Improving the Downscaling of Diurnal Land Surface Temperatures Using the Annual Cycle Parameters as Disaggregation Kernels

Sismanidis, Panagiotis; Keramitsoglou, Iphigenia; Bechtel, Benjamin; Kiranoudis, Chris T.

doi:10.3390/rs9010023

Cited by 41 publications

(22 citation statements)

References 55 publications

(149 reference statements)

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“…The final operation of the IAASARS/NOA nowcasting service is to enhance the spatial resolution of the air temperature data from~5 km to 1 km. This is done using a modified version of the statistical downscaling algorithm of [30,31], where the utilized disaggregation kernels set has been updated based on the findings of [32]. In particular, the employed downscaling algorithm is based on a support vector machine (SVM) coupled with gradient boosting and uses the elevation, vegetation indices, and information about the LST annual climatology as disaggregation kernels.…”

Section: Iaasars/noa Gridded Surface Air Temperature Data Productmentioning

confidence: 99%

PLANHEAT’s Satellite-Derived Heating and Cooling Degrees Dataset for Energy Demand Mapping and Planning

et al. 2019

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The urban heat island (UHI) effect influences the heating and cooling (H&C) energy demand of buildings and should be taken into account in H&C energy demand simulations. To provide information about this effect, the PLANHEAT integrated tool—which is a GIS-based, open-source software tool for selecting, simulating and comparing alternative low-carbon and economically sustainable H&C scenarios—includes a dataset of 1 × 1 km hourly heating and cooling degrees (HD and CD, respectively). HD and CD are energy demand proxies that are defined as the deviation of the outdoor surface air temperature from a base temperature, above or below which a building is assumed to need heating or cooling, respectively. PLANHEAT’s HD and CD are calculated from a dataset of gridded surface air temperatures that have been derived using satellite thermal data from Meteosat-10 Spinning Enhanced Visible and Near-Infrared Imager (SEVIRI). This article describes the method for producing this dataset and presents the results for Antwerp (Belgium), which is one of the three validation cities of PLANHEAT. The results demonstrate the spatial and temporal information of PLANHEAT’s HD and CD dataset, while the accuracy assessment reveals that they agree well with reference values retrieved from in situ surface air temperatures. This dataset is an example of application-oriented research that provides location-specific results with practical utility.

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Section: Iaasars/noa Gridded Surface Air Temperature Data Productmentioning

confidence: 99%

PLANHEAT’s Satellite-Derived Heating and Cooling Degrees Dataset for Energy Demand Mapping and Planning

et al. 2019

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“…Between the yearly and sub-daily timescales, ATC models have recently received particular attention due to their potentials in various applications [7,12]. These applications include the generation of spatio-temporally seamless LST products [21,22], improvement of the spatio-temporal downscaling of LSTs [23][24][25], and examination of surface urban heat islands (SUHIs) [9,[26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Modeling of Annual Temperature Cycles with Temporally Discrete Remotely Sensed Thermal Observations

et al. 2018

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Satellite thermal remote sensing provides land surface temperatures (LST) over extensive areas that are vital in various applications, but this technique suffers from its sampling style and the impenetrability of clouds, which frequently generates data gaps. Annual temperature cycle (ATC) models can fill these gaps and estimate continuous daily LST dynamics from a number of thermal observations. However, the standard ATC model (termed ATC S ) remains incapable of quantifying the short-term LST variations caused by synoptic conditions. By incorporating in-situ surface air temperatures (SATs) and satellite-derived normalized difference vegetation indexes (NDVIs), here we proposed an enhanced ATC model (ATC E ) to describe the daily LST fluctuations. With Aqua/MODIS LST products as validation data, we implemented and tested the ATC E over the Yangtze River Delta region of China. The results demonstrate that, when compared with the ATC S , the overall root mean square errors of the ATC E decrease by 1.0 and 0.8 K for the day and night, respectively. The accuracy improvements vary with land cover types with greater improvements over the forest, grassland, and built-up areas than over cropland and wetland. The assessments at different time scales further confirm that LST fluctuations can be better described by the ATC E . Though with limitations, we consider this new model and its associated parameters hold great potentials in various applications.

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“…The basic idea is to use all available acquisitions per pixel to fit a seasonal LST model and, thus, split the time series into a mean annual temperature cycle and short-term fluctuations. Besides various other potential applications, including geothermal energy [57], water masking [58], ecological modelling [59], topo-climatology [60], and LST downscaling [61][62][63][64], it has mainly been used to study and provide a more robust description of the SUHI [65][66][67]. Figure 3 shows an aerial image and the mean annual surface temperature at 13:30 local solar time from Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua LST data for Hamburg, Germany.…”

Section: Surface Temperaturementioning

confidence: 99%

Is It Possible to Distinguish Global and Regional Climate Change from Urban Land Cover Induced Signals? A Mid-Latitude City Example

et al. 2018

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Abstract:The local climate in cities differs from the one in rural areas, most prominently characterized by increased surface and air temperatures, known as the "(surface) urban heat island". As climate has changed and continues to change in all areas of the world, the question arises whether the effects that are noticeable in urban areas are "homemade", or whether some of them originate from global and regional scale climate changes. Identifying the locally induced changes of urban meteorological parameters is especially relevant for the development of adaptation and mitigation measures. This study aims to distinguish global and regional climate change signals from those induced by urban land cover. Therefore, it provides a compilation of observed and projected climate changes, as well as urban influences on important meteorological parameters. It is concluded that evidence for climate change signals is found predominantly in air temperature. The effect of urban land cover on local climate can be detected for several meteorological parameters, which are air and surface temperature, humidity, and wind. The meteorology of urban areas is a mixture of signals in which the influencing parameters cannot be isolated, but can be assessed qualitatively. Blending interactions between local effects and regional changes are likely to occur.

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Improving the Downscaling of Diurnal Land Surface Temperatures Using the Annual Cycle Parameters as Disaggregation Kernels

Cited by 41 publications

References 55 publications

PLANHEAT’s Satellite-Derived Heating and Cooling Degrees Dataset for Energy Demand Mapping and Planning

PLANHEAT’s Satellite-Derived Heating and Cooling Degrees Dataset for Energy Demand Mapping and Planning

Enhanced Modeling of Annual Temperature Cycles with Temporally Discrete Remotely Sensed Thermal Observations

Is It Possible to Distinguish Global and Regional Climate Change from Urban Land Cover Induced Signals? A Mid-Latitude City Example

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