Temperature Probability Distribution Research Articles

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

Risk changes of compound temperature and precipitation extremes in China under 1.5 °C and 2 °C global warming

Based on the simulated temperature and precipitation from CMIP6 models, the joint distribution characteristics of summer temperature and precipitation in China are described in the Copula approach. The occurrence risk of compound extremes (i.e., warm/dry, and warm/wet events) and corresponding univariate events (i.e., warm, wet, and dry events) in historical period are compared; and the risk changes of compound extremes are discussed under global warming 1.5 °C and 2 °C. Results show that: 1) the Copula approach can describe the joint probability distribution of summer temperature and precipitation in observation and model simulations in most parts of China except for the Qinghai-Tibet Plateau; 2) The average risk of warm and wet events in China increases by 2.3 and 1.16 times under 1.5 °C warming respectively, increases by 2.83 and 1.29 times under 2 °C warming respectively while the risk of dry events decreases in most parts of China; 3) The average of warm/wet events in China increases by 5.48 and 10.01 times under 1.5 °C and 2 °C warming, respectively. Regions over Northern China and South China experience the most increased risk about more than 8 times. The warm/dry events show less increase magnitude with 1.82 and 2.04 times under two warming climates, respectively. The highest increase in risk is mainly located in Northern China. At an additional 0.5 °C warming from 1.5 °C to 2 °C, the regional average risk of warm/dry and warm/wet events increases by 1.40 and 2.74 times, respectively. In most areas, the risk of warm/wet events increases significantly and are greater than that of warm/dry events. This indicates that controlling global warming up to 1.5 °C can avoid more intense warm/dry and warm/wet events. Our work highlights that the risk of compound extremes may be underestimated if we only consider the univariate events.

Evaluation of Extreme Temperatures Over Australia in the Historical Simulations of CMIP5 and CMIP6 Models

AbstractHistorical simulations of models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) are evaluated over 10 Australian regions for their performance in simulating extreme temperatures, among which three models with initial‐condition large ensembles (LEs) are used to estimate the effects of internal variability. Based on two observational data sets, the Australian Water Availability Project (AWAP) and the Berkeley Earth Surface Temperatures (BEST), we first analyze the models' abilities in simulating the probability distributions of daily maximum and minimum temperature (TX and TN), followed by the spatial patterns and temporal variations of the extreme indices, as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). Overall, the CMIP6 models are comparable to CMIP5, with modest improvements shown in CMIP6. Compared to CMIP5, the CMIP6 ensemble tends to have narrower interquartile model ranges for some cold extremes, as well as narrower ensemble ranges in temporal trends for most indices. Over southeast, tropical, and southern regions, both CMIP ensembles generally exhibit relatively large deficiencies in simulating temperature extremes. We also confirm that internal variability can affect the trends of the extremes and there is uncertainty in representing the irreducible variability among different LEs in CMIP6. Furthermore, the evaluation based on Perkins' skill score (PSS) and root‐mean‐square error (RMSE) in the three LEs does not directly correlate with the ranges of the trends for extreme temperatures. The findings of this study are useful in informing and interpreting future projections of temperature‐related extremes over Australia.

RETRACTED ARTICLE: Probability distribution of seasonal temperature based on GIS and management of employment information of college students

RETRACTED ARTICLE: Probability distribution of seasonal temperature based on GIS and management of employment information of college students

Mapping temperature contours for a long-span steel truss arch bridge based on field monitoring data

The temperature-induced response of long-span steel bridges can be more significant than the structural responses associated with operational loads or structural damage. These responses depend on the spatio-temporal temperature variation in bridge members, including the effective temperature and temperature difference within members. Bridges are designed to withstand the extreme temperature variations predicted for a given site. Hence, numerous studies have employed statistical analysis techniques to provide critical information for the design and maintenance of bridges during life cycles. However, the correlation between the effective temperature and temperature difference is usually ignored, which can result in inaccurate assessment of the extreme temperatures in members. In this work, the joint probability distribution for the temperature variation in a long-span steel truss arch bridge is investigated based on field monitoring data. The extreme temperature variations are mapped on contours with relevant return periods; the results show that the probability distribution of the effective temperature can be described using the normal distribution; the weighted sum of two lognormal distributions can describe the distribution of temperature difference. Moreover, extreme values of the effective temperature and temperature difference do not occur concurrently. The effective temperature and temperature difference in the structural member directly exposed to solar radiation vary significantly, while the temperature of the shaded member can be assumed uniform, which is mainly affected by air temperature. The study leads to more accurate estimation of the temperature extremes in long-span steel truss arch bridges, which is of great importance for proper design and maintenance of bridges.

