Local Weather Station Research Articles

Urban building energy and microclimate modeling – From 3D city generation to dynamic simulations

Dynamic urban simulations often face three main challenges: 3D digital city generations, building archetype creations, and inclusions of urban microclimate impacts due to limited data and computing resources available. This study introduces a new approach for the 3D city generation by integrating publicly available data sets (OpenStreetMap and Microsoft footprints) and a free program (Google Earth). These data sets provide 2D building footprints, whereas Google Earth provides digital surface models of terrains and buildings. The building archetype library of non-geometrical properties was created based on building types and years of constructions in the form of shapefiles joined with the 3D city data through QGIS. The proposed workflow also includes the dynamic integration of urban microclimate (CityFFD) and building thermal/energy models (CityBEM). The dynamic simulations were achieved using weather station data as boundary conditions, including air temperature, solar radiation, and wind speed and direction, instead of typical meteorological year data. The transient microclimate results were validated using local weather station data, and dynamic energy simulation results were validated using measured power consumption data. The study provides a solution to dynamic urban building energy and microclimate modeling by publicly available data sets and tools.

Water vapor estimation based on 1-year data of E-band millimeter wave link in North China

Abstract. The amount of water vapor in the atmosphere is very small, but its content varies greatly in different humidity areas. The change in water vapor will affect the transmission of microwave link signals, and most of the water vapor is concentrated in the lower layer, so the water vapor density can be measured by the change in the near-ground microwave link transmission signal. This study collected 1-year data of the E-band millimeter wave link in Hebei, China, and used a model based on the International Telecommunication Union Radiocommunication Sector (ITU-R) to estimate the water vapor density. An improved method of extracting the water-vapor-induced attenuation value is also introduced. It has a higher time resolution, and the estimation error is lower than the previous method. In addition, this paper conducts the seasonal analysis of water vapor inversion for the first time. The monthly and seasonal evaluation index results show a high correlation between the retrieved water vapor density and the actual water vapor density value measured by the local weather station. The correlation value for the whole year is up to 0.95, the root mean square error is as low as 0.35 g m−3, and the average relative error is as low as 5.00 %. Compared with European Center for Medium-Range Weather Forecast (ECMWF) reanalysis, the correlation of the daily water vapor density estimation of the link has increased by 0.17, the root mean square error has been reduced by 3.14 g m−3, and the mean relative error has been reduced by 34.00 %. This research shows that millimeter wave backhaul link provides high-precision data for the measurement of water vapor density and has a positive effect on future weather forecast research.

Temporal Correlation Between Urban Microclimate, Vector Mosquito Abundance, and Dengue Cases.

Dengue Hemorrhagic Fever (DHF) is a major mosquito-borne viral disease. Studies have reported a strong correlation between weather, the abundance of Aedes aegypti, the vector of DHF virus, and dengue incidence. However, this conclusion has been based on the general climate pattern of wide regions. In general, however, the human population, level of infrastructure, and land-use change in rural and urban areas often produce localized climate patterns that may influence the interaction between climate, vector abundance, and dengue incidence. Thoroughly understanding this correlation will allow the development of a customized and precise local early warning system. To achieve this purpose, we conducted a cohort study, during January-December 2017, in 16 districts in Bandung, West Java, Indonesia. In the selected areas, local weather stations and modified light mosquito traps were set up to obtain data regarding daily weather and the abundance of adult female Ae. aegypti. A generalized linear model was applied to analyze the effect of local weather and female adult Ae. aegypti on the number of dengue cases. The result showed a significant non-linear correlation among mosquito abundance, maximum temperature, and dengue cases. Using our model, the data showed that the addition of a single adult Ae. aegypti mosquito increased the risk of dengue infection by 1.8%, while increasing the maximum temperature by one degree decreased the risk by 17%. This finding suggests specific actionable insights needed to supplement existing mosquito eradication programs.

