From Point to Area: Worldwide Assessment of the Representativeness of Monthly Surface Solar Radiation Records

Abstract

The representativeness of surface solar radiation (SSR) point observations is an important issue when using them in combination with gridded data. We conduct a comprehensive near‐global (50°S to 55°N) analysis on the representativeness of SSR point observations on the monthly mean time scale. Thereto, we apply the existing concepts of decorrelation lengths (δ), spatial sampling biases (β), and spatial sampling errors (ε) to three high‐resolution gridded monthly mean SSR data sets (CLARA, SARAH‐P, and SARAH‐E) provided by the Satellite Application Facility on Climate Monitoring. While δ quantifies the area for which a point observation is representative, β and ε are uncertainty estimates with respect to the 1‐degree reference grid (G). For this grid we find a near‐global average δG=3.4°, βG=1.4 W/m2, and εG=7.6 W/m2 with substantial regional differences. Disregarding tropical, mountainous, and some coastal regions, monthly SSR point observations can largely be considered representative of a 1‐degree grid. Since ε is an uncorrectable error the total uncertainty when combining point with 1‐degree gridded data is roughly 40% higher than the uncertainty of station‐based SSR measurements alone if a rigorous bias correction is applied. Cloud cover and terrain data can at maximum explain 50% of the patterns of the representativeness metrics. We apply our methodology to the stations of the Baseline Surface Radiation Network. Overall, this study shows that representativeness is strongly dependent on local conditions and that all three metrics (δ, β, and ε) must be considered for a comprehensive assessment of representativeness.