Temperature Estimates Research Articles

Changes in soil water content and lateral flow exert large effects on soil thermal dynamics across Alaskan landscapes

Abstract Both lateral surface and subsurface water flow affect soil moisture dynamics, yet most land surface models only solve subsurface water movement vertically. Here we use a 3-D ecosystem model that considers both land surface and subsurface hydrologic processes to simulate soil moisture, which is then used to drive a 1-D vertical soil thermal model to simulate the soil moisture effects on soil thermal dynamics in central Alaska. Our coupled model improves soil temperature estimates by 43.5% in comparison with observational data. Soil moisture has little effect on soil temperature during the wet season (-1.5 %) and a substantial influence during the dry season (60%). Spatially, water lateral flow has significant impacts on both soil moisture and soil temperature, causing model estimates for thawed areas in the transition season to increase by ~10% in the study area. Our results highlight the importance of considering dynamical soil moisture, as well as lateral flow effects, on soil thermal dynamics in permafrost regions.

Reconstructing the experienced temperature during the larval oceanic migration of anguillid eels from otolith stable oxygen isotopes

Migration routes and the depth patterns of anguillid eel larvae migrating long distances from spawning grounds in the ocean remain poorly understood. We used otolith stable isotope analysis to study the oceanic migrations of anguillid eels by reconstructing experienced water temperature histories of larvae. The otolith stable oxygen isotopes (δ18Ootolith) of recruited Anguilla japonica glass eels were analyzed to assess the relationship with the experienced water temperature of the early larval stage in laboratory experiments. A negative linear relationship between rearing water temperature and δ18Ootolith values was observed for eel leptocephali reared at five different temperatures between 19 °C and 27 °C. Subsequently, the obtained equation between water temperature and δ18Ootolith was applied to estimate the water temperature experienced by wild-caught glass eels during their larval oceanic migration according to recruitment latitude, season, and species. The δ18Ootolith values of A. japonica glass eels were significantly higher at higher recruitment latitudes from Taiwan to Japan and later recruitment periods of 6 months, from November to March, indicating that the individuals were exposed to lower water temperatures along the Kuroshio flowing northward as they reached the higher latitudes in the later recruitment seasons. Furthermore, the δ18Ootolith values of the temperate A. japonica were higher than those of the tropical Anguilla marmorata, recruited at the same locations in southern Japan and Taiwan. This suggests that larval migratory behavior in the ocean may differ between the two eel species, although they have sympatric spawning areas in the western North Pacific. Collectively, these results suggest that δ18Ootolith provides a reasonable estimation of the experienced water temperature and may prove useful for reconstructing the early migratory history of anguillid eels in the ocean.

Estimation of All-Sky Gridded Diurnal Near-Surface Air Temperatures at Regional Scale From FY-4B Measurements

Estimation of All-Sky Gridded Diurnal Near-Surface Air Temperatures at Regional Scale From FY-4B Measurements

Online core temperature estimation method for lithium-ion batteries over the entire lifecycle

Online core temperature estimation method for lithium-ion batteries over the entire lifecycle

A fast real-time motor temperature estimation method taking less than 0.2 s

A fast real-time motor temperature estimation method taking less than 0.2 s

Postfire Estimation of Heating Temperatures Experienced by Fire Retardant Coatings Using Smartphone Videos and Machine Learning

ABSTRACTAccurate estimation of heating temperatures experienced by fire retardant coatings (FRCs) is crucial in identifying the ignition source during fire investigations. While traditional methods, such as spectroscopy, effectively capture the compositional changes in FRC at various heating temperatures, they are typically bulky, costly, and unsuitable for rapid field analysis. This study proposes the use of smartphone and machine learning to predict the heating temperatures of FRC. A smartphone is employed to capture short videos of FRC samples illuminated by its color‐changing screen. Video frames are then decomposed into color images and converted into spectral data for further processing. Linear and nonlinear regression models are applied to identify key variables and enhance predictive accuracy. The performance of smartphone‐based temperature estimation is compared to that of hyperspectral imaging and laser‐induced breakdown spectroscopy. In the test phase, the coefficient of determination for smartphone‐based estimation ranges from 0.946 to 0.962, often surpassing that of benchmark methods. These results indicate that smartphones can provide a low‐cost, effective means for estimating heating temperatures of FRC in fire investigations.

