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For microgrids to operate optimally and minimize the effects of uncertainty, anticipating solar PV measurements is essential. For residential and commercial microgrids that use solar PV, the predicting of solar energy over a short period is crucial for managing grid-connected PV effectively. Therefore, this work develops a Recurrent Neural Network (RNN) for forecasting temperature measurements as time series records, where a combination of long short-term memory (LSTM) architecture with RNN is used to process input measurements by updating the RNN state and winding over time degrees. Data from the entire prior time steps is stored in the RNN state. A dataset of temperature waveform measurements is used, which includes 2000 unnaturally produced signals of three channels with varying length. An LSTM neural network can be used to expect future values of a time series or sequence utilizing data from earlier time steps as input. Training of a regression LSTM neural network through the output of a sequence is performed, where the goals are the training sequence with records shifting one-time step, for training the LSTM neural architecture with time series forecasting. In other words, the weights of the LSTM neural structure learn to predict the following time step values of the input sequence at every time step. By considering the past forecasts as inputs, the closed-loop prediction forecasts the next time steps of sequences. The model makes the forecast without using the true data. The cross-entropy loss serves as the loss function. It is found that the mean RMSE overall test observations were about 0.5080 which promises to make better predictions from learning the temporal context of input sequences. [ABSTRACT FROM AUTHOR]

Long time series can be potentially influenced by breakpoints. This study presents an automatic procedure to detect and homogenize breakpoints in German daily and monthly mean temperature time series. To verify breakpoints metadata information is used. The homogenization tool is evaluated with a synthetic data set with the result of smaller mean bias and RMSE than the unhomogenized data. For homogenized German temperature data in most cases, smaller and less breakpoints are detected compared to the raw data. The mean differences of trends before and after homogenization is small (0.05 K/length of each time series). The result of the homogenization is presented in three case studies. After homogenization, the trend in these cases is closer to the calculated trend for Germany calculated using a gridded data set. [ABSTRACT FROM AUTHOR]

The coexistence of water, vapor, and solid phases and the spatiotemporal changes in thermal conductivities in response to variations in water content pose a challenge to the heat‐tracing method used to calculate water flux in variably saturated zones. In this study, a new approach, which adopted finite difference numerical schemes along with the limited‐memory Broyden–Fletcher–Goldfarb–Shanno bounded (L‐BFGS‐B) parameter optimization algorithm, was developed to inversely trace vertical soil water fluxes in variably saturated zones using temperature‐depth time‐series data. The newly developed method did not utilize hydraulic parameters, and only temperature time‐series data were used at the adjacent depths as boundary conditions. The soil water, vapor flux, and spatiotemporal variations in thermal conductivity at a given location were optimized by fitting the temperature time‐series at the target depth using the L‐BFGS‐B algorithm. Further, the method was verified through laboratory experiments under different saturated conditions that were conducted in two columns with saturated and free drainage bottom boundaries, and five sets of different flow rates with a relatively stable flow condition. The soil water flux calculated using the proposed method was consistent with the observed data, thus, verifying the reliability of the approach in applications with relatively stable flow rates. Furthermore, the new method was applied to a field site to estimate the time‐varying soil water flux using the measured temperature data. The trends of the calculated soil water flux at different depths were consistent with the field hydrologic conditions and observations. Thus, this study provides a novel computationally efficient and easy‐to‐implement tool to determine soil water flux using temperature measurements. Plain Language Summary: The method for estimating water flux with temperature data is widely used in saturated zone; however, its application in the vadose zone is difficult due to the coexistence of solid, vapor, and water and the variable thermal conductivities. This study developed a new method to estimate the soil water flux in both saturated and unsaturated zones using temperature time‐series data. The newly developed method doesn't need hydraulic parameters such as permeability and only requires three measured temperature time‐series at consecutive locations along soil profile. The time‐series data at upper‐ and lower‐depths were used to define boundary conditions, and the middle‐depth time‐series data were used as the fitting target for the model. The new method could then inversely optimize thermal conductivities and soil water and vapor flux to fit the measured temperatures. This method was tested to be reliable when predicting the water flux in the laboratory column experiments under the stable flow rate, and also successfully applied to predict soil water flux that changes with time at the field site. This method provides a robust tool to estimate the soil water flux in both saturated and unsaturated zone with temperature data. Key Points: A new method to estimate water flux in variably saturated soil was developed using temperature dataA numerical solution of the L‐BFGS‐B algorithm was appliedThe method was validated by laboratory experiments under varying saturated conditions and flow rates, and successfully applied at a field site [ABSTRACT FROM AUTHOR]