Investigating the influence of weather on public transit passenger’s travel behaviour: Empirical findings from Brisbane, Australia

Drawing on transit smart card data allied with local weather station records over a 12-month period, this paper takes Brisbane, Australia as the study context and examines the way in which weather impose influences on public transit passenger’s travel behaviour. In terms of transit passenger’s spatiotemporal origin–destination (OD) information, a new travel behaviour indicator called stickiness is developed to reflect passenger’s travel similarity in using the transit service over a period. By applying a suite of regression models, it shows that weather’s effects on transit passenger’s travel behaviour vary by temporal period and passenger type. In general, weather is shown to exert a stronger effect on passenger’s stickiness during midday off-peak hours in comparison with either AM peak or PM peak. Across all types of passengers, child passengers are found to be most tolerant to weather changes and even be stickier to their regular travel pattern during AM peak. The modelling results also reveal that when it relates to journey-to-work or journey-to-school, weather’s effects on alighting location are much smaller than other three OD-related travel behaviour features (i.e., boarding time, boarding location, and alighting time). In contrast with the postponement during AM peak, transit passengers are prone to bring forward their trip back home during PM peak in response to poor weather conditions. Moreover, this paper verifies that weather parameters are not singly perceived by transit passengers but as an interrelated unity in shaping transit passenger’s travel behaviour. By enriching the weather–travel behaviour scholarship, the empirical findings of this study are helpful to render a holistic understanding of the weather–travel behaviour relationship and have important implications for transit operators in building a more weather-resilient transit system.

Estimating Urban Wind Speeds and Wind Power Potentials Based on Machine Learning with City Fast Fluid Dynamics Training Data

Wind power is known as a major renewable and eco-friendly power generation source. As a clean and cost-effective energy source, wind power utilization has grown rapidly worldwide. A roof-mounted wind turbine is a wind power system that lowers energy transmission costs and benefits from wind power potential in urban areas. However, predicting wind power potential is a complex problem because of unpredictable wind patterns, particularly in urban areas. In this study, by using computational fluid dynamics (CFD) and the concept of nondimensionality, with the help of machine learning techniques, we demonstrate a new method for predicting the wind power potential of a cluster of roof-mounted wind turbines over an actual urban area in Montreal, Canada. CFD simulations are achieved using city fast fluid dynamics (CityFFD), developed for urban microclimate simulations. The random forest model trains data generated by CityFFD for wind prediction. The accuracy of CityFFD is investigated by modeling an actual urban area and comparing the numerical data with measured data from a local weather station. The proposed technique is demonstrated by estimating the wind power potential in the downtown area with more than 250 buildings for a long-term period (2020–2049).

Observations of Delayed Changes in Respiratory Function among Allergy Clinic Patients Exposed to Wildfire Smoke.

Wildfires have increased in frequency and magnitude and pose a significant public health challenge. The principal objective of this study was to assess the impact of wildfire smoke on respiratory peak flow performance of patients exposed to two different wildfire events. This longitudinal study utilized an observational approach and a cohort study design with a patient-level clinical dataset from a local outpatient allergy clinic (n = 842). Meteorological data from a local weather station served as a proxy for smoke exposure because air quality measurements were not available. This study found that there were decreases in respiratory peak flow among allergy clinic patients one year after each wildfire event. For every one percent increase in wind blowing from the fire towards the community, there was, on average, a 2.21 L per minute decrease in respiratory peak flow. This study observed an effect on respiratory peak flow performance among patients at a local allergy clinic one year after suspected exposure to wildfire smoke. There are likely multiple reasons for the observation of this relationship, including the possibility that wildfire smoke may enhance allergic sensitization to other allergens or that wildfire smoke itself may elicit a delayed immune response.

Suitable indicator of heat stress for genetic evaluation of heat tolerance in Holstein cows in Japan.

