Mean Dew Point Temperature Research Articles

Updated Observations Provide Stronger Evidence for Increases in Subhourly to Hourly Extreme Rainfall in Canada

Abstract Global warming is expected to lead to increases in atmospheric moisture and intensify subhourly to hourly rainfall extremes. However, signal-to-noise ratios are low, especially at the local scale, making detection of changes in the observational record difficult. For Canada, previous studies based on short data records from 1965 to 2005 did not show conclusive evidence of increases in short-duration extreme rainfall. This study updates single-site and regional trend analyses of 5-min–24-h annual maximum rainfall in Canada using data from 1950 to 2021. Estimates of temporal trends are extended to also consider the association between rainfall intensity and dewpoint temperature, a measure of moisture availability. With longer records, evidence for increases in extreme rainfall at individual sites is stronger. Field significant increasing trends are found for the majority of durations, whereas before results were mixed and typically not statistically significant. Intensification is even more pronounced in single-site scaling of rainfall intensity with summer mean dewpoint temperature. Field significant positive scaling rates are detected for all durations. When data are pooled in space—irrespective of choice of regionalization—the results are even more clear. Notably, the strongest and most spatially homogeneous intensification of short-duration extreme rainfall is detected in subhourly to 2-h durations. When data are pooled across Canadian climate regions, field significant positive scaling is found in 72.7%–81.8% of regions for 5-min–2-h durations, with median scaling rates ranging from 5.3% to 9.4% °C−1. For durations ≥ 6 h, this falls to 27.3%–53% of regions, with scaling rates less than 4% °C−1.

Exploring Meteorological Conditions and Microscale Temperature Inversions above the Great Barrier Reef through Drone-Based Measurements

Understanding the atmospheric conditions in remote areas contributes to assessing local weather phenomena. Obtaining vertical profiles of the atmosphere in isolated locations can introduce significant challenges for the deployment and maintenance of equipment, as well as regulatory obstacles. Here, we assessed the potential of consumer drones equipped with lightweight atmospheric sensors to collect vertical meteorological profiles off One Tree Island (Great Barrier Reef), located approximately 85 km off the east coast of Australia. We used a DJI Matrice 300 drone with two InterMet Systems iMet-XQ2 UAV sensors, capturing data on atmospheric pressure, temperature, relative humidity, and wind up to an altitude of 1500 m. These flights were conducted three times per day (9 a.m., 12 noon, and 3 p.m.) and compared against ground-based weather sensors. Over the Austral summer/autumn, we completed 72 flights, obtaining 24 complete sets of daily measurements of atmospheric characteristics over the entire vertical profile. On average, the atmospheric temperature and dewpoint temperature were significantly influenced by the time of sampling, and also varied among days. The mean daily temperature and dewpoint temperature reached their peaks at 3 p.m., with the temperature gradually rising from its morning low. The mean dewpoint temperature obtained its lowest point around noon. We also observed wind speed variations, but changes in patterns throughout the day were much less consistent. The drone-mounted atmospheric sensors exhibited a consistent warm bias in temperature compared to the reference weather station. Relative humidity showed greater variability with no clear bias pattern, indicating potential limitations in the humidity sensor’s performance. Microscale temperature inversions were prevalent around 1000 m, peaking around noon and present in approximately 27% of the profiles. Overall, the drone-based vertical profiles helped characterise atmospheric dynamics around One Tree Island Reef and demonstrated the utility of consumer drones in providing cost-effective meteorological information in remote, environmentally sensitive areas.

Construction of Influenza Early Warning Model Based on Combinatorial Judgment Classifier: A Case Study of Seasonal Influenza in Hong Kong.

