scholarly journals Increase in Air Temperature in The Region of South Sumatra Province as the Indicator of Global Warming and the Effect on Transpiration of Lansium domesticum Corr.

2019 ◽  
Author(s):  
Ari Sugiarto ◽  
Hanifa Marisa ◽  
Sarno
Keyword(s):  
Global Warming ◽  
Air Temperature ◽  
Transpiration Rate ◽  
Temperature Increase ◽  
Secondary Data ◽  
Temperature Data ◽  
Randomized Design ◽  
Maximum Air Temperature ◽  
Lansium Domesticum ◽  
Minimum Air Temperature

Abstract Global warming is one of biggest problems faced in the 21st century. One of the impacts of global warming is that it can affect the transpiration rate of plants that °Ccur. This study purpose to see how much increase in air temperature that occurred in the region of South Sumatra Province and to know the effect of increase in ari temperature in the region of South Sumatra Province on transpiration rate of Lansium domesticum Corr. This study used a complete randomized design with 9 treatments (22.9 °C, 23.6 °C, 24.6 °C, 26.3 °C, 27 °C, 27.8 °C, 31.7 °C, 32.5 °C, and 32.9 °C) and 3 replications. Air temperature data as secondary data obtained from the Meteorology, Climatology and Geophysics Agency (MCGA) Palembang Climatology Station in South Sumatra Province. The measurement of transpiration rate is done by modified potometer method with additional glass box. The data obtained are presented in the form of tables and graphs. Transpiration rate (mm3/g plant/hour) at temperture 22.9 °C = 4.37, 23.6 °C = 7.03, 24.6 °C = 8.03, 26.3 °C = 10.11, 27 °C = 13.13, 27.8 °C = 17.87, 31.7 °C = 23.21, 32.5 °C= 25.45 and 32.9 °C= 27.24. At the minimum air temperature in the region of South Sumatra Province there is increase in air temperature of 1.5 °C, average daily air temperature increase 1.3 °C and maximum air temperature increase 1.2 °C.

scholarly journals Investigation on Airport Landscape Cooling Associated with Irrigation: A Case Study of Adelaide Airport, Australia

2020 ◽  
Vol 12 (19) ◽  
pp. 8123
Author(s):  
Jingming Qian ◽  
Shujiang Miao ◽  
Nigel Tapper ◽  
Jianguang Xie ◽  
Greg Ingleton
Keyword(s):  
Global Warming ◽  
Thermal Comfort ◽  
Air Temperature ◽  
Trial Period ◽  
Land Utilization ◽  
Irrigation Area ◽  
Human Thermal Comfort ◽  
Loading And Unloading ◽  
Maximum Air Temperature

Extreme summertime heat is becoming a major issue for aircraft operations. As global temperatures continue to rise, some of the heaviest planes on the longest flights may eventually be unable to depart during the hottest part of summer days. During summer days, some airports have to reduce the payload of aircraft, including cargo and/or passengers in the hotter days of summer. Nonetheless, there is no existing body of research on the potential for airport cooling. Furthermore, extreme heat on the ground also affects airport workers; loading and unloading luggage and servicing platforms between flights could become more arduous. With global warming proceeding, it is becoming increasingly urgent to find a suitable strategy to cool airport environments, perhaps by irrigation of a vegetated landscape. All airports have large enclosed areas (usually of grass) acting as a buffer between airport activities and the adjacent industrial, commercial and residential land utilization. This paper describes the trial of irrigating the buffer area of Adelaide airport and analyzes the performance of irrigation cooling for Adelaide airport, examining whether this can benefit human thermal comfort. Results indicate that irrigation provides cooling, and the cooling effect reduces along with the increasing instance from the middle of the irrigation area. At 15:00, the average air temperature was 1.8 °C cooler in the middle of the irrigation area than in the non-irrigation area, and the relative humidity was 5.8% higher during the trial period. On an extremely hot day (the maximum air temperature was 45.4 °C), it was 1.5 °C cooler in the middle of the irrigation area than upwind the of irrigation area, and 0.8 °C cooler than downwind of the irrigation area at 13:00. Human thermal comfort (HTC) is unfavorable in the runway, but greater improvements can be made through promotion of irrigation.

