Temperature Probability Distribution Research Articles

Probabilistic CDF-based load forecasting model in a power distribution system

The study searches for a probabilistic forecasting model for electric power demand based on the correlation between weather temperature and electric power demand using hourly time series data spanning a year. The model is based on the temperature and power demand data's cumulative probability distribution functions or CDFs. In probabilistic forecasting, a range of potential values and their associated probabilities are predicted in addition to a single value for a given range. The qualities of CDF modeling allow it to cover these uncertainties. For scenario 1, case 2, and case 3, the mean absolute percentage error (MAPE)-based forecasting accuracy for the hourly load demand is 3.93, 4.64, and 2.56, respectively. The Gaussian distribution-based CDFs' capacity to forecast outside of the present data range is an extra advantage. The world's electric power networks are growing increasingly intricate and unpredictable through the integration of several distributed energy resources (DERs) and cutting-edge technology from renewable energy resources (RERs). Other variables, such as variations in weather patterns, shifts in the status of the economy, or adjustments to energy policies, can also add to uncertainty and not be foreseen. A body of literature demonstrates a shortage of research on LTLF and general-purpose forecasting methods. The proposed approach is flexible and might establish a standard for many different forecasting tasks. The proposed method is intended to be a flexible strategy that may be used for different utilities with varying time horizons.

Distinct anthropogenic greenhouse gas and aerosol induced marine heatwaves

In the era of escalating climate change, understanding human impacts on marine heatwaves (MHWs) becomes essential. This study harnesses climate model historical and single forcing simulations to delve into the individual roles of anthropogenic greenhouse gases (GHGs) and aerosols in shaping the characteristics of global MHWs over the past several decades. The results suggest that GHG variations lead to longer-lasting, more frequent, and intense MHWs. In contrast, anthropogenic aerosols markedly curb the intensity and growth of MHWs. Further analysis of the sea surface temperature (SST) probability distribution reveals that anthropogenic GHGs and aerosols have opposing effects on the tails of the SST probability distribution, causing the tails to expand and contract, respectively. Climate extremes such as MHWs are accordingly promoted and reduced. Our study underscores the significant impacts of anthropogenic GHGs and aerosols on MHWs, which go far beyond the customary concept that these anthropogenic forcings modulate climate extremes by shifting global SST probabilities via modifying the mean-state SST.

Non-Invasive Detection of Hidden Corrosion in Aluminum Sheets

Non-invasive detection of hidden defects using infrared thermography (IRT) mainly focuses on defects involving material loss. Of equal importance is the detection of hidden defects resulting from corrosion. This study proposes an improved method of detecting hidden defects caused by corrosion through active IRT. Aluminum sheets with or without hidden corrosion were identified using two simple analysis approaches, (1) observing differences in their mean surface temperature and visual thermal contrast and (2) comparing the temperature probability distribution of identified surfaces at a given reference temperature. The developed method only requires heating the region of interest to <66 °C with a portable heat source and monitoring the surface temperature distribution using IRT. This method may aid in building surveys and post-monitoring of metallic building envelopes, especially indoors or when environmental conditions cannot provide the needed thermal contrast for detecting hidden corrosion.

Simulation Analysis of Dynamic Damage Probability Modelling for Laser Systems

The paper proposes a method that analyses the dynamic damage probability of laser systems to address the shortcomings of the quantitative model for the damage probability of laser systems. Firstly, the far-field energy density distribution model is constructed according to the power spectrum inversion method. Then, the instantaneous on-target spot power density distribution is equivalently portrayed based on the combination of the far-field power density and the missile target characteristics. Next, the instantaneous on-target spot is combined with the tracking and aiming error to obtain the probability distribution of the energy density of the long-period on-target spot. Finally, the temperature probability distribution is obtained by analyzing the relation between the target energy density and the temperature of the inner wall of the warhead. Consequently, the damage probability was calculated. The simulation shows that there is a unique maximum damage probability when the target is flying in a straight line and the laser system strikes the missile sideways. The method can provide support for the shooting timing of high-energy laser systems.

