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Fog and low stratus clouds (FLS) form as a result of complex interactions of multiple factors in the atmosphere and at the land surface and impact both the anthropogenic and natural environments. Here, we analyze the role of synoptic conditions and aerosol loading on FLS occurrence and persistence in the Po valley in northern Italy. By applying k‐means clustering to reanalysis data, we find that FLS formation in the Po valley is either based on radiative processes or moisture advection from the Mediterranean sea. Satellite‐based data on FLS persistence shows longer persistence of radiatively formed FLS events, likely due to air mass stagnation and a temperature inversion. Ground‐based aerosol optical depth observations further reveal that FLS event duration is significantly higher under high aerosol loading. The results underline the combined effect of topography, moisture advection and aerosol loading on the FLS life cycle in the Po valley. Plain Language Summary: Fog and low stratus clouds (FLS) are influenced by various drivers in the atmosphere and near the ground. Here, the impact of the large‐scale weather situation and aerosols is analyzed over the Po valley in northern Italy. Using reanalysis and satellite data we find that FLS events driven by nighttime cooling under stable conditions can persist longer than FLS events formed as a result of moisture transport. Investigating ground‐based observations of aerosols, particles on which moisture can condensate and fog and cloud droplets form, we find that FLS event duration is higher when a higher amount of aerosols is present. These results can help when predicting the duration of FLS events, which is particularly important for traffic safety. Key Points: Fog and low stratus (FLS) cloud formation in the Po valley is primarily controlled by radiative processes or moisture advectionFLS persistence from the satellite perspective is highest for radiatively formed FLS events likely due to a stable boundary layerFLS persistence is significantly higher under high aerosol loading [ABSTRACT FROM AUTHOR]

High mountain rainforests are vital in the global energy and carbon cycle. Understanding the exchange of energy and carbon plays an important role in reflecting responses to climate change. In this study, an eddy covariance (EC) measurement system installed in the high Andean Mountains of southern Ecuador was used. As EC measurements are affected by heterogeneous topography and the vegetation height, the main objective was to estimate the effect of the sloped terrain and the forest on the turbulent energy and carbon fluxes considering the energy balance closure (EBC) and the heat storage. The results showed that the performance of the EBC was generally good and estimated it to be 79.5%. This could be improved when the heat storage effect was considered. Based on the variability of the residuals in the diel, modifications in the imbalances were highlighted. Particularly, during daytime, the residuals were largest (56.9 W/m2 on average), with a clear overestimation. At nighttime, mean imbalances were rather weak (6.5 W/m2) and mostly positive while strongest underestimations developed in the transition period to morning hours (down to −100 W/m2). With respect to the Monin–Obukhov stability parameter ((z − d)/L) and the friction velocity (u*), it was revealed that the largest overestimations evolved in weak unstable and very stable conditions associated with large u* values. In contrast, underestimation was related to very unstable conditions. The estimated carbon fluxes were independently modelled with a non-linear regression using a light-response relationship and reached a good performance value (R2 = 0.51). All fluxes were additionally examined in the annual course to estimate whether both the energy and carbon fluxes resembled the microclimatological conditions of the study site. This unique study demonstrated that EC measurements provide valuable insights into land-surface–atmosphere interactions and contribute to our understanding of energy and carbon exchanges. Moreover, the flux data provide an important basis to validate coupled atmosphere ecosystem models. [ABSTRACT FROM AUTHOR]

