Development and Application of a Statistically-Based Quality Control for Crowdsourced Air Temperature Data

In urban areas, dense atmospheric observational networks with high-quality data are still a challenge due to high costs for installation and maintenance over time. Citizen weather stations (CWS) could be one answer to that issue. Since more and more owners of CWS share their measurement data publicly, crowdsourcing, i.e., the automated collection of large amounts of data from an undefined crowd of citizens, opens new pathways for atmospheric research. However, the most critical issue is found to be the quality of data from such networks. In this study, a statistically-based quality control (QC) is developed to identify suspicious air temperature (T) measurements from crowdsourced data sets. The newly developed QC exploits the combined knowledge of the dense network of CWS to statistically identify implausible measurements, independent of external reference data. The evaluation of the QC is performed using data from Netatmo CWS in Toulouse, France, and Berlin, Germany, over a 1-year period (July 2016 to June 2017), comparing the quality-controlled data with data from two networks of reference stations. The new QC efficiently identifies erroneous data due to solar exposition and siting issues, which are common error sources of CWS. Estimation of T is improved when averaging data from a group of stations within a restricted area rather than relying on data of individual CWS. However, a positive deviation in CWS data compared to reference data is identified, particularly for daily minimum T. To illustrate the transferability of the newly developed QC and the applicability of CWS data, a mapping of T is performed over the city of Paris, France, where spatial density of CWS is especially high.DFG, 322579844, Hitzewellen in Berlin, Deutschland - StadtklimamodifkationenBMBF, 01LP1602A, Verbundprojekt Stadtklima: Evaluierung von Stadtklimamodellen (Modul B), 3DO Teilprojekt 1: Dreidimensionales Monitoring atmosphärischer Prozesse in Berli

Denial of Death? Death-Related Words are Suppressed in a Think/No-Think Paradigm

According to terror management theory, humans automatically suppress the thought of death when reminded of their mortality (mortality salience; MS), leading to a hyper- accessibility of death-related thoughts under MS. Here, we tested the claim of automatic death-thought-suppression using a think/no-think paradigm. Participants were reminded of death or a painful tooth treatment (control) before learning word associations between cue words and neutral, negative, or death-related target words. First analyses indicate that in the study phase, participants under MS performed worse in acquiring the target words. In the test phase, these general performance differences disappeared. However, death-related words were generally remembered worse than negative words, but better with multiple attempts of suppression under MS. This effect stands in line with the assumption of suppressed thoughts becoming hyper-accessible. Participants in the control group did remember less death-related words than participants under MS. This effect hints at an automatic thought suppression of death-thoughts

Implications of atmospheric conditions for analysis of surface temperature variability derived from landscape-scale thermography

Thermal infrared (TIR) cameras perfectly bridge the gap between (i) on-site measurements of land surface temperature (LST) providing high temporal resolution at the cost of low spatial coverage and (ii) remotely sensed data from satellites that provide high spatial coverage at relatively low spatio-temporal resolution. While LST data from satellite (LSTsat) and airborne platforms are routinely corrected for atmospheric effects, such corrections are barely applied for LST from ground-based TIR imagery (using TIR cameras; LSTcam). We show the consequences of neglecting atmospheric effects on LSTcamof different vegetated surfaces at landscape scale. We compare LST measured from different platforms, focusing on the comparison of LST data from on-site radiometry (LSTosr) and LSTcamusing a commercially available TIR camera in the region of Bozen/Bolzano (Italy). Given a digital elevation model and measured vertical air temperature profiles, we developed a multiple linear regression model to correct LSTcamdata for atmospheric influences. We could show the distinct effect of atmospheric conditions and related radiative processes along the measurement path on LSTcam, proving the necessity to correct LSTcamdata on landscape scale, despite their relatively low measurement distances compared to remotely sensed data. Corrected LSTcamdata revealed the dampening effect of the atmosphere, especially at high temperature differences between the atmosphere and the vegetated surface. Not correcting for these effects leads to erroneous LST estimates, in particular to an underestimation of the heterogeneity in LST, both in time and space. In the most pronounced case, we found a temperature range extension of almost 10 K

