Local Weather Station Research Articles

The cloud cover and meteorological parameters at the Lenghu site on the Tibetan Plateau

Abstract The cloud cover and meteorological parameters serve as fundamental criteria for an astronomical observatory working in optical and infrared wavelengths. In this paper, we present a systematic assessment of key meteorological parameters at the Lenghu astronomical observing site on the Tibetan Plateau. The datasets adopted includes the meteorological parameters collected at the local weather stations at the site and in the Lenghu Town, the sky brightness acquired by the Sky Quality Meters and all-sky images from a digital camera, the ERA5 reanalysis database and global climate monitoring data. From 2019 to 2023, the fractional observable time of photometric condition is 69.70%, 74.97%, 70.26%, 74.27% and 65.12%, respectively, which is influenced by a variety of meteorological parameters. Large-scale air-sea interactions affect the climate at Lenghu site, which in fact delivers a clue to understand the irregularity of 2023. Specifically, precipitable water vapor at Lenghu site is correlated to both the westerly wind index and the summer North Atlantic Oscillation index, the yearly average temperature of Lenghu site is observed to increase significantly during the occurrence of a strong El Niño event, and the relative humidity anomaly at Lenghu site is correlated to the Pacific Decadal Oscillation index. The decrease of fractional observing time in 2023 was due to the ongoing strong El Niño event and relevant global climate change. We underscore the substantial role of global climate change in regulating astronomical observing conditions and the necessity for long-term continuous monitoring of the astronomical meteorological parameters at Lenghu site.

Daytime land surface temperature and its limits as a proxy for surface air temperature in a subtropical, seasonally wet region

Land surface temperatures (LSTs) captured via satellite remote sensing are widely used as a proxy for the surface air temperatures (SATs) experienced outdoors, a key component of human heat exposure. However, LST’s accuracy in capturing SAT can vary through space and time across climate types and geographies and has been less explored in subtropical, seasonally wet regions (where summer precipitation exceeds 570 mm). Utilizing daytime (11 AM/12 PM local time, ET/EST) Landsat 8 remote sensing data, this study derived LST and evaluated its spatiotemporal patterns, as well as its relationship with SAT retrieved from local weather stations, using the case of Miami-Dade County, Florida, USA. Over 2013–2022, a surface urban heat island effect is distinctly present (mean SUHII = 3.43°C)—most intense during spring months rather than summer months (mean spring SUHII = 4.09°C). As such, LST peaks in May/June as opposed to July/August for many other parts of the northern hemisphere. In contrast, Miami-Dade SAT is greatest in August, and the strength of its relationship with LST varies by season. LST and SAT are most correlated in winter (R = 0.91) and spring (R = 0.59) months and least correlated during the wetter fall (R = 0.40) months. The relationship between LST and SAT during the summer is statistically insignificant. In this subtropical region with a seasonally wet climate, LST effectively reflects the spatial heterogeneity of the urban thermal landscape, consistent with the literature across urban regions globally. However, because the strength of the LST-SAT relationship considerably weakens during wet season months, LST data therefore have limits as a proxy for the heat exposure people experience outdoors annually, as they may not accurately represent the magnitude of localized potential heat risks. These findings underscore important considerations in using LST data to identify urban heat exposures and inform potential adaptive responses in seasonally wet, subtropical-to-tropical regions.

Methodology to assess the impact of urban vegetation on the energy consumption of residential buildings. Case study in a Mediterranean city

