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Journal articles on the topic 'Daily maximum temperature'
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1
Goncharevskyi, S., and V. Martynyuk. "Daily temperature dynamics of human skin representative areas." Bulletin of Taras Shevchenko National University of Kyiv. Series: Problems of Physiological Functions Regulation 21, no. 2 (2016): 86–91. http://dx.doi.org/10.17721/2616_6410.2016.21.86-91.
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The main aim of our research was to study the temperature variation of representative are a soft the cranial part of the autonomic nervous system of the human skin during the day. The temperature of representative are a soft the thoracic autonomic nervous system we measured by infrared thermometer (Medisana FTO D-53340, with anaccuracy of 0.1 degree Celsius). During the study identified minimums and maximums temperatures for representative are as during the day: the hypothalamus – 13 (maximum), 3 (minimum) an hour, midbrain – 15 (maximum), 5 (minimum) an hour, pons- not found, the medulla oblongata – 9, 15 (maximum), 3.21 (minimum) an hour, the vagus nerve (right side) – 15 (maximum), 5 (at least) an hour, the vagus nerve (left side) – 15 (maximum), 21 (minimum) an hour. The presence of minimums and maximums temperature in representative areas indicates different activity related to their brain structures.
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Muna, Siti Umamah Naili. "Crop Insurance Model Based on Maximum Daily Rainfall and Maximum Daily Temperature Index." ASTONJADRO 12, no. 2 (2023): 599–612. http://dx.doi.org/10.32832/astonjadro.v12i2.13207.
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A loss insurance model of risk for agricultural commodities that considers maximum daily rainfall and maximum daily temperature is introduced in this paper. This model requires bivariate distribution of maximum daily rainfall and maximum daily temperature in a specific region. Characteristics of particular agricultural commodity is also needed in the region where the two variables are being insured. The bivariate distribution and commodity characteristics are combined to obtain exit. exit is a benchmark value that causes the total crop failure and gives full benefit claim to policyholder. The case study was demonstrated by using data on maximum daily rainfall and temperature in Dramaga Bogor from September to December during 38 years (1984-2021) . Data was collected from Jawa Barat Climatology Station. Frank Copula is better to represent the bivariate distribution of data. Furthermore, two scenarios had given the premiums as IDR 2 482 925 per hectare and IDR 1 495 660 per hectare. This crop insurance model based on maximum daily rainfall and maximum daily temperature index could be the basis for the next developing of crop insurance model.
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van Wijngaarden, W. A., and A. Mouraviev. "Seasonal and Annual Trends in Australian Minimum/Maximum Daily Temperatures." Open Atmospheric Science Journal 10, no. 1 (2016): 39–55. http://dx.doi.org/10.2174/1874282301610010039.
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Seasonal and annual trends in Australian minimum and maximum temperatures were studied. Records of daily minimum and maximum temperatures averaged over each month, extending as far back as 1856 were examined. Over 1/2 million monthly temperature values were retrieved from the Australian Bureau of Meteorology for 299 stations. Each station had an average of 89 years of observations. Significant step discontinuities affected the maximum temperature data in the 19th century when Stevenson screens were installed. The temperature trends were found after such spurious data were removed and averaged over all stations. The resulting trend in the minimum (maximum) daily temperature was 0.67 ± 0.19 (0.58 ± 0.26) oC per century for the period 1907-2014. Decadal fluctuations were evident in the maximum daily temperature with most of the increase occurring in the late 20th century. The minimum and maximum daily temperature trends were also found for the various seasons. The minimum daily temperature trend exceeded the maximum daily temperature trend for all seasons except during June to August. The largest increases in minimum temperature as well as the smallest maximum temperature increases were found for the region north of 30 oS latitude and east of 140 oE longitude. There was also evidence that urban stations had greater increases in maximum daily temperature than those located in a rural environment.
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Abdullah, Ahmad Makmom, Marzuki Ismail, Fong Si Yuen, Samsuri Abdullah, and Rasheida Elhadi. "The Relationship between Daily Maximum Temperature and Daily Maximum Ground Level Ozone Concentration." Polish Journal of Environmental Studies 26, no. 2 (2017): 517–23. http://dx.doi.org/10.15244/pjoes/65366.
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RAJ, Y. E. A. "On forecasting daily summer maximum temperature at Madras." MAUSAM 49, no. 1 (2021): 95–102. http://dx.doi.org/10.54302/mausam.v49i1.3602.
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Forecasting schemes based on statistical techniques have been developed to forecast daily summer (March-May) maximum temperatures of Madras. A set of optimal number of predictors were chosen from a large number of parameters by employing stepwise forward screening. Separate forecasting schemes for Madras city and airport, with lead time of 24 and 9 hr were developed from the data of 12 years and tested in an independent sample of 4 years. Maximum temperature of the previous day, normal daily maximum temperature, temperature advection index and morning zonal wind at Madras at 900 hPa level were among the predictors selected. The schemes yielded good results providing 77-87% correct, forecasts with skill scores of 0.29-0.57.
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DeGaetano, Arthur T., Keith L. Eggleston, and Warren W. Knapp. "A Method to Estimate Missing Daily Maximum and Minimum Temperature Observations." Journal of Applied Meteorology 34, no. 2 (1995): 371–80. http://dx.doi.org/10.1175/1520-0450-34.2.371.
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Abstract A method to estimate missing daily maximum and minimum temperatures is presented. Temperature estimates are based on departures from daily temperature normals at the three closest stations with similar observation times. Although applied to Cooperative Observer Network stations in the northeastern United States, the approach can be used with any network of stations possessing an adequate station density and period of record. Generally, 75% of the estimates for both daily maximum and minimum temperature are within 1.7°C of the observed value. Median absolute differences between estimated and observed minimum temperatures, however, tend to be greater than those associated with maximum temperatures. For minimum temperatures, median absolute differences are approximately 1.0°C, whereas for maximum temperatures these differences are near 0.5°C. The accuracy of the estimates is independent of observation time, geographic location, and observed temperature but is influenced somewhat by station density.
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Neumann, David W., Balaji Rajagopalan, and Edith A. Zagona. "Regression Model for Daily Maximum Stream Temperature." Journal of Environmental Engineering 129, no. 7 (2003): 667–74. http://dx.doi.org/10.1061/(asce)0733-9372(2003)129:7(667).
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Kundzewicz, Zbigniew W., and Damian Józefczyk. "Temperature-related climate extremes in the Potsdam observation record." Geografie 113, no. 4 (2008): 372–82. http://dx.doi.org/10.37040/geografie2008113040372.
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This paper examines temperature-related climate extremes in the unique long-term gapfree record at the Secular Meteorological Station in Potsdam. Increasing tendencies in daily minimum temperature in winter and daily maximum temperature in summer, as well as monthly means of daily minimum temperatures in winter months and of daily maximum temperatures in summer months are illustrated. Also the numbers of hot days and of summer days (with maximum daily temperature exceeding 30 °C and 25 °C, respectively) have been increasing. In agreement with warming of winter minimum temperatures, the numbers of frost days (with minimum daily temperature below 0 °C) and of ice days (with maximum daily temperature below 0 °C) have been decreasing. However, low correlation coefficient and huge scatter illustrate strong natural variability, so that the occurrence of extremes departs from the general underlying tendency.