Temperature data, like many other measurements in quantitative fields, are usually modelled using a normal distribution. However, some distributions can offer a better fit while avoiding underestimation of tail event probabilities. To this point, we extend Pearson's notions of skewness and kurtosis to build a powerful family of goodness-of-fit tests based on Rao's score for the exponential power distribution EPD λ (μ , σ) , including tests for normality and Laplacity when λ is set to 1 or 2. We find the asymptotic distribution of our test statistic, which is the sum of the squares of two Z-scores, under the null and under local alternatives. We also develop an innovative regression strategy to obtain Z-scores that are nearly independent and distributed as standard Gaussians, resulting in a χ 2 2 distribution valid for any sample size (up to very high precision for n ≥ 20). The case λ = 1 leads to a powerful test of fit for the Laplace( μ , σ) distribution, whose empirical power is superior to all 39 competitors in the literature, over a wide range of 400 alternatives. Theoretical proofs in this case are particularly challenging and substantial. We applied our tests to three temperature datasets. The new tests are implemented in the R package PoweR. [ABSTRACT FROM AUTHOR]

Data normalization techniques are a very important initial stage to be carried out in order to obtain a good predictive data approach. Many researchers get different prediction and error results in each use of these data normalization techniques. Thereby, in this article discusses the accuracy rate of the seven normalization techniques at the preprocessing stage in the Neural Network Backpropagation (NNBP) architecture including decimal scaling, Z-score, min-max (there are 6 types), sigmoid, tanh estimators, mean-MAD, and median-MAD. We used two data patterns: seasonal data (rainfall) and stationary data (air humidity) that taken over the past 10 years (at 10-day intervals). We use accuracy rate parameters including number of epochs, MAE, and MSE when conducting training, testing, and predictions. The results showed that the Z-score technique was very good for the normalization of rainfall data with epochs of 10, MAE of 0.051, and MSE of 0.004. In the case of air humidity data, mean-MAD and Z-score techniques can be recommended with the number of mean-MAD technique epochs of 8, MAE of 0.013, MSE of 0.0004, while the number of epochs of Z-score techniques of 7, MAE of 0.018, and MSE of 0.0006. Thus, we conclude that when other researchers predict seasonal data or stationary data can use the Z-score technique for data normalization. [ABSTRACT FROM AUTHOR]

As the field of study expands, or as the number of observations in a sample increases, data observed at discrete points is generally assumed to be sampled from an underlying real function. As the number of observation points increases, those observations are likely to be sampled from a real-valued function. In this case, the derived data will be examples of a functional structure. We analyzed the daily average temperature data at 65 discrete points in 18 cities in Turkey's Black Sea Region. Fourier basis functions were used as a basis function approach because the temperature data had a periodic structure. The data were then transformed into a continuous function using the basis function and roughness penalty approach. Functional data were obtained using the roughness penalty approach. The generalized cross-validation method was used to determine the smoothing parameter of the variable (temperature variable). Finally, smoothed functional principal components analysis was applied to the functional data. In this way, changes in temperature functions, which seem hard to tackle, were evaluated on the same graph using the mean function generated for the principal component function and using the functions and the mean function obtained using the principal component function. [ABSTRACT FROM AUTHOR]

In this research, the surface temperature of imbed parts was used as the control parameter of the ice melting device in the gate slot. In order to verify the feasibility of this study, an experimental platform was established for the Cihe control gate in Jingshi section of the South-to-North Water Diversion Middle Route Project. Taking the temperature in the cavity and the surface of the imbed parts as the control parameters of different gate ice melting devices, the collected temperature data were compared, and the change of imbed parts temperature value, thermal losses and power consumption were analyzed and discussed. The results show that the surface temperature control scheme of imbed parts can meet the needs of ice melting and 5 ℃ is the best control reference temperature. Compared with the original control method, it has obvious economy. At the same time, the temperature sensor has simple installation and precise control. The heating time and power consumption of electric tracing band are reduced. The service life is greatly extended. The results provide some reference for the improvement of gate slot ice melting device technology in the future. [ABSTRACT FROM AUTHOR]

Various environmental building applications use temperature and relative humidity data for controlling and monitoring the user environment to deliver greater comfort to occupants. We present a multi-modal sensor system for delivering temperature and relative humidity data around the user workspace, where each multi-modal sensor is integrated into a luminaire of a connected lighting system. Multiple challenges need to be addressed in such a system: the sensors located at the ceiling need to monitor the remote environment around the user workspace, and the sensor measurements are impacted by thermal variations due to luminaire dimming controls. To address these challenges, fusion methods are presented to process raw sensor measurements to estimate the temperature and relative humidity reliably around the user workspace. The proposed sensing method is evaluated in experimental office setups and shown to meet required levels of application performance. [ABSTRACT FROM AUTHOR]

Seeking to contribute with the management of water resources in the Itaipu Binacional reservoir region, this research aimed to characterize the dynamics of evapotranspiration and evaluate the performance of different mathematical methods, which determine reference evapotranspiration (ETo) from air temperature values. Fifteen municipalities were analyzed. After obtaining the data series, ETo was estimated using six methods: Camargo (C), Hargreaves-Samani (HG-S), Jensen-Haise (JH), Penman-Monteith with missing data (PMm), Thornthwaite-Camargo (TW-C) and Penman-Monteith (PM) – the latter was the reference method. The performance of the methods was analyzed by several statistical indexes. Daily ETo values ranged from 2 to 10 mm d-1, with an annual mean of 5.78 mm d-1. We also observed that it is determined predominantly by radioactive parameters, which annually represent 76% of the evapotranspiration process, while aerodynamic parameters represent 24%. Among the ETo estimation methods, HG-S and PMm were the most suitable for estimating.

