Trend Analysis of Air Temperature in the Federal District of Brazil: 1980–2010

This study was designed to identify trends in maximum, minimum, and average air temperatures in the Federal District of Brazil from 1980 to 2010, measured at five weather stations. Three statistical tests (Wald–Wolfowitz, Cox–Stuart, and Mann–Kendall) were tested for their applicability for this purpose, and the ones found to be most suitable for the data series were validated. For this data sample, it was observed that the application of the Wald–Wolfowitz test and its validation by the Cox–Stuart and Mann–Kendall tests was the best solution for analyzing the air temperature trends. The results showed an upward trend in average and maximum air temperature at three weather stations, a downward trend at one, and the absence of any trend at two. If the trend of increasing air temperature in the Federal District persists, it could have a negative impact on various sectors of society, mainly on the health of the population, especially during the dry season when more cases of respiratory diseases are registered. These results could serve as inputs for public administrators involved in the planning and formulation of public policies.

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The Street Air Warming Phenomenon in a High-Rise Compact City

Atmosphere ◽

10.3390/atmos9100402 ◽

2018 ◽

Vol 9 (10) ◽

pp. 402 ◽

Cited By ~ 4

Author(s):

Xiaoxue Wang ◽

Yuguo Li ◽

Xinyan Yang ◽

Pak Chan ◽

Janet Nichol ◽

...

Keyword(s):

Thermal Comfort ◽

Air Temperature ◽

Thermal Environment ◽

Urban Climate ◽

Pollutant Dispersion ◽

Urban Morphology ◽

Air Temperatures ◽

Significant Difference ◽

Sensitivity Studies ◽

Weather Stations

The street thermal environment is important for thermal comfort, urban climate and pollutant dispersion. A 24-h vehicle traverse study was conducted over the Kowloon Peninsula of Hong Kong in summer, with each measurement period consisting of 2–3 full days. The data covered a total of 158 loops in 198 h along the route on sunny days. The measured data were averaged by three methods (direct average, FFT filter and interpolated by the piecewise cubic Hermite interpolation). The average street air temperatures were found to be 1–3 °C higher than those recorded at nearby fixed weather stations. The street warming phenomenon observed in the study has substantial implications as usually urban heat island (UHI) intensity is estimated from measurement at fixed weather stations, and therefore the UHI intensity in the built areas of the city may have been underestimated. This significant difference is of interest for studies on outdoor air temperature, thermal comfort, urban environment and pollutant dispersion. The differences were simulated by an improved one-dimensional temperature model (ZERO-CAT) using different urban morphology parameters. The model can correct the underestimation of street air temperature. Further sensitivity studies show that the building arrangement in the daytime and nighttime plays different roles for air temperature in the street. City designers can choose different parameters based on their purpose.

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Observational Evidence of Neighborhood Scale Reductions in Air Temperature Associated with Increases in Roof Albedo

Climate ◽

10.3390/cli6040098 ◽

2018 ◽

Vol 6 (4) ◽

pp. 98 ◽

Cited By ~ 6

Author(s):

Arash Mohegh ◽

Ronnen Levinson ◽

Haider Taha ◽

Haley Gilbert ◽

Jiachen Zhang ◽

...

Keyword(s):

Los Angeles ◽

Air Temperature ◽

Robust Regression ◽

Spatial Density ◽

Daily Average ◽

Air Temperatures ◽

San Fernando ◽

Weather Stations ◽

Meteorological Observations ◽

Neighborhood Scale

The effects of neighborhood-scale land use and land cover (LULC) properties on observed air temperatures are investigated in two regions within Los Angeles County: Central Los Angeles and the San Fernando Valley (SFV). LULC properties of particular interest in this study are albedo and tree fraction. High spatial density meteorological observations are obtained from 76 personal weather-stations. Observed air temperatures were then related to the spatial mean of each LULC parameter within a 500 m radius “neighborhood” of each weather station, using robust regression for each hour of July 2015. For the neighborhoods under investigation, increases in roof albedo are associated with decreases in air temperature, with the strongest sensitivities occurring in the afternoon. Air temperatures at 14:00–15:00 local daylight time are reduced by 0.31 °C and 0.49 °C per 1 MW increase in daily average solar power reflected from roofs per neighborhood in SFV and Central Los Angeles, respectively. Per 0.10 increase in neighborhood average albedo, daily average air temperatures were reduced by 0.25 °C and 1.84 °C. While roof albedo effects on air temperature seem to exceed tree fraction effects during the day in these two regions, increases in tree fraction are associated with reduced air temperatures at night.

