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Durant les vingt dernières années, l’inlandsis du Groenland a progressivement diminué en taille suite à une augmentation du ruissellement de l’eau de fonte en été sans compensation par une augmentation des précipitations. De plus, le taux de perte de la glace s’est aussi accéléré, ce qui a comme conséquence une élévation plus rapide du niveau général des mers et davantage d’eau douce rejetée dans l’océan (Vaughan et al., 2013; Fettweis et al., 2013(a)). Les principales incertitudes lorsqu’on estime le bilan de mase en surface (BMS) de l’inlandsis du Groenland proviennent des marges où plusieurs processus particuliers ont lieu. Par exemple, la rétroaction de l’albédo de la neige et le regel de l’eau fondue en surface peut renforcer ou au contraire diminuer la fonte. De plus, les fortes pentes en bordure d’inlandsis sont responsables de la distribution spatiale des précipitations qui correspondent à l’accumulation de masse de l’inlandsis. En modélisation, cela signifie qu’il est nécessaire d’utiliser des modèles avec une (très) haute résolution pour résoudre ces caractéristiques, ce qui est très couteux en temps de calcul. C’est pourquoi cette étude présente l’évaluation d’une nouvelle méthode de régionalisation couplée dans le modèle MAR qui permet d’utiliser une grille à haute résolution dans le module de surface (SISVAT) couplé au module atmosphérique de MAR utilisant une résolution deux fois plus basse. La méthode corrige l’humidité spécifique et la température de proche-surface de MAR à l’aide d’un gradient d’altitude avant de forcer le module de surface. Des simulations ont été lancées avec deux résolutions différentes et sont forcées avec les réanalyses ERA-Interim sur la période allant de 1979 à 2014. La régionalisation couplée est évaluée par rapport à la base de données PROMICE et montrent de meilleurs résultats avec les observations de BMS sur l’inlandsis par rapport aux résultats de MAR dans sa version standard. La comparaison de la régionalisation couplée à une régionalisation a posteriori a révélé peu de différence significative sauf près de la ligne d’équilibre. Seule la méthode couplée permet de faire regeler entièrement l’eau fondue et la pluie en surface tandis que ce processus n’est qu’implicitement pris en compte dans la méthode a posteriori. Comparé à MAR dans sa version standard à résolution équivalente, les résultats régionalisés de façon couplée montrent une surestimation de l’accumulation au centre de l’inlandsis et une surestimation de l’ablation aux marges dus aux biais que la méthode implique sur les précipitations renforçant la rétroaction de l’albédo de la neige. En outre, les gradients de température sont légèrement trop importants entraînant plus de fonte. En conclusion, la méthode de régionalisation couplée doit encore être améliorée en intégrant une correction des précipitations. Pour ce qui est du couplage entre modèle de dynamique glaciaire à très haute résolution et modèle climatique à haute résolution, la méthode a posteriori reste suffisante.

Greenland --- Surface Mass Balance --- Downscaling --- MAR --- Groenland --- Bilan de Masse en Surface --- Regionalisation --- MAR --- Physique, chimie, mathématiques & sciences de la terre > Sciences de la terre & géographie physique

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The papers included in this Special Issue tackle multiple aspects of how cities, districts, and buildings could evolve along with climate change and how this would impact our way of conceiving and applying design criteria, policies, and urban plans. Despite the multidisciplinary nature of the collection, some transversal take-home messages emerge: • Today’s energy-efficient paradigms may lose their virtuosity in the future unless accurate estimates of future scenarios are used to design modelling platforms and to inform legislative frameworks; • Acting at the local scale is key. Future climate change adaptation will be implemented at the local level. Overlooking regional and local specificities will contribute to inaccurate and inefficient action plans. As such, the smaller scale will become vital in predicting future urban metabolic rates and corresponding comfort-driven strategies; • Energy poverty, heat vulnerability, and social injustice are emerging as critical factors for planning and acting for future-proof cities on par of micro- and meso-climatological factors; • Given that the impacts of climate change will persist for many years, adaptation to this phenomenon should be prioritized by removing any prominent barrier and by enabling combinations of different mitigation technologies. These topics will receive a global reach in few decades, since also developing and underdeveloped countries are starting their fight against local climate change, with cities at the forefront.