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Serrano-Notivoli, Roberto, Santiago Beguería, and Martín de Luis. "STEAD: a high-resolution daily gridded temperature dataset for Spain." Earth System Science Data 11, no. 3 (2019): 1171–88. http://dx.doi.org/10.5194/essd-11-1171-2019.
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Abstract. Using 5520 observatories covering the whole territory of Spain (about 1 station per 90 km2 considering the whole period), a daily gridded maximum and minimum temperature was built covering a period from 1901 to 2014 in peninsular Spain and 1971 to 2014 in the Balearic and Canary Islands. A comprehensive quality control was applied to the original data, and the gaps were filled on each day and location independently. Using the filled data series, a grid of 5 km × 5 km spatial resolution was created by estimating daily temperatures and their corresponding uncertainties at each grid point. Four daily temperature indices were calculated to describe the spatial distribution of absolute maximum and minimum temperature, number of frost days and number of summer days in Spain. The southern plateau showed the maximum values of maximum absolute temperature and summer days, while the minimum absolute temperature and frost days reached their maximums at the northern plateau. The use of all the available information, the complete quality control and the high spatial resolution of the grid allowed for an accurate estimate of temperature that represents a precise spatial and temporal distribution of daily temperatures in Spain. The STEAD dataset is publicly available at https://doi.org/10.20350/digitalCSIC/8622 and can be cited as Serrano-Notivoli et al. (2019).
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Matthiessen, J. N., and M. J. Palmer. "Prediction of temperatures in cattle dung for estimating development times of coprophilous organisms." Bulletin of Entomological Research 78, no. 2 (1988): 235–40. http://dx.doi.org/10.1017/s0007485300013006.
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AbstractIn studies in Western Australia, temperatures in air and one- and two-litre pads of cattle dung set out weekly and ranging from one to 20 days old were measured hourly for 438 days over all seasons, producing 1437 day x dung-pad observations. Daily maximum temperatures (and hence thermal accumulation) in cattle dung pads could not be accurately predicted using meteorological data alone. An accurate predictor of daily maximum dung temperature, using multiple regression analysis, required measurement of the following factors: maximum air temperature, hours of sunshine, rainfall, a seasonal factor (the day number derived from a linear interpolation of day number from day 0 at the winter solstice to day 182 at the preceding and following summer solstices) and a dung-pad age-specific intercept term, giving an equation that explained a 91·4% of the variation in maximum dung temperature. Daily maximum temperature in two-litre dung pads was 0·6°C cooler than in one-litre pads. Daily minimum dung temperature equalled minimum air temperature, and daily minimum dung temperatures occurred at 05.00 h and maximum temperatures at 14.00 h for one-litre and 14.30 h for two-litre pads. Thus, thermal summation in a dung pad above any threshold temperature can be computed using a skewed sine curve fitted to daily minimum air temperature and the calculated maximum dung temperature.
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MOHAN, V., NK JANGLE, and PD KULKARANI. "NUMERICAL PREDICTION OF DAILY MAXIMUM TEMPERATURE OVER OZAR." MAUSAM 40, no. 2 (2022): 129–30. http://dx.doi.org/10.54302/mausam.v40i2.2082.
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Debusho, Legesse Kassa, and Tadele Akeba Diriba. "Bayesian Modelling of Summer Daily Maximum Temperature Data." PROOF 2 (March 17, 2022): 51–58. http://dx.doi.org/10.37394/232020.2022.2.7.
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The extremes of summer daily maximum temperature was analyzed using the generalized Pareto distribution (GPD) to the Bisho weather station data, Eastern Cape Province, South Africa. Since the extreme events are naturally scarce it is expected that the use of a Bayesian inference may improve the efficiency of the parameters estimates of the distribution compared to the maximum likelihood method. Therefore, the Bayesian approach was also applied in the paper using the Markov Chain Monte Carlo for the generalized Pareto distribution. The expected improvement in efficiency is not fully achieved in this study using the noninformative and informative priors. However, the effects of informative prior constructed from historical data depends on the distance.
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YE, Qianping, and Faisal AHAMMED. "Quantification of relationship between annual daily maximum temperature and annual daily maximum rainfall in South Australia." Atmospheric and Oceanic Science Letters 13, no. 4 (2020): 286–93. http://dx.doi.org/10.1080/16742834.2020.1755599.
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Al-Jiboori, Monim, Mahmoud Jawad Abu Al-Shaeer, and Ahemd S. Hassan. "Statistical Forecast of Daily Maximum Air Temperature in Arid Areas at Summertime." Journal of Mathematical and Fundamental Sciences 52, no. 3 (2020): 353–65. http://dx.doi.org/10.5614/j.math.fund.sci.2020.52.3.8.
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Based on historical observations of summers for the period from 2004 to 2018 with a focus on daily maximum and minimum air temperatures and wind speed recorded at 0600 GMT, a non-linear regression hypothesis is developed for forecasting daily maximum air temperature (Tmax) in arid areas such as Baghdad International airport station, which has a hot climate with no cloud cover or rain. Observations with dust storm events were excluded, thus this hypothesis could be used to predict daily Tmax on any day during summers characterized by fair weather. Using mean annual daily temperature range, daily minimum temperature, and the trend of maximum temperature with wind speed, Tmax was forecasted and then compared to those recorded by meteorological instruments. To improve the accuracy of the hypothesis, daily forecast errors, bias, and mean absolute error were analyzed to detect their characteristics through calculating relative frequencies of occurrence. At the end of this analysis, a value of (-0.45ºC) was added to the hypothesis as a bias term.
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Liu, Binhui, Ming Xu, Mark Henderson, Ye Qi, and Yiqing Li. "Taking China's Temperature: Daily Range, Warming Trends, and Regional Variations, 1955–2000." Journal of Climate 17, no. 22 (2004): 4453–62. http://dx.doi.org/10.1175/3230.1.
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Abstract In analyzing daily climate data from 305 weather stations in China for the period from 1955 to 2000, the authors found that surface air temperatures are increasing with an accelerating trend after 1990. They also found that the daily maximum (Tmax) and minimum (Tmin) air temperature increased at a rate of 1.27° and 3.23°C (100 yr)−1 between 1955 and 2000. Both temperature trends were faster than those reported for the Northern Hemisphere, where Tmax and Tmin increased by 0.87° and 1.84°C (100 yr)−1 between 1950 and 1993. The daily temperature range (DTR) decreased rapidly by −2.5°C (100 yr)−1 from 1960 to 1990; during that time, minimum temperature increased while maximum temperature decreased slightly. Since 1990, the decline in DTR has halted because Tmax and Tmin increased at a similar pace during the 1990s. Increased minimum and maximum temperatures were most pronounced in northeast China and were lowest in the southwest. Cloud cover and precipitation correlated poorly with the decreasing temperature range. It is argued that a decline in solar irradiance better explains the decreasing range of daily temperatures through its influence on maximum temperature. With declining solar irradiance even on clear days, and with decreases in cloud cover, it is posited that atmospheric aerosols may be contributing to the changing solar irradiance and trends of daily temperatures observed in China.
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Stefanova, Lydia, Philip Sura, and Melissa Griffin. "Quantifying the Non-Gaussianity of Wintertime Daily Maximum and Minimum Temperatures in the Southeast." Journal of Climate 26, no. 3 (2013): 838–50. http://dx.doi.org/10.1175/jcli-d-12-00161.1.
