Hourly Temperature Research Articles

Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning Algorithms

Wind energy is one of the fastest growing sources of energy worldwide. This is clear from the high volume of wind power applications that have been increased in recent years. However, the uncertain nature of wind speed induces several challenges towards the development of efficient applications that require a deep analysis of wind speed data and an accurate wind energy potential at a site. Therefore, wind speed forecasting plays a crucial rule in reducing this uncertainty and improving application efficiency. In this paper, we experimented with several forecasting models coming from both machine-learning and deep-learning paradigms to predict wind speed in a metrological wind station located in East Jerusalem, Palestine. The wind speed data were obtained, modelled, and forecasted using six machine-learning techniques, namely Multiple Linear Regression (MLR), lasso regression, ridge regression, Support Vector Regression (SVR), random forest, and deep Artificial Neural Network (ANN). Five variables were considered to develop the wind speed prediction models: timestamp, hourly wind speed, pressure, temperature, and direction. The performance of the models was evaluated using four statistical error measures: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2). The experimental results demonstrated that the random forest followed by the LSMT-RNN outperformed the other techniques in terms of wind speed prediction accuracy for the study site.

Evidence of horizontal urban heat advection in London using six years of data from a citizen weather station network

Recent advances in citizen weather station (CWS) networks, with data accessible via crowd-sourcing, provide relevant climatic information to urban scientists and decision makers. In particular, CWS can provide long-term measurements of urban heat and valuable information on spatio-temporal heterogeneity related to horizontal heat advection. In this study, we make the first compilation of a quasi-climatologic dataset covering six years (2015–2020) of hourly near-surface air temperature measurements obtained via 1560 suitable CWS in a domain covering south-east England and Greater London. We investigated the spatio-temporal distribution of urban heat and the influences of local environments on climate, captured by CWS through the scope of Local Climate Zones (LCZ)—a land-use land-cover classification specifically designed for urban climate studies. We further calculate, for the first time, the amount of advected heat captured by CWS located in Greater London and the wider south east England region. We find that London is on average warmer by about 1.0 ∘C–1.5 ∘C than the rest of south-east England. Characteristics of the southern coastal climate are also captured in the analysis. We find that on average, urban heat advection (UHA) contributes to 0.22 ± 0.96 ∘C of the total urban heat in Greater London. Certain areas, mostly in the centre of London are deprived of urban heat through advection since heat is transferred more to downwind suburban areas. UHA can positively contribute to urban heat by up to 1.57 ∘C, on average and negatively by down to −1.21 ∘C. Our results also show an important degree of inter- and intra-LCZ variability in UHA, calling for more research in the future. Nevertheless, we already find that UHA can impact green areas and reduce their cooling benefit. Such outcomes show the added value of CWS when considering future urban design.

An hourly ground temperature dataset for 16 high-elevation sites (3493–4377 m a.s.l.) in the Bale Mountains, Ethiopia (2017–2020)

Abstract. Tropical mountains and highlands in Africa are under pressure because of anthropogenic climate and land-use change. To determine the impacts on the afro-alpine environment and to assess the potential socio-economic consequences, the monitoring of essential climate and environmental variables at high elevation is fundamental. However, long-term temperature observations on the African continent above 3000 m are very rare. Here we present a consistent multiannual dataset of hourly ground temperatures for the Bale Mountains in the southern Ethiopian Highlands, which comprise Africa's largest tropical alpine area. The dataset covers the period from January 2017 to January 2020. To characterise and continuously monitor the mountain climate and ecosystem of the Bale Mountains along an elevation gradient from 3493 to 4377 m, ground temperature data loggers have been installed at seven sites at 2 cm depth; at four sites at 10 cm depth; and at five sites at 2, 10, and 50 cm depth. The statistical analysis of the generated time series reveals that ground temperatures in the Bale Mountains are subject to large daily fluctuations of up to 40 ∘C and minor seasonal variations on the order of 5 to 10 ∘C. Besides incoming short-wave radiation, ground moisture, and clouds at night, slope orientation and the type of vegetation coverage seem to be the main factors controlling daily and seasonal ground temperature variations. On the central Sanetti Plateau above 3800–4000 m, the mean annual ground temperature ranges from 9 to 11 ∘C. However, nocturnal ground frost down to a depth of 5 cm occurs frequently during the dry season from November to February. At the five sites where ground temperature is measured at three depths, the monitoring will be continued to trace long-term changes. To promote the further use of the ground temperature dataset by the wider research community dealing with the climate and geo-ecology of tropical mountains in eastern Africa, it is made freely available via the open-access repository Zenodo: https://doi.org/10.5281/zenodo.6047457 (Groos et al., 2022).