Temperature measurement system malfunction and sensor failure in grain storage warehouses can lead to missing grain temperature data on some days. Missing data is not conducive to the monitoring of grain storage conditions. This paper establishes mathematical models of temporal correlation coefficients of grain temperature and storage time in different planes, and analyzes the influence of storage state change on grain temperature correlation. The historical grain situation data for about one year were selected from 27 flat grain storage warehouses distributed in the second to seventh grain storage ecological zones in China. In addition, correlation coefficients of grain temperature were then calculated on the XOY, XOZ and YOZ planes of each warehouse. During this process, the time interval included 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days, meaning that the correlation coefficients between the grain temperature on the day and the grain temperature after storage for 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days were calculated. Next, the correlation coefficients from the same time intervals and planes in each warehouse were sequentially connected to form arrays of correlation coefficients. Then, the 3σ-threshold setting methods and DBSCAN (density-based spatial clustering of applications with noise) method were used to analyze the correlation coefficients those arrays. According to the results, we set the correlation coefficient thresholds for each plane (XOY, XOZ and YOZ planes) at each time interval. The models were then established regarding the correlation coefficient thresholds and storage time intervals. Subsequently, the sum of squares for error (SSE), coefficient of determination (R2), and root mean square error (RMSE) were chosen to evaluate the models, with the results showing that the effect of the model established by the threshold set by the 3σ-setting method, with SSE, R2 and RMSE of 0.056, 0.9771 and 0.0748, respectively, was better than the model established using the DBSCAN method. Finally, the correlation coefficients of grain temperatures with empty warehouse, new grain addition, aeration and self-heating were analyzed. The results show that the four modes in a certain time interval (e.g., 30 days) does not meet the correlation coefficient threshold during normal storage. The result can provide a theoretical basis for grain storage condition detection when grain temperature data is intermittently missing.

Seeking to contribute with the management of water resources in the Itaipu Binacional reservoir region, this research aimed to characterize the dynamics of evapotranspiration and evaluate the performance of different mathematical methods, which determine reference evapotranspiration (ETo) from air temperature values. Fifteen municipalities were analyzed. After obtaining the data series, ETo was estimated using six methods: Camargo (C), Hargreaves-Samani (HG-S), Jensen-Haise (JH), Penman-Monteith with missing data (PMm), Thornthwaite-Camargo (TW-C) and Penman-Monteith (PM) - the latter was the reference method. The performance of the methods was analyzed by several statistical indexes. Daily ETo values ranged from 2 to 10 mm d-1, with an annual mean of 5.78 mm d-1. We also observed that it is determined predominantly by radioactive parameters, which annually represent 76% of the evapotranspiration process, while aerodynamic parameters represent 24%. Among the ETo estimation methods, HG-S and PMm were the most suitable for estimating. [ABSTRACT FROM AUTHOR]

Frost damage significantly reduces global wheat production. Temperature development in wheat crops is a complex and dynamic process. During frost events, a vertical temperature gradient develops from soil to canopy due to the heat loss from the soil and canopy boundary. Understanding these temperature gradients is essential for improving frost management strategies in wheat crops. We hypothesise that the relationship between the temperatures of the canopy, plant and ground can be an early indicator of frost. We collected infrared thermal (IRT) images from field-grown wheat crops and extracted the temperatures from the canopy, plant and ground layers. We analysed these temperatures and applied four machine learning (ML) models to detect coldness scales leading to frost nights with different degrees of severity. We implemented a gated recurrent unit, convolutional neural network, random forest and support vector machines to evaluate the classification. Our study shows that in these three layers, temperatures have a relationship that can be used to determine frost early. The patterns of these three temperatures on a frost night differ from a cold no-frost winter night. On a no-frost night we observed that the canopy is the coldest, plant is warm, and the soil is warmest, and these three temperatures did not converge. On the other hand, on a frost night, before the frost event, the canopy and plant temperatures converged as the cold air penetrated through the canopy. These patterns in temperature distribution were translated into an ML problem to detect frost early. We classified coldness scales based on the temperatures conducive to frost formation of a certain severity degree. Our results show that the ML models can determine the coldness scales automatically with 93%–98% accuracy across the four models. The study presents a strong foundation for the development of early frost detection systems. • We demonstrate that vertical temperature gradient is an early indicator of frost. • The gradient is obtained from infrared thermal (IRT) images of in-field wheat crops. • IRT-based Machine learning (ML) model is used for frost detection. • Results show that ML can detect suspected frost night and the severity. • Highly accurate results indicate suitability for early & automatic frost detection. [ABSTRACT FROM AUTHOR]

