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 An attempt to assess the results of air temperature measurements from automatic weather stations in comparison to glass thermometer measurements in the context of weather types

2015 ◽  
Vol 9 (1) ◽  
pp. 67-79 ◽  
Author(s):  
Grzegorz Urban
Keyword(s):  
Air Temperature ◽  
Temperature Measurements ◽  
Normal Range ◽  
Weather Types ◽  
Accuracy Of Measurements ◽  
Weather Stations ◽  
Mean Differences ◽  
Annual Scale ◽  
Maximum Air Temperature ◽  
Minimum Air Temperature

Abstract In the article, an attempt was undertaken to compare the results of air temperature measurements made using automatic weather stations (AWS) to those of glass thermometers. The analysis considered the aspect of weather types. On the basis of simultaneous measurements carried out with the use of AWS and glass thermometers, the accuracy of measurements for 6 synoptic stations of IMGW-PIB was assessed. The stations represented the Lower and Opole Silesia regions. Mean differences in mean monthly and seasonal air temperature values (T) between AWS and glass results are not high. They are equal to ±0.1°C, only rarely reaching −0.2°C. In cold seasons and in particular months as well, they are negative. On an annual scale, differences hardly ever occur. No connection between mean difference for mean air temperature and weather types was found. The values of mean differences for mean monthly and seasonal maximum air temperature (Tx) are equal to ±0.1°C (except Śnieżka). The differences for T and Tx are of low significance, being within the normal range. Mean differences for mean monthly and seasonal minimum air temperature (Tn) are usually positive. In warm seasons they can reach 1.1°C. In the case of most of the stations under consideration, for positive differences for Tn, an increase in average (from +0.1°C to +0.5°C) and high (+0.5°C to +1.0°C) differences is noticed. The only exceptions are the Śnieżka and Opole stations. The differences for each category are not regular, therefore no universal corrections can be implemented.

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 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 EVALUASI DATA CLIMATE HAZARDS GROUP INFRARED PRECIPITATION WITH STATION (CHIRPS) DENGAN DATA PEMBANDING AUTOMATIC WEATHER STATIONS (AWS) DALAM MENGESTIMASI CURAH HUJAN HARIAN DI PROVINSI PAPUA BARAT

2021 ◽  
Vol 10 (1) ◽  
pp. 64
Author(s):  
Budiyono Budiyono ◽  
Arif Faisol
Keyword(s):  
Pearson Correlation ◽  
Daily Rainfall ◽  
Absolute Error ◽  
Satellite Observation ◽  
Probability Of Detection ◽  
T Test ◽  
Data Recording ◽  
Significant Difference ◽  
West Papua ◽  
Weather Stations

This research aims to evaluate the CHIRPS data in estimating daily rainfall in West Papua compared with automatic weather stations (AWS) data recording. The data used in this research are daily CHIRPS data and AWS daily data recording 1996 to 2020 from AWS Rendani–Manokwari, AWS Jefman–Raja Ampat, AWS Torea–Fakfak, and AWS Kaimana–Kaimana. CHIRPS data were evaluated using the Point to Pixel method based on numerical and categorical parameters i.e., root mean square error (RMSE), mean error (ME), mean absolute error (MAE), Pearson correlation (r), probability of detection (POD), critical success index (CSI), and T-test. The research showed that CHIRPS had a significant difference to AWS data in estimating daily rainfall in West Papua based on a T-test. However CHIRPS has a moderate accuracy in estimating daily rainfall in West Papua with RMSE = 8.59 mm, ME=2.75 mm, and MAE = 5.15 mm and had a moderate positive correlation with AWS data with r= 0.43. Besides, CHIRPS has good accuracy in detecting rain events in West Papua indicated by a POD = 0.72 and CSI = 0.43. Therefore, CHIRPS data can be used as an alternative solution for providing rainfall data in West Papua.   Keywords:  satellite observation, rainfall predictor, point to pixel 

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.

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 ◽  
2011 ◽  
Vol 49 (4) ◽  
pp. 305 ◽  
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.

Warm and Cold Waves in South-Eastern (V) Bioclimatic Region in Years 1981-2010 / Fale ciepła i fale chłodu w południowo-wschodnim (V) regionie bioklimatycznym w latach 1981–2010 / Fale ciepła i fale chłodu w południowo-wschodnim (V) regionie bioklimatycznym w latach 1981–2010

2014 ◽  
Vol 69 (2) ◽  
Author(s):  
Agnieszka Krzyżewska
Keyword(s):  
Standard Deviation ◽  
Air Temperature ◽  
High Frequency ◽  
Research Data ◽  
Eastern Region ◽  
Cold Waves ◽  
South Eastern ◽  
The Waves ◽  
Maximum Air Temperature ◽  
Minimum Air Temperature

