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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #293976

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Timing of the diurmal temperature cycle in remote sensing and model

item Holmes, Thomas
item Crow, Wade
item Hain, Christopher

Submitted to: Hydrology and Earth System Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/21/2013
Publication Date: 10/1/2013
Publication URL:
Citation: Holmes, T.R., Crow, W.T., Hain, C. 2013. Timing of the diurmal temperature cycle in remote sensing and model. Hydrology and Earth System Sciences. 17:3695-3706.

Interpretive Summary: In recent decades, earth observation by satellite has progressed from limited experimental datasets to routine methods for monitoring many aspects of the hydrologic cycle over land. Examples of remote-sensing products commonly integrated into hydrologic models include estimates of vegetation health/productivity, precipitation, soil moisture, and evaporative fluxes. Conspicuously missing from this list is land surface temperature. Even though it has been routinely measured since the first earth observation satellites, land surface temperature has yet to be successfully exploited as a stand-alone model input. This is striking since it is tightly linked to land-atmosphere energy exchange that significanlty impacts weather predictions. The utilization of land surface temperature observations is hampered by the fact that the relationship between different model and remotely-sensed based estimates of surface temperature is poorly understood. This paper aims to shed new light on the structural differences between remotely-sensed and modeled land surface temperature. Specifically it investigates the timing of the diurnal temperature cycle, as estimated by thermal infrared sensors, microwave radiometers and a land surface energy balance model. The results of this paper will be used to better align various land surface temperature sources, which is the first step in a proper blending of model and observations for hydrologic and weather prediction applications.

Technical Abstract: This paper investigates the structural difference in timing of the diurnal temperature cycle (DTC) over land resulting from variations in measuring devise or model framework. It is shown that the timing can be reliably estimated from temporally sparse observations acquired from a constellation of low Earth orbiting satellites given record lengths of at least three months. Based on a year of data, the spatial patterns of mean DTC timing are compared between Ka-band temperature estimates, geostationary thermal infrared (TIR) temperature estimates and numerical weather prediction model output from the Global Modeling and Assimilation Office (GMAO). It is found that the spatial patterns can be explained by vegetation effects, sensing depth differences and more speculatively the orientation of orographic relief features. In absolute terms, the NWP model puts the peak of the DTC on average at 12:20 local solar time, 50 minutes before TIR with a peak temperature at 13:10. Since TIR is the shallowest observation of the land surface, this hour difference represents a structural error in MERRA that possibly affects its ability to assimilate observations that are closely tied to the DTC. For non-desert areas, the Ka-band observations have only a small delay of about 10 minutes with the TIR observations which is in agreement with their respective theoretical sensing depth. The results of this comparison provide insights into the structural differences between temperature measurements and models, and can be used as a first step to account for these differences in a coherent way.