Typical Weather Conditions Research Articles

Determining the differences in O3-NOx-VOCs sensitivity between sauna and roast days

Temperature is a key meteorological factor that affects tropospheric ozone (O3), with both humid-heat (sauna days) and dry-heat (roast days) conditions leading to O3 exceedances. However, the mechanisms influencing O3 formation and degradation under these two weather conditions remain unclear. Therefore, experiments were conducted in Beijing from 2019-2021 to observe O3, its precursors, and related meteorological elements. A total of 18 days with O3 exceedances were selected, including 10 sauna days and 8 roast days. The results of this study revealed that on roast days, the sensible heat flux was 143.5 W/m2 greater and the wind speed gradient was 0.018 s-1 greater than those on sauna days, indicating more intense thermal and dynamic turbulence. The strong turbulence enhanced the vertical cycle of nitrogen dioxide (NO2) and O3, resulting in a 58.2 μg/(m2·h) increase in NO2 upward transport rate and a 1,034.4 μg/(m2·h) increase in O3 downward transport rate on roast days than sauna days. Subsequently, a box model analysis was used to examine O3 formation under the two types of weather conditions, revealing that the NO2-O3 vertical cycling speed dominated the O3 sensitivity. The O3 sensitivity was synergistically controlled by nitrogen oxides (NOx) and volatile organic compounds on sauna days, while it tended to be NOx-limited on roast days. The aim of this study was to provide a scientific theoretical basis for the control of O3 under different types of high temperature weather conditions.

Urbisphere-Berlin Campaign: Investigating Multiscale Urban Impacts on the Atmospheric Boundary Layer

Abstract For next-generation weather and climate numerical models to resolve cities, both higher spatial resolution and subgrid parameterizations of urban canopy–atmosphere processes are required. The key is to better understand intraurban variability and urban–rural differences in atmospheric boundary layer (ABL) dynamics. This includes upwind–downwind effects due to cities’ influences on the atmosphere beyond their boundaries. To address these aspects, a network of >25 ground-based remote sensing sites was designed for the Berlin region (Germany), considering city form, function, and typical weather conditions. This allows investigation of how different urban densities and human activities impact ABL dynamics. As part of the interdisciplinary European Research Council Grant urbisphere, the network was operated from autumn 2021 to autumn 2022. Here, we provide an overview of the scientific aims, campaign setup, and results from 2 days, highlighting multiscale urban impacts on the atmosphere in combination with high-resolution numerical modeling at 100-m grid spacing. During a spring day, the analyses show systematic upwind-city-downwind effects in ABL heights, largely driven by urban–rural differences in surface heat fluxes. During a heatwave day, ABL height is remarkably deep, yet spatial differences in ABL heights are less pronounced due to regionally dry soil conditions, resulting in similar observed surface heat fluxes. Our modeling results provide further insights into ABL characteristics not resolved by the observation network, highlighting synergies between both approaches. Our data and findings will support modeling to help deliver services to a wider community from citizens to those managing health, energy, transport, land use, and other city infrastructure and operations.

Comparative analysis of solar-powered heat pump systems for decarbonization of process heating and cooling applications: Case of milk pasteurization

The dairy processing industry is heavily dependent on fossil fuels for heating applications at different temperature levels and contributes to greenhouse gas (GHG) emissions. Besides, process cooling, product refrigeration, and storage are mainly reliant on vapour compression refrigeration technologies. This study investigates the performance of solar photovoltaic (PV) and solar thermal (ST) powered heat pumping technologies for simultaneous heating (at 75 °C) and cooling (at 2 °C) applications in a selected typical thermal process in a dairy processing plant (milk pasteurization). An ammonia/water absorption heat pump (AHP) and a two-stage R717 vapour compression heat pump with an open intercooler (VCHP) are used in the present study. The VCHP and AHP are powered by market-available solar PV modules and ST collector technologies. The electrical COP and exergy COP of the VCHP were about 4.710 and 0.535, respectively. The AHP operates at a thermal COP and exergy COP of 0.979 and 0.301 when the driving hot water and absorber cooling water inlet temperatures are 160 °C and 30 °C. The grid-connected PV-VCHP system and ST-AHP with natural gas-fired auxiliary heater operate at an annual solar fraction of 0.533 and 0.572, respectively, for a typical weather condition in Barcelona (Spain).

