Relative Sunshine Duration Research Articles

Daily sunshine grids for Austria since 1961 – combining station and satellite observations for a multi-decadal climate-monitoring dataset

Grid datasets of sunshine duration at high spatial resolution and extending over many decades are required for quantitative applications in regional climatology and environmental change (e.g., modelling of droughts and snow/ice covers, evaluation of clouds in numerical models, mapping of solar energy potentials). We present a new gridded dataset of relative (and derived absolute) sunshine duration for Austria at a grid spacing of 1 km, extending back until 1961 at daily time resolution. Challenges in the dataset construction were consistency issues in the available station data, the scarcity of long time series, and the high variation of cloudiness in the study region. The challenges were addressed by special efforts to correct evident breaks in the station series and by adopting an analysis method, which combines station data with satellite data. The methodology merges the data sources non-contemporaneously, using statistical patterns distilled over a short period, which allowed involving satellite data even for the early part of the study period. The resulting fields contain plausible mesoscale structures, which could not be resolved by the station network alone. On average, the analyses explain 47% of the spatial variance in daily sunshine duration at the stations. Evaluation revealed a slight systematic underestimation (− 1.5%) and a mean absolute error of 9.2%. The average error is larger during winter, at high altitudes, and around the 1990s. The dataset exhibits a conditional bias, which can lead to considerable systematic errors (up to 15%) when calculating sunshine-related climate indices.

Radiative Regime According to the New RAD-MSU(BSRN) Complex in Moscow: The Roles of Aerosol, Surface Albedo, and Sunshine Duration

The radiative budget is one of the key factors that influences climate change. The aim of this study was to analyze the radiative regime in Moscow using the RAD-MSU(BSRN) complex and to estimate the radiative effects of the main geophysical factors during the 2021–2023 period. This complex is equipped and maintained according to the recommendations of the Baseline Surface Radiation Network; however, it is not a part of this network. In cloudless conditions, the decrease in global shortwave irradiance (Q) is about 18–22% due to the aerosol content with a pronounced change in the direct to diffuse ratio. In winter, the increase in Q is about 45 W/m2 (or 9%) at h = 30° due to a high surface albedo and reduced aerosol and water vapor contents, while the net shortwave irradiance (Bsh) demonstrates a significant decrease due to the prevailing effects of snow albedo. In cloudy conditions, a nonlinear dependence of Q and Bsh cloud transmittance on the relative sunshine duration is observed. The mean changes in Q for the 2021–2023 against the 1955–2020 period are characterized by negative anomalies (−22%) in winter and positive anomalies in summer (+3%) due to the changes in cloudiness. This is in line with the global tendencies in the long-term changes in shortwave irradiance in moderate climates in Europe in recent years.

INSOLATION AT THE EARTH'S SURFACE

The (variable) transparency of our atmosphere regulates the amount of solar energy reaching the ground. Also known as clearness index or transmissivity, it has been related linearly to relative sunshine hours ever since the seminal work of Angstr5m, whose regression model was Iater modified and generalized, to become a classic too! in the statistical assessment of the global radiation at the surface of the Earth. In this paper we submit a physico­meteorological rationale for the polynomial regression between clearness index and relative sunshine duration.

Tracing climatic and human disturbance in diverse vegetation zones in China: Over 20 years of NDVI observations