• Extends the streamline-based inversion algorithm to incorporate DTS data and thermal process. • Proposes a hierarchical history matching workflow combining multi-objective Genetic Algorithm (MOGA) and streamline-based inversion. • Better monitoring of the CO 2 plume propagation is achieved by integration of temperature and pressure measurements. The scientific community has become increasingly interested in geological CO 2 sequestration and CO 2 enhanced oil recovery (EOR). The tracking of the CO 2 propagation in both space and time during geologic sequestration is necessary to ensure the secure and effective handling of a site for CO 2 injection. Our objective is to develop efficient and novel models and monitoring techniques for visualizing CO 2 plumes using field measurements. As a first step, the streamline-based data integration approach is extended to include data from distributed temperature sensors (DTS). The DTS and pressure data are then jointly history matched using a hierarchical workflow combining evolutionary and streamline methods. As a final step, we will create maps that visualize CO 2 propagation during the sequestration process based on saturation and streamline maps. We validate the extended streamline-based inversion method using a synthetic model. An application of the hierarchical workflow is then made to the CO 2 geologic storage test site in Michigan, USA. Monitoring data includes bottom-hole pressure of the injection well, DTS data at the monitoring well, and distributed pressure measurements from several downhole sensors along the monitoring well. Based on the history matching results, the CO 2 movement is largely limited to the zones intended for injection, which is in agreement with an independent warmback analysis of the temperature data. The novelty of this work is the extension of the streamline-based inversion algorithm for the DTS data, its field application to the Department of Energy regional carbon sequestration project, and potential extensions to other CO 2 -EOR and/or associated geological storage projects. [ABSTRACT FROM AUTHOR]

Citizen science or community science (CS) programmes are engaging volunteers in specific stages of the scientific research, mostly data collection and processing. They are less likely to have an explicit objective to support and promote volunteers' learning. In response to that, “citizen inquiry” emphases citizens' learning and community education, by viewing CS as an opportunity to educate the general public in thinking and acting scientifically. In citizen inquiry, citizens can take part in all the stages of the scientific research, from setting up an inquiry of personal interest, to deciding on the methods of data collection, analysis, and reporting. To ensure data quality when non-professionals design their own or take part in existing investigations, we have designed a bespoke online technological solution, the nQuire platform (nquire.org.uk), with support from the Open University/BBC partnership. nQuire scaffolds the design of high quality scientific inquiries through an authoring functionality and a process of data quality review by experts. In this paper, we detail how nQuire can support data quality assurance and control. We present case studies of how data quality was managed in two projects: “Heatwaves: Are you coping?” and “Pollinator Watch.”

An update of the geothermal gradient and heat flow maps for offshore Malaysia based on oil and gas industry data is long overdue. In this article we present an update based on available data and information compiled from PETRONAS and operator archives. More than 600 new datapoints calculated from bottom-hole temperature (BHT) data from oil and gas wells were added to the compilation, along with 165 datapoints from heat flow probe measurements at the seabed in the deep-water areas off Sarawak and Sabah. The heat flow probe surveys also provided direct measurements of seabed sediment thermal conductivity. For the calculation of heat flows from the BHT-based temperature gradients, empirical relationships between sediment thermal conductivity and burial depth were derived from thermal conductivity measurements of core samples in oil/gas wells (in the Malay Basin) and from ODP and IODP drillholes (as analogues for Sarawak and Sabah basins). The results of this study further enhanced our insights into the similarities and differences between the various basins and their relationships to tectonic settings. The Malay Basin has relatively high geothermal gradients (average ~47 °C/km). Higher gradients in the basin centre are attributed to crustal thinning due to extension. The Sarawak Basin has similar above-average geothermal gradients (~45 °C/km), whereas the Baram Delta area and the Sabah Shelf have considerably lower gradients (~29 to ~34 °C/km). These differences are attributed to the underlying tectonic settings; the Sarawak Shelf, like the Malay Basin, is underlain by an extensional terrane, whereas the Sabah Basin and Baram Delta east of the West Baram Line are underlain by a former collisional margin (between Dangerous Grounds rifted terrane and Sabah). The deep-water areas off Sarawak and Sabah (North Luconia and Sabah Platform) show relatively high geothermal gradients overall, averaging 80 °C/km in North Luconia and 87 °C/km in the Sabah Platform. The higher heat flows in the deep-water areas are consistent with the region being underlain by extended continental terrane of the South China Sea margin. From the thermal conductivity models established in this study, the average heat flows are: Malay Basin (92 mW/m²), Sarawak Shelf (95 mW/m2) and Sabah Shelf (79 mW/m²). In addition, the average heat flows for the deep-water areas are as follows: Sabah deep-water fold-thrust belt (66 mW/m²), Sabah Trough (42 mW/m2), Sabah Platform (63 mW/m2) and North Luconia (60 mW/m²). [ABSTRACT FROM AUTHOR]

This paper presents our winning entry for the EVA 2019 data competition, the aim of which is to predict Red Sea surface temperature extremes over space and time. To achieve this, we used a stochastic partial differential equation (Poisson equation) based method, improved through a regularization to penalize large magnitudes of solutions. This approach is shown to be successful according to the competition's evaluation criterion, i.e. a threshold-weighted continuous ranked probability score. Our stochastic Poisson equation and its boundary conditions resolve the data's non-stationarity naturally and effectively. Meanwhile, our numerical method is computationally efficient at dealing with the data's high dimensionality, without any parameter estimation. It demonstrates the usefulness of stochastic differential equations on spatio-temporal predictions, including the extremes of the process. [ABSTRACT FROM AUTHOR]