The global growth of urban areas is unstoppable, and this growth is accompanied by an intensification of urban heat island effects, exacerbating the challenges of climate change and sustainable urban development in warm climates. In this context, understanding the intricate dynamics of these phenomena and their implications on the thermal behaviour of buildings becomes paramount. This study focuses on València, a Spanish city characterized by a Mediterranean climate, where the interplay between ground temperature variations, vegetation levels, and the thermal demands of buildings is investigated.Land surface temperature measurements derived from satellite data, specifically from the Landsat-8 mission, provide a valuable lens through which to assess the heat island effect. These measurements are harmonized with data collected from local weather stations to establish a robust foundation for evaluating the thermal dynamics of the urban environment. European standards, coupled with Geographic Information System technologies, enable the simulation of temperature variations, and facilitate a nuanced analysis of their impact on the thermal demands of a building.Moreover, recognizing the crucial role played by the urban climate in the influencing of heating and cooling needs, this study explores nature-based solutions implemented in València. By leveraging satellite-derived temperature and vegetation data over an extended period, it is possible to identify actions and elements that contribute positively to mitigating UHI effects and improving the overall climatic conditions. Results indicate that vegetation has a notable impact on local temperature, with distinct patterns observed in different seasons. The research incorporated the simulation of climate scenarios, introducing varying levels of vegetation. Results demonstrated a substantial reduction in cooling demand, particularly during the summer months. Buildings with a lower exterior surface-to-volume ratio exhibited a more pronounced reduction in energy consumption.

A Three-Step Weather Data Approach in Solar Energy Prediction Using Machine Learning

Solar energy plays a critical part in lowering CO2 emissions and other greenhouse gases when integrated into the grid. Higher solar energy penetration is hindered by its intermittency leading to reliability issues. To forecast solar energy production, this study suggests a three-step forecasting method that selects weather variables with a moderate to strong positive correlation to solar radiation using Pearson correlation coefficient analysis. Low-level data fusion is used to combine weather inputs from a reliable local weather station and an on-site weather station, significantly improving the forecasting model’s accuracy regardless of the machine learning method used. Weather data was obtained from the Kisanhub Weather Station located in Cranfield University, UK and the meteorological station in Bedford, UK. In addition, PV power supply data was obtained from four solar plants. Using the Regression Learner app in MATLAB, the proposed architecture is tested on a utility scale solar plant (1 MW), showing a 6% and 13% prediction accuracy improvement when compared to solely using data from the on-site and local weather station respectively. It is further validated using data from three residential rooftop solar systems (8 kW, 10.5 kW and 15 kW), achieving root-mean square values of 0.0984, 0.0885, and 0.1425 respectively. The data was pre-processed using both rescaling and list-wise deletion methods. Training and testing data from the 1 MW solar plant was divided into 75% and 25% respectively, while 100% of the residential rooftop solar plants was used for validation.

Correlated spatiotemporal downscaling of Euro‐CORDEX climatic data for infrastructure resilience assessment

AbstractA methodology is presented for downscaling the Euro‐CORDEX climatic projections in order to derive spatially and temporally correlated weather fields that can be used for risk and resilience assessment of large‐scale asset portfolios or interconnected infrastructure. The temporal resolution of the Euro‐CORDEX data is downscaled to a 10 min basis by employing a modified analogue‐type approach that utilizes the k‐NN algorithm along with measurements from weather stations. The aim is to generate composite “Frankenstein” days comprising 144 jigsaw pieces of observed 10 min timeseries that are scaled and/or shifted, and matched together to form a continuous daily record. These point‐estimates, valid only at the locations of the weather stations, are expanded spatially by employing high‐fidelity weather intensity measure fields that provide variable yet synchronous patterns of weather parameters at all locations of interest. As a case study, the Euro‐CORDEX projections for wind, temperature, and precipitation are downscaled for the Metsovo‐Panagia segment of Egnatia Odos highway in Greece, by employing high‐fidelity Computational Fluid Dynamic simulations that account for the topography of the site to simulate turbulent wind flows. These are combined with measurements of two local weather stations to generate the Frankenstein timeseries and corresponding weather fields that can be used for estimating operability, recovery and direct/indirect loss statistics on an event‐by‐event basis for an ensemble of interconnected highway assets.

Assessment of Heat Exposure and Health Outcomes in Rural Populations of Western Kenya by Using Wearable Devices: Observational Case Study.

