Local Weather Parameters Research Articles

Comprehensive Dataset on Electrical Load Profiles for Energy Community in Ireland

This paper describes a comprehensive energy-related dataset, collected from residential electricity households within an energy community in Ireland, as part of StoreNet project. The data includes local weather parameters and per household power (W) and energy (Wh) components for various aspects such as active power consumption, PV generation, grid import and export, charging and discharging, and the state of charge of energy storage. Additionally, it provides weather data for the location at a 1-minute temporal resolution for the year 2020. The dataset has been validated by comparing measurements that should yield identical results to standard load profiles, with no significant inconsistencies discovered. Validation examples have also been given through the published articles where this dataset has been used to analyse peer-to-peer energy trading benefits for the energy community and decision-making support for aggregators. The dataset aids in understanding patterns in electrical load curves and the duty cycle of energy storage within an energy community. Furthermore, it can assist in comprehending the impact on distribution networks caused by distributed energy storage.

Analysis of power system load forecasting based on neural networks

In this research, our main goal was to improve power load forecasting accuracy by considering the impact of meteorological factors on the total power of the electrical system, examining existing load data, local weather, wind direction, and other parameters affecting total power load. We divided the data from the past three years into a training dataset, comprising 75% of the data, and a testing dataset with the remaining 25%. We employed a basic machine learning technique (Support Vector Machine) and three distinct neural network approaches (Artificial Neural Network, Convolutional Neural Network, and Long-Short Term Memory Network) to develop analytical models. Through experimentation, the LSTM model achieved a loss value of 0.0034 and required 1426.78 seconds of training time across 100 epochs. Considering the time expense and model complexity, we chose the LSTM model to forecast power load at 15-minute intervals for the subsequent ten days, achieving a satisfactory prediction and fitting outcome. Our results suggest that the LSTM model is a promising method for optimizing performance and reliability in electrical power systems.

Spatiotemporal modeling to predict soil moisture for sustainable smart irrigation

In this paper, we have proposed spatiotemporal soil moisture modeling to improve understanding of soil moisture and variability for sustainable irrigation practices. Increasing population growth and climatic changes have intensified the need to implement effective measures to conserve water in irrigation practices. Effective irrigation in parkland influences the overall plant growth processes including the final appearance of the plants. Furthermore, over-irrigation and poor water management in parks and gardens lead to wastage of water which may result in seepage, runoff, and leaching of nutrients into nearby streams. Conversely, under-irrigation results in reduced plant growth and unappealing appearance. Thus, appropriate irrigation management is required to maintain a lush green landscape. Due to large variability in soil properties, environmental conditions and landscape features, the soil moisture level might not be uniformly distributed within a given landscape. Hence, it is essential to understand the soil moisture distribution in the field. There are few existing soil moisture modeling exist but these modelings have not considered multiple vertical depth and time domain. Using Internet of Things(IoT) enabled Cyber–physical system and machine learning techniques, this paper has presented a soil moisture modeling for multiple vertical soil depth for a robust understanding of soil’s and plants need for sustainable sprinkling of the water. The proposed model has used both statistical and deep learning techniques to understand moisture variability in both vertical depth and time. The proposed model has used Correlation Index (CI) to understand multi depth moisture variability on the influence of soil types and local weather parameters, such as precipitation and temperature. To predict moisture data in multi depth, the model has evaluated the use of both statistical machine learning models, such as Support vector regression(SVR) and linear regression (LR), and deep learning models, such as Long Short-Term Memory (LSTM) using seasonal and non-seasonal dataset. The experiment has revealed closely related variability pattern between wind speed and soil moisture in multi depth whereas soil type shows a loosely related variability pattern with moisture in higher depth. With these diverse datasets, the proposed machine learning model has achieved almost 90% success rate to predict moisture content in higher depth using data of lower depth to reduce cumbersome sensors array deployment in higher depth for live moisture visualization. This proposed multiple depth and time domain-based model can enable a smart water dispersing system to conserve water and make a positive contribution to a sustainable future.

