Turkish State Meteorological Service Research Articles

Analysis of historical and future cooling degree days over Türkiye for facade design and energy efficiency in buildings

In today’s examination of optimal facade design for buildings, facade performance has a significant influence on cooling energy consumption. Cooling degree days (CDD) is regarded as a basic and straightforward approach for predicting building cooling energy consumption. Accurate seasonal trend forecasting of the CDD plays a vital role in estimating the energy demand for building cooling. Unfortunately, there are no published statistics or research on Turkey’s CDD data for the following years. The monthly mean CDD data of Türkiye were created in this study utilizing data from the Turkish State Meteorological Service for the historical period from 1991 to 2022. The Seasonal Autoregressive Integrated Moving Average (SARIMA) approach was used to model the CDD data, and a time series forecast was performed. The SARIMA model was used to forecast the future trend of CDD from 2023 to 2040. The monthly and yearly CDD statistics were presented as figures, while the yearly CDD maps for Türkiye were created using the ArcGIS software program. Finally, CDD zones that require more attention to the most appropriate facade design in terms of energy efficiency for buildings in Türkiye were identified and classified.

A Comparative Study on the Estimation of Wind Speed and Wind Power Density Using Statistical Distribution Approaches and Artificial Neural Network-Based Hybrid Techniques in Çanakkale, Türkiye

In recent years, wind energy has become remarkably popular among renewable energy sources due to its low installation costs and easy maintenance. Having high energy potential is of great importance in the selection of regions where wind energy investments will be made. In this study, the wind power potential in Çanakkale Province, located in the northwest of Türkiye, is examined, and the wind speed is estimated using hourly and daily data over a one-year period. The data, including 12 different meteorological parameters, were taken from the Turkish State Meteorological Service. The two-parameter Weibull and Rayleigh distributions, which are the most widely preferred models in wind energy studies, are employed to estimate the wind power potential using hourly wind speed data. The graphical method is implemented to calculate the shape (k) and scale (c) parameters of the Weibull distribution function. Daily average wind speed estimation is performed with artificial neural network–genetic algorithm (ANN-GA) and ANN–particle swarm optimization (ANN-PSO) hybrid approaches. The proposed hybrid ANN-GA and ANN-PSO algorithms provide correlation coefficient values of 0.94839 and 0.94042, respectively, indicating that the predicted and measured wind speed values are notably close. Statistical error indices reveal that the ANN-GA model outperforms the ANN-PSO model.

Analysis of temperature and precipitation series of Hirfanli Dam Basin by Mann Kendall, Spearman’s Rho and Innovative Trend Analysis

In this study long-term trend analysis of precipitation and temperature series are determined in the Hirfanli dam basin of Turkey. Data is obtained from the Turkish State Meteorological Service for the period of 1968 to 2017 for Gemerek, Kayseri, Kirsehir, Nevsehir, Sivas and Zara. Mann-Kendall, Spearman's Rho and Innovative Trend Analysis are used for trend analysis with 95% confidence levels. According to the results of the temperature series upward trend were determined. The results of all methods are similar but increasing significant trends were determined by Mann Kendall and Spearman's Rho except Zara. According to the precipitation series results, with decreasing trends in Gemerek, Kirsehir, Nevsehir and Zara, increasing trends were determined in Kayseri and Sivas. The results of Mann Kendall and Spearman's Rho methods show parallelism with each other. Contrary to other methods, Innovative Trend Analysis determined a decreasing trend in Kayseri. As a result of the analysis, the trends in the precipitation series are not significant at the 95% confidence level. In addition to statistical analyzes, evaluations were made in terms of integrated disaster management for drought disaster in the basin with arid climate characteristics.

From Anatolian Plateau to American Plains: A transcontinental assessment of the EUMETSAT H SAF’s new generation snow water equivalent product over Türkiye and the conterminous U.S.

The main frame of this paper is to present the first validation results of the new generation hemi-spherical daily snow water equivalent (SWE) product of the EUMETSAT H SAF, namely, H65. It utilizes data from passive microwave radiometry sensors to estimate SWE. Operating at a suitable spatial scale, it offers insights into snow accumulation and melting dynamics, advancing satellite-based snow monitoring across diverse regions. The validation period covers the 2021 snow year, from January to March 2021, during which the dry snow conditions prevail. The validation is conducted in two distinct geographic regions, Türkiye, and the conterminous U.S. For Türkiye, the in-situ snow depth measurements provided by the Turkish State Meteorological Service are employed. On the other hand, the 1-km gridded SWE dataset of NOAA National Ice and Snow Data Center is used in the validation over the U.S. The validation results over Türkiye yields an overall RMSE of 39.27 mm, whereas it reads 15.19 mm for the U.S. These results indicate that the H65 SWE product complies with the product requirement thresholds for both flat (40 mm) and mountainous (45 mm) areas.