Contrasting changes of urban heat island intensity during hot weather episodes

Cities typically exhibit higher air temperatures than their rural surroundings, a phenomenon known as the urban heat island (UHI) effect. Contrasting results are reported as to whether UHI intensity (UHII) is exacerbated or reduced during hot weather episodes (HWEs). This contrast is investigated for a four-year period from 2015 to 2018, utilising a set of observational data from high-quality meteorological stations, as well as from hundreds of crowdsourced citizen weather stations, located in the urban region of Berlin, Germany. It can be shown that if HWEs, defined here as the ten percent hottest days or nights during May–September, are identified via daytime conditions, or by night-time conditions at inner-city sites, then night-time UHII is exacerbated. However, if HWEs are identified via night-time conditions at rural sites, then night-time UHII is reduced. These differences in UHII change can be linked with prevalent weather conditions, namely radiation, cloud cover, wind speed, precipitation, and humidity. This highlights that, beside land cover changes, future changes in weather conditions due to climate change will control UHIIs, and thus heat-stress hazards in cities.BMBF, 01LP1602C , Verbundprojekt Stadtklima: Dreidimensionale Beobachtung atmosphärischer Prozesse in Städten, Modul B - 3DODFG, 322579844, Hitzewellen in Berlin, Deutschland - StadtklimamodifkationenDFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Quantification of heat-stress related mortality hazard, vulnerability and risk in Berlin, Germany

Many studies have addressed the challenge of heat stress for human health in recent years. However, appropriate concepts and methods for quantifying heat-stress hazards, vulnerabilities and risks are yet under development. The objective of this study is to test the applicability of a risk concept and associated event-based risk-analysis method for quantifying heat-stress related mortality. The study reveals that about 5 % of all deaths between 2001 and 2010 in Ber­lin can statistically be related to elevated air temperatures. Most of the affected people are 65 years or older, while the mortality of people below 65 years shows only weak statistical correlation to air temperature. Mean daily air tempera­ture was best suitable for risk analysis. The results demonstrate that the novel approach for quantitative risk analysis delivers statistically highly significant results on the city scale when analysing heat stress on an event basis. Performing the risk analysis on a spatially distributed data basis for city districts would allow to account for spatial variations of ur­ban climates and demographic properties. Using indoor climate data is expected to provide new insight into heat-stress related mortality risks, particularly for highly vulnerable persons like elderly persons or patients residing in hospitals

Evidence of horizontal urban heat advection in London using 6 years of data from a citizen weather station network

Recent advances in citizen weather station (CWS) networks, with data accessible via crowd-sourcing, provide relevant climatic information to urban scientists and decision makers. In particular, CWS can provide long-term measurements of urban heat and valuable information on spatio-temporal heterogeneity related to horizontal heat advection. In this study, we make the first compilation of a quasi-climatologic dataset covering six years (2015–2020) of hourly near-surface air temperature measurements obtained via 1560 suitable CWS in a domain covering south-east England and Greater London. We investigated the spatio-temporal distribution of urban heat and the influences of local environments on climate, captured by CWS through the scope of Local Climate Zones (LCZ)—a land-use land-cover classification specifically designed for urban climate studies. We further calculate, for the first time, the amount of advected heat captured by CWS located in Greater London and the wider south east England region. We find that London is on average warmer by about 1.0 ∘C–1.5 ∘C than the rest of south-east England. Characteristics of the southern coastal climate are also captured in the analysis. We find that on average, urban heat advection (UHA) contributes to 0.22 ± 0.96 ∘C of the total urban heat in Greater London. Certain areas, mostly in the centre of London are deprived of urban heat through advection since heat is transferred more to downwind suburban areas. UHA can positively contribute to urban heat by up to 1.57 ∘C, on average and negatively by down to −1.21 ∘C. Our results also show an important degree of inter- and intra-LCZ variability in UHA, calling for more research in the future. Nevertheless, we already find that UHA can impact green areas and reduce their cooling benefit. Such outcomes show the added value of CWS when considering future urban design

Estimates of water partitioning in complex urban landscapes with isotope‐aided ecohydrological modelling