Self-attention mechanism to enhance the generalizability of data-driven time-series prediction: A case study of intra-hour power forecasting of urban distributed photovoltaic systems

The emergence of small-scale urban distributed solar generation (DSG) has urged the exploration of site-adaptive forecasting models designed to accurately predict future power outputs for unseen DSGs. In such scenarios, with numerous DSGs spread across utility-scale cities and a lack of historical data, it is not economically viable to use conventional approaches that develop individual models for each DSG. Therefore, this work aims to tackle this real-world challenge by adapting the state-of-the-art, attention-based temporal fusion transformer (TFT) model to 188 real-world operational DSG data, thereby validating the generalizability of self-attention mechanism for multi-step time series forecasting. When adapted to unseen DSGs without training data, the experiment results demonstrate that the proposed solar TFT (STFT) improves by 11.07%, 17.58%, and 22.76% over the persistence model at the 10-, 20-, and 30-minute forecasts, respectively. Even when compared to representative deep-learning models, such as a long short-term memory model specialized in time series forecasting, STFT has demonstrated improved forecast accuracy, achieving 3.34%, 4.18%, and 5.85% enhancements at the 10-, 20-, and 30-minute forecast horizons, respectively. However, the model architecture of STFT is more complex, and the computational cost associated with it is relatively higher compared to other deep learning models. This trade-off between accuracy and computational efficiency should be considered in practical applications. The forecast performance is analyzed in three typical weather conditions, namely, clear, partly cloudy, and overcast. STFT demonstrates advantages in high variability periods, especially during weather transition periods, where reference models experience lagged predictions yielding relatively large errors.

A Systematic Approach to Map and Evaluate the Wildfire Behavior at a Territorial Scale in the Northwestern Iberian Peninsula

In the current context of extreme wildfires, understanding fire behavior at a territorial level has proven crucial for territory planning. This type of analysis is usually conducted by analyzing past wildfire statistics. In this study, we forego the past information related to wildfires and analyze, instead, the behavior of the entire territory in the face of wildfires. This allows for the distribution of ignition points to be systematized and for typical and atypical weather scenarios to be considered. This analysis relies on the use of wildfire simulation software. Ignition points used for the simulations were distributed using a systematic 1 × 1 km grid throughout the whole study area. Wildfires were simulated for each ignition point using eight different weather scenarios representing both typical and atypical weather conditions. The fire behavior on the territory was analyzed using rate of spread and intensity parameters for each simulated wildfire. It was observed that this territory is extremely prone to large wildfires both in typical and atypical weather conditions and that there is a tendency for extreme behaviors to develop. Some features were identified as prevention issues that ought to be addressed. This study develops a strategy to evaluate, in a systematic manner, the response of the territory to the threat of wildfires.

Evaluating the applicability of a machine learning methodology to improve TMY weather file generation for different Canadian climate zones

Typical meteorological year (TMY) weather files are used in building energy simulations to represent typical weather conditions for a location. The conventional approach to generating TMY weather files uses a set of universal weighting factors determined based on expert judgment. These universal weighting factors can overlook the distinct weather patterns between different locations and their influence on energy performance. A previous study used machine learning to generate location-specific weighting factors with respect to building energy performance. The objectives of this paper are: (1) To assess the applicability of the previous study across varying Canadian climates; (2) To investigate the feasibility of using standardized climate zone-based weighting factors to reduce the computational time associated with extracting location-based weighting factors while still considering local climate conditions to facilitate wider adoption of the proposed methodology. The investigation is conducted for a medium office building for 18 locations across six Canadian climate zones and generates two weather files, TMYSTATION and TMYCZ, for each location. The TMYSTATION weather files are generated using location-based weighting factors for each location whereas the TMYCZ weather files are generated using climate zone-based weighting factors for each location. The coefficient of variation of the root mean square error (CVRMSE) and normalized mean bias error (NMBE) are used to compare the performance of the proposed weather files with conventional weather files using the energy demands obtained through building simulation. The TMYSTATION and TMYCZ weather files outperform the conventional weather files in predicting the long-term energy performance of buildings. Although the TMYSTATION files marginally outperform the TMYCZ weather files in representing the long-term weather data, the convenience of standardized climate zone-based weighting factors allows the approach to be widely deployed.

