scholarly journals Daily minimum air temperatures in the Serra da Estrela, Portugal

Finisterra ◽  
2012 ◽  
Vol 36 (71) ◽  
Author(s):  
Carla Mora ◽  
Gonçalo Teles Vieira ◽  
Maria João Alcoforado
Keyword(s):  
Air Temperature ◽  
Morphological Diversity ◽  
Temperature Data ◽  
Air Temperatures ◽  
Data Loggers ◽  
Higher Temperature ◽  
The Times ◽  
Serra Da Estrela ◽  
Central Cordillera ◽  
Cluster 2

The Serra da Estrela is part of the Iberian Central Cordillera and isthe highest mountain in Portugal (1,993 m ASL). The Torre-Penhas Douradas and Alto da Pedrice-Malhada Alta plateaus with altitudes between 1,400 and 1,993m, which are separated by the Alforfa and Zêzere valleys dominate the highest part of the range. The central massif is dissected by several glacially sculpted valleys thatoriginate reliefs from 200 to 700m. This morphological diversity controls to a great extent the local climates of the mountain. Nine air temperature data loggers were installed in contrasting topographic situations, with special emphasis to valley floors and interfluve sites. Data collection was made each 2-hours from 27th December 1999 to 27th March 2000. Minima temperature most of the times occurs at 7 UTC. The minimum air temperature patterns based on the data from the nine sites were classified using k-means. Two contrasting events were chosen for the centroids of the classification. Cluster 1 represents the stable events with thermal inversions in the valleys and higher temperature in the interfluves. The valley floors at higher altitudes present lower temperatures than the ones at lower positions. Cluster 2 groups the unstable episodes with more turbulence and a temperature decrease controlled by altitude. In this group temperature does not depends on thetopographic position.

scholarly journals Effect of the duration of high air temperature on cow’s milking performance in moderate climate conditions

2018 ◽  
Vol 18 (1) ◽  
pp. 195-207 ◽  
Author(s):  
Piotr Herbut ◽  
Sabina Angrecka ◽  
Dorota Godyń
Keyword(s):  
Milk Yield ◽  
Air Temperature ◽  
Thermal Conditions ◽  
Climate Conditions ◽  
Air Temperatures ◽  
Holstein Friesian ◽  
Temperature Ranges ◽  
The Times ◽  
The Individual ◽  
Moderate Climate

Abstract The main aim of the presented investigation was to determine the effect of the air thermal conditions variability on cow’s milking performance in summer in a moderate climate. The analyses covered the summer months of 2012-2013 (June-September) and shorter, several-day periods characterized by the times of elevated or high air temperatures and by the declines and increases in milking performance. The research was conducted in a free stall barn for Holstein-Friesian cows. The study showed that the thermoneutral temperature for high yielding cows decreases gradually with the registered increasingly warmer summer periods. The decreases in milk yield already commence at an air temperature equal to 20°C and also depend on the dairy cattle sensitivity. July and August, with a high number of hot days, caused that in September the cows responded faster to a worsening of thermal conditions and the decline in milking performance happened almost simultaneously with the air temperature change, at milking yield recovery after the period of 3-4 d (r=-0.84, P<0.04). The percent duration in the individual temperature ranges which caused a decrease of milk yield was also determined. In June, and at the beginning of July, this was 90% of the time with temperatures above 20°C, and simultaneously 45% above 25°C occurred to milking performance decrease (r=-0.89, P<0.02). In September, this was only 30% of the time with temperatures above 20°C (r=-0.91, P<0.01).