How Do Different Processes Shape Temperature Probability Distributions? A Percentile-Averaged Temperature Tendency Decomposition

Abstract Studying temperature probability distributions and the physical processes that shape them is important for understanding extreme temperature events. Previous work has used a conditional mean temperature framework to reveal whether horizontal temperature advection drives temperature to extreme or median values at a specific location as a method to dynamically interpret temperature probability distributions. In this paper, we generalize this method to study how other processes shape temperature probability distributions and explore the diverse effects of horizontal temperature advection on temperature probability distributions at different locations and different temperature percentiles. We apply this generalized method to several representative regions to demonstrate its use. We find that temperature advection drives temperatures toward more extreme values over most land in the midlatitudes (i.e., cold air advection occurs during cold anomalies and warm air advection occurs during warm anomalies). In contrast, we find that horizontal temperature advection dampens temperature anomalies in some coastal summer monsoon regions, where extreme temperatures result from other processes, such as horizontal humidity advection and vertical temperature advection. By calculating the mean of processes conditioned on the temperature percentile, this method enables composite analysis of processes that contribute to events for all percentiles and a range of processes. We show examples of composites at different percentiles for certain processes and regions to illustrate the conditional mean analysis. This general approach may benefit future studies related to temperature probability distributions and extreme events.

Physical meaning of temperature and evaluation of distribution laws (in the area of the Lake Sevan basin)

Formulation of the problem. In the work, the most general definition of temperature was discussed and presented, the temporal probability distribution of air temperature was analyzed and evaluated. Temperature has no specific definition. Thus it will be difficult to study the nature of any geophysical phenomena, including the characteristics of temperature distribution, without understanding the modern scientific definition and adjustment of temperature. The aim of the work is to give the most modern reasonable definition of temperature or what the thermometer measures, the answer to which requires a more specific scientific justification, as well as to evaluate the patterns of possible spatiotemporal distribution of air temperature in the territory of Armenia and in the area of the Lake Sevan basin. Methods. To solve the set tasks, the authors used corresponding research and published works as a theoretical basis in the work. In the work, the average monthly data of actual observations of the temperature of the ground surface air layer and the amount of precipitation of the «Center for Hydrometeorology and Monitoring» SNCO of the Ministry of Environment of the Republic of Armenia were used. It was found that temperature is a quantity characterizing the thermal state and radiation of the terrestrial and celestial bodies and, in general, the environment, which is strongly related to the entropy change of the system; temperature is the main thermodynamic characteristic of thermal equilibrium; the thermodynamic and microscopic concepts of temperature coincide; the temperature-heat (energy) difference becomes known in the sense that a system can have high energy but low temperature. The authors applied the following research methods in the article: mathematical and statistical, extrapolation, analysis, analogy, correlation, cartographic. Results. Energy depends on the geometry (dimensions) of the system, but temperature does not. A trend of increasing air temperature is observed in the RA territory, which is also a result of the entropy change of the system. It can change very quickly, depending on the environmental factors in the given area (the growth rate of greenhouse farms, the artificial filling of valleys, which are wave carriers of air flow, the relentless use of green spaces for the purpose of public buildings, and other factors) in the process of disrupting the excessively permissible norms, which are currently separate needs serious research and prevention. The long-term variability of surface air temperature in the area of the Lake Sevan basin is analyzed. The analysis carried out made it possible to give a quantitative assessment characterizing the climate change in this region over the past 98 years. An analysis of the observational data showed that the trend of climate warming is confirmed by an increase in air temperature both in winter and over a long period of time. The change in surface air temperature occurs at a rate of 0.002 ºС/year to 0.012 ºС/year and is generally 0.008 ºС/year (or 0.08 ºС/10 years) for all analyzed stations. The results obtained confirm the presence of two periods of warming observed in 1927–1970 and 1971–2021. All the results obtained in the course of the work testify to the trend of climate mitigation in the area of the Lake Sevan basin at the end of the 20th - beginning of the 21st century.