An assessment of Iran's seasonal temperature probability distribution variations in the future decades

Global warming in arid and semi-arid regions such as Iran is characterized by water scarcity and drought. In this paper, climate change impact on seasonal maximum and minimum temperature was done. To do so, three global climate models including CANESM2, GFDL-ESM2M, and HADGEM2-ES were used to simulate future climate change across Iran. SDSM model was used to downscale the CANESM2 model data. The data of GFDL-ESM2M and HADGEM2-ES models were downloaded from CORDEX database. In the present paper, the time period of 1976–2005 was considered as the base period and the time scales of 2011–2040, 2041–2070, and 2071–2099 as the future periods. The RCP2.6, RCP4.5, and RCP8.5 scenarios were chosen to future projection of minimum and maximum temperature. Taylor diagram was used to model evaluation. The results showed that the performance of SDSM model in minimum and maximum temperature downscaling in the base period is better than CORDEX database. One reason could be the smaller number of stations selected compared to CORDEX grid points. Results showed the highest positive anomalies of average maximum and minimum temperature compared to the base period studied (1976–2005), related to time period 2071–2099 and RCP8.5 by 6.69 and 6.61°C, respectively. The results showed that the maximum temperature will increase between 0.82 and 3.7°C on average depending on the season, time, and type of scenario. This value is in the range of 0.4–3.87°C for the minimum temperature. Results showed that hot days will increase. Results also showed that cold nights of winter in the coming decades will be warmer than the base period. The results also indicate that the frequency distributions of minimum and maximum temperatures in the future decades will shift to warmer temperatures in response to global warming.

Revisiting cosmic microwave background radiation using blackbody radiation inversion

Blackbody radiation inversion is a mathematical process for the determination of probability distribution of temperature from measured radiated power spectrum. In this paper a simple and stable blackbody radiation inversion is achieved by using an analytical function with three determinable parameters for temperature distribution. This inversion technique is used to invert the blackbody radiation field of the cosmic microwave background, the remnant radiation of the hot big bang, to infer the temperature distribution of the generating medium. The salient features of this distribution are investigated and analysis of this distribution predicts the presence of distortion in the cosmic microwave background spectrum.

Selecting the probability distribution of annual maximum temperature in Malaysia

The issues on global warming have become very popular and been discussed both locally and internationally. This phenomenon due to the temperature rises will increase the variability of climate and more natural disasters were expected to occur. Increasing of global temperature will affect the agricultural sector, increase some of the infectious diseases that may lead to high mortality rates in humans, high demand for electricity, water and food which eventually affecting the economy of Malaysia. Hence, this work aims to study the best fitted probability distribution that describes the annual maximum temperature recorded at seventeen meteorological stations in Malaysia. The Normal, Lognormal, Gamma, Weibull and Generalized Skew Logistic distributions are considered using the maximum likelihood estimation method to estimate the parameters. The goodness of fit test and model selection criteria such as Kolmogorov-Smirnov and AndersonDarling tests, Corrected Akaike Information Criterion and Bayesian Information Criterion are used to measure the accuracy of the predicted data using theoretical probability distributions. The results show that most of the stations favour the Generalized Skew Logistic distribution as the best fitted probability distribution. Also, some stations favour the Normal, Lognormal as well as Weibull distribution as the best fitted distribution to describe the annual maximum temperature.

Estimate the probability density function of maximum temperature for the Middle East

Pollution is one reasons for increase temperature which leads to increase the heat waves which have large socioeconomic and healthy impacts on Middle East. By using monthly daily mean of maximum temperature (C°) at height (2m) covered middle east as a grid of (1581) points for selected months (March, April, May) represent spring and (Jun, July, August) represent Summer for the period 1979 to2018, from the ECMWF, model ERA-interim. Many PDFs have been proposed in recent past, but in present study Logistic, Rayleigh and Gamma distribution are used to describe the characteristics of maximum temperature. This paper attempts to determine the best fitted probability distribution of maximum temperature. To check the accuracy of the predicted data using theoretical probability distributions the goodness of fit criteria Z-test used in this paper. According to the goodness-of-fit criteria and from the graphical comparisons it can be said that Logistic distribution provides the best fit for the observed monthly daily mean of maximum temperature data.