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Abstract In this paper the statistics of daily maximum and minimum surface air temperature at weather stations in the southeast United States are examined as a function of the El Niño–Southern Oscillation (ENSO) and Arctic Oscillation (AO) phase. A limited number of studies address how the ENSO and/or AO affect U.S. daily—as opposed to monthly or seasonal—temperature averages. The details of the effect of the ENSO or AO on the higher-order statistics for wintertime daily minimum and maximum temperatures have not been clearly documented. Quality-controlled daily observations collected from 1960 to 2009 from 272 National Weather Service Cooperative Observing Network stations throughout Florida, Georgia, Alabama, and South and North Carolina are used to calculate the first four statistical moments of minimum and maximum daily temperature distributions. It is found that, over the U.S. Southeast, winter minimum temperatures have higher variability than maximum temperatures and La Niña winters have greater variability of both minimum and maximum temperatures. With the exception of the Florida peninsula, minimum temperatures are positively skewed, while maximum temperatures are negatively skewed. Stations in peninsular Florida exhibit negative skewness for both maximum and minimum temperatures. During the relatively warmer winters associated with either a La Niña or AO+, negative skewnesses are exacerbated and positive skewnesses are reduced. To a lesser extent, the converse is true of the El Niño and AO−. The ENSO and AO are also shown to have a statistically significant effect on the change in kurtosis of daily maximum and minimum temperatures throughout the domain.
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Wang, Shunjiu. "Variations in Maximum and Minimum Temperature in Mount Qomolangma during 1971–2020." Atmosphere 15, no. 3 (2024): 358. http://dx.doi.org/10.3390/atmos15030358.
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Based on the daily maximum and minimum temperature observational data during 1971–2020, the variabilities of the maximum and minimum temperature of Mount Qomolangma are analyzed. The daily maximum temperature is 25.8 °C and the daily minimum temperature is −31.4 °C during the study period in Mount Qomolangma. Overall, there has been an upward trend with decadal laps for both maximum and minimum temperature. On monthly, seasonal, and annual scales, neither maximum temperature nor minimum temperature time series exhibit an increasing trend from 1971 to 2020. The increasing trends in monthly minimum temperature are even more pronounced than those in maximum temperature. Abrupt changes are noted in both monthly, seasonal, and annual maximum and minimum temperature time series. Specifically, an abrupt change in annual maximum temperature occurred in the 1980s, while an abrupt change in annual minimum temperature occurred in the 1990s. Differences between the north and south slope of Mount Qomolangma are evident, with temperature fluctuations of the north slope being more extreme than those of south slope. The seasonal and annual maximum temperature of the north slope is higher than that of the south slope, except for winter, and the seasonal and annual minimum temperatures of the north slope are all lower than those of the south slope. The tendences of maximum and minimum temperatures in the north slope are more dominant than those in the south slope. The findings are beneficial for understanding the characteristics of local climate change on the Tibetan plateau and to underscore the significant role of Mount Qomolangma in the context of global warming.
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Vasconcelos, Vitor Vieira, and Helenice Maria Sacht. "Influence of Canopy Cover on Surface Temperature." Revista Brasileira de Geografia Física 13, no. 07 (2020): 3275. http://dx.doi.org/10.26848/rbgf.v13.07.p3275-3286.
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Trees affect the microclimate, which influences thermal comfort and ecosystem processes. This study investigated the influence of the canopy cover on daily maximum and minimum temperatures. The data are from a collaborative database, and each measurement consists of the minimum and maximum temperatures under the canopy and in an open adjacent area over a 24-hour period. Paired sample t-tests indicated that the canopy decreased the maximum and minimum daily temperatures and narrowed the daily temperature range. Multiple regression showed that the canopy cover percentage decreased the maximum daily temperatures, and this effect was greater in rural areas than in urbanized areas. Another multiple regression indicated that the canopy cover percentage and the distance to the edge of the canopy decreased the daily temperature range. An independent sample t-test also indicated that the effect of the canopy on the daily temperature range was higher in rural areas when analysed by parametric and non-parametric tests but not when measured by a robust test. Other independent sample t-tests indicated that the distance from a light source also decreased the canopy effect on the minimum daily temperature and the daily temperature range. The main plausible underlying processes include the canopy shade and wind insulation, litter insulation of the ground surface, heat pumps through evapotranspiration and lateral heat fluxes from light bulbs and other anthropogenic sources, especially in urbanized areas. These results provide a greater understanding of the effects of arborization in rural and urban ecosystems, as well as their respective benefits to human communities.
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M., Irwanto, Alam H., Masri M., Ismail B., Z. Leow W., and M. Irwan Y. "Solar energy density estimation using ANFIS based on daily maximum and minimum temperature." International Journal of Power Electronics and Drive System (IJPEDS) 10, no. 4 (2019): 2206–13. https://doi.org/10.11591/ijpeds.v10.i4.pp2206-2213.
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The data of solar energy density in one area is very important when the area will constructed photovoltaic (PV) system. The data is as preliminary study to decide what the area is suitable or not to be constructed the PV application system. But, sometime the available data is missing because the limitation of weather equipment. An alternative technique for the available data of solar energy density should be done for the continuity of PV application system decision. An estimation technique of solar energy density is one part of good alternative to solve this problem. This paper presents the estimation of solar energy density using Adaptive Neuron Fuzzy Inference System (ANFIS). The ANFIS system has two input data of the measured daily minimum, maximum temperature and difference between maximum and minimum temperature. The measured solar energy density is as target data of ANFIS system. The data is recorded from Medan meteorological station through the web site of world weather online for the year of 2018. The result shows that the average estimated solar energy density is classified in the very high solar energy density and based on the percentage error shows that the estimated solar energy density is acceptable
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Parsons, Danny, David Stern, Denis Ndanguza, and Mouhamadou Bamba Sylla. "Evaluation of Satellite-Based Air Temperature Estimates at Eight Diverse Sites in Africa." Climate 10, no. 7 (2022): 98. http://dx.doi.org/10.3390/cli10070098.
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High resolution satellite and reanalysis-based air temperature estimates have huge potential to complement the sparse networks of air temperature measurements from ground stations in Africa. The recently released Climate Hazards Center Infrared Temperature with Stations (CHIRTS-daily) dataset provides daily minimum and maximum air temperature estimates on a near-global scale from 1983 to 2016. This study assesses the performance of CHIRTS-daily in comparison with measurements from eight ground stations in diverse locations across Africa from 1983 to 2016, benchmarked against the ERA5 and ERA5-Land reanalysis to understand its potential to provide localized temperature information. Compared to ERA5 and ERA5-Land, CHIRTS-daily maximum temperature has higher correlation and lower bias of daily, annual mean maximum and annual extreme maximum temperature. It also exhibits significant trends in annual mean maximum temperature, comparable to those from the station data. CHIRTS-daily minimum temperatures generally have higher correlation, but larger bias than ERA5 and ERA5-Land. However, the results indicate that CHIRTS-daily minimum temperature biases may be largely systematic and could potentially be corrected for. Overall, CHIRTS-daily is highly promising as it outperforms ERA5 and ERA5-Land in many areas, and exhibits good results across a small, but diverse set of sites in Africa. Further studies in specific geographic areas could help support these findings.
