Average Monthly Mean Research Articles

Assessing Climate Variability and Its Impacts on Selected Forest Ecosystems in Taraba State: Insights from the Mann-Kendall Trend Analysis of Rainfall and Temperature

This study examines climate variability and its effects on selected forest ecosystems in Taraba State, Nigeria, using Mann-Kendall trend analysis of rainfall and temperature data from 1991 to 2021. Climatic variables, including average monthly rainfall and mean minimum and maximum temperatures, were sourced from the Upper Benue River Basin Development Authority. The Mann-Kendall test was applied to assess trends, revealing slight upward trends in annual rainfall and maximum temperatures, and a subtle downward trend in minimum temperatures, although none were statistically significant. Hypothesis testing was conducted, with results suggesting no significant long-term trends in annual rainfall, maximum temperatures, or minimum temperatures over the study period. Specifically, for annual rainfall, the Mann-Kendall slope was 0.03 with a p-value of 0.96, indicating no significant trend. Similarly, for minimum and maximum temperatures, the Mann-Kendall slopes were -0.01 and 0.01, respectively, with p-values of 0.99, indicating no significant trends. It was recommended that strengthening monitoring efforts to track long-term climate trends accurately, utilizing advanced statistical techniques to discern meaningful patterns from natural variability among other recommendations.

Time Series Simulation and Forecasting of Air Quality Using In-situ and Satellite-Based Observations Over an Urban Region

Air quality is directly associated with the health of society. So, it becomes essential to forecast air pollution, which assumes an imperative part in air pollution warnings and control. A time-series simulation approach was adapted for the forecasting of monthly mean ambient air pollutants (PM2.5, O3, NO2) concentration and Aerosol Optical Depth (AOD) at an urban traffic site (Mathura Road, CSIR-CRRI) in New Delhi, India. Satellite-based aerosol loading (AOD550) retrieved from the Terra MODIS (Collection 6) enhanced Deep Blue (DB) algorithm was used for further analysis. The analysis considered the average monthly mean concentration of air pollutants and AOD between 2012-2017 and, simulates the concentrations of PM2.5, O3, NO2, and AOD for the same period and then forecasts air quality for the years 2020-2023. The forecasted results were validated with 24 months of in-situ and satellite data from 2018-to and 2019. In the year 2020, observed and simulated results are in lower agreement due to the shutdown of anthropogenic activities to combat pandemic situations. Otherwise, modeled and forecasted results are in good harmony with the in-situ and satellite observations. The results also signify that the time series Autoregressive Integrated Moving Average (ARIMA) modeling approach can be an effective and simple tool for air pollution simulation and future forecast. The results are evocative concerning the forecast of near future aerosol loading information and will also be profound to address the problems.

Analysis of the annual pollen integral in Albuquerque, New Mexico, shows a negative trend with temperatures for Juniper, Cottonwood, Elm, and Mulberry

The goal of this study is to determine if the annual pollen integral (APIn) for the top tree allergens in the City of Albuquerque is correlated with meteorological variables. This analysis would be the first of its kind for this area. We used 17 consecutive years from 2004 to 2020 and data collected by the city of Albuquerque using a Spore Trap (Burkard) volumetric air sampler in a location designed to represent a typical desert environment. The pollen studied include Juniper, Elm, Ash, Cottonwood, and Mulberry. We found a negative linear correlation with early summer temperatures of the previous year and APIn for Elm, Cottonwood, and Mulberry, and early fall temperatures for Juniper. Linear regression models developed for Elm, Cottonwood, and Mulberry used the monthly mean maximum temperature for the month of June of the prior year as the independent variable to yield a R squared statistic (R2) of 0.88, 0.91 and 0.78, respectively. For Juniper, the average monthly mean minimum temperature for the previous September and October served as the independent variable and yielded the R2 value of 0.80. We also observed a positive trend for the annual maximum temperature over time and a negative trend for the total APIn. Summers in New Mexico are hot and dry, and they may be getting hotter and drier because of climate change. Our analysis predicts that climate change in this area may lead to reduced allergies if temperatures continue to increase and if precipitation patterns remain the same.