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Comparison of satellite, thermochron and air temperatures at Summit, Greenland, during the winter of 2008/09

Journal of Glaciology ◽

10.3189/002214310793146269 ◽

2010 ◽

Vol 56 (198) ◽

pp. 735-741 ◽

Cited By ~ 31

Author(s):

Lora S. Koenig ◽

Dorothy K. Hall

Keyword(s):

Surface Temperature ◽

Air Temperature ◽

Satellite Data ◽

Ice Sheet ◽

Greenland Ice Sheet ◽

Seasonal Temperature ◽

Temperature Trends ◽

Air Temperatures ◽

Winter Time ◽

Arctic Surface

AbstractCurrent trends show a rise in Arctic surface and air temperatures, including over the Greenland ice sheet where rising temperatures will contribute to increased sea-level rise through increased melt. We aim to establish the uncertainties in using satellite-derived surface temperature for measuring Arctic surface temperature, as satellite data are increasingly being used to assess temperature trends. To accomplish this, satellite-derived surface temperature, or land-surface temperature (LST), must be validated and limitations of the satellite data must be assessed quantitatively. During the 2008/09 boreal winter at Summit, Greenland, we employed data from standard US National Oceanic and Atmospheric Administration (NOAA) air-temperature instruments, button-sized temperature sensors called thermochrons and the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument to (1) assess the accuracy and utility of thermochrons in an ice-sheet environment and (2) compare MODIS-derived LSTs with thermochron-derived surface and air temperatures. The thermochron-derived air temperatures were very accurate, within 0.1 ± 0.3°C of the NOAA-derived air temperature, but thermochron-derived surface temperatures were ∼3°C higher than MODIS-derived LSTs. Though surface temperature is largely determined by air temperature, these variables can differ significantly. Furthermore, we show that the winter-time mean air temperature, adjusted to surface temperature, was ∼11°C higher than the winter-time mean MODIS-derived LST. This marked difference occurs largely because satellite-derived LSTs cannot be measured through cloud cover, so caution must be exercised in using time series of satellite LST data to study seasonal temperature trends.

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A weighted coefficient model for estimation of Australian daily soil temperature at depths of 5cm to 100cm based on air temperature and rainfall

Soil Research ◽

10.1071/sr10151 ◽

2011 ◽

Vol 49 (4) ◽

pp. 305 ◽

Cited By ~ 10

Author(s):

Brian Horton ◽

Ross Corkrey

Keyword(s):

Soil Temperature ◽

Air Temperature ◽

Cross Validation ◽

Soil Depth ◽

Mean Square ◽

Air Temperatures ◽

Proposed Model ◽

Soil Temperatures ◽

Weather Stations ◽

Fold Cross Validation

Soil temperatures are related to air temperature and rainfall on the current day and preceding days, and this can be expressed in a non-linear relationship to provide a weighted value for the effect of air temperature or rainfall based on days lag and soil depth. The weighted minimum and maximum air temperatures and weighted rainfall can then be combined with latitude and a seasonal function to estimate soil temperature at any depth in the range 5–100 cm. The model had a root mean square deviation of 1.21–1.85°C for minimum, average, and maximum soil temperature for all weather stations in Australia (mainland and Tasmania), except for maximum soil temperature at 5 and 10 cm, where the model was less precise (3.39° and 2.52°, respectively). Data for this analysis were obtained from 32–40 Bureau of Meteorology weather stations throughout Australia and the proposed model was validated using 5-fold cross-validation.