Emergence and Incidence of Coffee White Stemborer, Xylotrechus quadripes Chevrolat, 1863 (Coleoptera: Cerambycidae), under Different Rainfall Regimes across the Coffee-growing Regions of Karnataka, India

The coffee white stem borer (CWSB), Xyloterchus quadripes Chevrolat, 1863 (Coleoptera: Cerambycidae) is one of the most destructive pests of arabica coffee in India. As coffee cultivation in India is mostly confined to the hilly tracts of the Western Ghats of the southern states and it is mainly grown in Kodagu, Chikmagalur and Hassan districts of Karnataka with varying rainfall patterns ranging from more than 3000 mm to less than 1000 mm. The study of the incidence of WSB and emergence of adults in relation to local weather conditions in the estates at different rainfall patterns in Karnataka [High(<2500mm), Medium (2000 to 1000mm) and low(<1000mm) rainfall areas] for two years (2016 and 2017). The emergence data and local weather parameters viz rainfall, relative humidity and temperature were correlated with adult emergence. The results indicated that the emergence of CWSB during the years 2016 and 2017 exhibited a significant positive correlation with maximum temperature and minimum temperature. While beetle emergence was negatively correlated with the quantity of rainfall and relative humidity across the six locations. From these observations, it is evident that the beetles required a higher temperature for their emergence and peak beetle emergence occurred during the middle of November in the winter flight season and during the end of April and the first fortnight of May in the summer flight periods. Multiple linear regression analysis revealed that measured abiotic factors have significant effects on emergence of CWSB adults confirming results of correlation analysis. During the same period 2016-17, coffee plantations were surveyed randomly in all the three rainfall regimes to know the influence of annual rainfall on incidence of CWSB and the percent incidence was 10.81% at rainfall <1000, 7.67% at medium rainfall and 6.16% at high rainfall areas and was non-significant across rainfall regimes.

Long-Term Trends in Laying date and Fledged Young of Barn Swallow Hirundo rustica in Two Regions of the Netherlands

Since 1992, Dutch volunteers have monitored the breeding attempts of Barn Swallows on a systematic basis. For two regions – Noord-Veluwe and Twente – a dataset of 27 years (1992–2018) was available. This paper examines the timing of breeding and the number of fledglings per successful breeding attempt. As spring and summer weather in Western Europe became on average warmer during this period, we analysed whether Barn Swallows adapted to this shift. The laying date of first broods advanced on average 2.3 days per ten years. The average advancement of laying date of second broods was 2.9 days per decade. The average number of fledglings per successful breeding attempt was constant over time, but showed large inter-year-variation. First broods consistently produced 0.6 fledglings more than second broods. These results are in line with results from similar studies in Western Europe. Contrary to our expectations, neither large scale nor local weather parameters were found to explain the advancement of laying and the number of fledged young. It is essential to study key species like Barn Swallows during all stages of their life cycle over a long time span and large spatial scale to be able to unravel potential effects of climate change. Our study demonstrates that volunteers can contribute essential demographic data. Ideally, these demographic parameters would be monitored with individually marked (colour-ringed) birds. Frequent quality checks and feedback to volunteers are key to maximize the quality of the data collected through long-term volunteer surveys.

Effect of Sampietrini Pavers on Urban Heat Islands.

Cool pavements are reflective and/or permeable pavements that improve microclimate of urban areas where heat islands cause discomfort to citizens. Stone pavements lower surface temperatures and reduce the amount of heat absorbed. This study assessed, using ENVI-met 4.3 LITE software, how air temperature and predicted mean vote depend on physical properties of the road pavement. A comparative microclimatic analysis was implemented on a rectangular square in Rome (Italy) in the summer, paved in three different ways: asphalt, traditional sampietrini, and permeable sampietrini. The model considered local weather parameters, surrounding fabric, and vegetation to give reliable results in terms of numerical and graphical output using the application tool Leonardo. The tested pavement types affected air temperature during the day, but did not influence this variable in the early morning. Permeable sampietrini pavement was more effective than traditional sampietrini pavement in reducing air temperature compared to the current asphalt surface. The road pavement did not, however, affect human comfort in terms of predicted mean vote. The obtained results are useful for further investigation of parameters that could modify the microclimatic conditions of urban areas.