Predicting the Effects of Land Use Land Cover and Climate Change on Munneru River Basin Using CA-Markov and Soil and Water Assessment Tool

It is important to understand how changing climate and Land Use Land Cover (LULC) will impact future spatio-temporal water availability across the Munneru river basin as it aids in effective water management and adaptation strategies. The Munneru river basin is one of the important sub-basins of the Krishna River in India. In this paper, the combined impact of LULC and Climate Change (CC) on Munneru water resources using the Soil and Water Assessment Tool (SWAT) is presented. The SWAT model is calibrated and validated for the period 1983–2017 in SWAT-CUP using the SUFI2 algorithm. The correlation coefficient between observed and simulated streamflow is calculated to be 0.92. The top five ranked Regional Climate Models (RCMs) are ensembled at each grid using the Reliable Ensemble Averaging (REA) approach. Predicted LULC maps for the years 2030, 2050 and 2080 using the CA-Markov model revealed increases in built-up and kharif crop areas and decreases in barren lands. The average monthly streamflows are simulated for the baseline period (1983–2005) and for three future periods, namely the near future (2021–2039), mid future (2040–2069) and far future (2070–2099) under Representation Concentration Pathway (RCP) 4.5 and 8.5 climate change scenarios. Streamflows increase in three future periods when only CC and the combined effect of CC and LULC are considered under RCP 4.5 and RCP 8.5 scenarios. When compared to the CC impact in the RCP 4.5 scenario, the percentage increase in average monthly mean streamflow (July–November) with the combined impact of CC and LULC is 33.9% (near future), 35.8% (mid future), and 45.3% (far future). Similarly, RCP 8.5 increases streamflow by 33.8% (near future), 36.5% (mid future), and 38.8% (far future) when compared to the combined impact of CC and LULC with only CC. When the combined impact of CC and LULC is considered, water balance components such as surface runoff and evapotranspiration increase while aquifer recharge decreases in both scenarios over the three future periods. The findings of this study can be used to plan and develop integrated water management strategies for the basin with projected LULC under climate change scenarios. This methodology can be applied to other basins in similar physiographic regions.

Determinants of Willingness to pay for Employment-Based Health Insurance Among Governmental School Workers in Saudi Arabia.

School workers constitute the bulk of public workers in Saudi Arabia. Most of these workers seek public health care services through the Ministry of Health, which is characterised by an overloaded service. Consequently, the government is planning to introduce employment-based health insurance. However, government employees’ willingness to pay (WTP) for health insurance was not investigated. This study explores the feasibility and acceptability of employment-based health insurance by examining public school workers’ WTP. Methods: six hundred and twenty-second number participants from Riyadh city were interviewed from September to October 2020 using an online structured questionnaire. The contingent valuation method with a payment card was used to assess WTP among the participants. This study also determined the association between the willingness to participate and WTP for health insurance respondents’ demographic and socio-economic characteristics. Chi-square and multiple linear regression analyses were used to analyse the data. The majority (76%) with an average monthly mean of 77.9 Saudi Riyal (SAR) ($20.7) per capita. Male, had higher educated, and those diagnosed with chronic disease were more likely to report a willingness to participate and/or pay for health insurance (P > .005). This study demonstrates that WTP for employment-based health insurance depends on workers’ characteristics. The results of this study may be of use to policymakers to help with a set insurance premium, priority setting and fund allocation.