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Thrasher, B., E. P. Maurer, C. McKellar, and P. B. Duffy. "Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping." Hydrology and Earth System Sciences 16, no. 9 (2012): 3309–14. http://dx.doi.org/10.5194/hess-16-3309-2012.
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Abstract. When applying a quantile mapping-based bias correction to daily temperature extremes simulated by a global climate model (GCM), the transformed values of maximum and minimum temperatures are changed, and the diurnal temperature range (DTR) can become physically unrealistic. While causes are not thoroughly explored, there is a strong relationship between GCM biases in snow albedo feedback during snowmelt and bias correction resulting in unrealistic DTR values. We propose a technique to bias correct DTR, based on comparing observations and GCM historic simulations, and combine that with either bias correcting daily maximum temperatures and calculating daily minimum temperatures or vice versa. By basing the bias correction on a base period of 1961–1980 and validating it during a test period of 1981–1999, we show that bias correcting DTR and maximum daily temperature can produce more accurate estimations of daily temperature extremes while avoiding the pathological cases of unrealistic DTR values.
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Thrasher, B. L., E. P. Maurer, C. McKellar, and P. B. Duffy. "Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping." Hydrology and Earth System Sciences Discussions 9, no. 4 (2012): 5515–29. http://dx.doi.org/10.5194/hessd-9-5515-2012.
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Abstract. When applying a quantile-mapping based bias correction to daily temperature extremes simulated by a global climate model (GCM), the transformed values of maximum and minimum temperatures are changed, and the diurnal temperature range (DTR) can become physically unrealistic. While causes are not thoroughly explored, there is a strong relationship between GCM biases in snow albedo feedback during snowmelt and bias correction resulting in unrealistic DTR values. We propose a technique to bias correct DTR, based on comparing observations and GCM historic simulations, and combine that with either bias correcting daily maximum temperatures and calculating daily minimum temperatures or vice versa. By basing the bias correction on a base period of 1961–1980 and validating it during a test period of 1981–1999, we show that bias correcting DTR and maximum daily temperature can produce more accurate estimations of daily temperature extremes while avoiding the pathological cases of unrealistic DTR values.
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Hunter, Mark A., and Timothy Quinn. "Summer Water Temperatures in Alluvial and Bedrock Channels of the Olympic Peninsula." Western Journal of Applied Forestry 24, no. 2 (2009): 103–8. http://dx.doi.org/10.1093/wjaf/24.2.103.
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Abstract To better understand how stream geomorphology may affect water temperature, we recorded water temperatures along two channels, one with deep alluvium and the other composed of bedrock and shallow alluvium. Study channels were located in managed forestlands on the Olympic Peninsula. Water temperatures were recorded hourly at 75-m intervals along 1.6 and 1.4 km of the alluvial and bedrock channels, respectively, during the summers of 2003 and 2004. Seasonal maximum and minimum daily water temperatures (i.e., season-long means for individual temperature dataloggers) in the alluvial channel tended to vary less over the course of the summer than temperatures in the bedrock channel. In addition, the means of all the individual dataloggers' daily maximums for each stream (reach mean maximum) and, similarly, the daily minimums (reach mean minimum) varied less for the alluvial channel. Changes in temperature from the upstream to downstream were greater for the bedrock channel, but only at low flow.
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Hana Yulia Anggraeni, Riski Aspriyani, and Mizan Ahmad. "Forecasting Daily Maximum and Minimum Air Temperatures in The Cilacap District Using Arima and Exponential Smoothing." Jurnal Matematika Sains dan Teknologi 24, no. 2 (2023): 48–61. http://dx.doi.org/10.33830/jmst.v24i2.5078.2023.
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This research aims to predict daily maximum and minimum air temperatures in Cilacap Regency using ARIMA and Exponential Smoothing. Data was obtained from recordings carried out by BMKG Cilacap using maximum and minimum thermometers taken from January 1, 2016, to December 31, 2021. The results show that the best forecasting model uses the ARIMA (2,1,2) model for maximum temperature and the ARIMA (1,1,1) model for minimum temperature, with the MAPE value of 2.09% for the maximum temperature and 2.44% for the minimum temperature, while the RMSE value obtained is 0.9177 for the maximum temperature and 0.8001 for the minimum temperature. Based on the ARIMA model, Cilacap's daily maximum temperature in 2022 was predicted to be around 30.6ᵒC, with a 95% confidence interval between 28ᵒC - 35ᵒC, while the minimum temperature was predicted to be around 25.1ᵒC, with a 95% confidence interval between 23ᵒC - 28ᵒC.
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Acar Deniz, Zahide, and Barbaros Gönençgil. "Trends of summer daily maximum temperature extremes in Turkey." Physical Geography 36, no. 4 (2015): 268–81. http://dx.doi.org/10.1080/02723646.2015.1045285.
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Honda, Yasushi, Michinori Kabuto, Masaji Ono, and Iwao Uchiyama. "Determination of optimum daily maximum temperature using climate data." Environmental Health and Preventive Medicine 12, no. 5 (2007): 209–16. http://dx.doi.org/10.1265/ehpm.12.209.
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Kleiber, William, Richard W. Katz, and Balaji Rajagopalan. "Daily minimum and maximum temperature simulation over complex terrain." Annals of Applied Statistics 7, no. 1 (2013): 588–612. http://dx.doi.org/10.1214/12-aoas602.
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Lian, Xu, Zhenzhong Zeng, Yitong Yao, Shushi Peng, Kaicun Wang, and Shilong Piao. "Spatiotemporal variations in the difference between satellite‐observed daily maximum land surface temperature and station‐based daily maximum near‐surface air temperature." Journal of Geophysical Research: Atmospheres 122, no. 4 (2017): 2254–68. http://dx.doi.org/10.1002/2016jd025366.
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Niu, Yun, Jing Yun, Xinchuan Li, and Qilong Ren. "Variation Trend and Abrupt Change in Temperature Around Hongze Lake from 1981 to 2020." International Journal of Environmental Sustainability and Protection 3, no. 1 (2023): 7–16. http://dx.doi.org/10.35745/ijesp2023v03.01.0002.
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In global warming, the influence of lakes on climate change in surrounding areas has attracted extensive attention from many scholars. Based on 40 years of observation of the daily mean temperature, maximum temperature, and minimum temperature of 16 meteorological observation stations around Hongze Lake, we analyzed the trend and characteristics of temperature change in the area around Hongze Lake from 1981 to 2020. The Mann-Kendall trend analysis method and mutation detection method were used for the analysis. The daily mean, maximum, and minimum temperatures have fluctuated but increased. The daily minimum temperature showed the largest increase (0.55℃/10 years). The daily mean, minimum, and maximum temperatures changed significantly with the seasonal variation, and the warming trend in spring was most significantly observed, followed by those in winter and summer. The historic increase in temperature in autumn was the least significant. The abrupt change points of daily mean, minimum, and maximum temperatures were observed in 1996. Fluctuation became more severe and frequent after such abrupt changes. The increase in temperature in seasons was significant before and after 1998. The abrupt change air temperature of Hongze Lake was observed most frequently in winter, followed by autumn but in spring and summer, there were no such outliers observed in temperature. The closer the area to Hongze Lake, the temperature changes became smaller, the minimum temperature became higher, and the maximum temperature became lower. This verified the climate regulation effect of Hongze Lake on the surrounding area. The research results provide a reference for creating a protocol for possible natural disasters in the future and for transportation in water and agricultural structure adjustment.
