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Deng, Q.-G., G.-Y. Cao, Z.-C. Liu, Z.-S. Wang, Y. Yang, X.-Y. He, and J.-J. Yu. "Annual daylight glare evaluation: Impact of weather file selection." Lighting Research & Technology 50, no. 3 (August 31, 2016): 446–55. http://dx.doi.org/10.1177/1477153516664206.
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This paper illustrates the results of daylight glare simulations of a simple office carried out using five different five weather data files (Chinese typical year weather, CSWD(C), CSWD(A), international weather for energy calculations and Meteonorm), for three Chinese locations (Beijing, Shanghai and Guangzhou). The aim is to analyse the impact of choosing one weather file or the other on the prediction of daylight glare in the interior environment. The results show that using the Chinese typical year weather and CSWD(C) files can make a better prediction of the time distribution of daylight glare than the others. Analysis of the total amount of daylight glare leads to the conclusion that the Chinese typical year weather file should be recommended when considering extreme weather conditions and the CSWD(C) or Meteonorm files should be recommended in normal weather conditions.
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Lauzet, N., T. Colinart, M. Musy, and K. Lapray. "Selecting extreme weather file to assess overheating in residential building." Journal of Physics: Conference Series 2069, no. 1 (November 1, 2021): 012231. http://dx.doi.org/10.1088/1742-6596/2069/1/012231.
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Abstract Climate change is great challenge for current and newly built buildings. Nowadays, TMY weather file can be easily generated following the IPCC scenarios. Nevertheless, since these data are extrapolated with stochastic model from monthly mean values, they do not show a real pattern and do not include extreme events like heatwaves. In order to get more representative data, we propose in this work a methodology to select real measured files from a large database in light of heatwaves and climate change. This methodology is applied to the city of Lyon, for which 26 years of weather data are available. Three measured weather files projected for the time periods 2020, 2050 and 2080 are selected. These files are used in building thermal simulation of residential building with low or high thermal inertia. Summer overheating is analysed through two different comfort indicators: adaptative comfort and Givoni chart. Results indicates that summer overheating risk is obviously increased with future weather files. When compared to usual TMY files, this risk is also enhanced by using weather file including extreme events like heatwaves. Last, we note that discomfort is mainly encountered during this extreme events.
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Costanzo, Vincenzo, Gianpiero Evola, Marco Infantone, and Luigi Marletta. "Updated Typical Weather Years for the Energy Simulation of Buildings in Mediterranean Climate. A Case Study for Sicily." Energies 13, no. 16 (August 9, 2020): 4115. http://dx.doi.org/10.3390/en13164115.
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Building energy simulations are normally run through Typical Weather Years (TWYs) that reflect the average trend of local long-term weather data. This paper presents a research aimed at generating updated typical weather files for the city of Catania (Italy), based on 18 years of records (2002–2019) from a local weather station. The paper reports on the statistical analysis of the main recorded variables, and discusses the difference with the data included in a weather file currently available for the same location based on measurements taken before the 1970s but still used in dynamic energy simulation tools. The discussion also includes a further weather file, made available by the Italian Thermotechnical Committee (CTI) in 2015 and built upon the data registered by the same weather station but covering a much shorter period. Three new TWYs are then developed starting from the recent data, according to well-established procedures reported by ASHRAE and ISO standards. The paper discusses the influence of the updated TWYs on the results of building energy simulations for a typical residential building, showing that the cooling and heating demand can differ by 50% or even 65% from the simulations based on the outdated weather file.
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Manapragada, Naga Venkata Sai Kumar, Anoop Kumar Shukla, Gloria Pignatta, Komali Yenneti, Deepika Shetty, Bibhu Kalyan Nayak, and Venkataramana Boorla. "Development of the Indian Future Weather File Generator Based on Representative Concentration Pathways." Sustainability 14, no. 22 (November 16, 2022): 15191. http://dx.doi.org/10.3390/su142215191.
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India’s fossil-fuel-based energy dependency is up to 68%, with the commercial and residential sectors contributing to the rise of building energy demand, energy use, and greenhouse gas emissions. Several studies have shown that the increasing building energy demand is associated with increased space-cooling ownership and building footprint. The energy demand is predicted to grow further with the conditions of global warming and the phenomenon of urban heat islands. Building designers have been using state-of-the-art transient simulation tools to evaluate energy-efficient envelopes with present-day weather files that are generated with historical weather datasets for any specific location. Designing buildings with historical climatic conditions makes the buildings vulnerable to the predicted climate change impacts. In this paper, a weather file generator was developed to generate Indian future weather files using a geo-filtering-based spatial technique, as well as the temporal downscaling and machine learning (ML)-based bias correction approach proposed by Belcher et al. The future weather files of the three representative concentration pathways of 2.6, 4.5, and 8.5 could be generated for the years 2030, 2050, 2070, 2090, and 2100. Currently, the outputs of the second-generation Canadian Earth System Model are being used to create future weather files that will aid architects, urban designers, and planners in developing a built environment that is resilient to climate change. The novelty lies in using observed historical data from present-day weather files on the typical meteorological year for testing and training ML models. The typical meteorological weather files are composed of the concatenation of the monthly weather datasets from different years, which are referred to for testing and training ML models for bias correction.
