Surface Temperature Estimates Research Articles

Assessing homogeneity of land surface air temperature observations using sparse‐input reanalyses

AbstractState‐of‐the‐art homogenisation approaches for any test site rely upon the availability of a sufficient number of neighbouring sites with similar climatic conditions and a sufficient quantity of overlapping measurements. These conditions are not always met, particularly in poorly sampled regions and epochs. Modern sparse‐input reanalysis products which are constrained by observed sea surface temperatures, sea‐ice and surface pressure observations, continue to improve, offering independently produced surface temperature estimates back to the early 19th century. This study undertakes an exploratory analysis on the applicability of sparse‐input reanalysis to identify breakpoints in available basic station data. Adjustments are then applied using a variety of reanalysis and neighbour‐based approaches to produce four distinct estimates. The methodological independence of the approach may offer valuable insights into historical data quality issues. The resulting estimates are compared to Global Historical Climatology Network version 4 (GHCNMv4) at various aggregations. Comparisons are also made with five existing global land surface monthly time series. We find a lower rate of long‐term warming which principally arises in differences in estimated behaviour prior to the early 20th century. Differences depend upon the exact pair of estimates, varying between 15 and 40% for changes from 1850–1900 to 2005–2014. Differences are much smaller for metrics starting after 1900 and negligible after 1950. Initial efforts at quantifying parametric uncertainty suggest this would be substantial and may lead to overlap between these new estimates and existing estimates. Further work would be required to use these data products in an operational context. This would include better understanding the reasons for apparent early period divergence including the impact of spatial infilling choices, quantification of parametric uncertainty, and a means to update the product post‐2015 when the NOAA‐CIRES‐DOE 20CRv3 sparse input reanalysis product, upon which they are based, presently ceases.

Downwelling longwave radiation and sensible heat flux observations are critical for surface temperature and emissivity estimation from flux tower data

Land surface temperature (LST) is a preeminent state variable that controls the energy and water exchange between the Earth’s surface and the atmosphere. At the landscape-scale, LST is derived from thermal infrared radiance measured using space-borne radiometers. In contrast, plot-scale LST estimation at flux tower sites is commonly based on the inversion of upwelling longwave radiation captured by tower-mounted radiometers, whereas the role of the downwelling longwave radiation component is often ignored. We found that neglecting the reflected downwelling longwave radiation leads not only to substantial bias in plot-scale LST estimation, but also have important implications for the estimation of surface emissivity on which LST is co-dependent. The present study proposes a novel method for simultaneous estimation of LST and emissivity at the plot-scale and addresses in detail the consequences of omitting down-welling longwave radiation as frequently done in the literature. Our analysis uses ten eddy covariance sites with different land cover types and found that the LST values obtained using both upwelling and downwelling longwave radiation components are 0.5–1.5 K lower than estimates using only upwelling longwave radiation. Furthermore, the proposed method helps identify inconsistencies between plot-scale radiometric and aerodynamic measurements, likely due to footprint mismatch between measurement approaches. We also found that such inconsistencies can be removed by slight corrections to the upwelling longwave component and subsequent energy balance closure, resulting in realistic estimates of surface emissivity and consistent relationships between energy fluxes and surface-air temperature differences. The correspondence between plot-scale LST and landscape-scale LST depends on site-specific characteristics, such as canopy density, sensor locations and viewing angles. Here we also quantify the uncertainty in plot-scale LST estimates due to uncertainty in tower-based measurements using the different methods. The results of this work have significant implications for the combined use of aerodynamic and radiometric measurements to understand the interactions and feedbacks between LST and surface-atmosphere exchange processes.

Investigations of Middle-Caliber Anti-Aircraft Cannon Interior Ballistics including Heat Transfer Problem in Estimation of Critical Burst Length

Numerical and experimental investigations of armament systems are an important part of modern design processes. The presented paper reports problems that were encountered on the theoretical analysis of the performance of 35 mm anti-aircraft cannon and the way in which they were solved. The first problem concerns the application of results of closed vessel tests of used propellant in interior ballistics simulations. The use of a nonstandard form of the gas generation rate equation solved this problem. The second problem concerned the assessment of projectile–barrel interaction. The barrel resistance was estimated making use of finite element analysis. The third problem arose from the need to determine the heat transfer from propellant gases to the barrel. The employed formula for the heat exchange coefficient and 2D modelling of the heat conduction in the barrel provided the solution. Selected elements of the theoretical model were validated by shooting range experiments and data provided by the ammunition producer. Using the considered approach, crucial ballistic parameters (maximum propellant gas pressure and muzzle velocity) were estimated with an error of less than 6.0%, without application of additional fitting coefficients. The numerical estimation of the barrel external surface temperature provided a relative discrepancy with the experimental data lower than 6% and enabled the estimation of the critical burst length, equal to 14 shots.

