Spatiotemporal characteristics of ultraviolet radiation in recent 54 years from measurements and reconstructions over the Tibetan Plateau

Liu, H.; Hu, Bo; Zhang, L.; Wang, Y. S.; Tian, Pengfei

doi:10.1002/2015jd024378

Cited by 13 publications

(6 citation statements)

References 63 publications

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“…The UVDOWN is measured by CM-11 pyranometers at five sites from Chinese Ecosystem Research Network in the NCP, including Beijing, Luancheng, Yucheng, Jiaozhouwan, and Fengqiu (SI Appendix, Fig. S1B) (45).…”

Section: Methodsmentioning

confidence: 99%

Aerosol–photolysis interaction reduces particulate matter during wintertime haze events

Bei

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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Aerosol–radiation interaction (ARI) plays a significant role in the accumulation of fine particulate matter (PM2.5) by stabilizing the planetary boundary layer and thus deteriorating air quality during haze events. However, modification of photolysis by aerosol scattering or absorbing solar radiation (aerosol–photolysis interaction or API) alters the atmospheric oxidizing capacity, decreases the rate of secondary aerosol formation, and ultimately alleviates the ARI effect on PM2.5 pollution. Therefore, the synergetic effect of both ARI and API can either aggravate or even mitigate PM2.5 pollution. To test the effect, a fully coupled Weather Research and Forecasting (WRF)-Chem model has been used to simulate a heavy haze episode in North China Plain. Our results show that ARI contributes to a 7.8% increase in near-surface PM2.5. However, API suppresses secondary aerosol formation, and the combination of ARI and API results in only 4.8% net increase of PM2.5. Additionally, API increases the solar radiation reaching the surface and perturbs aerosol nucleation and activation to form cloud condensation nuclei, influencing aerosol–cloud interaction. The results suggest that API reduces PM2.5 pollution during haze events, but adds uncertainties in climate prediction.

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Section: Methodsmentioning

confidence: 99%

Aerosol–photolysis interaction reduces particulate matter during wintertime haze events

Bei

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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“…Latitudinally, there was an increasing trend from south to north. The daily UV radiation presented a decreasing trend from 1961 to 2014 throughout China, except over the Tibetan Plateau region (Liu et al 2016(Liu et al , 2017a. There was a decreasing linear trend from the early 1960s to the late 1980s, which then tapered off and reached a steadily low level from the 1990s throughout China.…”

Section: Long-term Variation In Uv Radiation Over Chinamentioning

confidence: 94%

Spatiotemporal characteristics of ultraviolet solar radiation in China

Liu

2019

Atmospheric and Oceanic Science Letters

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UV radiation plays an important role in climate change and photochemical reactions, and in Ecosystem Research. In this study, the authors presented study results of China's National Basic Research Program Study on the climatic characteristics and reconstruction method of UV radiation in China. The spatiotemporal variation of UV radiation in China has been discussed, and then an efficient modeling method has been established to obtain history UV radiation data to analyse the variation trends of UV radiation in China. Finally, the influence of aerosol, cloud, ozone, and water vapor on UV radiation has been discussed.

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“…Conventional statistical methods often focus on a single ground site [29] by establishing the relationship between satellite observations and ground measurements. Wang et al [13] used the UV radiation observation data at Yanting, China, to establish the regression relationship between the hourly observed UV irradiation and relative optical air mass, cloud modification factor, and clearness index; the root mean square error (RMSE) under all-sky conditions was 10.3%.…”

Section: Introductionmentioning

confidence: 99%

Estimation of 1-km Resolution All-Sky Instantaneous Erythemal UV-B with MODIS Data Based on a Deep Learning Method

Zhao

2022

Remote Sensing

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Although ultraviolet-B (UV-B) radiation reaching the ground represents a tiny fraction of the total solar radiant energy, it significantly affects human health and global ecosystems. Therefore, erythemal UV-B monitoring has recently attracted significant attention. However, traditional UV-B retrieval methods rely on empirical modeling and handcrafted features, which require expertise and fail to generalize to new environments. Furthermore, most traditional products have low spatial resolution. To address this, we propose a deep learning framework for retrieving all-sky, kilometer-level erythemal UV-B from Moderate Resolution Imaging Spectroradiometer (MODIS) data. We designed a deep neural network with a residual structure to cascade high-level representations from raw MODIS inputs, eliminating handcrafted features. We used an external random forest classifier to perform the final prediction based on refined deep features extracted from the residual network. Compared with basic parameters, extracted deep features more accurately bridge the semantic gap between the raw MODIS inputs, improving retrieval accuracy. We established a dataset from 7 Surface Radiation Budget Network (SURFRAD) stations and 1 from 30 UV-B Monitoring and Research Program (UVMRP) stations with MODIS top-of-atmosphere reflectance, solar and view zenith angle, surface reflectance, altitude, and ozone observations. A partial SURFRAD dataset from 2007–2016 trained the model, achieving an R2 of 0.9887, a mean bias error (MBE) of 0.19 mW/m2, and a root mean square error (RMSE) of 7.42 mW/m2. The model evaluated on 2017 SURFRAD data shows an R2 of 0.9376, an MBE of 1.24 mW/m2, and an RMSE of 17.45 mW/m2, indicating the proposed model accurately generalizes the temporal dimension. We evaluated the model at 30 UVMRP stations with different land cover from those of SURFRAD and found most stations had a relative RMSE of 25% and an MBE within ±5%, demonstrating generalization in the spatial dimension. This study demonstrates the potential of using MODIS data to accurately estimate all-sky erythemal UV-B with the proposed algorithm.

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Spatiotemporal characteristics of ultraviolet radiation in recent 54 years from measurements and reconstructions over the Tibetan Plateau

Cited by 13 publications

References 63 publications

Aerosol–photolysis interaction reduces particulate matter during wintertime haze events

Aerosol–photolysis interaction reduces particulate matter during wintertime haze events

Spatiotemporal characteristics of ultraviolet solar radiation in China

Estimation of 1-km Resolution All-Sky Instantaneous Erythemal UV-B with MODIS Data Based on a Deep Learning Method

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