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Aliabadi, Amir A., and Rachel M. McLeod. "The Vatic Weather File Generator (VWFG v1.0.0)." Journal of Building Engineering 67 (May 2023): 105966. http://dx.doi.org/10.1016/j.jobe.2023.105966.
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Aram, Kimiya, Roohollah Taherkhani, and Agnė Šimelytė. "Multistage Optimization toward a Nearly Net Zero Energy Building Due to Climate Change." Energies 15, no. 3 (January 28, 2022): 983. http://dx.doi.org/10.3390/en15030983.
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Climate change is one of the major problems of the planet. The atmosphere is overloaded with carbon dioxide caused by fossil fuels that are burned for energy. Almost 40 percent of the total energy worldwide is used by the building sector, which comes from non-renewable sources and contributes up to 30% of annual greenhouse gas emissions globally. The building sector in Iran accounts for 33.8% of Iran’s total energy usage. Within the building sector, the energy consumption of Iranian educational buildings is 2.5 times higher than educational buildings in developed countries. One of the most effective ways of reducing global energy consumption and greenhouse gas emissions is retrofitting existing buildings. This study aims to investigate whether a particular energy-optimized design under the present climate conditions would respond effectively to future climate change. This can help designers make a better decision on an optimal model, which can remain optimal over the years based on climate change. For methodological purposes, multistage optimization was used to retrofit an existing educational building. Specifically, the non-dominated sorting genetic algorithm (NSGA-II) was chosen to minimize the cooling and heating load, as well as consider investment costs for present and future weather files, using the jEPlus tool. Furthermore, the TOPSIS method was used to identify the best set of retrofit measures. For this purpose, a four-story educational building in Tehran was modeled on Design Builder software v7.0.0.116 as a case study to provide a better understanding for researchers of how to effectively retrofit a building to achieve a nearly zero energy building considering climate change. The results show that the optimized solution for the present weather file does not remain the optimized solution in 2080. Moreover, it is shown that to have an optimized building in regard to future weather files, the model should be designed for the future weather conditions. This study shows that if the building becomes optimized using the present weather file the total energy consumption will be reduced by 65.14% and 86.18% if using the future weather file. These two figures are obtained by implementing active and passive measures and show the priority of using the future weather file for designers. Using PV panels also, this building is capable of becoming a nearly net zero building, which would produce about 90% of its own energy demands.
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Baharun, Azhaili, Siti Halipah Ibrahim, Mohammad Omar Abdullah, and Ooi Koon Beng. "Experimental Validation of EnergyPlus® Simulation of a Single Storey Building." Journal of Civil Engineering, Science and Technology 2, no. 1 (March 1, 2011): 12–17. http://dx.doi.org/10.33736/jcest.82.2011.
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EnergyPlus® simulated indoor temperatures of a single storey building at the east campus of Universiti Malaysia Sarawak, for April and June, are validated with measurements taken in 2007.The measured local outdoor temperature was used together with the global solar radiation, wind velocity, relative humidity and cloud cover measured at the Kuching airport to replace the typical meteorological year (TMY) values in the EnergyPlus® weather (EPW) file to fonn a Modified EPW weather file at the time/date of experiments. The remaining fields of the Modified EPW contain TMY data including the direct and diffuse solar radiations and the 'sky's' infrared radiation, which is also present at night.Analysis of the temperatures at the windows simulated with the EPW and Modified EPW weather files for the April and June experiments show the strong influence of the outdoor temperature and importance of the global solar radiation in the weather file and local outdoor temperature is used in the Modified EPW.Day time peak mismatches between the measured indoor air temperature and the indoor air temperature simulated with the Modified EPW is 2 to 3 deg C. These are due to the use of the TMY direct and diffuse solar radiations in the heat balance algorithms at the outside surfaces. The corresponding night time mismatches are less than 1 deg C because the TMY values of the long wave infra-red radiation emitted from molecules and particles in the atmosphere in the Modified EPW are used in simulation.
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Demanuele, C., A. Mavrogianni, M. Davies, M. Kolokotroni, and I. Rajapaksha. "Using localised weather files to assess overheating in naturally ventilated offices within London's urban heat island." Building Services Engineering Research and Technology 33, no. 4 (September 9, 2011): 351–69. http://dx.doi.org/10.1177/0143624411416064.