Sea ice changes in the southwest Pacific sector of the Southern Ocean during the last 140 000 years

Abstract. Sea ice expansion in the Southern Ocean is believed to have contributed to glacial–interglacial atmospheric CO2 variability by inhibiting air–sea gas exchange and influencing the ocean's meridional overturning circulation. However, limited data on past sea ice coverage over the last 140 ka (a complete glacial cycle) have hindered our ability to link sea ice expansion to oceanic processes that affect atmospheric CO2 concentration. Assessments of past sea ice coverage using diatom assemblages have primarily focused on the Last Glacial Maximum (∼21 ka) to Holocene, with few quantitative reconstructions extending to the onset of glacial Termination II (∼135 ka). Here we provide new estimates of winter sea ice concentrations (WSIC) and summer sea surface temperatures (SSST) for a full glacial–interglacial cycle from the southwestern Pacific sector of the Southern Ocean using the modern analog technique (MAT) on fossil diatom assemblages from deep-sea core TAN1302-96. We examine how the timing of changes in sea ice coverage relates to ocean circulation changes and previously proposed mechanisms of early glacial CO2 drawdown. We then place SSST estimates within the context of regional SSST records to better understand how these surface temperature changes may be influencing oceanic CO2 uptake. We find that winter sea ice was absent over the core site during the early glacial period until MIS 4 (∼65 ka), suggesting that sea ice may not have been a major contributor to early glacial CO2 drawdown. Sea ice expansion throughout the glacial–interglacial cycle, however, appears to coincide with observed regional reductions in Antarctic Intermediate Water production and subduction, suggesting that sea ice may have influenced intermediate ocean circulation changes. We observe an early glacial (MIS 5d) weakening of meridional SST gradients between 42 and 59∘ S throughout the region, which may have contributed to early reductions in atmospheric CO2 concentrations through its impact on air–sea gas exchange.

Analytical and Machine Learning-based approaches to estimate the steady-state temperature limit on the surface of Cu powder beds when heated by a concentrated laser energy source

Surface temperature induced by concentrated energy sources, such as a laser beam used during a selective laser sintering process, has been measured, modeled, and estimated before by several authors. Estimating surface temperature by models including radiation heat transfer has commonly been pursued using elaborated numerical schemes such as finite differences, finite volumes, or finite element schemes, which could be costly to implement. A systematic study of the evolution of the surface temperature at a given fixed point in Cu powder beds exposed to the focused laser beam of a selective laser sintering system is presented here. Incorporation of radiation heat transfer effect into the classical 1-D surface heating-cooling heat transfer model allowed the development of an implicit predictive model for the surface temperature evolution during the heating and cooling of the specimen, which in turn and at its time limit, arrived at a closed-form analytical expression for the plateau temperature of a given superficial point when heated up by a focused laser beam. On the other hand, this latter asymptotic expression, as expected, corresponds to the surface temperature, which can be isolated directly from Stefan-Boltzmann's radiation law—making it possible to predict plateau temperatures (i.e., Stefan-Boltzmann temperature limit) of the Cu powder bed surface at different laser powers with an error of less than 2% compared to surface temperature measurements done using an infrared pyrometer. Concerning the prediction capacity of this expression, the developed technique could be applied not only to different material systems but could also be adapted to handle a moving laser in a given scan pattern, thus obtaining an analytical tool to model better the heat transfer phenomena of, for example, laser powder bed - sintering/melting processes. In parallel, three Machine Learning techniques were implemented, an artificial neural network, specifically the Multi-layer Perceptron, a Support Vector Regression, and Random Forests Regressor, to study the feasibility of these approaches in predicting the steady-state temperature as well, depending on the laser power and the measurement time. From the achieved results, both methodologies are considered suitable to model the temperature behavior of the analyzed specimens.

