Long-term Meteorological Dataset Research Articles

Optimization Analysis of Sustainable Solar Power System for Mobile Communication Systems

Cellular mobile technology has witnessed tremendous growth in recent times. One of the challenges facing the operators to extend the coverage of the networks to meet the rising demand for cellular mobile services is the power sources used to supply cellular towers with energy, especially in remote. Thus, switch from the conventional sources of energy to a greener and sustainable power model became a target of the academic and industrial sectors in many fields; one of these important fields is the telecommunication sector. Accordingly, this study aims to find the optimum sizing and techno-economic investigation of a solar photovoltaic scheme to deploy cellular mobile technology infrastructure cleanly and sustainably. The optimal solar-powered system is designed by employing the energy-balance procedures of the HOMER software tool. The problem objective is considered in terms of cost, but the energy system is constrained to meet the power demand reliably. Process simulations were performed to determine the optimum sizing, performance and monetary cost of the power system, using long-term meteorological datasets for a case study site with defined longitude (31° 25′ E) and latitude (30° 06′ N). From the observed results, the total net present cost (NPC) of the proposed system is $28,187. Indeed, these outcomes can provide profound economic, technical, and ecological benefits to cellular operators. It also ensures a sizeable reduction in greenhouse gas that supports sustainable green wireless network (WN) deployment in remote areas.

Integration of photovoltaic and hydrogen fuel cell system for sustainable energy harvesting of a university ICT infrastructure with an irregular electric grid

This study investigates the prospect of using an integrated renewable power system to enhance an unreliable grid network for sustainable electricity generation. A grid coupled microgrid comprising photovoltaic and hydrogen fuel cell system, simulated for an information and communication technology (ICT) building is considered. The overall objectives are to decide the technical, ecological and monetary gains of the addition of green energy sources to an unreliable grid for electricity harvesting within university campuses. The energy-balance procedures of the hybrid optimisation model for electric renewables software tool are employed for this study. The system design is implemented for the electrical load of an ICT building at Ambrose Alli University (6°44.0′N, 6°5.1′E) using long-term meteorological datasets collected for the study site. The results show that the integration of 323 kW photovoltaic array, 75 kW power converter, and 25 kW fuel cell system with 26 kW electrolyser and 20 kg hydrogen tank can reliably enhance the existing unreliable electric grid system in a clean and sustainable way. Using this integrated energy system requires an initial capital ($ 247,100), net present cost ($ 445,266), yearly operating cost (3,901 $/y), and cost of energy (0.05976 $/kWh). Consequently, the renewable fraction increases by 91.2%, CO2 cut by 95.2%, while the grid electricity purchases reduce by 80.7%, at a cost saving of 91.6%.

AATTENUATION—The Atmospheric Attenuation Model for CSP Tower Plants: A Look-Up Table for Operational Implementation

Attenuation of solar radiation between the receiver and the heliostat field in concentrated solar power (CSP) tower plants can reduce the overall system performance significantly. The attenuation varies strongly with time and the average attenuation at different sites might also vary strongly from each other. If no site specific attenuation data is available, the optimal plant design cannot be determined and rough estimations of the attenuation effect are required leading to high uncertainties of yield analysis calculations. The attenuation is caused mainly by water vapor content and aerosol particles in the lower atmospheric layer above ground. Although several on-site measurement systems have been developed during recent years, attenuation data sets are usually not available to be included during the plant project development. An Atmospheric Attenuation (AATTENUATION) model to derive the atmospheric transmittance between a heliostat and receiver on the basis of common direct normal irradiance (DNI), temperature, relative humidity, and barometric pressure measurements was developed and validated by the authors earlier. The model allows the accurate estimation of attenuation for sites with low attenuation and gives an estimation of the attenuation for less clear sites. However, the site-dependent coefficients of the AATTENUATION model had to be developed individually for each site of interest, which required time-consuming radiative transfer simulations, considering the exact location and altitude, as well as the pre-dominant aerosol type at the location. This strongly limited the application of the model despite its typically available input data. In this manuscript, a look-up table (LUT) is presented which enables the application of the AATTENUATION model at the site of interest without the necessity to perform the according complex radiative transfer calculations for each site individually. This enables the application of the AATTENUATION model for virtually all resource assessments for tower plants and in an operational mode in real time within plant monitoring systems around the world. The LUT also facilitates the generation of solar attenuation maps on the basis of long-term meteorological data sets which can be considered during resource assessment for CSP tower plant projects. The LUTs are provided together with this manuscript as supplementary files. The LUT for the AATTENUATION model was developed for a solar zenith angle (SZA) grid of 1°, an altitude grid of 100 m, 7 different standard aerosol types and the standard AFGL atmospheres for mid-latitudes and the tropics. The LUT was tested against the original version of the AATTENUATION model at 4 sites in Morocco and Spain, and it was found that the additional uncertainty introduced by the application of the LUT is negligible. With the information of latitude, longitude, altitude above mean sea level, DNI, relative humidity (RH), ambient temperature (Tair), and barometric pressure (bp), the attenuation can be now derived easily for each site of interest.