Only a few, principal, weather stations in Japanese prefectures have the daily humidity records required to calculate the temperature-humidity index (THI) as a dairy cow heat-stress indicator. We compared three heat-stress indices: (1) THI calculated from daily average temperature and daily relative humidity at a principal weather station (PTHI); (2) daily average temperature at each herd's closest local weather station (TEMP); and (3) THI calculated from daily average temperature at each herd's closest local weather station and daily relative humidity at the principal weather station (HTHI). We used daily records from 532 provincial weather stations and test-day records of milk production from Days 6 to 305 post-first-calving in Holsteins to compare the indices as indicators of heat-stress effects on milk yield and somatic cell score (SCS). The models used the BLUPF90 package to analyze the effects of herd-year, calving age, days in milk, and PTHI, TEMP, or HTHI. We estimated each model's mean square error (MSE) and compared suitabilities among indices for each trait. TEMP heat-stress thresholds were ~18°C (milk yield) and 15-20°C (SCS). The MSE of the HTHI model was the smallest, but no significant differences were found among the indices for milk yield.

Tea Plantation Frost Damage Early Warning Using a Two-Fold Method for Temperature Prediction

As the source and main producing area of tea in the world, China has formed unique tea culture, and achieved remarkable economic benefits. However, frequent meteorological disasters, particularly low temperature frost damage in late spring has seriously threatened the growth status of tea trees and caused quality and yield reduction of tea industry. Thus, timely and accurate early warning of frost damage occurrence in specific tea garden is very important for tea plantation management and economic values. Aiming at the problems existing in current meteorological disaster forecasting methods, such as difficulty in obtaining massive meteorological data, large amount of calculation for predicted models and incomplete information on frost damage occurrence, this paper proposed a two-fold algorithm for short-term and real-time prediction of temperature using field environmental data, and temperature trend results from a nearest local weather station for accurate frost damage occurrence level determination, so as to achieve a specific tea garden frost damage occurrence prediction in a microclimate. Time-series meteorological data collected from a small weather station was used for testing and parameterization of a two-fold method, and another dataset acquired from Tea Experimental Base of Zhejiang University was further used to validate the capability of a two-fold model for frost damage forecasting. Results showed that compared with the results of autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR), the proposed two-fold method using a second order Furrier fitting model and a K-Nearest Neighbor model (K = 3) with three days historical temperature data exhibited excellent accuracy for frost damage occurrence prediction on consideration of both model accuracy and computation (98.46% forecasted duration of frost damage, and 95.38% for forecasted temperature at the onset time). For field test in a tea garden, the proposed method accurately predicted three times frost damage occurrences, including onset time, duration and occurrence level. These results suggested the newly-proposed two-fold method was suitable for tea plantation frost damage occurrence forecasting.

Revealing the spatial variation in vehicle travel time with weather and driver travel frequency impacts: Findings from the Guangdong–Hong Kong–Macao Greater Bay Area, China

<abstract> <p>Vehicle travel time information is an essential location-based services that can be used to assess highway traffic conditions and provide valuable insights for transit agencies and travelers. To reveal the spatial variation in vehicle travel time with multiple factors, a multiple regression model and a geographically weighted regression model are used to investigate the associations between travel time and various factors. This study draws on freeway toll data in combination with local weather station records on Fridays over 12 months (286, 406 travel information data points), and the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), China, is used as a case study for examining the influence of weather and driver travel frequency on vehicle travel time. The results show that i) travel frequency along an origin-destination (OD) route has a significant effect on travel time, and this effect is approximately 3 to 100 times that of other explanatory variables; ii) rainfall significantly impacts travel time, with an effect that is 1.9 to 8.26 times that of other weather factors; and iii) both weather and driver travel frequency factors display spatial heterogeneity. These findings provide valuable insights for both traffic management and freeway travelers.</p> </abstract>

Assessing Post-Fire Effects on Soil Loss Combining Burn Severity and Advanced Erosion Modeling in Malesina, Central Greece

