Prediction Of Surface Temperature Research Articles

Droplet on oil impregnated surface: Temperature and velocity fields

The water droplet dynamics on silicon oil impregnated polydimethylsiloxane (PDMS) replicated and inclined surface is considered and the droplet sliding velocity incorporating force and energy balance is formulated. Since silicon oil film is kept at 308 K and water droplet temperature is initially at 300 K, the droplet heat transfer due to temperature disturbance is examined during droplet sliding. Velocity and temperature fields are computed using the experimental conditions. The droplet sliding velocity resulted from the analytical formulation is compared with the high-speed camera data. In addition, the predictions of the droplet surface temperature are validated with the thermal camera data. The findings reveal that the droplet sliding velocity obtained from the analytical formulation is in agreement with the high-speed camera data. The predictions of droplet temperature also agree well with the thermal camera data. Droplet heat transfer results in two contour rotating circulating structures in the droplet, which contribute to convection heat transfer in the droplet fluid. Silicon oil cloaking on water droplet surface alters the heating rates and flow structure in the droplet. As the distance along the silicon oil surface increases, a unique circulation structure is developed inside the droplet, which enhances the Nusselt and the Bond numbers. Formation of a single circulation is attributed to increased silicon oil ridge height in the frontal region of the droplet.

Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach

Sea surface temperature (SST) is one of the most important parameters in the global ocean-atmospheric system, changes of which can have profound effects on the global climate and may lead to extreme weather events such as droughts and floods. Therefore, predicting the dynamics of future SSTs is of vital importance which can help identify these extreme events and alleviate the losses they cause. In this paper, a machine learning method combining the long short-term memory (LSTM) deep recurrent neural network model and the AdaBoost ensemble learning model (LSTM-AdaBoost) is proposed to predict the short and mid-term daily SST considering that LSTM is good at modelling long-term dependencies but suffers from overfitting, while AdaBoost has strong prediction capability and is not easily overfitted. By combining these two strong and heterogeneous models, the prediction errors related to variance may cancel out each other and the final results can be improved. In this method, the historical time-series satellite data of SST anomaly (SSTA) is used instead of SST itself considering that the fluctuations of SSTs are very small compared to their absolute magnitudes. The seasonality of the SSTA time series is first modelled using polynomial regression and then removed. Then, the deseasonalized time series are used to train the developed LSTM model and AdaBoost model independently. Daily SSTA predictions are made using these two models, and eventually, their predictions are combined as final predictions using the averaging strategy. A case study in the East China Sea that predicts the daily SSTA 10 days ahead shows that the proposed LSTM-AdaBoost combination model outperforms the LSTM and AdaBoost separately, as well as the optimized support vector regression (SVR) model, the optimized feedforward backpropagation neural network model (BPNN), and the stacking LSTM-AdaBoost model (S_LSTM-AdaBoost), when judged using multiple error statistics and from different perspectives. The results suggest that the LSTM-AdaBoost combination model using the averaging strategy is highly promising for short and mid-term daily SST predictions.

Prediction of Sea Surface Temperature in the South China Sea by Artificial Neural Networks

Sea surface temperature (SST) significantly affects the processes of air–sea interactions, and, thus, forms an important indicator of climate changes. In SST predictions, the approach of artificial neural networks (ANNs) is data-driven, unlike that of the numerical models, which are physics-based. In this letter, Operational SST and Ice Analysis (OSTIA) data set were used for training ANN models and verifying prediction results. Typically, time series SST data of a certain period were directly used for ANN’s training. To reduce the prediction errors caused by SST variations, the authors propose to separate SST time series data into climatological monthly mean and monthly anomaly data sets and construct two neural network models. The combination of these two models gives the final SST prediction results. This method was used for 12-month lead time SST prediction in the South China Sea. The average bias and standard deviation between the predicted SST and OSTIA SST are −0.16 °C and 0.37 °C, respectively. The percentage of SST difference between the predicted SST and OSTIA SST, within ±0.5 °C and ±1 °C, is 71.24% and 95.22%, respectively. The results indicate that the proposed training method gives good prediction accuracy.