Climate change increasingly impacts health, particularly of rural populations in sub-Saharan Africa due to their limited resources for adaptation. Understanding these impacts remains a challenge, as continuous monitoring of vital signs in such populations is limited. Wearable devices (wearables) present a viable approach to studying these impacts on human health in real time. The aim of this study was to assess the feasibility and effectiveness of consumer-grade wearables in measuring the health impacts of weather exposure on physiological responses (including activity, heart rate, body shell temperature, and sleep) of rural populations in western Kenya and to identify the health impacts associated with the weather exposures. We conducted an observational case study in western Kenya by utilizing wearables over a 3-week period to continuously monitor various health metrics such as step count, sleep patterns, heart rate, and body shell temperature. Additionally, a local weather station provided detailed data on environmental conditions such as rainfall and heat, with measurements taken every 15 minutes. Our cohort comprised 83 participants (42 women and 41 men), with an average age of 33 years. We observed a positive correlation between step count and maximum wet bulb globe temperature (estimate 0.06, SE 0.02; P=.008). Although there was a negative correlation between minimum nighttime temperatures and heat index with sleep duration, these were not statistically significant. No significant correlations were found in other applied models. A cautionary heat index level was recorded on 194 (95.1%) of 204 days. Heavy rainfall (>20 mm/day) occurred on 16 (7.8%) out of 204 days. Despite 10 (21%) out of 47 devices failing, data completeness was high for sleep and step count (mean 82.6%, SD 21.3% and mean 86.1%, SD 18.9%, respectively), but low for heart rate (mean 7%, SD 14%), with adult women showing significantly higher data completeness for heart rate than men (2-sided t test: P=.003; Mann-Whitney U test: P=.001). Body shell temperature data achieved 36.2% (SD 24.5%) completeness. Our study provides a nuanced understanding of the health impacts of weather exposures in rural Kenya. Our study's application of wearables reveals a significant correlation between physical activity levels and high temperature stress, contrasting with other studies suggesting decreased activity in hotter conditions. This discrepancy invites further investigation into the unique socioenvironmental dynamics at play, particularly in sub-Saharan African contexts. Moreover, the nonsignificant trends observed in sleep disruption due to heat expose the need for localized climate change mitigation strategies, considering the vital role of sleep in health. These findings emphasize the need for context-specific research to inform policy and practice in regions susceptible to the adverse health effects of climate change.

Analysing the impacts of extreme torrential events using multi‐temporal LiDAR datasets—The Schöttlbach catchment, Upper Styria, Austria

AbstractExtreme precipitation events in small alpine catchments trigger hazardous hydro‐geomorphic processes that cause considerable damage to settlements and infrastructure. In summer 2011 and 2017, two flood events mobilizing large amounts of sediments struck the town of Oberwölz (Styria, Austria) located at the outlet of the Schöttlbach catchment. We used data from local weather stations and precipitation radar to analyse the meteorological settings that caused the flooding. We compiled a consistent sediment budget for the 2017 event by combining geomorphic mapping, connectivity analysis, high‐resolution airborne LiDAR (ALS) and uncrewed aerial vehicle (UAV)‐borne LiDAR (ULS), data from other authors for the 2011 event and external information (e.g., event analysis and excavation data). The 2017 event mobilized higher sediment volumes than the 2011 event (131 000 m3 vs 90 000 m3) even though 24‐h precipitation and peak discharges were lower in 2017. First assumptions that the larger sediment output was caused by the reworking of the 2011 flood deposits proved to be incorrect. The impacts of the 2011 event affected the resilience of the geomorphic system resulting in a significantly higher hillslope sediment supply. We conclude that sediment transport in alpine catchments can increase disproportionately when recurrence intervals fall below a critical level.

Cropland Microclimate and Leaf-nesting Behavior Shape the Growth of Caterpillar under Future Warming.