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Singirankabo, Edouard, Emmanuel Iyamuremye, Alexis Habineza, and Yunvirusaba Nelson. "Statistical modelling of maximum temperature in Rwanda using extreme value analysis." Open Journal of Mathematical Sciences 7, no. 1 (2023): 180–95. http://dx.doi.org/10.30538/oms2023.0206.
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This study aims to model the statistical behaviour of extreme maximum temperature values in Rwanda. To achieve such an objective, the daily temperature data from January 2000 to December 2017 recorded at nine weather stations collected from the Rwanda Meteorological Agency were used. The two methods, namely the block maxima (BM) method and the Peaks Over Threshold (POT), were applied to model and analyse extreme temperatures in Rwanda. Model parameters were estimated, while the extreme temperature return periods and confidence intervals were predicted. The model fit suggests that Gumbel and Beta distributions are the most appropriate for the annual maximum daily temperature. Furthermore, the results show that the temperature will continue to increase as estimated return levels show it.
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Faust, James E., and Royal D. Heins. "Modeling Leaf Development of the African Violet (Saintpaulia ionantha Wendl.)." Journal of the American Society for Horticultural Science 118, no. 6 (1993): 747–51. http://dx.doi.org/10.21273/jashs.118.6.747.
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Leaf unfolding rate (LUR) was determined for `Utah' African violet plants grown in growth chambers under 20 combinations of temperature and photosynthetic photon flus (PPF). A nonlinear model was used to predict LUR as a function of shoot temperature and daily integrated PPF. The maximum predicted LUR was 0.27 leaves/day, which occurred at 25C and a daily integrated PPF of 10 mol/m2 per day. The optimum temperature for leaf unfolding decreased to 23C, and the maximum rate decreased to 0.18 leaves/day as the daily integrated PPF decreased from 10 to 1 mol/m2 per day. A greenhouse experiment using 12 combinations of air temperature and daily integrated PPF was conducted to validate the LUR model. Plant temperatures used in the model predicted leaf development more accurately than did air temperatures, but using average hourly temperature data was no more accurate than using average daily temperature data.
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Lussana, Cristian, Ole Einar Tveito, Andreas Dobler, and Ketil Tunheim. "seNorge_2018, daily precipitation, and temperature datasets over Norway." Earth System Science Data 11, no. 4 (2019): 1531–51. http://dx.doi.org/10.5194/essd-11-1531-2019.
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Abstract. seNorge_2018 is a collection of observational gridded datasets over Norway for daily total precipitation: daily mean, maximum, and minimum temperatures. The time period covers 1957 to 2017, and the data are presented over a high-resolution terrain-following grid with 1 km spacing in both meridional and zonal directions. The seNorge family of observational gridded datasets developed at the Norwegian Meteorological Institute (MET Norway) has a 20-year-long history and seNorge_2018 is its newest member, the first providing daily minimum and maximum temperatures. seNorge datasets are used for a wide range of applications in climatology, hydrology, and meteorology. The observational dataset is based on MET Norway's climate data, which have been integrated by the “European Climate Assessment and Dataset” database. Two distinct statistical interpolation methods have been developed, one for temperature and the other for precipitation. They are both based on a spatial scale-separation approach where, at first, the analysis (i.e., predictions) at larger spatial scales is estimated. Subsequently they are used to infer the small-scale details down to a spatial scale comparable to the local observation density. Mean, maximum, and minimum temperatures are interpolated separately; then physical consistency among them is enforced. For precipitation, in addition to observational data, the spatial interpolation makes use of information provided by a climate model. The analysis evaluation is based on cross-validation statistics and comparison with a previous seNorge version. The analysis quality is presented as a function of the local station density. We show that the occurrence of large errors in the analyses decays at an exponential rate with the increase in the station density. Temperature analyses over most of the domain are generally not affected by significant biases. However, during wintertime in data-sparse regions the analyzed minimum temperatures do have a bias between 2 ∘C and 3 ∘C. Minimum temperatures are more challenging to represent and large errors are more frequent than for maximum and mean temperatures. The precipitation analysis quality depends crucially on station density: the frequency of occurrence of large errors for intense precipitation is less than 5% in data-dense regions, while it is approximately 30 % in data-sparse regions. The open-access datasets are available for public download at daily total precipitation (https://doi.org/10.5281/zenodo.2082320, Lussana, 2018b); and daily mean (https://doi.org/10.5281/zenodo.2023997, Lussana, 2018c), maximum (https://doi.org/10.5281/zenodo.2559372, Lussana, 2018e), and minimum (https://doi.org/10.5281/zenodo.2559354, Lussana, 2018d) temperatures.
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Hopkinson, Ron F., Daniel W. McKenney, Ewa J. Milewska, Michael F. Hutchinson, Pia Papadopol, and Lucie A. Vincent. "Impact of Aligning Climatological Day on Gridding Daily Maximum–Minimum Temperature and Precipitation over Canada." Journal of Applied Meteorology and Climatology 50, no. 8 (2011): 1654–65. http://dx.doi.org/10.1175/2011jamc2684.1.
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AbstractOn 1 July 1961, the climatological day was redefined to end at 0600 UTC at all principal climate stations in Canada. Prior to that, the climatological day at principal stations ended at 1200 UTC for maximum temperature and precipitation and 0000 UTC for minimum temperature and was similar to the climatological day at ordinary stations. Hutchinson et al. reported occasional larger-than-expected residuals at 50 withheld stations when the Australian National University Spline (ANUSPLIN) interpolation scheme was applied to daily data for 1961–2003, and it was suggested that these larger residuals were in part due to the existence of different climatological days. In this study, daily minimum and maximum temperatures at principal stations were estimated using hourly temperatures for the same climatological day as local ordinary climate stations for the period 1953–2007. Daily precipitation was estimated at principal stations using synoptic precipitation data for the climatological day ending at 1200 UTC, which, for much of the country, was close to the time of the morning observation at ordinary climate stations. At withheld principal stations, the climatological-day adjustments led to the virtual elimination of large residuals in maximum and minimum temperature and a marked reduction in precipitation residuals. Across all 50 withheld stations the climatological day adjustments led to significant reductions, by around 12% for daily maximum temperature, 15% for daily minimum temperature, and 22% for precipitation, in the residuals reported by Hutchinson et al.
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Davidson, J. I., P. D. Blankenship, R. J. Henning, W. R. Guerke, R. D. Smith, and R. J. Cole. "Geocarposphere Temperature as It Relates to Florunner Peanut Production." Peanut Science 18, no. 2 (1991): 79–85. http://dx.doi.org/10.3146/i0095-3679-18-2-5.
