Spatio-temporal structure of surface air temperature fluctuations in the Southern Urals

2018 ◽  
Keyword(s):  
Air Temperature ◽  
Temporal Structure ◽  
Surface Air Temperature ◽  
Temperature Fluctuations ◽  
Southern Urals ◽  
The Southern Urals ◽  
Spatio Temporal

scholarly journals Analysis of the Spatio-Temporal Variability of Air Temperature Near the Ground Surface in the Central Baltic Area from 2005 to 2019

Atmosphere ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 60
Author(s):  
Agu Eensaar
Keyword(s):  
Air Temperature ◽  
Ground Surface ◽  
Surface Air Temperature ◽  
Entire Study ◽  
Significance Level ◽  
Average Surface ◽  
Air Temperatures ◽  
Spatio Temporal ◽  
The Difference ◽  
Baltic Area

In this study, we analyzed the changes in the average daily, monthly, seasonal, and annual surface air temperatures based on the temperature data obtained from seven stations (1 January 2005–31 December 2019; 15 years) belonging to the central Baltic area (Stockholm, Tallinn, Helsinki, Narva, Pärnu, Tartu, and Võru). The statistical analysis revealed that there was a strong correlation between the daily average surface air temperature of the studied cities (range: 0.95–0.99). We analyzed the frequency distribution of the average surface air temperatures in addition to the Kruskal–Wallis and Dunn’s tests (significance level of 0.05) to demonstrate that the difference in air temperatures between Narva, Tallinn, Tartu, and Stockholm are critical. The Welch’s t-test (significance level 0.05), used to study the differences in the average monthly air temperature of the cities in question, showed that the surface air temperatures in Stockholm do not differ from Tallinn air temperatures from May to August. However, the surface air temperatures of Narva were similar to those of Tallinn in September. According to our results, the trends in the changes of monthly average surface air temperatures have a certain course during the year (ranging from 1.8 °C (Stockholm) to 4.5 °C (Võru and Tartu) per decade in February). During the entire study period, in addition to February, the surface air temperature increased in all the studied cities in March, May, June, and December, and the surface air temperature did not increase in January or from July to October. During the study period, the average annual surface air temperature in the cities of the central Baltic area increased by 0.43 °C per decade. The results also confirm that the surface air temperature in the study area is changing differently in different cities. The acceleration of the surface air temperature is very alarming and requires a significant intensification of the measures taken to slow down the temperature rise.

Constructing gridded hourly air temperature dataset

2020 ◽  
Author(s):  
Alexandru Dumitrescu ◽  
Sorin Cheval
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Meteorological Data ◽  
Surface Air Temperature ◽  
Shuttle Radar Topography Mission ◽  
National Network ◽  
Research And Innovation ◽  
Air Temperatures ◽  
Spatio Temporal ◽  
Mean Square Errors

<p>Air temperature is one of the most important meteorological element, with major impact on the earth-atmosphere energy balance. The characteristics of the surface air temperature in locations without surface meteorological measurements are usually acquired by employing spatial statistics methods. Gridded surface meteorological data are essential for evaluating the performance of climatological models, for applying statistical downscaling methods and as input data for hydrological and agrometeorological models.</p><p>In this work, we tested two categories of statistical methods (spatial and spatio-temporal) used for interpolating ground-based hourly air temperature data. The main input dataset used in this work was the quality controlled and homogenized hourly air temperatures measured between 2016 and 2017, obtained from four networks: Romanian National Meteorological Administration (ANM), National Network for Monitoring Air Quality (RNMCA), Regional Basic Synoptic Network (RBSN), and Meteorological Terminal Aviation Routine Weather Report network (METAR). </p><p>The principal covariate used in the spatial interpolation procedures was the gap filled hourly LST data over Romania, available between 2016 to 2017, based on MSG-Seviri satellite images, which is an operational product of the Land Surface Analysis – Satellite Application Facility (LSA-SAF).  The other predictors were derived from SRTM (Shuttle Radar Topography Mission) data and from CORINE Land Cover 2018 product. The gridding was performed in a Romanian National Grid (Stereo 70), at 1000 m × 1000 m spatial resolution.</p><p>The results of the tested methods show that the mean absolute errors (MAE) and root mean square errors (RMSE) of space–time predictions are considerably lower than those of the pure spatial estimation.</p><p>This work was supported by a grant of Ministry of Research and Innovation, Romania, CNCS - UEFISCDI, project number PN-III-P1-1.1-PD-2016-1579, within PNCDI III.</p>

Spatio-temporal variability of near-surface air temperature in the Dokriani glacier catchment (DGC), central Himalaya

2018 ◽  
Vol 136 (3-4) ◽  
pp. 1513-1532 ◽  
Author(s):  
Jairam Singh Yadav ◽  
Bhanu Pratap ◽  
Anil K. Gupta ◽  
D. P. Dobhal ◽  
R. B. S. Yadav ◽  
...  
Keyword(s):  
Air Temperature ◽  
Temporal Variability ◽  
Surface Air Temperature ◽  
Central Himalaya ◽  
Near Surface ◽  
Spatio Temporal

scholarly journals Geosphere Coupling and Hydrothermal Anomalies before the 2009 Mw 6.3 L'Aquila Earthquake in Italy

2016 ◽  
Author(s):  
L. X. Wu ◽  
S. Zheng ◽  
A. De Santis ◽  
K. Qin ◽  
R. Di Mauro ◽  
...  
Keyword(s):  
Air Temperature ◽  
3D Visualization ◽  
Three Dimensional ◽  
Surface Air Temperature ◽  
Precipitable Water ◽  
B Value ◽  
Near Surface ◽  
Coupling Process ◽  
Spatio Temporal ◽  
High Level

Abstract. The earthquake (EQ) anomalies associated with the April 6, 2009 Mw 6.3 L'Aquila EQ have been widely reported. Nevertheless, the reported anomalies have not been so far synergically analyzed to interpret or prove the potential LCA coupling process. Previous studies on b-value are also insufficient. In this work, the spatio-temporal evolution of several hydrothermal parameters related to the coversphere and atmosphere, including soil moisture, soil temperature, near-surface air temperature, and precipitable water, was comprehensively investigated. Air temperature and atmospheric aerosol were also statistically analyzed in time series with ground observations. An abnormal enhancement of aerosol occurred on March 30, 2009 and thus proved quasi-synchronous anomalies among the hydrothermal parameters from March 29 to 31 in particular places geo-related to tectonic thrusts and local topography. The three-dimensional (3D) visualization analysis of b-value revealed that regional stress accumulated to a high level, particularly in the L'Aquila basin and around regional large thrusts. Finally, the coupling effects of geospheres were discussed, and a conceptual LCA coupling mode was proposed to interpret the possible mechanisms of the multiple quasi-synchronous anomalies preceding the L’Aquila EQ. Results indicate that CO2-rich fluids in deep crust might have played a significant role in the local LCA coupling process.

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