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Ocean surface radiation measurement best practices have been developed as a first step to support the interoperability of radiation measurements across multiple ocean platforms and between land and ocean networks. This document describes the consensus by a working group of radiation measurement experts from land, ocean, and aircraft communities. The scope was limited to broadband shortwave (solar) and longwave (terrestrial infrared) surface irradiance measurements for quantification of the surface radiation budget. Best practices for spectral measurements for biological purposes like photosynthetically active radiation and ocean color are only mentioned briefly to motivate future interactions between the physical surface flux and biological radiation measurement communities. Topics discussed in these best practices include instrument selection, handling of sensors and installation, data quality monitoring, data processing, and calibration. It is recognized that platform and resource limitations may prohibit incorporating all best practices into all measurements and that spatial coverage is also an important motivator for expanding current networks. Thus, one of the key recommendations is to perform interoperability experiments that can help quantify the uncertainty of different practices and lay the groundwork for a multi-tiered global network with a mix of high-accuracy reference stations and lower-cost platforms and practices that can fill in spatial gaps. [ABSTRACT FROM AUTHOR]

Satellite and in situ sensors do not observe exactly the same measurand. This introduces a mismatch between both types of measurements in the spatial or temporal. The mismatch differences can be the dominant component in their comparison, so they have to be removed for an adequate validation of satellite products. With this aim, we propose a methodology to characterize the mismatch between satellite and in situ measurements of surface solar radiation, evaluating the impact of the mismatch on validations. The Surface Solar Radiation Data Set—Heliosat (SARAH‐2) and the Baseline Surface Radiation Network are used to characterize the spatial and temporal mismatch, respectively. The mismatch differences in both domains are driven by cloud variability. At least 5 years are needed to characterize the mismatch, which is not constant throughout the year due to seasonal and diurnal cloud cover patterns. Increasing the mismatch can artificially improve the validation metrics under some circumstances, but the mismatch must be always minimized for a correct product assessment. Finally, we test two types of up‐scaling methods based on SARAH‐2 in the validation of degree‐scale products. The fully data‐driven correction removes all the mismatch effects (systematic and random) but fully propagates SARAH‐2 uncertainty to the corrections. The model‐based correction only removes the systematic mismatch difference, but it can correct measurements not covered by the high‐resolution data set and depends less SARAH‐2 uncertainty. Plain Language Summary: Satellite and in situ measurements are frequently not directly comparable due to the different temporal, spectral, or spatial extents covered by each sensor. The principle of comparing apples against apples is often violated because a mismatch exists between both types of measurements. This study proposes a methodology to estimate the mismatch between satellite and in situ measurements of solar radiation measurements. The paper fully characterizes the spatial and temporal mismatch, evaluating the impact of the mismatch on product validation. Increasing the mismatch generally worsens the validation metrics but there are some situations where validation metrics are artificially improved giving an unrealistic overview of the product performance. The study also shows how the mismatch can be corrected with high‐resolution measurements, highlighting the high sensitivity of the results obtained to the quality of the high‐resolution data. Key Points: Solar radiation mismatch is driven by cloud cover variability and changes with cloud seasonal and diurnal cycles of each siteThe mismatch can either artificially improve or worsen the validation metrics giving an unrealistic picture of product performanceUncertainty estimates are needed, but currently missing, to assess the suitability of high‐res products for up‐scaling in situ measurement [ABSTRACT FROM AUTHOR]