Spatial and temporal variation of surface air temperature at different altitude zone in recent 30 years over Nepal

MAUSAM ◽  
2021 ◽  
Vol 68 (3) ◽  
pp. 417-428
Author(s):  
JANAK LAL NAYAVA ◽  
SUNIL ADHIKARY ◽  
OM RATNA BAJRACHARYA
Keyword(s):  
Air Temperature ◽  
Surface Air Temperature ◽  
Temperature Data ◽  
Lapse Rate ◽  
Spatial And Temporal Variation ◽  
Mountain Regions ◽  
Temperature Lapse Rate ◽  
Air Temperatures ◽  
Long Term Trend

This paper investigates long term (30 yrs) altitudinal variations of surface air temperatures based on air temperature data of countrywide scattered 22 stations (15 synoptic and 7 climate stations) in Nepal. Several researchers have reported that rate of air temperature rise (long term trend of atmospheric warming) in Nepal is highest in the Himalayan region (~ 3500 m asl or higher) compared to the Hills and Terai regions. Contrary to the results of previous researchers, however this study found that the increment of annual mean temperature is much higher in the Hills (1000 to 2000 m asl) than in the Terai and Mountain Regions. The temperature lapse rate in a wide altitudinal range of Nepal (70 to 5050 m asl) is -5.65 °C km-1. Warming rates in Terai and Trans-Himalayas (Jomsom) are 0.024 and 0.029 °C/year respectively.  

scholarly journals Evaluation of ERA-Interim Air Temperature Data over the Qilian Mountains of China

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Peng Zhao ◽  
Lu Gao ◽  
Jianhui Wei ◽  
Miaomiao Ma ◽  
Haijun Deng ◽  
...  
Keyword(s):  
Air Temperature ◽  
Warming Trend ◽  
Temperature Data ◽  
Qilian Mountains ◽  
Positive Trend ◽  
Climate Change Research ◽  
Weather Forecasts ◽  
Air Temperatures ◽  
Grid Points ◽  
The Qilian Mountains

In this study, 2 m air temperature data from 24 meteorological stations in the Qilian Mountains (QLM) are examined to evaluate ERA-Interim reanalysis temperature data derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) for the period of 1979–2017. ERA-Interim generally captures the monthly, seasonal, and annual variation very well. High daily correlations ranging from 0.956 to 0.996 indicate that ERA-Interim captures the daily temperature observations very well. However, an average root-mean-square error (RMSE) of ±2.7°C of all stations reveals that ERA-Interim should not be directly applied at individual sites. The biases are mainly attributed to the altitude differences between ERA-Interim grid points and stations. The positive trend (0.457°C/decade) is significant over the Qilian Mountains based on the 1979–2017 observations. ERA-Interim captures the warming trend very well with an increase rate of 0.384°C/decade. The observations and ERA-Interim both show the largest positive trends in summer with the values of 0.552°C/decade and 0.481°C/decade, respectively. We conclude that in general ERA-Interim captures the trend very well for observed 2 m air temperatures and ERA-Interim is generally reliable for climate change research over the Qilian Mountains.

scholarly journals EVALUATION OF A HEATING SYSTEM WITH A HEAT PUMP ACCORDING TO THE MODE OF INPUT DATA

2015 ◽  
Vol 1 (2) ◽  
pp. 65-71
Author(s):  
Vladimíra Linhartová
Keyword(s):  
Heat Pump ◽  
Air Temperature ◽  
Input Data ◽  
Heating System ◽  
Temperature Data ◽  
Air Source Heat Pump ◽  
Air Temperatures ◽  
The Difference ◽  
Bin Method

The paper is focused on evaluating a heating system with an air source heat pump using the bin method. The main goal of the paper is to find the difference between three modes of input outside air temperature data in the calculation. Outside air temperatures are used in three modes, an hour based calculation, monthly frequencies and annual frequencies based calculations.

scholarly journals GPRChinaTemp1km: a high-resolution monthly air temperature dataset for China (1951–2020) based on machine learning

2021 ◽  
Author(s):  
Qian He ◽  
Ming Wang ◽  
Kai Liu ◽  
Kaiwen Li ◽  
Ziyu Jiang
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Air Temperature ◽  
Spatial Interpolation ◽  
Gaussian Process Regression ◽  
Temperature Data ◽  
Air Temperatures ◽  
Maximum Air Temperature ◽  
Maximum Minimum

