Temperature Data Research Articles

The Socio-Environmental Determinants of Childhood Malnutrition: A Spatial and Hierarchical Analysis.

Despite a remarkable reduction in global poverty and famines, substantial childhood malnutrition continues to persist. In 2017, over 50 million and 150 million young children suffered from acute malnutrition (wasting) and chronic malnutrition (stunting), respectively. Yet, the measurable impact of determinants is obscure. We evaluate proposed socio-environmental related determinants of stunting and wasting across Kenya and Nigeria and quantify their effectiveness. We combine health and demographic data from Kenya and Nigeria Demographic Health Surveys (2003, 2008-2009, 2013, 2014) with spatially explicit precipitation, temperature, and vegetation data. Geospatial and disaggregated data help to understand better who is at risk and where to target mitigation efforts. We evaluate the responsiveness of malnutrition indicators using a four-level random intercept hierarchical generalized logit model. We find that spatial and hierarchical relationships explain 28% to 36% of malnutrition outcome variation. Temporal variation in precipitation, temperature, and vegetation corresponds with more than a 50% change in malnutrition rates. Wasting is most impacted by mother's education, family wealth, clinical delivery, and vaccinations. Stunting is most impacted by family wealth, mother's education, clinical delivery, vaccinations, and children asymptomatic of fever, cough, or diarrhea. Remotely monitored climatic variables are powerful determinants, however, their effects are inconsistent across different indicators and locations.

Analysis and Prospects of an Antarctic Krill Detection Experiment Using Drifting Sonar Buoy

To reduce costs associated with the detection and population assessment of Antarctic krill and diversify the single detection approach, our team designed and deployed a drifting sonar buoy for krill detection in the waters surrounding Antarctica. The experimental results indicate that the drifting sonar buoy fulfills its primary functions and meets the requirements for krill detection in designated marine areas. The initial experiment lasted seven days, during which the buoy collected 157 records of speed and location data as well as 82 records of sea surface temperature and acoustic data, demonstrating its potential for krill detection. The experiment also revealed shortcomings in the initial design of the drifting sonar buoy, leading to proposed improvements. The paper further compares the advantages and disadvantages of the drifting sonar buoy and traditional fishing vessels in krill detection with the buoy offering unique benefits in low-cost deployment, labor savings, broad monitoring range, and continuous real-time data monitoring. The drifting sonar buoy serves as an excellent complement to fishing vessels in krill detection.

A data-driven method for estimating sewer inflow and infiltration based on temperature and conductivity monitoring

Quantitation of sewer inflow and infiltration (I/I) is important for maintaining efficient wastewater transport and treatment. I/I flows can be quantified based on flow rate and water quality measurements. Flow rate-based methods require continuous monitoring of flow rates using flow meters that are costly and prone to fouling. In comparison, conductivity and temperature, as simple water quality parameters, are more easily measurable with more cost-effective and reliable sensors. In this study, a data-driven methodology is developed for estimating I/I flows based on online conductivity and temperature measurements. A Prophet-model-based analytic algorithm is first developed to reconstruct the temperature and conductivity profiles of the base wastewater flow (BWF) from the measured temperature and conductivity time series. The algorithm is shown to be able to reconstruct the BWF temperature and conductivity profiles in two monitored catchments. The reconstructed BWF data are then incorporated into mass/energy balance equations for estimating I/I flows from the measured temperature and conductivity data. The overall I/I quantification method is finally demonstrated using simulation studies of a real-life sewer network and validated against the known I/I flows. This work provides a reliable method for I/I quantification based on simple measurements.

Numerical Reconstruction of Heat Source on a Semi-Infinite Rod

Inverse heat conduction on an unbounded domain has always been the focus and difficulty in engineering and mechanical fields. In this paper, we consider the inverse problem of identifying the heat source by using the terminal measured temperature data. Being different from other ordinary heat conduction problems, our mathematical model is defined in a semi-infinite region, which may lead to many numerical methods that cannot be used directly. Based on the artificial boundary method, we first introduce the truncated boundary and derive the exact boundary conditions on it. Then we use the finite difference method to establish an approximate difference scheme for the forward problem. For the inverse problem, we design the Landweber and conjugate gradient iterative algorithm and do numerical experiments. Numerical results verify the correctness and effectiveness of our algorithm.