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About the features of the time course of the average annual air temperature in the territory of the Debed river basin (Armenia)

E3S Web of Conferences ◽

10.1051/e3sconf/202022303009 ◽

2020 ◽

Vol 223 ◽

pp. 03009

Author(s):

Varduhi Margaryan ◽

Gennady Tsibulskii ◽

Ksenia Raevich

Keyword(s):

River Basin ◽

Air Temperature ◽

Time Course ◽

Data Series ◽

Observation Data ◽

Air Temperatures ◽

Daily Data ◽

Long Term Observation ◽

Surface Air Layer

The article examines the features of the time course of the average annual air temperature in the Debed river basin in Armenia. As a starting material, we used daily data of actual observations of the temperature of the surface air layer for a year in the Debed river basin. The study was carried out at 6 meteorological stations in the Debed river basin based on long-term observation data series from 1930 to the present (2018). Analysis of the trend lines of temporal changes in air temperatures shows that at all meteorological stations currently operating on the territory of the basin, there is mainly a tendency for an increase in temperatures of annual values.

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VARIABILITY OF THE MAIN CLIMATIC INDICATORS IN THE TERRITORY OF THE VOLGA FEDERAL DISTRICT IN THE PERIOD 1966-2018

Bulletin of Udmurt University. Series Biology. Earth Sciences ◽

10.35634/2412-9518-2021-31-1-65-75 ◽

2021 ◽

Vol 31 (1) ◽

pp. 65-75

Author(s):

Yu.P. Perevedentsev ◽

E.M. Parubova ◽

K.M. Shantalinsky ◽

M.A. Myagkov ◽

B.G. Sherstyukov

Keyword(s):

Air Temperature ◽

Annual Variation ◽

Water Vapor Pressure ◽

Atmospheric Precipitation ◽

Statistical Processing ◽

Federal District ◽

Warming Trend ◽

A Value ◽

Weather Stations ◽

Spatio Temporal

The spatio-temporal variability of air temperature and humidity, atmospheric precipitation in the Volga Federal District in 1966-2018 is considered. As a result of statistical processing of data from 20 weather stations, a clear warming trend in recent decades and a weak increase in annual precipitation are revealed, except for the south-east of the region. The annual variation of water vapor pressure and relative humidity is considered, statistics of the distribution of "dry" and "wet" days by stations in different months are carried out. Correlations between individual circulation modes (AO, NAO, SCAND, EAWR) and air temperature are revealed. It is shown that positive relationships are closer in winter with the AO and NAO indices than in summer. A sufficiently high negative correlation is established with the SCAND index in winter (r = -0,7), and with the EAWR index in summer (the correlation coefficient reaches a value of -0,6).

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Detection of Changes in Hydrological Time Series During Recent Decades

Slovak Journal of Civil Engineering ◽

10.2478/sjce-2020-0016 ◽

2020 ◽

Vol 28 (2) ◽

pp. 56-62

Author(s):

Mária Ďurigová ◽

Kamila Hlavčová ◽

Jana Poórová

Keyword(s):

Time Series ◽

Air Temperature ◽

Time Series Data ◽

Kendall Test ◽

Change Points ◽

Series Data ◽

Data Series ◽

Time Data ◽

Air Temperatures ◽

Pettitt’S Test

AbstractAn analysis of a hydrological time-series data offers the possibility of detecting changes that have arisen due to climate change or change in land use. This paper deals with the detection of changes in the hydrological time data series. The trend analysis was applied at 58 stage-discharge gauging stations that are located throughout Slovakia, with the measurement period from 1962 to 2017. The Mann-Kendall test show a declining trends in the summer and a few rising trends in the winter in discharges. In the town of Banská Bystrica at a station on the Hron River, decades of discharges, air temperatures, and precipitation totals were analyzed. The five decades from the 1960s to the 2000s were used. The hydrological time data series were also analyzed by the Pettitt’s test, which is used to detect change points. The decadal analysis at the Banská Bystrica station shows an increase in the air temperature but insignificant changes in discharges and precipitation. Pettitt’s test identified many change points in the 1990s in the air temperature.