Through space and time: Predicting numbers of an eruptive pine tree pest and its predator under changing climate conditions

Forest biotic populations and communities are undergoing irreversible changes due to climatic perturbations, and these effects may be exacerbated by insect pests. In the southeastern United States (U.S.), Dendroctonus frontalis is one of the most important forest pests that can cause substantial tree mortality over large geographic areas. As life-history of D. frontalis is dependent on environmental conditions, our objective was to determine the effects of climatic variables on the species occurrence of D. frontalis and their main predator, Thanasimus dubius. U.S. Department of Agriculture - Forest Service beetle trapping data (proxy for true abundance) for 2014–2018 were assessed with nine climatic variables including: (1) average monthly minimum, maximum, and mean temperatures; (2) mean winter temperature; (3) minimum, maximum and mean annual temperature; and (4) cumulative winter and annual precipitation. Variable selection was performed using least absolute selection and shrinkage operator (LASSO), and selected variables were fit to varying coefficient generalized additive models (GAMs). Minimum temperatures for February and March and maximum temperatures for January and prior year August were found to have a significant effect on D. frontalis numbers, and model accuracy was ~89%. Beetle suitability was assessed for 1981–2100 under three different shared socioeconomic pathways (SSP). The model indicated that the suitability of D. frontalis has increased from 1981 to 2019 in certain regions of Alabama, Mississippi, and Georgia, while Arkansas, Kentucky, Louisiana, North Carolina, Tennessee, East Texas, and Virginia had low suitability. Future projections indicated comparable D. frontalis numbers to current numbers under the lowest SSP of 2.1 °C by year 2100, but D. frontalis may decrease under higher warming projections. The same climatic predictors influenced T. dubius, except average September maximum temperature replaced August maximum temperatures in the model. Understanding how these abiotic factors influence forest pest populations may better guide prevention and management practices aimed at identifying high risk areas and reducing pest outbreaks.

Increasing access to care for rural veterans through intensive hands‐on‐training of advanced practice pharmacists

AbstractIntroductionThe “Increasing Access to Care for Rural Veterans by leveraging Clinical Pharmacy Specialist (CPS) Providers,” known as the CPS Rural Veteran Access Initiative (CRVA), focused on providing access to comprehensive medication management (CMM) for rural Veterans in the areas of primary care, mental health, and pain management. Clinical pharmacy boot camps trained CPS to accomplish this goal.ObjectivesThe objectives of the boot camps were to address training needs ensuring consistency and quality for the provision of CMM for the CRVA CPS to increase access to care.MethodsA needs assessment survey was administered to determine curriculum content. Participants completed pre‐ and post‐boot camp knowledge assessments for the topics taught in the curriculum. An analysis was performed comparing the results before and after the training program for participants completing one pre‐ and one post‐boot camp survey.ResultsThere were 87 primary care boot camp attendees and 52 mental health and pain attendees included in the analysis. For primary care, 13 courses (59%) saw ≥50% growth in the number of participants deeming themselves to have a working knowledge or better from pre to post boot camp. For mental health/pain, seven courses (29%) saw ≥50% growth in the number of participants deeming themselves to have a working knowledge or better from pre to post. Direct patient care encounter tracking 6 months pre‐ and post‐training showed an average monthly mean encounter total increase for the primary care group from 134.4 pre‐training to 146.1 post‐training (P = .08) and for the mental health/pain group from 124.5 pre‐training to 136.1 post‐training (P = .32).ConclusionBased on the results, this program was successful in accomplishing its goals of improving the participants' knowledge, especially in areas lacking practice experience. These results were found across participants in both programs. Successes seen in these programs are now serving as models for CPS training in the VA for the future.

Is Aerated Soft Drink and Packaged Juice Consumption an Independent Risk Factor to Cause Non-Alcoholic Fatty Liver Disease