Over the past two decades (2000–2021), China has experienced a significant increase in vegetation cover. However, the intricate spatial and temporal variations in vegetation cover, arising from the interaction of climate change and human activities, remain enigmatic, particularly across diverse vegetation zones. This research endeavors to unravel the dynamics of vegetation cover using the MOD13A1 Normalized Difference Vegetation Index (NDVI) dataset, delving into China's seven distinct vegetation zones (VZ1-VZ7). This research employs residual analysis, relative analysis, and geo-detector techniques to investigate the drivers of NDVI variation and spatial heterogeneity across China's seven distinct vegetation zones. The findings reveal a compelling upward trajectory in NDVI, predicted NDVI, and residual NDVI across China during the 2000–2021 period. Notably, significant and extremely significant increases are discernible in approximately 53.90%, 50.13%, and 23.29% of China's NDVI, respectively. Among the vegetation zones, VZ3 displays the highest change rates, yet the proportion with a significant increase in VZ1 surpasses other zones. The relative analysis underscores climate change as the predominant catalyst for vegetation enhancement, although an exception is noted in VZ3. Furthermore, the spatial heterogeneity of China's NDVI is primarily influenced by the interactions between land use types and relative humidity, sunshine duration, and precipitation, especially the interaction between relative humidity and land use types. Significant changes in climate can lead to marked alterations in the structure and function of terrestrial ecosystems, while changes in land use patterns at the regional level also alter the type of land cover, which together exert a significant impact on the vegetation NDVI. These results accentuate the importance of formulating tailored ecosystem management strategies tailored to the distinct characteristics of each vegetation zone to ensure the sustainable development of terrestrial ecosystems across China.

A New Insight for Daily Solar Radiation Prediction by Meteorological Data Using an Advanced Artificial Intelligence Algorithm: Deep Extreme Learning Machine Integrated with Variational Mode Decomposition Technique

Reliable and precise estimation of solar energy as one of the green, clean, renewable and inexhaustible types of energies can play a vital role in energy management, especially in developing countries. Also, solar energy has less impact on the earth’s atmosphere and environment and can help to lessen the negative effects of climate change by lowering the level of emissions of greenhouse gas. This study developed thirteen different artificial intelligence models, including multivariate adaptive regression splines (MARS), extreme learning machine (ELM), Kernel extreme learning machine (KELM), online sequential extreme learning machine (OSELM), optimally pruned extreme learning machine (OPELM), outlier robust extreme learning machine (ORELM), deep extreme learning machine (DELM), and their versions combined with variational mode decomposition (VMD) as integrated models (VMD-DELM, VMD-ORELM, VMD-OPELM, VMD-OSELM, VMD-KELM, and VMD-ELM), for solar radiation estimation in Kurdistan region, Iraq. The daily meteorological data from 2017 to 2018 were used to implement suggested artificial models at Darbandikhan and Dukan stations, Iraq. The input parameters included daily data for maximum temperature (MAXTEMP), minimum temperature (MINTEMP), maximum relative humidity (MAXRH), minimum relative humidity (MINRH), sunshine duration (SUNDUR), wind speed (WINSPD), evaporation (EVAP), and cloud cover (CLOUDCOV). The results show that the proposed VMD-DELM algorithm considerably enhanced the simulation accuracy of standalone models’ daily solar radiation prediction, with average improvement in terms of RMSE of 13.3%, 20.36%, 25.1%, 27.1%, 34.17%, 38.64%, and 48.25% for Darbandikhan station and 5.22%, 10.01%, 10.26%, 21.01%, 29.7%, 35.8%, and 40.33% for Dukan station, respectively. The outcomes of this study reveal that the VMD-DELM two-stage model performed superiorly to the other approaches in predicting daily solar radiation by considering climatic predictors at both stations.

Investigation of the effect of climate change on reference evapotranspiration in Tekirdag, Turkiye

It is important to determine the impact of global climate change on water resources. In the effective use of water resources, the amount of water required for agricultural irrigation should be planned under these conditions. In this study, the trend analysis of the climate data required for the calculation of reference evapotranspiration (ET0) between 2011 and 2020 for the meteorology station of Tekirdag, Turkey was carried out. Analysis of change in the study was carried out according to the methods of Linear Regression Test, Mann-Kendall Test and Sen T Test. As a result of the change analysis, it was determined that the reference evapotranspiration (ET0) values had a decreasing trend in June and July and this decrease was statistically significant. As a result of the analysis of the changes in the climate parameters used in the calculation of reference evapotranspiration (ET0), it was determined that the values of maximum temperature, average relative humidity and average sunshine duration were in a decreasing trend, while minimum temperature and wind speed values were in an increasing trend. In the study, the change in monthly precipitation amounts during the measurement periods was also examined. As a result of the analysis of the changes, it was determined that the monthly precipitation amounts have a decreasing trend and the changes especially in April, September, October and December are statistically significant. It is thought that the results obtained will contribute to the future agricultural production planning in the regional conditions.