Dynamic treeline and cryosphere response to pronounced mid-Holocene climatic variability in the US Rocky Mountains

Climate-driven changes in high-elevation forest distribution and reductions in snow and ice cover have major implications for ecosystems and global water security. In the Greater Yellowstone Ecosystem of the Rocky Mountains (United States), recent melting of a high-elevation (3,091 m asl) ice patch exposed a mature stand of whitebark pine (Pinus albicaulis) trees, located ~180 m in elevation above modern treeline, that date to the mid-Holocene (c. 5,950 to 5,440 cal y BP). Here, we used this subfossil wood record to develop tree-ring-based temperature estimates for the upper-elevation climate conditions that resulted in ancient forest establishment and growth and the subsequent regional ice-patch growth and downslope shift of treeline. Results suggest that mid-Holocene forest establishment and growth occurred under warm-season (May-Oct) mean temperatures of 6.2 °C (±0.2 °C), until a multicentury cooling anomaly suppressed temperatures below 5.8 °C, resulting in stand mortality by c. 5,440 y BP. Transient climate model simulations indicate that regional cooling was driven by changes in summer insolation and Northern Hemisphere volcanism. The initial cooling event was followed centuries later (c. 5,100 y BP) by sustained Icelandic volcanic eruptions that forced a centennial-scale 1.0 °C summer cooling anomaly and led to rapid ice-patch growth and preservation of the trees. With recent warming (c. 2000-2020 CE), warm-season temperatures now equal and will soon exceed those of the mid-Holocene period of high treeline. It is likely that perennial ice cover will again disappear from the region, and treeline may expand upslope so long as plant-available moisture and disturbance are not limiting.

Soil temperature estimation at different depths using machine learning paradigms based on meteorological data.

Knowledge of soil temperature (ST) is important for analysing environmental conditionsand climate change. Moreover, ST is a vital element of soil that impacts crop growth as well as the germination of the seeds. In this study, four machine-learning (ML) paradigms including random forest (RF), radial basis neural network (RBNN), multi-layer perceptron neural network (MLPNN), and co-active neuro-fuzzy inference system (CANFIS) were used for estimation of daily ST at different soil depths (i.e. 5cm: ST5; 15cm: ST15; and 30cm: ST30) during 2016-2019 at Bathinda weather station, located in South-western Punjab (India). Five different combinations were formulated using four meteorological data, namely Tmean (mean air temperature), RH (relative humidity), WS (wind speed), and SSH (bright sunshine hours), and the optimal one was nominated by employing the gamma test (GT) for each soil depths, respectively. During the validation period, the outcomes of the RF, RBNN, MLPNN, and CANFIS models were evaluated according to performance metrics such as mean absolute error (MAE), root mean square error (RMSE), scatter index (SI), coefficient of efficiency (COE), Pearson correlation coefficient (PCC), and index of agreement (IOA), as well as through pictorial interpretation (Taylor diagram, box-whisker plots, time-variation, scatter plot, and radar chart). The comparison of theresults of ML paradigms revealed the highest accuracy was achieved by the CANFIS model at all depths with MAE (RMSE) = 0.788, 0.636, 0.806 (1.074, 0.854, 1.041) °C, SI = 0.040, 0.033, 0.040, and COE (PCC)/IOA = 0.986, 0.991, 0.985 (0.994, 0.995, 0.993)/0.996, 0.998, 0.996. Thus, the results highlight the capability of the CANFIS model with Tmean, RH, WS, and SSH inputs for daily ST estimation at different soildepths on the study site.

