Evaluation of the simulation performance of WRF-Solar for a summer month in China using ground observation network data DOI Creative Commons
Yue Xin, Xiao Tang, Bo Hu

и другие.

Atmospheric and Oceanic Science Letters, Год журнала: 2024, Номер unknown, С. 100532 - 100532

Опубликована: Июнь 1, 2024

Solar energy is a pivotal clean source in the transition to carbon neutrality from fossil fuels. However, intermittent and stochastic characteristics of solar radiation pose challenges for accurate simulation prediction. Accurately simulating predicting its variability are crucial optimizing utilization. This study conducted experiments using WRF-Solar model 25 June July 2022, evaluate accuracy performance simulated across China. The simulations covered whole country with grid spacing 27 km were compared ground observation network data Chinese Ecosystem Research Network. results indicated that can accurately capture spatiotemporal patterns global horizontal irradiance over China, but there still an overestimation radiation, underestimates total cloud cover. root-mean-square error ranged 92.83 188.13 Wm−2 mean bias (MB) 21.05 56.22 Wm−2. showed smallest MB at Lhasa on Qinghai–Tibet Plateau, while largest was observed Southeast To enhance simulation, authors Fast All-sky Radiation Model Rapid Radiative Transfer General Circulation Models found former provides better simulation. 摘要 准确模拟和预测太阳辐射及其变化对于优化太阳能利用至关重要. 本研究使用WRF-Solar模式对中国2022 年 6 月 日至 7 日的太阳辐射情况进行了深入模拟, 模式网格水平分辨率为27 km, 通过与中国生态系统研究网络 (CERN) 的地面观测网络数据的37个观测站点进行对比, 以评估模式性能. 结果表明: WRF-Solar模式能较好地捕捉到中国上空的辐照度GHI (Global Horizontal Irradiance) 的时空分布特征, 但存在高估太阳辐射量, 以及低估总云量的情况.全国的总辐射模拟均方根误差范围为92.83–188.13 W m−2, 平均偏差范围为21.05–56.22 m−2. 青藏高原拉萨站的偏差最小, 在中国东南部的偏差最大. 为了进一步提升太阳辐射模拟的精确度, 本研究还对比了FARMS与RRTMG辐射方案, 发现FARMS方案的模拟精度更高.

Язык: Английский

Development of a high-resolution dataset of future monthly surface solar radiation by combining CMIP6 projections and satellite-based retrievals DOI Creative Commons
Junmei He, Liang Hong, Ning Lü

и другие.

Advances in Climate Change Research, Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1
Predicting Solar Radiation in Manabí: A Machine Learning Approach DOI
Daniel Arteaga-Subiaga, Jorge Parraga-Alava, Lucía Rivadeneira

и другие.

Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 335 - 350

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Improvement of Stable Atmospheric Boundary Simulation with High-Spatiotemporal-Resolution Nudging over the North China Plain DOI Creative Commons
Tingting Xu,

Zhuohao Peng,

Yan Wang

и другие.

Atmosphere, Год журнала: 2024, Номер 15(3), С. 277 - 277

Опубликована: Фев. 25, 2024

The WRF model often struggles to accurately replicate specific characteristics of the atmospheric boundary layer, particularly under highly stable conditions. In this study, we reconstructed an OBS-nudging module using meteorological data with high spatiotemporal resolution, then coupled it in (WRF-OBS) improve layer (SBL) simulation over North China Plain (NCP). results showed that WRF-OBS improved SBL and reduced deviation from observations significantly. correlations (R2) between simulations 2 m temperature, relative humidity, 10 wind speed at 460 stations across NCP were 0.72, 0.56, 0.75, respectively, which much higher than values for unassimilated (WRF-BS). simulated vertical profiles generally consistent Pingyuan station. factors caused heavy air pollution was also investigated based on simulation. obtained light persisted region during period pollution, affected by warm humid air. Vertically, persistent temperature inversion station one main drivers pollution. captured two layers very well. covered NCP, a horizontal scale approximately 200 km, created conditions, preventing diffusion pollutants maintaining PM2.5 concentrations.

Язык: Английский

Процитировано

1
Spatial Downscaling of Downward Surface Shortwave Radiation Based on Image Super-Resolution DOI
Fei Cheng, Shuhua Zhang, Qianqian Tian

и другие.

