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.

As climate change progresses, urban areas are increasingly affected by water scarcity and the urban heat island effect. Evapotranspiration (ET) is a crucial component of urban greening initiatives of cities worldwide aimed at mitigating these issues. However, ET estimation methods in urban areas have so far been limited. An expanding number of flux towers in urban environments provide the opportunity to directly measure ET by the eddy covariance method. In this study, we present a novel approach to model urban ET by combining flux footprint modeling, remote sensing and geographic information system (GIS) data, and deep learning and machine learning techniques. This approach facilitates spatio-temporal extrapolation of ET at a half-hourly resolution; we tested this approach with a two-year dataset from two flux towers in Berlin, Germany. The benefit of integrating remote sensing and GIS data into models was investigated by testing four predictor scenarios. Two algorithms (1D convolutional neural networks (CNNs) and random forest (RF)) were compared. The best-performing models were then used to model ET values for the year 2019. The inclusion of GIS data extracted using flux footprints enhanced the predictive accuracy of models, particularly when meteorological data was more limited. The best-performing scenario (meteorological and GIS data) showed an RMSE of 0.0239 mm/h and R2 of 0.840 with RF and an RMSE of 0.0250 mm/h and a R2 of 0.824 with 1D CNN for the more vegetated site. The 2019 ET sum was substantially higher at the site surrounded by more urban greenery (366 mm) than at the inner-city site (223 mm), demonstrating the substantial influence of vegetation on the urban water cycle. The proposed method is highly promising for modeling ET in a heterogeneous urban environment and can support climate change mitigation initiatives of urban areas worldwide.

Urban areas tend to be warmer than their rural surroundings, well-known as the "urban heat island" effect. Higher nocturnal air temperature (Tair) is associated with adverse effects on human health, higher mortality rates, and higher energy consumption. Prediction of the spatial distribution of Tair is a step towards the "Smart City" concept, providing an early warning system for vulnerable populations. The study of the spatial distribution of urban Tair was thus far limited by the low spatial resolution of traditional data sources. Volunteered geographic information (VGI) provides alternative data with higher spatial density, with citizen weather stations monitoring Tair continuously in hundreds or thousands of locations within a single city. In this study, the aim was to predict the spatial distribution of nocturnal Tair in Berlin, Germany, one day in advance at a 30-m resolution using open-source remote sensing and geodata from Landsat and Urban Atlas, crowdsourced Tair data, and machine learning (ML) methods. Results were tested with a "leave-one-date-out" training scheme (testingcrowd) and reference Tair data (testingref). Three ML algorithms were compared - Random Forest (RF), Stochastic Gradient Boosting (GBM), and Model Averaged Neural Network (avNNet). The optimal model based on accuracy and computational speed is RF, with an average RMSE for testingcrowd of 1.16°C (R2 = 0.512) and RMSE for testingref of 1.97°C (R2 = 0.581). Overall, the most important GIS predictors were morphometric parameters and albedo. The proposed method relies on open-source datasets and can, therefore, be adapted to many cities worldwide.

This research quantifies and discusses atmospheric effects, which alter the radiance observed by a ground-based thermal-infrared (TIR) camera. The TIR camera is mounted on a boom at a height of 125 m above ground on top of a high-rise building in the city of Berlin, Germany (52.4556° N, 13.3200° E) and observes the Earth's surface. The study shows that atmospheric correction of TIR imagery of the three-dimensional (3-D) urban environment acquired in oblique viewing geometry has to account for spatial variability of line-of-sight (LOS) geometry. We present an atmospheric correction procedure that uses these spatially distributed LOS geometry parameters, the radiative transfer model MODTRAN^TM5.2 and atmospheric profile data derived from meteorological measurements in the field of view (FOV) of the TIR camera. The magnitude of atmospheric effects varies during the analysed 24-hourly period (6 August 2009) and is particularly noticeable for surfaces showing a strong surface-to-air temperature difference. The differences between uncorrected and corrected TIR imagery reach up to 6.7 K at 12:00. The use of non-spatially distributed LOS parameters leads to errors of up to 3.7 K at 12:00 and up to 0.5 K at 24:00.