Geographical Distribution of Global Radiation and Sunshine Duration over the Island of Cyprus

In this work, hourly measurements of global solar irradiances obtained from pyranometers and sunshine duration data using either Kipp & Zonen CSD3 automatic sensors or Campbell–Stokes sunshine recorders were assessed through an extensive quality control procedure and statistical analysis on the measured and derived solar parameters for all the actinometric stations installed in various locations over the island of Cyprus, covering mainly the period 2019–2021. This information is useful for engineers concerning the solar energy capture systems and energy efficiency who can therefore take knowledge of the local radiation levels. Monthly mean hourly values of global radiation and sunshine duration are calculated and shown through isoline diagrams. During June or July, daily global irradiations ranged between 25 MJ/m2 and 30 MJ/m2, with the lowest occurring in the mountainous locations. On the other hand, in January or December, they ranged between 6.5 MJ/m2 and 10.5 MJ/m2. The total annual number of hours of sunshine duration ranged between 2500 and 3500, with the lowest values recorded at the mountainous sites. The clearness index and relative sunshine duration were used for the classification of the weather conditions over the island. Furthermore, the interrelationships between the said indices were used for the estimation of global radiation. This work has specifically contributed to the characterization and analysis of hourly and daily solar global radiation. Furthermore, the measurements on the ground level could be compared with satellite observations in order to improve the geographical distribution of global radiation, especially in areas where no measurements exist. The analysis could be also extended for the other shortwave radiation components (Direct, Diffuse and Photosynthetic Active Radiation (PAR)) in order to assess the solar radiation regime over the island.

Intelligent optimization of Reference Evapotranspiration (ETo) for precision irrigation

Reference Evapotranspiration (ETo) is the basis for the conservation of irrigation water for sustainable development in agriculture. The FAO-56 Penman-Monteith (FAO-56 PM) is the standard method of ETo determination that is complex due to the involvement of many parameters. Many machine learning approaches were proposed to simplify ETo determination with limited parameters. The existing machine learning-based solutions rely on a fixed number of limited parameters for the determination of ETo. The study proposed a solution to determine the raw ETo with only temperature and optional optimization of the raw ETo according to the availability of Relative Humidity (RH), Wind Speed (WS), and sunshine duration ratio (n/N). The study proposed an ensembled Artificial Neural Network (ANN) model (Model-1) to determine ETo according to available minimal environmental parameters. The environmental conditions from the year 2001–2021 of Pakistan are used to develop a dataset with the FAO-56 PM method. The unique attribute of the proposed solutions is the flexibility in the use of environmental parameters for ETo determination according to the FAO-56 PM method. The performance of the proposed ensembled model (Model-1) is compared against the individual ANN model with all parameters (Model-2) and with the individual ANN model with only temperature (Model-3). The predictions by the proposed solution from Model-1 are also compared against the FAO-56 PM method. It is observed that the proposed Model-1 is more accurate with 91.44% accuracy, as compared to Model-2 and Model-3. The predictions by the proposed Model-1 are more correlated with the ETo by FAO-56 PM with a Pearson correlation of 0.996 as compared to the ETo predictions by Model-2 and Model-3.

Modeling of diffuse solar radiation exergy in Turkey: Comparison of diffuse fraction and diffusion coefficient models

AbstractIn this study, 19 regression models were established for the prediction of exergy value of monthly average diffuse solar radiation with a single or multiple input predictor (Relative sunshine duration, clearness index, relative humidity, and air temperature). Five model categories were defined depending on the diffuse fraction and diffusion coefficient. The most frequently used statistical indicators were used to determine the performance of the empirical models. The global performance indicator (GPI) method was used to determine the accuracy ranking of the models. Consequently, the results show a good agreement with the values ​​estimated from the regression models and the measurement data. The regression models established in this study can be used with great accuracy to evaluate the monthly average diffuse solar radiation exergy in the regions under consideration or in similar climatic conditions.