СТАТИСТИЧЕСКИЙ АНАЛИЗ МНОГОЛЕТНИХ ДАННЫХ О СХОДЕ СНЕЖНЫХ ЛАВИН И СНЕЖНО-МЕТЕОРОЛОГИЧЕСКИХ УСЛОВИЙ В ГОРАХ ИЛЕ АЛАТАУ

Based on long-term observations at snow avalanche stations in the vicinity of Almaty, more than 5 thousand snow avalanches have been recorded since 1966. After statistical analysis of the data, the main characteristics of the avalanches were determined and snow and meteorological conditions were analyzed. The average volume of avalanches is 4 thousand m3, but this value is distorted due to the nonparametric distribution law. The median value of the volume of avalanches is 750-800 m3. 50% of cases within the upper and lower quartiles are 250-2500 m3. Catastrophic avalanches of more than 30 thousand m3 account for 3% of cases, and more than 100 thousand m3 only 0.3% of cases. Typical weather conditions during periods of avalanche danger are: snow height on the slopes 80-90 cm, on the meteorological site 50-60 cm, precipitation 15-19 mm, snow growth 25-30 cm. The dominant factors in avalanche formation were the height of the snow cover on the slopes, the amount of snowfall precipitation, snow cover increase, maximum air temperature. Spearman's correlation coefficients were found to be statistically significant for these parameters at the standard 5% significance level (a-level) with a probability of error (p-level) of less than 0.01. The results of factor analysis showed that avalanches are influenced by three groups of predictors: snow depth, precipitation and snow growth, and temperature conditions. Analysis of catastrophic avalanches in the indicator avalanche collection in the Kotyrbulak gorge showed typical conditions for mass avalanches in Ile Alatau in the spring - snow height at the weather site 50 cm, precipitation amount 53 mm, snow increase 28 cm. The agreement criteria we calculated turned out to be statistically significant by 5% level of significance. The standard errors of some data were within the recommended limits of 10%, which indicates the reliability of the results.

Thermoelectric performance analysis of the novel direct-expansion photovoltaic thermal heat pump/power heat pipe compound cycle system in summer

Photovoltaic thermal heat pump technology has outstanding cogeneration ability by utilizing renewable energy to be accepted as a decarbonization pathway in the residential sector. However, existing photovoltaic thermal heat pumps consume higher energy under better external heat source conditions. To address this problem, a novel direct-expansion photovoltaic thermal heat pump/power heat pipe compound cycle system for heating and power generation as well as refrigeration, whose core equipment was a photovoltaic thermal module with straggled honeycomb fluid channel pattern on the serpentine arrangement, was proposed, manufactured, and studied with the method of experiment. The thermoelectric performance was compared between the heat pump and power heat pipe cycle modes, investigated under typical weather conditions, and discussed in contrast to the literature. The results showed that the average photoelectric conversion efficiency and coefficient of photothermal performance of the PVT modules, as well as COP of the novel system were 10.6%, 51.0%, and 6.19, respectively, at the average ambient temperature of 27.0 °C, average solar radiation intensity of 581 W/m2 and water temperature of 18.3–55.6 °C. And daily average COPs of the novel system under sunny, cloudy, and overcast conditions were 15.12, 7.40, and 3.17, respectively. The conclusion drawn from the experimental results is that the COP of the novel system was 10.0%–60.0% higher than that in the literature under similar outdoor conditions. The payback period of the additional cost of the novel system compared with the existing direct-expansion photovoltaic thermal heat pump was within 2.5 years. The novel system represents a practical response to the need to achieve total decarbonization of the residential sector.