The prediction of city expansion and land surface temperature in Bogor City, Indonesia

In recent year, rapid population growth and urban development were observed in Bogor City as a satellite city of Jakarta. In this study, we predicted the city expansion and land surface temperature (LST) in the future. Landsat images of 1997, 2007, and 2017 were used to detect the land use changes. Land use maps in 2007 and 2017 were used for land use prediction in 2027. Slope, distance from the roads, distance from the toll road, and elevation were also used as variables which influenced the land use change. The combination of Multi-Layer Perceptron and Markov Chain (MLP-MC) were applied to forecast the city expansion. We found that the urbanized area will expand by 3759.6 ha between 2017 and 2027. Based on the land use prediction of 2027, we predicted the future LST of 2027. The average LST of the analyzed area in 2027 increased compared to 2017. The future city expansion will increase the area with high LST. This study gives information to the city planners for making a comfortable and sustainable city.

Effectiveness of CMIP5 Decadal Experiments for Interannual Rainfall Prediction Over Australia

AbstractPhase five of the Coupled Model Intercomparison Project enabled a range of decadal modeling experiments where climate models were initialized with observations and allowed to evolve freely for 10–30 years. However, climate models struggle to realistically simulate rainfall and the skill of rainfall prediction in decadal experiments is poor. Here, we examine how predictions of sea surface temperature anomaly (SSTA) indices from Coupled Model Intercomparison Project Phase 5 decadal experiments can provide skillful rainfall forecasts at interannual timescales for Australia. Forecasts of commonly used SSTA indices relevant to Australian seasonal rainfall are derived from decadal hindcasts of six different climate models and corrected for model drift. The corrected indices are then combined to form a multimodel ensemble. The resultant forecasts are used as predictors in a statistical rainfall model developed in this study. As SSTA forecasts lose skill with increasing lead time, a new methodology for predicting interannual rainfall is proposed. We allow our statistical prediction model to evolve with lead time while accounting for the loss of skill in SSTA forecasts instead of using one statistical model for all lead times. Results in this pilot study across two of the largest climate zones in Australia show that SSTA outputs from the decadal experiments provide enhanced skill in rainfall prediction over using the conventional model (based purely on lagged observed indices) up to a maximum of three years ahead. This methodology could be used more broadly for other regions around the world where rainfall variability is known to have strong links to ocean temperatures.

Decadal predictability of late winter precipitation in western Europe through an ocean–jet stream connection

The characteristics of the North Atlantic jet stream play a key role in the weather and climate of western Europe. Although much of the year-to-year variability in the jet stream arises from internal atmospheric processes that are inherently unpredictable on timescales beyond a few days to weeks, any low-frequency variability or long-term trends that can be considered forced by slowly varying boundary conditions offer the potential for extended range predictability of climatological conditions in western Europe. Here we demonstrate that station-based precipitation observations have displayed pronounced multidecadal variability over the past century in western Europe during the late winter. We then use these precipitation observations as an independent verification of the multidecadal Atlantic jet stream variability found in reanalysis products. Both signals are highly correlated with sea surface temperature variability in the North Atlantic that is well predicted in initialized decadal prediction experiments with a coupled general circulation model. Combining the model-based predictions of the sea surface temperature with the observed relationship between precipitation and sea surface temperature, we show that there is great potential for skilful predictions of the forthcoming decadal average of March precipitation in western Europe, with hindcasts for the UK and Portugal yielding anomaly correlation coefficients of 0.82 and 0.69, respectively. Decadal averages of March precipitation in western Europe can be predicted by exploiting links with the jet stream and ocean along with skilful predictions of sea surface temperatures, according to an analysis of observations and reanalysis products.