Spatiotemporal variations in meteorological influences on ambient ozone in China: A machine learning approach

Considering the increase in ambient ozone (O3) levels with harmful health effects, this study aims to evaluate the spatiotemporal variations in meteorological influences on the daily maximum 8-h average O3 concentrations ([O3]MDA8) across China. Leveraging the high capacity of the random forest in simulating complicated relationships between the predictor variables and [O3]MDA8, we proposed a new method (named LVIG) to derive local variable importance from the global model (i.e., the random forest) for specific locations and months. On the basis of the LVIG results, the [O3]MDA8 in the northern China was more associated with the evaporation and temperature, while the [O3]MDA8 in the southern China was more associated with the relative humidity and sunshine duration. For the whole China, relative humidity was more influential during April to August, while evaporation, temperature and sunshine duration exhibited higher importance from November to February. The varying patterns of the meteorological influences could be explained by the Liebig law of the minimum, i.e., the limiting factors were the driving factors. Compared to the method of building multiple (geographically weighted) local models, the LVIG method gave more stable and specific estimates of local variable importance. As a generic method, the LVIG would be potentially applied in a wide range of fields.

Feasibility analysis of coexistence between plantation and tower concentrating solar system

There are large-scale thermal utilization needs in rural and urban fringes in China that can provide many employment opportunities. However, due to the limited use of small and medium-sized coal-fired boilers, thermal energy is actually in short supply. If embedding these spaces including plantations with solar heliostat fields, a large number of nodes for cheap thermal energy supply can be obtained through solar energy, which can create more small-scale industrial and commercial employment opportunities near user side. However, to ensure the crop harvest, plantations are often prohibited from being used for other purposes. In order to verify the idea of coexistence between tower concentrating solar system with sparse heliostat field and plantation, the clean-sky model was used to analyze the shading rate of the heliostat to the ground in each month and the proportion of the area with a relative sunshine duration of less than 80% for the heliostat fields at low, middle, and high latitude areas, and further gave the correction of the above analysis results using typical meteorological year conditions. The results show that heliostats in mid latitude area have obvious adverse effects on overwintering crops. In high latitude area, although the heliostats are more severely blocked in the cold season, there is no planting activity in this season, so the impact on planting can be ignored. The shade ratio of the heliostats to the ground in low, middle and high latitude regions is tolerable in the months suitable for planting, and shade-tolerant crops can also be interplanted in the shaded area to enrich the variety structure. Therefore, by embedding the tower concentrating solar system with a sparse heliostat field, the plantation can meet the two functions of planting and thermal energy supply at the same time to further increase its income.

Projected changes in mild weather frequency over China under a warmer climate

Previous studies have largely focussed on changes in mean climate state and climate extremes under a warmer climate, and little is known about changes in mild weather, which is a positive and pleasant condition and is highly related to human outdoor activities. Although changes in observed mild weather frequency over China, and their drivers, have been revealed, the understanding of how mild weather evolves with projected warming is still limited. Here, we examine future changes in mild weather frequency over China based on comprehensive thermal comfort indices, and dynamically downscaled climate projections produced by the Regional Climate Model version 4 within the framework of the Coordinated Regional Climate Downscaling Experiment—Coordinated Output for Regional Evaluations. We demonstrate that changes in mild weather frequency in a warmer future exhibit remarkable regional discrepancy. In particular, densely populated southeastern China will experience a robust decrease in mild weather relative to the current level, although a general increasing trend is observed in this area over recent decades. On a seasonal scale, the decrease in mild weather in summer overwhelms the increase in spring and autumn, and this is more prominent in warmer regions. Regarding the drivers, it is suggested that changes in mild weather frequency are dominated by elevated temperatures, with little contribution from relative humidity, wind speed, and sunshine duration.