Air temperature and light intensity in the tropical rainforest of Brunei Darussalam was recorded at a 20-min interval from 2011 to 2017. Five units of the HOBO Pendant® data loggers were attached to tree trunks within a circle of 5 m radius at 2 m above the ground. Approximately once a year, data were downloaded, and the sensors serviced. The test site is in a peat swamp forest classified as a mixed dipterocarp forest. The redundancy of the sensors allows for the assessment of the relative precision of the datasets. The root means squared error (RMSE) for the temperature is 0.37 °C, below the manufacturer's precision statement (0.53 °C). The RMSE for the light intensity data was 569.5 lux. There was no detection of systematic errors in the data. The sensors were calibrated before deployment. The data sets were captured in 2017. Ancillary data were collected in 2018, consisting of RGB orthoimagery and Light Detection And Ranging (LiDAR) data collected from an airborne platform. The data is a unique source of information on the diurnal, intraannual periodic effects, as well as random weather-related and phenology-related phenomenons. Multiannual datasets in the same ecosystem will be published in the future, allowing for a multidisciplinary analysis of the data in the context of climate change and the impact on the tropical rainforest ecosystems. The raw and auxiliary datasets are hosted in the Mendeley repository [1].

External and internal surface temperatures values of coated steel plates and the interior air temperature of a box-like model were collected throughout the time under different conditions: an artificial infrared radiation source was placed facing the external side of the plate to promote the temperature change, the energy power was variated by changing the distance from the plate and simultaneously different airflow conditions were inputted, data was collected by making experiments using the combination of distance and airflow changes. Reflectance raw data, of the materials, was also acquired by measuring the reflectance index of the samples according to ASTM E903 “Standard Test Method for Solar Absorptance, Reflectance, and Transmittance of Materials Using Integrating Spheres”. Amplified optical images and infrared images of the samples were taken (using a “Flir e4” infrared camera) while they were under the influence of the source in the three chosen distances (60 cm, 40 cm and 20 cm). Considering that researches at this particular field lack of more direct comparisons of temperature, reflectance and heat flux behaviours; between special high reflectance coatings and common ones when the substrate is steel, when the direction of airflow is changed and when the source is an infrared one; measurements intending to give faster insights and directions were done. These insights can be used by anyone who intends to develop new coating materials and compare them with already existing ones to streamline their researches in this area. Finally, the buying costs of these coatings, similar to commercially used ones, are showed.

Heating degree days (HDDs) and cooling degree days (CDDs) are widespread climatic indicators that depict the extremity and duration of ambient air temperature values. Over the years, they have been proven to be a useful tool for a reasonably accurate estimation of space heating and cooling requirements, as well as for the buildings' energy performance rating and their compliance with legislative requirements. Reliable degree-days (DDs) data are a prerequisite for using the degree-day methods, so is determining the DDs values on a local and regional level, with respect to climate differentiations. The aim of this study is the calculation of HDDs and CDDs for Cyprus at various base temperatures, as such data cannot be found for Cyprus in the literature. The temperature data used were provided by the Cyprus Department of Meteorology, and these are the average daily dry-bulb ambient air temperature values as well as the daily maximum and minimum air temperature values for the period 2006–2017. Data were recorded at five meteorological stations at Paphos, Larnaca, Athalassa, Limassol and Prodromos. Monthly and annually HDDs and CDDs were calculated, for 11 base temperatures, as well as monthly and annually average temperatures for the period 2006–2017, by utilizing the DDs calculation technique of average daily temperature as proposed by ASHRAE. Additionally, an analysis of the variability and the trends of the HDDs and CDDs at the five meteorological stations during the 12-year period were performed. Finally, conclusions were drawn concerning the development of climate over the last 12 years in Cyprus. [ABSTRACT FROM AUTHOR]

This study presents a method of anomaly detection within a gearbox by way of standardising temperature data. Assessing measured parameters in isolation is not sufficient to detect faults within a wind turbine. This technique uses temperature, rotational speed, and generator torque to detect a bearing fault within the gearbox. Standardising data allows a parameter to be analysed which also takes into consideration the operating state of the wind turbine, therefore providing a more holistic view of the health of the wind turbine and component being monitored.