Abstract. An accurate spatially continuous air temperature dataset is crucial for multiple applications in environmental and ecological sciences. Existing spatial interpolation methods have relatively low accuracy and the resolution of available long-term gridded products of air temperature for China is coarse. Point observations from meteorological stations can provide long-term air temperature data series but cannot represent spatially continuous information. Here, we devised a method for spatial interpolation of air temperature data from meteorological stations based on powerful machine learning tools. First, to determine the optimal method for interpolation of air temperature data, we employed three machine learning models: random forest, support vector machine, and Gaussian process regression. Comparison of the mean absolute error, root mean square error, coefficient of determination, and residuals revealed that Gaussian process regression had high accuracy and clearly outperformed the other two models regarding interpolation of monthly maximum, minimum, and mean air temperatures. The machine learning methods were compared with three traditional methods used frequently for spatial interpolation: inverse distance weighting, ordinary kriging, and ANUSPLIN. Results showed that the Gaussian process regression model had higher accuracy and greater robustness than the traditional methods regarding interpolation of monthly maximum, minimum, and mean air temperatures in each month. Comparison with the TerraClimate, FLDAS, and ERA5 datasets revealed that the accuracy of the temperature data generated using the Gaussian process regression model was higher. Finally, using the Gaussian process regression method, we produced a long-term (January 1951 to December 2020) gridded monthly air temperature dataset with 1 km resolution and high accuracy for China, which we named GPRChinaTemp1km. The dataset consists of three variables: monthly mean air temperature, monthly maximum air temperature, and monthly minimum air temperature. The obtained GPRChinaTemp1km data were used to analyse the spatiotemporal variations of air temperature using Theil–Sen median trend analysis in combination with the Mann–Kendall test. It was found that the monthly mean and minimum air temperatures across China were characterized by a significant trend of increase in each month, whereas monthly maximum air temperature showed a more spatially heterogeneous pattern with significant increase, non-significant increase, and non-significant decrease. The GPRChinaTemp1km dataset is publicly available at https://doi.org/10.5281/zenodo.5112122 (He et al., 2021a) for monthly maximum air temperature, at https://doi.org/10.5281/zenodo.5111989 (He et al., 2021b) for monthly mean air temperature and at https://doi.org/10.5281/zenodo.5112232 (He et al., 2021c) for monthly minimum air temperature.

scholarly journals Soil temperature prediction from air temperature for alluvial soils in lower Indo-Gangetic plain

2017 ◽  
Vol 31 (1) ◽  
pp. 9-22 ◽  
Author(s):  
D. Barman ◽  
D.K. Kundu ◽  
Soumen Pal ◽  
Susanto Pal ◽  
A.K. Chakraborty ◽  
...  
Keyword(s):  
Soil Temperature ◽  
Air Temperature ◽  
Temperature Data ◽  
Alluvial Soils ◽  
Gangetic Plain ◽  
Standard Time ◽  
Daily Data ◽  
Soil Temperatures ◽  
The Times ◽  
Indo Gangetic Plain

AbstractSoil temperature is an important factor in biogeochemical processes. On-site monitoring of soil temperature is limited in spatiotemporal scale as compared to air temperature data inventories due to various management difficulties. Therefore, empirical models were developed by taking 30-year long-term (1985-2014) air and soil temperature data for prediction of soil temperatures at three depths (5, 15, 30 cm) in morning (0636 Indian standard time) and afternoon (1336 Indian standard time) for alluvial soils in lower Indo-Gangetic plain. At 5 cm depth, power and exponential regression models were best fitted for daily data in morning and afternoon, respectively, but it was reverse at 15 cm. However, at 30 cm, exponential models were best fitted for both the times. Regression analysis revealed that in morning for all three depths and in afternoon for 30 cm depth, soil temperatures (daily, weekly, and monthly) could be predicted more efficiently with the help of corresponding mean air temperature than that of maximum and minimum. However, in afternoon, prediction of soil temperature at 5 and 15 cm depths were more precised for all the time intervals when maximum air temperature was used, except for weekly soil temperature at 15 cm, where the use of mean air temperature gave better prediction.

