Wall surface temperatures are important components of urban climates but are under-sampled by satellite and airborne remote sensing and at the microscale are under-sampled in observational studies. In urban canopy models, they are represented with simplistic geometries. This study examines the effect of microscale (sub-facet) surface structure geometries on wall surface brightness temperature distributions at micro-to neighbourhood scales using mobile sampling traverses of two suburban neighbourhoods with different sub-facet geometries. Visible and thermal imagery were recorded simultaneously and combined and classified to create a database of temperatures with associated geographic and thermal attributes. This study investigates (1) if sub-facet scale geometries affect temperature distributions, (2) if these cause canyon scale biases, and (3) if there are therefore inter-neighbourhood biases. It is shown that sub-facet geometries modify wall surface temperatures predominantly by cooling due to self-shading. Surface-sun geometry thus leads to intra-and inter-neighbourhood temperature differences of several degrees Celsius. The observed effects have important implications for modelling of urban surface temperatures, where simplified geometries may overestimate wall surface temperatures.

The Namib Turbulence EXperiment (NamTEX) was a multi-national micrometeorological campaign conducted in the Central Namib Desert to investigate three-dimensional surface layer turbulence and the spatio-temporal patterns of heat transfer between the sub-surface, surface, and atmosphere. The Namib provides an ideal location for fundamental research that revisits some key assumptions in micrometeorology that are implicitly included in the parameterizations describing energy exchange in weather forecasting and climate models: Homogenous flat surfaces, no vegetation, little moisture, and cloud-free skies create a strong and consistent diurnal forcing, resulting in a wide range of atmospheric stabilities. A novel combination of instruments was used to simultaneously measure variables and processes relevant to heat transfer: A three km fibre-optic distributed temperature sensor (DTS) was suspended in a pseudo-three-dimensional array within a 300 m x 300 m domain to provide vertical cross-sections of air temperature fluctuations. Aerial and ground-based thermal imagers recorded high resolution surface temperature fluctuations within the domain and revealed the spatial thermal imprint of atmospheric structures responsible for heat exchange. High-resolution soil temperature and moisture profiles together with heat flux plates provided information on near-surface soil dynamics. Turbulent heat exchange was measured with a vertical array of five eddy-covariance point measurements on a 21-m mast, as well as by co-located small- and large-aperture scintillometers. This contribution first details the scientific goals and experimental set-up of the NamTEX campaign. Then using a typical day, we demonstrate i) the coupling of surface layer, surface, and soil temperatures using high-frequency temperature measurements, ii) differences in spatial and temporal standard deviations of the horizontal temperature field using spatially distributed measurements, and iii) horizontal anisotropy of the turbulent temperature field.

<p>Field observation networks are becoming denser, more diverse, and more mobile, while being required to provide real-time results. The ERC <em>urbisphere</em> program is coordinating multiple field campaigns simultaneously to collect datasets on urban atmospheric and environmental conditions and processes in cities of different sizes. The datasets are used for improving climate and weather models and services, including assessing the impact of cities on the atmosphere (e.g. aerosols, greenhouse gases) as well as the exposure of urban populations in the context of atmospheric extreme events (heat waves, heavy precipitation, air pollution). For model development and evaluation, we are using meshed networks of in-situ observations with ground-based and airborne (remote-)sensing platforms. This contribution describes the <em>urbisphere</em> data management infrastructure and processes required to handle a variety of data streams from primarily novel modular observation systems deployed in complex urban environments.</p> <p>The modular observation systems consist of short-term deployed instrumentation and are separated into three thematic modules. Module A aims at characterizing urban form and function affecting urban climates. Module B quantifies the impact of urban emissions (heat, pollutants, greenhouse gases, etc.) on the urban boundary layer over and downwind of cities. Module C provides data on human exposure at street and indoor-level. The three modules are served with consistent data management, documentation and calibration. Systems deployed in Modules A, B and C include customized automatic weather stations, (Doppler) lidars and ceilometers, scintillometers, balloon radio sounding and spectral camera imaging. Systems are street-light-mounted, located on building roof-tops or indoors as well as on mobile platforms (vehicles, drones). Data ingestion processes are automated, delivering moderate data volumes in real-time to central data infrastructure through mobile phone and IOT connectivity. A meta data system helps keep track of the location and configuration of all deployed components and forms the backbone for conversion of instrument records into location-aware, conventions-aligned and quality-assured F.A.I.R. data products. Furthermore, the data management infrastructure provides services (APIs, Apps, IDEs, etc.) for data inspection and computations by scientists and students involved in the campaigns. Select datasets are integrated in near real-time into other global or local data systems such as, e.g., AERONET, the Phenocam Network, ICOS, or PANAME, for multiple uses.</p> <p>Besides technical aspects and design considerations, we discuss how cooperation and attribution are safeguarded when data are being accessed for immediate academic and citizen data science.</p> <div> <div> <div> </div> </div> </div>

<p>Taylor’s frozen turbulence hypothesis is the most critical assumption through which time-resolving sensors may be used to derive statistics of the turbulent spatial field. Namely, it relates temporal autocorrelation to spatial correlation via the mean wind speed and is invoked in almost all boundary layer field work. Nevertheless, the conditions and scales over which Taylor’s hypothesis is valid remain poorly understood in the atmospheric boundary layer.</p> <p>As part of the Namib Turbulence Experiment (NamTEX) campaign in March 2020, a pseudo-3D fibre-optic distributed temperature sensing (DTS) array was installed within a 300 x 300 m area in the Namib desert. The array is X-shaped in plan view and contains 16 measurement heights from 0.45 m to 2.85 m. Fibre-optic sensing provides air temperature measurements at unprecedented spatio-temporal density (0.25 m horizontally, 0.17 m vertically, and 1 Hz) and was coupled with a vertical array of traditional sonic anemometer point measurements to investigate the relationship between spatial and temporal temperature fields. The Namib provides an ideal location for fundamental boundary layer research: homogenous flat surfaces, no vegetation, little moisture, strong solar forcing, regular and repeated clear-sky conditions, and a wide range of atmospheric stabilities.</p> <p>Using the NamTEX DTS array we present the first field investigation of Taylor’s hypothesis that considers boundary layer stability and is independent of wind direction. A novel method of 2d horizontal cross-correlation between all possible points of a single height of the DTS is employed to produce spatial ‘maps’ of the turbulent flow, whose velocity, direction, and size may be tracked through time.</p>