Research & information: general --- outdoor space --- thermal environment --- radiation environment --- wind environment --- heat-related mortality --- built environment --- urban resilience --- extreme heat --- climate change --- urban heat island --- heat stress from outside --- indoor environments --- tropics --- multi-level office buildings --- coastal cities --- Mediterranean climate --- urban heat island intensity --- sample year --- climate change adaptation --- barriers --- focus group discussion --- Tehran --- structural equation modeling --- urban management --- near-zero energy buildings --- future scenarios --- energy efficiency --- adaptive comfort --- long-term performance --- urban heat --- Australia --- UHI effect --- mitigation --- bushfire smoke --- indoor air quality --- filtration --- building envelope --- energy --- future weather data --- building energy performance --- thermal comfort --- statistical downscaling of climate models --- dynamical downscaling of climate models --- urban modelling --- cities --- buildings --- decarbonization --- urbanisation --- climate --- densification --- population --- temperature --- outdoor space --- thermal environment --- radiation environment --- wind environment --- heat-related mortality --- built environment --- urban resilience --- extreme heat --- climate change --- urban heat island --- heat stress from outside --- indoor environments --- tropics --- multi-level office buildings --- coastal cities --- Mediterranean climate --- urban heat island intensity --- sample year --- climate change adaptation --- barriers --- focus group discussion --- Tehran --- structural equation modeling --- urban management --- near-zero energy buildings --- future scenarios --- energy efficiency --- adaptive comfort --- long-term performance --- urban heat --- Australia --- UHI effect --- mitigation --- bushfire smoke --- indoor air quality --- filtration --- building envelope --- energy --- future weather data --- building energy performance --- thermal comfort --- statistical downscaling of climate models --- dynamical downscaling of climate models --- urban modelling --- cities --- buildings --- decarbonization --- urbanisation --- climate --- densification --- population --- temperature

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The papers included in this Special Issue tackle multiple aspects of how cities, districts, and buildings could evolve along with climate change and how this would impact our way of conceiving and applying design criteria, policies, and urban plans. Despite the multidisciplinary nature of the collection, some transversal take-home messages emerge: • Today’s energy-efficient paradigms may lose their virtuosity in the future unless accurate estimates of future scenarios are used to design modelling platforms and to inform legislative frameworks; • Acting at the local scale is key. Future climate change adaptation will be implemented at the local level. Overlooking regional and local specificities will contribute to inaccurate and inefficient action plans. As such, the smaller scale will become vital in predicting future urban metabolic rates and corresponding comfort-driven strategies; • Energy poverty, heat vulnerability, and social injustice are emerging as critical factors for planning and acting for future-proof cities on par of micro- and meso-climatological factors; • Given that the impacts of climate change will persist for many years, adaptation to this phenomenon should be prioritized by removing any prominent barrier and by enabling combinations of different mitigation technologies. These topics will receive a global reach in few decades, since also developing and underdeveloped countries are starting their fight against local climate change, with cities at the forefront.

Research & information: general --- outdoor space --- thermal environment --- radiation environment --- wind environment --- heat-related mortality --- built environment --- urban resilience --- extreme heat --- climate change --- urban heat island --- heat stress from outside --- indoor environments --- tropics --- multi-level office buildings --- coastal cities --- Mediterranean climate --- urban heat island intensity --- sample year --- climate change adaptation --- barriers --- focus group discussion --- Tehran --- structural equation modeling --- urban management --- near-zero energy buildings --- future scenarios --- energy efficiency --- adaptive comfort --- long-term performance --- urban heat --- Australia --- UHI effect --- mitigation --- bushfire smoke --- indoor air quality --- filtration --- building envelope --- energy --- future weather data --- building energy performance --- thermal comfort --- statistical downscaling of climate models --- dynamical downscaling of climate models --- urban modelling --- cities --- buildings --- decarbonization --- urbanisation --- climate --- densification --- population --- temperature --- n/a

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This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research.

