Hourly Air Temperature Research Articles

Analysis of Diurnal Air Temperature Trends and Pattern Similarities in Highland and Lowland Stations of Italy and UK

ABSTRACTIn this study, an analysis of hourly air temperatures in four groups of 32 stations from the UK highland (5 stations), UK lowland (4 stations), Italian highland (11 stations), and Italian lowland (12 stations) at different altitudes was carried out over the period from 2002 to 2021. The study aimed to examine the trends of each hour of the day during this period, over different averaging time windows (10‐day, 30‐day, and 60‐day). The trends were computed using the Mann–Kendall trend test and Sen's slope estimator. The similarity of trends within and across the groups of stations was assessed using the hierarchical clustering with dynamic time warping technique. An additional analysis was conducted to show the correlation of trends among the group of stations using the correlation distance matrix. Hierarchical clustering and distance correlation analysis show trend similarities and correlations, also indicating dissimilarities among different groups. Using 30‐day averages, significant warming trends in specific months at the Italian stations are evident, especially in February, July, August, and December. The UK highland stations did not show statistically significant trends, but clear pattern similarities were found within the groups, especially in certain months. The ultimate goal of this article is to provide insights into temperature dynamics and climate change characteristics on regional and diurnal scales.

Predictive Modeling of Hourly Air Temperature Based on Atmospheric Conditions of Karak in Jordan

Predictive Modeling of Hourly Air Temperature Based on Atmospheric Conditions of Karak in Jordan

Models for Estimating the Cold Hardiness of Sweet Cherry (Prunus avium cv. Sweetheart and Lapins) in Cold Climate Regions

Plant cold hardiness is a dynamic process, and seasonal changes occur through cold acclimation and deacclimation to help prevent lethal injury from the cold. Cold weather injury resulting from inadequate plant cold hardiness can result in significant economic losses to growers of perennial crops in temperate climates. The objective of the current study was to develop models that estimate the lethal temperature that causes 10%, 50%, and 90% mortality (LT10, LT50, LT90) to two cultivars of sweet cherry (Prunus avium) flower buds from the early fall through to spring. We parameterized regression models using lethal temperature data collected in the Okanagan Valley, British Columbia, Canada, over six seasons (2013–17, 2019–20, 2021–22) for ‘Sweetheart’ sweet cherry and three seasons (2013–15, 2016–17) for ‘Lapins’ sweet cherry. These models incorporate parameters that are based on equations that describe chill and heat accumulation that rely on measures of hourly air temperature. Model evaluation and validation using several seasons of lethal temperature data not included in model development were completed. Models for estimating the cold hardiness of sweet cherry showed good agreement between model lethal temperature predictions and observed values for both sweet cherry cultivars. In addition, an open-access, interactive, web-based application was developed to access the outputs of these models in real time for use by growers, researchers, and extension workers. These current models of sweet cherry cold hardiness have potential application for use as a decision support tool for cold damage management as well as crop site suitability modeling.

Identifying appropriate prediction models for estimating hourly temperature over diverse agro-ecological regions of India

The present study tests the accuracy of four models in estimating the hourly air temperatures in different agroecological regions of the country during two major crop seasons, kharif and rabi, by taking daily maximum and minimum temperatures as input. These methods that are being used in different crop growth simulation models were selected from the literature. To adjust the biases of estimated hourly temperature, three bias correction methods (Linear regression, Linear scaling and Quantile mapping) were used. When compared with the observed data, the estimated hourly temperature, after bias correction, is reasonably close to the observed during both kharif and rabi seasons. The bias-corrected Soygro model exhibited its good performance at 14 locations, followed by the WAVE model and Temperature models at 8 and 6 locations, respectively during the kharif season. In the case of rabi season, the bias-corrected Temperature model appears to be accurate at more locations (21), followed by WAVE and Soygro models at 4 and 2 locations, respectively. The pooled data analysis showed the least error between estimated (uncorrected and bias-corrected) and observed hourly temperature from 04 to 08 h during kharif season while it was 03 to 08 h during the rabi season. The results of the present study indicated that Soygro and Temperature models estimated hourly temperature with better accuracy at a majority of the locations situated in the agroecological regions representing different climates and soil types. Though the WAVE model worked well at some of the locations, estimation by the PL model was not up to the mark in both kharif and rabi seasons. Hence, Soygro and Temperature models can be used to estimate hourly temperature data during both kharif and rabi seasons, after the bias correction by the Linear Regression method. We believe that the application of the study would facilitate the usage of hourly temperature data instead of daily data which in turn improves the precision in predicting phenological events and bud dormancy breaks, chilling hour requirement etc.

