@article{Morrisona2018,

title = {A novel method to obtain three-dimensional urban surface temperature from ground-based thermography},

author = {William Morrison and Simone Kotthaus and CSB Grimmond and Atsushi Inagaki and Tiangang Yin and Jean-Philippe Gastellu-Etchegorry and Manabu Kanda and Christopher J. Merchant},

url = {https://urbanfluxes.eu/wp-content/uploads/2018/07/2018_Morrison_et_al.pdf},

doi = {10.1016/j.rse.2018.05.004},

year  = {2018},

date = {2018-09-15},

journal = {Remote Sensing of Environment},

volume = {Volume 215},

pages = {268-283},

abstract = {Urban geometry and materials combine to create complex spatial, temporal and directional patterns of longwave infrared (LWIR) radiation. Effective anisotropy (or directional variability) of thermal radiance causes remote sensing (RS) derived urban surface temperatures to vary with RS view angles. Here a new and novel method to resolve effective thermal anisotropy processes from LWIR camera observations is demonstrated at the Comprehensive Outdoor Scale MOdel (COSMO) test site. Pixel-level differences of brightness temperatures reach 18.4 K within one hour of a 24-h study period. To understand this variability, the orientation and shadowing of surfaces is explored using the Discrete Anisotropic Radiative Transfer (DART) model and Blender three-dimensional (3D) rendering software. Observed pixels and the entire canopy surface are classified in terms of surface orientation and illumination. To assess the variability of exitant longwave radiation (MLW) from the 3D COSMO surface (), the observations are prescribed based on class. The parameterisation is tested by simulating thermal images using a camera view model to determine camera perspectives of fluxes. The mean brightness temperature differences per image (simulated and observed) are within 0.65 K throughout a 24-h period. Pixel-level comparisons are possible with the high spatial resolution of and DART camera view simulations. At this spatial scale (<0.10 m), shadow hysteresis, surface sky view factor and building edge effects are not completely resolved by . By simulating apparent brightness temperatures from multiple view directions, effective thermal anisotropy of is shown to be up to 6.18 K. The developed methods can be extended to resolve some of the identified sources of sub-facet variability in realistic urban settings. The extension of DART to the interpretation of ground-based RS is shown to be promising.},

keywords = {COSMO, DART, Ground-based thermography, Image classification, Sensor view modelling, Thermographic camera modelling, Upwelling longwave radiation, Urban meteorology},

pubstate = {published},

tppubtype = {article}

}

@article{Chrysoulakis2018,

title = {Urban energy exchanges monitoring from space},

author = {Nektarios Chrysoulakis and Sue Grimmond and Christian Feigenwinter and Fredrik Lindberg and Jean-Philippe Gastellu-Etchegorry and Mattia Marconcini and Zina Mitraka and Stavros Stagakis and Ben Crawford and Frans Olofson and Lucas Landier and William Morrison and Eberhard Parlow },

editor = {nature.com},

url = {https://urbanfluxes.eu/wp-content/uploads/2018/07/UF_Overview_final.pdf},

doi = {10.1038/s41598-018-29873-x},

issn = {2045-2322},

year  = {2018},

date = {2018-07-31},

journal = { nature.com },

volume = {Scientific Reports volume},

number = {8},

pages = {11498},

abstract = {One important challenge facing the urbanization and global environmental change community is to understand the relation between urban form, energy use and carbon emissions. Missing from the current literature are scientific assessments that evaluate the impacts of different urban spatial units on energy fluxes; yet, this type of analysis is needed by urban planners, who recognize that local scale zoning affects energy consumption and local climate. Satellite-based estimation of urban energy fluxes at neighbourhood scale is still a challenge. Here we show the potential of the current satellite missions to retrieve urban energy budget fluxes, supported by meteorological observations and evaluated by direct flux measurements. We found an agreement within 5% between satellite and in-situ derived net all-wave radiation; and identified that wall facet fraction and urban materials type are the most important parameters for estimating heat storage of the urban canopy. The satellite approaches were found to underestimate measured turbulent heat fluxes, with sensible heat flux being most sensitive to surface temperature variation (−64.1, +69.3 W m−2 for ±2 K perturbation).  They also underestimate anthropogenic heat fluxes. However, reasonable spatial patterns are obtained for the latter allowing hot-spots to be identified, therefore supporting both urban planning and urban climate modelling.},

keywords = {anthropogenic heat emission, Anthropogenic Heat Flux, Concentration profiles, Copernicus Sentinels, Earth Observation, Eddy-covariance, Flux measurements, Ground-based thermography, Image classification, reducing, Sensor view modelling, Street canyon, Thermographic camera modelling, Understanding, Upwelling longwave radiation, urban, Urban Climate, Urban Energy Budget, urban heat fluxes, Urban meteorology},

pubstate = {published},

tppubtype = {article}

}

@article{Feigenwinter2018,

title = {Spatial Distribution of Sensible and Latent Heat Flux in the city of Basel (Switzerland)},

author = {Christian Feigenwinter and Roland Vogt and Eberhard Parlow and Fredrik Lindberg and Mattia Marconcini and Fabio Del Frate and Nektarios Chrysoulakis},