Earth’s ecosystems are extremely valuable to humanity, playing a key role ecologically, economically, and socially. Wildfires constitute a significant threat to the environment, especially in vulnerable ecosystems, such as those that are commonly found in the Mediterranean. Due to their strong impact on the environment, they provide a crucial factor in managing ecosystems behavior, causing dramatic modifications to land surface processes dynamics leading to land degradation. The soil erosion phenomenon downgrades soil quality in ecosystems and reduces land productivity. Thus, it is imperative to implement advanced erosion prediction models to assess fire effects on soil characteristics. This study focuses on examining the wildfire case that burned 30 km2 in Malesina of Central Greece in 2014. The added value of remote sensing today, such as the high accuracy of satellite data, has contributed to visualizing the burned area concerning the severity of the event. Additional data from local weather stations were used to quantify soil loss on a seasonal basis using RUSLE modeling before and after the wildfire. Results of this study revealed that there is a remarkable variety of high soil loss values, especially in winter periods. More particularly, there was a 30% soil loss rise one year after the wildfire, while five years after the event, an almost double reduction was observed. In specific areas with high soil erosion values, infrastructure works were carried out validating the applied methodology. The approach adopted in this study underlines the significance of using remote sensing and geoinformation techniques to assess the post-fire effects of identifying vulnerable areas based on soil erosion parameters on a local scale.

Using local weather stations to generate growing degree‐day data to predict the flowering pattern of an annual bluegrass fairway in Michigan

AbstractSix 1‐yr observational studies were conducted from 2001 to 2006 at the Hancock Turfgrass Research Center in East Lansing, MI to characterize the timing, duration, and amplitude of annual bluegrass (AB) (Poa annua L.) seedhead emergence in a 10‐ to 15‐yr‐old AB fairway. The objective of this research was to collect data that could be used in the development of a growing degree‐day (GDD) model to predict AB seedhead emergence at other locations by using readily available weather station data. New GDD models were compared with previously published models. Plots were established on two adjacent perennial stands of AB maintained at 1.5 cm, receiving 0.5 to 0.6 cm of automatic daily irrigation throughout the growing season and 120 kg N ha–1 yr–1 from 2001 to 2006. The soil type is a Marlette sandy loam (fine‐loamy, mixed, semiactive, mesic Oxyaquic Glossudalf). Line intersects and visual estimations of seedhead cover were evaluated multiple times throughout the spring seedhead emergence period. A base temperature of –5 °C most accurately predicted the onset, peak duration, and completion of the AB seedhead emergence period for all 5 yr. The final model flowering rate = (–3.331599 × 10–6 × gdd2) + (6.968782 × 10–3 × gdd) + –2.841894, accurately predicted (R2 = .64) flowering stages of an AB fairway turf over 6 yr in Michigan.

Evaluation of Regulating and Provisioning Services Provided by a Park Designed to Be Resilient to Climate Change in Bangkok, Thailand

Understanding the ecosystem services provided by urban green spaces, in terms of their environmental, economic, and social benefits, is essential for a better management of area. Chulalongkorn University Centenary Park (CU 100) was established to mitigate the effects of climate change, especially flood prevention. This study focused on quantifying the ecosystem services provided by the trees in the park in terms of regulating and provisioning services. A publicly available tool, the i-Tree Eco international software, was used with data obtained from a local weather station as proxies to determine the accuracy of the analysis. Services, quantified in terms of monetary value, included avoided runoff, carbon storage, carbon sequestration, pollution removal, and timber price. The total monetary benefits, obtained from 697 trees (56 species, 49 genera, and 22 families), were estimated at USD 101,400. Of the total services, provisioning services contributed 75% to the total monetary value. Among all regulating services, the avoided runoff contributed about 60%, which was considered as the goal achieved by the park design. Azadirachta indica A. Juss (USD 518.75/tree−1/year−1), Shorea roxburghii G. Don (USD 417.17/tree−1/year−1) and Millettia leucantha Kurz (USD 414.87/tree−1/year−1) provided the greatest benefit, as indicated by a high value of provisioning services in terms of a high timber quality. These results can be used when planning the composition of trees to be planted in urban areas to increase both green spaces and maximize ecosystem services to improve the vitality of human well-being.