Differences between soil and air temperatures: Implications for geological reconstructions of past climate

AbstractAmong quantities of interest in paleoclimate, the mean annual air temperature, Ta, directly over the surface looms prominently. Most geologic estimates of past temperatures from continental regions, however, quantify temperatures of the soil or other material below the surface, Ts, and in general Ta < Ts. Both theory and data from the FLUXNET2015 data set of surface energy balance indicate systematic dependences of temperature differences ΔT = Ts − Ta and also of Bowen ratios—ratios of sensible to latent heat fluxes from surface to the atmosphere—on the nature of the land-surface cover. In cold regions, with mean annual temperatures ≲5 °C, latent heat flux tends to be small, and values of ΔT can be large, 3–5 °C or larger. Over wet surfaces, latent heat fluxes dominate sensible heat fluxes, and values of both ΔT and Bowen ratios commonly are small. By contrast, over arid surfaces that provide only limited moisture to the overlying atmosphere, the opposite holds. Both theory and observation suggest the following, albeit approximate, mean annual values of ΔT: for wetlands, 1 °C; forests, 1 ± 1 °C; shrublands, 3–4 °C; savannas, 3.5 °C < ΔT < 5.5 °C; grasslands, 1 °C where wet to 3 °C where arid; and deserts, 4–6 °C. As geological tools for inferring past land-surface conditions improve, these approximate values of ΔT will allow geologic estimates of past mean annual surface temperatures, Ts, to be translated into estimates of past mean annual air temperatures, Ta.

COMPARISON OF SPLIT WINDOWS ALGORITHM AND PLANCK METHODS FOR SURFACE TEMPERATURE ESTIMATION BASED ON REMOTE SENSING DATA IN SEMARANG

Surface temperature is one of the parameters in land–surface physical processes and is applied to global warming, climate change, and cycle hydrology. Two thermal bands in Landsat 8 imagery can be used as input for surface temperature extraction using the Split Windows Algorithm (SWA) and Planck method. This study aims to compare surface temperature estimates using the SWA and Planck methods and determine the surface temperature distribution based on the condition of land cover and its changes. The remote sensing data used are Landsat-8 OLI/TIRS Aqua MODIS images on August 27, 2013, and October 1, 2020. The results showed that Landsat 8 could obtain land cover information with an accuracy of 90% in 2013 and 91% in 2020. Planck surface temperature in 2013 was 1-3°C higher than SWA, while in 2020, Planck was 0.001-0.05°C lower than SWA but had similar distribution and pattern. The vegetation in the study area's central and south sides has a lower surface temperature than the built-up area on the north side. Land cover changes from non-built up to build-up area cause surface temperatures to increase. Each land cover has a different emissivity value and affects the surface temperature value, i.e., the lower the emissivity, the higher the surface temperature. The emissivity with surface temperature has a pearson correlation value ≥-0.8**.Keywords: Surface Temperature, Split Windows Algorithm, Planck

Using ground-based thermography to analyse surface temperature distribution and estimate debris thickness on Gran Zebrù glacier (Ortles-Cevedale, Italy)

In this paper we used ground-based thermal infrared imaging to investigate the spatial variability of surface temperatures on a mountain glacier at high spatial resolution and to estimate the supraglacial debris thickness distribution. The surveyed area is the eastern tongue of Gran Zebrù glacier, a small mountain glacier of the Ortles-Cevedale group (Italy). A FLIR E85 Thermal Camera was used to obtain a panoramic thermal image of the glacier surface on 30 September 2019 that was calibrated and georeferenced in order to obtain surface temperatures.Based on field data, debris thickness and debris surface temperature were correlated by using an exponential equation. The equation was used to estimate debris thickness on the glacier from the temperature data.The results show a spatial variability of surface temperatures, with the lowest temperatures found on snow and ice surfaces, and the highest on supraglacial debris. The estimated debris thicknesses show an inhomogeneous distribution, with a calculated mean debris thickness of 0.09 m in the areas of continous debris coverage. We found a good correspondence between measured and estimated debris thickness at 30 validation points (RMSE = 0.04 m and r = of 0.92). We demonstrated that ground-based thermal imaging can provide very high resolution maps of glacier surface temperatures and of debris thickness that can be used for the estimation of glacier ablation, with a spatial resolution more suitable than that offered by satellite data, especially for glaciers with a small area and with a heterogeneous surface.