Conditional maximum entropy and superstatistics

Superstatistics describes nonequilibrium steady states as superpositions of canonical ensembles with a probability distribution of temperatures. Rather than assume a certain distribution of temperature, recently [2020 J. Phys. A: Math. Theor. 53 045004] we have discussed general conditions under which a system in contact with a finite environment can be described by superstatistics together with a physically interpretable, microscopic definition of temperature. In this work, we present a new interpretation of this result in terms of the standard maximum entropy principle using conditional expectation constraints, and provide an example model where this framework can be tested.

Projected Increases in Monthly Midlatitude Summertime Temperature Variance Over Land Are Driven by Local Thermodynamics

AbstractThe increasing frequency of very high temperatures driven by global warming has motivated growing interest in how the probability distribution of summertime temperatures will evolve in the future. Climate models forced by increasing CO2 simulate increasing monthly‐averaged temperature variance across the midlatitudes. In this study we present evidence that these projections are credible and driven primarily by the magnitude of local warming. A first‐principles analytic theory reproduces the increased midlatitude summertime temperature variance in climate models extremely well by considering only the warming‐induced change in the climatological vapor pressure deficit. The impacts of local warming on saturation specific and relative humidity are shown to have roughly equal contributions to increases in summertime temperature variance. The vegetation response to increasing CO2 is found to be an important contributor to the uncertainty in modeled temperature variance change, highlighting the role of plants in shaping the summertime temperature distribution.

Risks of temperature extremes over China under 1.5 °C and 2 °C global warming

The Paris Agreement aims to keep global warming to well below 2 °C above pre-industrial levels and to pursue efforts to limit it to 1.5 °C, recognizing this will reduce the risks of natural disasters significantly. As changes in the risks of temperature extremes are often associated with changes in the temperature probability distribution, further analysis is still needed to improve understanding of the warm extremes over China. In this study, changes in the occurrence probability of temperature extremes and statistic characteristics of the temperature distribution are investigated using the fifth phase of the Coupled Model Intercomparison Project (CMIP5) multimodel simulations from 1861 to 2100. The risks of the once-in-100-year TXx and TNx events are projected to increase by 14.4 and 31.4 times at 1.5 °C warming. Even, the corresponding risks under 2 °C global warming are 23.3 and 50.6, implying that the once-in-100-year TXx and TNx events are expected to occur about every 5 and 2 years over China, respectively. The Tibetan Plateau, Northwest China and south of the Yangtze River are in greater risks suffering hot extremes (both day and night extremes). Changes in the occurrence probability of warm extremes are generally well explained by the combination of the shifts in location and scale parameters in areas with grown variability, i.e., the Tibetan Plateau for TXx, south of the Yangtze River for both TXx and TNx. The location (scale) parameter leading the risks of once-in-20-year TXx to increase by more than 5 (0.25) and 3 (0.75) times under 2 °C warming in the Tibetan Plateau and south of the Yangtze River, respectively. The location parameter is more important for regions with decreased variability e.g., the Tibetan Plateau for TNx, Northwest China for both TXx and TNx, with risks increase by more than 3, 6 and 4 times due to changes in location.

Compounding impact of deforestation on Borneo’s climate during El Niño events

Both deforestation and El Niño events influence Borneo’s climate, but their interaction is not well understood. Borneo’s native forest cover decreased by 37.1% between 1980 and 2015 with large areas being replaced by oil palm and a mosaic of plantations and regrowth vegetation. The island is also affected by El Niño events, resulting in severe droughts and fires. Here, we used a high-resolution climate model to simulate and evaluate how deforestation and El Niño episodes interact during the 1980–2016 period. Simulations revealed that deforestation resulted in a warmer and drier climate with the most pronounced changes in the extensively deforested regions of eastern and southern Borneo. Deforestation-linked impacts were more pronounced under El Niño than neutral (non El Niño/La Niña) conditions. Changes in climate mainly corresponded with areas with the most deforestation. There was a significant increase in the frequency of hotter and drier climatic extremes, with the probability distribution of temperature, humidity and aridity shifting from narrow to a broadening distribution. For example, the frequency of 90th percentile of the hot temperatures (defined as average monthly temperatures >28.9 °C) during the dry season increased from 10% for neutral conditions for the 1980 forest cover to 22% for neutral conditions for the 2015 forest cover. For strong El Niño events, the frequency increased from 15.6% to 32.5%. Replacement of intact native forest with oil palm resulted in increased frequency of hot temperatures to 49% for neutral and 74% for El Niño conditions. Hotter and drier conditions are likely to increase tree mortality and forest flammability (and fire-driven deforestation). The continued reduction and fragmentation of Borneo’s forests diminishes the ability to moderate regional climate impacted by larger scale and other regional/local human climate forcings.