Characteristics of drought, low temperature, and concurrent events of maize in Songliao Plain

AbstractMaize is susceptible to drought and low temperatures. In recent years, drought, low temperatures, and their composite events have often occurred during the growth period of maize, causing a huge impact. In this study, maize in the Songliao Plain was used as the research object. Based on the crop water surplus deficit index (CWSDI) and heat index (F(T)), combined with the Theil–Sen median and Mann–Kendall tests (Sen–MK) and empirical orthogonal function (EOF), the temporal trends and spatial patterns of drought and low temperatures in different growth periods of maize were obtained. Copula modelling was used to analyse the joint probability distribution and return period of drought and low temperatures. The results showed that the temporal variation trend of the CWSDI was significantly different at different growth periods of maize, and there were different trends and spatial modal distributions. However, the temporal variation trend of F(T) was similar, showing a downward trend over the entire region. There was no obvious asymmetry or skew‐dependent structure in the distribution of concurrent events of drought and low temperature; however, the joint probability contours of different growth periods were biased to the upper left. The recurrence period of drought and low‐temperature concurrent events was within 2 years, and the recurrence period was basically between 2 and 5 years, with a reduction in drought or low temperatures.

A COMPREHENSIVE METHOD OF A DEFECT MAP FORMING BASED ON THERMAL RESPONSE PATTERNS

The study considers contemporary approaches used in the analysis of infrared thermography results of a test object aimed at identifying structural features for defect map formation. Emphasis is placed on the advantages of employing statistical analysis methods in thermogram analysis, involving the quantitative assessment of signal-to-noise ratio and optimizing the thermographic analysis procedure through global extremum search of the objective function. According to the developed methodology, a set of four possible outcomes resulting from the thermographic analysis of a test object is examined, including true positive, false positive, true negative, and false negative results. The analysis of thermographic procedure results for the detection of a potential defect in an individual thermogram element is determined through the probability distribution function of temperature, which is compared with a similar function for a homogeneous section of the test object. In the basic methodology, probability distribution parameters for a homogeneous section of the test object and a section with structural features differ in terms of mean and variance. By dividing the temperature probability distribution area with a threshold value, four zones are formed: a zone where a defect is guaranteed to be absent, a zone where a defect is not detected according to the chosen threshold value, a zone where a defect is detected according to the chosen threshold value, and a zone where a defect is guaranteed to be present. The basic approach, basedon statistical methods, allows determining the accuracy of thermographic analysis for an individual thermogram element by calculating the signal-to-noise ratio based on the fundamental indicators of mean and variance of the temperature probability distribution. Within the extended scheme of statistical analysis of thermographic investigation results, a z-value is determined, based on the number of neighboring thermogram elements corresponding to a homogeneous section and a section with potential structural features.

Cascading shocks and heated electrons probability distributions on the surface and inside silicon target irradiated by femto-second laser pulse

The femtosecond laser effects on shock cascades at the surface as well as inside the silicon target are studied by using the two temperature method (TTM) for simulating the non-equilibrium system. The laser wavelength is varied from 500 to 1500 nm and significant modifications are noted in the shock cascades, an electron temperature probability distributions, cumulative probability distributions, and electron-lattice equilibration temperature. It is found that the highest and lowest electron temperatures occurred for 500 and 1500 nm lasers respectively. To explain silicon heating sensitivity to the interacting laser wavelength, fundamental parameters including reflection coefficient, absorption, extinction coefficients, refractive index, specific heat, thermal conductivity, electron-hole collision frequency, and lattice-electron coupling coefficient are comprehensively studied. The temperature distributions included non-Gaussian tails and cumulative probability distributions included shock-like jumps at distinctly different temperatures for considered five types of laser wavelengths. The findings of this study can be helpful for understanding silicon crystals interaction with an ultra-short and an intense femtosecond laser pulses.

Evaluation of Modeled Hyperspectral Infrared Spectra Against All-Sky AIRS Observations Using Different Cloud Overlap Schemes.

