ALBERT

Description: One of the main goals of the study was to demonstrate a low-power efficient Nd:YAG laser oscillator for applications in remote coherent Doppler anemometry. An electrical-to-optical slope efficiency of 6.5 percent has been achieved by using commercially available CW laser diodes of up to 100 mW to pump monolithic Nd:YAG rod lasers. The observed Nd:YAG oscillation threshold is at 2.3 mW of laser-diode output power, i.e., a small fraction of the rated output power. The highest Nd:YAG CW output power reached is 4.4 mW at an overall electrical-to-optical efficiency of 1.5 percent. The frequency jitter is less than 10 kHz in 0.3 s.

Description: Existing techniques for the frequency stabilization of Nd:YAG lasers operating at 1.06 micron, and the high-gain amplification of radiation at that wavelength, make possible the construction of a coherent Doppler wind velocity lidar using Nd:YAG. Velocity accuracy and range resolution are better at 1.06 micron than at 10.6 microns at the same level of the SNR. Backscatter from the atmosphere at 1.06 micron is greater than that at 10.6 microns by about 2 orders of magnitude, but the quantum-limited noise is higher by 100 also. Near-field attenuation and turbulent effects are more severe at 1.06 micron. In some configurations and environments, the 1.06-micron wavelength may be the better choice, and there may be technological advantages favoring the use of solid-state lasers in satellite systems.

Description: Excellent spatial resolution (34 microns) has been obtained using dimethylether, (CH3)2O at 1 atm in a small drift tube detector. This is better by at least a factor of 2 compared to previous work (about 80 microns) with a conventional gas (a 50-50 argon-ethane mixture) in the same detector. The Lorentz angle and the gas amplification have been measured over a wide range of electric and magnetic fields.

Description: Urban climate is determined by a variety of factors, whose knowledge can help to attenuate heat stress in the context of ongoing urbanization and climate change. We study the influence of city size and urban form on the Urban Heat Island (UHI) phenomenon in Europe and find a complex interplay between UHI intensity and city size, fractality, and anisometry. Due to correlations among these urban factors, interactions in the multi-linear regression need to be taken into account. We find that among the largest 5,000 cities, the UHI intensity increases with the logarithm of the city size and with the fractal dimension, but decreases with the logarithm of the anisometry. Typically, the size has the strongest influence, followed by the compactness, and the smallest is the influence of the degree to which the cities stretch. Accordingly, from the point of view of UHI alleviation, small, disperse, and stretched cities are preferable. However, such recommendations need to be balanced against e.g. positive agglomeration effects of large cities. Therefore, trade-offs must be made regarding local and global aims.

Description: Mapping spatial and temporal variability of urban microclimate is pivotal for an accurate estimation of the ever‐increasing exposure of urbanized humanity to global warming. This particularly concerns cities in arid/semi‐arid regions which cover two fifths of the global land area and are home to more than one third of the world's population. Focusing on the desert city of Be'er Sheva Israel, we investigate the spatial and temporal patterns of urban–rural and intra‐urban temperature variability by means of satellite observation, vehicular traverse measurement, and computer simulation. Our study reveals a well‐developed nocturnal canopy layer urban heat island in Be'er Sheva, particularly in the winter, but a weak diurnal cool island in the mid‐morning. Near surface air temperature exhibits weak urban–rural and intra‐urban differences during the daytime (〈1°C), despite pronounced urban surface cool islands observed in satellite images. This phenomenon, also recorded in some other desert cities, is explained by the rapid increase in surface skin temperature of exposed desert soils (in the absence of vegetation or moisture) after sunrise, while urban surfaces are heated more slowly. The study highlights differences among the three methods used for describing urban temperature variability, each of which may have different applications in fields such as urban planning, climate change mitigation, and epidemiological research.

Description: Human development has far-reaching impacts on the surface of the globe. The transformation of natural land cover occurs in different forms, and urban growth is one of the most eminent transformative processes. We analyze global land cover data and extract cities as defined by maximally connected urban clusters. The analysis of the city size distribution for all cities on the globe confirms Zipf’s law. Moreover, by investigating the percolation properties of the clustering of urban areas we assess the closeness to criticality for various countries. At the critical thresholds, the urban land cover of the countries undergoes a transition from separated clusters to a gigantic component on the country scale. We study the Zipf-exponents as a function of the closeness to percolation and find a systematic dependence, which could be the reason for deviating exponents reported in the literature. Moreover, we investigate the average size of the clusters as a function of the proximity to percolation and find country specific behavior. By relating the standard deviation and the average of cluster sizes—analogous to Taylor’s law—we suggest an alternative way to identify the percolation transition. We calculate spatial correlations of the urban land cover and find long-range correlations. Finally, by relating the areas of cities with population figures we address the global aspect of the allometry of cities, finding an exponent δ ≈ 0.85, i.e., large cities have lower densities.