doi = {10.1109/JSTARS.2018.2807815},

issn = {2151-1535},

year  = {2018},

date = {2018-03-18},

journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},

abstract = {Urban surfaces are a complex mixture of different land covers and surface materials; the relative magnitudes of the surface energy balance components therefore vary widely across a city. Eddy covariance (EC) measurements provide the best estimates of turbulent heat fluxes but are restricted to the source area. Land surface modeling with earth observation (EO) data is beneficial for extrapolation of a larger area since citywide information is possible. Turbulent sensible and latent heat fluxes are calculated by a combination of micrometeorological approaches (the aerodynamic resistance method, ARM), EO data, and GIS techniques. Input data such as land cover fractions and surface

temperatures are derived from Landsat 8 OLI and TIRS, urban morphology was calculated from high-resolution digital building models and GIS data layers, and meteorological data were provided by flux tower measurements. Twenty-two Landsat scenes covering all seasons and different meteorological conditions were analyzed. Sensible heat fluxes were highest for industrial areas, railway stations, and areas with high building density, mainly corresponding to the pixels with highest surface-to-air  temperature differences.

The spatial distribution of latent heat flux is strongly related to the

saturation deficit of vapor and the (minimum) stomatal resistance

of vegetation types. Seasonal variations are highly dependent on

meteorological conditions, i.e., air temperature, water vapor saturation

deficit, and wind speed. Comparison of measured fluxes

with modeled fluxes in the weighted source area of the flux towers

is moderately accurate due to known drawbacks in the modeling

approach and uncertainties inherent to EC measurements, particularly

in urban areas.},

keywords = {Basel, Flux measurements, Urban Energy Budget},

pubstate = {published},

tppubtype = {article}

}

@article{Kent2017b,

title = {Aerodynamic roughness parameters in cities: Inclusion of vegetation},

author = {Christoph Kent and Sue Grimmond and David Gatey},

editor = {Journal of Wind Engineering & Industrial Aerodynamics},

url = {https://urbanfluxes.eu/wp-content/uploads/2018/01/2017_Kent_et_al_JWEIA.pdf},

year  = {2017},

date = {2017-10-01},

journal = {Journal of Wind Engineering and Industrial Aerodynamics},

volume = {Volume 169},

pages = {168-176},

abstract = {A widely used morphometric method (Macdonald et al. 1998) to calculate the zero-plane displacement (zd) and aerodynamic roughness length (z0) for momentum is further developed to include vegetation. The adaptation also applies to the Kanda et al. (2013) morphometric method which considers roughness-element height variability. Roughness-element heights (mean, maximum and standard deviation) of both buildings and vegetation are combined with a porosity corrected plan area and drag formulation. The method captures the influence of vegetation (in addition to buildings), with the magnitude of the effect depending upon whether buildings or vegetation are dominant and the porosity of vegetation (e.g. leaf-on or leaf-off state). Application to five urban areas demonstrates that where vegetation is taller and has larger surface cover, its inclusion in the morphometric methods can be more important than the morphometric method used. Implications for modelling the logarithmic wind profile (to 100 m) are demonstrated. Where vegetation is taller and occupies a greater amount of space, wind speeds may be slowed by up to a factor of three},

keywords = {Aerodynamic roughness length, Drag coefficient for vegetation, Logarithmic wind profile, Morphometric method, urban, Zero-plane displacement},

pubstate = {published},

tppubtype = {article}

}

@article{Sun2017,

title = {The Analytical Objective Hysteresis Model (AnOHM v1.0): methodology to determine bulk storage heat flux coefficients},

author = {Ting Sun and Zhi-Hua Wang and Walter Oechel and Sue Grimmond },

editor = {EGU},

url = {https://urbanfluxes.eu/wp-content/uploads/2018/01/2017_Sun_et_al_Geosci.pdf},

year  = {2017},

date = {2017-07-27},

journal = {Geoscientific Model Development  Vol. 10, 2875-2890, 2017},

volume = {10},

pages = {2875–2890},

abstract = {The net storage heat flux (ΔQS) is important in the urban surface energy balance (SEB) but its determination remains a significant challenge. The hysteresis pattern of the diurnal relation between the ΔQS and net all-wave radiation (Q∗) has been captured in the Objective Hysteresis Model (OHM) parameterization of ΔQS. Although successfully used in urban areas, the limited availability of coefficients for OHM hampers its application. To facilitate use, and enhance physical interpretations of the OHM coefficients, an analytical solution of the one-dimensional advection–diffusion equation of coupled heat and liquid water transport in conjunction with the SEB is conducted, allowing development of AnOHM (Analytical Objective Hysteresis Model). A sensitivity test of AnOHM to surface properties and hydrometeorological forcing is presented using a stochastic approach (subset simulation). The sensitivity test suggests that the albedo, Bowen ratio and bulk transfer coefficient, solar radiation and wind speed are most critical. AnOHM, driven by local meteorological conditions at five sites with different land use, is shown to simulate the ΔQS flux well (RMSE values of ∼ 30 W m−2). The intra-annual dynamics of OHM coefficients are explored. AnOHM offers significant potential to enhance modelling of the surface energy balance over a wider range of conditions and land covers.},

keywords = {advection–diffusion equation, albedo, all-wave radiation, Analytical Objective Hysteresis Model, AnOHM, Bowen ratio, bulk transfer coefficient, net storage heat flux, Objective Hysteresis Model, SEB, solar radiation, surface energy balance, urban surface energy balance, wind speed, ΔQS},