The aim of this paper is to set out a strategy for improving the inference for statistical models for the distribution of annual maxima observed temperature data, with a particular focus on past and future trend estimation. The observed data are on a 25-km grid over the UK. The method involves developing a distributional linkage with models for annual maxima temperatures from an ensemble of regional and global climate numerical models. This formulation enables additional information to be incorporated through the longer records, stronger climate change signals, replications over the ensemble and spatial pooling of information over sites. We find evidence for a common trend between the observed data and the average trend over the ensemble with very limited spatial variation in the trends over the UK. The proposed model, which accounts for all the sources of uncertainty, requires a very high-dimensional parametric fit, so we develop an operational strategy based on simplifying assumptions and discuss what is required to remove these restrictions. With such simplifications, we demonstrate more than an order of magnitude reduction in the local response of extreme temperatures to global mean temperature changes. [ABSTRACT FROM AUTHOR]

According to more than 2,000 years of written records, migratory birds have adapted to the climate circulation, surface temperature and the distribution and seasonal changes of wetland ecosystem in the earth system, and have chosen the migration as a survival mode. Young migratory birds grow up in the migration path, and the fittest migratory bird population survives in the migration path of thousands of miles. At present, the rise of ecotourism has seriously threatened the migration of migratory birds. The traditional method of monitoring migratory birds' migration path generally adopts the ring-banding method. The recovery rate of this method is about 10%. In recent years, the Environmental Research Institute have studied the remote sensing tracking method of the migration path of bar-headed geese, and carried out the collection, processing and analysis of satellite tracking data, remote sensing imaging data and surface temperature data, and preliminary results are obtained. This paper provides a scientific basis for the layout and development of the rapid economic developing areas in the southwest of China, as well as the protection of bar-headed geese. It also helps to study the remote sensing tracking method of migration path of bar-headed geese under the influence of ecotourism. [ABSTRACT FROM AUTHOR]

Abstract This paper is focused on the development and verification of a heat attitude model (HAM) for satellite attitude determination. Within this context, the Sun and the Earth are considered as the main external sources of radiation that could effect the satellite surface temperature changes. Assuming that the satellite orbital position (navigational data) is known, the proposed HAM provides the satellite surface temperature with acceptable accuracy and also relates the net heat flux (NHF) of three orthogonal satellite surfaces to its attitude via the inertial to satellite transformation matrix. The proposed HAM simulation results are verified through comparison with commercial thermal analysis tools. The proposed HAM has been successfully utilized in some researches for attitude estimation, and further studies for practical implementations are still ongoing. [ABSTRACT FROM AUTHOR]

The measurement of solar radiation is difficult to carry out due to the cost and techniques involved especially in developing countries like South Africa. A variety of solar models with different weather parameters as inputs have been used to estimate the global solar radiation around the world. Since air temperature records are readily available around the world, models based on air temperature for estimating solar radiation have been widely accepted. Estimation of global solar radiation (H) from the daily range of air temperature (ΔT) offers an important alternative in the absence of the measured H or sunshine duration. In our present work, the estimation of the monthly average daily global solar radiation for Pretoria has been determined, based on two models: Hargreaves - Samani and Clemence. The performance of the two models is validated by comparing the measured and the estimated global solar radiation for the three different stations under study. The statistical comparison between the estimated and measured value leads to the conclusion that the two temperature models have potential to estimate the global solar radiation in the area under study. [ABSTRACT FROM AUTHOR]

Summary: Methodology is proposed to uncover structural breaks in functional data that is ‘fully functional’ in the sense that it does not rely on dimension reduction techniques. A thorough asymptotic theory is developed for a fully functional break detection procedure as well as for a break date estimator, assuming a fixed break size and a shrinking break size. The latter result is utilized to derive confidence intervals for the unknown break date. The main results highlight that the fully functional procedures perform best under conditions when analogous estimators based on functional principal component analysis are at their worst, namely when the feature of interest is orthogonal to the leading principal components of the data. The theoretical findings are confirmed by means of a Monte Carlo simulation study in finite samples. An application to annual temperature curves illustrates the practical relevance of the procedures proposed. [ABSTRACT FROM AUTHOR]

Measuring and analyzing internal dam temperature may provide insight into evaluating the integrity of earthen dams. Temperature in a dam, with the advent of modern distributed temperature sensing (DTS) technique, is conveniently measured. The analysis of the temperature is conducted based on a hydro-thermal coupled analysis technique. In this study, DTS-based temperature data and VS2DHI (a finite difference code for analyzing two-dimensional heat transport in porous media) were used to analyze the hydro-thermal coupled behavior in a dam. The results of this analysis show that the temperature variation in an earthen dam is closely related to seepage conditions. Additionally, a localized high-temperature (26 °C) zone found in the measured data of the dam, which raised concern to engineers on site, is explained through either hot water infiltration into the foundation layer or lower permeability of the foundation layer than the magnitude that appeared in the design document. These findings demonstrate that hydro-thermal coupled analysis has the potential for evaluating the integrity of earthen dams. [ABSTRACT FROM AUTHOR]

Till this moment, the model of interconnected power systems in the automatic generation control (AGC) loops relies only on the synchronous generating units. In today's world, a high level of penetration of renewable energy sources (RES) is integrated to the power grids. This paper presents a novel power system model, which includes both conventional generating units and RES for studying the AGC problem of such systems. The control strategy in the AGC loops is based on the proportional–integral–derivative (PID) controller, which is optimally designed by the whale optimization algorithm (WOA). It represents a great challenge to this controller to deal with the RES uncertainties. The effectiveness of the WOA‐based PID controller is compared with other computation evolutionary algorithms‐based PID controller. The system performance is evaluated under different operating conditions. For achieving a realistic study, 1) real wind speed data that extracted from Zafarana location in Egypt are used, 2) solar irradiation and temperature data that extracted from a field test are implemented, and 3) an irregular wave energy condition is applied. The validity of the control strategy is verified using the simulation results, which are carried out using MATLAB environment. [ABSTRACT FROM AUTHOR]