Hourly forecasting of the photovoltaic electricity at any latitude using a network of artificial neural networks

Nowadays, special attention is paid to the importance of using photovoltaic (PV) systems to tackle the problem of climate change and the energy crisis. Artificial intelligence is currently used in different science fields for its great potential and accuracy in forecasting problems.In this work, a network of artificial neural networks (ANNs) was trained and validated to forecast the hourly worldwide electrical power produced by various PV modules, with different electrical characteristics. Each ANN describes the worldwide performance of each PV module on the optimal inclination angle. The training data consists of the hourly air temperature, horizontal total solar radiation as input data and electrical power produced as output. The power is obtained from the hourly simulation of PV modules with an electrical circuit model in 24 localities at very different latitudes.The validation and generalization of the network were obtained by considering the six PV modules in further 24 localities and by considering two further PV modules in all 48 localities considered.The excellent results in terms of accuracy metrics confirmed that the network of ANNs is a reliable, simple and accurate tool that can be used to predict the hourly performance of any PV module in any location worldwide.

Impact of refined 2D/3D urban morphology on hourly air temperature across different spatial scales in a snow climate city

Urban morphology profoundly affects the air temperature, and a better understanding of their relationships is crucial for improving outdoor comfort and urban planning. However, how their complicated associations change across different times and spaces is not clear. This study compared the influence of the refined urban 2D/3D morphology on hourly air temperature in a snow climate city by using high spatial resolution GF-2 images, correlation analysis and multiple regression models at different spatial scales. Results indicated that: (1) the air temperature differences among the sites were much larger during nighttime (4.14 °C) as compared to daytime (2.22 °C). The spatial pattern of the hourly air temperature had similar distributions except at the hottest time of day. (2) The relationships between air temperature and urban morphology had an obvious spatial-temporal dependency. Overall, their connections became stronger as the spatial scale increased and tended to become stale at the 500 m scale; the effect of urban characteristics on average nighttime, lowest, and daily air temperature was larger than daytime air temperature, and there was almost no correlation between the highest air temperature and urban morphology. (3) The effects of 2D and 3D urban morphology on air temperature vary depending on the selection of spatial scales and the choice of different indicator of air temperature. Typically, the building height and volume had the strongest warming effect. The 3D metrics performed better in predicting air temperature than 2D metrics at the 500 m scale, while the influence of 2D features was stronger at the 1000 m scale. Regardless of the spatial analysis scale, 3D features had stronger correlations with average daily, daytime, and nighttime air temperature. Based on our results, 3D urban morphology indications, such as building height and volume, should be emphasized at the 500 m scale for urban planning. Arranging buildings and vegetation in a reasonable way could generate a better thermal comfort environment for city dwellers.

Integrating Copernicus land cover data into the i-Tree Cool Air model to evaluate and map urban heat mitigation by tree cover

ABSTRACT Cities host more than half of the world’s population and due to global warming and land use change their vulnerability to deadly heat waves has increased. A healthy vegetated landscape can abate heat wave severity and diminish the related urban heat island through the process of evapotranspiration. This research aimed to develop a methodology for cities to use publicly available Copernicus land cover maps within the i-Tree Cool Air water and energy balance model to map air temperature and humidity. The manuscript presents proof of concept using Naples, Italy with its Mediterranean climate characterized by limited soil water for cooling via evapotranspiration. The approach achieved strong correlations between predicted and observed air temperatures across the city (r ≥ 0.89). During the warm season of 2020, forested land cover was 5°C cooler than land cover dominated by impervious cover. Simulated land cover change, limited to a 10% increase or decrease in tree cover, generated an inverse change of 0.2°C in maximum hourly air temperature, with more trees obtaining cooler air. Soil water limited the cooling, with the generally wetter spring season enabling greater cooling of air temperatures, and summer droughts without irrigation had constrained cooling. Sustainable urban design will likely require an increase in plant cover along with a reduction of impervious surfaces that absorb and reradiate heat in order to improve community resilience to heat waves.