Determination of the change in variance components due to heat stress in dairy cattle using a random regression model

The aim of this study is to evaluate changes in variance components for dairy cows under heat stress conditions using a random regression model. The daily milk yield and pedigree records used in the research were obtained from a dairy farm in Sanliurfa, affiliated with The General Directorate of Agricultural Enterprises (TIGEM), a public institution. Records were from Holstein dairy cows registered between 2017 and 2019 in the farm. A total of 690 lactations from healthy dairy cows were used in the study. Among these lactations, 278, 130, 135, and 147 were the first, second, third, and fourth or higher lactations, respectively. In addition to this, the total number of cow-days was 207 003. In order to evaluate heat stress on animals meteorological data were used and collected from a public weather station in Sanliurfa, which is operated by the Turkish State Meteorological Service authorized by the Ministry of Environment, Urbanization, and Climate Change of the Republic of Türkiye. In the study, variance components were separately estimated for the comfort period (CP) and the heat stress period (HSP) using a random regression test-day model and six-knot linear spline function was used. In the study, it was observed that heat stress resulted in an increase in additive genetic, permanent environmental, and consequently, phenotypic variance. During the lactation period, the average heritability was determined to be 0.13±0.007 for CP, while it was found to be 18±0.010 for HSP. According to the findings obtained from the study, it was concluded that the time periods for selection should coincide with the peak milk yield under heat stress conditions, while for the period without heat stress, it should be around the 120th day of lactation. These results indicate that climatic factors such as temperature and humidity should be included in the models used for genetic parameter and breeding value estimation. Thus, it may be possible to identify dairy cattle that are genetically more tolerant to hot conditions. In this way, more successful outcomes can be achieved in selection studies.

Climatic variations and pollution on benign paroxysmal positional vertigo in Kars, Türkiye

Benign paroxysmal positional vertigo (BPPV) is the most common diagnosis for peripheral vertigo. Although pathophysiological mechanisms remain unclear, BPPV is mostly idiopathic and factors related to BPPV are still being investigated. Knowing these factors can contribute to the prevention and management of BPPV. In this study, we investigated the correlations between climatic variations, pollution, and BPPV retrospectively. 262 patients diagnosed with BPPV between 2019 and 2021 in Kars, Türkiye, were included in our study. Meteorological parameters were obtained from Turkish State Meteorological Service. Horizontal BPPV increased significantly with the humidity (p < 0.05). In addition, carbon monoxide levels significantly increased the potantial of BPPV (p < 0.05). Surprisingly, BPPV increased in the summertime and showed a significant relationship with humidity. We believe this change is related with the city-specific features as it is the coldest place in the country, emigrant province and crowded in the summer times.

Clustering of Precipitation in the Black Sea Region with by Fuzzy C-Means and Silhouette Index Analysis

In recent years, there has been a noticeable increase in the number of disasters caused by the effects of global climate change. In this context, various studies are carried out in our country and in the world in order to reduce the effects of climate change. The classification of regions affected by climate change into similar classes in terms of climate parameters is important in terms of applying similar methods in studies to be carried out in these regions. Thus, a correct strategy will be determined in the studies to be carried out in order to reduce the effects of climate change. The observation records evaluated within the scope of the study were used from 31 stations in the Black Sea Region of the Turkish State Meteorological Service, covering the period between 1982 and 2020. Cluster analysis was carried out using the Fuzzy C-Means. As a result of the study, the optimum cluster among the clusters formed by Fuzzy C-Means was determined by Silhouette index analysis. The optimal number of clusters is suggested as 4.