A New Look at the Variance of Summertime Temperatures over Land

AbstractThe increasing frequency of very high summertime temperatures has motivated growing interest in the processes determining the probability distribution of surface temperature over land. Here, we show that on monthly time scales, temperature anomalies can be modeled as linear responses to fluctuations in shortwave radiation and precipitation. Our model contains only three adjustable parameters, and, surprisingly, these can be taken as constant across the globe, notwithstanding large spatial variability in topography, vegetation, and hydrological processes. Using observations of shortwave radiation and precipitation from 2000 to 2017, the model accurately reproduces the observed pattern of temperature variance throughout the Northern Hemisphere midlatitudes. In addition, the variance in latent heat flux estimated by the model agrees well with the few long-term records that are available in the central United States. As an application of the model, we investigate the changes in the variance of monthly averaged surface temperature that might be expected due to anthropogenic climate change. We find that a climatic warming of 4°C causes a 10% increase in temperature variance in parts of North America.

A Stochastic Representation of Temperature Fluctuations Induced by Mesoscale Gravity Waves

AbstractUbiquitous mesoscale gravity waves generate high cooling rates important for cirrus formation. We make use of long‐duration balloon observations to devise a probabilistic model describing mesoscale temperature uctuations (MTF) away from strong wave sources. We define background conditions based on observed probability distributions of temperature and underlying vertical wind speed fluctuations. We show theoretically that MTF are subject to damping at a rate near the Coriolis frequency when the vertical wind speed fluctuations are autocorrelated over a fraction of a Brunt‐Väisälä period. We find that for background wave activity, a decrease in temperature of 1K translates into cooling rate standard deviations and mean updraft speeds of 4–8Kh−1 and ≈ 10–20 cms−1, respectively, depending on latitude and stratification. We introduce an effective Coriolis frequency to generate cooling rates in equatorial regions consistent with balloon data. Above ice saturation, MTF are large enough to affect ice crystal nucleation. Our results help constrain uncertainty in aerosol‐cirrus interactions, provide insights to better meet challenges in comparing measurement data with model simulations, and support the development of cutting‐edge ice cloud schemes in global models.

Linkage between the Arctic Oscillation and summer climate extreme events over the middle reaches of Yangtze River Valley

The Arctic Oscillation (AO) is commonly recognized as a dominant large-scale mode influencing climate over the Northern Hemisphere. Here, the influences of May AO on summer (JJA) extreme precipitation events and summer extreme warm days over the middle reaches of Yangtze River Valley for the period 1961-2014 are investigated. Following a positive May AO, there are usually fewer summer extreme precipitation events but more summer extreme warm days over the middle reaches of Yangtze River Valley. Composite analyses show that positive May AO induces the northward displacement of the East Asian jet stream and northeastward displacement of the western Pacific subtropical high (WPSH), and causes a stronger, more northwestern subtropical northwest Pacific cyclone/anticyclone anomaly, as well as an anticyclonic circulation anomaly on the north side of the South China Sea, resulting in a northward shift of the rainfall belt and an enhancement of the East Asia summer monsoon. Therefore, the cumulative distribution probability of daily precipitation values shift significantly to a lower precipitation value, indicating lower probabilities of summer extreme precipitation events following positive May AO. A weakening of WPSH induces an anomalous sinking motion over the middle reaches of the Yangtze River Valley. The 850 hPa wind field shows southerly wind anomalies over the Jiang-Huai River Basin, which cause a decrease in total cloud cover, resulting in an increase in solar radiation flux. A significant shift of the daily maximum temperature probability distribution towards to higher values indicates higher probabilities of summer extreme warm day occurrences following positive May AO. This study will provide useful insights to help improve the understanding of the dynamics and projections of future regional extreme precipitation changes over the middle reaches of Yangtze River Valley.