ObjectiveThe annual influenza epidemic is a heavy burden on the health care system, and has increasingly become a major public health problem in some areas, such as Hong Kong (China). Therefore, based on a variety of machine learning methods, and considering the seasonal influenza in Hong Kong, the study aims to establish a Combinatorial Judgment Classifier (CJC) model to classify the epidemic trend and improve the accuracy of influenza epidemic early warning.MethodsThe characteristic variables were selected using the single-factor statistical method to establish the influencing factor system of an influenza outbreak. On this basis, the CJC model was proposed to provide an early warning for an influenza outbreak. The characteristic variables in the final model included atmospheric pressure, absolute maximum temperature, mean temperature, absolute minimum temperature, mean dew point temperature, the number of positive detections of seasonal influenza viruses, the positive percentage among all respiratory specimens, and the admission rates in public hospitals with a principal diagnosis of influenza.ResultsThe accuracy of the CJC model for the influenza outbreak trend reached 96.47%, the sensitivity and specificity change rates of this model were lower than those of other models. Hence, the CJC model has a more stable prediction performance. In the present study, the epidemic situation and meteorological data of Hong Kong in recent years were used as the research objects for the construction of the model index system, and a lag correlation was found between the influencing factors and influenza outbreak. However, some potential risk factors, such as geographical nature and human factors, were not incorporated, which ideally affected the prediction performance to some extent.ConclusionIn general, the CJC model exhibits a statistically better performance, when compared to some classical early warning algorithms, such as Support Vector Machine, Discriminant Analysis, and Ensemble Classfiers, which improves the performance of the early warning of seasonal influenza.

Ensemble ecological niche modeling of West Nile virus probability in Florida.

Ecological Niche Modeling is a process by which spatiotemporal, climatic, and environmental data are analyzed to predict the distribution of an organism. Using this process, an ensemble ecological niche model for West Nile virus habitat prediction in the state of Florida was developed. This model was created through the weighted averaging of three separate machine learning models—boosted regression tree, random forest, and maximum entropy—developed for this study using sentinel chicken surveillance and remote sensing data. Variable importance differed among the models. The highest variable permutation value included mean dewpoint temperature for the boosted regression tree model, mean temperature for the random forest model, and wetlands focal statistics for the maximum entropy mode. Model validation resulted in area under the receiver curve predictive values ranging from good [0.8728 (95% CI 0.8422–0.8986)] for the maximum entropy model to excellent [0.9996 (95% CI 0.9988–1.0000)] for random forest model, with the ensemble model predictive value also in the excellent range [0.9939 (95% CI 0.9800–0.9979]. This model should allow mosquito control districts to optimize West Nile virus surveillance, improving detection and allowing for a faster, targeted response to reduce West Nile virus transmission potential.

Association of Recent Incidence of Foliar Disease in Pine Species in the Southeastern United States with Tree and Climate Variables

Pine forests in the southern United States are a major contributor to the global economy. Through the last three decades, however, there have been concerns about the decline of pine forests attributed mostly to pests and pathogens. A combination of biotic agents and environmental factors and their interaction often influences outbreaks and the resultant damage in the forests. Southern pines experience periodic mortality from bark beetles and root rot fungi and losses from fusiform rust and pitch canker have long been important for management. In recent years, there is also growing evidence of increasing damage from foliar disease in southern pines. Early detection of diseases following changes in foliar characteristics and assessment of potential risks will help us better utilize our resources and manage these forests sustainably. In this study, we used Forest Inventory and Analysis (FIA) data to explore the intensity of foliar disease in three common pines: loblolly (Pinus taeda L.), longleaf (Pinus palustris Mill.), and slash (Pinus elliottii Engelm.) in spatial and temporal terms using tree-level and climatic variables. Results from a tree-level model suggests that crown ratio may be an important factor in pine foliar disease (p < 0.1). We applied the MaxEnt model, a presence-only species distribution model (SDM), to explore any association of foliar disease incidences with the climatic variables at a landscape level. Results indicate that mean dew point temperature, maximum vapor pressure deficit, and precipitation during cold months had more influence over disease incidences than other climatic variables. While the sample size is limited as this is an emerging disease in the region, our study provides a basis for further exploration of disease detection methods, disease etiology studies, and hazard mapping.