Land Surface Temperature (LST) --- satellite retrievals of LST --- LST from GOES satellites --- land surface temperature --- drones --- unmanned aerial vehicles --- thermal remote sensing --- MODIS --- Bayesian Maximum Entropy --- interpolation --- Himalaya --- air temperature --- topography --- Landsat --- split window algorithm --- TIRS --- thermal --- Landsat 8 --- stray light correction --- split-window algorithm --- single-channel algorithm --- AMSR2 --- annual cycle parameters --- random forest --- cloudy sky LST --- evapotranspiration --- data fusion --- field-scale --- machine-learning --- physical model --- Sentinel-2 --- Sentinel-3 --- Downscaling --- thermal infrared --- disaggregation --- Copernicus --- hyperspectral thermal infrared --- spectral smoothness --- temperature-emissivity separation --- sensitivity analysis --- noise --- land surface temperature (LST) --- daytime LST --- nighttime LST --- validation --- land surface emissivity (LSE) --- single channel algorithm --- radiative transfer equation --- mono window algorithm --- SURFRAD data --- GK2A --- split-window method --- BSRN --- LST --- downscaling --- LSA-SAF --- Sentinel 2 --- DEM --- spatial averaging biases --- land surface emissivity --- measurement uncertainties --- emissivity box method --- Fourier transform infrared spectrometer --- portable spectrometer --- n/a

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• Reference Manager
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This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research.

Environmental science, engineering & technology --- Land Surface Temperature (LST) --- satellite retrievals of LST --- LST from GOES satellites --- land surface temperature --- drones --- unmanned aerial vehicles --- thermal remote sensing --- MODIS --- Bayesian Maximum Entropy --- interpolation --- Himalaya --- air temperature --- topography --- Landsat --- split window algorithm --- TIRS --- thermal --- Landsat 8 --- stray light correction --- split-window algorithm --- single-channel algorithm --- AMSR2 --- annual cycle parameters --- random forest --- cloudy sky LST --- evapotranspiration --- data fusion --- field-scale --- machine-learning --- physical model --- Sentinel-2 --- Sentinel-3 --- Downscaling --- thermal infrared --- disaggregation --- Copernicus --- hyperspectral thermal infrared --- spectral smoothness --- temperature-emissivity separation --- sensitivity analysis --- noise --- land surface temperature (LST) --- daytime LST --- nighttime LST --- validation --- land surface emissivity (LSE) --- single channel algorithm --- radiative transfer equation --- mono window algorithm --- SURFRAD data --- GK2A --- split-window method --- BSRN --- LST --- downscaling --- LSA-SAF --- Sentinel 2 --- DEM --- spatial averaging biases --- land surface emissivity --- measurement uncertainties --- emissivity box method --- Fourier transform infrared spectrometer --- portable spectrometer --- Land Surface Temperature (LST) --- satellite retrievals of LST --- LST from GOES satellites --- land surface temperature --- drones --- unmanned aerial vehicles --- thermal remote sensing --- MODIS --- Bayesian Maximum Entropy --- interpolation --- Himalaya --- air temperature --- topography --- Landsat --- split window algorithm --- TIRS --- thermal --- Landsat 8 --- stray light correction --- split-window algorithm --- single-channel algorithm --- AMSR2 --- annual cycle parameters --- random forest --- cloudy sky LST --- evapotranspiration --- data fusion --- field-scale --- machine-learning --- physical model --- Sentinel-2 --- Sentinel-3 --- Downscaling --- thermal infrared --- disaggregation --- Copernicus --- hyperspectral thermal infrared --- spectral smoothness --- temperature-emissivity separation --- sensitivity analysis --- noise --- land surface temperature (LST) --- daytime LST --- nighttime LST --- validation --- land surface emissivity (LSE) --- single channel algorithm --- radiative transfer equation --- mono window algorithm --- SURFRAD data --- GK2A --- split-window method --- BSRN --- LST --- downscaling --- LSA-SAF --- Sentinel 2 --- DEM --- spatial averaging biases --- land surface emissivity --- measurement uncertainties --- emissivity box method --- Fourier transform infrared spectrometer --- portable spectrometer