scholarly journals Modeling hourly air temperature based on internationally agreed times and the daily minimum temperature

2019 ◽  
Vol 23 (11) ◽  
pp. 807-811
Author(s):  
Sidinei Z. Radons ◽  
Arno B. Heldwein ◽  
Luís H. Loose ◽  
Mateus P. Bortoluzzi ◽  
Silvane I. Brand ◽  
...  
Keyword(s):  
Temperature Variation ◽  
Air Temperature ◽  
Linear Models ◽  
Daily Minimum Temperature ◽  
Mean Deviation ◽  
Data Series ◽  
Climatic Data ◽  
Daily Minimum ◽  
Model Adjustment ◽  
Minimum Air Temperature

ABSTRACT There are several fields that require knowledge of air temperature variation throughout the day, such as disease prediction or calculation of chill-hours. However, automatic meteorological stations are not always located in the vicinity to accurately monitor this variable. In this sense, models that describe the daily temporal variation of air temperature can be used to meet this demand, and transform the climatic data series of conventional meteorological stations into an estimated hourly series. The aim of this study was to adjust and validate models for the hourly air temperature variation through data obtained at internationally agreed times (0, 12 and 18 h Universal Time Coordinated: UTC) and the daily minimum air temperature. The hourly database of the automatic station was used for model adjustment and validation. Functions were adjusted based on values measured at internationally agreed times and the daily minimum air temperature for certain daily variation patterns. The air temperature estimation was performed on an hourly basis using sinusoidal and linear models. The model that presented the lowest root mean square error (RMSE) was used for the estimation. The accuracy of the air temperature estimates varied according to the time, presenting RMSE from 0.7 to 1.6 °C, with maximum mean deviation of 0.4 °C. The results of this study showcase the necessity of knowledge of the daily air temperature variation, as well as a series of data from conventional meteorological stations, which can be estimated using hourly models.

scholarly journals Winter Nocturnal Air Temperature Distribution for a Mesoscale Plain of a Snow-Covered Region: Field Meteorological Observations and Numerical Simulations

2017 ◽  
Vol 56 (2) ◽  
pp. 519-533 ◽  
Author(s):  
Tomotsugu Yazaki ◽  
Hirokazu Fukushima ◽  
Tomoyoshi Hirota ◽  
Yukiyoshi Iwata ◽  
Atsushi Wajima ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Temperature Distribution ◽  
Wind Speed ◽  
Air Temperature ◽  
Daily Minimum Temperature ◽  
Strong Wind ◽  
Daily Minimum ◽  
Air Temperatures ◽  
Winter Air Temperature ◽  
Minimum Air Temperature

AbstractWinter air temperatures strongly affect crop overwintering and cold resource usage. To clarify how winter air temperature distributions are formed in a mesoscale plain, field observations and simulations were conducted for the Tokachi region in Japan. Results elucidating the winter climate within the plain revealed that the winter mean air temperature at each site was correlated closely with the mean daily minimum air temperature. The daily minimum air temperature was not correlated with altitude, suggesting that local variation of the daily minimum temperature influences the temperature distribution. Observations at different distances from the upwind mountains revealed that nocturnal air temperatures were higher for stronger winds closer to the mountain foot. Low temperatures associated with wind speed suggest that radiative cooling strongly affects the temperature distribution. Wind and temperature conditions in the boundary layer influence the degree of drop in nocturnal air temperature and its distribution. The wind speed and direction, respectively, affect the extent and direction of the high-temperature zone from the northwest mountain foot. Simulations with a spatial resolution of 2 km reproduced the observed temperatures, but the error exceeded 5°C at sites having complex terrain under moderate or strong wind conditions. A higher-resolution model of 0.5 km showed that simulated temperatures approach the observed temperatures in association with a local wind system of down-valley drainage flow. In conclusion, the synoptic background, wind strength and direction over the plain, and microscale valleys affect boundary layer mixing and, thereby, determine the winter air temperature distribution.

Prediction of temperatures in cattle dung for estimating development times of coprophilous organisms

1988 ◽  
Vol 78 (2) ◽  
pp. 235-240 ◽  
Author(s):  
J. N. Matthiessen ◽  
M. J. Palmer
Keyword(s):  
Air Temperature ◽  
Meteorological Data ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Threshold Temperature ◽  
Cattle Dung ◽  
Daily Maximum ◽  
Daily Minimum ◽  
Maximum Temperatures ◽  
Minimum Air Temperature

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.

Empirical downscaling of daily minimum air temperature at very fine resolutions in complex terrain

2011 ◽  
Vol 151 (8) ◽  
pp. 1066-1073 ◽  
Author(s):  
Zachary A. Holden ◽  
John T. Abatzoglou ◽  
Charles H. Luce ◽  
L. Scott Baggett
Keyword(s):  
Air Temperature ◽  
Complex Terrain ◽  
Daily Minimum ◽  
Empirical Downscaling ◽  
Minimum Air Temperature

scholarly journals Minimum trends in air temperature in the municipality of Porto Velho - RO from 1971 to 2016

2019 ◽  
Vol 23 ◽  
pp. 39
Author(s):  
Camila Bermond Ruezzene ◽  
Renata Gonçalves Aguiar ◽  
Graciela Redies Fischer ◽  
Nara Luísa Reis de Andrade ◽  
Renato Billia de Miranda ◽  
...  
Keyword(s):  
Air Temperature ◽  
Sudden Change ◽  
Kendall Test ◽  
Data Series ◽  
Positive Trend ◽  
Pettitt Test ◽  
Temperature Trends ◽  
Mann Kendall Test ◽  
Porto Velho ◽  
Minimum Air Temperature