Predicting performance responses of insects to climate change is crucial for biodiversity conservation and pest management. While most projections on insects' performance under climate change have used macro-scale weather station data, few incorporated the microclimates within vegetation that insects inhabit and their feeding behaviors (e.g., leaf-nesting: building leaf nests or feeding inside). Here, taking advantage of relatively homogenous vegetation structures in agricultural fields, we built microclimate models to examine fine-scale air temperatures within two important crop systems (maize and rice) and compared microclimate air temperatures to temperatures from weather stations. We deployed physical models of caterpillars and quantified effects of leaf-nesting behavior on operative temperatures of two Lepidoptera pests: Ostrinia furnacalis (Pyralidae) and Cnaphalocrocis medinalis (Crambidae). We built temperature-growth rate curves and predicted the growth rate of caterpillars with and without leaf-nesting behavior based on downscaled microclimate changes under different climate change scenarios. We identified widespread differences between microclimates in our crop systems and air temperatures reported by local weather stations. Leaf-nesting individuals in general had much lower body temperatures compared to non-leaf-nesting individuals. When considering microclimates, we predicted leaf-nesting individuals grow slower compared to non-leaf-nesting individuals with rising temperature. Our findings highlight the importance of considering microclimate and habitat-modifying behavior in predicting performance responses to climate change. Understanding the thermal biology of pests and other insects would allow us to make more accurate projections on crop yields and biodiversity responses to environmental changes.

Urban microclimate prediction based on weather station data and artificial neural network

Urban microclimate has a significant impact on building energy consumption. Building energy modeling (BEM) requires accurate local weather conditions near a target building, whereas Typical Meteorological Year (TMY) weather inputs often use remote airport weather data. An artificial neural network (ANN) model is presented in this study to predict urban microclimates based on long-term measurements from local weather stations near urban buildings and their significance in analyzing building energy consumption. By utilizing only a few months of data, the ANN model could connect local and remote meteorological parameters for a whole year. The 20-year historical weather data at the airport was then used to generate a local TMY. Based on the original and local TMYs, this study compared building heating and cooling loads. This method was evaluated for five weather stations within the city of Montreal to assess the impact of the local microclimate on the energy consumption of buildings. Based on locations, urban microclimate contributed to an additional 2 % to 14 % cooling energy consumption and a reduction of 1 % to 10 % winter heating energy consumption.

Sex-Specific Association of Ambient Temperature With Urine Biomarkers in Southwest Coastal Bangladesh

IntroductionMen are vulnerable to ambient heat-related kidney disease burden, but limited evidence exists on how vulnerable women are when exposed to high ambient heat. We evaluated the sex-specific association between ambient temperature and urine electrolytes (concentration and 24-hour excretion), and 24-hour urine total protein, and volume. MethodsWe pooled a longitudinal data of 1,175 participants' concentration and 24-hour excretion of urine electrolytes and other biomarkers (24-hour urine total protein and volume) (5624 person-visits) collected in southwest coastal Bangladesh (Khulna, Satkhira, and Mongla districts) during November 2016 - April 2017. We then spatiotemporally linked ambient temperature data from local weather stations to participants' health outcomes. For evaluating the relationships between average ambient temperature and urine electrolytes and other biomarkers, we plotted confounder-adjusted restricted cubic spline (RCS) plots using participant-, household-, and community-level random intercepts. We then used piece-wise linear mixed-effects models for different ambient temperature segments determined by inflection points in RCS plots and reported the maximum likelihood estimates and cluster robust standard errors. By applying interaction terms for sex and ambient temperature, we determined the overall significance using the Wald test. Bonferroni correction was used for multiple comparisons. ResultsThe RCS plots demonstrated non-linear associations between ambient heat and urine biomarkers for males and females. Piecewise linear mixed-effects models suggested sex did not modify the relationship of ambient temperature with any of the urine parameters after Bonferroni correction (p-value <0.004). ConclusionOur findings suggest that women are as susceptible to the effects of high ambient temperature exposure as men.