Comparing three types of data-driven models for monthly evapotranspiration prediction under heterogeneous climatic conditions

Evapotranspiration is one of the most important hydro-climatological components which directly affects agricultural productions. Therefore, its forecasting is critical for water managers and irrigation planners. In this study, adaptive neuro-fuzzy inference system (ANFIS) model has been hybridized by differential evolution (DE) optimization algorithm as a novel approach to forecast monthly reference evapotranspiration (ET0). Furthermore, this model has been compared with the classic stochastic time series model. For this, the ET0 rates were calculated on a monthly scale during 1995–2018, based on FAO-56 Penman–Monteith equation and meteorological data including minimum air temperature, maximum air temperature, mean air temperature, minimum relative humidity, maximum relative humidity & sunshine duration. The investigation was performed on 6 stations in different climates of Iran, including Bandar Anzali & Ramsar (per-humid), Gharakhil (sub-humid), Shiraz (semi-arid), Ahwaz (arid), and Yazd (extra-arid). The models’ performances were evaluated by the criteria percent bias (PB), root mean squared error (RMSE), normalized RMSE (NRMSE), and Nash-Sutcliff (NS) coefficient. Surveys confirm the high capability of the hybrid ANFIS-DE model in monthly ET0 forecasting; so that the DE algorithm was able to improve the accuracy of ANFIS, by 16% on average. Seasonal autoregressive integrated moving average (SARIMA) was the most suitable pattern among the time series stochastic models and superior to its competitors, ANFIS and ANFIS-DE. Consequently, the SARIMA was suggested more appropriate for monthly ET0 forecasting in all the climates, due to its simplicity and parsimony. Comparison between the different climates confirmed that the climate type significantly affects the forecasting accuracies: it’s revealed that all the models work better in extra-arid, arid and semi-arid climates, than the humid and per-humid areas.

Multilayer perceptron neural network based models for prediction of the rainfall and reference crop evapotranspiration for sub-humid climate of Dapoli, Ratnagiri District, India

Rainfall and reference crop evapotranspiration (ETo) are two important climatic parameters pertaining to agriculture production. Rainfall displays variability whereas reference crop evapotranspiration is nonlinear dynamic and complex phenomena. Reference crop evapotranspiration is an important component in calculation of crop water requirements, climatological and hydrological studies. In the present study, eight independent models are developed by using widely employed artificial intelligence (AI) formalism, namely, multilayer perceptron-artificial neural network (MLP-ANN). The proposed models predict two important weather parameters, viz. rainfall and reference crop evapotranspiration (ETo) using meteorological data of 34 calendar years (1983–2016) at Dapoli, District Ratnagiri, Maharashtra, India. The input space of the models consists of six climatic variables, namely, maximum temperature (Tmax), minimum temperature (Tmin), maximum relative humidity (RHmax), minimum relative humidity (RHmin), wind speed (WS), and sunshine duration (SSD). The high correlation coefficient (CC) values (0.900 to 0.957), and low root mean square error (RMSE) values (0.543 to 22.057) for both training along with test data set indicate very good prediction accuracy and generalization performance of the proposed models. Significant features of this study are (i) it is first instance where in MLP-ANN based models are developed for the rainfall and ETo prediction for meteorological data at Dapoli, Dist. Ratnagiri, Maharashtra, India. (ii) MLP-ANN modeling formalism is found to be an efficient tool for ETo prediction, and the developed models can be gainfully utilized for the further prediction of similar weather parameters in future.

Spatiotemporal Variations in Reference Evapotranspiration and Its Contributing Climatic Variables at Various Spatial Scales across China for 1984–2019