Prediction of hourly air temperature based on CNN–LSTM

The prediction accuracy of hourly air temperature is generally poor because of random changes, long time series, and the nonlinear relationship between temperature and other meteorological elements, such as air pressure, dew point, and wind speed. In this study, two deep-learning methods—a convolutional neural network (CNN) and long short-term memory (LSTM)—are integrated into a network model (CNN–LSTM) for hourly temperature prediction. The CNN reduces the dimensionality of the time-series data, while LSTM captures the long-term memory of the massive temperature time-series data. Training and validation sets are constructed using 60,133 hourly meteorological data (air temperature, dew point, air pressure, wind direction, wind speed, and cloud amount) obtained from January 2000 to October 2020 at the Yinchuan meteorological station in China. Mean absolute error (MAE), mean absolute percentage error (MAPE), and goodness of fit are used to compare the performances of the CNN, LSTM, and CNN–LSTM models. The results show that MAE, MAPE, RMSE, and PBIAS from the CNN–LSTM model for hourly temperature prediction are 0.82, 0.63, 2.05, and 2.18 in the training stage and 1.02, 0.8, 1.97, and −0.08 in the testing stage. Average goodness of fit from the CNN–LSTM model is 0.7258, higher than the CNN (0.5291), and LSTM (0.5949) models. The hourly temperatures predicted by the CNN–LSTM model are highly consistent with the measured values, especially for long time series of hourly temperature data.

Seasonal Aspects of Radiative and Advective Air Temperature Populations: A Canadian Perspective

Canadian high-frequency temperature time series exhibit physical heterogeneity in the coexistence of radiative and advective populations in the total air temperature sample. This work examines forty-five Canadian hourly air temperature records to study seasonal characteristics and variability of radiative and advective population counts and their corresponding temperature biases and trends. The Linear Pattern Discrimination algorithm, conceptualized in a previous study, was adjusted to seasonal analysis on the equinox-to-equinox time scale. Count analysis of radiative and advective days supports the existence of two distinct thermal regimes, Spring–Summer and Fall–Winter. Further, seasonal advective counts for the majority of examined stations typically decrease in numbers. The consistently warmer winter radiative temperature extrema points to the critical role of the advective population in control of the overall temperature magnitude. Canadian northwest warming trends are found to be the highest, indicating the amplifying effect of decreasing advective counts with rapidly increasing temperatures that weaken the advective population’s moderating ability to control the magnitude of the total temperature population.

Integrating weather observations and local-climate-zone-based landscape patterns for regional hourly air temperature mapping using machine learning

Air temperature is a crucial variable of urban meteorology and is essential to many urban environments, urban climate and climate-change-related studies. However, due to the limited observational records of air temperature and the complex urban morphology and environment, it might not be easy to map the hourly air temperature with a fine resolution at the surface level within and around cities via conventional methods. Thus, this study employed machine learning (ML) algorithms and meteorological and landscape data to develop hourly air temperature mapping techniques and methods at the 1-km resolution over a multi-year warm seasons period. Guangdong Province, China was selected for the case study. Random forest algorithm was employed for the hourly air temperature mapping. The validation results showed that the hourly air temperature maps exhibit good accuracy from 2008 to 2019, with mean R2, root mean square error (RMSE) and mean absolute error (MAE) values of 0.8001, 1.4821 °C and 1.0872 °C, respectively. The importance assessment of the driving factors showed that meteorological factors, especially relative humidity, contributed the most to the air temperature mapping. Simultaneously, landscape factors also played a non-negligible role. Further analysis revealed that the maps steadily maintained high accuracy at nighttime (20:00–7:00), which is essential for investigating nighttime urban climate conditions, especially the urban heat island effect. Moreover, a correlation existed between the nighttime air temperature changes and urban morphology represented by the local climate zones. Air temperatures tended to fall more slowly in the core of metropolitan areas than in the urban fringe. Using ML, this study reliably improves the spatial refinement of hourly air temperature mapping and reveals the spatially explicit air temperature patterns in and around cities at different times in a day during the warm seasons. Moreover, it provides a novel valuable and reliable dataset for air-temperature-related implementation and studies.