Smart Core and Surface Temperature Estimation Techniques for Health-Conscious Lithium-Ion Battery Management Systems: A Model-to-Model Comparison

Estimation of core temperature is one of the crucial functionalities of the lithium-ion Battery Management System (BMS) towards providing effective thermal management, fault detection and operational safety. It is impractical to measure the core temperature of each cell using physical sensors, while at the same time implementing a complex core temperature estimation strategy in onboard low-cost BMS is also challenging due to high computational cost and the cost of implementation. Typically, a temperature estimation scheme consists of a heat generation model and a heat transfer model. Several researchers have already proposed ranges of thermal models with different levels of accuracy and complexity. Broadly, there are first-order and second-order heat resistor–capacitor-based thermal models of lithium-ion batteries (LIBs) for core and surface temperature estimation. This paper deals with a detailed comparative study between these two models using extensive laboratory test data and simulation study. The aim was to determine whether it is worth investing towards developing a second-order thermal model instead of a first-order model with respect to prediction accuracy considering the modeling complexity and experiments required. Both the thermal models along with the parameter estimation scheme were modeled and simulated in a MATLAB/Simulink environment. Models were validated using laboratory test data of a cylindrical 18,650 LIB cell. Further, a Kalman filter with appropriate process and measurement noise levels was used to estimate the core temperature in terms of measured surface and ambient temperatures. Results from the first-order model and second-order models were analyzed for comparison purposes.

A comparison of orbital-resolution, Late Pleistocene Alkenone and foraminiferal assemblage-based sea surface temperature reconstructions from the Southwest Pacific

Global and regional reconstructions of past climate conditions often incorporate sea surface temperature (SST) estimates from multiple proxies because not every paleotemperature proxy is applicable in all geographic locations. This practice of assimilating estimates from different proxies in global or regional temperature syntheses makes the implicit assumption that estimates derived from different proxies can be meaningfully intercompared. However, evidence to support the validity of this assumption is limited. Using paleotemperature data from sediments collected from ODP Site 1125 in the Southwest Pacific, we conduct a ∼1 Myr, orbital-scale SST proxy comparison of a recently published alkenone-derived SST record with a previously published foraminiferal assemblage-based SST record. These alkenone and foraminiferal assemblage SST datasets show strong structural similarity and yield remarkably similar estimates for basic climate metrics, including mean, median, standard deviation, and range. Statistical analysis indicates that the correlation between the two SST records is highly significant. In the spectral domain, the records share the same dominant 100 kyr beat, are coherent and in phase with each other at this frequency, and have the same coherence and phase relationship with benthic foraminiferal δ18O. Results from this work demonstrate that these two proxies would yield very similar estimates for the paleoclimate metrics most commonly used in empirical paleoclimate reconstructions that seek to document the evolution of climate over this interval. However, significant disparities between SST estimates derived from the two proxies exist for some time periods, particularly during glacial and interglacial extrema. This comparison suggests that treating estimates from these proxies as equivalent in studies that focus on short time windows (e.g. a few thousand to tens-of-thousands of years), particularly in investigations that seek to characterize glacial or interglacial extrema, could be potentially problematic. However, the sensitive location of Site 1125, just north of the Subtropical Front, likely accentuates the difference between temperature estimates from these proxies, which may be attenuated in other oceanographic settings. We attribute the discrepancies between the two SST records to two main causes: seasonal leakages of cold water across the Subtropical Front during glacial extrema and differential seasonality of maximum alkenone and foraminiferal production.

Prediction of actual fatigue test temperature and isothermal fatigue life curves for non-crystallising rubber under fully relaxing uni-axial loading conditions

The time to complete fatigue tests can be rather long, especially with high cycle fatigue. Increasing frequency in fatigue tests decreases the time needed to complete material characterisation. For rubber materials, considerable internal heat generation and relatively low thermal conductivity causes temperature rises that can greatly reduce fatigue lifetime. Based on both experimental tests under fully relaxing uni-axial loading conditions on a non-crystallising rubber compound and numerical simulations, a method for estimating test temperature and isothermal fatigue life curves is presented. The results of the comparison between measured and estimated surface temperature of the specimen show that the maximum estimation error is below 10 %.