pubstate = {published},

tppubtype = {article}

}

@article{Wicki2017bc,

title = {Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment},

author = {Andreas Wicki and Eberhard Parlow },

editor = {MDPI},

url = {https://urbanfluxes.eu/wp-content/uploads/2018/01/2017_Wicki_Parlow_RemSens.pdf},

year  = {2017},

date = {2017-07-04},

journal = {Remote Sensing},

volume = {Vol 9},

pages = {684},

abstract = {Global climate change and increasing urbanization worldwide intensify the need for a better understanding of human heat stress dynamics in urban systems. During heat waves, which are expected to increase in number and intensity, the development of urban cool islands could be a lifesaver for many elderly and vulnerable people. The use of remote sensing data offers the unique possibility to study these dynamics with spatially distributed large datasets during all seasons of the year and including day and night-time analysis. For the city of Basel 32 high-quality Landsat 8 (L8) scenes are available since 2013, enabling comprehensive statistical analysis. Therefore, land surface temperature (LST) is calculated using L8 thermal infrared (TIR) imagery (stray light corrected) applying improved emissivity and atmospheric corrections. The data are combined with a land use/land cover (LULC) map and evaluated using administrative residential units. The observed dependence of LST on LULC is analyzed using a thermal unmixing approach based on a multiple linear regression (MLR) model, which allows for quantifying the gradual influence of different LULC types on the LST precisely. Seasonal variations due to different solar irradiance and vegetation cover indicate a higher dependence of LST on the LULC during the warmer summer months and an increasing influence of the topography and albedo during the colder seasons. Furthermore, the MLR analysis allows creating predicted LST images, which can be used to fill data gaps like in SLC-off Landsat 7 ETM+ data. Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment (PDF Download Available). Available from: https://www.researchgate.net/publication/318206742_Multiple_Regression_Analysis_for_Unmixing_of_Surface_Temperature_Data_in_an_Urban_Environment [accessed Jan 18 2018].},

keywords = {atmospheric corrections, land surface temperature, land use/land cover, Landsat 8, LST analysis, multiple linear regression, thermal infrared data, urban},

pubstate = {published},

tppubtype = {article}

}

@article{Kent2017,

title = {Evaluation of Urban Local-Scale Aerodynamic Parameters: Implications for the Vertical Profile of Wind Speed and for Source Areas},

author = {Christoph Kent and Sue Grimmond and Janet Barlow and David Gatey and Simone Kotthaus and Fredrik Lindberg and Christos Halios},

editor = {Boundary-Layer Meteorology},

url = {https://urbanfluxes.eu/wp-content/uploads/2018/01/2017_Kent_et_al_BLM.pdf},

doi = {10.1007/s10546-017-0248-z},

year  = {2017},

date = {2017-04-28},

journal = {Boundary-Layer Meteorol (2017) 164:183–213},

volume = {Vol: 164(2)},

pages = {183-213},

abstract = {Nine methods to determine local-scale aerodynamic roughness length (z0) and zero-plane displacement (zd ) are compared at three sites (within 60 m of each other) in London, UK. Methods include three anemometric (single-level high frequency observations), six morphometric (surface geometry) and one reference-based approach (look-up tables). A footprint model is used with the morphometric methods in an iterative procedure. The results are insensitive to the initial zd and z0 estimates. Across the three sites, zd varies between 5 and 45 m depending upon the method used. Morphometric methods that incorporate roughness-element height variability agree better with anemometric methods, indicating zd is consistently greater than the local mean building height. Depending upon method and wind direction, z0 varies between 0.1 and 5 m with morphometric z0 consistently being 2–3 m larger than the anemometric z0. No morphometric method consistently resembles the anemometric methods. Wind-speed profiles observed with Doppler lidar provide additional data with which to assess the methods. Locally determined roughness parameters are used to extrapolate wind-speed profiles to a height roughly 200 m above the canopy. Wind-speed profiles extrapolated based on morphometric methods that account for roughness-element height variability are most similar to observations. The extent of the modelled source area for measurements varies by up to a factor of three, depending upon the morphometric method used to determine zd and z0},

keywords = {Aerodynamic roughness length, Anemometric methods, Logarithmic wind-speed profile, Morphometric methods, Source area, Zero-plane displacement},

pubstate = {published},

tppubtype = {article}

}

@article{Gastellu-Etchegorry2017,

title = {DART: Recent Advances in Remote Sensing Data Modeling With Atmosphere, Polarization, and Chlorophyll Fluorescence},

author = {Jean-Philippe Gastellu-Etchegorry and Nicolas Lauret and Tiangang Yin and Lucas Landier and Abdelazis Kallel and Zbynek Malenovský and Ahmad Al-Bitar and Josselin Aval and Sahar Benhmida and Jianbo Qi and Ghania Medjdoub and Jordan Guilleux and Eric Chavanon and Bruce Cook and Douglas Morton and Nektarios Chrysoulakis and Zina Mitraka },

editor = {Journal of Selected Topics in Applied Earth Observations and Remote Sensing},

url = {https://urbanfluxes.eu/wp-content/uploads/2018/01/2017_Gastellu-Etchegorry_et_al_IEEE.pdf},

doi = {10.1109/JSTARS.2017.2685528},

issn = {2151-1535},

year  = {2017},

date = {2017-04-14},

journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Volume: 10, Issue: 6, June 2017)},