The determination of geothermal potential area based on remote sensing, Micromine software, and land surface temperature calculation

Indonesia has a significant geothermal energy potential because it is in the Ring of Fire. However, only 7% of identified potential geothermal has been utilized until now. This research aims to determine and predict geothermal potential area by using Landsat 8 imagery, Micromine software, and Land Surface Temperature Calculation. Land Surface Temperature calculation can be carried out by thermal infrared bands which can be obtained from Landsat 8 imagery. Land Surface Temperature calculation is based on a Split-Window algorithm which providing a surface temperature value and its distribution. Furthermore, the method can support to generate the distribution of Land Surface Temperature in research area using Micromine software. The remote sensing analysis also being done to determine geological structures development in the research area which assumed to control manifestation occurrence. It also shows the appearances of geothermal surface manifestation. The result of Land Surface Temperature analyses could predict surface temperature zone in the research area. The area that has high surface temperature value tends to be an area that has a potential geothermal area. The application of this method is being done on a potential geothermal field around Mt. Patuha, Ciwidey, West Java to verify these approaches. This research shows that Land Surface Temperature method to determine geothermal potential has a positive result.

Simulated Phase-dependent Spectra of Terrestrial Aquaplanets in M Dwarf Systems

Orbital phase-dependent variations in thermal emission and reflected stellar energy spectra can provide meaningful constraints on the climate states of terrestrial extrasolar planets orbiting M dwarf stars. Spatial distributions of water vapor, clouds, and surface ice are controlled by climate. In turn, water, in each of its thermodynamic phases, imposes significant modulations to thermal and reflected planetary spectra. Here we explore these characteristic spectral signals, based on 3D climate simulations of Earth-sized aquaplanets orbiting M dwarf stars near the habitable zone. By using 3D models, we can self-consistently predict surface temperatures and the location of water vapor, clouds, and surface ice in the climate system. Habitable zone planets in M dwarf systems are expected to be in synchronous rotation with their host star and thus present distinct differences in emitted and reflected energy fluxes depending on the observed hemisphere. Here we illustrate that icy, temperate, and incipient runaway greenhouse climate states exhibit phase-dependent spectral signals that enable their characterization.

Prediction of land surface temperatures for surface urban heat island assessment over Chandigarh city using support vector regression model

Rapid urbanization is one of the primary reasons for changing the local climate, and there is a high impact on the surrounding areas. Chandigarh is one of the fastest developing cities in India showing rapidly urbanizing agglomeration. Due to the rapid urbanization, natural land surfaces are being replaced by the anthropogenic materials which negatively impacts the ecosystem resulting in urban heat island (UHI) effect. Land surface temperature (LST) is the primary and vital step for the analysis of UHI effect. The present study has been conducted to predict the LSTs for the assessment of UHI effect of the area surrounding Chandigarh city. Remote sensing data from Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) have been used for the prediction of LST. In the study, support vector regression (SVR) model has been developed from LST values of previous three years along with enhanced vegetation index (EVI), road density (RD) and elevation as input parameters to predict LST. The results of the SVR model have been validated using the data of the year 2014. A comparison of the model estimated LST and measured LST indicates that the range of mean absolute error (MAE) and mean absolute percentage error (MAPE) varies between 0.521 K and 0.525 K and 0.181–0.187%, respectively. Hence, SVR model can be used as a significant tool to predict LST for the assessment of heat island effect at any location. From the sensitivity analysis, it is observed that LST was ultimately the most sensitive to the RD compared to EVI and elevation. The SVR model has been compared with artificial neural networks (ANN) model to estimate the skill score factor of the SVR model (Forecasted) with reference to the ANN (Referred) model. Skill scores calculated for the periods show positive values which clearly depicts the efficacy of SVR model compared to ANN model for better LST prediction.

Determining asphalt surface temperature using weather parameters

Abstract Temperature affects the structural response of asphalt concrete (AC) pavement. To evaluate the performance, it is important to accurately predict the pavement surface temperature. This study develops three regression models to determine the surface temperature of an asphalt pavement using field measured data such as air temperature, wind speed, wind direction, relative humidity, and solar radiation. Three models namely, 24-h model, day-time model and night-time model were developed based on one-year continuous data. The weather data were measured from October 2012 to October 2013 at an instrumented pavement section on Interstate I-40 near Albuquerque, New Mexico. The developed regression models were validated using October 2014 to June 2015 weather data, which were not used in developing the regression models. Surface temperatures predicted using the developed regression models were then compared to those predicted using the pavement mechanistic-empirical (ME) software's default model. It is observed that the regression models are better predictors of pavement surface temperatures than the ME default models. A parametric study was also performed in this study to identify the effects of weather parameters on the pavement surface temperature prediction. The authors of this study expect that this study will be useful in pavement design especially in cold regions.