BACKGROUND:
 There has been an increase in consumption of aerated sweetened soft drinks and packaged juices high in carbohydrates/fructose by humans across the globe. There have been various studies with contradictory inferences of association of chronic sweetened soft drink (rich in high fructose corn syrup) intake and NAFLD. This study was undertaken with the aim to determine the quantity, frequency, duration and type of sweet soft drinks /packaged fruit juices consumption in patients with NAFLD as compared to that in control population. Other objectives included assessment of the independent role of sweetened soft drinks as a risk factor for NAFLD in the absence of metabolic syndrome and to correlate the level of steatosis (objectively graded by ultrasonography) with the amount of intake of soft drinks in patients of NAFLD.
 METHOD :
 50 patients of NAFLD as diagnosed clinically and by ultrasonography were identified and compared with 50 age matched control population reporting to the hospital with ailments other than NAFLD. The presence or absence of metabolic syndrome was noted in all the patients. The history of soft drink intake was obtained from the patient under following heads: a) quantity consumed, b) type of soft drink/packaged juice consumed, c) period of ingestion and d) frequency of consumption.
 RESULT :
 The average monthly consumption, duration of consumption and the mean total sugar consumption of sweetened soft drinks / packaged juices were significantly higher in patients with NAFLD versus those in the control group (p=0.0002; <0.0001; p=0.002 respectively). The quantity and chronicity of intake correlated well with the grade of NAFLD. As the average monthly consumption and mean sugar consumption increased the grade of fatty liver on USG also increased (p= 0.004). A total of 11 patients out of all the 100 patients (study and control groups combined) had a daily intake of 150-300ml of SSD for a period ranging from 5 to 20 years. Two of these had grade I NAFLD whereas 9 had grade II-III NAFLD. Five of these 11 patients had a daily intake of 250ml or greater. All these 5 patients had grade II-III NAFLD documented on ultrasound findings.
 Conclusion
 The consumption of sweetened soft drinks and packaged juice is on the rise in India. The intake of these drinks is significantly higher in patients with NAFLD.

Calculated the diffuse and direct parts of global solar radiation in Baghdad city for the period (1983-2005) depending on clearness index by applying the two world models of Liu -Jordan

In this paper solar radiation was studied over a region of Baghdad (Latitude 33.3o and longitude 44.4o). The two parts of global solar radiation: diffuse and direct solar radiation were estimated depending on the clearance index of measured data (Average Monthly mean global solar radiation). Metrological data of measured (average monthly mean diffuse and direct solar radiation) were used to comparison the results and show the agreement between them. Results are determined by applying Liu and Jordan two models (1960). Excel 2007program is used in calculation, graphics and comparison the results.

Sensitivity of vegetation to climate variability and its implications for malaria risk in Baringo, Kenya.

The global increase in vector borne diseases has been linked to climate change. Seasonal vegetation changes are known to influence disease vector population. However, the relationship is more theoretical than quantitatively defined. There is a growing demand for understanding and prediction of climate sensitive vector borne disease risks especially in regions where meteorological data are lacking. This study aimed at analyzing and quantitatively assessing the seasonal and year-to-year association between climatic factors (rainfall and temperature) and vegetation cover, and its implications for malaria risks in Baringo County, Kenya. Remotely sensed temperature, rainfall, and vegetation data for the period 2004–2015 were used. Poisson regression was used to model the association between malaria cases and climatic and environmental factors for the period 2009–2012, this being the period for which all datasets overlapped. A strong positive relationship was observed between the Normalized Difference Vegetation Index (NDVI) and monthly total precipitation. There was a strong negative relationship between NDVI and minimum temperature. The total monthly rainfall (between 94 -181mm), average monthly minimum temperatures (between 16–21°C) and mean monthly NDVI values lower than 0.35 were significantly associated with malaria incidence rates. Results suggests that a combination of climatic and vegetation greenness thresholds need to be met for malaria incidence to be significantly increased in the county. Planning for malaria control can therefore be enhanced by incorporating these factors in malaria risk mapping.

Curva de crecimiento poblacional de Spodoptera frugiperda en maíz en Irapuato, Guanajuato, México

The fall armyworm Spodoptera frugiperda Smith (Lepidoptera: Noctuidae) is an important pest of maize (Zea mays L.) in Irapuato, Guanajuato, Mexico. The objective of the study was to obtain the population growth curve (and its estimation) of the adults of S. frugiperda in the maize crop in Irapuato, Guanajuato, Mexico. Applying the multiple regression technique, the functional form of a third-degree polynomial was obtained, in terms of temperature and precipitation, using the monthly averages available from 1993 to 2002. The estimated equation was: Y = 821.347 – 104.728T + 2.737P + 3.448T2 – 0.019P2 – 0.180TP + 0.001TP2 (1), where T was the average monthly mean temperature, P was the average monthly accumulated precipitation, and Y was the average of adults of S. frugiperda per month. In addition, using standard mathematical programming techniques, the critical temperature and the critical precipitation were calculated (T=13.70 °C, P=38.70 mm) obtaining the average of maximum number of individuals of this specie per month, which was 39 adults. The prediction model (1) provided information to reach deep understanding of the fluctuation of this species population, and contributed, therefore, to make the integrated pest management in maize crop more efficient, in the region of “El Bajío” of Guanajuato.