New input selection procedure for machine learning methods in estimating daily global solar radiation

Selection of optimal model inputs is a challenging issue particularly for non-linear and dynamic systems. In this study, a new input selection method, procrustes analysis (PA), was implemented and compared with gamma test (GT) for estimating daily global solar radiation (Rs). The PA and GT were applied for modeling with the non-linear models of artificial neural networks (ANNs) and support vector machines (SVMs). Goodness-of-fit of the models was evaluated by the coefficient of correlation (CC), root-mean-square error (RMSE), and Nash-Sutcliffe model efficiency coefficient (NS). The uncertainty of the model outputs was determined using 95PPU% (p-factor) and d-factor. In this study, we used maximum wind speed, mean wind speed, maximum temperature, minimum temperature, mean temperature, maximum sea surface pressure, minimum sea surface pressure, mean sea surface pressure, mean vapor pressure, total rainfall, maximum cloudiness, mean cloudiness, maximum humidity, minimum humidity, mean humidity, sunshine hours, evaporation, mean dew point temperature, mean wet point temperature, maximum air pressure, minimum air pressure, mean air pressure, and mean vapor saturation as input variables. Maximum and mean temperature; maximum wind speed; maximum, minimum, and mean sea surface pressure; maximum, minimum, and mean air pressure; mean vapor pressure; mean cloudiness; mean humidity; sunshine hours; mean dew point temperature; mean wet point temperature; and mean vapor saturation pressure were identified as significant input variables by GT in five or more of the eight studied stations. Also, mean air pressure, mean cloudiness, and mean temperature were identified as significant input variables for Rs modeling by the PA method for more than four stations. Results indicated that although ANN-GT and SVM-GT showed better goodness-of-fit metrics, ANN-PA and SVM-PA had lower uncertainties for estimating Rs. According to the obtained results, almost all models showed that the higher the bandwidth (95PPU or P-factor), the greater the d-factor, and the lower the bandwidth, the lower the d-factor, SVM-PA has the lowest uncertainty among the four models. So, it can be seen that the lowest bandwidth also belonged to the SVM-PA model for Kiashahr with a P-factor of 0.8% and a d-factor of 0.06, although the Aliabad-E-Katoul had the lowest d-factor of 0.017 and a p-factor of 1%. The highest d-factor belonged to the ANN-GT model for a Bandar-E-Torkman with a d-factor of 0.817 and a p-factor of 76%. One reason for the high uncertainty in this model might be due to the number of input variables selected by the GT. Lower uncertainty is a major scale for choosing the optimal model for solving a given problem, suggesting results of the SVM-PA model with lower uncertainty are more reliable.

Assessing climate sensitivity of peak electricity load for resilient power systems planning and operation: A study applied to the Texas region

Accurate forecasting of peak electricity load has long been an active area of research in electricity markets, and power systems planning and operation. Unanticipated climate-induced surges in peak load can lead to supply shortages causing frequent brownouts and blackouts, and large-scale socioeconomic impacts. In this paper, the climate sensitivity of daily peak load is characterized by leveraging advanced statistical machine learning algorithms. More specifically, a rigorously tested and validated predictive model based on the Bayesian additive regression trees algorithm is proposed. Results from this study revealed that maximum daily temperature followed by mean dew point temperature are the most important predictors of the climate-sensitive portion of daily peak load. Among the non-climatic predictors, electricity price was found to have a strong positive association with the daily peak load. Economic growth was observed to have an inverse association with the daily peak load. While the proposed framework is established for the state of Texas, one of the most energy-intensive states with geographic and demographic susceptibility to climatic change, the methodology can be extended to other states/regions. The model can also be used to make short-term predictions of the climate-sensitive portion of daily peak load.