Urban green space is increasingly viewed as essential infrastructure to build resilience to climate change by retaining water in the city landscape and balancing ecohydrological partitioning into evapotranspiration for cooling and groundwater recharge. Quantifying how different vegetation types affect water partitioning is essential for future management, but paucity of data and the complex heterogeneity of urban areas make water balance estimates challenging. Here, we provide a preliminary assessment of water partitioning from different sized patches of trees and grass as well as from sealed surfaces. To do this, we used limited field observations together with an advanced, process-based tracer-aided ecohydrological model at a meso-scale (5 km2) in central Berlin, Germany. Transpiration was the dominant green water flux accounting for over 50% of evapotranspiration in the modelled area. Green water fluxes were in general greater from trees compared with grass, but grass in large parks transpired more water compared with grass in small parks that were intensively used for recreation. Interception evaporation was larger for trees compared with grass, but soil water evaporation was greater for grass compared with trees. We also show that evapotranspiration from tree-covered areas comprise almost 80% of the total evapotranspiration from the whole model domain while making up less than 30% of the surface cover. The results form an important stepping-stone towards further upscaling over larger areas and highlights the importance of continuous high-resolution hydrological measurements in the urban landscape, as well as the need for improvements to ecohydrological models to capture important urban processes.Berlin University Alliance / Einstein Stiftung Berlin, Climate and Water under ChangeDeutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Einstein Stiftung Berlin http://dx.doi.org/10.13039/501100006188Leverhulme Trust http://dx.doi.org/10.13039/501100000275Urban Climate Observatory (UCO) BerlinPeer Reviewe

How Cool Are Allotment Gardens? A Case Study of Nocturnal Air Temperature Differences in Berlin, Germany

Urban green infrastructures have been extensively studied for their ability to mitigate the urban heat island (UHI) effect. However, allotment gardens (AGs)—a prominent type of urban green infrastructure within many European cities—have not yet been comprehensively investigated concerning their microclimates. In this study, nocturnal air temperatures (TN) in 13 AG complexes (AGCs) were measured during the summer of 2018 in Berlin, Germany. These were compared to measurements in densely built-up urban areas (URB), two large inner-city parks and rural areas (RUR). On average, the assessed AGCs were 2.7 K cooler at night than URB. Most of the investigated AGCs (11/13) displayed a larger mean TN difference to URB (∆TN AGC ) than the examined urban parks. RUR showed the largest differences to URB (∆TN RUR), indicating a UHI effect. Furthermore, the influence of land surface characteristics of the AGCs on ∆TN AGC was analyzed. ∆TN AGC decreased significantly as the floor space index around AGCs increased. The analysis of the shape complexity also produced a significant positive correlation with ∆TN AGC. In contrast, size and distance to the city center of an AGC decreased significantly with increasing ∆TN AGC. This study provides first insights into the microclimate of AGs and influencing variables concerning TN.DFG, 322579844, Hitzewellen in Berlin, Deutschland - StadtklimamodifkationenBMBF, 01LP1602A, Verbundprojekt Stadtklima: Evaluierung von Stadtklimamodellen (Modul B), 3DO Teilprojekt 1: Dreidimensionales Monitoring atmosphärischer Prozesse in BerlinDFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

BioCAS: Biometeorological Climate impact Assessment System for building-scale impact assessment of heat-stress related mortality

An urban climate analysis system for Seoul was combined with biometeorological models for the spatially distributed assessment of heat stress risks. The Biometeorological Climate impact Assessment System (BioCAS) is based on the Climate Analysis Seoul (CAS) workbench which provides urban planners with gridded data relevant for local climate assessment at 25 m and 5 m spatial resolutions. The influence of building morphology and vegetation on mean radiant temperature Tmrt was simulated by the SOLWEIG model. Gridded hourly perceived temperature PT was computed using the Klima-Michel Model for a hot day in 2012. Daily maximum perceived temperature PTmax was then derived from these data and applied to an empirical-statistical model that explains the relationship between PTmax and excess mortality rate rEM in Seoul. The resultant rEM map quantifies the detrimental impact of hot weather at the building scale. Mean (maximum) values of rEM in old and new town areas in an urban re-development site in Seoul were estimated at 2.3 % (50.7 %) and 0 % (8.6 %), respectively, indicating that urban re-development in the new town area has generally resulted in a strong reduction of heat-stress related mortality. The study illustrates that BioCAS can generally be applied for the quantification of the impacts of hot weather on human health for different urban development scenarios. Further improvements are required, particularly to consider indoor climate conditions causing heat stress, as well as socio-economic status and population structure of local residents