Estimate global risks of a forest disease under current and future climates using species distribution model and simple thermal model – Pine Wilt disease as a model case

Predicting distributions of a pest species is an important part of Pest Risk Analysis, but it is not always an easy task. Expert knowledge may help, yet, validation with field data is essential. Pine Wilt Disease (hereafter PWD) caused by the presence of the Pine wood nematode (hereafter PWN), Bursaphelenchus xylophilus is one of the most severe forest diseases in the world. The symptom development of this disease only occurs in a warmer region while the nematode itself could be widely distributed in cooler regions in its native range. Isotherm approaches have been used to estimate the distribution of this disease, but these models have not been well tested with field data. A correlative species distribution model, MaxEnt, is used to evaluate the climatic suitability for PWD at a global scale, along with a simple thermal model. The MaxEnt analysis indicated that most of the current distribution of PWD is explained by the average monthly mean temperatures of the warmest three months and aridity. The thermal model suggested larger PWD risk zones, particularly in hot and arid areas. The current distribution of some susceptible host species (e.g., Pinus sylvestris) is largely outside the area suitable for the development of PWD, but results using projected future climate showed that half of those areas become at risk from PWD in future. Species distribution models such as MaxEnt are useful tools for the evaluation of not only the likely potential distributions of pests but also areas where symptom development could occur under various climatic conditions, even exotic pests not yet present in a region. Our results suggest that PWD will undoubtedly pose a major threat to European pine species if climate change proceeds as currently projected.

Techno-economic feasibility of off-grid solar irrigation for a rice paddy in Guilan province in Iran: A case study

In this study, a typical rice paddy in Guilan province of Iran is considered and the technical and economical feasibility of the solar powered pumping system is studied. The monthly mean daily solar irradiance has been studied for this area and the measured data is compared with European Photovoltaic Geographic Information System (EU PVGIS) model for Middle East, including Iran. The investigations imply that the average monthly mean daily solar irradiance in irrigation months are reported equal to 5.92kWh/m2/day by Guilan Meteorological Administration and 5.95kWh/m2/day by EU PVGIS. A mean monthly clearness index from 0.54 to 0.57, in irrigation period, gives Guilan province a good potential to employ photovoltaic (PV) pumping system. In this study, the appropriate size of the PV panels and the lifecycle cost estimation of PV pumping system in comparison with conventional systems are presented. Also, the area of the PV solar panel to supply required power of the pumping system for a rice paddy with specified area is calculated. Results show that though the initial outlay of the PV system is about 9 times of the conventional systems but the total lifecycle costs of the PV pumping system is just 65.6% costs of the conventional pumping system. Also in spite of high initial costs of the PV pumping system, it is found that after around 9years, the total costs of both systems would be equal to conventional one i.e. gasoline pumping system and after this time, the costs of the conventional pumping system will exceed the PV solar panel system.

Comparative study of isotropic and anisotropic sky models to estimate solar radiation incident on tilted surface: A case study for Bhopal, India

Abstract The purpose of this study is to compare the different empirical models used for estimation of solar radiation on tilted surface. For this, three isotropic and same number of anisotropic sky models were employed by using average monthly mean value of solar radiation on daily basis at Bhopal, local climatic condition, located in central region of India. The tilt angle was fixed at 23.26°N (latitude of Bhopal). The models results were compared with ground measured data from one sample statistical test. It was found that Hays and Davis model (HD) estimated the highest amount of incident solar radiation in the whole year whereas Badescu model (BA) established the lowest among all isotropic as well as anisotropic models. Finally, Badescu model (BA) was preferred for estimation of solar radiation incident on tilted surface with smallest statistical errors among all models and closed agreement with measured data.