volume = {Vol: 10(6)},

pages = {2640–2649},

abstract = {To better understand the life-essential cycles and processes of our planet and to further develop remote sensing (RS) technology, there is an increasing need for models that simulate the radiative budget (RB) and RS acquisitions of urban and natural landscapes using physical approaches and considering the three dimensional (3-D) architecture of Earth surfaces. Discrete anisotropic radiative transfer (DART) is one of the most comprehensive physically based 3-D models of Earth-atmosphere radiative transfer, covering the spectral domain from ultraviolet to thermal infrared wavelengths. It simulates the optical 3-D RB and optical signals of proximal, aerial, and satellite imaging spectrometers and laser scanners, for any urban and/or natural landscapes and for any experimental and instrumental configurations. It is freely available for research and teaching activities. In this paper, we briefly introduce DART theory and present recent advances in simulated sensors (LiDAR and cameras with finite field of view) and modeling mechanisms (atmosphere, specular reflectance with polarization and chlorophyll fluorescence). A case study demonstrating a novel application of DART to investigate urban landscapes is also presented.},

keywords = {Discrete anisotropic radiative transfer (DART), model, radiative transfer (RT), remote sensing},

pubstate = {published},

tppubtype = {article}

}

@article{Wicki2017b,

title = {Attribution of local climate zones using a multitemporal land use/land cover classification scheme},

author = {Andreas Wicki and Eberhard Parlow},

editor = {SPIE },

url = {https://urbanfluxes.eu/wp-content/uploads/2018/01/2017_Wicki_Parlow_JARS.pdf},

doi = {DOI: 10.1117/1.JRS.11.026001},

year  = {2017},

date = {2017-04-03},

journal = {J. Appl. Remote Sens. 11(2), 026001 (2017)},

volume = {Vol. 11},

pages = {026001-1 – 026001-16},

abstract = {Worldwide, the number of people living in an urban environment exceeds the rural population with increasing tendency. Especially in relation to global climate change, cities play a major role considering the impacts of extreme heat waves on the population. For urban planners, it is important to know which types of urban structures are beneficial for a comfortable urban climate and which actions can be taken to improve urban climate conditions. Therefore, it is essential to differ between not only urban and rural environments, but also between different levels of urban densification. To compare these built-up types within different cities worldwide, Stewart and Oke developed the concept of local climate zones (LCZ) defined by morphological characteristics. The original LCZ scheme often has considerable problems when adapted to European cities with historical city centers, including narrow streets and irregular patterns. In this study, a method to bridge the gap between a classical land use/land cover (LULC) classification and the LCZ scheme is presented. Multitemporal Landsat 8 data are used to create a high accuracy LULC map, which is linked to the LCZ by morphological parameters derived from a high-resolution digital surface model and cadastral data. A bijective combination of the different classification schemes could not be achieved completely due to overlapping threshold values and the spatially homogeneous distribution of morphological parameters, but the attribution of LCZ to the LULC classification was successful. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.},

keywords = {land use/land cover, Landsat 8, local climate zones, morphology, urban},

pubstate = {published},

tppubtype = {article}

}

@article{Lindberg2017,

title = {Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services},

author = {Fredrik Lindberg and Sue Grimmond and Andrew Gabey and Bei Huang and Christoph Kent and Ting Sun and Natalie Theeuwes and Leena Jarvi and Helen Ward and Isabella Capel-Timms and Yuanyong Chang and Per Jonsson and Niklas Krave and Dongwei Liu and D Meyer and Frans Olofson and Jianguo Tan and Dag Wastberg and Lingbo Xue and Zhe Zhang},

editor = {EGU},

url = {https://urbanfluxes.eu/wp-content/uploads/2018/01/2018_Lindberg_et_al_-EnvMod.pdf},

year  = {2017},

date = {2017-01-01},

journal = {Environmental Modelling & Software Vol. 99, 70-87, (2018)},

volume = {Vol. 99},

pages = {70-87},

abstract = {UMEP (Urban Multi-scale Environmental Predictor), a city-based climate service tool, combines models and tools essential for climate simulations. Applications are presented to illustrate UMEP's potential in the identification of heat waves and cold waves; the impact of green infrastructure on runoff; the effects of buildings on human thermal stress; solar energy production; and the impact of human activities on heat emissions. UMEP has broad utility for applications related to outdoor thermal comfort, wind, urban energy consumption and climate change mitigation. It includes tools to enable users to input atmospheric and surface data from multiple sources, to characterise the urban environment, to prepare meteorological data for use in cities, to undertake simulations and consider scenarios, and to compare and visualise different combinations of climate indicators. An open-source tool, UMEP is designed to be easily updated as new data and tools are developed, and to be accessible to researchers, decision-makers and practitioners.},

keywords = {Green infrastructure, Heat risk, QGIS, Solar energy, Urban climate services},

pubstate = {published},

tppubtype = {article}

}

@article{Lindberg2016,

title = {Influence of ground surface characteristics on the mean radiant temperature in urban areas},

author = {Fredrik Lindberg and Shiho Onomura and Sue Grimmond   },

url = {https://urbanfluxes.eu/wp-content/uploads/2017/02/02_2016_Lindberg_et_al_IntJBiomet.pdf},

year  = {2016},

date = {2016-12-31},

keywords = {ground surface characteristics, Influence, mean radiant temperature, urban areas},

pubstate = {published},

tppubtype = {article}

}

@article{Kotthaus2016,

title = {Recommendations for processing atmospheric attenuated backscatter profiles from Vaisala CL31 ceilometers},

