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Abstract Heat storage change (HSC) is a crucial component of lake's thermal energy budget. Conventional temperature profile based models of HSC require location specific parameters such as lakebed topography. Based on the half‐order time‐derivative formula of heat fluxes, an analytical model was formulated for estimating HSC from water surface temperature and solar radiation without using geography dependent parameters. The proposed model was tested against field measurements at Poyang Lake, a shallow inland lake, which has pronounced seasonal variations in water level and lake area. Our analysis indicates that the model accurately simulates diurnal HSC with a coefficient of determination of 0.94 and a root mean squared error (RMSE) of 77.5 ± 21.6 Wm⁻²for the study period. Larger nighttime RMSE (75.0 ± 26.8 Wm⁻²) than the daytime value (55.1 ± 19.7 W m⁻²) is attributable to larger measurement errors of nighttime turbulent fluxes. The estimation of HSC independent of temperature profile and lake‐specific parameters by the proposed model facilitates remote sensing monitoring the HSC of global water bodies. 

The term “in situ processing” has evolved over the last decade to mean both a specific strategy for visualizing and analyzing data and an umbrella term for a processing paradigm. The resulting confusion makes it difficult for visualization and analysis scientists to communicate with each other and with their stakeholders. To address this problem, a group of over 50 experts convened with the goal of standardizing terminology. This paper summarizes their findings and proposes a new terminology for describing in situ systems. An important finding from this group was that in situ systems are best described via multiple, distinct axes: integration type, proximity, access, division of execution, operation controls, and output type. This paper discusses these axes, evaluates existing systems within the axes, and explores how currently used terms relate to the axes.