The co‐evolutionary arms race between herbivores and plants forces plants to evolve protection strategies that reduce the palatability of the plant modules attacked by the herbivores. These characteristics of traits have consequences for both the survival of plant individuals and the composition of plant communities. Thus, correlating traits of for instance leaves with herbivory is an important step toward understanding the dynamics of plant populations and communities. Traits can either be measured using conventional lab methods or recently developed spectral sensing techniques. We examined whether leaf traits of trees are related to herbivory in a multispecies approach. Furthermore, we explored whether leaf traits characterized by spectral sensing provide similar relations to herbivory as lab‐based leaf traits. We established nine 1‐ha square plots evenly distributed over three different forest types in Ecuadorian tropical montane rainforests where we estimated herbivory as the leaf area loss (in square centimeters) of 20 (±5) leaves sampled from the canopies of 380 tree individuals belonging to 51 tree species (7 ± 1 individuals/species) using lab‐ and spectral‐sensing‐based methods. For each methodological approach, we ran 100 linear mixed‐effects models with all respective leaf traits as predictor and herbivory as response variables for data subsets containing one randomly selected tree individual of each species to estimate the range of the regression coefficients for each trait. Automated stepwise backward selections determined the frequency of each trait having an important influence on herbivory. We found no clear relations between leaf traits and herbivory for neither lab‐ nor spectral‐sensing‐based traits. A nested variance component analysis demonstrated that the observed variability was mainly due to the variation in trait concentrations between tree individuals of a species. Our results suggest that snapshot data lead to a mismatch between herbivory and the concentrations of traits during the peak of herbivory. Another explanation could be that environmental conditions or processes along the food web are more important in structuring herbivory than leaf traits. [ABSTRACT FROM AUTHOR]

High spatio‐temporal variability is a characteristic of extreme rainfall. In mountainous regions like the Tropical Andes, where intricate orography and mesoscale atmospheric dynamics greatly impact rainfall systems, this particularly holds for mountain areas like the Tropical Andes. Thus, the absence of operational rainfall monitoring networks with high spatio‐temporal resolution has imposed difficulties for a proper analysis of extreme rainfall events in the Ecuadorian Andes. Nowhere, we present our improved knowledge on rainfall extremes based on newly available rainfall radar data of this region. In our study we employ a clustering approach to identify types of extreme rainfall events and analyze their spatio‐temporal characteristics. Based on 3 years of data obtained from an X‐band scanning weather radar data, the study was conducted in the southern Ecuadorian Tropical Andes at 4450 m a.s.l. By applying a rainfall threshold, 67 extreme rainfall events were selected. The rainfall characteristics of each extreme rainfall event, such as the amount of rain, its duration, its hour, and month of occurrence were determined and used as input variables of a k‐means clustering analysis to group the events into different classes. The result revealed three main classes of extreme rainfall events. The first class is characterized by highest rain intensity and lowest duration. The second class is characterized by its month of occurrence, during the first 5 months of the year. The third class showed lowest rain intensity and highest duration mainly occurred at higher elevations. The typology of events advances our understanding of the spatio‐temporal characteristics of extreme rainfall in the Tropical Andes. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement Mission (GPM) improved spaceborne precipitation data. The GPM dual-frequency precipitation radar (DPR) provides information on total precipitation (TP), snowfall precipitation (SF) and snowfall flags (surface snowfall flag (SSF) and phase near surface (PNS)), among other variables. Especially snowfall data were hardly validated. This study compares GPM DPR TP, SF and snowfall flags on the Tibetan Plateau (TiP) against TP and SF from six well-known model-based data sets used as ground truth: ERA 5, ERA 5 land, ERA Interim, MERRA 2, JRA 55 and HAR V2. The reanalysis data were checked for consistency. The results show overall high agreement in the cross-correlation with each other. The reanalysis data were compared to the GPM DPR snowfall flags, TP and SF. The intercomparison performs poorly for the GPM DPR snowfall flags (HSS = 0.06 for TP, HSS = 0.23 for SF), TP (HSS = 0.13) and SF (HSS = 0.31). Some studies proved temporal or spatial mismatches between spaceborne measurements and other data. We tested whether increasing the time lag of the reanalysis data (+/−three hours) or including the GPM DPR neighbor pixels (3 × 3 pixel window) improves the results. The intercomparison with the GPM DPR snowfall flags using the temporal adjustment improved the results significantly (HSS = 0.21 for TP, HSS = 0.41 for SF), whereas the spatial adjustment resulted only in small improvements (HSS = 0.12 for TP, HSS = 0.29 for SF). The intercomparison of the GPM DPR TP and SF was improved by temporal (HSS = 0.3 for TP, HSS = 0.48 for SF) and spatial adjustment (HSS = 0.35 for TP, HSS = 0.59 for SF). [ABSTRACT FROM AUTHOR]