Synoptic analysis of the January 2004 snowstorm: Example of Çanakkale

Many natural disasters have occurred on the Earth we live in. Humans have always been in a struggle with these natural phenomena. Today, people are still struggling with atmospheric extreme events. One of the most important disasters like this is snowstorms that occur suddenly in winter. Especially countries in the middle belt (30-60 latitudes in the Northern Hemisphere) are constantly affected by snowstorms. Turkey, located in the Middle Belt, is also exposed to these storms during the winter season. From this perspective, the analysis of synoptic conditions and weather forecast is an important factor for risk analysis and take precautions for winter storms. Thus, the predictions made with the tracking of air masses and synoptic analysis make it easier to understand how the storm occurred. For this purpose, synoptic analyses were conducted to understand the development of the snowstorm that occurred in the high atmosphere in Çanakkale on January 20-30, 2004. This study examined how weather conditions change. Radiosonde data, surface maps, and daily bulletins provided from the Turkish State Meteorological Service were used in the study. Archival data were used to track air mass orbits using the Hysplit trajectory method. Stability and instability assessments of air masses were evaluated using the data from radiosonde stations using the K Index. Spatial analysis software was used to analyze the data and show the thematic distribution of topography. According to the findings, during the passage of a cold front of air masses coming from the north to Çanakkale on January 22, gusty winds began, suddenly dropped the temperatures, and began heavy snow events. Later, it turned into a blizzard and formed a type of snow.

Evaluation of wind comfort with computational fluid dynamics simulations for pedestrian sidewalks around buildings

Wind power could be one of the most clean and powerful renewable resources for electrical energy production, but on the other hand, uncontrolled wind flow especially in urban places could cause undesired situations as damage to buildings, decrease in pedestrian comfort, environmental damage, or even life loss. Construction of high-rise buildings, widely spread structures within cities, and environmental changes forces, engineers to find quick, reliable, and also economically viable solutions during design stages, but wind comfort of sidewalks generally not considered enough even if they are located in crowded areas. The web-based computer aided engineering (CAE) program named Simscale which runs on the basis of sophisticated graphical interface was used as computational fluid dynamics (CFD) software to determine wind speeds under influence of buildings in the Nuh Naci Yazgan University campus. Also, field measurements carried out in campus area for a short term period were compared with long term hourly wind speed data obtained from the Turkish State Meteorological Service (MGM) station located in Kayseri to identify most optimal wind speed data for the research area. Results of analysis showed that wind speed increased in the mostly used paths of campus, which means that the layout of buildings negatively affected the wind comfort. CFD analysis softwares could be used to determine the possible consquences of wind with less economic investment in a short time, and they could be used in accordance with comfort criterias as well as safety regulations.

Homogeneity Analysis of Turkish Rainfall Intensity Series

In this study, homogeneity checking the annual maximum rainfall intensity series for periods ranging from 30 to 78 years were obtained from 103 stations operated by the Turkish State Meteorological Service. Absolute homogeneity tests namely Standard Normal Homogeneity Test (SNHT), Buishand Range Test (BRT), Pettitt Test (PT), and Von Neumann Ratio Test (VNRT) were applied at a confidence level of 95%. Stations were classified inhomogeneous if at least one of the standard durations data classified suspect or doubtful. Assuming that the factor destabilizing the homogeneity is the trend, a detrended methodology (DFA) was performed. After the implementation of DFA, absolute homogeneity tests were reapplied to the series of rainfall intensity. As a result of this study 49 of 103 stations called useful all 14 standard durations. 45 of the remaining 54 stations classified useful all standard durations after trend components separated from rainfall intensity series. As a result of the analysis, it was determined that the remaining 8 of 103 stations had inhomogeneous values after the trend components were separated.

Artificial neural network approach for monthly air temperature estimations and maps

Accurate air temperature estimation plays a crucial role in weather planning, and hydrological modeling techniques including water supply, drought analysis, and demand problems of a country. In this study, monthly maximum air temperature (Tmax), minimum air temperature (Tmin), and mean air temperature (T) values for Türkiye were modeled using the Feed-forward neural network (FNN) and Elman neural network (ENN) approaches. In the developed models, monthly Tmax, Tmin, and T values at 81 measurement stations over the period of 1927 through 2021 recorded by the Turkish State Meteorological Service (known as MGM in Türkiye) were used. While the periodicity (month of the year) and geographical variables (latitude, longitude, and altitude) were used as inputs in the developed models, the Tmax, Tmin, and T variables were defined as the output. First of all, long-term monthly air temperature maps were created with the help of the ArcGIS software program using the observed values. Then, maps of long-term air temperatures were created with the ArcGIS software program using the predicted values of the developed artificial neural network models. To prove the accuracy of the models, the predicted long-term air temperature maps were compared with those actual maps. The obtained results showed that the long-term monthly air temperature maps of the studied regions could be created with a small and acceptable error as a function of geographical information and periodicity component. By creating artificial intelligence maps in this way, it will be possible to accurately predict the long-term monthly air temperatures as a function of latitude, longitude, altitude, and the number of months at any point where no measurement is made.