Solar panel photovoltaic (PV) systems are widely used in Korea to generate solar energy, which is one of the most promising renewable energy sources. With regard to solar electricity providers and a grid operator, it is critical to accurately predict solar power generation for supply–demand planning in an electrical grid, which directly affects their profit. This prediction is, however, a challenging task because solar power generation is weather dependent and uncontrollable. In this study, a daily prediction model based on the weather forecast information for solar power generation is proposed. In the case of the proposed model, the cloud and temperature data available from the weather forecast information is used to predict the amount of solar radiation as well as a loss adjustment factor to reflect the possible loss of power generation due to the degradation or failure of the PV module. Using the proposed model, solar power generation for the following day can be predicted. The proposed model is embedded into a solar PV monitoring system that is commercially used in Korea, and it is shown to perform better than the existing prediction models. [ABSTRACT FROM AUTHOR]

The dependence structure of max-stable random vectors can be characterized by their Pickands dependence function. In many applications, the extremal dependence measure varies with covariates. We develop a flexible, semi-parametric method for the estimation of non-stationary multivariate Pickands dependence functions. The proposed construction is based on an accurate max-projection allowing to pass from the multivariate to the univariate setting and to rely on the generalized additive modeling framework. In the bivariate case, the resulting estimator of the Pickands function is regularized using constrained median smoothing B-splines, and bootstrap variability bands are constructed. In higher dimensions, we tailor our approach to the estimation of the extremal coefficient. An extended simulation study suggests that our estimator performs well and is competitive with the standard estimators in the absence of covariates. We apply the new methodology to a temperature dataset in the US where the extremal dependence is linked to time and altitude. [ABSTRACT FROM AUTHOR]

Low‐voltage network is concerned on the penetration of low carbon technologies. Distribution transformer thermal capacity is one of the most concerned, due to the aged and capital‐intensive nature of the transformer fleet. Extensive studies have been done on this topic, and most of them have applied exiting thermal models such as IEC 60076 thermal model to estimate the hot‐spot temperature (HST). However, it is lack of consideration on how accurate the thermal model is or how to refine the thermal model for more accurate estimation of HST. In this study, a methodology is introduced to improve the accuracy of IEC thermal model by refining its thermal parameters based on measured temperature data during the heat run test. Verification of the methodology under one 1 MVA 6.6/0.415 kV distribution transformer shows that the accuracy of predicting the HST is improved by reducing the maximum error from 25.8 to 3.6 K under constant loads and 8.15 to 4.16 K under cyclic loads. Also, a worst‐case scenario study under the penetration of electric vehicles (EVs) shows the maximum penetration level of EVs is improved by 9% by applying refined thermal parameters to estimate the HST of the distribution transformer. [ABSTRACT FROM AUTHOR]

Accurate estimation of reference evapotranspiration (ET 0 ) is essential to agricultural water management. The present study developed two artificial intelligence models for daily ET 0 estimation only with temperature data, including extreme learning machine (ELM) and generalized regression neural network (GRNN) in 6 meteorological stations of Sichuan basin, southwest China, and compared the proposed ELM and GRNN with the corresponding temperature-based Hargreaves (HG) model and its calibrated version considering FAO-56 Penman-Monteith ET 0 as benchmark. Two data management scenarios were evaluated for estimation of ET 0 : (1) the models were trained/calibrated and tested using the local data of each station; and (2) the models were trained/calibrated using the pooled data from all the stations and tested in each station. In the first scenario, the results showed that the temperature-based ELM model provided the better estimation than the GRNN, HG and calibrated HG models, with average relative root mean square error (RRMSE) of 0.198, mean absolute error (MAE) of 0.267 mm/d and Nash-Sutcliffe coefficient (NS) of 0.891, respectively. In the second scenario, GRNN model provided the most accurate results among the considered models, with average RRMSE of 0.194, MAE of 0.263 mm/d and NS of 0.895, respectively. Both of the temperature-based GRNN and ELM performed much better than the HG and calibrated HG models for the two scenarios, and the temperature-based GRNN and ELM models are appropriate alternatives for accurate estimation of ET 0 for Sichuan basin of southwest China, which is very helpful for farmers or irrigation system operators to improve their irrigation scheduling. [ABSTRACT FROM AUTHOR]

