Chen et al. (2025) Validating physical and semi-empirical satellite-based irradiance retrievals using high- and low-accuracy radiometric observations in a monsoon-influenced continental climate

Identification

• Journal: Atmospheric measurement techniques
• Year: 2025
• Date: 2025-12-03
• Authors: Yun Chen, Dazhi Yang, Chunlin Huang, Hongrong Shi, Adam R. Jensen, Xiangao Xia, Yves‐Marie Saint‐Drenan, Christian A. Gueymard, Martin János Mayer, Yanbo Shen
• DOI: 10.5194/amt-18-7315-2025

Research Groups

• Public Meteorological Service Centre, China Meteorological Administration, Beijing, China
• Key Laboratory of Energy Meteorology, China Meteorological Administration, Beijing, China
• School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin, Heilongjiang, China
• Institute of Light Resources and Environmental Sciences, Henan Academy of Sciences, Zhengzhou, Henan, China
• Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
• University of Chinese Academy of Sciences, Beijing, China
• Department of Civil and Mechanical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark
• MINES ParisTech, PSL Research University, O.I.E. Centre Observation, Impacts, Energy, Sophia Antipolis, France
• Solar Consulting Services, Colebrook, NH, USA
• Department of Energy Engineering, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, Hungary

Short Summary

This study validates physical and semi-empirical satellite-based irradiance retrievals against both high- and low-accuracy ground observations in a monsoon-influenced continental climate. The findings reveal that using low-accuracy observations for validation introduces significant, non-unidirectional deviations in validation outcomes, comparable to commonly accepted error margins, thereby posing risks to scientific assertions.

Objective

• To conduct a systematic validation of physical and semi-empirical satellite irradiance retrievals under the monsoon-influenced dry-winter hot-summer continental climate (Dwa Köppen climate), a climatic regime remarkably underrepresented in existing studies.
• To demonstrate the methodological superiority of distribution-oriented validation compared to conventional measure-oriented approaches.
• To quantitatively assess the propagation of observational uncertainties from ground-based radiometers into validation outcomes.

Study Configuration

• Spatial Scale: Point validation at the Qiqihar (QIQ) station (47.7957° N, 124.4852° E, 170 m above sea level) in a Dwa Köppen climate. Satellite products have native resolutions ranging from 0.5 km to 4 km at nadir.
• Temporal Scale: One year, from April 2024 to March 2025. Data were processed at 15-minute, hourly, and daily resolutions.

Methodology and Data

• Models used:
  • Physical retrieval: Official Fengyun-4B (FY-4B) irradiance product from the National Satellite Meteorological Center (NSMC) of China Meteorological Administration (CMA), based on radiation transfer and a lookup table.
  • Statistical/Semi-empirical retrieval: Modified Heliosat-2 method (Huang et al., 2023), using the REST2 model for clear-sky Global Horizontal Irradiance (GHI) estimation.
  • Validation framework: Distribution-oriented validation (examining joint, marginal, and conditional probability density functions, and mean square error decompositions) and measure-oriented validation (Mean Bias Error (MBE), Root Mean Square Error (RMSE)).
  • Bias correction: Quantile mapping.
• Data sources:
  • High-accuracy ground observations (yH): Baseline Surface Radiation Network (BSRN) Qiqihar (QIQ) station, using secondary-standard Kipp & Zonen CMP22 pyranometers and a CHP1 pyrheliometer.
  • Low-accuracy ground observations (yL): CMA operational station (Fuyu County Meteorological Bureau), using a DFN1 thermopile pyranometer (Class B) from Huatron Environment.
  • Satellite data: Advanced Geostationary Radiation Imager (AGRI) on board the Fengyun-4B (FY-4B) geostationary satellite.
  • Snow cover data: United States National Ice Center's (NIC's) Interactive Multisensor Snow and Ice Mapping System (IMS).

Main Results

• Low-accuracy observations introduce measurable deviations in validation outcomes, which are comparable in magnitude (several W·m⁻² or a few percent) to commonly accepted error margins, and these deviations are not unidirectional (can lead to both overconfident and underconfident results).
• The physical retrieval product (xP) exhibits systematic irradiance underestimation across the 100 W·m⁻² to 1000 W·m⁻² range, particularly under clear-sky conditions, suggesting inherent algorithmic deficiencies likely due to imprecise atmospheric state inputs (e.g., aerosols, water vapor).
• The statistical retrieval product (xS) generally shows superior performance in terms of bias and accuracy compared to the physical product for the studied location.
• However, the Heliosat-2 algorithm (xS) experiences substantial performance degradation during winter months (November to March) due to snow cover, where high surface albedo is misidentified as clouds, leading to severe irradiance underestimation.
• Distribution-oriented validation provides a more comprehensive understanding of product quality (including bias, accuracy, association, calibration, resolution, and discrimination) compared to measure-oriented approaches, which can be ambiguous.
• After bias correction using quantile mapping, both products show effectively eliminated bias and improved accuracy. Combining the bias-corrected physical and statistical products through arithmetic mean further reduces RMSE by approximately 4%, indicating complementarity.

Contributions

• First formal investigation leveraging both high- and low-accuracy radiometric observations for validating satellite-retrieved irradiance in a monsoon-influenced continental climate (Dwa Köppen climate).
• Presentation of irradiance observations from a rarely reported climate regime and two gridded irradiance products from the latest Chinese FY-4B weather satellite.
• Identification of fundamental limitations in the operational physical retrieval algorithm (NSMC FY-4B), specifically systematic underestimation under clear-sky conditions due to atmospheric parameter quality.
• Identification of performance degradation in the statistical retrieval algorithm (Heliosat-2) under snow-covered conditions due to cloud misidentification.
• Novel application and demonstration of the methodological superiority of the distribution-oriented validation framework over conventional measure-oriented approaches, providing a more comprehensive understanding of product quality aspects.
• Quantitative assessment of the impact of low-accuracy ground observations on validation outcomes, showing measurable, non-unidirectional deviations comparable to accepted error margins, necessitating rigorous methodological caution.

Funding

• National Natural Science Foundation of China (grant no. 42375192)
• China Meteorological Administration Innovation and Development Special Project (grant no. CXFZ2025J045)

Citation

@article{Chen2025Validating,
  author = {Chen, Yun and Yang, Dazhi and Huang, Chunlin and Shi, Hongrong and Jensen, Adam R. and Xia, Xiangao and Saint‐Drenan, Yves‐Marie and Gueymard, Christian A. and Mayer, Martin János and Shen, Yanbo},
  title = {Validating physical and semi-empirical satellite-based irradiance retrievals using high- and low-accuracy radiometric observations in a monsoon-influenced continental climate},
  journal = {Atmospheric measurement techniques},
  year = {2025},
  doi = {10.5194/amt-18-7315-2025},
  url = {https://doi.org/10.5194/amt-18-7315-2025}
}