Effect of Weather Conditions on the Pediatric Supracondylar Humerus Fracture Incidence.

Some environmental factors pose as risk factors for children's supracondylar humerus fractures. This study aimed to evaluatethe relationship between weather conditions and pediatric supracondylar humerus fracture incidence. A total of 310 patients aged <16 years, admitted to our hospital with supracondylar humerus fractures,were evaluated. We evaluated patients' age, and also, season, day, and weather conditions. The Turkish State Meteorological Service database was used for meteorological data and data was analysed statistically. Most cases occurred in the spring (28.1%, n=87) and summer (27.1%, n=84). Cases of older children (aged six years and older) with supracondylar humerus fractures were recorded in the summer season, while fracture cases in preschool-aged(younger than six years old) children were seen in the winter season. We found that the overall incidence of pediatric supracondylar humerus fractures increased inspring and summer seasons. In addition, the fracture incidence in preschool- and school-aged children differed according to the season and temperature. Hence, the management of these fractures could also include the significance of weather conditions, making preventive measures more critical in the spring and summer seasons.

Performance of Machine Learning Methods in Location-Based Prediction

Thanks to the technological developments that have taken place in recent years, the number, variety and quality of the data obtained using IoT (Internet of Things) sensors have been increasing. Data obtained from IoT sensors have been used in many scientific fields such as land use, climate change, vegetation analysis and air quality forecasting. In this study, a location-based spatial analysis application was carried out using the data obtained from IoT sensors with machine learning. With this application, the average temperature information of the station was estimated with Artificial Neural Network (ANN), Random Forests (RF), and Support Vector Machines (SVM) methods using daily average humidity, average pressure, and station altitude information on real datas of Kayseri acquired from the Turkish State Meteorological Service, and then performances of the methods were compared. In the experimental evaluations, the ANN, RF and SVM methods obtained an average of 0.83, 0.75 and 0.50 R2 values. The ANN method outperformed the RF and SVM methods in location-based temperature estimation.

Makine Öğrenimi Yaklaşımlarını Kullanarak Günlük Deniz Suyu Sıcaklığı Tahmini

The efficiency of turbines in seaside nuclear or coal-fired power plants is directly proportional to sea water temperature (SWT). The cooling medium temperature is critical in the design of any power plant when considering long-term average climatic conditions. As a result, the deviation in the SWT affects the efficiency of electricity generation. Accurate SWT estimation is critical for electrical output from power plant applications in this regard. Three different data-driven models such as long short-term memory (LSTM) neural network, adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM) and grid partition (GP) were used to perform one-day ahead short-term SWT prediction, in this paper. The analyses were performed using 5-year daily mean SWTs measured by the Turkish State Meteorological Service in Canakkale Province between 2014 and 2018. The measured data was also used to validate the data produced by the proposed techniques. Performance criteria for the techniques suggested are mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R). With the ANFIS-FCM technique, the best outcomes for MAE, RMSE and R values were obtained as 0.113oC, 0.191oC, and 0.9994, respectively, according to daily SWT forecasting.

Güneş Işınım Tahmini için Farklı Güneşlenme Durumlarından Faydalanan Hibrit Bir Yöntem Tasarımı

The constant expansion of solar energy has made the accurate forecasting of radiation an important issue. An accurate prediction of solar energy production is crucial for the effective integration of photovoltaic (PV) and wind generators in smart grids. The intermittent nature of solar energy poses many challenges to renewable energy system operators in terms of operational planning and scheduling. For this reason, forecasting solar radiation by means of the hybrid methods is becoming widespread. In this paper, a hybrid method for predicting solar radiation is proposed, wherein the prediction model is determined based on the clearness index. The study used two-year solar radiation data of the province of Mardin obtained from the Turkish State Meteorological Service (TSMS). As predictors, ANN, NARX networks, and Ridge regression methods were used, and the training data were modeled with all three approaches in the first stage of the study. The clearness index was determined into three ranges; slightly cloudy, cloudy, and mostly cloudy. The training data were modeled with three methods used as estimators, and the success of each method was examined in each defined clearness index range. As a result, in the hybrid prediction algorithm, the clearness index is first estimated using artificial neural networks, and then the future solar radiation value is predicted by using the most successful model within the predicted clearness index range. Experimental results show that more successful predictions are made with the proposed hybrid method than when models are used individually.