Reference evapotranspiration (ET0) is of great significance in studies of hydrological cycle, agricultural water resources, and hydrometeorology. The present study collected daily meteorological data at 536 meteorological stations in China from 1984 to 2019, calculated daily ET0 using the FAO Penman–Monteith equation, analyzed the spatial distribution and temporal variation characteristics of ET0 and meteorological variables at four different spatial scales (continental, regional, provincial, and local), and discussed the sensitivity of ET0 to the meteorological variables and the contribution rates of the meteorological variables to the ET0 variations. The results showed that ET0 increased at 406 out of the 536 stations (75.7%), with the trends being significant at 65 stations at the 5% significance level, and 147 at the 1% significance level. The slope calculated using Sen’s method and linear trend method showed that the annual ET0 at the continental scale increased by approximately 12 mm/decade. Most of the stations showed decreasing trends in relative humidity (Hm), sunshine duration (SD), and wind speed at 2 m height (U2) while increasing trends in the maximum air temperature (Tmax) and minimum air temperature (Tmin). ET0 was most sensitive to Hm (sensitivity coefficient, St = −0.66), followed by Tmax (St = 0.29), SD (St = 0.18), U2 (St = 0.16), and Tmin (St = 0.07). Most of the stations showed increasing trends in St for Hm (56.16%), Tmax (72.95%), Tmin (87.31%), and U2 (90.49%), and decreasing trends for SD (69.78%). The variations in Hm, Tmax, and Tmin increased the ET0 at most of the stations (82.28%, 98.13%, and 69.03%, respectively). The variations in SD and U2 decreased ET0 at most of the stations (66.04% and 56.34%, respectively). Some ET0 characteristics in a few regions can be well described using a single spatial scale. However, most regions exhibited significantly different ET0 characteristics across spatial scales. The results of this project can provide reference for hydrological analysis and agricultural water management under climate change conditions and provide data and information for other hydrology-related applications.

Reconstruction and variability of high daily erythemal ultraviolet doses and relationship with total ozone, cloud cover, and albedo in Novi Sad (Serbia)

AbstractIn this paper, a method previously developed for reconstructing daily erythemal ultraviolet (UVery) doses in Novi Sad (Serbia) was improved and used for estimating daily UVery over the period 1971–2018. The proposed new method uses reanalysed total ozone column (TOC) data and snow cover as an input and is based on the empirical relationship between relative sunshine duration and relative UVery doses. Introducing reanalysed TOC data and snow cover data significantly improved the quality and accuracy of the reconstructed UVery time series, particularly in the winter season. The reconstructed time series revealed the statistically significant increase in summer and winter UVery in the 1980s and the first half of the 1990s (+8.78% and + 13.14% per decade, respectively), which was linked to the statistically significant decline in TOC (−4.13 and −7.36% per decade, respectively). The study focuses on days with high erythemal UV dose (highUVery), which is defined based on the 90th percentile of the daily UVery for each month. Moreover, the influence of low TOC, low cloud cover conditions, and high surface albedo on highUVery was analysed on a seasonal basis. The results of this study show that highUVery days increased from the 1980s, particularly strongly in the period of TOC depletion. A fraction of highUVery days in the last two decades was large (12.29%), which is higher than in the previous periods (6.97 and 8.68% in the period before and during TOC depletion, respectively). The occurrence of highUVery days was influenced mostly by low cloud cover (89.71% of days), and low TOC (82.23% of days). In the winter season, high surface albedo influenced the appearance of 19.18% of highUVery days. However, overall the interaction of low cloud cover and low TOC in all seasons most often led to the appearance of highUVery days.

Responding Mechanism of Vegetation Cover to Climate Change and Human Activities in Southwest China from 2000 to 2020

Studying vegetation cover variation and its responding mechanism to climate change and human activities is of great significance for regional ecological protection and vegetation restoration. In this study, on the basis of MODIS NDVI, in situ climate data, and land use type data using Theil-Sen Median analysis, the Mann-Kendall significance test, residual analysis, partial correlation analysis, and multi-correlation analysis, the spatial and temporal variation in vegetation cover and its response to climate change and the land use/land cover change in each geomorphological unit in southwest China were analyzed. The vegetation cover showed a fluctuant increasing trend, and the changing trend exhibited obvious spatial heterogeneity, with the increasing rate being higher in the southeast and lower in the northwest of southwest China from 2000 to 2020. The vegetation variation was dominated by positive effects of the climate change and human activities in southwest China, and the positive effects were stronger in Guangxi Hill than those in other geomorphological units. Furthermore, from 2000 to 2020 the vegetation cover was positively associated with precipitation and temperature and negatively correlated with relative humidity and sunshine duration in southwest China. Temperature was considered to be the dominate climate factor controlling the vegetation variation in the study area. Urban expansion had decreased the region vegetation cover, but the overall vegetation cover had increased in southwest China due to the suitable regional climate conditions and the implementation of ecological reforestation projection. These results can provide scientific references for ecological protection and economic sustainable development in southwest China.