scholarly journals GPRChinaTemp1km: a high-resolution monthly air temperature dataset for China (1951–2020) based on machine learning

2021 ◽  
Author(s):  
Qian He ◽  
Ming Wang ◽  
Kai Liu ◽  
Kaiwen Li ◽  
Ziyu Jiang
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Air Temperature ◽  
Spatial Interpolation ◽  
Gaussian Process Regression ◽  
Temperature Data ◽  
Air Temperatures ◽  
Maximum Air Temperature ◽  
Maximum Minimum

Abstract. An accurate spatially continuous air temperature dataset is crucial for multiple applications in environmental and ecological sciences. Existing spatial interpolation methods have relatively low accuracy and the resolution of available long-term gridded products of air temperature for China is coarse. Point observations from meteorological stations can provide long-term air temperature data series but cannot represent spatially continuous information. Here, we devised a method for spatial interpolation of air temperature data from meteorological stations based on powerful machine learning tools. First, to determine the optimal method for interpolation of air temperature data, we employed three machine learning models: random forest, support vector machine, and Gaussian process regression. Comparison of the mean absolute error, root mean square error, coefficient of determination, and residuals revealed that Gaussian process regression had high accuracy and clearly outperformed the other two models regarding interpolation of monthly maximum, minimum, and mean air temperatures. The machine learning methods were compared with three traditional methods used frequently for spatial interpolation: inverse distance weighting, ordinary kriging, and ANUSPLIN. Results showed that the Gaussian process regression model had higher accuracy and greater robustness than the traditional methods regarding interpolation of monthly maximum, minimum, and mean air temperatures in each month. Comparison with the TerraClimate, FLDAS, and ERA5 datasets revealed that the accuracy of the temperature data generated using the Gaussian process regression model was higher. Finally, using the Gaussian process regression method, we produced a long-term (January 1951 to December 2020) gridded monthly air temperature dataset with 1 km resolution and high accuracy for China, which we named GPRChinaTemp1km. The dataset consists of three variables: monthly mean air temperature, monthly maximum air temperature, and monthly minimum air temperature. The obtained GPRChinaTemp1km data were used to analyse the spatiotemporal variations of air temperature using Theil–Sen median trend analysis in combination with the Mann–Kendall test. It was found that the monthly mean and minimum air temperatures across China were characterized by a significant trend of increase in each month, whereas monthly maximum air temperature showed a more spatially heterogeneous pattern with significant increase, non-significant increase, and non-significant decrease. The GPRChinaTemp1km dataset is publicly available at https://doi.org/10.5281/zenodo.5112122 (He et al., 2021a) for monthly maximum air temperature, at https://doi.org/10.5281/zenodo.5111989 (He et al., 2021b) for monthly mean air temperature and at https://doi.org/10.5281/zenodo.5112232 (He et al., 2021c) for monthly minimum air temperature.

Phenology and ecology of hibernation in spotted turtles (Clemmys guttata) near the northern limit of their range

1999 ◽  
Vol 77 (9) ◽  
pp. 1348-1357 ◽  
Author(s):  
Jacqueline D Litzgus ◽  
Jon P Costanzo ◽  
Ronald J Brooks ◽  
Richard E Lee, Jr.
Keyword(s):  
Body Temperature ◽  
Temperature Data ◽  
Temperature Sensitive ◽  
Data Logger ◽  
Ambient Temperatures ◽  
Air Temperatures ◽  
Data Loggers ◽  
Rock Cavern ◽  
Clemmys Guttata ◽  
Radio Transmitters