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Abstract Geocarposphere (GCS) temperature was correlated to yield, grade, jumbos (outturns), aflatoxin, and germination of Florunner peanuts grown in Southwest Georgia during CY 1981–1987. Maximum daily GCS temperatures usually provided better correlations with yield and quality factors than minimum and mean daily GCS temperatures. Maximum daily GCS temperatures were also more indicative of plant stress. Minimum GCS temperatures were important for rapid emergence, root growth, and maturation. Both maximum and minimum daily GCS temperatures were important for reducing impact by wet and dry weather pests. Maximum yield and quality will be produced when production practices are managed to maintain GCS temperature in the range of 20–35 C (68–95 F) at planting time, 20–31 C (68–87 F) prior to and during the early part of fruiting, 21–28 C (70–83 F) during primary pod addition and 21–29 C (70–85 F) during primary pod maturation period. However, maximum GCS temperatures below 27 C (80 F) should be avoided to minimize impact of wet weather pests. This and other information that relate scouting data and field history to yield and quality have proven useful in developing an Expert System and models for managing peanut production and marketing.
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Sang, Yan-Fang, Zhonggen Wang, Changming Liu, and Tongliang Gong. "Temporal–Spatial Climate Variability in the Headwater Drainage Basins of the Yangtze River and Yellow River, China." Journal of Climate 26, no. 14 (2013): 5061–71. http://dx.doi.org/10.1175/jcli-d-12-00523.1.
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Abstract Variability of the climate in the headwater drainage basins of the Yangtze River and Yellow River during 1961–2010 was investigated by examining four typical climatic variables: daily minimum, mean, and maximum temperatures and daily precipitation. The results indicate that the temporal trends vary among the climatic variables and the time periods examined. The increase in daily minimum temperature began later than the daily mean and maximum temperatures, but the increase rate of the former was relatively greater after 1985. The abrupt increases in precipitation that occurred near 1978 were much clearer than the three temperature variables. Four dominant periodicities (3, 7, 11, and 18–20 yr) of temperature and precipitation were identified, and these variation patterns directly determined the periodic discharge variations in the two rivers. Under climate change impacts, periodic variations in temperature and precipitation at long temporal scales were intensified after the 1980s. Comparatively, climate change more severely impacts the minimum temperature and precipitation than the maximum and mean temperatures in the study area, and the variability of daily precipitation is more complex than the three temperature variables. Overall, the headwater drainage basin of the Yellow River is more susceptible to climate change compared with the other basin. After 2008, the increase rate of temperature (especially the daily mean and minimum temperatures) became greater, and precipitation showed a downward trend in the study areas. These trends are unfavorable for the safety of water resources and for eco-environmental safety in the two basins.
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Seginer, Ido, and Bryan M. Jenkins. "Temperature exposure of greenhouses from monthly means of daily maximum and minimum temperatures." Journal of Agricultural Engineering Research 37, no. 3-4 (1987): 191–208. http://dx.doi.org/10.1016/s0021-8634(87)80017-3.
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Petropoulos, Zoe E., Oriana Ramirez-Rubio, Madeleine K. Scammell, et al. "Climate Trends at a Hotspot of Chronic Kidney Disease of Unknown Causes in Nicaragua, 1973–2014." International Journal of Environmental Research and Public Health 18, no. 10 (2021): 5418. http://dx.doi.org/10.3390/ijerph18105418.
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An ongoing epidemic of chronic kidney disease of uncertain etiology (CKDu) afflicts large parts of Central America and is hypothesized to be linked to heat stress at work. Mortality rates from CKDu appear to have increased dramatically since the 1970s. To explore this relationship, we assessed trends in maximum and minimum temperatures during harvest months between 1973 and 2014 as well as in the number of days during the harvest season for which the maximum temperature surpassed 35 °C. Data were collected at a weather station at a Nicaraguan sugar company where large numbers of workers have been affected by CKDu. Monthly averages of the daily maximum temperatures between 1996 and 2014 were also compared to concurrent weather data from eight Automated Surface Observing System Network weather stations across Nicaragua. Our objectives were to assess changes in temperature across harvest seasons, estimate the number of days that workers were at risk of heat-related illness and compare daily maximum temperatures across various sites in Nicaragua. The monthly average daily maximum temperature during the harvest season increased by 0.7 °C per decade between 1973 and 1990. The number of days per harvest season with a maximum temperature over 35 °C increased by approximately five days per year between 1974 and 1990, from 32 days to 114 days. Between 1991 and 2013, the number of harvest days with a maximum temperature over 35 °C decreased by two days per year, and the monthly average daily maximum temperature decreased by 0.3 °C per decade. Comparisons with weather stations across Nicaragua demonstrate that this company is located in one of the consistently hottest regions of the country.
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Petropoulos, Zoe E., Oriana Ramirez-Rubio, Madeleine K. Scammell, et al. "Climate Trends at a Hotspot of Chronic Kidney Disease of Unknown Causes in Nicaragua, 1973–2014." International Journal of Environmental Research and Public Health 18, no. 10 (2021): 5418. https://doi.org/10.3390/ijerph18105418.
Full textAbstract:
An ongoing epidemic of chronic kidney disease of uncertain etiology (CKDu) afflicts large parts of Central America and is hypothesized to be linked to heat stress at work. Mortality rates from CKDu appear to have increased dramatically since the 1970s. To explore this relationship, we assessed trends in maximum and minimum temperatures during harvest months between 1973 and 2014 as well as in the number of days during the harvest season for which the maximum temperature surpassed 35 °C. Data were collected at a weather station at a Nicaraguan sugar company where large numbers of workers have been affected by CKDu. Monthly averages of the daily maximum temperatures between 1996 and 2014 were also compared to concurrent weather data from eight Automated Surface Observing System Network weather stations across Nicaragua. Our objectives were to assess changes in temperature across harvest seasons, estimate the number of days that workers were at risk of heat-related illness and compare daily maximum temperatures across various sites in Nicaragua. The monthly average daily maximum temperature during the harvest season increased by 0.7 °C per decade between 1973 and 1990. The number of days per harvest season with a maximum temperature over 35 °C increased by approximately five days per year between 1974 and 1990, from 32 days to 114 days. Between 1991 and 2013, the number of harvest days with a maximum temperature over 35 °C decreased by two days per year, and the monthly average daily maximum temperature decreased by 0.3 °C per decade. Comparisons with weather stations across Nicaragua demonstrate that this company is located in one of the consistently hottest regions of the country.
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Zwiers, Francis W., Xuebin Zhang, and Yang Feng. "Anthropogenic Influence on Long Return Period Daily Temperature Extremes at Regional Scales." Journal of Climate 24, no. 3 (2011): 881–92. http://dx.doi.org/10.1175/2010jcli3908.1.
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Abstract Observed 1961–2000 annual extreme temperatures, namely annual maximum daily maximum (TXx) and minimum (TNx) temperatures and annual minimum daily maximum (TXn) and minimum (TNn) temperatures, are compared with those from climate simulations of multiple model ensembles with historical anthropogenic (ANT) forcing and with combined anthropogenic and natural external forcings (ALL) at both global and regional scales using a technique that allows changes in long return period extreme temperatures to be inferred. Generalized extreme value (GEV) distributions are fitted to the observed extreme temperatures using a time-evolving pattern of location parameters obtained from model-simulated extreme temperatures under ANT or ALL forcing. Evaluation of the parameters of the fitted GEV distributions shows that both ANT and ALL influence can be detected in TNx, TNn, TXn, and TXx at the global scale over the land areas for which there are observations, and also regionally over many large land areas, with detection in more regions in TNx. Therefore, it is concluded that the influence of anthropogenic forcing has had a detectable influence on extreme temperatures that have impacts on human society and natural systems at global and regional scales. External influence is estimated to have resulted in large changes in the likelihood of extreme annual maximum and minimum daily temperatures. Globally, waiting times for extreme annual minimum daily minimum and daily maximum temperature events that were expected to recur once every 20 yr in the 1960s are now estimated to exceed 35 and 30 yr, respectively. In contrast, waiting times for circa 1960s 20-yr extremes of annual maximum daily minimum and daily maximum temperatures are estimated to have decreased to fewer than 10 and 15 yr, respectively.