Correlation between mumps and meteorological factors in Xiamen City, China: A modelling study.

ObjectiveMumps is a seasonal infectious disease, always occurring in winter and spring. In this study, we aim to analyze its epidemiological characteristics, transmissibility, and its correlation with meteorological variables.MethodA seasonal Susceptible–Exposed–Infectious/Asymptomatic–Recovered model and a next-generation matrix method were applied to estimate the time-dependent reproduction number (Rt).ResultsThe seasonal double peak of annual incidence was mainly in May to July and November to December. There was high transmission at the median of Rt = 1.091 (ranged: 0 to 4.393). Rt was seasonally distributed mainly from February to April and from September to November. Correlations were found between temperature (Pearson correlation coefficient [r] ranged: from 0.101 to 0.115), average relative humidity (r = 0.070), average local pressure (r = -0.066), and the number of new cases. In addition, average local pressure (r = 0.188), average wind speed (r = 0.111), air temperature (r ranged: -0.128 to -0.150), average relative humidity (r = -0.203) and sunshine duration (r = -0.075) were all correlated with Rt.ConclusionA relatively high level of transmissibility has been found in Xiamen City, leading to a continuous epidemic of mumps. Meteorological factors, especially air temperature and relative humidity, may be more closely associated with mumps than other factors.

Estimating relative sunshine duration from commonly available meteorological variables for simulating biome distribution in the Carpathian Region

Bright sunshine duration (BSD) data are required for simulating biomes using process-based vegetation models. However, monthly global paleoclimate datasets that can be used in paleo data–model comparisons do not necessarily contain BSD or radiation data. Considering the theoretical and practical aspects, the scheme of Yin, X. (1999) is here recommended to estimate monthly time series of relative BSD using only monthly climate and location data. As a case study for the Carpathian Region, the efficiency of both the original and a variant of that scheme is analysed in this paper. The alternative scheme has high applicability in paleoenvironmental studies. Comparison of the estimated and observed BSD data shows that from May to August, the value of relative root mean squared error in more than 90 percent of the study area does not exceed the threshold of 20 percent, indicating an excellent performance of the original estimation scheme. It is also found that though the magnitude of overestimation for the alternative algorithm is significant in the winter period, the proposed method performs similarly well in the growing season as the original. Furthermore, concerning modelling the distribution of biomes, simulation experiments are performed to assess the effects of modifying some configuration settings: (a) the generation of relative BSD data, and (b) the algorithm used to create quasi-daily weather data from the monthly values. Under both the recent humidity conditions of the study region and the spatial resolution of the climate dataset used, the results can be considered sufficiently robust, regardless of the configuration settings tested. Thus, using monthly temperature and precipitation climatologies, the spatial distribution of biomes can be properly simulated with the configuration settings proposed here.

Empirical Relationships Between Global and Diffuse Radiation and Sunshine Duration in Chad: Polynomial Regression Approach

Chad, in regards to its geographical position is considered as one of the countries in the world that receives an important annual solar radiation over its entire territory. The average sunshine duration is about 10 hours in the North, 8 hours in the Center and 7 hours in the South. This can be used to quantify the solar radiation over the whole territory and evaluate the solar potential. In this paper, some new correlations between sunshine duration and solar radiation have proposed in four cities (Abeche, Bitkine, Mongo and Ndjamena) located in the Sahelian climate in order to have tools for quantifying all components of solar radiation. The polynomial regression model approach is used and their parameters are estimated by the ordinary least squares method. A statistical evaluation was carried out to compare the different correlations obtained. The results show that the third order polynomial approach is the most appropriate for an empirical relationship between global, diffuse and relative sunshine duration in each of the cities. Considering the long dry seasons characterized by high temperatures in this region, the consideration of the temperature variable in the functional form could improve the quality and accuracy of the models in their estimation.