Validating Meteosat Second Generation and Himawari-8 Derived Solar Irradiance against Ground Measurements: Solarad AI’s Approach

Author:

Meher Jitendra Kumar1ORCID,Rizvi Syed Haider Abbas12ORCID,Choudhary Bhramar1,Choudhary Ravi1,Thakre Yash1,Kumar Ritesh1,Singh Vikram2

Affiliation:

1. Solarad AI, Building 145, 91 Springboard, Sector-44, Gurugram 122003, Haryana, India

2. Department of Chemical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India

Abstract

This study assesses the efficacy of the Heliosat-2 algorithm for estimating solar radiation, comparing its outputs against ground measurements across seven distinct countries: the Netherlands, Spain, Japan, Namibia, South Africa, Saudi Arabia, and India. To achieve this, the study utilizes two distinct satellite data sources—Himawari-8 for Japan and Metosat Second Generation-MSG for the rest of the countries—and spanning the time between January 2022 and April 2024. A robust methodology for determining albedo parameters specific to Heliosat-2 was developed. During cloudy days, the estimates provided by Heliosat-2 generally exceeded the ground measurements in all of the countries. Conversely, on clear days, there was a tendency for underestimation, as indicated by the median values of the mean bias (MB) across most of the countries. The Heliosat-2 model slightly underestimates daily radiation values, with a median MB ranging from −27.5 to +10.2 W·m−2. Notably, the median root mean square error (RMSE) on clear days is significantly lower, with values ranging from 24.8 to 108.7 W·m−2, compared to cloudy days, for which RMSE values lie between 75.3 and 180.2 W·m−2. In terms of R2 values, both satellites show strong correlations between the estimated and actual values, with a median value consistently above 0.86 on a monthly scale and over 92% of daily data points falling within ±2 standard deviations.

Funder

Solarad AI Private Limited

Publisher

MDPI AG

Reference62 articles.

1. Habte, A., Sengupta, M., and Lopez, A. (2024, April 10). Evaluation of the National Solar Radiation Database (NSRDB): 1998–2015, Available online: https://www.nrel.gov/docs/fy17osti/67722.pdf.

2. A Critical Review of the Models Used to Estimate Solar Radiation;Zhang;Renew. Sustain. Energy Rev.,2017

3. Sen, Z. (2008). Solar Energy Fundamentals and Modeling Techniques: Atmosphere, Environment, Climate Change, and Renewable Energy, Springer Science & Business Media.

4. Sengupta, M., Habte, A., Wilbert, S., Gueymard, C., and Remund, J. (2021). Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications, National Renewable Energy Lab (NREL). No. NREL/TP-5D00-77635.

5. Baseline Surface Radiation Network (BSRN): Structure and Data Description (1992–2017);Driemel;Earth Syst. Sci. Data,2018

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Assessment of clear-sky irradiance from 6S affected by local climatology of India;Journal of Quantitative Spectroscopy and Radiative Transfer;2024-12

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3