Windshear Detection in Rain Using a 30 km Radius Coherent Doppler Wind Lidar at Mega Airport in Plateau

Convective weather is often accompanied by precipitation and windshear, seriously endangering the safety of aircraft during takeoff and landing. However, under rainfall conditions, conventional wind lidars have a limited detection range due to significant signal attenuation. To solve this problem, a 200 mm temperature-controlled telescope coated with a hydrophobic film is applied in the coherent Doppler wind lidar system to improve the detection capability in rain. The maximum detection range of the lidar is extended to 30 km and demonstrated at Kunming Changshui International Airport at an altitude of 2102 m. Firstly, the detection accuracy and maximum detection range of the lidar are verified. Through the analysis of the horizontal wind field under two typical convective weather conditions, it is found that convective weather often accompanies low-level convergence and divergence structures, leading to headwind shear and crosswind shear on the airport runway. From the vertical profile, it is shown that the triggering of convective weather is accompanied by low-level southwest winds and high-altitude northeastern winds. According to the statistics of wind speed and direction on clear and rainy days over 9 months, rainy days are usually caused by the invasion of cold air from Northeast China, resulting in airport windshear. In summary, the enhanced lidar can effectively identify and analyze windshear during rainy days, which is very useful for aviation safety, especially for takeoff and landing in all weather conditions.

Enhancing climate resilience in buildings using Collective Intelligence: A pilot study on a Norwegian elderly care center

The combined challenge of climate change and population aging requires novel solutions that enhance the resilience of building energy systems and secure indoor comfort for vulnerable occupants in extreme weather conditions. This research investigates the performance of a newly developed Energy Management (EM) system based on Collective Intelligence (CI) and Reinforcement Learning (RL), called CIRLEM, managing the energy performance of an urban complex in Ålesund, Norway, including an elderly care center with decentralized PV generation, EV charging and storage, while connected to a main electricity grid. CIRLEM controls multiple flexibility assets including independent thermal zones (the demand-side agents) and Electric Vehicle (EV) charging stations (the local storage). In a novel approach, CIRLEM coordinates the distributed storage and generation together with the demand side to control energy systems and react collaboratively to environmental variations. Under extreme weather conditions, without applying CIRLEM, the demand can be more than double that of typical weather conditions. The implementation of the double-layer CIRLEM can reduce the total demand by 35 % over a month. Furthermore, the inclusion of photovoltaic (PV) systems allows the system to be independent from the grid for almost 40 % of its operational hours, while adding EV storage can increase it to around 70 %. Finally, the application of CIRLEM reduced overheating hours from 17 h ∙°C to 2 h ∙°C under extreme conditions, while maintaining comfortable conditions even during temperature ramps.

Energy savings potential and life cycle costs of deep energy retrofits in buildings with and without habitable style loft attic conversions: A case study of Irelands residential sector

This paper evaluated the potential energy and life cycle cost (LCC) savings arising from the deep energy retrofit of residential buildings. The cost optimal model was determined for material upgrades and the addition of an air-to-water heat pump, where the household level of savings at 2023 energy prices will be dependent on the typical housing characteristics and weather conditions. LCC savings post retrofit for a detached dwelling insulated at the ceiling/rafter including installation of a heat pump amount to a €20,031-€21,370 for an uninsulated dwelling pre-retrofit, rising to €38,448 for dwellings with a habitable loft conversion. Partially insulated dwellings, pre-retrofit did not all incur favourable returns in investment, and in some cases were unviable. Although, the application of a 50% grant results in a viable return on investment for home owners. A sensitivity analysis found high discount rates, fuel prices and increasing capital costs can negate positive returns on investment. Finally, at a national level energy saving of 7.72 TWh by 2030 could be realised if the Irish Climate Action Plan 2023 retrofit targets are adhered to. In addition, total LCC savings could be valued at €1.4 billion. Where only material upgrade are pursued a loss occurs of €0.56 billion.