Seasonal Dynamics of Photochemical Performance of PS II of Terrestrial Mosses from Different Elevations

Mosses are critical components of tropical forest ecosystems and have multiple essential ecological functions. The drying and rehydrating and often hot environments in tropical regions present some of the greatest challenges for their photosynthetic activities. There is limited knowledge available on the physiological responses to the changing environments such as temperature and water pattern changes for terrestrial mosses. We examined the seasonal dynamics of photochemical performance of PS II through the measuring of chlorophyll fluorescence of 12 terrestrial mosses in situ from five different elevations by Photosynthesis Yield Analyzer MINI-PAM-II, along with the seasonal changes of climatic factors (air temperature, dew point, relative humidity and rainfall), which were collected by local weather stations and self-deployed mini weather stations. The results showed a great seasonality during observing periods, which, mainly the changes of rainfall and relative humidity pattern, presented significant impacts on the photochemical performance of PS II of terrestrial mosses. All these tested moss species developed a suitable regulated and non-regulated strategy to avoid the detrimental effect of abiotic stresses. We found that only Hypnum plumaeforme, Pterobryopsis crassicaulis and Pogonatum inflexum were well adapted to the changes of habitat temperature and water patterns, even though they still experienced a lower CO2 assimilation efficiency in the drier months. The other nine species were susceptible to seasonality, especially during the months of lower rainfall and relative humidity when moss species were under physiologically reduced PS II efficiency. Anomobryum julaceum, Pogonatum neesii, Sematophyllum subhumile, Pseudotaxiphyllum pohliaecarpum and Leucobryum boninense, and especially Brachythecium buchananii, were sensitive to the changes of water patterns, which enable them as ideal ecological indicators of photosynthetic acclimation to stressed environments as a result of climate change.

Optimal switching control of an air to air heat pump operating under variable time-based electricity pricing

The electrical energy related expenditure in residential buildings may become substantial when it comes to peak pricing with regards to the Time-of-Use (TOU) tariff structure. Effective energy management strategies should be implemented at the large residential buildings scale to conserve energy, which in turn, can assist in relieving the burden on the national energy grid. This paper presents an optimal switching control strategy for an air to air heat pump used to provide space heating to a typical two-bedroom residential building in which two occupants are residing, is presented. Accurate and reliable data, which is obtained from a local weather station, is applied to the established model. The main purpose of this paper is to develop an optimal control algorithm, which minimizes the running energy cost of the air to air heat pump under time-based pricing and the level of discomfort with regards to the desired building temperature. Additionally, various exogenous disturbances, such as the ambient temperature, amount of occupants present and the amount of lights switched on during each moment in time, are taken into account. The thermostat-based control of an air to air heat pump which is adopted as the first baseline, illustrates a daily electrical energy cost of USD 4.8. The timer with thermostat-based control method which is adopted as the second baseline illustrates a daily electrical energy cost of USD 4.78. The optimal switching control of an air to air heat pump illustrates a daily electrical energy cost of USD 4.48. Therefore, the optimal switching control model achieved a 6.67% cost savings in electricity compared to the first baseline and a 6.28% cost savings in electricity to the second baseline.

A fully predictive model for aeolian sand transport, part 3: Verification and application of model for natural beaches

This paper presents extensive research results on the verification of a new transport equation for dry and moist sand at field sites and is a continuation of earlier work (Van Rijn and Strypsteen, 2020). A new trap-type instrument (LVRS-sampler) for measuring the sand transport rate of sliding, rolling and saltating particles was designed and used at many natural beach sites in Belgium and The Netherlands. A short mast equipped with three mini wind cup meters was used to measure the velocity distribution in the lowest 1 m of the boundary layer flow. Many new data sets (81 data) of aeolian sand transport for dry and moist beach surface conditions were obtained using the new equipment and used for verification of the new prediction method. Older field data (48 data points) from the literature were also used for verification. The score of the predicted sand transport based on the new VR-method is 87% within a factor of 2 of measured values for dry sand and 91% for moist sand. The modified Bagnold-transport equation was also used for verification of the available field data with scores of 79% and 67%. As a practical example, the new VR-sand transport equation was applied to calculate the annual aeolian sand transport on a Dutch beach site using wind velocity and rainfall data from a local weather station including rain fall effects (moist sand) and conversion of inland wind velocities to beach velocities (upscaling of results).