Hyperspectral infrared sounding contains information about clouds, which plays an important role in modulating Earth's climate. However, there is a great deal of uncertainty in modeling the radiative effect of clouds due to its complex dependence on various parameters. Therefore, cloudy scenarios are often neglected in retrievals of infrared spectral measurements and in data assimilation. One‐dimensional radiative transfer (RT) models have a limited capability to represent the cloud three‐dimensional multilayer structure. This issue is typically resolved by using a multiple independent column approach, which is computationally demanding. Therefore, it is necessary to find a balance between computational speed and accuracy for infrared RT all‐sky radiance simulations. In this study, we utilize the Community Radiative Transfer Model with four different cloud overlap schemes and compare against observations made by the Atmospheric Infrared Sounder (AIRS) using a statistical metric called the first Wasserstein distance. Our results show that the average cloud overlap scheme performs the best and successfully predicts the overall probability distribution of brightness temperature over nonfrozen oceans for a wide range of wavelengths. The mean absolute differences are less than 0.7 K for 846 selected AIRS channels between 790 cm−1 and 1231 cm−1.

Joint probability distribution of air temperature and global solar radiation for outdoor design conditions based on copula approach

In the common method of compiling outdoor design conditions by annual cumulative frequency of occurrence, the probability of simultaneous occurrence of the selected meteorological elements is not considered, which may lead to an overestimation of air-conditioning equipment capacity design. This paper aims at developing a novel approach for producing the outdoor design conditions, taking into consideration the dependency between the air temperature and global solar radiation, which have a strong influence on the HVAC capacity design. These two meteorological elements are analyzed for specific simultaneous occurrence probability based on the joint distribution. For this purpose, the copula approach which can describe the dependence between these two meteorological elements is used for modeling the joint distribution function. We investigated copula family selection for air temperature and global solar radiation at each hour of hourly weather data for 10 years from 2001 to 2010 in Osaka, Japan. The Joe-Frank copula has the best fitting results for the dependence between the air temperature and global solar radiation. According to the comparison of original exceeding probability and simultaneous occurrence probability, the results show that the design conditions based on the common method can significantly exceed that of the simultaneous occurrence probability method. Practical Application: Outdoor design conditions are used in the building design stage to estimate building performance and select the appropriate air conditioning equipment capacity. Excessive air-conditioning capacity may lead to a large amount of unnecessary energy wastage. Insufficient capacity may lead to not meeting the desired indoor environment conditions of utilization in extreme weather conditions, especially for large office buildings. Therefore, this paper provides a new approach to generate more realistic meteorological conditions for air-conditioning design to improve building energy efficiency.

MESMER-M: an Earth system model emulator for spatially resolved monthly temperature

Abstract. The degree of trust placed in climate model projections is commensurate with how well their uncertainty can be quantified, particularly at timescales relevant to climate policy makers. On inter-annual to decadal timescales, model projection uncertainty due to natural variability dominates at the local level and is imperative to describing near-term and seasonal climate events but difficult to quantify owing to the computational constraints of producing large ensembles. To this extent, emulators are valuable tools for approximating climate model runs, allowing for the exploration of the uncertainty space surrounding selected climate variables at a substantially reduced computational cost. Most emulators, however, operate at annual to seasonal timescales, leaving out monthly information that may be essential to assessing climate impacts. This study extends the framework of an existing spatially resolved, annual-scale Earth system model (ESM) emulator (MESMER, Beusch et al., 2020) by a monthly downscaling module (MESMER-M), thus providing local monthly temperatures from local yearly temperatures. We first linearly represent the mean response of the monthly temperature cycle to yearly temperatures using a simple harmonic model, thus maintaining month-to-month correlations and capturing changes in intra-annual variability. We then construct a month-specific local variability module which generates spatio-temporally correlated residuals with yearly temperature- and month-dependent skewness incorporated within. The emulator's ability to capture the yearly temperature-induced monthly temperature response and its surrounding uncertainty due to natural variability is demonstrated for 38 different ESMs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The emulator is furthermore benchmarked using a simple gradient-boosting-regressor-based model trained on biophysical information. We find that while regional-scale, biophysical feedbacks may induce non-uniformities in the yearly to monthly temperature downscaling relationship, statistical emulation of regional effects shows comparable skill to the more physically informed approach. Thus, MESMER-M is able to statistically generate ESM-like, large initial-condition ensembles of spatially explicit monthly temperature fields, providing monthly temperature probability distributions which are of critical value to impact assessments.

