A comment on temperature measurement at automatic weather stations in Australia

2019 ◽  
Vol 69 (1) ◽  
pp. 172
Author(s):  
G. P. Ayers
Keyword(s):  
Air Temperature ◽  
Thermal Inertia ◽  
Temperature Data ◽  
Maximum Temperature ◽  
Data Sets ◽  
Frequency Distributions ◽  
Measurement Systems ◽  
Weather Stations ◽  
The Difference ◽  
Temperature Records

Two versions of 1-min air-temperature data recorded at Bureau Automatic Weather Stations (AWSs) were compared in three case studies. The aim was to evaluate the difference between 1-min data represented by a measurement at the last second of each minute, compared with an average of four or five 1-s measurements made during the minute. Frequency distributions of the difference between these two values were produced for 44 000 min in three monthly data sets, January and July 2016 and September 2017. Diurnal and seasonal changes in standard deviation of the temperature differences showed that minute-to-minute fluctuations were driven by solar irradiance as the source of turbulent kinetic energy in the planetary boundary layer. Fluctuations in the difference between the two versions of 1-min data were so small overnight in all months that minimum temperature (Tmin) was the same using both methods. In midsummer, any difference between the two values for maximum temperature (Tmax) was greatest at midday. Tmax could be up by 0.1 K higher in the 1-s data compared with Tmax averaged from four measurements in the minute, but less often than 1 min in five. A follow-up test for September 2017 at Mildura when a new Tmax record was set found the difference immaterial, with Tmax the same for the averaged or 1-s values. Thus while the two versions of 1-min air-temperature data showed fluctuating small differences, largest at midday in summer, for the 3 months studied at both sites, fluctuations were too small to cause bias in climatological air-temperature records. This accorded with a numerical experiment confirming the Bureau’s advice that thermal inertia in the AWS measurement systems ensured that its 1-s data represented averages over the prior 40–80 s, providing a 1-min average of air temperature in accord with World Meteorological Organization requirements.

scholarly journals A Temperature-Based Model for Estimating Monthly Average Daily Global Solar Radiation in China

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Huashan Li ◽  
Fei Cao ◽  
Xianlong Wang ◽  
Weibin Ma
Keyword(s):  
Solar Radiation ◽  
Air Temperature ◽  
Statistical Error ◽  
Global Solar Radiation ◽  
Measured Data ◽  
Temperature Data ◽  
New Model ◽  
Climatic Regions ◽  
Monthly Average ◽  
Temperature Records

Since air temperature records are readily available around the world, the models based on air temperature for estimating solar radiation have been widely accepted. In this paper, a new model based on Hargreaves and Samani (HS) method for estimating monthly average daily global solar radiation is proposed. With statistical error tests, the performance of the new model is validated by comparing with the HS model and its two modifications (Samani model and Chen model) against the measured data at 65 meteorological stations in China. Results show that the new model is more accurate and robust than the HS, Samani, and Chen models in all climatic regions, especially in the humid regions. Hence, the new model can be recommended for estimating solar radiation in areas where only air temperature data are available in China.

scholarly journals Three novel copula-based bias correction methods for daily ECMWF air temperature data

2017 ◽  
Author(s):  
Fakhereh Alidoost ◽  
Alfred Stein ◽  
Zhongbo Su ◽  
Ali Sharifi
Keyword(s):  
Air Temperature ◽  
Bias Correction ◽  
Weather Forecast ◽  
Correction Method ◽  
Temperature Data ◽  
Weather Data ◽  
Conditional Quantiles ◽  
New Methods ◽  
Conditional Copula ◽  
Weather Stations

Abstract. Data retrieved from global weather forecast systems are typically biased with respect to measurements at local weather stations. This paper presents three copula-based methods for bias correction of daily air temperature data derived from the European Centre for Medium-range Weather Forecasts (ECMWF). The aim is to predict conditional copula quantiles at different unvisited locations, assuming spatial stationarity of the underlying random field. The three new methods are: bivariate copula quantile mapping (types I and II), and a quantile search. These are compared with commonly applied methods, using data from an agricultural area in the Qazvin Plain in Iran containing five weather stations. Cross-validation is carried out to assess the performance. The study shows that the new methods are able to predict the conditional quantiles at unvisited locations, improve the higher order moments of marginal distributions, and take the spatial variabilities of the bias-corrected variable into account. It further illustrates how a choice of the bias correction method affects the bias-corrected variable and highlights both theoretical and practical issues of the methods. We conclude that the three new methods improve local refinement of weather data, in particular if a low number of observations is available.

scholarly journals CrowdQC+—A Quality-Control for Crowdsourced Air-Temperature Observations Enabling World-Wide Urban Climate Applications