Does detailed hygrothermal transport analysis in respiratory tract affect skin surface temperature distributions by thermoregulation model?

ABSTRACT The prediction of the physiological response of the human body to the thermal environment is essential for healthy and comfortable indoor environmental design; hence, various rational thermoregulation models for estimating skin surface temperature have been developed based on the physics of heat and mass transfer between the human body and indoor environment, and on cybernetic models of the thermoregulatory system. Most of these models calculate the respiratory heat loss through the function of the pulmonary ventilation as well as the difference in water content between expiratory and inspiratory air, which describes a steady respiration process with a constant flow rate and fixed values of inspired/expired vapour pressure. In this study, a coupling numerical model combined with a thermoregulation model and a computer simulated person (CSP) with respiratory tract model that can be integrated with computational fluid dynamics has been developed. The coupling thermoregulation model used here are the multi-node model proposed by Stolwijk et al. and two-node model of Gagge, respectively. Based on this CSP with the thermoregulation model, a coupling analysis method combining the thermoregulation model and the model of dynamic heat and mass transfer/exchange in the respiratory tract is developed. This is followed by a discussion of the skin surface temperature predictions of the proposed model compared with those of the model using the traditional respiratory heat loss calculation method.

Seasonal prediction of equatorial Atlantic sea surface temperature using simple initialization and bias correction techniques

Due to strong mean state‐biases most coupled models are unable to simulate equatorial Atlantic variability. Here, we use the Kiel Climate Model to assess the impact of bias reduction on the seasonal prediction of equatorial Atlantic sea surface temperature (SST). We compare a standard experiment (STD) with an experiment that employs surface heat flux correction to reduce the SST bias (FLX) and, in addition, apply a correction for initial errors in SST. Initial conditions for both experiments are generated in partially coupled mode, and seasonal hindcasts are initialized at the beginning of February, May, August and November for 1981–2012. Surface heat flux correction generally improves hindcast skill. Hindcasts initialized in February have the least skill, even though the model bias is not particularly strong at that time of year. In contrast, hindcasts initialized in May achieve the highest skill. We argue this is because of the emergence of a closed Bjerknes feedback loop in boreal summer in FLX that is a feature of observations but is missing in STD.

Analytical Modeling of the Temperature Using Uniform Moving Heat Source in Planar Induction Heating Process

The planar induction heating possesses more difficulties in industry application compared with traditional spiral induction coils in mostly heat treatment processes. Numerical approaches are adopted in the power distribution and temperature prediction during the induction heating process, which has a relatively low computational efficiency. In this work, an analytical calculation model of the planar induction heating with magnetic flux concentrator is investigated based on the uniform moving heating source. In this model, the power density in the surface of the workpiece induced by coils is calculated and applied into the analytical model of the temperature calculation using a uniform moving heat source. Planar induction heating tests are conducted under various induction coil parameters and the corresponding temperature evolution is obtained by the infrared imaging device NEC R300W2-NNU and the thermocouples. The final surface temperature prediction is compared to the finite element simulation results and experimental data. The analytical results show a good match with the finite element simulation and the experimental results, and the errors are in reasonable range and acceptable. The analytical model can compute the temperature distribution directly and the computational time is much less than the finite element method. Therefore, the temperature prediction method in this work has the advantage of less experimental and computational complexity, which can extend the analytical modeling methodology in induction heating to a broader application.