Climate matching models for Ceratapion basicorne (Coleoptera: Apionidae), a biocontrol agent of yellow starthistle

Abstract Ceratapion basicorne (Illiger) (Coleoptera: Apionidae), a weevil native to Europe and western Asia, shows promise for enhancing the control of yellow starthistle (Centaurea solstitialis L.), an invasive annual forb in the western United States. However, a paucity of data on this biocontrol agent’s environmental constraints has made it difficult to assess the suitability of potential release locations. Climate matching models were developed for C. basicorne to help identify areas of the western United States with similar climates to the source area of breeding colonies being used for releases (home location). The models used climate variables derived from daily estimates of minimum temperature, maximum temperature, precipitation, and soil moisture for a 30-yr period spanning 1991–2020 at 1 km2 resolution. Of the areas where C. solstitialis is known to occur, the Central California Foothills, Eastern Cascades Foothills, Columbia Plateau, and mountainous parts of northcentral Utah had the most similar climates to the home location. Of these areas, the Eastern Cascades foothills in northeastern California and Wasatch Range in Utah occurred at a similar latitude as the home location, which may be important to consider if C. basicorne has photoperiodic diapause. The least similar climates occurred in wet coastal regions, high-elevation (cold) mountains, and hot deserts; however, C. solstitialis has not been detected in most of these areas. The development of process-based models for predicting the establishment of this agent will require a more detailed understanding of the agent’s requirements for development and survival.

The effect of state preparation on the probe-bath parameters estimation

Abstract Projective measurement is a popular method of initial state preparation, which always prepares a pure state. However, in various physical situations of interest, this selective measurement becomes unrealistic. In this paper, we investigate the role of pulsed measurement (a unitary operation) on the estimation of system-environment parameters and compare the estimation results obtained via projective measurement with the results obtained via unitary operation. We argue that in typical situations, parameters can be estimated with higher accuracy if the initial state is prepared with the unitary operator (a pulse). We consider the spin-spin model in which a central two-level system (probe) interacts with a collection of two-level systems (bath). The probe interacts with the bath and attains thermal equilibrium. Then, via unitary operation, the initial state is prepared which evolves unitarily. The properties of the bath are imprinted on the reduced dynamics. Due to the initial probe-bath correlations present in the thermal equilibrium state, an additional factor arises in the dynamics, which has an important role in the parameter estimation. In this paper, we study the estimation of bath temperature and probe-bath coupling strength which is quantified by the quantum Fisher information. Our results are promising as one can improve the precision of the estimates by orders of magnitude (especially in the coupling strength case) via unitary operation and by incorporating the effect of initial correlations.

Field sensing of temperature and degree of cure for composites manufacture using parameterized physical‐informed neural networks and sparse FBG sensors

AbstractMonitoring the curing process of polymer composite parts stands imperative for improving the quality and efficiency of manufacturing processes. This paper proposes a field sensing framework that fuses parameterized physics‐informed neural networks (PPINN) and sparse fiber Bragg grating sensors to provide real‐time estimates of temperatures and degree of cure field in composite parts. Moreover, a novel separation network is developed for constructing the PPINN predictor, which integrates the coupled partial differential equation of the thermochemical curing process, achieving data‐free training and real‐time performance. In the simulation experiment, temperature accuracy reached 99.7%, while in laboratory experiments, the accuracy reached 96.1%. Both simulation and experiment demonstrated the successful field sensing of the temperature and degree of cure field in a negligible time with noise resistance ability.Highlights Proposed framework integrates PPINN and sparse FBG sensors for field sensing. PPINN solves thermo‐chemical equations without simulation data required. Simulation accuracy reaches 99.7%, and experimental accuracy reaches 96.1%. The framework shows strong resistance to noise interference in experiments.