On the estimation of maximum surface temperature for cells

In paperwork is presented the estimation of the maximum surface temperature of cells exposed to specific tests for the intrinsic safety type of protection. Particularly, it presents the thermal resistance of the short-circuit test-stand results. The first part introduces the aspects regarding the risk of explosion. The risk of explosion occurs due to the presence of technical equipment in areas where flammable substances may occur. The second part itemizes the requirements for the testing of cells. Also, is introduced the stand configuration and performance aspects. The third part of the paper has been dedicated to the presentation and the discussion of the obtained results. The analysis of the test results highlighted the range of thermal resistance. This result could help estimation of the surface temperature of cells based on their capacity and internal resistance. The convection coefficients were determined. The process of deep discharge destroys the inner electrochemical of the cell system. Therefore, it permits energy recovery lower than a fraction of a tenth.

Estimation of Surface Temperature on Traction Roller by Using the Therom-Fluid Dynamic Analysis Model

Traction drives is devices that transmits power by the shear stress of the lubricating oil supplied between the rolling elements. Since the designing traction drives based on the traction coefficient, it is necessary to know the traction coefficient accurately. However, the necessary roller surface temperature is usually used experimental values in this process. Therefore, roller surface temperature was estimated by using thermo-fluid dynamic analysis in this study. In previous studies, a model was created with a thicker oil film than the actual the oil film thickness of the contact point. On the other hand, a model with an oil film thickness closer to the actual thickness of the contact point was created for the purpose of more accurate estimation of the roller surface temperature in this study. Furthermore, the roller surface temperature was compared between analysis result and experimental result. As a result, it was found that the analytical values tended to be higher than the experimental values under high peripheral speed conditions. It was considered as cause that there is no heat transfer from the roller to the shaft in this model.

ТОЧНОСТЬ СПУТНИКОВОЙ ОЦЕНКИ ТЕМПЕРАТУРЫ ПОВЕРХНОСТИ ГЕТЕРОГЕННЫХ УРБАНИЗИРОВАННЫХ УЧАСТКОВ

Temperatures in urban areas are always higher than in rural areas, “urban heat islands” noticeably affect the urban climate, which puts the theme under discussion in the category of acute environmental problems. The author suggests a possibility of accurate satellite-based estimation of surface temperature of heterogeneous urbanized areas. The object of the study is surfaces of urbanized areas. The subject of the study is satellite temperature estimation of “urban heat islands”. The aim of the study is to scientifically substantiate the probability of an accurate satellite assessment of surface temperature of heterogeneous urbanized areas. The realization of the aim involves solving a number of tasks, the main ones of which are: - to reveal the dependence of the depth of the carried out correction of the emissivity variability influence on the measured value of the degree and character of emissivity change from the wavelength; - to consider the emissivity properties of new and old asphalt pavement. The research methodology is presented by the ecological approach and structural-functional approach. The aim of the study and its objectives has determined the need to use the following methods: analysis, synthesis, induction, deduction, comparison, explanation. The task of increasing the accuracy of remote sensing of heat islands appearing in urbanized areas by taking into account the emissivity index spectral dependence of objects on the surface of the ground cover on the wavelength has been formulated and solved. It is determined that the optimum is the inverse dependence of the emissivity coefficient on wavelength. Emission properties of new and old asphalt pavement have been considered, and on the basis of the analysis the author has made a conclusion about the importance of taking into account the temperature of such a pavement. It has been found that if, according to the Planck curve family, asphalt pavement emits at an appropriate temperature in the 9…11 pm range at the maximum energy, the requirement for the correction may weaken, and, consequently, the error due to the failure to consider the influence of spectral variability in emissivity may diminish

Intracity Temperature Estimation by Physics Informed Neural Network Using Modeled Forcing Meteorology and Multispectral Satellite Imagery