Observations of the downwelling far-infrared atmospheric emission at the Zugspitze observatory

Abstract. Measurements of the spectrum of the atmospheric emission in the far-infrared (FIR) range, between 100 and 667 cm−1 (100–15 µm) are scarce because of the detection complexity and of the strong absorption of air at ground level preventing the sounding of the FIR from low altitude. Consequently, FIR measurements need to be made from high-altitude sites or on board airborne platforms or satellites. This paper describes the dataset of FIR spectral radiances of the atmosphere and snow surface emission measured in the 100–1000 cm−1 range by the Far-Infrared Radiation Mobile Observation System (FIRMOS) instrument during a 2-month campaign carried out from the ground at about 3000 m of altitude on the top of Mt. Zugspitze in the German Alps in 2018–2019. This campaign is part of the preparatory activity of a new space FIR mission, named Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM), which is under development by the European Space Agency (ESA). The dataset acquired during the campaign also includes all the additional measurements needed to provide a full characterisation of the observed atmospheric state and the local atmospheric and surface conditions. It includes co-located spectral measurements in the infrared range from 400 to 1800 cm−1; lidar backscatter profiles; radio soundings of temperature, humidity and aerosol backscatter profiles; local weather parameters; and snow/ice microphysical properties. These measurements provide a unique dataset that can be used to perform radiative closure experiments to improve modelling parameters in the FIR that are not well-characterised, such as water vapour spectroscopy, scattering properties of cirrus clouds, and the FIR emissivity of the surface covered by snow. The consolidated dataset is freely available via the ESA campaign dataset website at https://doi.org/10.5270/ESA-38034ee (Palchetti et al., 2020a).

Application of reflectance based vegetation index to derive dual crop co-efficient for summer sesame

Dual crop co-efficient approach was applied to estimate seasonal water requirement for summer sesame using reflectance based vegetation indices. Field experiment was conducted to collect the required various crop physiologic parameters and NDVI data for the study crop during 2018 and 2019. Basal crop co-efficients and soil evaporation co-efficients collected from FAO-56 for initial mid and end stages of summer sesame were adjusted for study area using local weather parameters. Spectrum® Field Scout CM 1000 NDVI Meter were used to collect the NDVI data at various stages of study crop. The NDVI was measured from crop canopy and soil surface at 7 days intervals between 12.00 to 13.00 clocks. NDVI Based Basal Crop Co-efficient and Soil evaporation co-efficient were derived using standard methods. FAO estimated crop co-efficients were compared with NDVI based crop co-efficients. The co-efficient of determination of the fitted regression equation was found to be 0.836 and 0.765 for drip irrigation and 0.783 and 0.867 for surface control irrigation system for summer sesame during 2018 and 2019, respectively. Crop growing stage wise water requirement per unit area was estimated for both treatments. Results indicates that among these two methods, NDVI method estimate lowest water requirement in both cases i.e. total water requirement and during all the crop growth stages for both irrigation systems while daily crop water requirement was lower for all growth stages in drip system as compare with control system.

Mapping of most frequent operating condition of photovoltaic module across India

The performance of photovoltaic (PV) module is mainly influenced by in-plane irradiance and module temperature parameters. At any site, variations of in-plane irradiance and module temperature depends on local weather parameters and these weather parameters vary significantly over large geographical area. The most frequently occurred value of in-plane irradiance and module temperature has major contribution in energy generation. In this manner, calculation of performance ratio (PR) based on most frequent operating condition (MFOC) will give an accurate indication of performance under local climatic conditions. Thus, estimation of MFOC is important and its mapping over a large geographical area will be useful for PV systems performance assessment. The objective of present study is to map MFOC of PV module across India. For this purpose, a new scheme of climate classification has been developed. The 58 different sites have been selected and weather data of each site has been used to estimate irradiance, module temperature, and output power, which are analyzed to identify MFOC. The estimation MFOC has been verified with experimental data. Based on present study, seven MFOC have been found for India. Results show that the PR under MFOC is lower than the conventional PR by up to 4%.

Validation and Performance of Satellite Meteorological Dataset MERRA-2 for Solar and Wind Applications

Fast-growing energy demand of the world makes the researchers focus on finding new energy sources or optimizing already-developed approaches. For an efficient use of solar and wind energy in an energy system, correct design and sizing of a power system is of high importance and improving or optimizing the process of data obtaining for this purpose leads to higher performance and lower cost per unit of energy. It is essential to have the most precise possible estimation of solar and wind energy potential and other local weather parameters in order to fully feed the demand and avoid extra costs. There are various methods for obtaining local data, such as local measurements, official organizational data, satellite obtained, and reanalysis data. In this paper, the Modern-Era Retrospective analysis for Research and Applications dataset version 2 (MERRA-2) dataset provided by NASA is introduced and its performance is evaluated by comparison to various locally measured datasets offered by meteorological institutions such as Meteonorm and Deutscher Wetterdienst (DWD, or Germany’s National Meteorological Service) around the world. After comparison, correlation coefficients from 0.95 to 0.99 are observed for monthly global horizontal irradiance values. In the case of air temperature, correlation coefficients of 0.99 and for wind speed from 0.81 to 0.99 are observed. High correlation with ground measurements and relatively low errors are confirmed, especially for irradiance and temperature values, that makes MERRA-2 a valuable dataset, considering its world coverage and availability.