The relationship between ambient temperatures and road traffic injuries: a systematic review and meta-analysis.

Traffic accidents cause considerable economic losses and injuries. Although the adverse effects of a change in ambient temperatures on human health have been widely documented, its effects on road traffic safety are still debated. This systematic review and meta-analysis was performed to synthesize available data on the association between ambient temperature and the risks of road traffic accidents (RTAs) and traffic accident injuries (TAIs). We searched 7 different databases to locate studies. The subgroup analyses were stratified by temperature type, temperature exposure, region, mean temperature, mortality, study period, statistical model, and source of injury data. This study was registered with PROSPERO under the number CRD42021264660. This is the first meta-analysis to investigate the association between ambient temperature and road traffic safety. A total of 34 high-temperature effect estimates were reported, and two additional studies reported the relationship between low temperatures and TAI risk. The meta-analysis results found a significant association between the high temperature and RTAs, and the pooled RR was 1.025 (95%CI 1.014, 1.035). The risk of TAI was also significantly associated with temperature increases. Subgroup analyses found that using daily mean temperatures, the RR value of road traffic accidents was 1.024 (95%CI 0.939, 1.116), and the RR value of road traffic injuries was 1.052 (95%CI 1.024, 1.080). Hourly temperatures significantly increased the risk of RTA, while the risk of TAI was not significantly increased by hourly temperature. The sensitivity analysis indicated that the results were stable, and no obvious publication bias was detected. The results of this systematic review and meta-analysis suggest that increases in ambient temperature are associated with an increased risk of RTAs and TAIs. These findings add to the evidence of the impact of ambient temperature on road traffic safety.

Development of Predictive Reliability Model of Solar Photovoltaic System Using Stochastic Diffusion Process for Distribution System

Reliability assessment of power distribution systems is an essential concern for electric utilities to maintain supply continuity. In the event of supply failure, distributed energy resources play a vital role in restoring the supply to customers. This paper presents the development of a predictive reliability model for distribution system with solar Photovoltaic (PV) system. At the initial stage, the hourly solar irradiance and temperature are forecasted using Stochastic Diffusion Process (SDP)-based Monte Carlo simulation from historical data. Later, the cloud transients are modeled using the Jump Diffusion Process integrated into SDP. The monthly failure rates of PV system components are evaluated from historical data using the Physics of Failure (PoF) reliability model. Finally, the predictive reliability evaluation of the practical distribution system integrated with the proposed solar PV model is carried out. Results demonstrate the capability of the developed model to handle continuous and discrete uncertainties with more accuracy.

Multi-step solar irradiation prediction based on weather forecast and generative deep learning model

With the rapid development of computer technology, more and more deep learning models are used in solar radiation (irradiation) prediction. There have been a lot of studies discussing the research of this type of model. However, how to better apply the deep learning model in the optimization method of building energy system, such as multi-step solar radiation (irradiation) prediction model in model predictive control (MPC), is still a challenging issue due to the complexity of the time series and the accumulation of errors in multi-step forecasts.In this research, a deep generative model based on LSTM is developed for multi-step solar irradiation prediction at least 24 h in the future. Measured data and temperature forecast data from the Tokyo Meteorological Agency were used for training and testing in this experiment. The results show that generating the model first can effectively avoid the problem of error accumulation. The generative model can produce an accuracy improvement of 7.7% against traditional regression LSTM model. Secondly, the introduction of the temperature forecast data from the previous one day can increase the forecast accuracy by about 18% points. When the earlier temperature forecast is used, the forecast accuracy will gradually decrease, and the use of the temperature forecast released 3 days before can hardly improve the forecast effect. In the end, using hourly temperature forecasts will result in better forecast accuracy than using daily temperature forecasts.