Türkiye'nin Kutup Bilim Diplomasisi

Bilim ve dış politikanın kesişim noktasında yer alan bilim diplomasisi kavramı, devletler arasında ikili ve çok taraflı bilimsel iş birliklerine ortak bir zemin sunma potansiyeli nedeniyle küresel iklim değişikliğinin olumsuz etkilerine paralel olarak son yıllarda sıklıkla gündeme gelmektedir. Bilim diplomasisinin kavramsallaşmasını Antarktika Antlaşmalar Zirvesi’ne dayandıran görüşler nedeniyle öncülleri, boyutları ve uygulama usullerine yönelik gerçekleştirilen güncel çalışmalarda konunun daha kapsamlı ele alınabilmesi için tarihsel perspektifin önemi vurgulanmıştır. Türkiye’nin kutuplara yönelik bilimsel araştırmalarına tarihsel perspektiften bakıldığında ise kutup araştırmalarına yönelik ilgisinin 1932 ve 1933 yılları arasında 34 devletin katılımıyla düzenlenen 2. Uluslararası Kutup Yılı’na dayandığı görülmektedir. Bu minvalde çalışma kapsamında tarihsel araştırma yöntemi kullanılarak T.C. Cumhurbaşkanlığı Devlet Arşivleri Başkanlığı’nda yer alan resmi dokümanların yanı sıra söz konusu dokümanlarda elde edilen bulguların ışığında kutup araştırmalarına katkı sağlayan Meteoroloji Genel Müdürlüğü ve Harita Genel Müdürlüğü gibi çeşitli kurumların arşivleri de taranarak bütüncül bir bakış açısıyla Türkiye’nin kutup bilim diplomasisi sürecine ışık tutulmaya çalışılmıştır. Bu çalışmanın amacı; başarılı bir kamu diplomasisi faaliyeti olarak yürütmekte olduğu kutuplara yönelik bilimsel araştırmalarına 2017 yılında İstanbul Teknik Üniversitesi (İTÜ) bünyesinde Kutup Araştırmaları Uygulama ve Araştırma Merkezi (PolReC)’in kurulmasıyla yaptığı atılım sonrasında 2019 yılında Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) bünyesinde Kutup Araştırmaları Enstitüsü (KARE)’nin kurulmasıyla kurumsal bir nitelik ve ivme kazandıran Türkiye’nin, kutup bilim diplomasisi potansiyelini geliştirmesine katkı sağlamaktır.

Agent-based simulation and simulation optimization approaches to energy planning under different scenarios: A hospital application case

In complex multi-energy systems, one of the main goals is that combining optimal circumstances to acquire minimum system cost as decreasing energy consumption without an outage of energy supply. This study mainly has focused on flexible solutions that include the controlling of a multi-energy system and its components such as electricity, heating, natural gas, and converters to minimize the overall system cost and reduce carbon emissions by observing of demand whether is satisfied. The technique of agent-based simulation and simulation optimization approaches has been adopted as solution methods for tackling these difficulties. As a case study to implement the flexible solutions, the multi-energy system of a large hospital complex placed in Turkey was selected. These flexible solutions based on the integration of cogeneration and photovoltaic systems to existing energy infrastructure have been simulated under multi-tariff and singe-tariff electricity price strategies for different years. The used data in the model has been gathered from the hospital electricity and natural gas bills, the energy projects realized for the hospital in the past, and other various sources including PVGIS database, Turkish State Meteorological Service, commercial cogeneration/PV panel manufacturers, and publicly available reports. The results indicate that the effective presence of cogeneration and photovoltaic is notable in the decreasing total system cost taking into consideration also lower carbon emissions. Once current system was simulated, it was compared with other proposed possible configurations with different two scenarios. Based on the simulation models that were optimized with different metaheuristics such as genetic algoritm, the economic results show that the both of two scenarios can be profitable (simple pay back periods around 5.28 and 6.6 years respectively) even without any public funding. Furthermore, it was determined that cogeneration-based scenario-2 provided a 12.35 % reduction in carbon emissions compared to the base scenario, while scenario-3 provided a 16.94 % reduction compared to the base scenario, and both scenarios are proven to be quite environmental friendly.