In recent years, studies focused on the climate of the Amazon have been the focus of major research mainly to identify possible temperature trends. The objective of this study was to analyze the trends and the years of abrupt changes in temperature that occurred in the Southwest Amazon from 1971 to 2016 in the municipality of Porto Velho-RO. The study area is located in the municipality of Porto Velho at the Meteorological Station of Surface, we used average daily temperature data and subdivided into climatological series from 1971 to 2006 and 1981 to 2016. The Mann-Kendall and Pettitt tests were used to verify trends. A positive trend was observed for the Mann-Kendall test for the months of January, February, March, April, June, September, October and December of 1971 to 2006 and for the years 1981 to 2016 only the months of March and June presented a trend temperature increase. The Pettitt test indicated a sudden change in the data series coinciding with most of the months that showed a tendency to increase the minimum air temperature by the Mann-Kendall test in the years 1971 to 2006. From 1981 to 2016 the Pettitt test indicated the months of March and June with changes in the minimum air temperature, this result also coincided with the months with positive trends.

scholarly journals Forecasting mean monthly maximum and minimum air temperature of Jalandhar district of Punjab, India using seasonal ARIMA model

2022 ◽  
Vol 24 (1) ◽  
Author(s):  
BALJEET KAUR ◽  
NAVNEET KAUR ◽  
K. K. GILL ◽  
JAGJEEVAN SINGH ◽  
S. C. BHAN ◽  
...  
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Goodness Of Fit ◽  
Time Series Data ◽  
Arima Model ◽  
Kendall Test ◽  
Series Data ◽  
Data Series ◽  
Augmented Dickey Fuller ◽  
Minimum Air Temperature

The long-term air temperature data series from 1971-2019 was analyzed and used for forecasting mean monthly air temperature at the district level. The Augmented Dickey-Fuller test, Kwiatkowski-Phillips-Schmidt-Shin test, and Mann-Kendall test were employed to test the stationarity and trend of the time series. The mean monthly maximum air temperature did not show any significant variation while an increasing trend of 0.04°C per annum was observed in mean monthly minimum air temperature, which was detrended. Box-Jenkins autoregressive integrated moving–averages were used to forecast the forthcoming 5 years (2020-2024) air temperature in the district Jalandhar of Punjab. The goodness of fit was tested against residuals, the autocorrelation function, and the histogram. The fitted model was able to capture dynamics of the time series data and produce a sensible forecast.

scholarly journals Klimatska obilježja otoka Palagruže, Hrvatska

Geoadria ◽  
2017 ◽  
Vol 8 (1) ◽  
pp. 39
Author(s):  
Živko Trošić ◽  
Dražen Jašić ◽  
Vinko Marinković
Keyword(s):  
Air Temperature ◽  
Precipitation Amount ◽  
Meteorological Station ◽  
Rank Statistic ◽  
Climatic Data ◽  
Air Temperatures ◽  
The Mean ◽  
Instrumental Period ◽  
C 30 ◽  
Minimum Air Temperature

The paper presents a brief review of contemporary research on climatic features of Palagruža Island, both in the pre-instrumental and instrumental period. The analysis was made upon climatic data provided by the meteorological station of Palagruža. For comparison purposes, data from the station in Split-Marjan was used, covering the standard climate period 1961-1990. The results show, among other things, that there is a decreasing trend of annual mean maximum air temperatures for 0.37 °C/30 years, average air temperature for 0.05 °C/30 years, and the mean minimum air temperature for 0.04 °C/30 years. There is a decreasing trend of annual mean of precipitation amount for 62 mm/30 years. Spearman rank statistic shows that neither of the trends noted above is statistically significant at 0.05 level (two-tailed t-test) 

scholarly journals An Improved QC Process for Temperature in the Daily Cooperative Weather Observations

2007 ◽  
Vol 24 (2) ◽  
pp. 206-213 ◽  
Author(s):  
Kenneth G. Hubbard ◽  
Nathaniel B. Guttman ◽  
Jinsheng You ◽  
Zhirong Chen
Keyword(s):  
Quality Assurance ◽  
Data Quality ◽  
Quality Assessment ◽  
Air Temperature ◽  
Spatial Regression ◽  
Daily Maximum ◽  
Correct Identification ◽  
Climatic Data ◽  
Regression Test ◽  
Minimum Air Temperature

Abstract TempVal is a spatial component of data quality assurance algorithms applied by the National Climatic Data Center (NCDC), and it has been used operationally for about 4 yr. A spatial regression test (SRT) approach was developed at the regional climate centers for climate data quality assurance and was found to be superior to currently used quality control (QC) procedures for the daily maximum and minimum air temperature. The performance of the spatial quality assessment procedures has been evaluated by assessing the rate with which seeded errors are identified. A complete dataset with seeded errors for the year 2003 for the contiguous United States was examined for both the maximum and minimum air temperature. The spatial regression quality assessment component (SRT), originating in the Automated Climate Information System (ACIS), and TempVal, originating in the NCDC database, were applied separately and evaluated through the ratio of identified seeded errors to the total number of seeds. The spatial regression test applied in the ACIS system was found to perform better in identifying the seeded errors. For all months, the relative frequency of correct identification of wrong data is 0.72 and 0.83 for TempVal and SRT, respectively. The goal of the comparison was to evaluate quality assurance techniques that could improve data quality assessment at the NCDC, and the results of the comparison led to the recommendation that the SRT be included in the NCDC quality assessment methodology.

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