Time invariant boundary data of regional climate models COSMO-CLM and WRF and their application in COSMO-CLM

Basic time invariant boundary parameters, such as topography, land use, soil texture, and derived vegetation and soil properties as defined in the regional climate models Consortium for Small-scale Modeling in Climate Mode (CCLM), and the Weather Research and Forecasting Model (WRF), are investigated in terms of their applicability. The focus is on the comparison of the four available data sets and the investigation of their influences on actual CCLM simulations. Several runs of the CCLM model with National Centers for Environmental Prediction (NCEP) and ERA40 boundary forcings in the Mediterranean Coordinated Regional Climate Downscaling Experiment indicate that the investigated data sets are suitable for the considered region. The variations introduced by the different time invariant boundary data are within the range of the variations resulting from different driving reanalysis data, but lower than the differences obtained in intermodel evaluations, e.g., in Prediction of Regional scenarios and Uncertainties for Defining European Climate Change Risks and Effects (PRUDENCE) and Ensembles-based predictions of climate changes and their impacts (ENSEMBLES). The variation in the monthly mean values resulting from differences in the land use, topography, and soil data are up to 1.1 K in the area average monthly mean temperature and up to 17% of the observed value of the area average monthly precipitation. The study indicates that CCLM would benefit from further improvements of the time invariant boundary data, especially the soil texture data.

Temporal assessment of wind energy resource at four locations in Algerian Sahara

This paper presents a statistical analysis of wind characteristics of four locations situated in Algerian Sahara, namely Bordj Badji Mokhtar, Djanet, Illizi and Tamanrasset, using Weibull distribution function on wind speed data at 10m height collected in the last decade by the Algerian Meteorological Office. The monthly, seasonal and annual variations were examined by statistical analyses, while wind speeds at 50m were assessed by extrapolating the 2-parameter Weibull using the power law. It is shown that Bordj Badji Mokhtar has the highest wind potential with the annual mean wind speed Vm=5m/s.The average monthly mean wind speed variation ranged from 2.54m/s at Tamanrasset to 4.94m/s at Bordj Badji Mokhtar, while the minimum seasonal mean wind speed is recorded at Djanet with 2.65m/s and the maximum is found to be 5.79m/s at Bordj Badji Mokhtar. Annual wind power density ranged from 48W/m2 at Tamanrasset to 204W/m2 at Bordj Badji Mokhtar is recorded. Weibull parameters k and A together were estimated and compared with the variation of air temperature for the respective locations. Results indicate that wind speed is most important and most uniform in hottest periods. Additionally, the analysis of energy efficiency for wind turbine of 600kW rated capacity was made for calculating electricity generation and capacity factor for all periods.

Simulating river discharge in a snowy region of Japan using output from a regional climate model

Abstract. Snowfall amounts have fallen sharply along the eastern coast of the Sea of Japan since the mid-1980s. Toyama Prefecture, located approximately in the center of the Japan Sea region, includes high mountains of the northern Japanese Alps on three of its sides. The scarcity of meteorological observation points in mountainous areas limits the accuracy of hydrological analysis. With the development of computing technology, a dynamical downscaling method is widely applied into hydrological analysis. In this study, we numerically modeled river discharge using runoff data derived by a regional climate model (4.5-km spatial resolution) as input data to river networks (30-arcseconds resolution) for the Toyama Prefecture. The five main rivers in Toyama (the Oyabe, Sho, Jinzu, Joganji, and Kurobe rivers) were selected in this study. The river basins range in area from 368 to 2720 km2. A numerical experiment using climate comparable to that at present was conducted for the 1980s and 1990s. The results showed that seasonal river discharge could be represented and that discharge was generally overestimated compared with measurements, except for Oyabe River discharge, which was always underestimated. The average correlation coefficient for 10-year average monthly mean discharge was 0.8, with correlation coefficients ranging from 0.56 to 0.88 for all five rivers, whereas the Nash-Sutcliffe efficiency coefficient indicated that the simulation accuracy was insufficient. From the water budget analysis, it was possible to speculate that the lack of accuracy of river discharge may be caused by insufficient accuracy of precipitation simulation.