Extended linear and non-linear auto-regressive models for forecasting the urban water consumption of a fast-growing city in an arid region

Accurate water consumption forecasting models are required for fast-growing cities in arid regions. In the present study, two forecasting models are proposed for urban-water consumption using extended Auto-Regressive Integrated Moving Average (ARIMA) and Nonlinear Auto-Regressive Exogenous (NARX) methods. Various extension of these models by adding new forecaster factors were tested to find superior linear and nonlinear models. The ARIMA and NARX model accuracy with and without additional forecaster factors (minimum, maximum and mean temperature, precipitation, mean relative humidity, sunshine hours, mean dew point temperature, mean wind speed, and population) were compared using accuracy indices. The ARIMA model which includes sunny hour in addition to the base model predictors was selected as the superior linear model and also the model NARX which includes sunny hours and population in addition to the base model’s predictors was selected as the superior nonlinear model. This infers that sunny hour has linear performance and population predictor has non-linear performance in forecasting water consumption. This shows adding sunny hours and population as forecaster factors can robust forecasting performance considerably. By extending the models with these additional forecasting factors, the precision of daily water consumption forecasts can be improved for providing a better urban water supply.

Evaluating regional climate-electricity demand nexus: A composite Bayesian predictive framework

Climatic variations significantly influence the shape of end-use electricity demand curves. Although the climate sensitivity of end-use electricity demand is well-established, projecting medium- and long-term future demand remains a significant challenge—mostly due to a multitude of uncertainties involved in the modeling process. In this paper, we leveraged a state-of-the-art Bayesian approach to develop rigorously validated regional prediction models of the climate–demand nexus conditioned on the intensity level of demand. The prediction models were developed for the residential and commercial sectors for the top eight energy-intensive states in the U.S. A key contribution of this work was to illustrate the asymmetry in the sensitivity of load to climate. More specifically, our results demonstrated a greater sensitivity of the high-intensity end-use demand to climate variability as compared to the moderate-intensity end-use demand. In addition, our results helped identify mean dew point temperature as the key predictor of the climate-sensitive portion of both residential and commercial electricity demands, irrespective of the demand intensity levels. Wind speed was identified as the second most important predictor of the high-intensity (i.e., ≥3rd quartile) end-use demand, while electricity price was found to be the key predictor of the moderate-intensity (i.e., <3rd quartile) end-use demand. The influence of precipitation on the residential and commercial sectors’ moderate end-use demand was found to be more variable. Precipitation was found to influence the commercial sector’s electricity demand more significantly compared to the residential sector's demand.

A Data-Driven Approach to Assessing Supply Inadequacy Risks Due to Climate-Induced Shifts in Electricity Demand.

The U.S. electric power system is increasingly vulnerable to the adverse impacts of extreme climate events. Supply inadequacy risk can result from climate-induced shifts in electricity demand and/or damaged physical assets due to hydro-meteorological hazards and climate change. In this article, we focus on the risks associated with the unanticipated climate-induced demand shifts and propose a data-driven approach to identify risk factors that render the electricity sector vulnerable in the face of future climate variability and change. More specifically, we have leveraged advanced supervised learning theory to identify the key predictors of climate-sensitive demand in the residential, commercial, and industrial sectors. Our analysis indicates that variations in mean dew point temperature is the common major risk factor across all the three sectors. We have also conducted a statistical sensitivity analysis to assess the variability in the projected demand as a function of the key climate risk factor. We then propose the use of scenario-based heat maps as a tool to communicate the inadequacy risks to stakeholders and decisionmakers. While we use the state of Ohio as a case study, our proposed approach is equally applicable to all other states.

Climate sensitivity of end-use electricity consumption in the built environment: An application to the state of Florida, United States

The link between climate and energy needed for space conditioning has been widely studied, but is still beset with uncertainties. The uncertainties are further compounded by the projected climate change impacts, which will likely increase the frequency of weather and climate extremes. Unlike previous approaches, we leveraged non-linear statistical learning methods to develop predictive models for residential and commercial electricity usage to understand the relationship between weather, climate and electric power consumption. Our analyses were implemented for the state of Florida, one of the most energy intensive states with geographic and demographic vulnerability to climatic change. Our results indicate that the method of Bayesian additive regression trees (BART) best captures the complex structure of the data. We conclude that mean dew point temperature is a more suitable predictor of climate sensitive load than the most widely used degree-days variables. Moreover, wind-speed and precipitation are key predictors of the climate sensitive portion of electricity consumption. We also find that the residential electricity consumption has more spatio-temporal heterogeneity and is more influenced by consumer behavior than the commercial sector.