author = {Simone Kotthaus and Ewan O’Connor and Christoph Münkel and Cristina Charlton-Perez and Martial Haeffelin and Andrew Gabey and Sue Grimmond  },

url = {https://urbanfluxes.eu/wp-content/uploads/2017/02/07_2016_Kotthaus_et_al_AtmosMeas.pdf},

year  = {2016},

date = {2016-12-31},

keywords = {atmospheric attenuated backscatter profiles, Recommendations, Vaisala CL31 ceilometers},

pubstate = {published},

tppubtype = {article}

}

@article{Ward2016,

title = {Surface Urban Energy and Water Balance Scheme (SUEWS): Development and evaluation at two UK sites},

author = {Helen Ward and Simone Kotthaus and Leena Järvi and Sue Grimmond      },

url = {https://urbanfluxes.eu/wp-content/uploads/2017/02/05_2016_Ward_et_al_UrbanClim.pdf},

year  = {2016},

date = {2016-12-30},

keywords = {SUEWS, Surface Urban Energy and Water Balance Scheme},

pubstate = {published},

tppubtype = {article}

}

@article{07_2015_Lindberg_et_al_UrbanClimate,

title = {Sunlit fractions on urban facets – Impact of spatial resolution and approach},

author = {Fredrik Lindberg and Sue Grimmond and Alberto Martilli      },

url = {https://urbanfluxes.eu/wp-content/uploads/2016/02/07_2015_Lindberg_et_al_UrbanClimate.pdf},

year  = {2016},

date = {2016-01-01},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Crawford2016,

title = {Spatial and temporal patterns of surface–atmosphere energy exchange in a dense urban environment using scintillometry},

author = {Benjamin Crawford and Sue Grimmond and Helen Ward and William Morrison and Simone Kotthaus  },

url = {https://urbanfluxes.eu/wp-content/uploads/2017/02/12_2016_Crawford_et_al_QJRMeteorol.pdf},

year  = {2016},

date = {2016-00-00},

keywords = {scintillometry, Spatial and temporal patterns of surface–atmosphere energy exchange in a dense, urban environment, using},

pubstate = {published},

tppubtype = {article}

}

@article{05_2015_Bjorkegren_et_al_AtmEnvir,

title = {CO2 emission estimation in the urban environment: Measurement of the CO2 storage term},

author = {Alex Bjorkegren and Sue Grimmond and Simone Kotthaus and Bruce Malamud      },

url = {https://urbanfluxes.eu/wp-content/uploads/2016/02/05_2015_Bjorkegren_et_al_AtmEnvir.pdf},

year  = {2015},

date = {2015-01-01},

journal = {Atmospheric Environment},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Morrisona2018,

title = {A novel method to obtain three-dimensional urban surface temperature from ground-based thermography},

author = {William Morrison and Simone Kotthaus and CSB Grimmond and Atsushi Inagaki and Tiangang Yin and Jean-Philippe Gastellu-Etchegorry and Manabu Kanda and Christopher J. Merchant},

url = {https://urbanfluxes.eu/wp-content/uploads/2018/07/2018_Morrison_et_al.pdf},

doi = {10.1016/j.rse.2018.05.004},

year  = {2018},

date = {2018-09-15},

journal = {Remote Sensing of Environment},

volume = {Volume 215},

pages = {268-283},

abstract = {Urban geometry and materials combine to create complex spatial, temporal and directional patterns of longwave infrared (LWIR) radiation. Effective anisotropy (or directional variability) of thermal radiance causes remote sensing (RS) derived urban surface temperatures to vary with RS view angles. Here a new and novel method to resolve effective thermal anisotropy processes from LWIR camera observations is demonstrated at the Comprehensive Outdoor Scale MOdel (COSMO) test site. Pixel-level differences of brightness temperatures reach 18.4 K within one hour of a 24-h study period. To understand this variability, the orientation and shadowing of surfaces is explored using the Discrete Anisotropic Radiative Transfer (DART) model and Blender three-dimensional (3D) rendering software. Observed pixels and the entire canopy surface are classified in terms of surface orientation and illumination. To assess the variability of exitant longwave radiation (MLW) from the 3D COSMO surface (), the observations are prescribed based on class. The parameterisation is tested by simulating thermal images using a camera view model to determine camera perspectives of fluxes. The mean brightness temperature differences per image (simulated and observed) are within 0.65 K throughout a 24-h period. Pixel-level comparisons are possible with the high spatial resolution of and DART camera view simulations. At this spatial scale (<0.10 m), shadow hysteresis, surface sky view factor and building edge effects are not completely resolved by . By simulating apparent brightness temperatures from multiple view directions, effective thermal anisotropy of is shown to be up to 6.18 K. The developed methods can be extended to resolve some of the identified sources of sub-facet variability in realistic urban settings. The extension of DART to the interpretation of ground-based RS is shown to be promising.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chrysoulakis2018,

title = {Urban energy exchanges monitoring from space},

author = {Nektarios Chrysoulakis and Sue Grimmond and Christian Feigenwinter and Fredrik Lindberg and Jean-Philippe Gastellu-Etchegorry and Mattia Marconcini and Zina Mitraka and Stavros Stagakis and Ben Crawford and Frans Olofson and Lucas Landier and William Morrison and Eberhard Parlow },