Ambient Room Temperatures in a Burn Intensive Care Unit-A Quality Improvement Project.

Introduction: Patients with major burn injuries are particularly susceptible to hypothermia. The ability to maintain and rapidly increase ambient temperatures may reduce the impact of hypothermia and the hypermetabolic response. The purpose of this study was to determine ambient patient room temperatures in a burn intensive care unit (ICU) and to evaluate our ability to adjust these temperatures. Methods: The ambient temperatures of 9 burn ICU patient rooms were recorded hourly over a 6-month period in an American Burn Association-verified burn centre. Temperatures were recorded using wall-mounted smart sensors, transmitted to a mobile smartphone application via Bluetooth, and then exported to Excel for analysis. On 2 predetermined dates, thermostats in all rooms were simultaneously set to maximum, and monitored over 3 h. This represented a sound change initiative, and replicated a medical order to increase the ambient temperature during critical stages of patient care. Results: We recorded 4394 individual hourly temperature measurements for each of the 9 rooms. The mean ambient temperature was 23.5 ± 0.3 °C (range 22.8-24). After intervention 1, ambient temperatures increased <2 °C in 7 rooms and by only 2 °C-3 °C in the other 2 rooms. The overall mean increase in temperature over 3 h across all rooms was 1.03 °C ± 1.19 °C (range -0.88 to 3.26). Following intervention 2, temperatures could be increased by ≥2 °C in only 2 rooms with an overall mean increase in temperature of only 0.76 °C ± 0.99 °C (range -0.29 to 2.43) across all rooms. Conclusions: The burn ICU rooms were relatively cool and our ability locally to adjust ambient temperatures quickly was limited. Burn centres should have regular facility assessments to assess whether ambient temperatures can be adjusted expeditiously when required.

Wind-driven upwelling and surface nutrient delivery in a semi-enclosed coastal sea

Abstract. Wind-driven upwelling is an important control on surface nutrients and water properties in stratified lakes and seas. In this study, a high-resolution biophysical coupled model is used to investigate upwelling in the Strait of Georgia on the Canadian Pacific coast. The model is forced with surface winds from a high-resolution atmospheric forecast and has been tuned in previous studies to reproduce extensive observations of water level, temperature, salinity, nutrients and chlorophyll with competitive skill relative to similar models of the study region. A total of 5 years of hourly surface nitrate and temperature fields are analyzed in order to characterize the dominant upwelling patterns of the basin. A prevailing along-axis wind pattern steered by mountainous topography produces episodic upwelling along the western shore during the spring and fall southeasterlies and along the eastern shore during the summer northwesterlies, as indicated by positive nitrate anomalies. Principal component analysis reveals that these cross-axis upwelling patterns account for nearly one-third of the surface nitrate variance during the summer productive season. By contrast, nearly half of the surface temperature variance over the same period is dominated by a single, combined mixing and diurnal heating–cooling pattern. The principal components associated with these patterns correlate with along-axis wind stress in a manner consistent with these physical interpretations. The cross-axis upwelling response to wind is similar to other dynamically wide basins where the baroclinic Rossby deformation radius is smaller than the basin width. However, the nitrate anomaly during upwelling along the eastern shore is stronger in the northern basin, which may be indicative of an along-axis pycnocline tilt or an effect of the background along-axis stratification gradient due to the Fraser River. Our findings highlight an important spatiotemporal consideration for future ecosystem monitoring.