Design and implementation of a low-cost datalogger for solar irradiance and PV module temperature

Photovoltaic module temperature and solar irradiance are two essential parameters that greatly affect the performance of solar plants. The measured information concerning these parameters is needed to size as well as predict future energy production of the plant. These data are often available at hourly intervals or more from meteorological stations and are expensive to acquire depending on the number of data points needed or from websites linked to satellites. Consequently, a method that can provide data at smaller time intervals is required to capture changes in irradiance and temperature. This paper presents a simple and cost-effective datalogger that measures the irradiance, relative humidity, module, and environmental temperatures. It used a 50 W photovoltaic module as an irradiance sensor. LM35 and DHT22 sensors were used for PV module and ambient temperature measurements, respectively. An interrupt service routine function implemented with the Arduino Mega microcontroller ensured a repetitive switching sequence of parallel resistance networks and the storage of desired current and voltage coordinates every 4s from 6 a.m. to 6 p.m. The irradiance computed was based on power at the maximum point with a load-switching network and in a short-circuit condition. The entire cost of the datalogger system was 153.12 euros, and major results show that the power at maximum power point method and ambient temperature give the best estimate of the photovoltaic module temperature. Consequently, the irradiance determined by the maximum power point method with ambient temperature can be used to evaluate the performance of photovoltaic modules using the single-diode model.

Childhood maltreatment is associated with lower exploration and disrupted prefrontal activity and connectivity during reward learning in volatile environments.

Atypical reward processing is implicated in a range of psychiatric disorders associated with childhood maltreatment and may represent a latent vulnerability mechanism. In this longitudinal study, we investigated the impact of maltreatment on behavioural and neural indices of reward learning in volatile environments and examined associations with future psychopathology assessed 18 months later. Thirty-seven children and adolescents with documented histories of maltreatment (MT group) and a carefully matched group of 32 non-maltreated individuals (NMT group) aged 10-16 were presented with a probabilistic reinforcement learning task featuring a phase of stable and a phase of volatile reward contingencies. Brain activation and connectivity were assessed simultaneously using functional magnetic resonance imaging (fMRI). Computational models were used to extract individual estimates of learning rates and temperature, and neural signals in prespecified regions of interest were analysed during volatile and stable environments. In regression analyses, behavioural measures and neural signals at baseline were used to predict psychological symptoms at follow-up. The MT group showed lower behavioural exploration, which predicted decreased internalising symptoms at follow-up. The MT group had lower activation in the orbitofrontal cortex (OFC) during outcome delivery in volatile relative to stable contexts. OFC connectivity with an area in the mid-cingulate cortex was also lower during outcome processing, which predicted higher general psychopathology at follow-up. These findings are consistent with the notion that low exploratory behaviour following childhood maltreatment is potentially a protective adaptation against internalising symptoms, while disrupted neural processing of reward learning in volatile environments may index latent vulnerability to mental illness.

ALMA-IMF. XVI: Mass-averaged temperature of cores and protostellar luminosities in the ALMA-IMF protoclusters

The ALMA-IMF Large Program imaged 15 massive protoclusters down to a resolution of sim 2 kau scales, identifying about $10^3$ star-forming cores. The mass and luminosity of these cores, which are fundamental physical characteristics, are difficult to determine, a problem greatly exacerbated at the distances ge 2 kpc of ALMA-IMF protoclusters. We combined new datasets and radiative transfer modeling to characterize these cores. We estimated their mass-averaged temperature and the masses these estimates imply. For one-sixth of the sample, we measured the bolometric luminosities, implementing deblending corrections when necessary. We used spectral energy distribution (SED) analysis obtained with the point process mapping ( Bayesian procedure, which aims to preserve the best angular resolution of the input data. We extrapolated the luminosity and dust temperature images provided by at $2.5 resolution to estimate those of individual cores, which were identified at higher angular resolution. To do this, we applied approximate radiative transfer relationships between the luminosity of a protostar and the temperature of its surrounding envelope and between the external heating of prestellar cores and their temperatures. For the first time, we provide data-informed estimates of dust temperatures for 883 cores identified with ALMA-IMF: 17--31 K and 28--79 K (5th and 95th percentiles, up to 127 K) for the 617 prestellar and 266 protostellar cores, respectively. We also measured protostellar luminosities spanning $20-80\,000 Dust temperatures previously estimated from SED-based analyses at a comparatively lower resolution validate our method. For hot cores, on the other hand, we estimated systematically lower temperatures than studies based on complex organic molecules. We established a mass-luminosity evolutionary diagram, for the first time at the core spatial resolution and for a large sample of intermediate- to high-mass protostellar cores. The ALMA-IMF data favor a scenario in which protostars accrete their mass from a larger mass reservoir than their host cores.