Adaptive SPP–CNN–LSTM–ATT wind farm cluster short-term power prediction model based on transitional weather classification

With the expansion of the scale of wind power integration, the safe operation of the grid is challenged. At present, the research mainly focuses on the prediction of a single wind farm, lacking coordinated control of the cluster, and there is a large prediction error in transitional weather. In view of the above problems, this study proposes an adaptive wind farm cluster prediction model based on transitional weather classification, aiming to improve the prediction accuracy of the cluster under transitional weather conditions. First, the reference wind farm is selected, and then the improved snake algorithm is used to optimize the extreme gradient boosting tree (CBAMSO-XGB) to divide the transitional weather, and the sensitive meteorological factors under typical transitional weather conditions are optimized. A convolutional neural network (CNN) with a multi-layer spatial pyramid pooling (SPP) structure is utilized to extract variable dimensional features. Finally, the attention (ATT) mechanism is used to redistribute the weight of the long and short term memory (LSTM) network output to obtain the predicted value, and the cluster wind power prediction value is obtained by upscaling it. The results show that the classification accuracy of the CBAMSO-XGB algorithm in the transitional weather of the two test periods is 99.5833% and 95.4167%, respectively, which is higher than the snake optimization (SO) before the improvement and the other two algorithms; compared to the CNN–LSTM model, the mean absolute error (MAE) of the adaptive prediction model is decreased by approximately 42.49%–72.91% under various transitional weather conditions. The relative root mean square error (RMSE) of the cluster is lower than that of each reference wind farm and the prediction method without upscaling. The results show that the method proposed in this paper effectively improves the prediction accuracy of wind farm clusters during transitional weather.

АГРОБИОЛОГИЧЕСКАЯ ОЦЕНКА МАСЛИЧНЫХ КУЛЬТУР В УСЛОВИЯХ ЛЕСОСТЕПИ ЦФО РФ

This article presents the results of environmental tests of the most promising very early soybean lines of Krasnodar breeding in the conditions of the Forest-steppe of the Central Federal District of the Russian Fed-eration, which were involved in hybridization. In the obtained hybrid populations of F4 in the conditions of Krasnodar, very early elite plants with a growing period of 90-100 days, plant height of at least 70 cm, and increased productivity were isolated. Their offspring in generations F6 and F7 underwent selection evalua-tion in the latitudinal and climatic conditions of the forest–steppe of the Central Black Soil region on the ba-sis of the Lipetsk Rapeseed Research Institute - a branch of the Federal State Budgetary Research Center V.S. Pustovoit All-Russian Research Institute of Oil crops, Lipetsk. As a result of the conducted studies, it was found that in the typical weather conditions of the Central Black Soil region in 2021 and with the dates of soybean sowing in the Lipetsk region in the 2nd decade of May, the earliest genotypes ripened in late August – early September. The duration of their growing seasons was 96-103 days. In the abnormally cold and rainy conditions of 2022, the growing season of these genotypes increased to 110-123 days. The yield of the earliest of the studied soybean genotypes in the conditions of 2021 varied in the range of 2.04-2.46 t/ha, in the condi-tions of 2022 – in the range of 2.07-2.91 t/ha. A two-year analysis of the productivity of the studied soybean genotypes in various weather conditions allowed us to identify 7 breeding lines that form a yield of more than 2.0 t/ha, both in close to optimal and in abnormally cold weather conditions with excessive precipitation.

Defining generation parameters with an adaptable data-driven approach to construct typical meteorological year weather files

Weather files play a critical role in building energy simulation by providing crucial information on the environmental condition. Typical meteorological year (TMY) weather files are often used in building energy simulation to approximate the long-term performance. These files are generated using long-term weather data and are meant to reflect the typical weather conditions within the dataset. Common generation methods used to construct TMY weather files typically rely on expert-defined universal generation parameters. However, the weather parameters and weighting factors used to generate these files, termed generation parameters, often overlook variations in climate and applications. This study introduces a data-driven approach to define the generation parameters used in the Sandia method to produce TMY weather files. The study aims to enhance the generation method by providing a framework to define the generation parameters which considers data availability, climate and application.The proposed methodology employs machine learning and feature importance to define the generation parameters used to construct TMY weather files. The generated TMY weather files are evaluated for a Montreal case study using building energy demand. The results show an improvement in the representativeness of the long-term average building energy demand for the weather file generated using the model-defined generation parameters.