Using mark-recapture techniques, temperature-sensitive radio transmitters, and miniature temperature data loggers we investigated the hibernation ecology of northern temperate zone spotted turtles (Clemmys guttata) in Georgian Bay, Ontario, over 4 winters (1993-1997). We observed 18 hibernacula that were occupied by 34 turtles; 11 hibernacula were apparently occupied by single turtles, and 7 were used communally by up to 9 individuals. Hibernacula were located in swamps and were of 2 types: sphagnum moss hummock (n = 15) and rock cavern (n = 3). Almost half of the individuals (16 of 34) used the same hibernaculum in at least 2 winters. Turtles entered hibernacula between mid-September and October, when their body temperature was between 12 and 16°C, and exited them in mid to late April, when ambient temperatures ranged between 1 and 5°C. A waterproof temperature data logger attached to a turtle indicated that this turtle was protected from freezing in a thermally stable hibernaculum (body temperature range 0.3-3.9°C) despite highly variable (a 37°C change over 5 days) and low air temperatures (minimum -35°C). Loss of body mass (2%) during hibernation was not significant. We observed no mortality within hibernacula over the 4 winters; however, 3 turtles were destroyed by predators near the hibernacula. These data provide insight into the role of climate in limiting the northern distribution of this species.

scholarly journals Activity and Pool Use in Relation to Temperature and Water Changes in Zoo Hippopotamuses (Hippopotamus amphibious)

Animals ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 1022
Author(s):  
Eduardo J. Fernandez ◽  
Martin Ramirez ◽  
Nancy C. Hawkes
Keyword(s):  
Water Temperature ◽  
Air Temperature ◽  
Daily Activity ◽  
Current Knowledge ◽  
Temperature Data ◽  
Future Directions ◽  
Pool Water ◽  
Data Loggers ◽  
In The Wild ◽  
Increased Activity

In the wild, hippopotamuses spend much of their daily activity in the water. In zoos, it is less clear the extent to which hippos spend time in the water. We examined how much time Woodland Park Zoo’s three hippos spent in their outdoor pool, based on: (a) temperature of the pool water, and (b) when the pool water was changed (approximately three times a week). Several digital temperature data loggers collected water and air temperature readings once every hour for six months. We correlated the water temperature readings with several behaviors the hippos could engage in, where the hippos were on exhibit (pool vs. land), and how many days it had been since a dump (0, 1, or 2 days). The results indicated that water changes had little effect on pool usage, while increasing water temperatures resulted in both increased activity and pool use. The results are discussed in terms of how these findings relate to wild hippo activity, current knowledge of zoo-housed hippo welfare, and future directions for zoo-housed hippo welfare and research.

On the dynamics of distribution of the American White Butterfly, Hyphantria cunea Drury, 1973 (Lepidoptera, Arctiidae), in Ukraine regarding the annual minimal air temperatures

2018 ◽  
Vol 14 (1) ◽  
pp. 44-57
Author(s):  
S. N. Shumov
Keyword(s):  
Air Temperature ◽  
Annual Reports ◽  
Complete Elimination ◽  
Hyphantria Cunea ◽  
White American ◽  
Gis Technologies ◽  
Data Analyses ◽  
Air Temperatures ◽  
Negative Temperatures

The spatial analysis of distribution and quantity of Hyphantria cunea Drury, 1973 across Ukraine since 1952 till 2016 regarding the values of annual absolute temperatures of ground air is performed using the Gis-technologies. The long-term pest dissemination data (Annual reports…, 1951–1985; Surveys of the distribution of quarantine pests ..., 1986–2017) and meteorological information (Meteorological Yearbooks of air temperature the surface layer of the atmosphere in Ukraine for the period 1951-2016; Branch State of the Hydrometeorological Service at the Central Geophysical Observatory of the Ministry for Emergencies) were used in the present research. The values of boundary negative temperatures of winter diapause of Hyphantria cunea, that unable the development of species’ subsequent generation, are received. Data analyses suggests almost complete elimination of winter diapausing individuals of White American Butterfly (especially pupae) under the air temperature of −32°С. Because of arising questions on the time of action of absolute minimal air temperatures, it is necessary to ascertain the boundary negative temperatures of winter diapause for White American Butterfly. It is also necessary to perform the more detailed research of a corresponding biological material with application to the freezing technics, giving temperature up to −50°С, with the subsequent analysis of the received results by the punched-analysis.

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