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The urban heat island, is a serious threat for the urban well-being, and can be determined by the local energy balance. The surface energy balance, with respect to incoming radiative energy and subsequent partitioning into reflected energy (albedo), absorbed energy and further partitioning of latter into convectional heat (Q H), radiative heat (Q R) and latent heat (Q E) by using commonly applied urban materials and vegetation types, was therefore experimentally quantified in this study. In agreement with previous studies it was found that materials convert most of absorbed energy into convectional heat (>92%) while vegetation channels a substantial part of absorbed radiative energy into latent heat (27–50%). It is for the first time experimentally demonstrated that significant differences in thermal behaviour between different types of urban vegetation surfaces occur. Of the investigated vegetation types ivy and moss showed respectively the highest (0.10) and lowest (0.07) albedo, but sedum and moss channelled respectively lowest (27%) and highest (50%) percentage of the absorbed radiative energy into latent heat production. Of the four investigated plant types, moss appeared most effective in preventing UHI, converting only 50% of incoming radiative energy into convectional heat, while sedum was least effective converting 73% of incoming radiative energy into convectional heat. These quantitative measurements show that strategic use of specific types of urban vegetation surfaces, instead of commonly applied building materials, can be an effective measure for mitigation of UHI leading to improved climate resilient cities. [ABSTRACT FROM AUTHOR]

Measurements of ice temperature provide crucial constraints on ice viscosity and the thermodynamic processes occurring within a glacier. However, such measurements are presently limited by a small number of relatively coarse-spatial-resolution borehole records, especially for ice sheets. Here, we advance our understanding of glacier thermodynamics with an exceptionally high-vertical-resolution (~0.65 m), distributed-fiber-optic temperature-sensing profile from a 1043-m borehole drilled to the base of Sermeq Kujalleq (Store Glacier), Greenland. We report substantial but isolated strain heating within interglacial-phase ice at 208 to 242 m depth together with strongly heterogeneous ice deformation in glacial-phase ice below 889 m. We also observe a high-strain interface between glacial- and interglacial-phase ice and a 73-m-thick temperate basal layer, interpreted as locally formed and important for the glacier's fast motion. These findings demonstrate notable spatial heterogeneity, both vertically and at the catchment scale, in the conditions facilitating the fast motion of marine-terminating glaciers in Greenland., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

Distributed temperature sensing (DTS) systems can be used to estimate the temperature along optic fibers of several kilometers at a sub-meter interval. DTS systems function by shooting laser pulses through a fiber and measuring its backscatter intensity at two distinct wavelengths in the Raman spectrum. The scattering-loss coefficients for these wavelengths are temperature-dependent, so that the temperature along the fiber can be estimated using calibration to fiber sections with a known temperature. A new calibration approach is developed that allows for an estimate of the uncertainty of the estimated temperature, which varies along the fiber and with time. The uncertainty is a result of the noise from the detectors and the uncertainty in the calibrated parameters that relate the backscatter intensity to temperature. Estimation of the confidence interval of the temperature requires an estimate of the distribution of the noise from the detectors and an estimate of the multi-variate distribution of the parameters. Both distributions are propagated with Monte Carlo sampling to approximate the probability density function of the estimated temperature, which is different at each point along the fiber and varies over time. Various summarizing statistics are computed from the approximate probability density function, such as the confidence intervals and the standard uncertainty (the estimated standard deviation) of the estimated temperature. An example is presented to demonstrate the approach and to assess the reasonableness of the estimated confidence intervals. The approach is implemented in the open-source Python package "dtscalibration".

Conventional in situ observations of meteorological variables are restricted to a limited number of levels near the surface, with the lowest observation often made around 1-m height. This can result in missed observations of both shallow fog, and the initial growth stage of thicker fog layers. At the same time, numerical experiments have demonstrated the need for high vertical grid resolution in the near-surface layer to accurately simulate the onset of fog; this requires correspondingly high-resolution observational data for validation. A two-week field campaign was conducted in November 2017 at the Cabauw Experimental Site for Atmospheric Research (CESAR) in the Netherlands. The aim was to observe the growth of shallow fog layers and assess the possibility of obtaining very high-resolution observations near the surface during fog events. Temperature and relative humidity were measured at centimetre resolution in the lowest 7 m using distributed temperature sensing. Further, a novel approach was employed to estimate visibility in the lowest 2.5 m using a camera and an extended light source. These observations were supplemented by the existing conventional sensors at the site, including those along a 200-m tall tower. Comparison between the increased-resolution observations and their conventional counterparts show the errors to be small, giving confidence in the reliability of the techniques. The increased resolution of the observations subsequently allows for detailed investigations of fog growth and evolution. This includes the observation of large temperature inversions in the lowest metre (up to 5 K) and corresponding regions of (super)saturation where the fog formed. Throughout the two-week observation period, fog was observed twice at the conventional sensor height of 2.0 m. Two additional low-visibility events were observed in the lowest 0-0.5 m using the camera-based observations, but were missed by the conventional sensors. The camera observations also showed the growth of shallow radiation fog, forming in the lowest 0.5 m as early as two hours before it was observed at the conventional height of 2 m., (© The Author(s) 2019.)