Impacts of high-albedo urban surfaces on outdoor thermal environment across morphological contexts: A case of Tianjin, China

The urban heat island (UHI) effect can exacerbate various environmental challenges related to high temperatures in urban areas. Increasing urban surface albedo is an effective strategy to mitigate UHI. However, the efficacy of high-albedo urban surfaces across varying urban contexts remains poorly understood. In this study, we leverage Urban Weather Generator to systematically simulate the effects of high-albedo roads, walls, and roofs on urban microclimate across Tianjin. Our simulation covers a typical meteorological year, representing typical weather conditions from January to December. Our results reveal that increasing road albedo is more effective in mitigating UHI in fringe areas, whereas increasing wall and roof albedo is more effective in mitigating UHI in central areas. Local climate zones with an urban aspect ratio of about 0.5 can obtain a maximum reduction of road surface temperature (-6 °C) and wall surface temperature (-3 °C). The temperature changes induced by albedo changes show evident seasonal characteristics: the road temperature decreases significantly in summer, while the wall temperature decreases significantly in spring and autumn. Our results could help guide UHI mitigation policies and urban planning in cities hoping to enhance urban surface albedo to balance urban growth and climate resilience.

Traffic flow prediction based on T-GCN in extreme weather: A case study of Beijing

In recent years, frequent extreme weather events have significantly impacted traffic management and urban planning. Consequently, accurate and real-time traffic prediction has become crucial for effective urban traffic planning, management, and control. This study aims to predict road traffic flow in Beijing under extreme weather conditions. To achieve this goal, the study analyses historical weather data of Beijing and identifies four typical extreme weather conditions. Using traffic data collected between 2014 and 2016, the study then employs the time series graph convolutional network (T-GCN) model to predict traffic flow under extreme weather conditions.

Internet of Things Based Horizontal Axis Tracking Solar Panel Performance Evaluation

Renewable energy is undergoing significant advancements through the utilization of new technologies. Among these technologies, fixed solar panels serve as a fundamental type of solar photovoltaic energy generator. Monitoring and optimizing the performance of these panels is achieved through the implementation of controller algorithms. However, there is a growing emphasis on axis tracking systems for solar panels, as that has the feasible to extract more power compared to fixed panels. In this context, the integration of the Internet of Things (IoT), big data analytics, deep learning, and cloud computing presents a contemporary solution for addressing various challenges associated with monitoring renewable solar energy systems. By employing sensors and actuators grouped within an IoT framework, real-time data can be extracted and stored in a cloud server, allowing remote access from anywhere. Furthermore, the use of meta-heuristic optimization algorithms, such as the Whale Optimization Algorithm, has become commonplace in solving engineering issues. In this proposed system, the performance of horizontal axis trackers is analyzed and compared with fixed panels using the WOA-based axis tracking analysis. Through the application of a control algorithm, optimal values can be obtained under typical weather conditions. The system’s simulation and performance are thoroughly assessed and evaluated.

UAV Communication Recovery under Meteorological Conditions

Our study proposes a UAV communications recovery strategy under meteorological conditions based on a ray tracing simulation of excessive path loss in four distinct three-dimensional (3D) urban environments. We start by reviewing the air-to-ground propagation loss model under meteorological conditions, as well as the specific attenuation of rain, fog, and snow, and we propose a new expression for line-of-sight (LoS) probability. Using the two frequency bands of 28 GHz and 71 GHz, we investigate the impact of specific attenuation caused by different weather conditions and analyze the relationship between the radius of the UAV coverage area and the elevation angle. Furthermore, we investigate the effects of the rainfall rate, liquid water density, and snowfall rate on the maximum coverage area and optimal height of the UAV. Eventually, we propose a strategy that involves compensating for the maximum path loss and adjusting the UAV’s position to recover the coverage of the UAV to ground users. Our results show that rain has the greatest impact on the UAV’s coverage area and optimum height among the three types of weather conditions. For various weather conditions, relative to Region 1, the percentage reduction in the maximum coverage radius of Region 2 to Region 4 increases gradually, and the extent of each increase is approximately 10%. Moreover, after adding the compensated path loss, the coverage radius of the UAV in the four regions is restored to a value slightly larger than that before the rain. In addition, rain caused the greatest reduction in UAV coverage for suburban environments and the lowest for high-rise urban environments.