Wpływ działalności antropogenicznej w dorzeczu górnego Dunaju na reżim temperatury wody Dunaju w Bratysławie

The man activity in the basin affects the water temperature in the increasingly higher levels (construction of water reservoirs, construction of thermal and nuclear power plants or drainage of waste water to streams). In this paper, we focused our attention on the evaluation of the impact of anthropogenic activity to increase the thermal load of the Danube River for the period 1926–2020 at the Bratislava (Slovakia) station. In the first part, the long-term trends of a series of monthly and annual water temperatures in the Danube (period 1931–2020) are identified. In the second part, the dependence of the range of daily water temperature values is analysed at the temperature of the atmosphere in Vienna. The impact of an increase in the temperature of the Danube water due to human activity was tried to identify for lower, average, and higher flows (for Danube discharge at Bratislava water gauge 1500 m3s-1, 2000 m3s-1, and 3000 m3s-1) by comparing two periods: 1932–1961, and 1991–2020. As the effect of water heating in the river stream is most noticeable during low flow (dry) periods and high air temperatures, only daily water and air temperature data from the warm months between May and September were used in the calculations. At monthly flow rates of 1500 m3s-1, the water temperature was, on average, 0.5°C higher during the period of 1991–2020 than it was during the period of 1932–1960. This growth could be attributed to human activities in the Danube basin above Bratislava (warming of water in the built tanks, the wastewater flow to the Danube flow, etc.)

Improved fusion model for generating hourly fine scale land surface temperature data under all-weather condition

Existing Land Surface Temperature (LST) fusion models encounter some challenges due to missing data, complex weather areas, and rapid land cover changes. To overcome these limitations, we proposed the Integrated SpatioTemporal Fusion Algorithm (ISFAT). ISFAT is developed based on contemporary fusion models but in addition incorporates data from partially contaminated LSTs using the masked weight function. This helps to predict fine-scale LST on prediction date while considering error resulting from landcover changes between the base and prediction date. This algorithm also factors in the calculation of model residuals, which are distributed back to the predicted fine-scale LST using the thin-plate spline function. The fine-scale LST on prediction can thereafter employed for predicting hourly fine-scale LST images by integrating a coarse resolution LST with hourly temporal resolution. Compared to contemporary LST fusion models, ISFAT demonstrates superior performance, with mean average differences of 0.1 K and 0.27 K over SADFAT and STITFM, respectively. Additionally, diurnal LST predictions from ISFAT compare well with air temperatures from automatic weather stations. Notably, on February 18, 2020, ISFAT effectively optimized fine-scale LST for Hong Kong, achieving an RMSE of 3.33 K, despite the limitation of cloud cover in the base date. The newly developed ISFAT could facilitate better LST retrieval over a large spatial coverage under different degrees of cloud contamination.

Lithium-Ion Battery SOH Estimation Method Based on Multi-Feature and CNN-BiLSTM-MHA

Electric vehicles can reduce the dependence on limited resources such as oil, which is conducive to the development of clean energy. An accurate battery state of health (SOH) is beneficial for the safety of electric vehicles. A multi-feature and Convolutional Neural Network–Bidirectional Long Short-Term Memory–Multi-head Attention (CNN-BiLSTM-MHA)-based lithium-ion battery SOH estimation method is proposed in this paper. First, the voltage, energy, and temperature data of the battery in the constant current charging phase are measured. Then, based on the voltage and energy data, the incremental energy analysis (IEA) is performed to calculate the incremental energy (IE) curve. The IE curve features including IE, peak value, average value, and standard deviation are extracted and combined with the thermal features of the battery to form a complete multi-feature sequence. A CNN-BiLSTM-MHA model is set up to map the features to the battery SOH. Experiments were conducted using batteries with different charging currents, and the results showed that even if the nonlinearity of battery SOH degradation is significant, this method can still achieve a fast and accurate estimation of the battery SOH. The Mean Absolute Error (MAE) is 0.1982%, 0.1873%, 0.1652%, and 0.1968%, and the Root-Mean-Square Error (RMSE) is 0.2921%, 0.2997%, 0.2130%, and 0.2625%, respectively. The average Coefficient of Determination (R2) is above 96%. Compared to the BiLSTM model, the training time is reduced by an average of about 36%.

Standardized Testing for Thermal Evaluation of Bone Drilling: Towards Predictive Assessment of Thermal Trauma.