2021 ◽  
Vol 9 ◽  
Author(s):  
Daniel Fenner ◽  
Benjamin Bechtel ◽  
Matthias Demuzere ◽  
Jonas Kittner ◽  
Fred Meier
Keyword(s):  
Quality Control ◽  
Air Temperature ◽  
World Wide ◽  
Meteorological Data ◽  
Urban Climate ◽  
Specific Humidity ◽  
Data Sets ◽  
User Friendliness ◽  
Weather Stations ◽  
Further Development

In recent years, the collection and utilisation of crowdsourced data has gained attention in atmospheric sciences and citizen weather stations (CWS), i.e., privately-owned weather stations whose owners share their data publicly via the internet, have become increasingly popular. This is particularly the case for cities, where traditional measurement networks are sparse. Rigorous quality control (QC) of CWS data is essential prior to any application. In this study, we present the QC package “CrowdQC+,” which identifies and removes faulty air-temperature (ta) data from crowdsourced CWS data sets, i.e., data from several tens to thousands of CWS. The package is a further development of the existing package “CrowdQC.” While QC levels and functionalities of the predecessor are kept, CrowdQC+ extends it to increase QC performance, enhance applicability, and increase user-friendliness. Firstly, two new QC levels are introduced. The first implements a spatial QC that mainly addresses radiation errors, the second a temporal correction of the data regarding sensor-response time. Secondly, new functionalities aim at making the package more flexible to apply to data sets of different lengths and sizes, enabling also near-real time application. Thirdly, additional helper functions increase user-friendliness of the package. As its predecessor, CrowdQC+ does not require reference meteorological data. The performance of the new package is tested with two 1-year data sets of CWS data from hundreds of “Netatmo” CWS in the cities of Amsterdam, Netherlands, and Toulouse, France. Quality-controlled data are compared with data from networks of professionally-operated weather stations (PRWS). Results show that the new package effectively removes faulty data from both data sets, leading to lower deviations between CWS and PRWS compared to its predecessor. It is further shown that CrowdQC+ leads to robust results for CWS networks of different sizes/densities. Further development of the package could include testing the suitability of CrowdQC+ for other variables than ta, such as air pressure or specific humidity, testing it on data sets from other background climates such as tropical or desert cities, and to incorporate added filter functionalities for further improvement. Overall, CrowdQC+ could lead the way to utilise CWS data in world-wide urban climate applications.

scholarly journals Os fatores geoecológicos, geourbanos e o clima urbano de Iporá-GO: uma análise a partir do Método de Correlação Linear

2018 ◽  
Vol 11 (3) ◽  
pp. 77
Author(s):  
Washington Silva Alves ◽  
Zilda De Fátima Mariano
Keyword(s):  
Statistical Analysis ◽  
Spatial Variation ◽  
Statistical Methods ◽  
Air Temperature ◽  
Urban Climate ◽  
Temperature Data ◽  
Absolute Minimum ◽  
El Sistema ◽  
Weather Stations ◽  
Maximum Air Temperature