Hybrid improved empirical mode decomposition and BP neural network model for the prediction of sea surface temperature

Abstract. Sea surface temperature (SST) is the major factor that affects the ocean–atmosphere interaction, and in turn the accurate prediction of SST is the key to ocean dynamic prediction. In this paper, an SST-predicting method based on empirical mode decomposition (EMD) algorithms and back-propagation neural network (BPNN) is proposed. Two different EMD algorithms have been applied extensively for analyzing time-series SST data and some nonlinear stochastic signals. The ensemble empirical mode decomposition (EEMD) algorithm and complementary ensemble empirical mode decomposition (CEEMD) algorithm are two improved algorithms of EMD, which can effectively handle the mode-mixing problem and decompose the original data into more stationary signals with different frequencies. Each intrinsic mode function (IMF) has been taken as input data to the back-propagation neural network model. The final predicted SST data are obtained by aggregating the predicted data of individual series of IMFs (IMFi). A case study of the monthly mean SST anomaly (SSTA) in the northeastern region of the North Pacific shows that the proposed hybrid CEEMD-BPNN model is much more accurate than the hybrid EEMD-BPNN model, and the prediction accuracy based on a BP neural network is improved by the CEEMD method. Statistical analysis of the case study demonstrates that applying the proposed hybrid CEEMD-BPNN model is effective for the SST prediction. Highlights include the following: Highlights. An SST-predicting method based on the hybrid EMD algorithms and BP neural network method is proposed in this paper. SST prediction results based on the hybrid EEMD-BPNN and CEEMD-BPNN models are compared and discussed. A case study of SST in the North Pacific shows that the proposed hybrid CEEMD-BPNN model can effectively predict the time-series SST.

Identification of homogeneous regions of near surface air temperature lapse rates across India

Spatial variation of near surface temperature lapse rate is usually analysed by subjectively dividing the study area in to different sub‐regions based on political or physiographic boundaries. This can often lead to unreliable results as near surface temperature lapse rate largely depends on regional conditions. In this study, homogeneous regions of near surface air temperature lapse rate across India have been identified for monthly mean minimum, mean, and mean maximum temperature using a probabilistic Gaussian mixture model clustering approach. For the delineated regions, lapse rates are estimated using weighted linear regression model. The weights corresponding to each station were obtained from the clustering approach. Results indicate that regions with uniform lapse rate vary across different months and a minimum of three regions is obtained for maximum temperature in April month. Further, temperature lapse rate estimates exhibit spatial and temporal variability, and are less steep than the Environmental lapse rate (−6.5°C/km). The maximum spatial variability in lapse rate is observed in May for both maximum and minimum temperature. The delineation of entire India into homogeneous regions improves temperature interpolation when compared to results obtained by considering entire India as one region. A maximum improvement of 34% in root mean square error is observed in prediction of monthly mean temperature in March. The identified regions and the associated lapse rates are expected to improve the prediction of near surface temperature at ungauged locations across India.

Modeling temperature evolution of wheel flat during formation

Predicting temperature evolution of sliding bodies plays a key role in many industrial designs. Temperature-dependent material properties, microstructure evolution of material while heating and quenching, and residual stress comprise these factors importance. Despite existing theoretical, numerical, and experimental methods for predicting surface temperature of sliding bodies, there are some restrictions relating to each one. This paper aims to present a strategy and numerical method for finding the temperature evolution of sliding bodies with arbitrary geometry of the contact patch. Preserving generality, temperature evolution of sliding railway flat wheels is the main problem of this study. A finite element model (FEM) is developed with ANSYS APDL software (Canonsburg, PA, USA). The model is validated with existing analytical formulas in steady state and transient cases and a good agreement is achieved. Six real cases from full-scale field tests are considered and a comparison is made between the results. As an application of the method, the obtained time-history of surface temperature is applied to a 3D FE model of a flat wheel as a boundary condition.

Regional Decadal Climate Predictions Using an Ensemble of WRF Parameterizations Driven by the MIROC5 GCM

