Modelling of diurnal cycles of brightness temperature extracted from METEOSAT data

Göttsche, Frank-M.; Olesen, F.

doi:10.1016/s0034-4257(00)00214-5

Cited by 179 publications

(137 citation statements)

References 25 publications

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“…In this paper we adapt a more sophisticated model of the DTC as described by Göttsche and Olesen (2001). This harmonic-exponential DTC model improves the fit along the cooling limb of the DTC and allows for better leveraging of observations at all times.…”

Section: Methodsmentioning

confidence: 99%

Spatial patterns in timing of the diurnal temperature cycle

Holmes

Crow

Hain

2013

Hydrol. Earth Syst. Sci.

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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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Section: Methodsmentioning

confidence: 99%

Spatial patterns in timing of the diurnal temperature cycle

Holmes

Crow

Hain

2013

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

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“…The unknown parameters are the segment-specific season amplitude α j,k and phase δ j,k , which can be determined using an unconstrained Levenberg-Marquardt minimization with a universal optimization [43] on the detrended data; and f is the known frequency (e.g., f = 7 annual observations for lakes area, AMT and AMP time series). The breakpoint number and breakpoint location in time sequence is needed to identify breakpoints of the trend and seasonal components.…”

Section: Trend and Season Decomposition Modelmentioning

confidence: 99%

Mapping Extent Dynamics of Small Lakes Using Downscaling MODIS Surface Reflectance

et al. 2017

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Lake extent is an indicator of water capacity as well as the aquatic ecological and environmental conditions. Due to the small sizes and rapid water dynamics, monitoring the extent of small lakes fluctuating between 2.5 and 30 km 2 require observations with both high spatial and temporal resolutions. The paper applied an improved surface reflectance (SR) downscaling method (i.e., IMAR (Improved Modified Adaptive Regression model)) to downscale the daily SR acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra platform to a consistent 250-m resolution, and derived monthly water extent of four small lakes in the Tibetan Plateau (Longre Co, Ayonggongma Co, Ayonggama Co, and Ayongwama Co)) from 2000 to 2014. Using Landsat ETM+ acquired on the same date, the downscaled MODIS SR and identified water extent were compared to the original MODIS, observations downscaled using an early SR downscaling method (MAR (Modified Adaptive Regression model)) and Wavelet fusion. The results showed IMAR achieved the highest correlation coefficients (R 2 ) (0.89-0.957 for SR and 0.79-0.933 for water extent). The errors in the derived water extents were significantly decreased comparing to the results of MAR and Wavelet fusion, and lakes morphometry of IMAR is more comparable to Landsat results. The detected lake extents dynamic between 2000 and 2014 were analyzed using the trend and season decomposition model (BFAST), indicating an increasing trend after 2005, and it likely had higher correlations with temperature and precipitation variation in the Tibetan region (R 2 : 0.598-0.728 and 0.61-0.735, respectively).

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“…DDL DDL was calculated by Equation (1) over the study area with the same spatial and temporal resolutions as LST [64]:…”

Section: Elvmentioning

confidence: 99%

Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data

et al. 2016

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Near surface air temperature (Ta) is one of the most critical variables in climatology, hydrology, epidemiology, and environmental health. In situ measurements are not efficient for characterizing spatially heterogeneous Ta, while remote sensing is a powerful tool to break this limitation. This study proposes a mapping framework for daily mean Ta using an enhanced empirical regression method based on remote sensing data. It differs from previous studies in three aspects. First, nighttime light data is introduced as a predictor (besides land surface temperature, normalized difference vegetation index, impervious surface area, black sky albedo, normalized difference water index, elevation, and duration of daylight) considering the urbanization-induced Ta increase over a large area. Second, independent components are extracted using principal component analysis considering the correlations among the above predictors. Third, a composite sinusoidal coefficient regression is developed considering the dynamic Ta-predictor relationship. This method was performed at 333 weather stations in China during 2001-2012. Evaluation shows overall mean error of −0.01 K, root mean square error (RMSE) of 2.53 K, correlation coefficient (R 2 ) of 0.96, and average uncertainty of 0.21 K. Model inter-comparison shows that this method outperforms six additional empirical regressions that have not incorporated nighttime light data or considered predictor independence or coefficient dynamics (by 0.18-2.60 K in RMSE and 0.00-0.15 in R 2 ).

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Modelling of diurnal cycles of brightness temperature extracted from METEOSAT data

Cited by 179 publications

References 25 publications

Spatial patterns in timing of the diurnal temperature cycle

Spatial patterns in timing of the diurnal temperature cycle

Mapping Extent Dynamics of Small Lakes Using Downscaling MODIS Surface Reflectance

Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data

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