editor = {nature.com},

url = {https://urbanfluxes.eu/wp-content/uploads/2018/07/UF_Overview_final.pdf},

doi = {10.1038/s41598-018-29873-x},

issn = {2045-2322},

year  = {2018},

date = {2018-07-31},

journal = { nature.com },

volume = {Scientific Reports volume},

number = {8},

pages = {11498},

abstract = {One important challenge facing the urbanization and global environmental change community is to understand the relation between urban form, energy use and carbon emissions. Missing from the current literature are scientific assessments that evaluate the impacts of different urban spatial units on energy fluxes; yet, this type of analysis is needed by urban planners, who recognize that local scale zoning affects energy consumption and local climate. Satellite-based estimation of urban energy fluxes at neighbourhood scale is still a challenge. Here we show the potential of the current satellite missions to retrieve urban energy budget fluxes, supported by meteorological observations and evaluated by direct flux measurements. We found an agreement within 5% between satellite and in-situ derived net all-wave radiation; and identified that wall facet fraction and urban materials type are the most important parameters for estimating heat storage of the urban canopy. The satellite approaches were found to underestimate measured turbulent heat fluxes, with sensible heat flux being most sensitive to surface temperature variation (−64.1, +69.3 W m−2 for ±2 K perturbation).  They also underestimate anthropogenic heat fluxes. However, reasonable spatial patterns are obtained for the latter allowing hot-spots to be identified, therefore supporting both urban planning and urban climate modelling.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Feigenwinter2018,

title = {Spatial Distribution of Sensible and Latent Heat Flux in the city of Basel (Switzerland)},

author = {Christian Feigenwinter and Roland Vogt and Eberhard Parlow and Fredrik Lindberg and Mattia Marconcini and Fabio Del Frate and Nektarios Chrysoulakis},

doi = {10.1109/JSTARS.2018.2807815},

issn = {2151-1535},

year  = {2018},

date = {2018-03-18},

journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},

abstract = {Urban surfaces are a complex mixture of different land covers and surface materials; the relative magnitudes of the surface energy balance components therefore vary widely across a city. Eddy covariance (EC) measurements provide the best estimates of turbulent heat fluxes but are restricted to the source area. Land surface modeling with earth observation (EO) data is beneficial for extrapolation of a larger area since citywide information is possible. Turbulent sensible and latent heat fluxes are calculated by a combination of micrometeorological approaches (the aerodynamic resistance method, ARM), EO data, and GIS techniques. Input data such as land cover fractions and surface

temperatures are derived from Landsat 8 OLI and TIRS, urban morphology was calculated from high-resolution digital building models and GIS data layers, and meteorological data were provided by flux tower measurements. Twenty-two Landsat scenes covering all seasons and different meteorological conditions were analyzed. Sensible heat fluxes were highest for industrial areas, railway stations, and areas with high building density, mainly corresponding to the pixels with highest surface-to-air  temperature differences.

The spatial distribution of latent heat flux is strongly related to the

saturation deficit of vapor and the (minimum) stomatal resistance

of vegetation types. Seasonal variations are highly dependent on

meteorological conditions, i.e., air temperature, water vapor saturation

deficit, and wind speed. Comparison of measured fluxes

with modeled fluxes in the weighted source area of the flux towers

is moderately accurate due to known drawbacks in the modeling

approach and uncertainties inherent to EC measurements, particularly

in urban areas.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kent2017b,

title = {Aerodynamic roughness parameters in cities: Inclusion of vegetation},

author = {Christoph Kent and Sue Grimmond and David Gatey},

editor = {Journal of Wind Engineering & Industrial Aerodynamics},

url = {https://urbanfluxes.eu/wp-content/uploads/2018/01/2017_Kent_et_al_JWEIA.pdf},

year  = {2017},

date = {2017-10-01},

journal = {Journal of Wind Engineering and Industrial Aerodynamics},

volume = {Volume 169},

pages = {168-176},

abstract = {A widely used morphometric method (Macdonald et al. 1998) to calculate the zero-plane displacement (zd) and aerodynamic roughness length (z0) for momentum is further developed to include vegetation. The adaptation also applies to the Kanda et al. (2013) morphometric method which considers roughness-element height variability. Roughness-element heights (mean, maximum and standard deviation) of both buildings and vegetation are combined with a porosity corrected plan area and drag formulation. The method captures the influence of vegetation (in addition to buildings), with the magnitude of the effect depending upon whether buildings or vegetation are dominant and the porosity of vegetation (e.g. leaf-on or leaf-off state). Application to five urban areas demonstrates that where vegetation is taller and has larger surface cover, its inclusion in the morphometric methods can be more important than the morphometric method used. Implications for modelling the logarithmic wind profile (to 100 m) are demonstrated. Where vegetation is taller and occupies a greater amount of space, wind speeds may be slowed by up to a factor of three},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sun2017,

title = {The Analytical Objective Hysteresis Model (AnOHM v1.0): methodology to determine bulk storage heat flux coefficients},

author = {Ting Sun and Zhi-Hua Wang and Walter Oechel and Sue Grimmond },

editor = {EGU},

url = {https://urbanfluxes.eu/wp-content/uploads/2018/01/2017_Sun_et_al_Geosci.pdf},