부산지역 고농도 오존일의 선행 기상 특성 연구

Comparing to the other air pollutants like <TEX>$SO_2$</TEX>, CO, the number of exceedance of the ozone national ambient air quality standard(NAAQS) and the ozone warning increased recently in Busan. The purpose of this study is to find out the preliminary symptoms for high ozone days in Busan area. In order to find out the preliminary symptoms, the hourly ozone data at air quality monitoring stations and the hourly meterological parameters at Busan regional meteorological 2007 to 2013 were used for the analysis. Averaged daily max ozone concentration was the highest(0.055 ppm) at Noksan and Youngsuri in the ozone season from 2007 to 2013. The horizontal distributions of daily max. ozone including all stations in Busan at high ozone days(the day exceeding 0.1 ppm of ozone concentration at least one station) were classified from two to five clusters by hierarchial cluster analysis. The meteorological variables showing strong correlation with daily max. ozone were the daily mean dew point temperature, averaged total insolation, the daily mean relative humidity and the daily mean cloud amount. And the most frequent levels were <TEX>$19-23^{\circ}C$</TEX> in dew point temperature, <TEX>$21-24 MJ/m^2$</TEX> in total insolation on the day before, <TEX>$2.6-3.0 MJ/m^2$</TEX> on the very day, 67-80% in relative humidity and 0-3 in cloud amount.

Estimation of daily solar radiation from routinely observed meteorological data in Chongqing, China

Solar radiation is a very important and major variable in crop simulation models. However, it is measured at a very limited number of meteorological stations worldwide. Models were developed to estimate daily solar radiation in Chongqing, one of the most important agricultural areas in China. Several routinely observed meteorological variables including daily maximum and minimum temperatures, daily mean dew point temperature, fog and rainfall had been obtained, investigated and analyzed from 1986 to 2000 for Chongqing. The monthly mean daily solar radiation at this location ranged from a maximum of 15.082 MJ m −2 day −1 in August and a minimum of 3.042 MJ m −2 day −1 in December. A newly developed model that included all selected variables proved the best method with a RMSE value of 2.522 MJ m −2 day −1. The best performed models for different seasons were further evaluated according to divide-and-conquer principle. The model using all selected variables provided the best estimates of daily solar radiation in winter and autumn with RMSE values of 1.491 and 2.037 MJ m −2 day −1, respectively. The method involving temperatures and rainfall information could be used to estimate daily solar radiation in summer with a RMSE value of 3.163 MJ m −2 day −1. The model using temperature, rainfall and dew point data performed better than other models in spring with a RMSE value of 2.910 MJ m −2 day −1.

Characterization of a three-phase response in gloved cold-stressed fingers.

Seven gloves were studied worn by eight sedentary subjects (six men and two women) exposed to cold-dry, C D, (mean dry bulb temperature Tdb = -17.2 degrees C; mean dew point temperature Tdp = -25.1 degrees C), and cold-wet, C-W, (Tdh = 0 degrees C; Tdp = -8.4 degrees C) conditions. Mean endurance times were 75 min for the C-D and 162 min for the C W conditions. A three-phase response pattern of the temperature in the fingers was characterized. Phase I comprised an initial period during which finger temperature remained close to the pre-exposed level, due to delayed vasoconstriction in the finger. Phase II involved an exponential-like decrease of finger temperature indicative of the onset of vasoconstriction in the finger. Phase III manifested periodic finger temperature changes due to cold induced vasodilatation (CIVD). Mean wave patterns for phase III indicated approximately 3.5 waves x h(-1) in the C D but only about 2 waves x h(-1) in the C-W condition. Extension of endurance time, due to CIVD, was defined as the difference in time between the actual end of the experiment and the time the finger-tip would have reached the set temperature endurance limit as extrapolated by a continued exponential drop. Three overall response patterns of fingers in the cold were characterized: type A exhibiting all 3 phases; type B1 or B2 exhibiting either phases I+ II or phases II+ III; and type C showing only phase II. Considerable inter- and intra-subject variability was found. In both test conditions the final physiological thermal states of the subjects were between comfortable and slightly uncomfortable but acceptable and thus did not correlate with the responses in the fingers.