Evaluation of long-term degree-days estimated with several methods for corn in Nebraska, USA

The concept of thermal time, measured in degree-days, is widely used among the agricultural community in Nebraska to make decisions in corn (Zea mays L.) production. Instead of the real-time temperatures that are experienced by corn plants, most of the widely available temperature data are limited to daily timescale observations from standard meteorological stations. And a variety of equations are used by different agricultural groups (e.g., researchers, advisors, farmers, and seed companies) to estimate thermal time for corn. Two problems could arise: (a) the estimation method is lacking in accuracy; and (b) different estimation methods are used for the same purpose by different groups. Consequently, citing these inaccurate and maybe inherently different thermal time results could lead to biased decisions in corn production. The goal of this study is to evaluate six commonly used estimation methods by comparing the estimated thermal time with the hourly temperature approximated thermal time. We analyzed the root mean square error and mean absolute error for six metrics of total growing season (from May through September) degree-days based on the temperature data from a total of 14 long-term observing locations in Nebraska. In particular, we selected four location-extreme year cases to demonstrate the six methods’ estimation performance on a daily timescale. We found that the most commonly used adjusted Tmax and Tmin rectangle method provided poor estimation in the study area. Instead, single-sine, double-sine, or Tavg-based method was more superior depending on the metric of degree-days.

Apparent thermal diffusivity of soil in ice-free areas of Keller peninsula in maritime Antarctica.

Heat transfer process in the soil active layer is important for the knowledge of its thermal properties linked with climate issues. The objective of this work was to analyze the energy flux in different soil profiles by estimating the apparent soil thermal diffusivity ($ATD$). The study was carried out in Keller Peninsula, located at King George Island in four different sites differing by soil characteristics, as well as vegetation coverage and landscape setting. The $ATD$ was estimated in function of the long-term hourly temperature records at different soil depths. In addition, we estimated the seasonal mean of the $ATD$ and the freezing $N$-factor. Results showed that $ATD$ values were smaller at shallow depths and increased with depth. The diffusivity values presented lower variability in colder conditions, especially at deeper soil layers. Water content was the main factor affecting soil thermal diffusivity at sites $1$ and $3$ (more than $70$ and $63\%$ of probability). At sites $3$ and $4$ lower $N$-factors were observed, suggesting higher snow pack and permafrost closer to the soil surface. Hence, positive $ATD$ appears in the summer due to thawing increases soil moisture, while negative $ATD$ appears during the freeze of the snow pack and precipitation.

MetPGNet: Meteorological Prior Guided Network for Temperature Forecasting

High temperature is one of the most severe disasters in the world, which causes the death of millions of people each year. Accurate temperature forecasting, as a key member of weather prediction, is of great application value. Many recent contributions treat weather forecasting as a spatio-temporal learning task, in which these methods mainly capture the motion of a rigid body while neglecting the generation and dispersion of fluid elements. However, typical spatio-temporal prediction is quite different from meteorological forecasting. To resolve this key issue, this paper proposes MetPGNet, a Meteorological Prior Guided Network for hourly temperature forecasting. Specifically, under the framework of atmospheric theory, three simple but effective multi-dimensional attention branches, i.e., the advection branch, the vertical branch and the temporal branch, are elaborately designed to depict the temperature variation of spatial atmospheric advection, vertical atmospheric movement and temporal heat exchange, respectively. Experiments compared with state-of-the-art spatio-temporal learning and weather prediction methods demonstrate the superiority of the proposed MetPGNet. Specifically, MetPGNet gets improvement of 0.52 on MAE compared with vanilla ConvGRU.