Investigating trend changes of annual mean temperature and precipitation in Iran

Climate change is a prevalent issue all around the world. Changes in the temperature and precipitation, as well as their distributions, are the evidence of climate change. Therefore, the objective of this study was to gather and standardize information about temperature (maximum and minimum temperatures and the discrepancy between them) and precipitation in Iran’s environmental zones. The long-term meteorological dataset was gathered from 47 synoptic stations for 66 years (1951–2017). Linear regression was employed to show the type and trend of changes in the studied variables. The Koppen method and UNESCO aridity index were adopted for climate classification based on temperature and precipitation criteria, respectively. Based on the Koppen method, the climate of Iran is defined into 4 environmental zones consisting of the arid desert (Bw), semi-arid (Bs), humid with mild winters (C), and humid with severe winters (D). On the basis of the UNESCO aridity index, the climate of Iran is classified into 5 environmental zones including very dry, dry, semi-dry, humid, and very humid. Results demonstrated that at 42 stations (89% of stations), the slope of the trend line was positive and long-term annual average temperatures of 5 stations (11% of stations) were negative. Furthermore, for precipitation, 11 stations (25% of stations) had positive trend and 33 stations had a negative trend (75% of stations). Finally, it was found that there are significant changes in both temperature and precipitation. On the other hand, an overall increasing trend in temperature and a decreasing trend in precipitation were observed from high-altitude or high-latitude cold areas to low-latitude regions such as costal lines in Iran.

Effects of climate change and episodic heat events on cyanobacteria in a eutrophic polymictic lake.

Mixing regime and CO2 availability may control cyanobacterial blooms in polymictic lakes, but the underlying mechanisms still remain unclear. We integrated detailed results from a natural experiment comprising an average-wet year (2011) and one with heat waves (2012), a long-term meteorological dataset (1960-2010), historical phosphorus concentrations and sedimentary pigment records, to determine the mechanistic controls of cyanobacterial blooms in a eutrophic polymictic lake. Intense warming in 2012 was associated with: 1) increased stability of the water column with buoyancy frequencies exceeding 40 cph at the surface, 2) high phytoplankton biomass in spring (up to 125 mg WW L-1), 3) reduced downward transport of heat and 4) depleted epilimnetic CO2 concentrations. CO2 depletion was maintained by intense uptake by phytoplankton (influx up to 30 mmol m-2 d-1) in combination with reduced, internal and external, carbon inputs during dry, stratified periods. These synergistic effects triggered bloom of buoyant cyanobacteria (up to 300 mg WW L-1) in the hot year. Complementary evidence from polynomial regression modelling using historical data and pigment record revealed that warming explains 78% of the observed trends in cyanobacterial biomass, whereas historical phosphorus concentration only 10% thereof. Together the results from the natural experiment and the long-term record indicate that effects of hotter and drier climate are likely to increase water column stratification and decrease CO2 availability in eutrophic polymictic lakes. This combination will catalyze blooms of buoyant cyanobacteria.

Implications of stubble management on snow hydrology and meltwater partitioning

Spring snowmelt is the most important hydrological event in agricultural cold regions, recharging soil moisture and generating the majority of annual runoff. Melting agricultural snowcovers are patchy, which leads to melt rate enhancement by energy advection from warm moist snow-free surfaces to cool dry snowcovers. Agricultural snowmelt is also impacted by crop residue. Adoption of zero-tillage agricultural practices means vast areas of the Canadian Prairies are now characterized by standing crop stubble. Stubble influences snow accumulation through blowing snow processes and snowmelt through the impact of emerging stubble upon the surface energy balance. Unfortunately, stubble emergence and advection to patchy snowcovers are unaccounted for in snow hydrology models and a complete process description has been unavailable. Here, both advection and stubble influences on snowmelt hydrology are modelled by coupling new stubble-snow-atmosphere surface energy balance and advection models to existing blowing snow and frozen soil infiltration models. Long-term meteorological datasets from sub-humid and semi-arid locations in Saskatchewan, Canada are used to quantify the influence of stubble characteristics on accumulation, melt, and meltwater partitioning processes with respect to interannual variability, antecedent soil moisture, and climatic differences on the Canadian Prairies. The hydrological response to increasing stubble height is increased meltwater, melt rate, infiltration, and runoff, and negligible changes in melt duration. The response of these processes to changes in stubble height was more pronounced at a semi-arid site versus a sub-humid site as stubble more effectively suppresses blowing snow sublimation in the windier, drier semi-arid environment of southwestern Saskatchewan. Recommendations for stubble management to meet specific runoff or infiltration objectives are summarised; stubble management can be an effective tool to influence infiltration where soils are dry and runoff where soils are wet. This framework allows the diagnosis of the influence of stubble management on meltwater partitioning in cold agricultural regions.

