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 Suitability of a constant air temperature lapse rate over an Alpine glacier: testing the Greuell and Böhm model as an alternative

2013 ◽  
Vol 54 (63) ◽  
pp. 120-130 ◽  
Author(s):  
Lene Petersen ◽  
Francesca Pellicciotti ◽  
Inge Juszak ◽  
Marco Carenzo ◽  
Ben Brock
Keyword(s):  
Air Temperature ◽  
Surface Air Temperature ◽  
Ambient Air ◽  
Lapse Rate ◽  
Spatial And Temporal Variability ◽  
Near Surface ◽  
Alpine Glacier ◽  
Temperature Lapse Rate ◽  
Boundary Layer Height ◽  
Air Temperatures

AbstractNear-surface air temperature, typically measured at a height of 2 m, is the most important control on the energy exchange and the melt rate at a snow or ice surface. It is distributed in a simplistic manner in most glacier melt models by using constant linear lapse rates, which poorly represent the actual spatial and temporal variability of air temperature. In this paper, we test a simple thermodynamic model proposed by Greuell and Böhm in 1998 as an alternative, using a new dataset of air temperature measurements from along the flowline of Haut Glacier d’Arolla, Switzerland. The unmodified model performs little better than assuming a constant linear lapse rate. When modified to allow the ratio of the boundary layer height to the bulk heat transfer coefficient to vary along the flowline, the model matches measured air temperatures better, and a further reduction of the root-mean-square error is obtained, although there is still considerable scope for improvement. The modified model is shown to perform best under conditions favourable to the development of katabatic winds – few clouds, positive ambient air temperature, limited influence of synoptic or valley winds and a long fetch – but its performance is poor under cloudy conditions.

scholarly journals Anthropogenic Influence on the Rhine water temperatures

2019 ◽  
Author(s):  
Alex Zavarsky ◽  
Lars Duester
Keyword(s):  
River Water ◽  
Air Temperature ◽  
Nuclear Power ◽  
Important Variable ◽  
Anthropogenic Influence ◽  
River Temperature ◽  
Air Temperatures ◽  
Long Term Trend ◽  
River Water Temperature

Abstract. River temperature is an important parameter for water quality and an important variable for physical, chemical and biological processes. River water is also used by production facilities as cooling agent.We introduce a new way of calculating a catchment-wide air temperature and regressing river temperature vs air temperatures. As a result the meteorological influence and the anthropogenic influence can be studied separately. We apply this new method at four monitoring stations (Basel, Worms, Koblenz and Cologne) along 5 the Rhine and show that the long term trend (1979–2018) of river water temperature is, next to the increasing air temperature, mostly influenced by decreasing nuclear power production. Short term changes on time scales

An analysis of a 34-year air temperature record from Fossil Bluff (71°S, 68°W), Antarctica

1997 ◽  
Vol 9 (3) ◽  
pp. 355-363 ◽  
Author(s):  
Stephen A. Harangozo ◽  
Steven R. Colwell ◽  
John C. King
Keyword(s):  
Air Temperature ◽  
Antarctic Peninsula ◽  
Surface Air Temperature ◽  
Temperature Record ◽  
West Antarctic ◽  
Air Temperatures ◽  
Marguerite Bay ◽  
First Time ◽  
Snow Temperature

An analysis of a long-term surface air temperature record for Fossil Bluff in the George VI Sound, West Antarctic Peninsula (WAP) documents in detail some important aspects of the climate of this area for the first time. The analysis identifies the close dependency of air temperatures on latitude in the WAP but reveals that the strength of this dependency is greatest in winter. This result along with others leads to the Fossil Bluff climate regime being characterized as ‘continental’ rather than ‘maritime’ as found further north. The WAP as a whole displays large interannual temperature variability but this is greatest in Marguerite Bay rather than the Fossil Bluff area. Evidence is also provided for secular climatic change appearing in summer throughout the WAP over the last few decades. The representativeness of existing Antarctic Peninsula annual air temperature climatologies, based mainly on snow temperature measurements, for the winter and summer periods is also noted.

scholarly journals A Comparison of Methods for Filling Gaps in Hourly Near-Surface Air Temperature Data

2013 ◽  
Vol 14 (3) ◽  
pp. 929-945 ◽  
Author(s):  
Brian Henn ◽  
Mark S. Raleigh ◽  
Alex Fisher ◽  
Jessica D. Lundquist
Keyword(s):  
Missing Data ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Accurate Method ◽  
Temperature Data ◽  
Lapse Rate ◽  
Horizontal Distance ◽  
Method Performance ◽  
Near Surface ◽  
Temperature Observations

Abstract Near-surface air temperature observations often have periods of missing data, and many applications using these datasets require filling in all missing periods. Multiple methods are available to fill missing data, but the comparative accuracy of these approaches has not been assessed. In this comparative study, five techniques were used to fill in missing temperature data: spatiotemporal correlations in the form of empirical orthogonal functions (EOFs), time series diurnal interpolation, and three variations of lapse rate–based filling. The method validation used sets of hourly surface temperature observations in complex terrain from five regions. The most accurate method for filling missing data depended on the number of available stations and the number of hours of missing data. Spatiotemporal correlations using EOF reconstruction were most accurate provided that at least 16 stations were available. Temporal interpolation was the most accurate method when only one or two stations were available or for 1-h gaps. Lapse rate–based filling was most accurate for intermediate numbers of stations. The accuracy of the lapse rate and EOF methods was found to be sensitive to the vertical separation of stations and the degree of correlation between them, which also explained some of the regional differences in performance. Horizontal distance was less significantly correlated with method performance. From these findings, guidelines are presented for choosing a filling method based on the duration of the missing data and the number of stations.

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 Models to estimate river temperature. Example for Danube, at Oltenița in the context of climate change and anthropic impact

2021 ◽  
Vol 286 ◽  
pp. 04003
Author(s):  
Daniela-Elena Gogoașe-Nistoran ◽  
Cristina Sorana Ionescu ◽  
Ioana Opriș
Keyword(s):  
Water Temperature ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Logistic Function ◽  
Danube River ◽  
Weather Data ◽  
River Temperature ◽  
Anthropic Impact ◽  
Long Term Trend

Daily variation of Danube River temperature measured at Oltenitț gauging station over 9 years (2008-2016) was analysed in comparison with the air temperature measured by satellite in the same location between 1979-2020. Air temperature shows a nearly 2°C increase over the 40-years period, which can be attributed to both climate warming and anthropic impact. Water temperature was modeled with a sinusoidal function and variation with discharge was discussed. Long-term trend of hourly surface air temperature variation was obtained from Open Weather data. Air - water temperature dependency was fitted with a logistic function with good approximation. Resulting correlations help predict water temperature as a function of satellite - measured air temperature.

scholarly journals Improving snow process modeling with satellite-based estimation of near-surface-air-temperature lapse rate

2016 ◽  
Vol 121 (20) ◽  
pp. 12,005-12,030 ◽  
Author(s):  
Lei Wang ◽  
Litao Sun ◽  
Maheswor Shrestha ◽  
Xiuping Li ◽  
Wenbin Liu ◽  
...  
Keyword(s):  
Air Temperature ◽  
Process Modeling ◽  
Surface Air Temperature ◽  
Lapse Rate ◽  
Near Surface ◽  
Temperature Lapse Rate

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.

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