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Tang, Qiuhong, and Guoyong Leng. "Changes in Cloud Cover, Precipitation, and Summer Temperature in North America from 1982 to 2009." Journal of Climate 26, no. 5 (2013): 1733–44. http://dx.doi.org/10.1175/jcli-d-12-00225.1.
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Abstract In North America (NA), trends in summer surface air temperatures vary on decadal time scales, and some regions have temperature trends that exhibit a lack of warming in 1982–2009. From a surface energy balance perspective, the summer mean daily maximum temperature change can be affected by changes in solar heating that are associated with cloud cover change and changes in surface evaporative cooling caused by different precipitation and land surface wetness, but little is known about regional cloud cover and precipitation feedbacks to decadal temperature trends. Changes in cloudiness and precipitation and their connections with summer mean daily maximum temperature variations in NA were investigated using observation-based products of temperature and precipitation and satellite-derived cloud cover and radiation products. Results show that summer mean daily maximum temperature variance is largely explained by changes in cloud cover and precipitation. Cloud cover effect dominates at the high and middle latitudes of NA, and precipitation is a more dominant factor in the southern United States. The results indicate that cloud cover is either the major indicator of the summer mean daily maximum temperature changes (the effect) or the important local factor influencing the changes (the cause). Cloud cover is negatively correlated with mean daily maximum temperature variation in spring and autumn at the middle latitudes of NA but not at the high latitudes.
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Pumphrey, Floyd V., and Robert E. Ramig. "Field Response of Peas to Excess Heat during the Reproductive Stage of Growth." Journal of the American Society for Horticultural Science 115, no. 6 (1990): 898–900. http://dx.doi.org/10.21273/jashs.115.6.898.
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Regression analysis was used to establish the effect of maximum daily temperatures on fresh pea (Pisum sativum L.) yield during the reproductive stage of growth. Maximum daily temperatures below 25.6C had little influence on pea yield. Temperatures above 25.6C depressed yield; this adverse effect increased exponentially as maximum daily temperature increased linearly. Predicted decrease in fresh pea yield ranged from 16 kg/ha per heat degree day above 27C to 67 kg/ha per heat degree day above 35C.
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Rust, Henning W., Tim Kruschke, Andreas Dobler, Madlen Fischer, and Uwe Ulbrich. "Discontinuous Daily Temperatures in the WATCH Forcing Datasets." Journal of Hydrometeorology 16, no. 1 (2015): 465–72. http://dx.doi.org/10.1175/jhm-d-14-0123.1.
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Abstract The Water and Global Change (WATCH) forcing datasets have been created to support the use of hydrological and land surface models for the assessment of the water cycle within climate change studies. They are based on 40-yr ECMWF Re-Analysis (ERA-40) or ECMWF interim reanalysis (ERA-Interim) with temperatures (among other variables) adjusted such that their monthly means match the monthly temperature dataset from the Climatic Research Unit. To this end, daily minimum, maximum, and mean temperatures within one calendar month have been subjected to a correction involving monthly means of the respective month. As these corrections can be largely different for adjacent months, this procedure potentially leads to implausible differences in daily temperatures across the boundaries of calendar months. We analyze day-to-day temperature fluctuations within and across months and find that across-months differences are significantly larger, mostly in the tropics and frigid zones. Average across-months differences in daily mean temperature are typically between 10% and 40% larger than their corresponding within-months average temperature differences. However, regions with differences up to 200% can be found in tropical Africa. Particularly in regions where snowmelt is a relevant player for hydrology, a few degrees Celsius difference can be decisive for triggering this process. Daily maximum and minimum temperatures are affected in the same regions, but in a less severe way.
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Westra, Seth, Lisa V. Alexander, and Francis W. Zwiers. "Global Increasing Trends in Annual Maximum Daily Precipitation." Journal of Climate 26, no. 11 (2013): 3904–18. http://dx.doi.org/10.1175/jcli-d-12-00502.1.
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Abstract This study investigates the presence of trends in annual maximum daily precipitation time series obtained from a global dataset of 8326 high-quality land-based observing stations with more than 30 years of record over the period from 1900 to 2009. Two complementary statistical techniques were adopted to evaluate the possible nonstationary behavior of these precipitation data. The first was a Mann–Kendall nonparametric trend test, and it was used to evaluate the existence of monotonic trends. The second was a nonstationary generalized extreme value analysis, and it was used to determine the strength of association between the precipitation extremes and globally averaged near-surface temperature. The outcomes are that statistically significant increasing trends can be detected at the global scale, with close to two-thirds of stations showing increases. Furthermore, there is a statistically significant association with globally averaged near-surface temperature, with the median intensity of extreme precipitation changing in proportion with changes in global mean temperature at a rate of between 5.9% and 7.7% K−1, depending on the method of analysis. This ratio was robust irrespective of record length or time period considered and was not strongly biased by the uneven global coverage of precipitation data. Finally, there is a distinct meridional variation, with the greatest sensitivity occurring in the tropics and higher latitudes and the minima around 13°S and 11°N. The greatest uncertainty was near the equator because of the limited number of sufficiently long precipitation records, and there remains an urgent need to improve data collection in this region to better constrain future changes in tropical precipitation.
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Putri, Jamiatul Khairunnisa, Suhadi, Muhammad Irfan, Hamdi Akhsan, and Iskhaq Iskandar. "Variability of Temperature Extreme Observes in Kalimantan." Jurnal Penelitian Pendidikan IPA 10, no. 11 (2024): 8734–44. https://doi.org/10.29303/jppipa.v10i11.9048.
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This study aims to analyze the trend of extreme temperatures in Kalimantan over the past few decades. The data used are daily temperatures from meteorological stations in West Kalimantan, Central Kalimantan, and South Kalimantan, with an observation period of January 1985 to December 2022. The research methodology involves four main stages: data collection, extreme temperature index calculation, trend detection, and correlation analysis with ENSO and IOD. The results showed an increasing trend in extreme temperatures (warming). The increase in mean annual maximum temperature (TXmean) ranged from 0.03°C to 2°C per century, while the mean annual minimum temperature increased from 0.2°C to 0.5°C per century. Monthly maximum value of daily max temperature (TXx) the increasing trend ranged from 0.19°C to 1.7°C, monthly maximum value of daily min temperature (TNx) increased from 0.1°C to 0.5°C. The monthly mean difference between daily max and min temperature (DTR) also shows an increase of 0.5°C to 1.7°C. This trend indicates that daytime (TXmean, TXx) and nighttime (TNmean, TNx) conditions in Kalimantan, especially in West, Central, and South Kalimantan, are getting hotter, with the daytime experiencing a more significant increase in temperature. The correlation between the extreme temperature index and ENSO and IOD is negative and positive, indicating that ENSO and IOD do not fully influence the increase in extreme temperatures. These findings have important implications for disaster mitigation planning and adaptation to climate change in the Kalimantan region.