Ultrafast laser excitation as a pathway to liquid-like transport rates in the solid state

This work presents an ultrafast laser-based mechanism for achieving liquid-like mass transport rates in solids. Transport at these rates was achieved by irradiating GaAs with a femtosecond laser (repetition rate = 1 kHz, τ = 150 fs, and λ = 780 nm). Following irradiation, the material exists in a highly excited state in which the atomic bonds are softened. The probability of hopping between lattice/interstitial sites for atoms within the softened interatomic potentials is greatly increased. Evidence of the enhanced diffusion is observable in the rate at which islands and corrugated structures form on the GaAs surface. Quantification of the mass transport rate necessary to form the structures via Fick's law produces a diffusion coefficient of 1.18×10−6 cm2/s, which is over 10 orders of magnitude higher than that for near-melt vacancy self-diffusion in crystalline GaAs. In fact, the calculated diffusion coefficient is on the order of magnitude of liquid GaAs, while estimates of temperature during mass transport place the crystal well below its melting point. These results suggest that ultrafast laser irradiation can be used to enhance the diffusion rate in materials without inducing a phase transition.

IGBT Junction Temperature Estimation Based on External Parameters of Multiple Drive Devices in Practical Industrial Scenario

ABSTRACTThe insulated gate bipolar transistor (IGBT) is one of the most important power semiconductor devices in power electronics and is also prone to failure. High junction temperature and junction temperature fluctuation of IGBT are the main causes of IGBT module aging failure. The high‐precision monitoring of the junction temperature of the IGBT module is a prerequisite for IGBT life prediction, which is crucial for reducing maintenance costs and improving equipment reliability. Therefore, an IGBT junction temperature estimation method based on long short‐term memory (LSTM) neural network and sliding window estimation model is proposed and applied in practical industrial scenarios. This method uses the operating data of the motor drive device in the actual industrial application scenario as the training and test data set and uses the external operating parameters of the IGBT module to estimate the junction temperature of the IGBT module. Compared with the internal operating parameters of the IGBT module based on switching transient, the external operating parameters are easier to collect and process, and more suitable for practical application scenes. A sliding window estimation model is proposed to estimate the junction temperature of the IGBT module. Compared with the point‐to‐point estimation method, the sliding window estimation method can capture the influence of historical operation data better and has a higher capability of time series data estimation. The IGBT junction temperature estimation of sliding windows is realized by the LSTM neural network, which is more suitable for time series estimation in real industrial scenarios. The experimental results show that the estimation accuracy of the sliding window estimation method is better than that of the point‐to‐point estimation method, and the accuracy of the sliding window estimation method based on LSTM is better than that of the sliding window estimation method based on other machine learning models. It proves that the proposed method can better capture the dynamic process of the system and has higher estimation accuracy.

The High-resolution Urban Meteorology for Impacts Dataset (HUMID) daily for the Conterminous United States

Many current gridded surface meteorological datasets are inadequate for quantifying near-surface spatiotemporal variability because they do not fully represent the impacts of land surface heterogeneity. Of note, explicit representation of the spatial structure and magnitude of local urban warming are usually lacking. Here we enhance the representation of spatial meteorological variability over urban areas in the conterminous United States (CONUS) by employing the High-Resolution Land Data Assimilation System (HRLDAS), which accounts for the fine-scale impacts of spatiotemporally varying land surfaces on weather. We also synthesize in situ meteorological data including local mesonets to create a 1 km grid spacing model-observation fusion product spanning 1981–2018 over the CONUS at daily temporal resolution. Daily maximum, minimum, and mean values for a variety of temperature estimates, humidity, and surface energy budget terms, among others, are included. This High-resolution Urban Meteorology for Impacts Dataset (HUMID) will be useful for studies examining spatial variability of near surface meteorology and the impacts of urban heat islands across many disciplines including epidemiology, ecology, and climatology.