The interrelationship between meteorological parameters and leptospirosis incidence in Hambantota district, Sri Lanka 2008-2017 and practical implications.

BackgroundLeptospirosis is a bacterial zoonosis. Leptospirosis incidence (LI) in Sri Lanka is high. Infected animals pass leptospires to the environment with their urine. Leprospires' survival in the environment to infect a new host depends on meteorological factors. El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) modulate the weather in Sri Lanka.ObjectivesThe determination of interrelationship between the LI in the Hambantota District, and local meteorological parameters, ENSO and IOD.MethodsWe acquired notified leptospirosis cases in the Hambantota District and population data. We calculated weekly leptospirosis incidences for 2008 to 2017.Weather data from two weather stations was obtained, averaged and converted into weekly data. We plotted time series graphs and observed the correlation between seven aggregated weather parameters and LI. We estimated cross-correlations between those weather parameters and LI. As our principal analysis we determined correlation between LI and seven local weather parameters, Nino 3.4, Nino4 and Dipole Mode Index (DMI) indices using wavelet analysis.ResultsOur wavelet analysis results showed troughs of minimum, maximum, mean temperatures, soil temperature, the evaporation rate, the duration of sunshine were followed by peaks in LI and peaks of rainfall followed by peaks of LI, all after lag periods. Our time series graphs and cross-correlation determination results are generally in agreement with these results. However there was no significant correlation between rainfall and LI in the cross-correlation analysis. There were peaks of LI following both peaks and troughs of DMI. There was no clear correlation between both Nino indices and LI.DiscussionThis may be the first long-term study demonstrating soil temperature, evaporation rate and IOD are correlating with LI. The correlation pattern of LI with temperature parameters differs from similar past studies and we explain the reasons. We propose ways to control high LI we observed after periods of weather favorable for transmission of leptospirosis.

A decade with climatic factors and seasonal activity of influenza A H1N1, Puducherry, India, 2009–2019

Background: Influenza A/H1N1(pdm09) strain is in circulation across the globe since 2009, with frequent seasonal outbreaks. The disease burden had varied across geographical regions and temporal spheres. Though studies have linked the role of climatic variables with influenza outbreaks in temperate zones, this is limited in tropical countries. We explored the relationship between climatic factors and incidence of influenza A/H1N1 in Puducherry, a southern district in India during 2009–2019. Methods and materials: We retrieved the laboratory confirmed influenza A/H1N1 patients done by standard polymerase chain reaction analysis during June 2009 to April 2019 from the state disease surveillance unit and accredited virology laboratories. The line list was epidemiologically analysed and patterns in the outbreaks were looked into. We collected monthly averages of local weather parameters such as absolute humidity, relative humidity, temperature, rainfall and insolation from the local meteorological department and respective month's incidence of Influenza was estimated using monthly projected population for the period. We conducted an ecological study to determine the relationship between incidence of influenza and weather parameters using negative binomial regression models. Results: There were 680 patients, lab confirmed with influenza A/H1N1 between June 2009 and April 2019 in Puducherry district. Three outbreaks occurred in the last five years and it was observed that mortality was often confined to the initial half of the outbreak period and healthcare personnel were the major patients in the first half. Between 2009 and 2019, the monthly incidence ranged between 0 and 14.5 per 100,000 population. Univariate negative binomial regression analysis of the influenza incidence with the local weather parameters demonstrated that absolute humidity, relative humidity, insolation and rainfall were associated. On multivariate regression analysis, absolute humidity (IRR: 1.37, 95% CI: 1.06–1.76), insolation (IRR: 1.02, 95% CI: 1.001–1.03) and rainfall (IRR: 1.01, 95% CI: 1.004–1.02) were significantly associated. Conclusion: We found the absolute humidity, insolation and rainfall were associated with incidence of influenza A/H1N1. It is suggested to include climatic factors in Disease surveillance system to detect influenza outbreaks at the earliest. Specific protective measures of the influenza could be planned at the earliest among the at risk population.