Seasonal bean yield forecast for non-irrigated croplands through climate and vegetation index data: Geospatial effects

Seasonal crop estimates at large scales are essential for national food security. Therefore, this study aimed to predict non-irrigated dry bean yields for 41 districts in the semi-arid region in central Mexico before estimations from crop census. 13-year period data included: bean yields from official statistics, climate data from local weather stations, and Remote Sensing Imagery. The study examined a suite of econometric modeling approaches to predict seasonal bean yields under different precipitation categorization scheme (normal-wet seasons and dry seasons), as well as predictand/predictor log-transformations. The method tested Ordinary least Squares (OLS) and OLS + Dummy (dummy variable with spatial regimes), and spatial regression methods: Spatial Lag (SAR) and Spatial Error (SER), and Geographically Weighted Regression (GWR). At first, exploratory OLS regressions were used to create a subset of specified models before testing the spatial regression models. The predictors that accounted for most of the bean yield variability were precipitation and Enhanced Vegetation Index. The models that incorporated explicit and implicit spatial effects (SAR and OLS + Dummy, respectively), with log-transformations of predictand and non-transformation of predictors, showed the best performances (r2 between 0.81 and 0.84 with an AIC between −81 and −99). Likewise, a prognosis of a13-year yield simulations (hindcasts) indicated that the latter models are adequate for normal-wet and dry seasons (mean absolute error less than 0.190 ton ha−1). Overall, spatial regression techniques have the potential to estimate bean yields as an early forecast for crop official statistics, eluding large amounts of monetary and human resources on gathering ground data in poor or developing countries, particularly.

A functional seasonal thermal hot-spot classification: Focus on industrial sites

This study was focused on the metropolitan area of Florence in Tuscany (Italy) with the aim to provide a functional spatial thermal anomaly indicator obtained throughout a thermal summer and winter hot-spot detection. The hot-spot analysis was performed by applying Getis-Ord Gi* spatial statistics to Land Surface Temperature (LST) layers, obtained from Landsat 8 remote sensing data during the 2015–2019 daytime summer and winter period, to delimitate summer hot- and cool-spots, and winter warm- and cold-spots. Further, these ones were spatially combined thus obtaining a comprehensive summer-winter Thermal Hot-Spot (THSSW) spatial indicator. Winter and summer mean daily thermal comfort profiles were provided for the study area assessing the Universal Thermal Climate Index (UTCI) by using meteorological data available from seven local weather stations, located at a maximum distance of 350 m from industrial sites. A specific focus on industrial sites was carried out by analyzing the industrial buildings characteristics and their surrounding areas (50 m buffer), through the following layers: industrial building area (BA), surface albedo of buildings (ALB), impervious area (IA), tree cover (TC), and grassland area (GA). The novel THSSW classification applied to industrial buildings has shown that about 50% of the buildings were located in areas characterized by summer hot-spots. Increases in BA and IA revealed warming effects on industrial buildings, whereas increases in ALB, TC, and GA disclosed cooling effects. A decrease of about 10% of IA replaced by TC and GA was associated with about 2 °C decrease of LST. Very strong outdoor heat stress conditions were observed during summer daytime, whereas moderate winter outdoor cold stress conditions were recorded during nighttime until the early morning. The thermal spatial hot-spot classification in industrial areas provides a very useful source of information for thermal mitigation strategies aimed to reduce the heat-related health risk for workers.