Identifying Challenges to 3D Hydrodynamic Modeling for a Small, Stratified Tropical Lake in the Philippines

Three-dimensional hydrodynamic modeling for small, stratified tropical lakes in the Philippines and in Southeast Asia in general is not deeply explored. This study pioneers investigating the hydrodynamics of a small crater lake in the Philippines with a focus on temperature simulation using a Fantom Refined 3D model that has been tested mostly for temperate and sub-tropical lakes. The lake’s monthly temperature during the dry season served as a reference for the model’s initial condition and validation. For the simulation to proceed, input data such as weather, inflow, and bathymetry were prepared. In the absence of hourly meteorological data from local weather stations, this paper adopted the satellite weather data from Solcast. Simple correlation analysis of daily weather data between local stations and Solcast showed valid and acceptable results. Inflow values were estimated using the rational method while the stream temperature was estimated from a regression equation using air temperatures as input. The validated satellite-derived data and runoff model can therefore be employed for 3D modeling. The simulations resulted in extremely higher temperatures compared with those observed when using previous default model settings. Direct modifications were then applied to weather parameters, compromising their integrity but resulting in reasonable profiles. By adding scaling factors to heat flux equations and multiplying their components by 0.75 (shortwave), 1.35 (longwave), 0.935 (air temperature), and 0.80 (wind), better results were achieved. This study identifies several challenges in performing 3D hydrodynamic modeling, such as paucity in input hydro-meteorologic and limnologic data and the need for heat flux model improvement. Overall, this study was successful in employing 3D hydrodynamic modeling in a tropical lake, which can pave directions and serve as an excellent reference for future modeling in the same region.

Climate Change and Human Health: Impacts and Vulnerability

Background: Climate change is a global health emergency, with its impacts being profoundly felt across various regions of Pakistan. Rising temperatures, changing precipitation patterns, and increased frequency of extreme weather events pose significant threats to public health, agriculture, and water resources. Understanding these impacts is crucial for developing targeted interventions to mitigate health risks associated with climate change. Objective: This study aims to assess the effects of climate change on health outcomes in Pakistan, focusing on temperature variations, precipitation changes, agricultural impacts, vector-borne diseases, and socio-economic vulnerabilities. Methods: A cross-sectional study was conducted in four regions of Pakistan (Punjab, Sindh, KPK, and Balochistan) from January 2022 to January 2023. A sample size of 3000 participants was selected through systematic random sampling. Meteorological data was collected from local weather stations, and health data was gathered through structured surveys, interviews, and medical examinations. Descriptive and inferential statistics were used to analyze the data, employing SPSS version 25 for statistical analysis. GIS mapping visualized the spatial patterns of climate change impacts. Results: The study found significant regional variations in climate change impacts. Temperature increases were most pronounced in Sindh (1.8°C), leading to heightened risks of heat-related ailments. Precipitation changes showed decreased rainfall in Punjab, causing water scarcity and an increase in waterborne diseases. Agricultural impacts revealed a 10% crop yield reduction in Punjab, resulting in increased malnutrition rates. Vector-borne diseases saw a noticeable rise in Sindh due to habitat expansion of disease vectors. Socio-economic vulnerability analysis indicated high vulnerability in urban areas of Sindh, with limited adaptive capacities. Conclusion: Climate change significantly affects health outcomes in Pakistan, with regional disparities necessitating targeted interventions. The study underscores the importance of incorporating climate change considerations into public health planning and policy-making to enhance resilience and adaptive capacities across vulnerable regions.

Radiative parameters specific to Braşov urban area

The paper objective is to present the input data needed for the renewable systems design, namely the recording and the analysis of the local meteorological and climatological parameters such as the solar radiation, the wind speed, the clearness index and the atmospheric turbidity factor. The diagrams presented are obtained on the basis of the meteorological data recorded during three years, 2006-2008. The used meteorological data, for this study, were recorded with a local weather station positioned on the building roof of the Transilvania University of Braşov. The paper ends with an analysis of some very important diagrams concerning the solar and wind power for Braşov area, and there are worded conclusions derived from the comparative analysis of these values.