Impact of Air Density Variation on a Simulated Earth-to-Air Heat Exchanger’s Performance

Due to their simple design and reliable operation, earth-to-air heat exchangers (EAHE) are used in modern buildings to reduce ventilation heat losses. EAHE operation in atmospheric conditions results in variation in ambient air temperature and pressure affecting air density. The paper presents the study on the impact of ambient air density variation on the calculated hourly air temperature at the EAHE outlet and the resulting energy use for space heating and cooling of an exemplary residential building. The ground temperature was computed from the model given in EN 16798-5-1. Then, air density was obtained using five various methods. Energy use for space heating and cooling of the building was computed using the 5R1C thermal network model of EN ISO 13790. Depending on the chosen method and concerning the base case without EAHE, a reduction in annual heating and cooling needs was obtained from 7.5% to 8.8% in heating and from 15.3% to 19% in cooling. Annual heating and cooling gain from EAHE were 600.9 kWh and 628.3 kWh for heating and 616.9 kWh and 603.5 kWh for cooling for the Typical Meteorological Years (TMY) and International Weather for Energy Calculation (IWEC) files, respectively. Unit heating and cooling gains per heat exchanger area were from 34.9 kWh/m2 to 36.8 kWh/m2 and from −35.1 kWh/m2 to −36.3 kWh/m2. Density variation with temperature from the relevant typical Polish meteorological year at constant pressure, in comparison to the method of EN 16798-5-1, resulted in an hourly difference of that unit gain up to 4.3 W/m2 and 2.0 W/m2 for heating and cooling, respectively. The same was true inthe case of IWEC files that resulted in differences of 5.5 W/m2 and 1.1 W/m2.

Extreme Air Temperatures at the Southwestern Slope of Pirin Mountain (Bulgaria) and Related Synoptic Conditions


 
 
 The article examines the occurrence of extremely cold and extremely warm days as an indicator of environmental changes in the Pirin Mountain (Bulgaria). The study is based on hourly air temperature data recorded by an automatic weather station. By averaging the hourly data, daily values are calculated. The occurrence of extremely cold and extremely warm days during the period 2015–2018 for January, April, July and October are analyzed. Extreme temperature days are determined by threshold values for the 10th and 90th percentiles. Characteristic synoptic situations related to the manifestation of extreme temperatures are presented. As a result of the research it was established that the advections of air masses are the leading factor for the occurrence of extreme temperatures. In addition very important for the extremely low temperatures is the high air pressure over the Balkan Peninsula.
 
 

Downscaling Hourly Air Temperature of WRF Simulations Over Complex Topography: A Case Study of Chongli District in Hebei Province, China

AbstractAccurate and high‐resolution air temperature prediction is important in many different applications. Hourly air temperature forecasting in mountainous areas is necessary and important because mountainous areas are becoming increasingly important areas of human activities. At present, scientists successfully employ numerical weather prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, to achieve reliable forecasts. However, air temperature forecasting and modeling over complex geographical zones are still difficult tasks. The WRF model is a mesoscale model and does not adequately account for the influence of terrain on the air temperature. It is important to downscale larger‐scale models to a much finer scale. In this paper, a statistical temperature downscaling method based on geographically weighted regression (GWR) and diurnal temperature cycle (DTC) models is proposed. A statistical downscaling scheme of WRF simulation data is designed to forecast the hourly air temperature from 1‐km spatial resolution to 30 m, up to 24 hr in advance. The combined downscaling model's root‐mean‐square error (RMSE) decreased by 0.87°C at the automatic weather station (AWS) level and 0.62°C over the domain when compared to WRF simulations, and the mean absolute error (MAE) decreased by 0.71°C and 0.51°C, respectively, at these two levels. The results reveal that the combined downscaling model performs very well in correcting and downscaling the air temperature in WRF simulations in the study areas.