Estimating urban surface temperature at high resolution is crucial for effective urban planning for climate-driven risks. This high-resolution surface temperature over broader scales can usually be obtained via satellite remote sensing for historical period. However, it can be hard for future predictions. This paper presents a Physics Informed Hierarchical Perception (PIHP) network, a novel approach for accurate, high-resolution and generalizable urban surface temperature estimation. The key to our approach is leveraging the implied temperature-related physics information of the land surface structure from high-resolution multi-spectral satellite images, thus achieving precise estimation or prediction for high spatial resolution urban surface temperature. Specifically, a semantic category histogram is first designed to describe the land surface structures. Based on this, a hierarchical urban surface perception network is proposed to capture the complex relationship between the underlying land surface features, upper atmosphere conditions and the intracity temperature. The proposed PIHP-Net makes it possible to generate models that can generalize across different cities, thus to estimating or predicting high-resolution urban surface temperature when the satellite land surface temperature (LST) observation is not available. Experiments over various cities in different climate regions in China show, for the first time, errors less than 2 Kelvin (for most of the cases) at the high resolution (60-by-60 meters grids), thus making it possible to predict future <i>intracity temperature</i> from forcing meteorology and multi-spectral satellite imagery.

DeepUrbanDownscale: A physics informed deep learning framework for high-resolution urban surface temperature estimation via 3D point clouds

Accurate high-resolution downscaling of surface climate variables (such as surface temperature) over urban areas has long been a critical yet unresolved research problem in the field of urban climate and environmental sciences. In this paper, we propose a novel physics informed neural network (PINN) based framework: DeepUrbanDownscale (DUD) for high-resolution urban surface temperature estimation. Anchored in process-based modeling and satellite remote sensing, the DUD network leverages the high-precision 3D point clouds to achieve accurate urban land surface temperature (LST) estimation at an ultra-high spatial resolution. This network, ingesting the high-precision land surface geometry information derived from 3D point clouds and guided by the atmospheric physics related to surface temperature, constructs a physics informed data-driven framework to fit high-resolution temperature distribution, which is otherwise difficult to be obtained by physical (numerical) simulations or traditional machine learning. Specifically, the proposed DUD network contains two branches: The Global Feature Perception (GPFP) branch and Local Urban Surface Perception (LUSP) branch. The former considers the broader-scale urban physical parameters, constraining the estimation results in accordance with the relevant physical laws. The latter, by employing a proposed local spatial coefficient index (LSCI), which is based on 3D point clouds, the estimation performance is further improved at a very high resolution. Results from designed experiments demonstrate that the proposed DUD network predicts the urban LST on a 30-by-30 m grid with the estimated error less than 0.2 Kelvin compared to the satellite measurement, which is well below the errors of other traditional methods.

Estimation of Surface and Near-Surface Air Temperatures in Arid Northwest China Using Landsat Satellite Images

Near-surface air (Ta) and land surface (Ts) temperatures are essential parameters for research in the fields of agriculture, hydrology, and ecological changes, which require accurate datasets with different temporal and spatial resolutions. However, the sparse spatial distribution of meteorological stations in Northwest China may not effectively provide high-precision Ta data. And it is not clear whether it is necessary to improve the accuracy of Ts which has the most influence on Ta. In response to this situation, the main objective of this study is to estimate Ta for Northwest China using multiple linear regression models (MLR) and random forest (RF) algorithms, based on Landsat 8 images and auxiliary data collected from 2014 to 2019. Ts, NDVI (Normalized Difference Vegetation Index), surface albedo, elevation, wind speed, and Julian day were variables to be selected, then used to estimate the daily average Ta after analysis and adjustment. Also, the Radiative Transfer Equation (RTE) method for calculating Ts would be corrected by NDVI (RTE-NDVI). The results show that: 1) The accuracy of the surface temperature (Ts) was improved by using RTE-NDVI; 2) Both MLR and RF models are suitable for estimating Ta in areas with few meteorological stations; 3) Analyzing the temporal and spatial distribution of errors, it is found that the MLR model performs well in spring and summer, and is lower in autumn, and the accuracy is higher in plain areas away from mountains than in mountainous areas and nearby areas. This study shows that through appropriate selection and combination of variables, the accuracy of estimating the pixel-scale Ta from satellite remote sensing data can be improved in the area that has less meteorological data.