Estimation of hourly global solar radiation using artificial neural network in Adana province, Turkey

Since global solar radiation (GSR) is an important parameter for the design, installation, and operation of solar energy-based systems, it is important to have precise information about it. As the indicating devices are expensive and their requirements such as operation and maintenance should be carried out, the measurement of solar radiation cannot be frequently taken. On the other hand, the measurements of different meteorological parameters such as relative humidity, sunshine duration, and ground surface temperature are more prevalent in meteorology stations. Therefore, the estimation of solar radiation is a significant parameter for the areas where the measurements could not be performed and to complete the missing information in databases. Many different models, software, and simulation programs are utilized to calculate solar radiation data, provide an economic advantage, and obtain high accuracy. The main purpose of this study is to perform an estimation of solar radiation in Adana, where is on the east of the Mediterranean in Turkey, by using an artificial neural network (ANN) model. The best estimation performance is obtained by optimizing the neuron numbers used in the network’s hidden layer with the trial and error method. With this aim, hourly data including wind speed, wind direction, humidity, sunshine duration, actual pressure, and average temperature are taken as inputs while solar radiation is taken as a target. All these data, which is for 2018, has taken from the Turkish State Meteorological Service. A linear correlation coefficient value has been obtained to be about 0.87313 with the mean square error (MSE) of 1.6184x104 W/m2 for the testing data set. The ANN’s testing/validation results show that it has a low MSE, indicating the accuracy and adequacy of the network model. Besides, the predicted ANN output is evaluated to be remarkably close to the measured target data by considering the linear correlation coefficient.

Statistical Models For Sunshine Duration Related To Precipitation And Relative Humidity

Energy usage leads to environmental problems especially climate change and air pollution. In urban areas, extensive consumption of fossil energy sources deteriorates urban air quality and causes health problems such as asthma and respiratory diseases. The usage of renewable energy sources mainly solar energy instead of fossil energy sources provide the healthier and cleaner environment. Some statistical approaches have been used to predict the solar radiation for many years. The formula which is named as Angström-Prescott is widely used for the estimation. This approach uses the value of relative sunshine duration which is computed by using the daily measurement of sunshine duration and the daylength. Sunshine duration has been measured at meteorological stations for a long time. But in some cases such as remote areas or non-exist stations, sunshine duration is forecasted by using statistical methods. In atmospheric environment, sunshine duration is affected from other climate parameters such as cloud cover, wind speed, relative humidity, precipitation, air temperature, pressure. This paper offers to gain the relationships for sunshine duration via precipitation and relative humidity. To construct the statistical models, cloud cover was entered the models firstly because of its association with sunshine duration. Many studies were shown that sunshine duration was highly correlated to the cloud cover. The observed daily mean values of sunshine duration and climate elements were the data of Samsun meteorological station measured by Turkish State Meteorological Service. To acquire the statistical models for sunshine duration, data was arranged as monthly mean values and then linear regression analysis method was operated. Also the graphs of time series of climate variables were visually created for a clear interpretation. According to the findings, sunshine duration can be estimated bu using statistical models over cloud cover, precipitation and relative humidity. The contributions of precipitation and relative humidity change for different time scales.

Evaluating The Sunshine Duration Characteristics In Association With Other Climate Variables

Many processes such as for industrial production, heating, cooling, health, education and etc. are depended on and use the energy. In recent years, the consumption of energy has been increased over the world. Fossil energy sources cannot meet the increase in energy demand. So, renewable energy sources especially solar energy become important in this way. The applications with solar energy present clear and environment friendly systems. Sunshine duration which is used for estimating the solar energy is very crucial parameter for human life. Climate variables in atmospheric environment have influences on sunshine duration. This study aims to demonstrate the relations between daily sunshine duration and other climate variables such as cloud amount, atmospheric pressure, maximum air temperature, minimum air temperature, relative humidity, precipitation and wind speed. The data of all climate variables were obtained from the measurements of ground based observation station belonging to the Turkish State Meteorological Service. The long term records based on daily measurements were examined for Rize meteorological station. The daily observation values for sunshine duration and other climate variables were transformed to mean montly values. A 51 years (1960-2010) database was statistically analyzed by using the method named as Pearson Correlation Analysis (%95 confidence interval). The obtained results over this analysis were shown that sunshine duration was associated with other climate variables. The grade of these relations was changed according the different time periods. The amount of relations were obtained generally in moderate, good and weak degrees as statistically significant (p<0.05). Sunshine duration was highly negatively correlated with cloud amount generally except the exceptional circumstances such as moderate for December. The correlations for sunshine duration and the other climate variables were found as negatively for relative humidity, atmospheric pressure and precipitation and positively for wind speed, maximum and minimum air temperature.