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

10.5194/essd-2021-442 ◽

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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A data portal for regional climatic trend analysis in a Peruvian High Andes region

Advances in Science and Research ◽

10.5194/asr-6-219-2011 ◽

2011 ◽

Vol 6 (1) ◽

pp. 219-226 ◽

Cited By ~ 17

Author(s):

M. Schwarb ◽

D. Acuña ◽

Th. Konzelmann ◽

M. Rohrer ◽

N. Salzmann ◽

...

Keyword(s):

Climate Change ◽

Trend Analysis ◽

Climate Change Adaptation ◽

Air Temperature ◽

Data Series ◽

Adaptation Measures ◽

Climatic Trend ◽

Temperature And Precipitation ◽

Temperature Trends ◽

Data Portal

Abstract. In the frame of a Swiss-Peruvian climate change adaptation initiative (PACC), operational and historical data series of more than 100 stations of the Peruvian Meteorological and Hydrological Service (SENAMHI) are now accessible in a dedicated data portal. The data portal allows for example the comparison of data series or the interpolation of spatial fields as well as download of data in various data formats. It is thus a valuable tool supporting the process of data homogenisation and generation of a regional baseline climatology for a sound development of adequate climate change adaptation measures. The procedure to homogenize air-temperature and precipitation data series near Cusco city is outlined and followed by an exemplary trend analysis. Local air temperature trends are found to be in line with global mean trends.

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Multiple Indicators of Extreme Changes in Snow-Dominated Streamflow Regimes, Yakima River Basin Region, USA

Water ◽

10.3390/w13192608 ◽

2021 ◽

Vol 13 (19) ◽

pp. 2608

Author(s):

Anna M. Wagner ◽

Katrina E. Bennett ◽

Glen E. Liston ◽

Christopher A. Hiemstra ◽

Dan Cooley

Keyword(s):

River Basin ◽

Air Temperature ◽

Snow Water Equivalent ◽

Hydrological Cycle ◽

Negative Impact ◽

Clear Trend ◽

Multiple Indicators ◽

Air Temperatures ◽

Yakima River ◽

Streamflow Trends

Snow plays a major role in the hydrological cycle. Variations in snow duration and timing can have a negative impact on water resources. Excluding predicted changes in snowmelt rates and amounts could result in deleterious infrastructure, military mission, and asset impacts at military bases across the US. A change in snowpack can also lead to water shortages, which in turn can affect the availability of irrigation water. We performed trend analyses of air temperature, snow water equivalent (SWE) at 22 SNOTEL stations, and streamflow extremes for selected rivers in the snow-dependent and heavily irrigated Yakima River Basin (YRB) located in the Pacific Northwest US. There was a clear trend of increasing air temperature in this study area over a 30 year period (water years 1991–2020). All stations indicated an increase in average air temperatures for December (0.97 °C/decade) and January (1.12 °C/decade). There was also an upward trend at most stations in February (0.28 °C/decade). In December–February, the average air temperatures were 0.82 °C/decade. From these trends, we estimate that, by 2060, the average air temperatures for December–February at most (82%) stations will be above freezing. Furthermore, analysis of SWE from selected SNOTEL stations indicated a decreasing trend in historical SWE, and a shift to an earlier peak SWE was also assumed to be occurring due of the shorter snow duration. Decreasing trends in snow duration, rain-on-snow, and snowmelt runoff also resulted from snow modeling simulations of the YRB and the nearby area. We also observed a shift in the timing of snowmelt-driven peak streamflow, as well as a statistically significant increase in winter maximum streamflow and decrease in summer maximum and minimum streamflow trends by 2099. From the streamflow trends and complementary GEV analysis, we show that the YRB basin is a system in transition with earlier peak flows, lower snow-driven maximum streamflow, and higher rainfall-driven summer streamflow. This study highlights the importance of looking at changes in snow across multiple indicators to develop future infrastructure and planning tools to better adapt and mitigate changes in extreme events.

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