Meteorological Conditions That Promote Persistent Contrails

Climate-impacting contrails need ice (super-)saturation to persist longer than a few minutes. However, this simple criterion cannot be easily applied for the prediction of persistent contrails. The current weather forecast models, which lack humidity data for assimilation in the upper troposphere, have difficulties coping with the enormous variability and sharp gradients in the relative humidity field. Thus, ice supersaturation, which is an extremal state of relative humidity, is hard to forecast at a precise location and time to allow contrail-avoiding flight routing. In this paper, we investigate the possibility of using dynamical proxy variables for improved contrail prediction. This idea is guided by the fact that the probability of ice supersaturation differs in different dynamical regimes. Therefore, we determine probability distributions of temperature, water vapour concentration, vertical velocity, divergence, relative and potential vorticity, geopotential height, and lapse rate conditioned on three situations: (a) contrail persistence not possible; (b) contrail persistence possible; and (c) strongly warming persistent contrails possible. While the atmospheric variables are taken from reanalysis data, the conditions (a–c) are based on airborne measurement data and radiation quantities from the reanalysis. It turns out that the vorticity variables, and in particular geopotential and lapse rate, show quite distinct conditional probabilities, suggesting a possibility to base an improved forecast of persistent contrails not only on the traditional quantities of temperature and relative humidity, but on these dynamical proxies as well. Furthermore, we show the existence of long flight tracks with the formation of strongly warming contrails, which are probably embedded in larger ice-supersaturated regions with conditions that foster such contrails. For forecasting purposes, this is a beneficial property since the humidity forecast is easier on large, rather than small, spatial scales.

Dynamic portfolio decisions with climate risk and model uncertainty

ABSTRACT We study the effect of investment horizon on the optimal stock–bond–cash portfolio in a dynamic model with uncertainty about climate change. The stock risk premium is assumed to be an affine function of the average global temperature and an unobserved factor which is estimated via Bayesian learning. We assume that the probability distribution of future temperature is uncertain. The optimal investment strategy, robust to the uncertainty about climate change, is derived in closed form and analyzed for returns on the S&P500 index and the S&P500 ESG index. We find that stock market investment is quite sensitive to climate uncertainty with allocation to the S&P500 index being the most sensitive. We also show that, even for relatively short time horizons, welfare losses from climate uncertainty could be large for investments in either the S&P500 index or the S&P500 ESG index.

Bivariate bias correction of the regional climate model ensemble over the Adriatic region

AbstractThe main goal of this analysis is to provide an assessment of the fine grid regional climate model (RCM) ensemble over the broader Adriatic region, which is the domain with orographically complex terrain and a developed coastline. The results of this study are a beneficial complement to earlier work on using modified bivariate bias correction methods. This analysis was based on an ensemble of 12 combinations of three RCMs and four global climate models (GCMs) from 1971 to 2004, divided into calibration and validation parts. A comparison of one univariate and three versions of bivariate bias correction methods of summer and winter monthly temperature and precipitation was performed. To examine the effect of various methods, we used different marginal distributions and distributions of dependence (copula). Bivariate bias correction was conducted with parametric and empirical marginal distributions. In the quantile mapping (univariate) and the parametric bivariate method, gamma (precipitation) and normal (temperature) probability distributions were used. We documented the bias and the influence of bias correction methods on precipitation and temperature, including their climate change signals in historical data. The considered methods maintained the spatial distribution of trends from the native ensemble. If the relationship between variables was not distinct and the correlation coefficient was low, bivariate bias correction methods tended to weaken the correlation coefficient from the model. Two experiments on how the bias correction methods influence statistical measures of the considered variables and their relationships showed that the bivariate method with empirical distributions (eeG) performed slightly better than other methods. Although the eeG method performed better than the other methods, none of the considered methods was able to fully preserve the correlation coefficient between variables detected from observations. Considering two copulas (Gaussian and Student's t), we conclude that the effect of different copula types on method performance is small.

