基于卫星和Ångström-Prescott估算的南非不同云量条件下全球水平辐照度的比较

IF 0.9 Q4 GEOCHEMISTRY & GEOPHYSICS Solar-Terrestrial Physics Pub Date : 2022-08-16 DOI:10.3390/solar2030021
Brighton Mabasa, M. Lysko, S. J. Moloi
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引用次数: 0

摘要

该研究比较了基于卫星的数据集和Ångström-Prescott (AP)模型在估计南非站点的每日全球水平辐照度(GHI)方面的性能。通过对2014- 2019年期间南非所有六个宏观气候区八个辐射站的地面观测数据进行验证,对来自四颗卫星(即SOLCAST、CAMS、NASA SSE和CMSAF SARAH)和Ångström-Prescott (AP)模型的每日GHI进行了评估。评估在晴空、全天和阴天条件下进行。CLAAS-2云分数覆盖数据用于确定晴天和阴天。观测到的GHI数据首先使用基线地表辐射网方法进行质量控制,然后使用HelioClim模式进行质量控制。传统的统计基准,即相对平均偏差误差(rMBE)、相对均方根误差(rRMSE)、相对平均绝对误差(rMAE)和决定系数(R2),提供了有关数据集性能的信息。在晴空条件下,估计的数据集表现出优异的性能,最大rMBE、rMAE和rRMSE均小于6.5%,最小R2为0.97。相比之下,在阴天条件下,最大rMBE(24%)、rMAE(29%)、rRMSE(39%)和最小R2(0.74)的性能明显较差。在全天候条件下,SOLCAST(0.948)、CMSAF(0.948)、CAMS(0.944)和AP模型(0.91)具有良好的相关性;R2大于0.91。SOLCAST(10%)、CAMS(12%)、CMSAF(12%)和AP模型(11%)的最大rRMSE均小于13%。SOLCAST(7%)、CAMS(8%)、CMSAF(8%)和AP模型(9%)的最大rMAE均小于10%,表现出良好的性能。而NASA SSE卫星GHI的R2相关性小于0.9(0.896),最大rRMSE为18%,最大rMAE为15%,表现出相当差的性能。在研究区域,SOLCAST、CAMS、CMSAF和AP模型的性能基本相同。CAMS、CMSAF和AP模型是可行的、可免费获得的数据集,可用于定量确定估计南非各地点的每日GHI。NASA SSE数据集在研究区表现相对较差,可能是由于其空间分辨率较低,为0.5°× 0.5°(~55 km × 55 km)。随着云层覆盖面积的增加,数据集的可行性显著降低。研究结果可为进一步研究提供基础/数据,以利用机器学习算法纠正原位观测与估计GHI数据集之间的偏差。
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Comparison of Satellite-Based and Ångström–Prescott Estimated Global Horizontal Irradiance under Different Cloud Cover Conditions in South African Locations
The study compares the performance of satellite-based datasets and the Ångström–Prescott (AP) model in estimating the daily global horizontal irradiance (GHI) for stations in South Africa. The daily GHI from four satellites (namely SOLCAST, CAMS, NASA SSE, and CMSAF SARAH) and the Ångström–Prescott (AP) model are evaluated by validating them against ground observation data from eight radiometric stations located in all six macro-climatological regions of South Africa, for the period 2014-19. The evaluation is carried out under clear-sky, all-sky, and overcast-sky conditions. CLAAS-2 cloud fractional coverage data are used to determine clear and overcast sky days. The observed GHI data are first quality controlled using the Baseline Surface Radiation Network methodology and then quality control of the HelioClim model. The traditional statistical benchmarks, namely the relative mean bias error (rMBE), relative root mean square error (rRMSE), relative mean absolute error (rMAE), and the coefficient of determination (R2) provided information about the performance of the datasets. Under clear skies, the estimated datasets showed excellent performance with maximum rMBE, rMAE, and rRMSE less than 6.5% and a minimum R2 of 0.97. In contrast, under overcast-sky conditions there was noticeably poor performance with maximum rMBE (24%), rMAE (29%), rRMSE (39%), and minimum R2 (0.74). For all-sky conditions, good correlation was found for SOLCAST (0.948), CMSAF (0.948), CAMS (0.944), and AP model (0.91); all with R2 over 0.91. The maximum rRMSE for SOLCAST (10%), CAMS (12%), CMSAF (12%), and AP model (11%) was less than 13%. The maximum rMAE for SOLCAST (7%), CAMS (8%), CMSAF (8%), and AP model (9%) was less than 10%, showing good performance. While the R2 correlations for the NASA SSE satellite-based GHI were less than 0.9 (0.896), the maximum rRMSE was 18% and the maximum rMAE was 15%, showing rather poor performance. The performance of the SOLCAST, CAMS, CMSAF, and AP models was almost the same in the study area. CAMS, CMSAF, and AP models are viable, freely available datasets for estimating the daily GHI at South African locations with quantitative certainty. The relatively poor performance of the NASA SSE datasets in the study area could be attributed to their low spatial resolution of 0.5° × 0.5° (~55 km × 55 km). The feasibility of the datasets decreased significantly as the proportion of sky that was covered by clouds increased. The results of the study could provide a basis/data for further research to correct biases between in situ observations and the estimated GHI datasets using machine learning algorithms.
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来源期刊
Solar-Terrestrial Physics
Solar-Terrestrial Physics GEOCHEMISTRY & GEOPHYSICS-
CiteScore
1.50
自引率
9.10%
发文量
38
审稿时长
12 weeks
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