Dual mixed refrigerant LNG process: Uncertainty quantification and dimensional reduction sensitivity analysis

The dual mixed refrigerant (DMR) liquefaction process is complicated and sensitive compared to the competitive propane pre-cooled mixed refrigerant liquefied natural gas (LNG) process. When any uncertainty is introduced to the process operation conditions, it is necessary for the DMR process to be re-optimized to maintain efficient operation at a minimal cost. However, in actual operation, re-optimization is a challenging task when the process operational input variables are varied, typically owing to the lack of information regarding the nature, impact, and levels of uncertainty. Within this context, this study investigates the uncertainty levels in the overall energy consumption and minimum internal temperature approach (MITA) inside LNG heat exchangers with variations in the operational variables of the DMR processes. Moreover, a global sensitivity analysis is conducted to identify the influence of random inputs on the process performance parameters. The required energy is significantly influenced by the variations in the variables in the cold mixed refrigerant (approximately 63%), while changes in the warm mixed refrigerant (WMR) section only slightly affect the uncertainty of the required specific energy. Furthermore, the probability distribution of the approach temperature (MITA1) inside the WMR exchanger is mainly affected by changes in the compositions of methane, ethane, and propane, as well as the high pressure of the cold mixed refrigerant (approximately 97%). Conversely, the flow rate of ethane and low pressure of the WMR significantly affect the uncertainty of the approach temperature (MITA2) inside the cold mixed refrigerant exchanger.

Future climate emulations using quantile regressions on large ensembles

Abstract. The study of climate change and its impacts depends on generating projections of future temperature and other climate variables. For detailed studies, these projections usually require some combination of numerical simulation and observations, given that simulations of even the current climate do not perfectly reproduce local conditions. We present a methodology for generating future climate projections that takes advantage of the emergence of climate model ensembles, whose large amounts of data allow for detailed modeling of the probability distribution of temperature or other climate variables. The procedure gives us estimated changes in model distributions that are then applied to observations to yield projections that preserve the spatiotemporal dependence in the observations. We use quantile regression to estimate a discrete set of quantiles of daily temperature as a function of seasonality and long-term change, with smooth spline functions of season, long-term trends, and their interactions used as basis functions for the quantile regression. A particular innovation is that more extreme quantiles are modeled as exceedances above less extreme quantiles in a nested fashion, so that the complexity of the model for exceedances decreases the further out into the tail of the distribution one goes. We apply this method to two large ensembles of model runs using the same forcing scenario, both based on versions of the Community Earth System Model (CESM), run at different resolutions. The approach generates observation-based future simulations with no processing or modeling of the observed climate needed other than a simple linear rescaling. The resulting quantile maps illuminate substantial differences between the climate model ensembles, including differences in warming in the Pacific Northwest that are particularly large in the lower quantiles during winter. We show how the availability of two ensembles allows the efficacy of the method to be tested with a “perfect model” approach, in which we estimate transformations using the lower-resolution ensemble and then apply the estimated transformations to single runs from the high-resolution ensemble. Finally, we describe and implement a simple method for adjusting a transformation estimated from a large ensemble of one climate model using only a single run of a second, but hopefully more realistic, climate model.

PÉGASE.3: A code for modeling the UV-to-IR/submm spectral and chemical evolution of galaxies with dust

A code computing consistently the evolution of stars, gas and dust, as well as the energy they radiate, is required to derive reliably the history of galaxies by fitting synthetic spectral energy distributions (SEDs) to multiwavelength observations. The new code PÉGASE.3 described in this paper extends to the far-infrared/submillimeter the ultraviolet-to-near-infrared modeling provided by previous versions of PÉGASE. It first computes the properties of single stellar populations at various metallicities. It then follows the evolution of the stellar light of a galaxy and the abundances of the main metals in the interstellar medium (ISM), assuming some scenario of mass assembly and star formation. It simultaneously calculates the masses of the various grain families, the optical depth of the galaxy and the attenuation of the SED through the diffuse ISM in spiral and spheroidal galaxies, using grids of radiative transfer precomputed with Monte Carlo simulations taking scattering into account. The code determines the mean radiation field and the temperature probability distribution of stochastically heated individual grains. It then sums up their spectra to yield the overall emission by dust in the diffuse ISM. The nebular emission of the galaxy is also computed, and a simple modeling of the effects of dust on the SED of star-forming regions is implemented. The main outputs are ultraviolet-to-submillimeter SEDs of galaxies from their birth up to 20 Gyr, colors, masses of galactic components, ISM abundances of metallic elements and dust species, supernova rates. The temperatures and spectra of individual grains are also available. The paper discusses several of these outputs for a scenario representative of Milky Way-like spirals. PÉGASE.3 is fully documented and its Fortran 95 source files are public. The code should be especially useful for cosmological simulations and to interpret future mid- and far-infrared data, whether obtained by JWST, LSST, Euclid or e-ELT.