Combining Hydrological Models and Remote Sensing to Characterize Snowpack Dynamics in High Mountains

Seasonal snowpacks, characterized by their snow water equivalent (SWE), can play a major role in the hydrological cycle of montane environments with months of snow accretion followed by episodes of melt controlling flood risk and water resource availability downstream. Quantifying the temporal and spatial patterns of snowpack accumulation and its subsequent melt and runoff is an internationally significant challenge, particularly within mountainous regions featuring complex terrain with limited or absent observational data. Here we report a new approach to snowpack characterization using open-source global satellite and modelled data products (precipitation and SWE) greatly enhancing the utility of the widely used Soil and Water Assessment Tool (SWAT). The paper focusses on the c. 23,000 km2 Chenab river basin (CRB) in the headwaters of the Indus Basin, globally important because of its large and growing population and increasing water insecurity due to climate change. We used five area-weighted averaged satellite, gridded and reanalysis precipitation datasets: ERA5-Land, CMORPH, TRMM, APHRODITE and CPC UPP. As well as comparison to local weather station data, these were used in SWAT to model streamflow for evaluation against observed streamflow at the basin outlet. ERA5-Land data provided the best streamflow match-ups and was used to infer snowpack (SWE) dynamics at basin and sub-basin scales. Snow reference data were derived from remote sensing and modelled SWE re-analysis products: ULCA-SWE and KRA-SWE, respectively. Beyond conventional auto-calibration and single-variable approaches we undertook multi-variable calibration using R-SWAT to manually adjust snow parameters alongside observed streamflow data. Characterization of basin-wide patterns of snowpack build-up and melt (SWE dynamics) were greatly strengthened using KRA-SWE data accompanied by improved streamflow simulation in sub-basins dominated by seasonal snow cover. UCLA-SWE data also improved SWE estimations using R-SWAT but weakened the performance of simulated streamflow due to under capture of seasonal runoff from permanent snow/ice fields in the CRB. This research highlights the utility and value of remote sensing and modelling data to drive better understanding of snowpack dynamics and their contribution to runoff in the absence of in situ snowpack data in high-altitude environments. An improved understanding of snow-bound water is vital in natural hazard risk assessment and in better managing worldwide water resources in the populous downstream regions of mountain-fed large rivers under threat from climate change.

Performance estimation technique for solar-wind hybrid systems: A machine learning approach

This study introduces a novel method to assess solar-wind hybrid energy systems, utilizing data analysis for key metrics (Performance Yields YR, Capacity Factor CF, and Performance Ratio PR), focused on their uncertainties estimation. Employing a bootstrapped-based K-means algorithm on features from a local weather station in Universidad de La Salle, Bogotá, the approach evaluates the technical feasibility of a photovoltaic system on the campus. It discusses the clustering algorithm advantages, emphasizing its suitability for modeling continuous processes in renewable energy systems. The hypothesis assumes independence and identically distributed centroid coordinates, aligning with large-scale energy system characteristics. Methods include data normalization, feature selection, statistical calculations, bootstrapping, and Probability Density Functions (PDF) estimation through a Maximum Likelihood Estimator (MLE) to assess uncertainties. Results suggest the unfeasibility of a wind energy system due to technological constraints. The study evaluates system flexibility, and design considerations, providing valuable decision-making insights. The conclusion highlights the potential use for accurate estimations, enhancing the system reliability.

Exploring Hybrid Models For Short-Term Local Weather Forecasting in IoT Environment