To ensure the prevention of thermal trauma and tissue necrosis during bone drilling in surgical procedures, it is crucial to maintain temperatures below the time- and temperature-dependent threshold of 50 °C for 30 s. However, the absence of a current standard for assessing temperatures attained during bone drilling poses a challenge when comparing findings across different studies. This article aims to address this issue by introducing a standardized testing method for acquiring thermal data during experimental bone drilling. The method requires the use of three controlled variables: infrared thermography, standard bone blocks, and a regulated drilling procedure involving a drill press with irrigation that simulates a surgeon. By utilizing this setup, we can obtain temperature data that can be effectively applied in the evaluation of other variables, such as surgical techniques or drill bit design, and translate the data into bone damage/clinical outcomes. Two surgical drill bits (2.0 mm-diameter twist drill bit and 3.3 mm-diameter multi-step drill bit) are compared using this experimental protocol. The results show the 2.0 mm bit reached significantly higher temperatures compared to the 3.3 mm bit when preparing an osteotomy (p < 0.05). The 2.0 mm drill bit reached temperatures over 100 °C while the 3.3 mm drill bit did not exceed 50 °C.

Recent advances in vertical temperature profiler instrumentation and flux estimation methods facilitate groundwater – Surface water exchange studies in environments with strong discharge zones

Groundwater fluxes to many surface water systems are spatially heterogeneous with discharge focused into discrete, high-flux zones. Quantifying fluxes in these preferential discharge zones is critical to a range of surface water habitat and water quality processes, but characterization can be difficult due to short-scale spatial and temporal variability. Passive heat-as-a-tracer methods employing vertical temperature profiler (VTP) data can provide the necessary spatial and temporal resolution, but upward fluid flow strongly attenuates the thermal signals used for estimating fluxes. In preferential discharge zones it becomes difficult to measure the signals in the subsurface and the flux parameter can become insensitive in the analysis models, leading to large uncertainties. We use data from a high-flux site of contaminant-loaded groundwater discharge to the Quashnet River on Cape Cod, Massachusetts, USA, to demonstrate how recent advances in VTP instrumentation that allow for the acquisition of high-resolution (0.001 °C) temperature data at short (1 cm) offsets near the ground surface, combined with advances in flux estimation methods that exploit the information content of the high-resolution data, facilitate heat-as-a-tracer approaches for characterizing groundwater-surface water exchanges and make it possible to obtain accurate and statistically robust results in a preferential discharge zone with a specific discharge of ∼1 m/d.

Trawling-Induced Sedimentary Dynamics in Submarine Canyons of the Gulf of Palermo (SW Mediterranean Sea)

Bottom trawling in submarine canyons can affect their sedimentary dynamics, but studies addressing this topic are still scarce. In the Gulf of Palermo (NW Sicily, SW Mediterranean Sea), bottom trawling occurs on the continental slope, but principally concentrates within Oreto Canyon. Hydrographic profiles and time series data of temperature, turbidity, and currents obtained by a CTD probe and by moored instruments, respectively, revealed increased turbidity values and the presence of bottom and intermediate nepheloid layers coinciding with periods of bottom trawling activity. The delay between the onset of trawling activities along the Oreto canyon axis and the increase in water turbidity at the mooring location indicate that trawling resuspended particles are progressively advected down-canyon by hydrodynamic processes. Topographic waves and near-inertial currents seem to contribute to the sediment transport of resuspended particles as bottom and intermediate nepheloid layers. Results presented in this paper highlight the complex relationship between hydrodynamic processes and sediment resuspension by trawling in submarine canyons.

Intercalation based synthesis of Chevrel phase superconductors

This work focuses on the superconducting and magnetic properties of Chevrel phase (CP) compounds (Cu2Mo6S8 and Ni2Mo6S8) processed via a novel self-propagating high-temperature synthesis (SHS) method involving ternary cation intercalation in MoS2 high-temperature polymorphs. Green pellets of the precursor materials sealed under vacuum and exposed to 1050 °C or 1200 °C rapidly transformed from a powder mixture into submicron CPs within minutes. X-ray diffraction analysis confirmed the presence of Cu2Mo6S8 CP (67%), and Ni2Mo6S8 (93%). This group has illustrated the first time CP were formed at low energy cost and full stoichiometry. Magnetization versus temperature data obtained for the samples under different field conditions confirmed the superconducting behavior of the Cu–CP at a critical temperature of 8.3 K. The Ni–CP behaved as a superparamagnetic at low temperature (4.2 K). The results of this work suggest that the novel SHS process offers a viable scalable synthesis route for the Chevrel family of compounds and that it is worth exploring the functional applications of the new compounds of this material system.