AbstractRegional climate models (RCMs) are expected to provide better representations of the climate dynamics because of their higher spatial resolutions. Here, we generated an ensemble of decadal (2006–36) RCM predictions for the area of Israel, which spans a considerable climatic gradient and comprises complex terrain. We used the WRF Model forced by the MIROC5 global climate model (GCM). The ensemble was generated by choosing different combinations of radiation, microphysics, surface layer, and planetary boundary layer parameterizations. The simulation results were compared with meteorological station data for the first simulated decade. For the minimum surface temperature, all the RCM configurations performed better than the driving GCM, while for the maximum surface temperature, only three out of eight configurations improved the predictions. The RCM configurations had higher errors in predicting the precipitation, but four configurations had comparable errors to the GCM. For the next two decades, the ensemble average predicts an increase of 0.51° and 0.40°C decade−1 for the average daily minimum and maximum surface temperatures, respectively. No significant change is predicted in the precipitation. We found that all the parameterizations affect the predictions of the surface temperatures and precipitation [e.g., the CAM radiation scheme simulates colder temperatures than the RRTM for GCMs (RRTMG)] but the PBL and surface layer scheme has the largest effect on the errors. Spectral nudging was found to have a considerable effect on the deviations of the precipitation predicted by the WRF configurations from the predictions of the GCM and a much smaller effect on the surface temperature predictions.

Calibration of a plug-type gauge for measurement of surface heat flux and temperature using data from in-depth thermocouples

This study presents an investigation of the calibration and testing of a stainless steel plug-type gauge that utilizes temperature data from its two in-depth thermocouples (TCs) to predict its surface thermal boundary conditions caused by an unknown heating scenario regardless of its back surface boundary condition. Calibration of the sensor was accomplished using the two-probe calibration integral equation method (CIEM) which was modified to incorporate the effects of known temperature dependent properties. The precise locations of the in-depth TCs are not required to implement this method. The two-probe CIEM requires data from two calibration tests consisting of accurate measurements of the surface boundary conditions along with data from the two in-depth TCs to estimate the surface boundary conditions of any unknown (reconstruction) heating process when data from its in-depth TCs are measured. High temperature data was collected from three calibration tests and four reconstruction tests. Accurate predictions of the surface heat flux and surface temperature of four high temperature reconstruction tests (up to 770 °C) were obtained where the back surface of the gauge was heated, cooled, insulated and unaltered. Amplified experimental noise was added to the reconstruction temperature data to simulate noisy temperature data that may be gathered in a field test. The addition of amplified noise to the reconstruction temperatures had almost no effect on the overall accuracy of the surface heat flux and temperature predictions.

Predictability of South China Sea Summer Monsoon Onset

Predicting monsoon onset is crucial for agriculture and socioeconomic planning in countries where millions rely on the timely arrival of monsoon rains for their livelihoods. In this study we demonstrate useful skill in predicting year-to-year variations in South China Sea summer monsoon onset at up to a three-month lead time using the GloSea5 seasonal forecasting system. The main source of predictability comes from skillful prediction of Pacific sea surface temperatures associated with El Nino and La Nina. The South China Sea summer monsoon onset is a known indicator of the broadscale seasonal transition that represents the first stage of the onset of the Asian summer monsoon as a whole. Subsequent development of rainfall across East Asia is influenced by subseasonal variability and synoptic events that reduce predictability, but interannual variability in the broadscale monsoon onset for East Asian summer monsoon still provides potentially useful information for users about possible delays or early occurrence of the onset of rainfall over East Asia.

Estimation of land surface temperature using Landsat satellite data: A case study of Mueang Maha Sarakham District, Maha Sarakham Province, Thailand for the years 2006 and 2015

At present, the climate has constantly been changing, especially the increase in global average temperature that results in the risk of severe climatic conditions such as heat wave, drought and flood. The objective of this study is to estimate land surface temperature (LST) by applying Landsat satellite data in Mueang Maha Sarakham District, Maha Sarakham Province, Thailand. The study focuses on investigating the temperature changes for the years 2006 and 2015. The research was conducted by analyzing the satellite data in the thermal infrared band with a geo-informatics package software mutually with mathematical models. The operation results indicated that the average LST was at 26.28°C in 2006 and 27.15°C in 2015. In order to verify the accuracy of the data in this study, the results of the annual satellite data analysis were brought to find out a statistical correlation with the LST data from the Meteorological Station of Thai Meteorological Department (TMD). The results indicated that there was a correlation of the data at a high level in 2006 and 2015. The results of this study indicated that the satellite data analysis method is reliable and can be used to analyze, track, and verify data to predict surface temperatures effectively.