scholarly journals Aumento das temperaturas extremas na cidade do Rio de Janeiro e o desvio ocasionado durante um evento de El Niño intenso (Thermal change in the city of Rio de Janeiro and the deviation caused during an intense El Niño event)

2019 ◽  
Vol 12 (4) ◽  
pp. 1291
Author(s):  
Henderson Silva Wanderley ◽  
Ronabson Cardoso Fernandes ◽  
André Luiz De Carvalho
Keyword(s):  
Air Temperature ◽  
El Niño ◽  
Urban Areas ◽  
Rio De Janeiro ◽  
El Nino ◽  
Pearson Correlation ◽  
Temperature Data ◽  
Kolmogorov Smirnov ◽  
The City ◽  
Maximum Air Temperature

O processo de urbanização tem o potencial de alterar a característica térmica e aerodinâmica da superfície dos grandes centros urbanos, possibilitando o aumento da temperatura do ar. No entanto, a correlação da intensificação da temperatura do ar em áreas urbanas em resposta a um evento extremo de El Niño é escassa, principalmente no que se refere à cidade do Rio de Janeiro. Assim, o objetivo deste estudo visa quantificar as mudanças ocorridas na temperatura do ar (máxima e mínima) na cidade do Rio de Janeiro e o desvio ocasionado às temperaturas extremas durante um evento de El Niño intenso. Os dados de temperatura do ar utilizados referem-se às normais climatológicas nos períodos climatológicos de 1961-1990 e 1980-2010, comparados entre si, e posteriormente, comparou-se as normais climatológicas do período de 1980-2010 com as do El Niño intenso de 2015-2016. Para a análise, dados de temperatura mínima e máxima do ar em uma escala mensal foram comparados. As médias mensais das temperaturas em análise foram submetidas ao ajuste do coeficiente de correlação de Pearson, ao teste t de Student e ao teste de Kolmogorov-Smirnov. Os resultados mostraram um aumento médio na temperatura do ar mínima (máxima) de +0,66 °C e +0,73 °C (+1,21 °C e +0,90 °C), respectivamente entre os períodos climatológicos e o último período climatológico com o evento El Niño intenso, entretanto, sem diferença estatística para o aumento da média e de sua distribuição.   A B S T R A C TUrbanization process has potential to change the thermal and aerodynamic characteristics of large urban centers surface, allowing the increase of air temperature. However, correlation of air temperature intensification in urban areas in response to an extreme event of El Niño is scarce, especially in relation to the city of Rio de Janeiro. Thus, the objective of this study is to quantify the changes occurred in the air temperature (maximum and minimum) in the city of Rio de Janeiro and the deviation caused to extreme temperatures during an intense event of El Niño. Data of air temperature data refer to the climatological normals in the periods of 1961-1990 and 1980-2010, and intense event of El Niño occurred in 2015-2016. For the analysis, minimum and maximum air temperature data on a monthly scale were compared. Monthly mean values of the air temperature under analysis were adjusted to the Pearson correlation coefficient, Student's t-test and Kolmogorov-Smirnov test. The results showed a mean increase in minimum (maximum) air temperature of +0.66 °C and +0.73 °C (+1.21 °C and +0.90 °C), respectively between the climatological periods and the last climatological period with the intense event of El Niño, however, with no statistical difference for the increase of the mean and its distribution.Keywords: Urban climate, ENSO, air temperature.