Description: Understanding the health impacts of particulate matter (PM) requires spatiotemporally continuous exposure estimates. We developed a multi-stage ensemble model that estimates daily mean PM2.5 and PM10 at 1 km spatial resolution across France from 2000 to 2019. First, we alleviated the sparsity of PM2.5 monitors by imputing PM2.5 at more common PM10 monitors. We also imputed missing satellite aerosol optical depth (AOD) based on modelled AOD from atmospheric reanalyses. Next, we trained three base learners (mixed models, Gaussian Markov random fields, and random forests) to predict daily PM concentrations based on AOD, meteorology, and other variables. Finally, we generated ensemble predictions using a generalized additive model with spatiotemporally varying weights that exploit the strengths and weaknesses of each base learner. The Gaussian Markov random field dominated the ensemble, outperforming mixed models and random forests at most locations on most days. Rigorous cross-validation showed that the ensemble predictions were quite accurate, with mean absolute error (MAE) of 2.72 μg/m3 and R2 of 0.76 for PM2.5; PM10 MAE was 4.26 μg/m3 and R2 0.71. Our predictions are available to improve epidemiological studies of acute and chronic PM exposure in urban and rural France.

Description: Mapping of near-surface air temperature (Ta) at high spatio-temporal resolution is essential for unbiased assessment of human health exposure to temperature extremes, not least given the observed trend of urbanization and global climate change. Data constraints have led previous studies to focus merely on daily Ta metrics, rather than hourly ones, making them insufficient for intra-day assessment of health exposure. In this study, we present a three-stage machine learning-based ensemble model to estimate hourly Ta at a high spatial resolution of 1 × 1 km2, incorporating remotely sensed surface skin temperature (Ts) from geostationary satellites, reanalysis synoptic variables, and observations from weather stations, as well as auxiliary geospatial variables, which account for spatio-temporal variability of Ta. The Stage 1 model gap-fills hourly Ts at 4 × 4 km2 from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which are subsequently fed into the Stage 2 model to estimate hourly Ta at the same spatio-temporal resolution. The Stage 3 model downscales the residuals between estimated and measured Ta to a grid of 1 × 1 km2, taking into account additionally the monthly diurnal pattern of Ts derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. In each stage, the ensemble model synergizes estimates from the constituent base learners—random forest (RF) and extreme gradient boosting (XGBoost)—by applying a geographically weighted generalized additive model (GAM), which allows the weights of results from individual models to vary over space and time. Demonstrated for Israel for the period 2004–2017, the proposed ensemble model outperformed each of the two base learners. It also attained excellent five-fold cross-validated performance, with overall root mean square error (RMSE) of 0.8 and 0.9 °C, mean absolute error (MAE) of 0.6 and 0.7 °C, and R2 of 0.95 and 0.98 in Stage 1 and Stage 2, respectively. The Stage 3 model for downscaling Ta residuals to 1 km MODIS grids achieved overall RMSE of 0.3 °C, MAE of 0.5 °C, and R2 of 0.63. The generated hourly 1 × 1 km2 Ta thus serves as a foundation for monitoring and assessing human health exposure to temperature extremes at a larger geographical scale, helping to further minimize exposure misclassification in epidemiological studies.

Description: This paper assesses the seasonality of the urban heat island (UHI) effect in the Greater London area (United Kingdom). Combining satellite-based observations and urban boundary layer climate modeling with the UrbClim model, the authors are able to address the seasonality of UHI intensity, on the basis of both land surface temperature (LST) and 2-m air temperature, for four individual times of the day (0130, 1030, 1330, and 2230 local time) and the daily means derived from them. An objective of this paper is to investigate whether the UHI intensities that are based on both quantities exhibit a similar hysteresis-like trajectory that is observed for LST when plotting the UHI intensity against the background temperature. The results show that the UrbClim model can satisfactorily reproduce both the observed urban–rural LSTs and 2-m air temperatures as well as their differences and the hysteresis in the surface UHI. The hysteresis-like seasonality is largely absent in both the observed and modeled 2-m air temperatures, however. A sensitivity simulation of the UHI intensity to incoming solar radiation suggests that the hysteresis of the LST can mainly be attributed to the seasonal variation in incoming solar radiation.