Impact of Climate Change on the Water Requirements of Oat in Northeast and North China

Crop water requirements are directly affected by climatic variability, especially for crops grown in the areas which are sensitive to climatic change. Based on the SIMETAW model and a long-term meteorological dataset, we evaluated the spatiotemporal variations of climatic change impacts on water requirement of oat in North and Northeast China. The results indicated that effective rainfall showed an increasing trend, while the crop water requirement and irrigation demand presented decreasing trends over the past decades. The water requirement of oat showed significant longitudinal and latitudinal spatial variations, with a downtrend from north to south and uptrend from east to west. Climatic factors have obviously changed in the growth season of oat, with upward trends in the average temperature and precipitation, and downward trends in the average wind speed, sunshine hours, relative humidity, and solar radiation. Declines in solar radiation and wind speed, accompanied with the increase in effective rainfall, have contributed to the reduced crop water requirement over these decades. Given the complex dynamic of climate change, when studying the impact of climate change on crop water requirements, we should not only consider single factors such as temperature or rainfall, we need to analyze the comprehensive effects of various climatic factors.

Mesoscale atmospheric modelling technology as a tool for creating a long-term meteorological dataset

A detailed hydrodynamic simulation of major meteorological parameters for the last 30 years (1985 – 2014) has been performed for the Sea of Okhotsk and the Sakhalin Island. The regional non-hydrostatic atmospheric model COSMO-CLM was used for this long-term simulation with horizontal resolutions of ~13.2, ~6.6 and ~2.2 km. This dataset was created to help in the investigation of statistical characteristics and physical mechanisms of formation of extreme weather events (primarily wind speed extremes) on small spatio-temporal scales. The detailed meteorological information thus obtained could be used to take into account the coast configuration, mountain systems, and other important mesoscale features of the terrain. This paper describes a proposed downscaling technology for long-term simulations with three “nested domains”. The results of verification of the dataset and estimation of extreme wind velocities are presented.

Benchmarking of Five Typical Meteorological Year Datasets Dedicated to Concentrated-PV Systems

This paper presents the benchmarking of different Typical Meteorological Year (TMY) datasets applied to a Concentrated-PV (CPV) system. Using 18-years of high quality meteorological and pyranometric ground measurements, five types of TMY datasets were generated using variable time period and following different methods: the standard Sandia method or only considering the Direct Normal Irradiation (DNI) or a more sophisticated DNI-based driver considering the characteristics of the CPV system. The results show that the Sandia method is not suitable for CPV systems. The TMY datasets obtained using dedicated drivers are more representative to derive TMY datasets from limited long-term meteorological dataset.

Two Types of Strong Local Wind Captured by Simultaneous Multiple-Site Radiosonde Soundings across a Mountain Range

AbstractA radiosonde observation method is presented, consisting of simultaneous radiosonde observations at closely spaced multiple sites using balloons with varied buoyancies. This method was employed during a strong wind event (Suzuka-oroshi) on the lee side of the Suzuka mountain range, Japan, to derive the detailed structure of the wind as it crossed the mountains. Batches of six radiosondes were launched simultaneously from a line of four sites, using balloons with three different degrees of buoyancy. The four sites were 13 km apart along a 35-km-long transect roughly aligned with the prevailing wind. The observations documented two flow regimes: a downslope flow perpendicular to the mountain range, similar to a windstorm, and an unexpectedly strong low-level jet flowing parallel to the mountain range. The method was more successful at delineating the first regime than the second. The first regime was well simulated by a numerical experiment, but the second regime was not. The vertical wind associated with the downslope windstorm was inferred from the changing slopes of potential temperature isentropes. Comparison of the balloon ascent rates with these isentropes meanders and the simulated vertical wind showed that fluctuations in balloon ascent rate provide reliable information on the vertical direction of the wind. An analysis of the second regime using a long-term meteorological dataset shows that the onset of the low-level jet is related to the synoptic-scale shift in vorticity from positive to negative in the observation area. This vorticity shift appears to be a useful indicator for the low-level jet regime.

Climate change and impacts in the Eastern Mediterranean and the Middle East.