IEEE Transactions on Geoscience and Remote Sensing, Год журнала: 2024, Номер 62, С. 1 - 11

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

1
Reconstructing 10-km-resolution direct normal irradiance dataset through a hybrid algorithm DOI
Jinyang Wu,

Jiayun Niu,

Qinghai Qi

и другие.

Renewable and Sustainable Energy Reviews, Год журнала: 2024, Номер 204, С. 114805 - 114805

Опубликована: Авг. 5, 2024

Язык: Английский

Процитировано

1
Evaluating direct and diffuse solar radiation components through global radiation measurements from three fixed directions DOI
Concettina Marino, Antonino Nucara,

Maria Francesca Panzera

и другие.

Energy Conversion and Management, Год журнала: 2024, Номер 315, С. 118741 - 118741

Опубликована: Июль 14, 2024

Язык: Английский

Процитировано

1
Recent Progress on Applications of Artificial Intelligence for Sustainability of Solar Energy Technologies: An Extensive Review DOI Creative Commons
Jamilu Ya'u MUHAMMAD, Abubakar Abdulkarim,

Nafi’u Muhammad Saleh

и другие.

Advances in Artificial Intelligence Research, Год журнала: 2024, Номер 4(1), С. 36 - 52

Опубликована: Авг. 30, 2024

Green energy sources are most promising in the globe, as they non-pollutant sources. Solar among green that free and abundant nature, yet solar have some shortcoming such faults on PV modules, improper maintenance climatic environmental impacts. Artificial intelligences employed to solve of these like prediction irradiance specific sites, parameters estimation fault detection modules surfaces forecasting power output. This paper presents extensive review application artificial problems related systems from 2009 2024. It was found literatures, intelligent algorithms were more accurate efficient than conventional methods it has an ability complex non-linear data. work will help scholars explore relationship between technologies intelligences.

Язык: Английский

Процитировано

0
A novel Terrain Correction Sinusoidal Model for improving estimation of daily clear-sky downward shortwave radiation DOI
Hui Liang, Bo Jiang, Shunlin Liang

и другие.

IEEE Transactions on Geoscience and Remote Sensing, Год журнала: 2024, Номер 62, С. 1 - 15

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0
Evaluation of the simulation performance of WRF-Solar for a summer month in China using ground observation network data DOI Creative Commons
Yue Xin, Xiao Tang, Bo Hu

и другие.

Atmospheric and Oceanic Science Letters, Год журнала: 2024, Номер unknown, С. 100532 - 100532

Опубликована: Июнь 1, 2024

Solar energy is a pivotal clean source in the transition to carbon neutrality from fossil fuels. However, intermittent and stochastic characteristics of solar radiation pose challenges for accurate simulation prediction. Accurately simulating predicting its variability are crucial optimizing utilization. This study conducted experiments using WRF-Solar model 25 June July 2022, evaluate accuracy performance simulated across China. The simulations covered whole country with grid spacing 27 km were compared ground observation network data Chinese Ecosystem Research Network. results indicated that can accurately capture spatiotemporal patterns global horizontal irradiance over China, but there still an overestimation radiation, underestimates total cloud cover. root-mean-square error ranged 92.83 188.13 Wm−2 mean bias (MB) 21.05 56.22 Wm−2. showed smallest MB at Lhasa on Qinghai–Tibet Plateau, while largest was observed Southeast To enhance simulation, authors Fast All-sky Radiation Model Rapid Radiative Transfer General Circulation Models found former provides better simulation. 摘要 准确模拟和预测太阳辐射及其变化对于优化太阳能利用至关重要. 本研究使用WRF-Solar模式对中国2022 年 6 月 日至 7 日的太阳辐射情况进行了深入模拟, 模式网格水平分辨率为27 km, 通过与中国生态系统研究网络 (CERN) 的地面观测网络数据的37个观测站点进行对比, 以评估模式性能. 结果表明: WRF-Solar模式能较好地捕捉到中国上空的辐照度GHI (Global Horizontal Irradiance) 的时空分布特征, 但存在高估太阳辐射量, 以及低估总云量的情况.全国的总辐射模拟均方根误差范围为92.83–188.13 W m−2, 平均偏差范围为21.05–56.22 m−2. 青藏高原拉萨站的偏差最小, 在中国东南部的偏差最大. 为了进一步提升太阳辐射模拟的精确度, 本研究还对比了FARMS与RRTMG辐射方案, 发现FARMS方案的模拟精度更高.

Язык: Английский

Процитировано

0