Feasibility and application of using Texas horned lizard scat to predict lizard size and age class

AbstractTexas horned lizards (Phrynosoma cornutum; hereafter THL) were once abundant throughout Texas; however, their distribution and abundance have decreased, and currently they are listed as a threatened species in the state. Detection of THL can be difficult because of their cryptic nature and sporadic distribution. Using indirect signs such as fecal pellets (i.e., scat) to determine occupancy is a widely practiced survey technique. However, it is unknown if scat size can be used to determine the age structure of THL. Our objectives were to determine 1) if THL scats were consistently produced and at consistent size in relation to lizard snout‐vent length (SVL), 2) if THL scats remain intact in the environment, and 3) if scat length and diameter could be used as an indicator of THL size, which could be used to determine population age structure. We tested the feasibility of using scat morphometrics to estimate age class ratios, body size, and population estimates for THL. Data collection was conducted in 3 phases, which occurred from 2001–2019. Scats were collected daily for 6 days from 6 hatchling, 10 juvenile, and 10 adult THL collected from southern Texas. Texas horned lizards were measured for snout‐to‐vent length (mm) and categorized as either hatchling, juvenile, or adults based on estimates from the literature. On average, THL produced one scat/day, typically in the morning. Scat length and width increased as lizard size (i.e., SVL) increased with each age class. Scat size of each age class of THL was consistent between days; thus, objective one was met. Scats were measured for length and width (mm) and placed in direct sunlight for 90 days, submerged in water for 2 days, and placed in typical summer weather conditions of southern Texas. Texas horned lizard scat did not shrink or swell in size or decay due to environmental conditions during the 90‐day experiment. Thus, THL scats do remain intact in the environment. We collected scat from 70 known‐size THL from south‐central Texas. Simple log‐log regression models demonstrated a linear relationship (r2 = 0.89) between loge (SVL) and loge (scat length, mm). Slopes were similar among age classes, but intercepts differed as expected. We tagged individual THL to follow growth rates through time. Thirteen of 20 tagged females produced 178 hatchlings, of which 56, 23, 19, and 6 were recaptured during their hatchling, 2nd, 3rd, and 4th year, respectively. Models for growth rates of known‐age THL were not equal (F6,69 = 44.6, P < 0.0001) among age classes. Growth rates were faster in year 1 (0.101 ± 0.003 mm d−1) and year 2 (0.099 ± 0.007 mm day−1) and slowed (0.076 ± 0.009 mm day−1) during their 3rd year. Scat from THL reliably can be used to estimate population age class structure, body size, and population estimates. However, our growth rate analyses suggest that THL appear to experience slower growth rates than during previous decades, and thus, THL require reevaluation as to what SVL size constitutes an adult THL.

The Design and Application of Microgrid Supervisory System for Commercial Buildings Considering Dynamic Converter Efficiency

This paper presents a supervisory system that considers converter efficiency for local microgrids of commercial buildings to solve the uncertainty problem of the sources and loads while also optimizing local microgrid operating costs and maintaining power supply quality for commercial buildings. The supervisory system includes an energy management layer and a power management layer. In the energy management layer, a long-term optimization approach is used to reduce the operating costs by considering the dynamic converter efficiency. In the power management layer, a real-time power optimization method is structured to deal with the uncertainty problem of the sources and loads, and to ensure that the direct current bus power is balanced while also guaranteeing the power quality by considering the dynamic converter efficiency. Four cases are proposed for the supervisory system, and these cases are simulated in MATLAB/Simulink under three typical weather conditions: cloud, sunshine, and rain. The comparison of simulation results for cases 1 and 2 illustrates the impact of converter efficiency on energy coordination in microgrids. The simulation results of cases 3 and 4 verify that the performance—in terms of the power supply quality and the operating costs—of the proposed microgrid supervisory system considering dynamic converter efficiency outperforms that of the microgrid supervisory system considering fixed converter efficiency.