Teleconnections and local weather orchestrate the reproduction of tit species in the Carpathian Basin

Variation in climatic conditions is an important driving force of ecological processes. Populations are under selection to respond to climatic changes with respect to phenology of the annual cycle (e.g. breeding, migration) and life‐history. As teleconnections can reflect climate on a global scale, the responses of terrestrial animals are often investigated in relation to the El Niño‐Southern Oscillation and North Atlantic Oscillation. However, investigation of other teleconnections and local climate is often neglected. In this study, we examined over a 33‐year period the relationships between four teleconnections (El Niño‐Southern Oscillation, North Atlantic Oscillation, Arctic Oscillation, East Atlantic Pattern), local weather parameters (temperature and precipitation) and reproduction in great tits Parus major and blue tits Cyanistes caeruleus in the Carpathian Basin, Hungary. Furthermore, we explored how annual variations in the timing of food availability were correlated with breeding performance. In both species, annual laying date was negatively associated with the Arctic Oscillation. The date of peak abundance of caterpillars was negatively associated with local temperatures in December–January, while laying date was negatively related to January–March temperature. We found that date of peak abundance of caterpillars and laying date of great tits advanced, while in blue tits clutch size decreased over the decades but laying date did not advance. The results suggest that weather conditions during the months that preceded the breeding season, as well as temporally more distant winter conditions, were connected to breeding date. Our results highlight that phenological synchronization to food availability was different between the two tit species, namely it was disrupted in blue tits only. Additionally, the results suggest that in order to find the climatic drivers of the phenological changes of organisms, we should analyze a broader range of global meteorological parameters.

Assessing the implementation of bias correction in the climate prediction

An issue of the climate changes nowadays becomes trigger and irregular. The increment of the greenhouse gases (GHGs) emission into the atmospheric system day by day gives huge impact to the fluctuated weather and global warming. It becomes significant to analyse the changes of climate parameters in the long term. However, the accuracy in the climate simulation is always be questioned to control the reliability of the projection results. Thus, the Linear Scaling (LS) as a bias correction method (BC) had been applied to treat the gaps between observed and simulated results. About two rainfall stations were selected in Pahang state there are Station Lubuk Paku and Station Temerloh. Statistical Downscaling Model (SDSM) used to perform the relationship between local weather and atmospheric parameters in projecting the long term rainfall trend. The result revealed the LS was successfully to reduce the error up to 3% and produced better climate simulated results.

Continuous Monitoring of High‐Rise Buildings Using Seismic Interferometry

Research Article| September 26, 2017 Continuous Monitoring of High‐Rise Buildings Using Seismic Interferometry Aurélien Mordret; Aurélien Mordret aDepartment of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139‐4307, mordret@mit.edutoksoz@mit.edu Search for other works by this author on: GSW Google Scholar Hao Sun; Hao Sun bDepartment of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139‐4307, haosun@mit.eduobuyuk@mit.edu Search for other works by this author on: GSW Google Scholar German A. Prieto; German A. Prieto cDepartamento de Geociencias, Universidad Nacional de Colombia ‐ Sede Bogotá, Cra 30 # 45‐03, Bogotá, Colombia, gaprietogo@unal.edu.co Search for other works by this author on: GSW Google Scholar M. Nafi Toksöz; M. Nafi Toksöz aDepartment of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139‐4307, mordret@mit.edutoksoz@mit.edu Search for other works by this author on: GSW Google Scholar Oral Büyüköztürk Oral Büyüköztürk bDepartment of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139‐4307, haosun@mit.eduobuyuk@mit.edu Search for other works by this author on: GSW Google Scholar Bulletin of the Seismological Society of America (2017) 107 (6): 2759–2773. https://doi.org/10.1785/0120160282 Article history first online: 27 Sep 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Aurélien Mordret, Hao Sun, German A. Prieto, M. Nafi Toksöz, Oral Büyüköztürk; Continuous Monitoring of High‐Rise Buildings Using Seismic Interferometry. Bulletin of the Seismological Society of America 2017;; 107 (6): 2759–2773. doi: https://doi.org/10.1785/0120160282 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyBulletin of the Seismological Society of America Search Advanced Search Abstract The linear seismic response of a building is commonly extracted from ambient vibration measurements. Seismic deconvolution interferometry performed on ambient vibrations can be used to estimate the dynamic characteristics of a building, such as its shear‐wave velocity and its damping. The continuous nature of the ambient vibrations allows us to measure these parameters repeatedly and to observe their temporal variations. We used 2 weeks of ambient vibrations, recorded by 36 accelerometers that were installed in the Green Building at the Massachusetts Institute of Technology campus, to monitor the shear wavespeed and the apparent attenuation factor of the building. Because of the low strain of the ambient vibrations, we observed small speed changes followed by recoveries. We showed that measuring the velocity variations for the deconvolution functions, filtered around the fundamental‐mode frequency, is equivalent to measuring the wandering of the fundamental frequency in the raw ambient vibration data. By comparing these results with local weather parameters, we showed that the air humidity is the dominating factor in the velocity variations of the waves in the Green Building, as well as the main force behind the wandering of the fundamental mode. The one‐day periodic variations are affected by both the temperature and the humidity. The apparent attenuation, measured as the exponential decay of the fundamental‐mode waveforms, is strongly biased due to the amplitude of the raw vibrations and shows a more complex behavior with respect to the weather measurements. We have also detected normal‐mode nonlinear interaction for the Green Building, likely due to heterogeneity or anisotropy of its structure. We found that the temporal behavior of the frequency singlets may be used for monitoring. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