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Streck, Nereu Augusto, Gizelli Moiano de Paula, Felipe Brendler Oliveira, Ana Paula Schwantes, and Nilson Lemos de Menezes. "Improving node number simulation in soybean." Pesquisa Agropecuária Brasileira 44, no. 7 (2009): 661–68. http://dx.doi.org/10.1590/s0100-204x2009000700002.
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The objective of this study was to improve the simulation of node number in soybean cultivars with determinate stem habits. A nonlinear model considering two approaches to input daily air temperature data (daily mean temperature and daily minimum/maximum air temperatures) was used. The node number on the main stem data of ten soybean cultivars was collected in a three-year field experiment (from 2004/2005 to 2006/2007) at Santa Maria, RS, Brazil. Node number was simulated using the Soydev model, which has a nonlinear temperature response function [f(T)]. The f(T) was calculated using two methods: using daily mean air temperature calculated as the arithmetic average among daily minimum and maximum air temperatures (Soydev tmean); and calculating an f(T) using minimum air temperature and other using maximum air temperature and then averaging the two f(T)s (Soydev tmm). Root mean square error (RMSE) and deviations (simulated minus observed) were used as statistics to evaluate the performance of the two versions of Soydev. Simulations of node number in soybean were better with the Soydev tmm version, with a 0.5 to 1.4 node RMSE. Node number can be simulated for several soybean cultivars using only one set of model coefficients, with a 0.8 to 2.4 node RMSE.
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Mote, Thomas L. "On the Role of Snow Cover in Depressing Air Temperature." Journal of Applied Meteorology and Climatology 47, no. 7 (2008): 2008–22. http://dx.doi.org/10.1175/2007jamc1823.1.
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Abstract This study empirically examines the role of snow depth on the depression of air temperature after controlling for effect of temperature changes above the boundary layer. In addition, this study examines the role of cloud cover, solar elevation angle, and maximum snow-covered albedo on the temperature depression due to snow cover. The work uses a new dataset of daily, gridded snow depth, snowfall, and maximum and minimum temperatures for North America from 1960 to 2000 in conjunction with 850-hPa temperature data for the same period from the NCEP–NCAR reanalysis, version 1. The 850-hPa temperatures are used as a control to remove the effect of temperature changes above the boundary layer on surface air temperatures. Findings from an analysis of variance demonstrate that snow cover can result in daily maximum (minimum) temperature depressions on average of 4.5°C (2.6°C) for snow depths greater than 10 cm over the grasslands of central North America, but temperature depressions average only 1.2°C (1.1°C) overall. The temperature depression of snow cover is shown to be reduced by increased cloud cover and decreased maximum albedo, which is indicative of denser forest cover. The role of snow melting on temperature depression is further explored by comparing days with maximum temperatures above or below freezing.
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Santos, Carlos Antonio Costa dos, Tantravahi Venkata Ramana Rao, and Ricardo Alves de Olinda. "Trends in temperature and growing season length in idaho-usa during the past few decades." Revista Brasileira de Meteorologia 30, no. 4 (2015): 359–70. http://dx.doi.org/10.1590/0102-778620130002.
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ABSTRACT This study attempts to provide new information on seasonal and annual trends, on a regional scale, using records of daily air temperature over Idaho, USA, through the analysis of the Growing Season Length (GSL), and maximum and minimum air temperature data from multiple stations in the region, as well as, to obtain the temporal correlation between the daily air temperature and Sea Surface Temperature (SST) indices. The analyses were conducted using long-term and high quality data sets for 35 meteorological stations for the period between 1970 and 2006. The results suggest that both daily maximum and minimum temperatures had increasing trends, but the minimum air temperature is increasing faster than the maximum air temperature. On average, the GSL has increased by 7.5 days/decade during the period 1970-2006, associated with increasing temperatures. Trends in regional air temperature and their indication of climate change are of interest to Idaho and the rest of the world. The trends obtained herein corroborate with the general idea that during the last century the globe has warmed.
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Wittich, Klaus-Peter. "Apple Sunburn Risk Detection—A Simple Model for Agricultural Decision Making and Some Fruit Temperature Measurements." Erwerbs-Obstbau 63, no. 1 (2021): 25–36. http://dx.doi.org/10.1007/s10341-021-00544-y.
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AbstractAs a response to strong radiative heating, the fruit temperature of apples may rise significantly above the air temperature. This may result in damage to the skin tissue and the epidermal and hypodermal cell layers. To avoid economic losses induced by sunburn symptoms on the skin, apple growers need forecasts of the fruit temperature, which will allow them to organize sun-protection measures in time.The temperatures of detached apples, which had been exposed to incident radiation and wind, were measured in order to quantify the extent of fruit heating under the climate conditions of northern Germany. On average, the daily maximum skin temperatures measured on the sunlit south side of the fruit during selected sunny and warm days were about 7 °C above the daily maximum air temperature. However, skin temperatures of more than 40 °C (the predefined warning threshold) were rarely detected, indicating that sunburn is still an uncommon phenomenon in northern Germany. Maximum apple-core temperatures were on average 4 °C higher than the maximum temperatures of ambient air.Based on energy balance principles, a simple model has been developed to predict the diurnal cycle of the sky- and ground-facing hemispherical apple temperatures. Focusing on fruit growers’ needs, daily temperature maxima of the modeled sky-facing hemisphere were compared with skin temperatures measured on the sunlit south side, resulting in a mean absolute error (MAE) of 1.7 °C. Comparison between daily maxima of total sphere temperatures and apple core temperatures provided better correlations (MAE = 0.8 °C).
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Nassar, Musab, and Lamya Amleh. "Transient Thermal Analysis of Concrete Box Girders: Assessing Temperature Variations in Canadian Climate Zones." Sensors 23, no. 19 (2023): 8206. http://dx.doi.org/10.3390/s23198206.
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This study examines the temperature distributions and thermal-induced responses in reinforced concrete bridge elements, focusing on the Canadian climate regions. The Canadian Highway Bridge Design Code (CHBDC) currently utilizes a fixed thermal gradient profile that does not account for regional climatic variations. Historical environmental data determines the effective maximum temperatures in the CHBDC. In order to investigate temperature behaviors and distributions, a transient finite element (FE) model is developed using recorded and calculated 3-month thermal loads data for representative cities in different climate regions. The results indicate that the predicted daily maximum effective mean temperatures and extreme daily positive vertical thermal gradients do not align. A linear correlation exists between the daily maximum effective mean temperature and the daily maximum air temperature, with a coefficient of determination (R2) of 0.935. The proposed effective mean temperatures obtained from the FE thermal analysis are higher than the CHBDC recommendations. New thermal gradient profiles are proposed for Canadian climate zones, consisting of two straight lines and a linear gradient at the top and bottom sections. A comparison between the proposed profiles and the CHBDC and AASHTO specifications reveals that a single fixed thermal gradient profile is inadequate to account for the variation in thermal gradients across Canadian climate regions.
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Tavosi, Mohammad, Mehdi Vafakhah, and Vahdi Moosavi. "Estimation of Maximum and Minimum Daily Air Temperature Using MODIS Surface Temperature Products." Journal of Remote Sensing and Gis 14, no. 4 (2022): 53–70. http://dx.doi.org/10.52547/gisj.14.4.53.
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