Spatial patterns in timing of the diurnal temperature cycle

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.

Estimating morning change in land surface temperature from MODIS day/night observations: Applications for surface energy balance modeling

“…Previous studies have successfully developed methods to use temporally sparse observations of LST, from sensors such as MODIS or AVHRR, to estimate the diurnal cycle of LST [Jin and Dickinson, 1999;Gottsche and Olesen, 2001;Aires et al, 2004;Sun and Pinker, 2005;Inamdar et al, 2008;Holmes et al, 2013;Duan et al, 2014]. Each of these methods relied on more than two observations per day, which allows for a more physically constrained diurnal fit for LST.…”

“…This necessitates that a sufficient number of observations be available to constrain the LST time series and limit the impact of data outliers during the diurnal fitting process. For example, Holmes et al [2013] required at a minimum of four observations to attempt a fit to the sparse LST When sufficient consistent diurnal thermal sampling is available from multiple platforms, a method like DTC would ideally be exploited to provide the ΔLST ALEXI inputs required by ALEXI. However, there is also value in developing single-sensor approaches.…”

Cloud tolerance of remote-sensing technologies to measure land surface temperature

“…This implementation (DTC3) is fully described in Holmes et al (2015). DTC3 summarizes the DTC with two daily parameters (daily minimum T 0 at the start and end of the day, and diurnal amplitude A) together with diurnal timing (ϕ), which is assumed a temporal constant (Holmes et al, 2013b). The daily mean is defined by the daily minimum and the amplitude (T = T 0 + A/2).…”

“…1 and 2) is applied to every day for which estimates of T Ka and A Ka are available. Together with the timing of the diurnal cycle of TIR-LST, ϕ TIR , as determined based on (Holmes et al, 2013b), we then calculate the diurnal MW-LST based on the same DTC3 model:…”

Frozen Soil Detection Based on Advanced Scatterometer Observations and Air Temperature Data as Part of Soil Moisture Retrieval

“…• GLDAS ST: linear interpolation between the two closest observations; • ISMN ST: due to the frequent temporal sampling, the matching was obtained using the nearest observation in a window of ± 1 h; • WMO AT: as only daily minimum and maximum temperature are available, the current temperature was estimated through linear interpolation based on an assumed diurnal cycle; the maximum temperature was taken to occur 2 h after solar noon and the minimum at sunrise [37].…”