls were estimated to reduce the annual BC emissions by 26 % during 2010–2020, reversing the 8 % growth during 2000–2010. BC emissions in the main coal-producing provinces declined by 2010 but rebounded afterwards. By contrast, provinces with higher economic and urbanization levels experienced emission growth (0.05–0.10 Mg km -2 yr -1) by 2010 and declined greatly (0.07–0.23 Mg km -2 yr -1) during 2010–2020. The national annual BC-associated premature mortality ranged between 733 910 (95 % confidence interval: 676 790–800 250) and 937 980 cases (864 510–1 023 400) for different years. The changing BC emissions contributed to an increase of 78 590 cases (72 520–85 600) during 2000–2005 and a decrease of 133 360 cases (123 150–145 180) during 2010–2015. Strategies differentiated by region are needed for further reducing BC emissions and their health and climate impacts. [ABSTRACT FROM AUTHOR]

Published
2024
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49. High-Resolution Remote Sensing of the Gradient Richardson Number in a Megacity Boundary Layer.

Author

Yang, Simin, Ma, Yongjing, Zhang, Wenyu, Ren, Xinbing, Peng, Kecheng, Ahmad, Masroor, Jia, Danjie, Zhao, Dandan, Kong, Lingbin, Ma, Yining, and Xin, Jinyuan

Subjects
RICHARDSON number, BOUNDARY layer (Aerodynamics), ATMOSPHERIC boundary layer, MEGALOPOLIS, REMOTE sensing
Abstract

The Gradient Richardson Number (Ri) is an important parameter for appraising the stability and turbulence exchange at the atmospheric boundary layer (ABL). However, high-resolution measurements of Ri profiles are rarely reported, especially in megacities. In this study, a Doppler wind lidar and a microwave radiometer were simultaneously utilized to measure the 2 km Ri vertical profile in downtown Beijing. These measurements were verified to have high accuracy compared with observations from a 325 m meteorological tower, with root-mean-square errors (RMSEs) of less than 1.66 K, 7.9%, and 1.45 m/s for the temperature, relative humidity, and wind speed (WS) for all altitudes and corresponding Pearson correlation coefficients (R) of 0.97, 0.93, and 0.81. The inter-comparisons of different spatial (25 m, 50 m, 100 m) and temporal resolutions (1 min, 30 min, 1 h) form a 3 × 3 resolution matrix of Ri, in which the 1 h temporal resolution of Ri overestimates the intensity and active area of turbulence. The Ri value retrieved from the 100 m spatial resolution data overestimates these by half as it misidentifies the height of the stable area at the near surface. There are significant differences between the data with a 1 min temporal resolution and a 25 m spatial resolution (defined as the standard resolution of Ri), and the rest of the data in the resolution matrix (defined as data at other resolutions), with an RMSE > 1 and an R < 0.8. The difference between data at the standard resolution and data at other resolutions increases with elevations, which results from frequent weather processes or from water-vapor blocking at higher altitudes. The Ri profiles reveal that the atmospheric layer at altitudes from 100 m to 500 m in daytime is unstable, with Ri < 0, while it is neutral, with 0 < Ri < 0.25, at night-time from 200 m to 400 m. The atmosphere above the ABL in a megacity is rather stable, with Ri > 0.25, whereas below the ABL, it is neutral or unstable, which is due to drastic changes in the WS and temperature that are affected by the topography and surface friction. [ABSTRACT FROM AUTHOR]

Published
2024
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