Identification of Multiple Thermal Events in High‐Grade Metacarbonate Rocks Using Carbon Isotope Thermometry: An Example From the Sør Rondane Mountains, East Antarctica

ABSTRACTNine metacarbonate layers from the regionally metamorphosed terrane of the Sør Rondane Mountains in the Eastern Dronning Maud Land in East Antarctica were examined in detail for constraining the thermal events using carbon isotope exchange between dolomite/calcite and graphite. Equilibrium carbon isotope fractionation between dolomite and graphite suggested peak metamorphic temperature conditions reaching up to 802°C ± 29°C were estimated at the Balchenfjella locality, where multiple samples from six thick layers of metacarbonate rocks were examined. However, some of the samples exhibit lower carbon isotope fractionation reflecting the possibility of ultrahigh‐temperature metamorphic conditions, which is consistent with recent reports. Furthermore, several metacarbonate rock samples display large variations in δ13CVPDB values for graphite grains, despite dolomite and calcite showing homogeneous carbon and oxygen isotopic composition indicating signatures of retrograde metamorphism and fluid infiltration events. Detailed textural observation suggested alteration of δ13CVPDB values of graphite during retrograde metamorphism might have resulted due to the overgrowth of graphite crystals by the infiltration of low δ13CVPDB‐bearing fluids, the extent of alteration being a direct function of the fluid–rock ratio. Field evidence indicates the presence of carbonate veins cutting across the metacarbonate rocks suggesting that carbon isotope thermometry can also be utilised to understand the effect of external fluid infiltration. At Perlebandet locality the metamorphic temperature conditions were estimated to be around 915°C, whereas those from Tanngarden and Menipa gave lower temperature estimates. Detailed textural analysis of graphite in combination with isotopic composition provided clear evidence for retrograde events. Thus, our results provide tight constraints of peak and post‐peak metamorphic temperature conditions and a regional thermal structure for the Sør Rondane Mountains and further testify the usefulness of carbon isotope thermometry in polymetamorphic terrains.

Assimilation of Scattered Ocean Observations Using the Localized Equivalent-Weights Particle Filter with Statistical Observations

Abstract Marine physical data are currently derived mainly from satellite remote sensing, sparsely distributed buoys, and mobile observation platforms. The buoys and mobile observation platforms are more suitable for acquiring deep ocean data. Traditional methods for dealing with such observations rely on linear and Gaussian assumptions in the assimilation of nonlinear marine characteristics, which may introduce bias in analysis and forecasting. The particle-filter assimilation method is of increasing interest because it has advantages in dealing with nonlinear assimilation problems, although it has limitations with sparsely distributed observations. To address this problem, we introduced inverse distance-weighted interpolation into the localization scheme of the localized equivalent-weights particle filter with time-distributed statistical observations method based on data from sparsely distributed buoys and mobile platforms. This improved the utilization rate of observations and increased forecast accuracy. Theoretical experiments were undertaken to highlight the characteristics of the improved method, using reanalysis data from the European Centre for Medium-Range Weather Forecasts as evaluation criteria in comparing the method with the localized weighted ensemble Kalman filter method—a hybrid particle-filter method. Results indicate that the method effectively improves assimilation and the accuracy of state estimation and forecasts of ocean temperature and salinity. Significance Statement This paper addresses the problem of assimilating sparsely distributed observations in processing ocean elements. We improved the localization scheme to deal with the sparse-observation problem based on the localized equivalent-weights particle filter with time distribution statistical observation (LEWPF–T–Sobs). This method can effectively handle the assimilation problem with sparse observations while retaining the advantages of traditional particle filtering assimilation, thus improving the accuracy of estimates of physical ocean data.

Estimation of land surface temperature in relation to land-use using remote sensing technology and geographic information system (Case study: Tabriz city(

Estimation of land surface temperature in relation to land-use using remote sensing technology and geographic information system (Case study: Tabriz city(