Enhancement of Texas wind turbine power predictions using fractional order neural network by incorporating machine learning models to impute missing data

In real-world datasets, missed data is often expected due to sensor errors, environmental conditions, communication errors, and other technical limitations. These factors can affect the accuracy of power predictions, particularly in wind speed and direction parameters. Enhancing the accuracy of wind energy forecasts and maintaining the electrical grid’s stability are essential in addressing related challenges. This paper proposes a bidirectional long short-term memory (Bi-LSTM) model to forecast missed wind speed and direction data to address these issues. In addition, a novel fractional-order neural network (FONN) with various developed fractional activation functions presents enhanced wind power prediction using forecasted missing wind speed and direction data. The performance of the FONN model is demonstrated in four comprehensive case studies on the Texas wind turbine dataset. The first case study uses wind speed, direction, pressure, and air temperature data. The second case study uses pressure, air temperature, wind speed, and forecasted wind direction data. Similarly, pressure, air temperature, wind direction, and forecasted wind speed data are used in the third case study. The final case study uses pressure, air temperature, and forecasted wind speed and direction to predict power. The proposed models’ performance is evaluated using mean square error and coefficient of determination metrics.

Surface temperature field real-time reconstruction of hot forging die based on 1DCNN

Accurate reconstruction of the die surface temperature field during the hot forging process is crucial for ensuring optimal control of die temperature and high-quality metal forgings. In this study, we propose a novel method that combines hot forging process parameters with a deep neural network to reconstruct the temperature field on the die surface in real-time. Firstly, a high-fidelity finite element model of the hot forging process is created and validated with a forging experiment. Secondly, temperature observation points are initially selected based on the simulated temperature distribution of the forging die, and the correlation coefficient method is used to optimize the layout of observation points to improve reconstruction accuracy and efficiency. Subsequently, multiple high-quality simulations with varying process parameters are conducted, generating a comprehensive dataset. The 1DCNN algorithm is then presented to produce a mapping from the input to the surface temperature field. Finally, the effectiveness of the surface temperature field reconstruction method is verified. The results indicate that the overall performance of the proposed 1DCNN algorithm is better than the traditional regression algorithms. In terms of reconstruction accuracy, it achieves a prediction accuracy within an absolute error of 3 °C for the outer surface points and within a relative error of 5 % for the cavity region. Regarding reconstruction efficiency, it requires an approximate execution time of 0.064 s for predicting a new sample. This paper presents a method for real-time reconstruction of the die surface temperature field, leveraging hot forging process parameters and temperature data from observation points. This approach enables reliable online monitoring of transient temperature variations on the hot forging die surface, offering valuable support for operational control and maintenance.

Performance Evaluation of Regression-Based Machine Learning Models for Modeling Reference Evapotranspiration with Temperature Data

Performance Evaluation of Regression-Based Machine Learning Models for Modeling Reference Evapotranspiration with Temperature Data

Low-grade heat to electricity conversion using the self-circulation of a magnet inside a single-turn pulsating heat pipe