This paper explores using and hybridizing simple prediction models to maximize the accuracy of local weather prediction while maintaining low computational effort and the need to process and acquire large volumes of data. A hybrid RF-LSTM model is proposed and evaluated in this research paper for the task of short-term local weather forecasting. The local weather stations are built within an acceptable radius of the measured area and are designed to provide a short period of forecasting - usually within one hour. The lack of local weather data might be problematic for an accurate short-term valuable prediction in sustainable applications like agriculture, transportation, energy management, and daily life. Weather forecasting is not trivial because of the non-linear nature of time series. Thus, traditional forecasting methods cannot predict the weather accurately. The advantage of the ARIMA model lies in forecasting the linear part, while the SVR model indicates the non-linear characteristic of the weather data. Both non-linear and linear approaches can represent the combined model. The hybrid ARIMA-SVR model strengthens the matched points of the ARIMA model and the SVR model in weather forecasting. The LSTM and random forest are both popular algorithms used for regression problems. LSTM is more suitable for tasks involving sequential data with long-term dependencies. Random Forest leverages the wisdom of crowds by combining multiple decision trees, providing robust predictions, and reducing overfitting. Hybrid Random forest-LSTM potentially leverages the robustness and feature importance of Random Forest along with the ability of LSTM to capture sequential dependencies. The comparison results show that the hybrid RF-LSTM model reduces the forecasting errors in metrics of MAE, R-squared, and RMSE. The proposed hybrid model can also capture the actual temperature trend in its prediction performance, which makes it even more relevant for many other possible decision-making steps in sustainable applications. Furthermore, this paper also proposes the design of a weather station based on a real-time edge IoT system. The RF-LSTM leverages the parallelized characteristics of each decision tree in the forest to accelerate the training process and faster inferences. Thus, the hybrid RF-LSTM model offers advantages in terms of faster execution speed and computational efficiency in both PC and Raspberry Pi boards. However, the RF-LSTM consumes the highest peak memory usage due to being a combination of two different models.

The vitality of native grassland plants in current urban climatic conditions in Gauteng, South Africa

Plants are essential components of urban microclimates, as they can help mitigate some of the adverse effects of urbanisation, enhance environmental quality, and thus contribute to overall species well-being and the sustainability of cities. Urban planners and policymakers often incorporate green infrastructure and urban greening into their strategies to create healthier and more livable urban environments. Plants are primary producers, serving as a baseline for the attraction and habitat of other species; they also provide vital ecosystem services, such as climate amelioration. Landscape designers and horticulturalists can influence plant selection through built environment interventions, increasing urban ecosystem services and benefits. Based on the evidence of native plant preferences by insects and people and their natural adaptation to regional climatic extremes, we tested, through an experimental study, the potential of native grassland plants to survive in assemblages in current urban environments. The study specifically monitored the tolerance of nine native grassland plant species to urban environments over six months in Gauteng, South Africa. A stratified random sampling was done by monitoring permanent quadrats in two purposefully engineered urban native gardens. Plant vitality was evaluated using chlorophyll a fluorescence. General climate data were obtained from a local weather station. Microclimatic temperature and humidity data were collected at each site and quadrat using Hygrochron High-Resolution Temperature and Humidity data loggers. The results indicated that all nine native plant species functioned with good photosynthetic health and can be recommended as resilient species that tolerate current urban conditions. Correlation studies indicated that two forb species, Haplocarpha lyrata and Scabiosa columbaria, showed great tolerance to current urban conditions. This vitality is likely contributed to their winter dormancy morphologically based on below-ground biomass, and their physiological adaptation to tolerate both wet and dry habitat conditions. This points to the potential of grassland forb species for urban use and the potential for climate adaptation in grassland areas.

Understanding greenhouse gas (GHG) column concentrations in Munich using the Weather Research and Forecasting (WRF) model