year  = {2017},

date = {2017-07-27},

journal = {Geoscientific Model Development  Vol. 10, 2875-2890, 2017},

volume = {10},

pages = {2875–2890},

abstract = {The net storage heat flux (ΔQS) is important in the urban surface energy balance (SEB) but its determination remains a significant challenge. The hysteresis pattern of the diurnal relation between the ΔQS and net all-wave radiation (Q∗) has been captured in the Objective Hysteresis Model (OHM) parameterization of ΔQS. Although successfully used in urban areas, the limited availability of coefficients for OHM hampers its application. To facilitate use, and enhance physical interpretations of the OHM coefficients, an analytical solution of the one-dimensional advection–diffusion equation of coupled heat and liquid water transport in conjunction with the SEB is conducted, allowing development of AnOHM (Analytical Objective Hysteresis Model). A sensitivity test of AnOHM to surface properties and hydrometeorological forcing is presented using a stochastic approach (subset simulation). The sensitivity test suggests that the albedo, Bowen ratio and bulk transfer coefficient, solar radiation and wind speed are most critical. AnOHM, driven by local meteorological conditions at five sites with different land use, is shown to simulate the ΔQS flux well (RMSE values of ∼ 30 W m−2). The intra-annual dynamics of OHM coefficients are explored. AnOHM offers significant potential to enhance modelling of the surface energy balance over a wider range of conditions and land covers.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wicki2017bc,

title = {Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment},

author = {Andreas Wicki and Eberhard Parlow },

editor = {MDPI},

url = {https://urbanfluxes.eu/wp-content/uploads/2018/01/2017_Wicki_Parlow_RemSens.pdf},

year  = {2017},

date = {2017-07-04},

journal = {Remote Sensing},

volume = {Vol 9},

pages = {684},

abstract = {Global climate change and increasing urbanization worldwide intensify the need for a better understanding of human heat stress dynamics in urban systems. During heat waves, which are expected to increase in number and intensity, the development of urban cool islands could be a lifesaver for many elderly and vulnerable people. The use of remote sensing data offers the unique possibility to study these dynamics with spatially distributed large datasets during all seasons of the year and including day and night-time analysis. For the city of Basel 32 high-quality Landsat 8 (L8) scenes are available since 2013, enabling comprehensive statistical analysis. Therefore, land surface temperature (LST) is calculated using L8 thermal infrared (TIR) imagery (stray light corrected) applying improved emissivity and atmospheric corrections. The data are combined with a land use/land cover (LULC) map and evaluated using administrative residential units. The observed dependence of LST on LULC is analyzed using a thermal unmixing approach based on a multiple linear regression (MLR) model, which allows for quantifying the gradual influence of different LULC types on the LST precisely. Seasonal variations due to different solar irradiance and vegetation cover indicate a higher dependence of LST on the LULC during the warmer summer months and an increasing influence of the topography and albedo during the colder seasons. Furthermore, the MLR analysis allows creating predicted LST images, which can be used to fill data gaps like in SLC-off Landsat 7 ETM+ data. Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment (PDF Download Available). Available from: https://www.researchgate.net/publication/318206742_Multiple_Regression_Analysis_for_Unmixing_of_Surface_Temperature_Data_in_an_Urban_Environment [accessed Jan 18 2018].},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kent2017,

title = {Evaluation of Urban Local-Scale Aerodynamic Parameters: Implications for the Vertical Profile of Wind Speed and for Source Areas},

author = {Christoph Kent and Sue Grimmond and Janet Barlow and David Gatey and Simone Kotthaus and Fredrik Lindberg and Christos Halios},

editor = {Boundary-Layer Meteorology},

url = {https://urbanfluxes.eu/wp-content/uploads/2018/01/2017_Kent_et_al_BLM.pdf},

doi = {10.1007/s10546-017-0248-z},

year  = {2017},

date = {2017-04-28},

journal = {Boundary-Layer Meteorol (2017) 164:183–213},

volume = {Vol: 164(2)},

pages = {183-213},

abstract = {Nine methods to determine local-scale aerodynamic roughness length (z0) and zero-plane displacement (zd ) are compared at three sites (within 60 m of each other) in London, UK. Methods include three anemometric (single-level high frequency observations), six morphometric (surface geometry) and one reference-based approach (look-up tables). A footprint model is used with the morphometric methods in an iterative procedure. The results are insensitive to the initial zd and z0 estimates. Across the three sites, zd varies between 5 and 45 m depending upon the method used. Morphometric methods that incorporate roughness-element height variability agree better with anemometric methods, indicating zd is consistently greater than the local mean building height. Depending upon method and wind direction, z0 varies between 0.1 and 5 m with morphometric z0 consistently being 2–3 m larger than the anemometric z0. No morphometric method consistently resembles the anemometric methods. Wind-speed profiles observed with Doppler lidar provide additional data with which to assess the methods. Locally determined roughness parameters are used to extrapolate wind-speed profiles to a height roughly 200 m above the canopy. Wind-speed profiles extrapolated based on morphometric methods that account for roughness-element height variability are most similar to observations. The extent of the modelled source area for measurements varies by up to a factor of three, depending upon the morphometric method used to determine zd and z0},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gastellu-Etchegorry2017,

title = {DART: Recent Advances in Remote Sensing Data Modeling With Atmosphere, Polarization, and Chlorophyll Fluorescence},

author = {Jean-Philippe Gastellu-Etchegorry and Nicolas Lauret and Tiangang Yin and Lucas Landier and Abdelazis Kallel and Zbynek Malenovský and Ahmad Al-Bitar and Josselin Aval and Sahar Benhmida and Jianbo Qi and Ghania Medjdoub and Jordan Guilleux and Eric Chavanon and Bruce Cook and Douglas Morton and Nektarios Chrysoulakis and Zina Mitraka },