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 High-temperature Stress and the Fate of Pollen Germination and Yield in Lentil (Lens culinaris Medikus)

2020 ◽  
Author(s):  
Ananya Baidya ◽  
Anjan Kumar Pal ◽  
Mohammed Anwar Ali ◽  
Rajib Nath
Keyword(s):  
High Temperatures ◽  
Air Temperature ◽  
Pollen Germination ◽  
High Temperature Stress ◽  
Cropping Pattern ◽  
Randomized Design ◽  
Pod Development ◽  
Drastic Effect ◽  
Maximum Air Temperature ◽  
Physiological Characters

Background: High temperatures adversely affect the growth and development of plants at vegetative, reproductive and maturity stages of their life cycle. The maximum air temperature of higher than 25oC frequently encountered in lentil at the reproductive stage mainly in West Bengal rice-lentil cropping pattern. Such high temperatures in rabi season impair the crop growth and productivity. Added to this, future climate predictions are indicating 27-28oC during this period. Methods: The field-laboratory experiment was conducted during 2014, with 20 genotypes of lentil. The field experiment was laid in split plot design with 3 different windows of sowing, while the laboratory investigation was carried out in completely randomized design with pollen collected from the field experiment.Result: The present investigation reveals that high air temperature greater than 25oC resulted in the failure of pollen germination, increase in abnormal pollen tube and aborted pollen. A drastic effect in seed filling and pod development in lentil was observed. Out of 20 lentil genotypes studied, ILL-10893 and L-13-113 emerged as tolerant genotypes while, BM-6 and L-4076 as susceptible with special emphasis on physiological characters.

scholarly journals Effect of meteorological factors on influenza-like illness from 2012 to 2015 in Huludao, a northeastern city in China

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6919 ◽  
Author(s):  
Ying-Long Bai ◽  
De-Sheng Huang ◽  
Jing Liu ◽  
De-Qiang Li ◽  
Peng Guan
Keyword(s):  
Relative Humidity ◽  
Vapor Pressure ◽  
Air Temperature ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Lag Time ◽  
Temperature Minimum ◽  
The Relationship ◽  
Maximum Air Temperature ◽  
Minimum Air Temperature

Background This study aims to describe the epidemiological patterns of influenza-like illness (ILI) in Huludao, China and seek scientific evidence on the link of ILI activity with weather factors. Methods Surveillance data of ILI cases between January 2012 and December 2015 was collected in Huludao Central Hospital, meteorological data was obtained from the China Meteorological Data Service Center. Generalized additive model (GAM) was used to seek the relationship between the number of ILI cases and the meteorological factors. Multiple Smoothing parameter estimation was made on the basis of Poisson distribution, where the number of weekly ILI cases was treated as response, and the smoothness of weather was treated as covariates. Lag time was determined by the smallest Akaike information criterion (AIC). Smoothing coefficients were estimated for the prediction of the number of ILI cases. Results A total of 29, 622 ILI cases were observed during the study period, with children ILI cases constituted 86.77%. The association between ILI activity and meteorological factors varied across different lag periods. The lag time for average air temperature, maximum air temperature, minimum air temperature, vapor pressure and relative humidity were 2, 2, 1, 1 and 0 weeks, respectively. Average air temperature, maximum air temperature, minimum air temperature, vapor pressure and relative humidity could explain 16.5%, 9.5%, 18.0%, 15.9% and 7.7% of the deviance, respectively. Among the temperature indexes, the minimum temperature played the most important role. The number of ILI cases peaked when minimum temperature was around −13 °C in winter and 18 °C in summer. The number of cases peaked when the relative humidity was equal to 43% and then began to decrease with the increase of relative humidity. When the humidity exceeded 76%, the number of ILI cases began to rise. Conclusions The present study first analyzed the relationship between meteorological factors and ILI cases with special consideration of the length of lag period in Huludao, China. Low air temperature and low relative humidity (cold and dry weather condition) played a considerable role in the epidemic pattern of ILI cases. The trend of ILI activity could be possibly predicted by the variation of meteorological factors.

scholarly journals Multi-Model Projections of Climate Change in Different RCP Scenarios in an Arid Inland Region, Northwest China