Resumo O objetivo desse trabalho consistiu em analisar a influência dos fatores geoecológicos e geourbanos no padrão da temperatura do ar máxima e mínima absoluta em Iporá-GO, por meio do método estatístico de correlação linear. Os fundamentos teóricos e metodológicos pautaram-se no sistema clima urbano de Monteiro (2003), com ênfase no subsistema termodinâmico. Os fatores geoecológicos (hipsometria, exposição de vertente, vegetação urbana e hidrografia) e geourbanos (densidade de construção e o uso do solo urbano), foram georreferenciado com auxílio dos softwares ArcGis 9.0, Spring 5.3 e Surfer 9.0. Os dados de temperatura do ar foram coletados entre outubro de 2012 e outubro de 2013, em intervalos de 30 minutos, com termohigrômetros (modelo HT-500) e estações meteorológicas automáticas distribuídos em seis pontos da área urbana e rural de Iporá. Posteriormente, os dados foram organizados em planilhas de cálculos para análise estatística. Os resultados demonstraram que os fatores geoecológicos e geourbanos citados foram decisivos na variação espacial da temperatura do ar máxima e mínima absoluta em Iporá.Palavras-chave: Climatologia, Cidade, Clima Urbano AbstractThe objective of this study is to analyze the influence of geoecological factors and geourbanos the standard maximum air temperature and absolute minimum in Iporá-GO, by means of statistical methods of correlation linear. The theoretical and methodological foundations guided in the urban climate system Monteiro (2003), with emphasis on thermodynamic subsystem. The geoecological factors (hipsometria, slop exposure, urban and Hydrography vegetation) and geourban (building density and the use of urban land), were georeferenced with the help of software ArcGIS 9.0, Sprint 5.3 and Surfer 9.0. The air temperature data were collected between October 2012 and October 2013, in 30-minute intervals, with hygrometer term (HT-500 model) and automatic weather stations distributed in six points of the urban and rural Iporá. Later, the data were organized into spreadsheets for statistical analysis. The results showed that geoecological mentioned factors and geourbanos were decisive in the spatial variation of the temperature of the air and maximum absolute minimum in Iporá.Keywords: Climatology, City, Urban Climate ResumenEl objetivo de este estudio fue analizar la influencia de los factores geoecológicos y geourbanos en el patrón de la temperatura máxima y mínima absoluta del aire en Iporá-GO, a través de lo método estadístico de correlación lineal. Los fundamentos teóricos y metodológicos se basan en el sistema de clima urbano de Monteiro (2003), con énfasis en el subsistema termodinámico. Los factores geoecológicos (hipsometría, hebras de exposición, hidrografía y vegetación urbana) y geourbanos (densidad de edificación y uso del suelo urbano) fueron georeferenciados con la ayuda del software ArcGIS 9.0, Spring 5.3 y Surfer 9.0. Los datos de temperatura del aire se recogieron entre octubre 2012 y octubre 2013, en intervalos de 30 minutos, con termohigrômetros (modelo HT-500) y estaciones meteorológicas automáticas distribuidas en seis puntos de las zonas urbanas y rurales. Posteriormente, los datos se organizaron en las hojas de cálculo para el análisis estadístico. Los resultados mostraron que los factores geoecológicos y geourbanos citados fueron decisivos en la variación espacial de la temperatura máxima y mínima absoluta del aire en Iporá.Palavras clave: Climatología, Ciudad, Clima Urbano 

scholarly journals Influence of Air Temperature on School Teachers’ Mood and the Perception of Students’ Behavior

2021 ◽  
Vol 13 (17) ◽  
pp. 9707
Author(s):  
Salvador Boix-Vilella ◽  
Elena Saiz-Clar ◽  
Eva León-Zarceño ◽  
Miguel Angel Serrano
Keyword(s):  
Air Temperature ◽  
Mental Fatigue ◽  
Temperature Data ◽  
The State ◽  
State Agency ◽  
School Teachers ◽  
Indoor Temperature ◽  
External Temperature ◽  
The Difference ◽  
Random Measurements

This study investigates how temperature, inside and outside the classroom, influence teachers’ mood and mental fatigue as well as the perceived students’ behavior. Two daily random measurements of the temperature inside various classrooms were taken for 7 months. Mood, mental fatigue, and perception of students’ behavior were evaluated for the teachers. Daily external temperature data were obtained from the State Agency of Meteorology. Results showed that indoor temperature, indoor humidity, and the difference between outdoor/indoor temperature significantly explain a worse perception of mood of the teachers and a worse perception of students’ behavior that influences perception of students’ behavior.

scholarly journals Prediction of solar radiation with air temperature data in a coastal location in Tamilnadu

MAUSAM ◽  
2021 ◽  
Vol 62 (1) ◽  
pp. 85-90
Author(s):  
A. MUGRAPAN ◽  
SUBBARAYAN SIVAPRAKASAN ◽  
S. MOHAN
Keyword(s):  
Solar Radiation ◽  
Air Temperature ◽  
Minimum Temperature ◽  
Reference Evapotranspiration ◽  
Temperature Data ◽  
Maximum Temperature ◽  
Adjustment Coefficient ◽  
Meteorological Observatory ◽  
Daily Sunshine ◽  
Sunshine Hour

The objective of this study is to evaluate the performance of the Hargreaves’ Radiation formula in estimating daily solar radiation for an Indian coastal location namely Annamalainagar in Tamilnadu State. Daily solar radiation by Hargreaves’ Radiation formula was computed using the observed data of maximum temperature, Tmax and minimum temperature, Tmin, sourced from the India Meteorological Observatory located at Annamalainagar and employing the adjustment coefficient KRS of 0.19. Daily solar radiation was also computed using Angstrom-Prescott formula with the measured daily sunshine hour data. The differences between the daily solar radiation values computed using the formulae were more pronounced in year around. Hence, the adjustment coefficient KRS is calibrated for the study location under consideration so that the calibrated KRS could be used to better predict daily solar radiation and hence better estimation of reference evapotranspiration.

