Scholarlink

<p>Surface downward shortwave radiation (DSR) is a critical element of the surface energy budgets, with significant applications in solar energy development, building energy simulation (BES), and agricultural planning. However, current radiation datasets are constrained by unevenly distributed observation stations and discontinuous records, limiting their utility for high-precision applications like refined building energy modeling. The ERA5-Land (ERA5L) reanalysis product, with high spatiotemporal resolution and long-term continuity, presents a promising solution to fill these observational gaps. Nevertheless, its performance across China and specific applicability in BES have not been systematically evaluated. This study provides the comprehensive multi-scale evaluation of the ERA5L DSR product across China and validates its utility for simulating heating and cooling loads of office buildings. Results show ERA5L effectively captures the spatial distribution of DSR well (spatial correlation coefficient = 0.87), despite a slight average overestimation of 6.1&#xa0;W m⁻². It exhibits higher accuracy in summer (June–August) and autumn (September–November), with diurnal biases varying regionally and seasonally. Aerosols are the dominant factor influencing DSR biases. For BES, ERA5L demonstrates high reliability for cooling load estimation. For heating loads, a quantitative linkage between radiation bias and load deviation is established: in representative cities, winter DSR overestimation (7–13%) systematically translates into heating load underestimation (6–14%). These region-specific error propagation patterns provide a quantitative basis for developing tailored correction factors. The findings provide scientific support for its optimized use in BES, offering data-driven insights to enhance the reliability of energy policy-making and low-carbon transition strategies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi-scale evaluation of ERA5-land downward shortwave radiation and its implications for building energy simulation over China

  • 期刊论文

  • 发表日期：

    2026年5月22日

查看全文
  • Song Jiang,
  • Jingfu Cao,
  • Mingcai Li,
  • Rui Miao,
  • Minjie Yu

摘要

Surface downward shortwave radiation (DSR) is a critical element of the surface energy budgets, with significant applications in solar energy development, building energy simulation (BES), and agricultural planning. However, current radiation datasets are constrained by unevenly distributed observation stations and discontinuous records, limiting their utility for high-precision applications like refined building energy modeling. The ERA5-Land (ERA5L) reanalysis product, with high spatiotemporal resolution and long-term continuity, presents a promising solution to fill these observational gaps. Nevertheless, its performance across China and specific applicability in BES have not been systematically evaluated. This study provides the comprehensive multi-scale evaluation of the ERA5L DSR product across China and validates its utility for simulating heating and cooling loads of office buildings. Results show ERA5L effectively captures the spatial distribution of DSR well (spatial correlation coefficient = 0.87), despite a slight average overestimation of 6.1 W m⁻². It exhibits higher accuracy in summer (June–August) and autumn (September–November), with diurnal biases varying regionally and seasonally. Aerosols are the dominant factor influencing DSR biases. For BES, ERA5L demonstrates high reliability for cooling load estimation. For heating loads, a quantitative linkage between radiation bias and load deviation is established: in representative cities, winter DSR overestimation (7–13%) systematically translates into heating load underestimation (6–14%). These region-specific error propagation patterns provide a quantitative basis for developing tailored correction factors. The findings provide scientific support for its optimized use in BES, offering data-driven insights to enhance the reliability of energy policy-making and low-carbon transition strategies.