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 347 ◽  
Author(s):  
Ruotong Wang ◽  
Qiuya Cheng ◽  
Liu Liu ◽  
Churui Yan ◽  
Guanhua Huang
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Air Temperature ◽  
Northwest China ◽  
Heihe River Basin ◽  
Heihe River ◽  
Rcp Scenarios ◽  
Maximum Air Temperature ◽  
The Heihe River Basin ◽  
Minimum Air Temperature

Based on three IPCC (Intergovernmental Panel on Climate Change) Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, and RCP8.5), observed meteorological data, ERA-40 reanalysis data, and five preferred GCM (general circulation model) outputs selected from 23 GCMs of CMIP5 (Phase 5 of the Coupled Model Intercomparison Project), climate change scenarios including daily precipitation, maximum air temperature, and minimum air temperature from 2021 to 2050 in the Heihe River basin, which is the second largest inland river basin in Northwest China, were generated by constructing a statistical downscaling model (SDSM). Results showed that the SDSM had a good prediction capacity for the air temperature in the Heihe River basin. During the calibration and validation periods from 1961 to 1990 and from 1991 to 2000, respectively, the coefficient of determination (R2) and the Nash–Sutcliffe efficiency coefficient (NSE) were both larger than 0.9, while the root mean square error (RMSE) was within 20%. However, the SDSM showed a relative lower simulation efficiency for precipitation, with R2 and NSE values of most meteorological stations reaching 0.5, except for stations located in the downstream desert areas. Compared with the baseline period (1976–2005), changes in the annual mean precipitation simulated by different GCMs during 2021–2050 showed great difference in the three RCP scenarios, fluctuating from −10 to +10%, which became much more significant at seasonal and monthly time scales, except for the consistent decreasing trend in summer and increasing trend in spring. However, the maximum and minimum air temperature exhibited a similar increasing tendency during 2021–2050 in all RCP scenarios, with a higher increase in maximum air temperature, which increased as the CO2 concentration of the RCP scenarios increased. The results could provide scientific reference for sustainable agricultural production and water resources management in arid inland areas subject to climate change.

scholarly journals Evaluasi Data TerraClimate Dalam Mengestimasi Suhu Udara Di Provinsi Papua Barat

2021 ◽  
Vol 14 (1) ◽  
pp. 15-25
Author(s):  
Baso Daeng ◽  
Arif Faisol
Keyword(s):  
Air Temperature ◽  
Research Unit ◽  
Mean Square ◽  
Data Recording ◽  
Climate Research ◽  
West Papua ◽  
Weather Stations ◽  
Papua Barat ◽  
Maximum Air Temperature ◽  
Minimum Air Temperature