COMPLEX NETWORK-BASED ANALYSIS OF AIR TEMPERATURE DATA IN CHINA

2009 ◽  
Vol 23 (14) ◽  
pp. 1781-1789 ◽  
Author(s):  
JIANBO WANG ◽  
HUIJIE YANG
Keyword(s):  
Complex Network ◽  
Air Temperature ◽  
Temperature Data ◽  
New Strategy ◽  
Temperature Records

Air temperature records in 34 cities of China are used to construct an area relation network. A new strategy to construct relation networks is proposed. The areas are clustered into mainly four modules, which may behave differently in disaster occurrences and may be helpful in compilations of accident emergency program of anti-disasters.

scholarly journals Estimasi Dampak Urban Heat Island terhadap Laju Evapotranspirasi: Studi Kasus di Kota Palembang

2021 ◽  
Vol 6 (1) ◽  
pp. 23-34
Author(s):  
Ari Sugiarto ◽  
Budi Indra Setiawan ◽  
Chusnul Arif ◽  
Satyanto Krido Saptomo
Keyword(s):  
Urban Heat Island ◽  
Air Temperature ◽  
Meteorological Station ◽  
Temperature Data ◽  
Water Evaporation ◽  
Heat Island ◽  
National Agency ◽  
Evapotranspiration Rate ◽  
Urban Heat ◽  
The Difference

A review of air temperature in the Palembang city by reviewing data from the National Agency for Meteorology, Climatology, and Geophysics/BMKG (Kenten Climatology Station and the SMB II Meteorological Station) shows a difference in air temperature can indicate the occurrence of Urban Heat Island (UHI). The difference in air temperature affects the evapotranspiration rate (ET) because air temperature very influencing water evaporation. ET rate estimation with air temperature data is the first step to prove this hypothesis. Hargreaves and Samani, Blaney and Criddle, Linacre, and Kharuffa models is the ET model that using air temperature as the variable was used to estimate the ET rate. Air temperature data used in the period 2011-2020 by reviewing data from the Kenten Climatology Station and the SMB II Meteorological Station. The results of this study of air temperature data from the Kenten Climatology Station and the SMB II Meteorology Station showed a difference in air temperature with the minimum ∆T of 0.42 oC, the maximum of 0.43 oC, and the daily average of 0.41 oC. This difference in air temperature has an impact on the difference in the ET rate with the average ∆ET of the Hargreaves and Samani model of 0.05 mm/day, the Blaney and Criddle model of 0.05 mm/day, the Linacre model of 0.06 mm/day, and the Kharuffa model of 0.14 mm/day. The results of this study predicted that an increase in air temperature causes an increase in the ET rate of ± 10-30%.

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 Determination of Step Check Quality Control Thresholds on Air Temperature Data at South Tangerang Climatological Station

2021 ◽  
Vol 893 (1) ◽  
pp. 012063
Author(s):  
M Halida ◽  
SA Pramono
Keyword(s):  
Quality Control ◽  
Air Temperature ◽  
Temperature Increase ◽  
Data Distribution ◽  
Threshold Value ◽  
Temperature Data ◽  
Temperature Decrease ◽  
Climatological Station ◽  
Check Method ◽  
The Difference

Abstract All data, including air temperature data, must be verified by conducting quality control using the step check method. Step check quality control is carried out by looking at the difference of a parameter in a certain period compared to the threshold value that was already determined. Therefore before carrying out step check quality control, it is necessary to determine the ceiling and floor boundaries of the difference in air temperature data every hour. The data used in this study are hourly air temperature data and hourly present weather data from weather observations at the South Tangerang Climatological Station during 2016 - 2020. In determining the threshold for air temperature step check quality control, the air temperature data is paired with weather condition data to obtain a threshold value according to rain and no rain conditions. The threshold conducted in this study is based on a check for unusual climatological values, where the limits for an unusual and impossible jump in hourly air temperature changes are determined based on a certain percentage of the data distribution. This study uses percentile analysis to determine the threshold, where 5% in the lower and upper part of the data distribution are used as the threshold. The results show various thresholds every hour. The increase in temperature dominates the changes of hourly air temperature in no-rain conditions. The highest threshold for temperature increase occurs at 00.00 – 01.00 UTC at 3.2°C and continues to decrease over time. The highest threshold for temperature decrease occurs at 09.00 UTC - 10.00 UTC at 2.2°C. In rain conditions, the increase in temperature can still occur. However, the decrease in temperature mainly occurs. The highest threshold for temperature increase during rainy conditions is 1.8°C at 01.00 - 02.00 UTC, while the highest threshold for the temperature decrease is 5.8°C at 06.00 UTC – 07.00 UTC. With these results, observers can first carry out quality control with the Step Check method before filling in the data into the system database. Thus, any suspect data either from reading errors or tool errors can be minimized and finally produce a valid dataset.

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