Condensate loss estimation and transient solar-to-vapor conversion efficiency for effective performance evaluation of the inclined solar still

Previously, freshwater yields of the solar still were quantified only based on the actual distillate recovery, not considering condensate losses by any means. Likewise, solar-to-vapor conversion efficiencies of the solar still were conventionally considered and evaluated as one-off -rigid values- based on the latent heat of the average water temperature. In most cases, these approaches do not give a comprehensive performance details of the solar still. Thus, we suggest two considerations for effective performance evaluation of the inclined solar still. The first consideration is theoretical estimation of the condensate loss due to the condensate collection channel slope, while the other is the use of a transient method to evaluate the solar-to-vapor conversion efficiency. We demonstrated, geometrically, that the condensate loss on the inclined solar still can be significant—hence the need to consider it alongside the overall yield. We formulated a model to estimate the condensate loss and validated the model by comparing an estimated condensate loss with experimental loss. Similarly, we demonstrated a transient approach to evaluate the solar-to-vapor conversion efficiency by using the latent heat of the hourly water temperature. Accordingly, the optimum hourly efficiency of the investigated solar still prototype was 161.4%, with a daily average of 113.4% versus 108.4% from the conventional method. Overall, no study on the solar still had previously accounted for condensate losses by any means whatsoever, making our current study a reference and a pioneer in this concept and suggesting an advancement in the approach to report the performance productivity of the solar still.•Condensate loss on the inclined solar still due condensate collection channel slope was estimated geometrically and demonstrated to be significant.•Solar-to-vapor conversion efficiency was evaluated using an hourly transient approach.

Real-Time Rainfall Nowcast Model by Combining CAPE and GNSS Observations

Precipitable water vapor (PWV), derived from the Global Navigation Satellite System (GNSS), has contributed significantly to rainfall forecasting. However, another key parameter, convective available potential energy (CAPE), is strongly correlated with increases in extreme rainfall under the background of global warming but has rarely been investigated for rainfall forecasting. Therefore, a real-time rainfall nowcast (RRN) model that combines CAPE and PWV is proposed in this study. In addition, seasonal factors and the time autocorrelation of the predictors were considered. Here, the previous hourly PWV, CAPE, temperature, and rainfall were used to establish the RRN model and simulate/nowcast the next hourly rainfall based on the support vector regression, which was performed in a time span of five years at 23 GNSS stations in Taiwan Province under four designed schemes to validate the performance of the proposed RRN model. The average root mean square (RMS) and correlation coefficients of the proposed RRN model reached 0.34 mm/h and 0.96, respectively. Additionally, the linear relationships between the daily CAPE/PWV and extreme rainfall were investigated, revealing that PWV may contribute more to trigger extreme rainfall than CAPE. Finally, compared to existing quantitative rainfall forecast studies, the RRN model achieved significantly improved rainfall nowcast accuracy and thus has more potential applications in rainfall forecasting.

Short-term load forecasting considering improved cumulative effect of hourly temperature

As the proportion of air-conditioning load in the total power load increases year by year, meteorological factors such as temperature and humidity have more significant influences on power load fluctuations. Power load fluctuations often lag behind changes in meteorological factors, which increases the difficulty of short-term load forecasting. This paper proposes an equivalent temperature model based on the heat island effect, the temperature and humidity effect, and the improved hourly air temperature cumulative effect. This paper improves the population initial value generation method of the traditional genetic algorithm to improve the algorithm's efficiency in solving the optimal temperature change parameter of the hourly temperature's cumulative effect. The equivalent temperature is then used as the input feature of the prediction model, and the Elman neural network is used for short-term load prediction. Finally, the PJM hourly load data and weather station data in the United States Washington area are used for example analysis. The results show that the improved genetic algorithm can effectively solve the optimal temperature change correction parameters, and the generated equivalent temperature has a higher correlation with the power load; compared with the prediction model considering the actual temperature, the model proposed has a higher prediction accuracy.