Accurate estimation of reference evapotranspiration is vital to hydrological and ecological processes. The FAO-56 Penman–Monteith (PM) model has the higher accuracy for ET 0 estimation, but it requires many meteorological inputs that are not commonly available. Therefore an ideal method is needed requiring as minimal as possible input data variables without affecting the accuracy of estimation. The temperature-based models are especially interesting due to its input data, air temperature, can be monitored easily and is one of the commonly available climatic inputs. Among the temperature-based models, the Hargreaves (HG) model requiring maximum and minimum air temperature as the inputs is considered as one of the simplest and accurate ET 0 methods, but this method needed a local calibration. The present study calibrated the HG model using Bayesian theory at 19 meteorological stations in Sichuan basin of southwest China. Meteorological data during 1961–1990 were used for the calibration and data during 1991–2014 were used for the validation. The results confirmed that the locally calibrated HG model (with average RRMSE, MAE and NS of 0.284, 0.433 mm/d and 0.783) performed better than the original HG model (with average RRMSE, MAE and NS of 0.567, 0.959 mm/d and 0.134). Both of the calibrated and original HG models overestimated ET 0 at daily, monthly and annual timescale, but the calibrated HG model provided closer average values with PM ET 0 , which could confirm the good performances of the calibrated HG model. Therefore, the calibrated HG model could be highly recommended for estimating ET 0 when only temperature data are available. [ABSTRACT FROM AUTHOR]

As the only connecting parameter between energy balance and water balance, evapotranspiration (ET) is the most excellent indicator for the activity of climate change and water cycle, and therefore, accurate estimation of ET is of importance for hydrologic, climatic and agricultural studies. ET is commonly computed by reference evapotranspiration (ET0), and this paper investigated the performance of generalized regression neural network(GRNN) algorithm in modeling FAO-56 Penman-Monteith(PM) ET0 only with the temperature data in 19 meteorological stations located at the western, middle, and eastern part of Sichuan basin, southwest China. Data of meteorological variables containing maximum air temperature (Tmax), minimum air temperature(Tmin) for the period of 1961-1990 were used as input variables to train the GRNN model, and data for the period of 1991-2014 were used to validate the GRNN model. The performance of GRNN model was compared with the empirical temperature-based Hargreaves(HS1) and calibrated Hargreaves(HS2) models considering the PM ET0 as the benchmarks. The evaluation criteria of root mean squared error(RMSE) and model efficiency (Ens) were used for the comparison. The statistical results indicated that the RMSE values of the GRNN, HS1 and HS2 models were 0.41, 1.16 and 0.70 mm/d, respectively, and the Ens values of the GRNN, HS1 and HS2 models were 0.88, 0.13 and 0.67, respectively, which manifested the performance of the GRNN model was encouraging. The RMSE of the HS1 model was the biggest every year at temporal scale in all 3 sub-zones of Sichuan basin, followed by the HS2 model, and the RMSE of the GRNN model was the smallest; the Ens of the GRNN model was bigger than HS1 and HS2 model every year at temporal scale in all 3 sub-zones. The RMSE of the HS1 model was the biggest in every meteorological station of Sichuan basin at spatial scale, followed by the HS2 model, and the RMSE of the GRNN model was the smallest; the Ens of the GRNN model was bigger than HS1 and HS2 model in every meteorological station at spatial scale. Based on the RMSE and Ens, the errors of GRNN, HS1 and HS2 models showed an increasing tendency, which indicated the error of all the 3 models would become bigger in the future. The ranges of the ET0 values computed by the PM, GRNN, HS1 and HS2 model were 695~837, 709~820, 1 029~1 178 and 818~975 mm, all of which showed an increase tendency with a rate of 2.7, 2.0, 2.2 and 2.4 mm/a respectively in recent 24 years. Compared with the PM model, GRNN, HS1 and HS2 overestimated the ET0 value by 0.8%, 45.1% and 17.3%, respectively. The analysis of the performance of GRNN, HS1 and HS2 models at temporal and spatial scale confirmed the good ability of the GRNN model in estimating ET0 when the data for PM model were not fully available, and thus the GRNN model should be adopted to compute ET0. This paper can provide the reference for estimating the crop water requirement in Sichuan basin when only temperature data are accessible. [ABSTRACT FROM AUTHOR]

Projecting the future distribution of permafrost under different climate change scenarios is essential, especially for the Qinghai–Tibet Plateau (QTP). The altitude-response model is used to estimate future permafrost changes on the QTP for the four RCPs (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). The simulation results show the following: (1) from now until 2070, the permafrost will experience different degrees of significant degradation under the four RCP scenarios. This will affect 25.68%, 40.54%, 45.95%, and 62.84% of the current permafrost area, respectively. (2) The permafrost changes occur at different rates during the periods 2030–2050 and 2050–2070 for the four different RCPs. (1) In RCP2.6, the permafrost area decreases a little during the period 2030–2050 but shows a small increase from 2050 to 2070. (2) In RCP4.5, the rate of permafrost loss during the period 2030–2050 (about 12.73%) is higher than between 2050 and 2070 (about 8.33%). (3) In RCP6.0, the permafrost loss rate for the period 2030–2050 (about 16.52%) is similar to that for 2050–2070 (about 16.67%). (4) In RCP8.5, there is a significant discrepancy in the rate of permafrost decrease for the periods 2030–2050 and 2050–2070: the rate is only about 3.70% for the first period but about 29.49% during the second. [ABSTRACT FROM PUBLISHER]