The relative performance of Australian CMIP5 models based on rainfall and ENSO metrics

We assess the performance of 30 CMIP5 and two CMIP3 models using metrics based on an all-Australia average rainfall and NINO3.4 sea surface temperatures (SSTs). The assessment provides an insight into the relative performance of the models at simulating long-term average monthly mean values, interannual variability and the seasonal cycles. It also includes a measure of the ability to capture observed rainfall-NINO3.4 SST correlations. In general, the rainfall features are reasonably simulated and there is relatively little difference amongst the models but the NINO3.4 SST features appear more difficult to simulate as evidenced by the greater range in metric scores. We find little evidence of consistency in the sense that a relatively good metric score for one feature does not imply a relatively good score for another related (but independent) feature. The assessment indicates that more recent models perform slightly better than their predecessors, especially with regard to the NINO3.4 metrics. We also focus on the ability of models to reproduce the observed seasonal cycle of rainfall-SST correlations since this is a direct indicator of a model's potential utility for seasonal forecasting over Australia. This indicates some relatively good models (CNRM, HadGEM2-ESM, MPI-ESM-LR and MPI-ESM-MR) and some relatively poor models (CSIRO-Mk3.5, FGOALS, GISS-E2-HP1 and INMCM4). We find that the ACCESS1.3 and CSIRO-Mk3.6 models rank as near median performers on this metric and represent improvements over their predecessors (ACCESS1.0, CSIRO-Mk3.0 and CSIRO-Mk3.5).

Prediction of clear-sky biologically effective erythematic radiation (EER) From global solar radiation (250–2800 nm) at Cairo, Egypt

The hourly and daily measured clear-sky global solar radiation (G) and biologically important effective erythematic radiation (EER) incident on a horizontal surface at Cairo, Egypt (latitude 30° 05′ N & Longitude 31° 15′ E), during the period from January 1995 to December 2005 are used in this paper. The relationship between daily integrated totals of EER and the daily totals of broadband global solar radiation (250–2800nm) is established. The temporal variability of the percentage ratio of the total daily erythema to total daily broadband solar global irradiation (EER/G) is determined. The monthly and the seasonal averages of the extraterrestrial UVB solar radiation, Mesurad and estímated UVB solar radiation and clearness index KtUVB of UVB radiation are discussed. The average monthly mean variation of slant ozone (Z) and UVB transmission (KtUVB) at the present work are found. The two variables show an opposite seasonal behavior, and the average monthly of slant ozone column and UVB transmission values shows the relationship between them in a clearer way than those of daily values. The estimated values of UVB solar radiation a good agreement with the measured values of the UVB solar radiation, the difference between the estimated and measured values of UVB solar radiation varies from 1.2% to 2.8%. The effect of the annual cycles of solar zenith angle (SZA) and total column ozone (TCO) on the ratios (EER/G) are presented and the correction factors are determined for removal of the ozone cycle. The seasonal variability of EER/G is also discussed. The effect of the annual cycles of solar zenith angle (SZA) and total column ozone (TCO) on the ratios (EER/G) is presented and the correction factors are determined for removal of the ozone cycle.

Review of typical vs. synthesized energy modeling weather files

This paper reviews the widely used weather file formats, namely, typical meteorological year (TMY) of evolved versions, test reference year (TRY), weather year for energy calculation (WYEC), design reference year (DRY) as well as synthetically generated meteorological year (SMY) used in energy simulation and renewable-energy studies. This paper examines the fundamentals underlying their structure as to how they support and interface with the available energy simulation and modeling software as well as computer systems on which they operate. The differences in TMY, TRY, WYEC, DRY, and SMY in the selection and weighting of weather parameters are discussed, outlining the pros and cons of each type. Finally, sample dry bulb temperature regimes from SMY and TMY weather files are presented to show the differences in average monthly maximum, minimum, and mean temperatures.