Comparison of Deep Learning Architectures for Short-Term Electrical Load Forecasting Based on Multi-Modal Data

Short-term load forecasting is a topic of considerable interest as it is of major importance for specifying and managing power resources and needs. In the literature, Deep Neural Networks have been successfully recently applied in load forecasting using single modalities as an improvement to traditional Artificial Neural Networks (ANN). In this paper, the main objective is to tackle the load forecasting problem with the intention of enhancing the prediction performance by combining and testing different multi-modal deep learning approaches and architectures in order to process and relate information from multiple modalities. The benefits of using multi-modality instead of one modality, applied to the time series modeling problem of short-term load forecasting is therefore investigated. The models are trained and evaluated using the hourly temperature and electrical consumption in addition to auto-regressive variables as the first modality. The day type characteristic, such as: weekends, week days, bank holidays, religious holidays etc., may be considered as a second modality. The approach liability is tested by comparing the empirical results of different Deep architectures namely: Stacked Denoising Auto- Encoders (SDAEs) and Convolutional Neural Network (CNN), with multiple and single modalities where processing the same task of one day ahead load forecasting.

Comparison of Ventilated and Unventilated Air Temperature Measurements in Inland Dronning Maud Land on the East Antarctic Plateau

Abstract Surface temperature measurements with naturally ventilated (NV) sensors over the Antarctic Plateau are largely subject to systematic errors caused by solar radiative heating. Here we examined the radiative heating error in Dronning Maud Land on the East Antarctic Plateau using both the newly installed automatic weather stations (AWSs) at NDF and Relay Station and the existing AWSs at Relay Station and Dome Fuji. Two types of NV shields were used in these AWSs: a multiplate radiation shield and a simple cylinder-shaped shield. In austral summer, the temperature bias between the force-ventilated (FV) sensor and the NV sensor never reached zero because of continuous sunlight. The hourly mean temperature errors reached up to 8°C at noon on a sunny day with weak wind conditions. The errors increased linearly with increasing reflected shortwave radiation and decreased nonlinearly with increasing wind speed. These features were observed in both the multiplate and the cylinder-shaped shields. The magnitude of the errors of the multiplate shield was much larger than that of the cylinder-shaped shield. To quantify the radiative errors, we applied an existing correction model based on the regression approach and successfully reduced the errors by more than 70% after the correction. This indicates that we can use the corrected temperature data instead of quality controlled data, which removed warm bias during weak winds in inland Dronning Maud Land.

Effects of ensiling length and storage temperature on the nutritive value and fibre-bound protein of three tropical legumes ensiled alone or combined with sorghum

Changes in the nutritive value of forages are imminent under different ensiling conditions. Thus, a study was conducted to assess the effects of ensiling length and storage temperature on the nutritive value, fermentation characteristics and fibre-bound protein of three tropical forage legumes, sorghum and mixtures of sorghum and the legumes. Soybean (Glycine max), jack bean (Cannavalia ensiformis), lablab (Lablab purpureus) and sorghum (Sorghum bicolor) were solely grown and harvested, and the legumes were wilted before ensiling. Mixtures of sorghum and each legume were handmade on a percentage fresh weight basis of 60:40. Each forage and mixtures (400 g) were ensiled in polythene vacuum bags with homofermentative lactic acid bacteria inoculation for 30, 75 and 180 days. A set of mini silos were stored indoors, and another batch was stored outdoors. HOBO Pro v2 data loggers were deployed to monitor the ambient temperature of the storage locations during the entire ensiling period (from day 0–180). Measurements included nutrient analysis, fermentation quality and fibre bound protein characteristics. The hourly ambient temperature for outdoor and indoor storage ranged from 16° to 61°C vs 18–35 °C, respectively. Proximate constituents of all silages were influenced by ensiling length. Significant changes were primarily detected in fermentation products of legume silages between 30 and 75 d of ensiling. There were reduced fermentation products for silages stored outdoors. The ensiling length influenced proportions of neutral detergent insoluble nitrogen (NDIN) and acid detergent insoluble nitrogen (ADIN) with outdoor silages resulting in a higher proportion of NDIN and ADIN compared to indoor silages. Overall, a short period of ensiling preserves the nutritional quality of ensiled forages compared to prolonged storage at high ambient temperatures typical of the tropics that increase nutrient losses. Thus, changes in the nutritional composition of forages during ensiling should be considered during ration formulations.

