An Online System for Nowcasting Satellite Derived Temperatures for Urban Areas

Keramitsoglou, Iphigenia; Kiranoudis, Chris T.; Sismanidis, Panagiotis; Zakšek, Klemen

doi:10.3390/rs8040306

Cited by 29 publications

(26 citation statements)

References 54 publications

(97 reference statements)

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“…The IAASARS/NOA nowcasting service [21] produces 1 × 1 km surface air temperature raster data of Europe every 5 min in almost real time. It has operational since mid-2015, and in its current implementation, it uses the 6.2, 7.3, 10.8, 12.0, and 13.4 µm SEVIRI brightness temperatures (BTs) with appropriate spatially, temporally, and vertically interpolated numerical weather predictions (NWPs) from the Global Forecast System (GFS).…”

Section: Iaasars/noa Gridded Surface Air Temperature Data Productmentioning

confidence: 99%

“…The workflow of IAASARS/NOA nowcasting service comprises four major operations and is presented in detail in [21]. The first operation is the real-time acquisition and decompression of the SEVIRI data and the masking of pixels containing clouds, dust, or snow/ice using the Satellite Application Facility on Support to Nowcasting and Very Short-Range Forecasting (SAFNWC) software package [26].…”

Section: Iaasars/noa Gridded Surface Air Temperature Data Productmentioning

confidence: 99%

“…PLANHEAT's HD and CD dataset is also based on satellite thermal data. In particular, it uses 1 × 1 km gridded, hourly surface air temperatures from the nowcasting service [21,22] of the Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing of the National Observatory of Athens (IAASARS/NOA) in Greece that are derived using Meteosat-10 Spinning Enhanced Visible and Near-Infrared Imager (SEVIRI) thermal images [23]. In this article we present the method for producing PLANHEAT's HD and CD dataset and also the results for one of the three validation cities of PLANHEAT, namely Antwerp (Belgium).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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%

Section: Iaasars/noa Gridded Surface Air Temperature Data Productmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…This is because the installation and operation of dense weather station networks is very expensive; whilst the model-based solutions are less accurate and spatially coarser than required to capture the intra-city variabilities. To that end, Earth Observation can offer a simultaneous and continuous view of the urban thermal environment with minimum cost and form the basis for the estimation of several environmental parameters relevant to UHIs (Keramitsoglou et al [6]) and also other higher level products as presented in Figure 2. Hence, understanding, monitoring, and mitigating the UHI effect is a key so as to maintain an acceptable quality of life for the foreseeable future.…”

Section: Urban Heat Islandsmentioning

confidence: 99%

ERA-PLANET, a European Network for Observing Our Changing Planet

et al. 2017

Self Cite

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ERA-PLANET is a wide European network comprised of 118 researchers from 35 partner institutions located in 18 countries, aiming to strengthen the European Research Area in the domain of Earth Observation (EO) in coherence with the European participation to the Group on Earth Observation (GEO) and the program for the establishment of a European capacity for Earth Observation, COPERNICUS. It will provide more accurate, comprehensive, and authoritative information to policy and decision-makers in key societal benefit areas (SBAs), under the umbrellas of dedicated projects in the topics of: smart cities and resilient societies; resource efficiency and environmental management; global changes and environmental treaties; polar areas and natural resources. ERA-PLANET will provide advanced decision support tools and technologies aimed to better monitor our global environment and share the information and knowledge in different domains of EO by launching joint transnational calls along the above four strands. The concept of the project that tackles with strand 1, as well as an example of a specific application fitting in, are described, aspiring to promote and coordinate the "smart-city" approach into a European network of cities and non-European follower cities, serving the need for a common approach to enhance environmental and societal resilience to air pollution, urban growth, and urban heat islands, as well as other natural/manmade stresses and relevant impacts. This is achieved through the synergy among technology, government, and society, while at the same time creating bridges between local/national initiatives with GEO/GEOSS, COPERNICUS, and other smart cities and GEO relevant projects. The project addresses initiatives in European cities but also specific issues dealing with air quality management in other parts of the world. Finally, it places major emphasis on fully exploiting key-enabling technologies and firmly addressing interoperability issues, in the context of big "smart city" data, and open science.

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“…Surface air temperature (Ta) not only serves as a direct indicator of climate change [1], but also plays a crucial role in a range of studies for understanding the complex land surface processes, such as drought monitoring [2,3], greenhouse effect [4], hydrological simulation [5], urban heat island analysis [6][7][8][9], epidemiological modeling [10], and snowpack investigation [11]. Spatially explicit Ta mapping has been traditionally relying spatial interpolation to scale up the point-based temperature measurements recorded by sample meteorological stations to the landscape level.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Land Cover and Seasonal Variation on Maximum Air Temperature Estimation Using MODIS Imagery

Cai

Chen

Wang

et al. 2017

Preprint

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Abstract:Daily maximum surface air temperature (Tamax) is a crucial factor for understanding complex land surface processes under rapid climate change. Remote detection of Tamax has widely relied on the empirical relationship between air temperature and land surface temperature (LST), a product derived from remote sensing. However, little is known about how such a relationship is affected by the high heterogeneity in landscapes and dynamics in seasonality. This study aims to advance our understanding of the roles of land cover and seasonal variation in the estimation of Tamax using the MODIS (Moderate Resolution Imaging Spectroradiometer) LST product. We developed statistical models to link Tamax and LST in the middle and lower reaches of the Yangtze River in China for five major land-cover types (i.e., forest, shrub, water, impervious surface, cropland, and grassland) and two seasons (i.e., growing season and non-growing season). Results show that the performance of modeling the Tamax-LST relationship was highly dependent on land cover and seasonal variation. Estimating Tamax over grasslands and water bodies achieved superior performance; while uncertainties were high over forested lands that contained extensive heterogeneity in species types, plant structure, and topography. We further found that all the landcover specific models developed for the plant non-growing season outperformed the corresponding models developed for the growing season. Discrepancies in model performance mainly occurred in the vegetated areas (forest, cropland, and shrub), suggesting an important role of plant phenology in defining the statistical relationship between Tamax and LST. For impervious surfaces, the challenge of capturing the high spatial heterogeneity in urban settings using the low-resolution MODIS data made Tamax estimation a difficult task, which was especially true in the growing season.

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An Online System for Nowcasting Satellite Derived Temperatures for Urban Areas

Cited by 29 publications

References 54 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

ERA-PLANET, a European Network for Observing Our Changing Planet

Impacts of Land Cover and Seasonal Variation on Maximum Air Temperature Estimation Using MODIS Imagery

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