Comparison of Vapor-Pressure-Deficit Calculation Methods—Southern High Plains

Vapor-pressure deficit ( VPD ) affects evapotranspiration, water-use efficiency, and radiation-use efficiency of crops. VPD calculation methods were evaluated for a semiarid environment in the Southern Great Plains. Air temperature and relative humidity were measured near Bushland, Texas, during 1992 and 1993. Temperature and relative humidity were measured at 0.17 Hz (6 s), averages were recorded for each 15-min period, and daily (24-hr) maximums, minimums, and averages were recorded. VPD , actual vapor pressure, and dew-point temperatures were computed and averaged for each 15-min period and day. Methods that used mean daily dew-point temperature to compute daily actual vapor pressure performed well, and methods that used hybrid calculations based on maximum and minimum air temperature and relative humidity performed the worst. Methods using one-time-of-day dew-point temperatures as recommended by the 1990 ASCE Manual No. 70 should be used with caution in this environment. Weather data sets containing maximum and minimum temperatures and daily mean dew-point temperature should provide the most accurate calculations of VPD in this environment.

Design and Performance of a Large, Field Exposure Chamber to Measure Effects of Air Quality on Plants

AbstractA 4.66‐m diam., 3.6 m tall, cylindrical open‐top field chamber was designed, constructed, and tested as a tool to measure the effects of air quality on plant function and yield. It is a larger version of the 3‐m diam. chamber that has been used to measure the effects of gaseous pollutants on crop plants. The new chamber has an aluminum‐channel frame covered with clear polyvinyl chloride plastic film. It is equipped with a frustum (truncated cone) that decreases ambient air ingress and can be fitted with a device to exclude rain (rain cap) for studies with simulated rain pH. During the daylight hours, the mean air temperature within the chamber was 0.6 °C greater than ambient on cloudy, cold days, 2.2 °C greater than ambient on partly cloudy, cool days, and 2.8 °C greater than ambient on sunny, warm days. The mean dew point temperature for a wide range of conditions was 0.7 ° greater inside than outside. Mean solar radiation in the chamber, with new plastic panels, was 15% less than ambient with a rain cap and 12% less with no rain cap. Charcoal filtration removed 78% of the O3 in ambient air; long‐term measurements during charcoal filtration showed that the mean O3 concentration in the chamber (all positions and heights) was 77% less than ambient suggesting little or no long‐term ingress through the top. Short‐term gradients in O3 concentrations existed (mostly near the top of the chamber) during infrequent periods of strong winds. During addition of approximately 0.09 µL L−1 of O3 to nonfiltered air, the mean (14‐d) O3 concentrations across all positions and heights varied by less than 0.005 µL L−1 of the overall mean.

Daily Mean Dew Point Temperature Prediction for Ventilation Control in Humid Climates

ABSTRACT MICROCOMPUTER control systems allow real-time decision making to be incorporated into livestock production facilities. Air moisture content is a necessary input for computer based ventilation control. Suitable sensors for air moisture measurement are lacking for many applications. Methods for its estimation are needed to be effectively integrate these improved control systems into the livestock industry. Two methods of predicting daily mean dew point temperature under humid weather conditions are presented. Method one is based on the measurement of early morning dry-bulb temperature. Method two is based on an equation for hourly dry-bulb which uses forecasts of daily high-low dry-bulb temperature. Results are compared to weather data for Montgomery, AL and the deviations are discussed. Method one over-estimates the daily mean dew point temperature by approximately 2 °C for Montgomery, AL and method two underestimates early morning dry-bulb temperature by approximately 2 °C.