Energy-efficient design protocol for sustainable pitched roof modification from flat roof

Modification of a pitched roof from a flat roof transforms the flat roofs of multi-storey buildings into pitched roofs to improve the building function and landscape effect, provided that the building structures and the foundation bearing capacity meet the requirements; it normally improves the thermal insulation performance of the top layer and solves problems such as leakage of the top layer. This study developed an optimal efficient design protocol for pitched roof modification from a flat roof through comparative analysis. The case study was conducted in an existing building in Hangzhou city, China. In the process of energy consumption simulation on pitched roof modification from a flat roof, control variables were used to keep the building materials, building environments and meteorological conditions consistent. The indoor environment of the building top floor with different gradient settings was simulated in groups, and the hourly temperature, monthly heat gains and losses, and energy consumption of each model were analysed. The results showed a consistent trend in building energy performance of the different models. With an increase in the slope gradient, the building energy performance is higher. This study provides an important reference for modification of pitched roofs from flat roofs in future in terms of building energy efficiency.

Influence of heating degree day calculation methods in designing the thermal envelope of buildings

Heating degree day is an essential parameter for estimation of energy demand and consumption in buildings. In the case of hourly temperature data availability, it is more appropriate to use the hourly method to calculate heating degree days. In the situation of reduced meteorological data, other methodologies must be selected for calculation. These methodologies include ASHRAE and UKMO methods. The aim of the present study is to analyse the effect of substitution in the heating degree day calculation method to modify building recommendations of current Chilean thermal building codes, maintaining the transmission heat losses through thermal envelope of buildings, in various geographical locations, with different climatic conditions. Analysing the data obtained from 165 meteorological stations, it has been discovered that there are significant differences in the geographical distribution of heating degree days calculated with ASHRAE and UKMO methods in desert areas, while heating degree days are similar in regions with a more temperate climate with both methods. When there is a significant frequency of days with large daily temperature amplitude, it is more advisable to use the UKMO method for the calculation of heating degree days. For all these reasons, new values of thermal transmittances have been proposed for the opaque elements of the thermal envelopes with the developed methodology. A reduction by up 65% of the values of thermal transmittances are required using UKMO method in desert areas and no variations are required in cold zones. The new values of thermal transmittances proposed will help to improve the energy performance of buildings and to achieve a better adaptation to the climatic conditions.

Hourly temperature variability and mortality in 31 major Chinese cities: Effect modification by individual characteristics, season and temperature zone

In the context of ongoing climate change, temperature variability (TV) has been considered as an important trigger of death. However, evidence of association between mortality and hourly temperature variability (HTV) is scarce at the multi-city level, and the time window of health effects of HTV is lack of investigation. This study aims at quantifying the mortality risk and burden of HTV and exploring subpopulations susceptible to HTV from a large-scale multi-city perspective. Data on daily number of deaths and meteorology were collected for 31 Chinese major cities during 2007-2013. HTV was calculated as the standard deviation of hourly temperature within a few days. The optimal exposure period of HTV was chosen according to multiple scientific criteria. A quasi-Poisson regression combined with distributed lag nonlinear model was used to assess the city-specific HTV-mortality associations. Then, meta-analysis was further applied to pool city-specific effect estimates. Finally, we calculated the fraction of mortality attributable to HTV. Stratification analyses were conducted by individual characteristics (i.e. age, sex, and educational attainment), season, and region. HTV calculated in a relatively long-time window like 18 d (HTV0-17) could capture the impact of HTV adequately. Per 1°C raise of HTV0-17 associated with 1.38% (95%CI: 0.77, 1.99) increase of non-accidental mortality. During the study period, 5.47% (95%CI: 1.06, 9.64) of non-accidental mortality could be attributed to HTV. The females, the elderly, and individuals with low education level were more susceptible to HTV than their counterparts, respectively. Moreover, a stronger HTV-mortality association was observed in individuals who live in warmer season and temperature zone. HTV is associated with a considerable mortality burden, which may be modified by season, geographic and individual-level factors. Our findings highlight the practical importance of establishing early warning systems and promoting health education to mitigate the impacts of temperature variability.