Non-Gaussian speed distributions: OMNI multisatellite data and simulations based on two temperature model

The multi-satellite OMNI solar wind speed data are analyzed, and six distinct types of non-Gaussian probability distributions are investigated. These include the three and four peak probability distributions, bell-shaped probability distribution with nipple-like single peak, bimodal, kinky-flat-top, and bump-on-tail speed distributions. It is also found that cumulative probability distributions for these included the shock-like, sudden jumps, and linear trends toward the peak value of one. This work also studies temperature probability distributions for laser-solid coupling, by analyzing the simulation data for the two temperature model. It is very surprising to find that non-Gaussian temperature distributions resulting from laser–solid interaction match with those found in solar wind data. Therefore, it is concluded that high frequency electromagnetic radiation is a valid candidate for shaping non-Gaussian speed distributions in solar wind.

Interpreting Observed Temperature Probability Distributions Using a Relationship between Temperature and Temperature Advection

Abstract The nonnormality of temperature probability distributions and the physics that drive it are important due to their relationships to the frequency of extreme warm and cold events. Here we use a conditional mean framework to explore how horizontal temperature advection and other physical processes work together to control the shape of daily temperature distributions during 1979–2019 in the ERA5 dataset for both JJA and DJF. We demonstrate that the temperature distribution in the middle and high latitudes can largely be linearly explained by the conditional mean horizontal temperature advection with the simple treatment of other processes as a Newtonian relaxation with a spatially variant relaxation time scale and equilibrium temperature. We analyze the role of different transient and stationary components of the horizontal temperature advection in affecting the shape of temperature distributions. The anomalous advection of the stationary temperature gradient has a dominant effect in influencing temperature variance, while both that term and the covariance between anomalous wind and anomalous temperature have significant effects on temperature skewness. While this simple method works well over most of the ocean, the advection–temperature relationship is more complicated over land. We classify land regions with different advection–temperature relationships under our framework, and find that for both seasons the aforementioned linear relationship can explain ∼30% of land area, and can explain either the lower or the upper half of temperature distributions in an additional ∼30% of land area. Identifying the regions where temperature advection explains shapes of temperature distributions well will help us gain more confidence in understanding the future change of temperature distributions and extreme events.

The Unconditional Probability Distribution of Future Emission and Temperatures

The Unconditional Probability Distribution of Future Emission and Temperatures

Probability prediction of pavement surface low temperature in winter based on bayesian structural time series and neural network

Reliable pavement surface temperature prediction models are important for decision making in winter road maintenance. Due to the complexity of thermal environment in winter, there is inevitably uncertainty in pavement temperature. Therefore, the prediction results of pavement surface temperature should not be a single value, but a probability distribution. This paper developed a prediction model for evaluating the probability distribution of pavement surface temperature in winter. The model consisted of two modules, namely, a Bayesian Structural Time Series module (BSTS) and a Bayesian Neural Network module (BNN). The BSTS module provided a relatively rough prediction of pavement surface temperature, while the BNN module provided refined prediction based on the results from the BSTS module. The model was validated with data collected from the field test. The impact of model structure, model parameters, and probability threshold on the predictions was analyzed. Results show that most measured pavement temperatures fell into the 95% confidence interval of the predicted values, indicating the prediction was reliable. For the BSTS module, the regression component plays the most important role in the prediction. The seasonal component also has obvious influence on the prediction results. While the influence of local linear trend component on the prediction result is not obvious. For the BNN module, although the slippery index and pavement surface condition do not directly contribute to the heat transfer process, these two factors have correlation with the pavement surface temperature. The value of probability threshold should be properly determined to balance the reliability and computational cost of the prediction model. The model presented in this paper is useful for transportation agencies in winter road maintenance

Retraction Note to: Probability distribution of seasonal temperature based on GIS and management of employment information of college students

Retraction Note to: Probability distribution of seasonal temperature based on GIS and management of employment information of college students