GPR Survey and Physical Measurements of Sea Ice in Quilty Bay, Larsemann Hills, East Antarctica and its Correlation with Local Atmospheric Parameters

ABSTRACT Sea ice formation in Quilty Bay, East Antarctica shows a strong relationship with climatic conditions and its spatial variation is established by GPR survey over the sea ice during the austral winters. The study also evaluates the variability of sea ice thickness and extent with local weather parameters between 2010 and 2014. Profiling on sea ice in Quilty Bay shows that sea ice thickness decreases gradually towards east. The overall thickness of sea ice is considerably less at the centre of the bay as compared to the coastal side. Two types of fast ice layers have been delineated from GPR surveys i.e. top layer fast ice which incorporates very low density surface snow and underlying high density fast ice i.e. with melt water pockets. Development of sea ice is influenced by persistent easterly winds, temperature fluctuations and ocean currents. Sea ice reveal average annual cyclic trend in Quilty Bay with maximum development in the year 2013 (157.54 cm) and the minimum development has been observed in the year 2010 (99.04 cm). The cyclic pattern of average sea ice accumulation data show perfect inverse correlation with surface air temperature, with measure of reliability R2 = 0.93. However, moderate (R2 = 0.52) and good (R2 = 0.69) degree of inverse relationship is observed with surface snow and overall sea ice (snow and ice) respectively. With 0.46 °C change in temperature, there is 33% change in sea ice condition in Quilty Bay which may remain same as far as ice extents in the region. Besides this easterly winds play a vital role in controlling the snow/ice distribution patterns as reflected by isopach maps.

Assessment of spatio-temporal variations in air quality of Jaipur city, Rajasthan, India

This paper presents the evaluation of air quality in different wards of Jaipur city. Geo-spatial and geo-statistical techniques were utilized to estimate the seasonal and temporal variations (2004–2015) of gaseous and particulate pollutants. Data of six fixed monitoring stations was collected from Central Pollution Control Board (CPCB) and Rajasthan Pollution Control Board (RPCB) and the relationship between air quality and local weather parameters were also analyzed for the Jaipur city. It was found that SPM and PM10 is the major contributor to the deterioration of air quality in Jaipur city, while NOX and SO2 concentrations were below the CPCB standards. Results show that the concentrations of the air pollutants are high in winter and summer in comparison to the monsoon. The spatio-temporal distribution of Air Quality Index (AQI) clearly depicts the severe air pollution in Vidhyadhar nagar, Civil lines, Hawa Mahal, Kishanpole and Aadarsh Nagar area of Jaipur city. Ordinary least square (OLS) regression reveals good correlation between AQI and weather conditions (temperature, relative humidity and wind speed) for summer and winter seasons. The spatial analysis was also conducted to ascertain the degree of spatial association between AQI and weather conditions in different wards of Jaipur city using bivariate Moran’s I and LISA (Local Indicator of Spatial Association). Moran’s I results imply strong positive autocorrelation between AQI and relative humidity followed by temperature and wind speed. The combination of GIS and statistical analyses contributes to characterize the spatial influences of weather conditions on air pollutants and provide a quick view of the relatively critical areas that need more attention from decision makers to initiate the policies geared towards developing appropriate strategies for reducing air pollution.