PSII-3 Daily weather patterns associated with colostrum production in dry cows

Abstract Insufficient consumption of colostrum and/or intake of poor-quality colostrum reduces thriftiness and survival of dairy calves. Additionally, heat stress during gestation of primiparous Holstein cows can impact hormone synthesis, leading to decreased colostrum and milk production. Previous research has primarily focused on Holstein cows who have a larger frame compared to other breeds. It is possible heat stress may impact Jersey cow colostrum production differently. Therefore, the objective of this research was to determine the relationship between temperature-humidity index (THI) and colostrum volume and quality in Jersey cows. Colostrum data was collected from Jersey cows (n = 75) from three farms and compared to the average THI values during the dry period. Colostrum data included volume, Brix score, fat, true protein (TP), solids non-fat (SNF), lactose, somatic cell count (SCC), and urea. Daily air temperature and humidity records were determined via local weather stations and used to calculate THI values. These values were averaged for the far-off and close-up periods. The data were analyzed separately for each farm using linear regression models in R version 1.3.1073. Covariates included season, dry period length, date of colostrum collection, and parity. Close-up and far-off THI tended to predict an increased volume (β=0.3656; P = 0.0724) and somatic cell count (β=82.07; P = 0.0964) for Farm 1. Far-off THI was a significant predictor for Brix for Farm 2 (β=0.2482; P ≤ 0.01) and Farm 3 (β=-0.1762; P ≤ 0.01) and increased Brix score in Farm 2 but decreased it in Farm 3. THI was not a significant predictor for any other colostrum values. Based on these results, we suggest that an increased THI during the far-off period potentially increases Brix score without compromising other values. However, more research is needed to further investigate the effects of the close-up period on volume and of covariates on other values.

Environmental Heat Exposure Among Pet Dogs in Rural and Urban Settings in the Southern United States.

With advancing global climate change, heat-related illnesses and injuries are anticipated to become more prevalent for humans and other species. Canine hyperthermia is already considered an important seasonal emergency. Studies have been performed on the risk factors for heat stroke in canine athletes and military working dogs; however there is limited knowledge on environmental risk factors for the average pet dog. This observational study explores variation in individually experienced environmental temperatures of pet dogs (N = 30) in rural and urban environments in central Alabama. Temperature data from dogs and their owners was collected using wearable personal thermometers. Demographic data on the dogs was collected using a brief survey instrument completed by their owners. Dogs included in the study varied in signalment, activity level, and home environment. Linear mixed effects regression models were used to analyze repeated measure temperature and heat index values from canine thermometers to explore the effect of environmental factors on the overall heat exposure risk of canine pets. Specifically, the heat exposures of dogs were modeled considering their owner's experienced temperatures, as well as neighborhood and local weather station measurements, to identify factors that contribute to the heat exposure of individual dogs, and therefore potentially contribute to heat stress in the average pet dog. Results show hourly averaged temperatures for dogs followed a diurnal pattern consistent with both owner and ambient temperature measurements, except for indoor dogs whose recordings remained stable throughout the day. Heat index calculations showed that owners, in general, had more hours categorized into the National Weather Station safe category compared to their dogs, and that indoor dogs had a greater proportion of hours categorized as safe compared to outdoor dogs. Our results suggest that the risk of the average pet dog to high environmental heat exposure may be greater than traditional measures indicate, emphasizing that more localized considerations of temperature are important when assessing a dog's environmental risk for heat-related injury or illness.

A statistical model of the spatial variability of weather for use in building simulation practice

Traditionally, statistically representative (e.g. TMY) weather data is used as boundary condition in building simulation practice and is deemed homogenous for a defined region, e.g., a city. However, this presumed homogeneity does not exist in most cases because due to the effect of variable urban morphology, local weather may be different in different parts of the region. This paper uses Multiple Tensor on Tensor Regression (MTOT) for creation of meso-scale weather for applications in building simulation practice, which is fast and efficient, provided that enough local weather station data and regional morphology data are available. The derived statistical weather model captures variation in local weather due to change in urban morphology with an ensemble mean of standardized mean square error 0.038, i.e, 96.2% of the variation in weather may be predicted by this method. Chicago is used as a demonstration region with a case study of single family dwelling to exemplify the impact of local weather on building performance indicators. A standard deviation of approximately 372 kWh for end use heating, 33.6 kWh for end use cooling and 4 h for hours of discomfort in winters is observed, highlighting the variation caused by the use of local weather in building performance simulation.