The Eastern Mediterranean and the Middle East (EMME) are likely to be greatly affected by climate change, associated with increases in the frequency and intensity of droughts and hot weather conditions. Since the region is diverse and extreme climate conditions already common, the impacts will be disproportional. We have analyzed long-term meteorological datasets along with regional climate model projections for the 21st century, based on the intermediate IPCC SRES scenario A1B. This suggests a continual, gradual and relatively strong warming of about 3.5–7°C between the 1961–1990 reference period and the period 2070–2099. Daytime maximum temperatures appear to increase most rapidly in the northern part of the region, i.e. the Balkan Peninsula and Turkey. Hot summer conditions that rarely occurred in the reference period may become the norm by the middle and the end of the 21st century. Projected precipitation changes are quite variable. Annual precipitation is expected to decrease in the southern Europe – Turkey region and the Levant, whereas in the Arabian Gulf area it may increase. In the former region rainfall is actually expected to increase in winter, while decreasing in spring and summer, with a substantial increase of the number of days without rainfall. Anticipated regional impacts of climate change include heat stress, associated with poor air quality in the urban environment, and increasing scarcity of fresh water in the Levant.Electronic supplementary materialThe online version of this article (doi:10.1007/s10584-012-0418-4) contains supplementary material, which is available to authorized users.

INCREASE IN SIMPLE PRECIPITATION INTENSITY INDEX IN PANAMA

The paper describes trends in climate change indices in the Republic of Panama by using long-term meteorological data sets from the Panama Canal and MRI-AGCM. Simple precipitation intensity index from eleven climate change indices shows the same trend, increasing at six meteorological stations around the Panama Canal. Forward projection (from 2080 to 2099) was carried out by using MRI-AGCM3.1S and 3.2S, which demonstrates an increase in simple precipitation intensity index in the entire area of Panama. The increase in simple precipitation intensity index may suggest that stronger precipitation will occur more frequently in the future in Panama.

A 59-year (1948-2006) global near-surface meteorological data set for land surface models. Part I: Development of daily forcing and assessment of precipitation intensity

This paper describes the development and assessment of global 0.5° near-surface atmospheric data from 1948 to 2006 at daily (for precipitation, snowfall, and specific humidity) to 3-hourly (for temperature, shortwave radiation, and longwave radiation) time scales, which can be used to drive land surface models. Using newly available monthly precipitation and temperature data extending to recent years, the variables were created by statistical methods, the parameters of which were obtained from available daily to 3-hourly observations. The daily precipitation developed in this paper produces reasonable numbers of precipitation days and heavy precipitation days, different from previous longterm meteorological data sets based on reanalysis. Together with its relatively high spatial resolution (0.5°) and availability of recent years, the newly obtained data may be preferred to other forcing data sets in case of hydrological and climate change studies, in particular if the study results are sensitive to daily variations in atmospheric conditions.

Methodik zur Identifikation von Klimatrends aus langjährigen meteorologischen Datenreihen

A method for nonlinear approximation of stochastic processes is derived for calculation of climatic trends of long-term meteorological data sets. The method mainly makes use of spline approximation, Green's function and spectral transfer function of the Chauchy problem. Generally applicable for a wider range of problems the method is specified for special conditions typical for meteorological and climatological data. The applicability of the method described is examined on the basis of long-term (40... 228 years) meteorological data sets for temperature and precipitation data from one Australian and five European locations. The results obtained are discussed in comparison with climate trend results from temperature and precipitation data taken from literature.

Analysis of the Barcelona precipitation series 1850-1991

Interest in climatic change has never been greater and it is inevitable that the search for periodicities in the climatic record should be a popular means of attempting to unravel at least some of the complexities of the atmospheric system. This paper reviews aspects of the methodologies of spectral analysis and takes the example of the Barcelona precipitation series (1850‐1991) to illustrate how both spectral analysis and other statistical analyses can provide information on climatic evolution as expressed through the medium of this one variable. Copyright © 1999 Royal Meteorological Society. The analysis of long-term meteorological data sets plays an important role in climatic studies and in recent years has been of unprecedented interest to the scientific community and the general public. Indeed the disputes surrounding climatic change and its possible repercussions have prompted almost daily items in the media. Objective numerical analysis based on standard techniques included within computer statistics packages, which are often beyond the close control of the investigator, can lead to unrepresentative and perhaps misleading conclusions concerning the nature of climatic series’ and their statistical character. Analysis based on inappropriate methods can lead to problems, particularly when non-homogenous series are used. Moreover, analysis of the temporal evolution of data sets is often executed using methods that employ arbitrarily selected windows or filters that often provide equally arbitrary and unpredictable outcomes. Spectral analysis based on standard and relatively inflexible methods poses further difficulties. One of the first attempts to provide a more rational basis for spectral analyses was made by the World Meteorological Organisation (WMO, 1966). Whilst this was an important development, it has failed to provide this particular form of analysis with a methodology that recognised the often peculiarly variable and highly distinctive characteristics of the data sets that are likely to be used.