Impact of Refined 2d/3d Urban Morphology on Hourly Air Temperature Across Different Spatial Scales Over Summer and Winter in a Snow Climate City

Impact of Refined 2d/3d Urban Morphology on Hourly Air Temperature Across Different Spatial Scales Over Summer and Winter in a Snow Climate City

Impact of Refined 2d/3d Urban Morphology on Hourly Air Temperature Across Different Spatial Scales in a Snow Climate City

Impact of Refined 2d/3d Urban Morphology on Hourly Air Temperature Across Different Spatial Scales in a Snow Climate City

Determination of Step Check Quality Control Thresholds on Air Temperature Data at South Tangerang Climatological Station

All data, including air temperature data, must be verified by conducting quality control using the step check method. Step check quality control is carried out by looking at the difference of a parameter in a certain period compared to the threshold value that was already determined. Therefore before carrying out step check quality control, it is necessary to determine the ceiling and floor boundaries of the difference in air temperature data every hour. The data used in this study are hourly air temperature data and hourly present weather data from weather observations at the South Tangerang Climatological Station during 2016 - 2020. In determining the threshold for air temperature step check quality control, the air temperature data is paired with weather condition data to obtain a threshold value according to rain and no rain conditions. The threshold conducted in this study is based on a check for unusual climatological values, where the limits for an unusual and impossible jump in hourly air temperature changes are determined based on a certain percentage of the data distribution. This study uses percentile analysis to determine the threshold, where 5% in the lower and upper part of the data distribution are used as the threshold. The results show various thresholds every hour. The increase in temperature dominates the changes of hourly air temperature in no-rain conditions. The highest threshold for temperature increase occurs at 00.00 – 01.00 UTC at 3.2°C and continues to decrease over time. The highest threshold for temperature decrease occurs at 09.00 UTC - 10.00 UTC at 2.2°C. In rain conditions, the increase in temperature can still occur. However, the decrease in temperature mainly occurs. The highest threshold for temperature increase during rainy conditions is 1.8°C at 01.00 - 02.00 UTC, while the highest threshold for the temperature decrease is 5.8°C at 06.00 UTC – 07.00 UTC. With these results, observers can first carry out quality control with the Step Check method before filling in the data into the system database. Thus, any suspect data either from reading errors or tool errors can be minimized and finally produce a valid dataset.

An Investigation of NEXRAD-Based Quantitative Precipitation Estimates in Alaska

Precipitation estimation by weather radars in Alaska is challenging. In this study, we investigate National Weather Service (NWS) precipitation products that are produced from the seven NEXRAD radar sites in Alaska. The NWS precipitation processing subsystem generates stages of data at each NEXRAD site which are then input to the weather forecast office to generate a regionwide precipitation product. Data from the NEXRAD sites and the operational rain gauges in the weather forecast region are used to produce this regionwide product that is then sent to the National Centers for Environmental Prediction (NCEP) to be included in the NCEP Stage IV distribution. The NCEP Stage IV product for Alaska has been available since 2017. We use the United States Climate Reference Network (USCRN) data from Alaska to compare to the NCEP Stage IV data. Given that the USCRN can be used in the production of the NCEP Stage IV data for Alaska, we also used the NEXRAD Digital Precipitation Array (DPA) that is generated at the site for comparison of the radar-only products. Comparing the NEXRAD-based data from Alaska to the USCRN gauge estimates using the USCRN site information on air temperature, we are able to condition the analysis based on the hourly or 6-hourly average air temperature. The estimates in the frozen phase of precipitation largely underestimate as compared to the gauge, and the correlation is low with larger errors as compared to other phases of precipitation. In the mixed phase the underestimation of precipitation improves, but the correlation is still low with relatively large errors as compared to the rain phases of precipitation. The difficulties in precipitation estimation in cold temperatures are well known and we show the evaluation for the NCEP Stage IV regional data for Alaska and the NEXRAD site specific Digital Precipitation Array (DPA) data. Results show the challenges of estimating mixed-phase and frozen precipitation. However, the DPA data shows somewhat better performance in the mixed precipitation phase, which suggests that the NWS Precipitation Processing Subsystem (PPS) is tuned to the climatology as it relates to precipitation in Alaska.