AbstractLublin and Roztocze regions are placed in bioclimatic division (by T. Kozłowska-Szczęsna) in 5th south-eastern region. This area can be characterized by a high number of days with high air temperature (Kozłowska-Szczęsna et al., 1997) and by highest number of frost days in Poland (Błażejczyk, Kunert 2011). In this region, there is high frequency of cold spells; an occurrence, which can last over 15 days (Kuchcik et al., 2013). In this paper, warm and cold waves are calculated by method elaborated by Wibig (2007), where waves are determined by maximum air temperature (warm and cold days) and minimum air temperature (warm and cold nights) based on standard deviation from the average, expressed in standard deviation. Days, where air temperature was higher than average by more than 1.28 standard deviation was regarded as very warm, and those with lower air temperature than average by more than 1.28 standard deviation was regarded as very cold (Wibig 2007). For the purpose of this research, data from stations Lublin-Radawiec, Zamość and Tomaszów Lubelski were used, for the 1981-2010 period. During that time, short (3-5 days) waves of warm days occurred slightly more often than for waves of cold days, but in case of long waves (11-20 days) cold waves dominated, which is very characteristic for south-eastern (V) bioclimatic region. The waves of cold days were particularly long at Tomaszów Lubelski and Zamość stations. The average number of short (3-5 days) cold waves (night) on examined stations of south-eastern bioclimatic region was 3-4 waves per year and this was more than average number of short warm waves (night), which fluctuated between 2 to 3 waves per year (inversely to the case of waves of warm days). In the first decade of 21st century, the decrease in number of cold days is visible, but number of warm nights has increased during that time.

scholarly journals VARIABILITAS IKLIM DAN KEJADIAN KEBAKARAN HUTAN DAN LAHAN GAMBUT DI KABUPATEN BENGKALIS, PROVINSI RIAU Variability of Climate and Forest and Peat Fires Occurrences in Bengkalis Regency, Riau

2018 ◽  
Vol 9 (1) ◽  
pp. 60-68
Author(s):  
Lailan Syaufina ◽  
Dinda Aisyah Fadhillah Hafni
Keyword(s):  
Air Temperature ◽  
Peat Soil ◽  
Research Unit ◽  
Climatic Research Unit ◽  
P Value ◽  
Suitable Model ◽  
Climate Data ◽  
Climate Patterns ◽  
Peat Fires ◽  
Maximum Air Temperature

Bengkalis is one of the regency in Riau which is vulnerable to forest and peatland fires due to land use change. This research aimed to analyze the distribution of forest and peat fires in Bengkalis, and observe the correlation between climate (rainfall, maximum air temperature, and average air temperature) and hotspot as an indicator of forest and peat fires occurrences in Bengkalis. The climate data derived from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG) and global climate data from Climatic Research Unit (CRU) satellite. The result revealed that climate patterns of CRU satellite are in line with climate patterns of BMKG observations. Hotspots spatial distribution showed the years of 1998, 2002, 2004, 2005, 2006, and 2009 as years with a high number of hotspots. Most hotspots are found in the peat soil types Hemists/Saprists (60/40), very deep and on land cover shrub swamp. The maximum air temperature and average air temperature showed significant correlation (p-value <0.05) to the number of hotspots while the rainfall did not significantly affect the number of hotspots. The best model illustrated the absence of a suitable model to describe the relationship between climate and the number of hotspots.Key words: climate, Climatic Research Unit, hotspot, peat fires

scholarly journals Study of temporal variations of nocturnal and daytime urban heat island in Baghdad

2020 ◽  
Vol 29 (3) ◽  
pp. 355-365
Author(s):  
Salwa Naif ◽  
Monim Al-Jiboor ◽  
Najlaa Hadi
Keyword(s):  
Urban Heat Island ◽  
Air Temperature ◽  
Temporal Variations ◽  
Rural Site ◽  
Heat Island ◽  
Mean Values ◽  
Four Seasons ◽  
Urban Heat ◽  
Weather Stations ◽  
Maximum Air Temperature

Based on daily minimum and maximum air temperature observations for three years: 2008, 2013 and 2019, measured by automatic weather stations located at two sites of Baghdad city were used to compute nocturnal and daytime urban heat island (UHI). First station fixed in campus of the Mustansiriyah University is considered as urban area, and another station followed to Iraqi meteorological organization installed at the International Baghdad Airport was chosen as the rural site. Daily, seasonal and annual averages of nocturnal and daytime UHIs were presented to study the variability and trends. The results show the evolution of a nocturnal UHI, whose high mean values were recorded in four seasons with largest value found in summer of 2019. Annual trend in nocturnal UHI intensities was found to be larger than that of daytime. Thus, this study propose that maintenance and increase urban parks and planting shading tall trees to mitigate UHI intensity in Baghdad city.

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