Abstract. To address ambitious goals of carbon neutrality set at national and city scales, a number of atmospheric networks have been deployed to monitor greenhouse gas (GHG) concentrations in and around cities. To convert these measurements into estimates of emissions from cities, atmospheric models are used to simulate the transport of various trace gases and help interpret these measurements. We set up a modelling framework using the Weather Research and Forecasting (WRF) model applied at a high spatial resolution (up to 400 m) to simulate the atmospheric transport of GHGs and attempt a preliminary interpretation of the observations provided by the Munich Urban Carbon Column Network (MUCCnet). Building on previous analyses using similar measurements performed within a campaign for the city of Berlin and its surroundings (Zhao et al., 2019), our modelling framework has been improved regarding the initialization of tagged tracers, model settings, and input data. To assess the model performance, we validate the modelled output against two local weather stations and two radiosonde observations, as well as observed column GHG concentrations. The measurements were provided by the measurement campaign that was carried out from 1 to 30 August 2018. The modelled wind matches well with the measurements from the weather stations, with wind speeds slightly overestimated. In general, the model is able to reproduce the measured slant column concentrations of CH4 and their variability, while for CO2, a difference in the slant column CO2 of around 3.7 ppm is found in the model. This can be attributed to the initial and lateral boundary conditions used for the background tracer. Additional mismatches in the diurnal cycle could be explained by an underestimation of nocturnal respiration in the modelled CO2 biogenic fluxes. The differential column method (DCM) has been applied to cancel out the influence from the background concentrations. We optimize its application by selecting suitable days on which the assumption of the DCM holds true: a relatively uniform air mass travels over the city, passing from an upwind site to a downwind site. In particular, the Stochastic Time-Inverted Lagrangian Transport (STILT) model is used here and driven by our WRF-modelled meteorological fields to obtain footprints (i.e. the potential areas of influence for signals observed at measurement stations), further used for interpreting measurement results. Combining these footprints with local knowledge of emission sources, we find evidence of CH4 sources near Munich that are missing or underestimated in the emission inventory used. This demonstrates the potential of this data–model framework to constrain local sources and improve emission inventories.

Relationship between Nest and Body Temperature and Microclimate in the Paper Wasp Polistes dominula.

The paper wasp Polistes dominula is a thermophilic species originating from the Mediterranean climate, but is now widely spread in Europe. They live in quite differing habitats; and as synanthropic species, they have been established in human settlement areas. They build a single small comb at protected places with a favorable microclimate. We measured the temperature of the wasps, the nests and their environment at typical nesting sides in Austria (Europe) in the temperate climate, in order to reveal relationships between nest and body temperature and the habitats' microclimate. The temperatures of the comb and of the wasps' body were in a wide range (~20-37 °C) above the ambient air temperature at the nest. This is an advantage as higher temperatures accelerate the development speed of the brood. However, the mean comb temperature did not exceed approximately 38.6 °C. This was managed by cooling efforts of the adult wasps. The ambient air temperature near the nest (~1-2 cm) was always clearly elevated above the ambient air temperature at a local standard weather station in the habitat. A comparison with climate-model-generated macroclimate data revealed the necessity of measuring microclimate data for a reliable description of the insects' thermal environment.

Local Weather Station Design and Development for Cost-Effective Environmental Monitoring and Real-Time Data Sharing.

Current weather monitoring systems often remain out of reach for small-scale users and local communities due to their high costs and complexity. This paper addresses this significant issue by introducing a cost-effective, easy-to-use local weather station. Utilizing low-cost sensors, this weather station is a pivotal tool in making environmental monitoring more accessible and user-friendly, particularly for those with limited resources. It offers efficient in-site measurements of various environmental parameters, such as temperature, relative humidity, atmospheric pressure, carbon dioxide concentration, and particulate matter, including PM 1, PM 2.5, and PM 10. The findings demonstrate the station's capability to monitor these variables remotely and provide forecasts with a high degree of accuracy, displaying an error margin of just 0.67%. Furthermore, the station's use of the Autoregressive Integrated Moving Average (ARIMA) model enables short-term, reliable forecasts crucial for applications in agriculture, transportation, and air quality monitoring. Furthermore, the weather station's open-source nature significantly enhances environmental monitoring accessibility for smaller users and encourages broader public data sharing. With this approach, crucial in addressing climate change challenges, the station empowers communities to make informed decisions based on real-time data. In designing and developing this low-cost, efficient monitoring system, this work provides a valuable blueprint for future advancements in environmental technologies, emphasizing sustainability. The proposed automatic weather station not only offers an economical solution for environmental monitoring but also features a user-friendly interface for seamless data communication between the sensor platform and end users. This system ensures the transmission of data through various web-based platforms, catering to users with diverse technical backgrounds. Furthermore, by leveraging historical data through the ARIMA model, the station enhances its utility in providing short-term forecasts and supporting critical decision-making processes across different sectors.