Artificial Neural Network (ANN) based Soil Temperature model of Highly Plastic Clay

ABSTRACT Artificial neural networks (ANNs) are one of the popular methods of artificial intelligence that seek to follow the human mind function and nervous system with its successful application increased in many areas of engineering. The current study is focused to develop an ANN-based predictive model of the soil temperature of Yazoo clay using based on field instrumentation data. To provide an acceptable dataset for developing the predictive model, the investigation was carried out in six instrumented slopes within the 25 miles (40.2 km) radius from metropolitan Jackson in Mississippi. The six selected slopes were instrumented with soil moisture sensors, automated rain gauge, air, and soil temperature sensors starting from mid-August 2018. Volumetric moisture content, precipitation, air, and soil temperature values at 1.5 m (5 ft) depth at the crest of the six slopes were collected using automated data loggers and observed for more than seventeen months. The established database consisting of 13650 datasets was implemented for ANN intelligent system and multiple-degree Fourier series non-linear regression technique for predicting the hourly soil temperature. The total hourly natural rainfall and time, average previous soil temperature, and average hourly air temperature were set to be the inputs of the model and the hourly soil temperature was set to be the output of the model. These datasets were used as the training data and validated with each target slope. Sensitivity analysis was also conducted, and the most influential input parameters on the data output were determined. In this study, the change of soil temperature with atmospheric temperature was investigated, and a predictor model was developed by adopting the Levenberg-Marquardt (LM) algorithm method and the Tan-sigmoid transfer function. The developed ANNs model showed an excellent fit with the observed field values.

Feasibility analysis of Earth-Air Heat Exchanger (EAHE) in a sports and culture center in Tianjin, China

This thesis calculated and analyzed the feasibility of EAHE in a building in Tianjin, China through theoretical calculation and numerical simulation. According to the theoretical model, the thermal performance of EAHE were obtained by calculating the soil temperature, the calculated temperature of the soil in intermittent operation, the heat release coefficient of tunnel wall and the heat transfer coefficient during intermittent operation. CFD software was used to model summer operating conditions, the hourly air temperature in July in Tianjin was taken as the air temperature of tunnel inlet, simulated the intermittent operation of EAHE for one week, recorded the average temperature of the soil on the tunnel walls and outlet temperature of the working hours of EAHE, data fitting was carried out for the cooling amplitude in the working period, the numerical simulation results accord with the theoretical calculation values. The results of the study indicate that the higher the inlet air temperature, the greater the temperature drop; the soil around the tunnel walls has the characteristic of reducing temperature; considering the modification of the fan temperature rise, COP in winter is greater than that in summer. The energy-saving effect is more significant in winter.

Improvement of community monitoring network data for urban heat island investigation in Hong Kong

The Community Weather Information Network (Co-WIN) was established by the Hong Kong Observatory (HKO) and Hong Kong Polytechnic University (PolyU) in 2007 for promoting weather and climate education. Under Co-WIN, participating members (mainly primary and secondary schools) operate automatic weather stations (AWS) onsite to collect weather data and transmit them to a central database for assimilation and archiving. These Co-WIN AWS are typically located on the rooftops of schools in densely populated areas surrounded by buildings, representing the living environment of the nearby inhabitants. With more than 100 AWSs geographically covering every district of Hong Kong (50 stations operated for 5 years or longer), Co-WIN offers a large set of weather data which are potentially useful in urban climate research. In this study, a Quality Control (QC) procedure tailored made for community network was developed to eliminate the potential errors associated with the power outage, loss in connection and instrumental malfunctions. In the process, the hourly air temperature data extracted from the Co-WIN database was screened with reference to the historical monthly extreme values and the hourly temperature data from nearby HKO stations. Regression analyses were then performed to assess the effects of different elements of urban development (e.g. building density, street configuration, amount of greening and water bodies) on the local temperature. At last, the data was applied to an urban climate study to investigate the relationship between the urban heat island (UHI) effect and the characteristics of the urban environment in Hong Kong. The results demonstrate the community network data can be a useful resource for supplementing the official HKO data for urban climatological study.