Low-temperature (≤100°C) heat sources are abundantly and ubiquitously available in nature, such as solar energy, and in the form of untapped waste heat. This work aims to harness low-grade heat sources by introducing a portable harvesting system capable of effectively converting small spatial temperature gradients to mechanical and then electrical energy while maintaining a relatively high heat transfer capability. The designed and studied pair of harvesters are based on single-turn pulsating heat pipes with different condenser locations. Asymmetrical heating and cooling of the looped capillary glass tube gives rise to the bulk circulation of the internal fluid with occasional oscillation. The thermally-driven two-phase flow carries a spherical neodymium magnet fitted inside the tube. Two external solenoids generate a small amount of electricity every time the magnet passes through them. The proper functioning of energy-harvesting pulsating heat pipes (EH-PHPs) was investigated for pure water, acetone, and ethanol as the working fluids. Only water demonstrated satisfactory results and was thus selected as the main working fluid. Subsequently, high-frame-rate imaging of the internal flow along with time-domain temperature and open-circuit voltage data were employed and discussed to study the working principles of the harvester. In the next step, the mechanical, thermal, and electrical performance of the two EH-PHPs were quantitatively investigated for five different filling ratios (FRs) between 20% to 80% and five levels of heat input from 20.0W to 40.0W for each FR. Among the studied conditions, the EH-PHP with horizontal condenser (HC) configuration and 50% FR demonstrated the best performance in all three aspects. Accordingly, when 40.0W of heat was supplied to this ideal case, the magnet mean circulation frequency around the loop, effective thermal conductivity, and average induced open-circuit voltage (peak-to-peak) reached their maximum values of 0.46Hz, 11.5kW/(mK), and 0.53V, respectively. Although the integration of electromagnetic generators had reduced the heat transfer performance, the thermal conductivity of the EH-PHPs was comparable to that of conventional heat pipes and at least 25 times better than copper. Finally, the EH-PHP with HC configuration and 50% FR, the superior case, produced up to 5μW of electrical power while its solenoids were connected to load resistances with an optimum impedance. Therefore, even in its early stage of development, the portable EH-PHP can be used to supply low-power electronics such as a quartz watch. The generated electricity corresponds to about 0.01% relative Carnot efficiency. The remarkable thermal conductivity and capability of electricity production with less than 5°C temperature difference across the tube distinguish the EH-PHPs from mature energy harvesting technologies with higher energy conversion efficiencies.

Change in water column total chlorophyll-a in the Mediterranean revealed by satellite observation

Chlorophyll-a (Chl-a) is a crucial pigment in algae and macrophytes, which makes the concentration of total Chl-a in the water column (total Chl-a) an essential indicator for estimating the primary productivity and carbon cycle of the ocean. Integrating the Chl-a concentration at different depths (Chl-a profile) is an important way to obtain the total Chl-a. However, due to limited cost and technology, it is difficult to measure Chl-a profiles directly in a spatially continuous and high-resolution way. In this study, we proposed an integrated strategy model that combines three different machine learning methods (PSO-BP, random forest and gradient boosting) to predict the Chl-a profile in the Mediterranean by using several sea surface variables (photosynthetically active radiation, spectral irradiance, sea surface temperature, wind speed, euphotic depth and KD490) and subsurface variables (mixed layer depth) observed by or estimated from satellite and BGC-Argo float observations. After accuracy estimation, the integrated model was utilized to generate the time series total Chl-a in the Mediterranean from 2003 to 2021. By analysing the time series results, it was found that seasonal fluctuation contributed the most to the variation in total Chl-a. In addition, there was an overall decreasing trend in the Mediterranean phytoplankton biomass, with the total Chl- decreasing at a rate of 0.048 mg/m2 per year, which was inferred to be related to global warming and precipitation reduction based on comprehensive analysis with sea surface temperature and precipitation data.

Overall and local environmental collaborative control based on personal comfort model and personal comfort system

Most methods for creating an indoor thermal environment are based on controlling heating, ventilation, and air conditioning (HVAC) systems and do not consider the various needs of individuals in a multiperson space. Personal comfort systems (PCS) and personal comfort models (PCM) are popular technologies for achieving personal thermal comfort. This paper presents a thermal environmental collaborative control system (TECCS) that regulates environments at different spatial scales by leveraging the advantages of the HVAC system, PCS, PCM, and PCM-based automatic control to address the issue of individual differences in thermal demand in multiperson environments. The TECCS predicts thermal sensation votes (TSV) by combining facial skin temperature data obtained by an infrared sensor with environmental parameters. Subsequently, it performs the corresponding PCS control and adjusts the air conditioner according to the operating state of the PCS. This study proposes a collaborative control strategy with PCS at the core, enabling communication between thermal state recognition, HVAC system, and PCS. Twenty-eight adult males participated in the experiments testing the TECCS's performance. The results indicate that the TECCS can automatically regulate environments at different spatial scales based on thermal sensation prediction and that the operating state of the PCS can effectively guide air conditioning operations. Compared with constant setpoint control, the TECCS offers the advantage of improving thermal comfort. This paper also proposes future optimization directions based on the research results, focusing on recognition, equipment, and control.