Floods cause significant damage to human life, infrastructure, agriculture, and the economy. Predicting peak runoffs is crucial for hazard assessment, but it is challenging in remote areas like the Andes due to limited hydrometeorological data. We utilized a 300 km2 catchment over the period 2015–2021 to develop runoff forecasting models exploiting precipitation information retrieved from an X-band weather radar. For the modeling task, we employed the Random Forest (RF) algorithm in combination with a Feature Engineering (FE) strategy applied to the radar data. The FE strategy is based on an object-based approach, which derives precipitation characteristics from radar data. These characteristics served as inputs for the models, distinguishing them as "enhanced models" compared to "referential models" that incorporate precipitation estimates from all available pixels (1210) for each hour. From 29 identified events, enhanced models achieved Nash-Sutcliffe efficiency (NSE) values ranging from 0.94 to 0.50 for lead times between 1 and 6 h. A comparative analysis between the enhanced and referential models revealed a remarkable 23% increase in NSE-values at the 3 h lead time, which marks the peak improvement. The enhanced models integrated new data into the RF models, resulting in a more accurate representation of precipitation and its temporal transformation into runoff. [ABSTRACT FROM AUTHOR]

Radiation fog nowcasting remains a complex yet critical task due to its substantial impact on traffic safety and economic activity. Current numerical weather prediction models are hindered by computational intensity and knowledge gaps regarding fog‐influencing processes. Machine‐Learning (ML) models, particularly those employing the eXtreme Gradient Boosting (XGB) algorithm, may offer a robust alternative, given their ability to learn directly from data, swiftly generate nowcasts, and manage non‐linear interrelationships among fog variables. However, unlike recurrent neural networks XGB does not inherently process temporal data, which is crucial in fog formation and dissipation. This study proposes incorporating preprocessed temporal data into the model training and applying a weighted moving‐average filter to regulate the substantial fluctuations typical in fog development. Using an ML training and evaluation scheme for time series data, we conducted an extensive bootstrapped comparison of the influence of different smoothing intensities and trend information timespans on the model performance on three levels: overall performance, fog formation and fog dissipation. The performance is checked against one benchmark and two baseline models. A significant performance improvement was noted for the station in Linden‐Leihgestern (Germany), where the initial F1 score of 0.75 (prior to smoothing and trend information incorporation) was improved to 0.82 after applying the smoothing technique and further increased to 0.88 when trend information was incorporated. The forecasting periods ranged from 60 to 240 min into the future. This study offers novel insights into the interplay of data smoothing, temporal preprocessing, and ML in advancing radiation fog nowcasting. [ABSTRACT FROM AUTHOR]

Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade‐off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real‐world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low‐cost, and open‐source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area‐wide ecosystem mapping tasks, thereby providing an exemplary cost‐efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystems and the provision of ecosystem services. [ABSTRACT FROM AUTHOR]

In tropical forests, herbivorous arthropods remove between 7% up to 48% of leaf area, which has forced plants to evolve defense strategies. These strategies influence the palatability of leaves. Palatability, which reflects a syndrome of leaf traits, in turn influences both the abundance and the mean body mass not only of particular arthropod taxa but also of the total communities. In this study, we tested two hypotheses: (H1) The abundance of two important chewer guilds ('leaf chewers' and 'rostrum chewers'), dominant components of arthropod communities, is positively related to the palatability of host trees. (H2) Lower palatability leads to an increased mean body mass of chewers (Jarman-Bell principle). Arthropods were collected by fogging the canopies of 90 tropical trees representing 31 species in three plots at 1000 m and three at 2000 m a.s.l. Palatability was assessed by measuring several 'leaf traits' of each host tree and by conducting a feeding trial with the generalist herbivore Gryllus assimilis (Orthoptera, Gryllidae). Leaf traits provided partial support for H1, as abundance of leaf chewers but not of rostrum chewers was positively affected by the experimentally estimated palatability. There was no support for H2 as neither leaf traits nor experimentally estimated palatability affected the mean body mass of leaf chewers. The mean body mass of rostrum chewers was positively related to palatability. Thus, leaf traits and experimentally estimated palatability influenced the abundance and mean body mass of chewing arthropods on the community level. However, the data were not consistent with the Jarman-Bell principle. Overall, our results suggest that the palatability of leaves is not among the dominant factors influencing abundance and mean body mass of the community of chewing arthropod herbivores. If other factors, such as the microclimate, predation or further (a-)biotic interactions are more important has to be analyzed in refined studies. [ABSTRACT FROM AUTHOR]