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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 10
Hydrol. Earth Syst. Sci., 17, 3695–3706, 2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 17, 3695–3706, 2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 01 Oct 2013

Research article | 01 Oct 2013

Spatial patterns in timing of the diurnal temperature cycle

T. R. H. Holmes1, W. T. Crow1, and C. Hain2 T. R. H. Holmes et al.
  • 1USDA-ARS Hydrology and Remote Sensing Lab, Beltsville, MD, USA
  • 2Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA

Abstract. This paper investigates the structural difference in timing of the diurnal temperature cycle (DTC) over land resulting from choice of measuring device 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 temperature estimates from microwave Ka-band, geostationary thermal infrared (TIR), 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 GMAO model puts the peak of the DTC on average at 12:50 local solar time, 23 min before TIR with a peak temperature at 13:13 (both averaged over Africa and Europe). Since TIR is the shallowest observation of the land surface, this small difference represents a structural error that possibly affects the model's ability to assimilate observations that are closely tied to the DTC. The equivalent average timing for Ka-band is 13:44, which is influenced by the effect of increased sensing depth in desert areas. For non-desert areas, the Ka-band observations lag the TIR observations by only 15 min, 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.

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