editor = {Journal of Selected Topics in Applied Earth Observations and Remote Sensing},

url = {https://urbanfluxes.eu/wp-content/uploads/2018/01/2017_Gastellu-Etchegorry_et_al_IEEE.pdf},

doi = {10.1109/JSTARS.2017.2685528},

issn = {2151-1535},

year  = {2017},

date = {2017-04-14},

journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Volume: 10, Issue: 6, June 2017)},

volume = {Vol: 10(6)},

pages = {2640–2649},

abstract = {To better understand the life-essential cycles and processes of our planet and to further develop remote sensing (RS) technology, there is an increasing need for models that simulate the radiative budget (RB) and RS acquisitions of urban and natural landscapes using physical approaches and considering the three dimensional (3-D) architecture of Earth surfaces. Discrete anisotropic radiative transfer (DART) is one of the most comprehensive physically based 3-D models of Earth-atmosphere radiative transfer, covering the spectral domain from ultraviolet to thermal infrared wavelengths. It simulates the optical 3-D RB and optical signals of proximal, aerial, and satellite imaging spectrometers and laser scanners, for any urban and/or natural landscapes and for any experimental and instrumental configurations. It is freely available for research and teaching activities. In this paper, we briefly introduce DART theory and present recent advances in simulated sensors (LiDAR and cameras with finite field of view) and modeling mechanisms (atmosphere, specular reflectance with polarization and chlorophyll fluorescence). A case study demonstrating a novel application of DART to investigate urban landscapes is also presented.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wicki2017b,

title = {Attribution of local climate zones using a multitemporal land use/land cover classification scheme},

author = {Andreas Wicki and Eberhard Parlow},

editor = {SPIE },

url = {https://urbanfluxes.eu/wp-content/uploads/2018/01/2017_Wicki_Parlow_JARS.pdf},

doi = {DOI: 10.1117/1.JRS.11.026001},

year  = {2017},

date = {2017-04-03},

journal = {J. Appl. Remote Sens. 11(2), 026001 (2017)},

volume = {Vol. 11},

pages = {026001-1 – 026001-16},

abstract = {Worldwide, the number of people living in an urban environment exceeds the rural population with increasing tendency. Especially in relation to global climate change, cities play a major role considering the impacts of extreme heat waves on the population. For urban planners, it is important to know which types of urban structures are beneficial for a comfortable urban climate and which actions can be taken to improve urban climate conditions. Therefore, it is essential to differ between not only urban and rural environments, but also between different levels of urban densification. To compare these built-up types within different cities worldwide, Stewart and Oke developed the concept of local climate zones (LCZ) defined by morphological characteristics. The original LCZ scheme often has considerable problems when adapted to European cities with historical city centers, including narrow streets and irregular patterns. In this study, a method to bridge the gap between a classical land use/land cover (LULC) classification and the LCZ scheme is presented. Multitemporal Landsat 8 data are used to create a high accuracy LULC map, which is linked to the LCZ by morphological parameters derived from a high-resolution digital surface model and cadastral data. A bijective combination of the different classification schemes could not be achieved completely due to overlapping threshold values and the spatially homogeneous distribution of morphological parameters, but the attribution of LCZ to the LULC classification was successful. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lindberg2017,

title = {Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services},

author = {Fredrik Lindberg and Sue Grimmond and Andrew Gabey and Bei Huang and Christoph Kent and Ting Sun and Natalie Theeuwes and Leena Jarvi and Helen Ward and Isabella Capel-Timms and Yuanyong Chang and Per Jonsson and Niklas Krave and Dongwei Liu and D Meyer and Frans Olofson and Jianguo Tan and Dag Wastberg and Lingbo Xue and Zhe Zhang},

editor = {EGU},

url = {https://urbanfluxes.eu/wp-content/uploads/2018/01/2018_Lindberg_et_al_-EnvMod.pdf},

year  = {2017},

date = {2017-01-01},

journal = {Environmental Modelling & Software Vol. 99, 70-87, (2018)},

volume = {Vol. 99},

pages = {70-87},

abstract = {UMEP (Urban Multi-scale Environmental Predictor), a city-based climate service tool, combines models and tools essential for climate simulations. Applications are presented to illustrate UMEP's potential in the identification of heat waves and cold waves; the impact of green infrastructure on runoff; the effects of buildings on human thermal stress; solar energy production; and the impact of human activities on heat emissions. UMEP has broad utility for applications related to outdoor thermal comfort, wind, urban energy consumption and climate change mitigation. It includes tools to enable users to input atmospheric and surface data from multiple sources, to characterise the urban environment, to prepare meteorological data for use in cities, to undertake simulations and consider scenarios, and to compare and visualise different combinations of climate indicators. An open-source tool, UMEP is designed to be easily updated as new data and tools are developed, and to be accessible to researchers, decision-makers and practitioners.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lindberg2016,

title = {Influence of ground surface characteristics on the mean radiant temperature in urban areas},

author = {Fredrik Lindberg and Shiho Onomura and Sue Grimmond   },

url = {https://urbanfluxes.eu/wp-content/uploads/2017/02/02_2016_Lindberg_et_al_IntJBiomet.pdf},

year  = {2016},

date = {2016-12-31},

keywords = {},

pubstate = {published},

tppubtype = {article}

}