On the effect of summer heatwaves and urban overheating on building thermal-energy performance in central Italy

Local climate phenomena impose a serious threat to the built environment. In particular, urban heat island and heatwaves can significantly affect buildings’ thermal-energy performance. The purpose of this study is to investigate summer hot periods and their impact on building thermal-energy behavior in an urban area in Italy. To this aim, the statistical analysis of the microclimate variation during monitored hot periods is performed by the analysis of local weather parameters collected from a dedicated meteorological station. Moreover, the numerical analysis of representative Italian residential buildings is carried out to determine the role of such phenomena on indoor thermal comfort and cooling energy requirements, by considering the consequences arising from heatwaves due to the indoor overheating stress. The analysis showed a strong negative correlation between temperature and relative humidity during extreme hot periods (∼−0.92). Positive correlation was noted between temperature and solar irradiance (∼0.62) and temperature and wind velocity (∼0.33). The southern winds registered in normal hot periods reduced the heat stress by cooling-down the south-facing urban surfaces. The numerical analysis demonstrated higher indoor temperature for insulated buildings, requiring more than three times the cooling requirement of traditional non-insulated buildings in extreme hot periods and exacerbating the occupants’ vulnerability.

Bird collisions at an offshore platform in the North Sea

Capsule Collisions with offshore structures in the North Sea could account for the mortality of hundreds of thousands of nocturnally migrating birds.Aims To assess, for the first time, the circumstances of mass fatalities at an offshore structure, including the species involved, their numbers, ages, body conditions and injuries.Methods At an unmanned tall offshore research platform in the southeastern North Sea, bird corpses were collected on 160 visiting days from October 2003 to December 2007. Corpses were identified to species and kinds of injury, ages, and fat and muscle scores were determined. Nocturnal bird calls were recorded, identified to species and quantified. Local and large-scale weather parameters were also considered.Results A total of 767 birds of 34 species, mainly thrushes, European Starlings and other passerines, were found at 45 visits. Most carcasses were in good body condition and young birds were not more affected than adults. Three quarters of 563 examined individuals had collision induced injuries. Birds in poor body condition were less likely to be collision victims than those in good condition. Mass collision events at the illuminated offshore structure coincided with increasingly adverse weather conditions and an increasing call intensity of nocturnal birds.Conclusions Assuming an average of 150 dead birds per year at this single offshore structure and additionally assuming that a considerable proportion of the corpses were not found, we estimate that mortality at the 1000 + human structures in the North Sea could reach hundreds of thousands of birds. Since offshore industrialization will progress and collision numbers at offshore turbines will consequently increase considerably, we recommend reinforced measures to reduce bird strikes at offshore structures, especially in the light of substantial declines in some migrant species.

Determination of Crop Coefficient for Capsicum (Capsicum annumm L.) in Eastern Himalayan Region through Field Lysimeter

A Crop coefficient (kc) was determined for Capsicum annumm L. (hot pepper) with the help of UMS-GmBH cylindrical field lysimeter of 30 cm diameter and 120 cm deep and Penman-Monteith FAO-56 model. Penman-Monteith model is the universally adopted standard model for estimation of reference evapo-transpiration (ET0) based on local weather parameters. Eight other models viz. Modified Penman Method, Hargreaves equation, Samini Hargreaves equation, Thorthwaite equation, Solar Radiation Method, Net Radiation Method, Blaney-Criddle Method and Radiation Method were also used for estimation of ET0 and compared with Penman-Monteith model to find out the accuracy of prediction with limited weather parameters. Scatter plot and paired t-test were used for comparison. Out of all these models, Blaney-Criddle Method was found to yield similar results as given by Penman-Monteith model. The values of crop evapo-transpiration (ETc) were varying from 1.11 mm d−1 to 3.12 mm d−1. kc is the ratio of ETc to ET0. The highest kc value was obtained during the maximum vegetative growth in 8th week after transplantation. The crop-coefficients for three growth stages viz. initial, mid and maturity were found to be 0.33, 0.64 and 0.30, respectively.