The Yorktown Formation: Improved Stratigraphy, Chronology, and Paleoclimate Interpretations from the U.S. Mid-Atlantic Coastal Plain

The Yorktown Formation records paleoclimate conditions along the mid-Atlantic Coastal Plain during the mid-Piacenzian Warm Period (3.264 to 3.025 Ma), a climate interval of the Pliocene in some ways analogous to near future climate projections. To gain insight into potential near future changes, we investigated Yorktown Formation outcrops and cores in southeastern Virginia, refining the stratigraphic framework. We analyzed 485 samples for alkenone-based sea surface temperature (SST) and productivity estimates from the Holland and Dory cores, an outcrop at Morgarts Beach, Virginia, and the lectostratotype of the Yorktown Formation at Rushmere, Virginia, and analyzed planktonic foraminferal assemblage data from the type section. Using the structure of the SST record, we improved the chronology of the Yorktown Formation by establishing the maximum age ranges of the Rushmere (3.3–3.2 Ma) and Morgarts Beach (3.2–3.15 Ma) Members. SST values for these members average ~26 °C, corroborating existing sclerochronological data. Increasing planktonic foraminifer abundance, productivity, and species diversity parallel increasing SST over the MIS M2/M1 transition. These records constitute the greatest temporal concentration of paleoecological estimates within the Yorktown Formation, aiding our understanding of western North Atlantic temperature patterns, seasonality and ocean circulation during this interval. We provide a chronologic framework for future studies analyzing ecological responses to profound climate change.

A Novel Electro-Thermal Model of Lithium-Ion Batteries Using Power as the Input

Considering that use of measured current as input of a battery model may cause distortion of the model due to low accuracy of the on-board current sensor and that power can be used to indicate energy transmission in an electric vehicle model, the power input internal resistance model is widely used in simulation of whole electric vehicles. However, since no consideration is given to battery polarization and electro-thermal coupling characteristics, the foregoing model cannot be used to describe the internal temperature change of batteries under working conditions. Three contributions are made in the present study: (1) ternary lithium-ion batteries were taken as the research objects and a second-order RC equivalent circuit model with power as the input was established in the present study; (2) A dynamic heat generation rate model suitable for RC equivalent circuits was built based on coupled electrical and thermal characteristics of lithium-ion batteries; (3) An electric model and a two-state equivalent thermal network model were further built and combined by using the heat generation rate model to form a power input electro-thermal model. Parameters of the model so formed were identified offline, and the battery model was verified with respect to accuracy under seven working conditions. The results show that the maximum root mean square error in voltage estimation, current estimation, and surface temperature estimation is 19.38 mV, 9.51 mA, and 0.19 °C respectively, which indicates that the power input electro-thermal model can describe the electrical and thermal dynamic behavior of batteries more accurately and comprehensively than the traditional power input internal resistance model.

Estimation of land surface temperature in response to land use/land cover transformation in Kolkata city and its suburban area, India

ABSTRACT The land transformation in Kolkata city and its suburban area is mainly due to intensive population pressure and rapid urban sprawling. Consequently, the land surface temperature (LST) is continuously increasing and gradually intensifying the effects of the urban heat island. The aim of this study is to assess the spatiotemporal variation of LST in response to land use land cover change (LULC) during 1995–2020. The maximum likelihood classifier was used for the supervised classification of LULC and the accuracy assessment was done using the confusion matrix. Quin’s Mono-window algorithms for Landsat TM data of 1995 and 2010 and split-window algorithms for Landsat 8 OLI data of 2020 were applied to retrieve LST. Several spectral indices such as Normalized difference built-up index (NDBI), Normalized difference vegetation index (NDVI), and Modified normalized difference water index (MNDWI) were calibrated and pixel-specific overlay analysis was done for correlation study between spectral indices and LST. This work revealed that the rapid urban sprawling causes massive land transformation in the study area. The land conversions from trees outside forests (TOF) and agricultural land to built-up were significantly contributing to an overall increase in the mean LST during 1995–2020. The mean LST was comparatively high over Kolkata city than its suburban area. During 1995–2020, the mean LST was increased by nearly 8.43°C in the summer season and 4.32°C in the winter season. The increasing rate of LST was found relatively high over the built-up (7.06°C), agricultural land without crop (5.55°C), and open land (5.54°C). However, it was comparatively low over TOF (4.66°C) and water bodies (3.68°C). The LST was positively correlated to NDBI and negatively correlated to NDVI and MNDWI. In order to combat urban warming, this study will promote green city initiatives through sustainable land use planning.