Abstrak. Terra Climate merupakan seperangkat data iklim yang mengkombinasikan antara data WorldClim, Climate Research Unit (CRU), dan Japanese 55-year Reanalysis (JRA 55). TerraClimate menyediakan data iklim bulanan tahun 1958 – 2019  pada resolusi spasial ~4 km. Penelitian ini bertujuan untuk mengevaluasi data TerraClimate dalam mengestimasi suhu udara di Provinsi Papua Barat. Data yang digunakan pada penelitian ini adalah data TerraClimate dan data suhu udara perekaman tahun 1996 – 2019 yang diperoleh dari automatic weather stations (AWS) Rendani – Kabupaten Manokwari, AWS Jefman – Kabupaten Raja Ampat, AWS Torea – Kabupaten Fakfak, dan AWS Kaimana – Kabupaten Kaimana. Data TerraClimate dievaluasi dengan dibandingkan data AWS menggunakan metode point to pixel berdasarkan 5 (lima) parameter statistik, yaitu mean error (ME), root mean square error (RMSE), relative bias (RBIAS), percent bias (PBIAS), dan koefisien korelasi Pearson (r). Hasil penelitian menunjukkan bahwa data TerraClimate cenderung overestimated dalam mengestimasi suhu udara minimum bulanan dan cenderung underestimated dalam mengestimasi suhu udara maksimum bulanan di Provinsi Papua Barat. Namun TerraClimate memiliki akurasi yang sangat baik dalam mengestimasi suhu udara bulanan di Provinsi Papua Barat  dengan nilai ME= 0,87 oC, RMSE = 1,19 oC, RBIAS = 0,04, dan PBIAS = 3,71 dalam mengestimasi suhu udara minimum, dan ME = 0,54 oC, RMSE = 0,88 oC,  RBIAS = 0,02, dan PBIAS = 1,79 dalam mengestimasi suhu udara maksimum. Disamping itu TerraClimate memiliki korelasi yang sedang terhadap data AWS nilai r = 0,40 - 0,68. Sehingga TerraClimate dapat digunakan sebagai solusi alternatif untuk penyedia data suhu udara di Provinsi Papua Barat.An Evaluation of TerraClimate Data in Estimating Monthly Air Temperature in West PapuaAbstract. TerraClimate is a climate dataset that combines WorldClim data, Climate Research Unit (CRU) data, and Japanese 55-year Reanalysis (JRA 55) data at ~4 km spatial resolution. TerraClimate provides monthly climate data from 1958 to recent years. This research aims to evaluate the TerraClimate data in estimating monthly air temperature in West Papua compared with automatic weather stations (AWS) data recording. The data used in this research are TerraClimate data and AWS data recording from 1996 to 2019 obtained from AWS Rendani – Manokwari, AWS Jefman – Raja Ampat, AWS Torea – Fakfak, and AWS Kaimana – Kaimana. TerraClimate data were evaluated using the Point to Pixel method based on 5 (five) statistical parameters i.e., mean error (ME), root mean square error (RMSE), relative bias (RBIAS), percent bias (RBIAS), and Pearson correlation coefficient (r). The research showed that TerraClimate is overestimated in estimating monthly minimum air temperature and underestimated in estimating monthly maximum air temperature in West Papua. However, TerraClimate and has very good accuracy in estimating the monthly temperature in West Papua with ME = 0.87 oC, RMSE = 1.19 oC, RBIAS = 0.04, and PBIAS = 3.71 in estimating monthly minimum air temperature, and ME=0.54 oC, RMSE = 0.88 oC, RBIAS = 0.02, PBIAS = 1.79 in estimating monthly maximum air temperature. Besides, TerraClimate data has a moderate correlation with AWS data in estimating monthly air temperature with r= 0.40 - 0.68. Therefore, TerraClimate can be used as an alternative solution for providing air temperature data in West Papua. 

scholarly journals Construção de cenários futuros da temperatura máxima do ar: Capitais do Nordeste Brasileiro

2021 ◽  
pp. 2427-2445
Author(s):  
Ana Carla dos Santos Gomes ◽  
Maytê Duarte Leal Coutinho ◽  
Fábio de Paula Viana ◽  
Losany Branches Viana ◽  
Sivaldo Filho Seixas Tavares ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Wind Speed ◽  
Air Temperature ◽  
Temperature Data ◽  
Northeastern Brazil ◽  
Future Scenarios ◽  
General Terms ◽  
Dynamic Regression Model ◽  
Maximum Air Temperature

This research aims to analyze and estimate future scenarios of maximum air temperature in the capitals of northeastern Brazil, in order to highlight the importance of climate change today and in the future. For this, rainfall, wind speed, relative humidity and maximum air temperature data were used by the database meteorological activities of the National Institute of Meteorology, of the nine capitals of the northeastern region of Brazil from 1980 to 2019, and the dynamic regression technique that combines the dynamics of time series and the effect of explanatory variables.The main results showed that the dynamic regression model satisfactorily adjusted the association between meteorological variables.Trend (without lag) and seasonality (lag) functions were considered in all capitals, presenting the occurrence of different lags according to the capital and the variable. Thus, the highest temperatures among the capitals analyzed occurred in Teresina/PI and the least high, in Salvador/BA. In general terms, the optimistic scenarios (C1) presented temperature between 32.5 and 35 ºC, the pessimists (C2) between 37.5 ºC and extremes (C3) 35 and 39 ºC, evidencing that all future scenarios present danger to the population. It is expected that the results obtained can help public policies.

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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