Published in

European Geosciences Union, Hydrology and Earth System Sciences, 10(17), p. 3695-3706, 2013

DOI: 10.5194/hess-17-3695-2013

European Geosciences Union, Hydrology and Earth System Sciences Discussions, 5(10), p. 6019-6048

DOI: 10.5194/hessd-10-6019-2013

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Spatial patterns in timing of the diurnal temperature cycle

Journal article published in 2013 by T. R. H. Holmes ORCID, W. T. Crow ORCID, C. Hain
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

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.