Greening of Svalbard

Svalbard, located between 76°30′N and 80°50′N, is among the regions in the world with the most rapid temperature increase. We processed a cloud-free time-series of MODIS-NDVI for Svalbard. The dataset is interpolated to daily data during the 2000–2022 period with 232 m pixel resolution. The onset of growth, with a clear phenological definition, has been mapped each year. Then the integrated NDVI from the onset (O) of growth each year to the time of average (2000–2022) peak (P) of growth (OP NDVI) have been calculated. OP NDVI has previously shown high correlation with field-based tundra productivity. Daily mean temperature data from 11 meteorological stations are compared with the NDVI data. The OP NDVI values show very high and significant correlation with growing degree days computed from onset to time of peak of growth for all the meteorological stations used. On average for the entire Svalbard, the year 2016 first had the highest greening (OP NDVI values) recorded since the year 2000, then the greening in 2018 surpassed 2016, then 2020 surpassed 2018, and finally 2022 was the year with the overall highest greening by far for the whole 2000–2022 period. This shows a rapid recent greening of Svalbard very strongly linked to temperature increase, although there are regional differences: the eastern parts of Svalbard show the largest variability between years, most likely due to variability in the timing of sea-ice break-up in adjacent areas. Finally, we find that areas dominated by manured moss-tundra in the polar desert zone require new methodologies, as moss does not share the seasonal NDVI dynamics of tundra communities.

An approach-based machine learning and automated thermal images to predict the dark-cutting incidence in cattle management of healthcare supply chain

The healthcare supply chain is a network made up of various systems, processes, and elements that function and interact seamlessly to offer healthcare services and products. Food safety is an essential component of the healthcare supply chain. In the cattle industry, the healthcare supply chain contributes positively to the global economy through providing high-quality products such as milk and meat. Stress in cattle is one of main factor that cause low quality meat called “dark meat”. Numerous studies have been conducted on the development of different non-invasive methods based on Infrared Thermography Technology (IRT) to enhance the meat quality by detecting stress in cattle pre-slaughtering. These studies have the following issues: lack of automating in detecting body temperature of cattle and ignoring detecting stress with prediction dark meat incidence. The present study endeavors a new fully automated system for detecting stress, and dark meat incidence, incorporating the following new approaches: Multiview face detecting, Automatic eye localisation for detecting body temperature automatically employing computer vision and image processing, respectively. Furthermore, machine learning algorithms like Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), and Decision Tree (DT) have been developed specifically for stress detection and the prediction of dark meat. To develop automated system, two forms of data were collected: statistical temperature and infrared thermal images. Infrared thermal images are used to develop Multiview face detection and Automatic eye localisation. Temperature data used to develop the machine learning model. Results reveal that Multiview face detection better than the current methods in term of Precision 0.99, Recall 0.91, F-score 0.95 with high True positive rate 0.90 and zero False-positive rate. Automatic eye localisation has high accuracy, with the following values: sensitivity 0.9780, precision 0.7212, F measure of 0.8024, and misclassification 0.0455. Lastly, results elaborate that the decision tree model can attain a notable level of accuracy in terms of specificity, recall, F-measure, and Area Under the Curve (AUC), all at an optimal rate of 98%.

Investigation of the variability applying classical (MK-SR) and modern (ITTA-TPSC) trend methods to meteorological parameters of Marmara Basin in Turkey

Scientific studies for countries experiencing climate change have accelerated recently. Turkey is among the countries experiencing climate change. Studies carried out for this country or its basins are of great importance both for the basin and the country. Therefore, Marmara Basin, where Turkey's population density is highest and industrialization is at the most advanced level, is selected in this study. The length of meteorological data is 45 years. The analysis of these data is made with Mann-Kendall (MK) and Spearman Rho (SR), which are classical trend methods, and Innovative Triangle Trend Analysis (ITTA) and Trend Polygon Star Concept (TPSC), which are modern trend methods. When the studies on Marmara basin are examined, there is no study that includes these four methods. Therefore, this study stands out in this respect. Because these analysis methods will reveal a new idea by applying them to hydro-meteorological data within the basin area. Before applying MK method, serial correlation method is applied to the meteorological parameter data of all stations. When the analysis results are examined; a general increasing trend is observed in precipitation data for ITTA and TPSC methods. No trend is observed in precipitation data for MK and SR methods. A general increasing trend is observed in temperature data for ITTA, MK and SR methods. However, a generally decreasing trend is observed in temperature data for TPSC method. While there is generally no trend in relative humidity data for MK and SR methods, an increasing trend is observed for ITTA and TPSC methods.