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Urban environments typically experience increased average air temperatures compared to surrounding rural areas – a phenomenon referred to as the Urban Heat Island (UHI). The impact of the UHI on comfort in naturally ventilated buildings is the main focus of this article. The overheating risk in urban buildings is likely to be exacerbated in the future as a result of the combined effect of the UHI and climate change. In the design of such buildings in London, the usual current practice is to view the use of one generic weather file as being adequate to represent external temperatures. However, the work reported here demonstrates that there is a considerable difference between the overheating performance of a standard building at different sites within London. This implies, for example, that a building may wrongly pass or fail criteria used to demonstrate compliance with building regulations as a result of an inappropriate generic weather file being used. The work thus has important policy implications. Practical application: The Greater London Authority has recently developed, with the Chartered Institute of Building Services Engineers, guidance for developers to address the risk of overheating in buildings via the provision of weather files for London relating to three zones. While such an initiative is welcomed, it may be that a weather file tailored to the building location would be preferable. Of course, this would add further complexity to the process and a view would have to be taken as the viability of such an approach. The work presented in this article, however, suggests that serious consideration should be given to the use of tailored weather data for regulatory purposes.
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Kwak, Young-Hoon, Yong-Woo Jeong, Hey-Sim Han, Cheol-Yong Jang, and Jung-Ho Huh. "Forecasted Weather based Weather Data File Generation Techniques for Real-time Building Simulation." Journal of the Korean Solar Energy Society 34, no. 1 (February 28, 2014): 8–18. http://dx.doi.org/10.7836/kses.2014.34.1.008.
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P.Tootkaboni, Mamak, Ilaria Ballarini, Michele Zinzi, and Vincenzo Corrado. "A Comparative Analysis of Different Future Weather Data for Building Energy Performance Simulation." Climate 9, no. 2 (February 23, 2021): 37. http://dx.doi.org/10.3390/cli9020037.
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The building energy performance pattern is predicted to be shifted in the future due to climate change. To analyze this phenomenon, there is an urgent need for reliable and robust future weather datasets. Several ways for estimating future climate projection and creating weather files exist. This paper attempts to comparatively analyze three tools for generating future weather datasets based on statistical downscaling (WeatherShift, Meteonorm, and CCWorldWeatherGen) with one based on dynamical downscaling (a future-typical meteorological year, created using a high-quality reginal climate model). Four weather datasets for the city of Rome are generated and applied to the energy simulation of a mono family house and an apartment block as representative building types of Italian residential building stock. The results show that morphed weather files have a relatively similar operation in predicting the future comfort and energy performance of the buildings. In addition, discrepancy between them and the dynamical downscaled weather file is revealed. The analysis shows that this comes not only from using different approaches for creating future weather datasets but also by the building type. Therefore, for finding climate resilient solutions for buildings, care should be taken in using different methods for developing future weather datasets, and regional and localized analysis becomes vital.
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Bastos Porsani, Gabriela, and Carlos Fernández Bandera. "A Case Study of Empirical Validation of EnergyPlus Infiltration Models Based on Different Wind Data." Buildings 13, no. 2 (February 13, 2023): 511. http://dx.doi.org/10.3390/buildings13020511.
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Building retrofitting is an efficient means of reducing greenhouse gas emissions. Its first focus is on building façade, as transmission and air leakage are the main sources of energy loss in buildings. Nowadays, building modellers cannot easily implement envelope air leakage and assume constant values, which results in erroneous energy estimates. Additionally, in energy simulations, a weather file is usually inserted with measurements provided by a weather station. In this study, we revealed the use of wind data from the weather file (herein as global wind) to calculate the infiltration of a test case in Spain, using the three algebraic equations of EnergyPlus. Furthermore, four other wind data were applied: eastbound and westbound winds from the weather file and two from in situ measurements (on the southeast and on the northwest façades). The fifteen combinations of the three infiltration models and the five wind data were empirically evaluated, using the tracer gas results performed during three different periods. The combinations were validated according to the American Society for Testing Materials D5157 standard criteria, and the best and the only ones that complied with the standard were those using the wind data from the southeast in situ sensor and the west wind from the weather station. The global wind was not able to generate accurate infiltration models, which raises doubts about its use in the highly-time calibration of energy models. However, its disaggregation was a cost-effective strategy to estimate the infiltration of this case study.
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Bezručka, Juraj. "Non-observation RINEX compression." Contributions to Geophysics and Geodesy 43, no. 4 (December 1, 2013): 351–64. http://dx.doi.org/10.2478/congeo-2013-0020.
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Abstract RINEX format is a result of an effort to standardize exchanging GNSS related data in human readable receiver independent format. Besides observation data, RINEX is also de facto standard for exchanging navigation message or meteorological data. The paper describes simple compression methods for navigation messages and meteorological data files. Both formats have their specific features, therefore different methods were used. Followed by standard compression the algorithms reduce the file size by more than 20 percent compared to standard compression. Thanks to the properties of weather and regular parameter observations it allows to reduce the file size of meteorological RINEX to a mere fraction of its original size.
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Yassaghi, Hamed, Patrick L. Gurian, and Simi Hoque. "Propagating downscaled future weather file uncertainties into building energy use." Applied Energy 278 (November 2020): 115655. http://dx.doi.org/10.1016/j.apenergy.2020.115655.
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Segarra, Eva Lucas, Germán Ramos Ruiz, Vicente Gutiérrez González, Antonis Peppas, and Carlos Fernández Bandera. "Impact Assessment for Building Energy Models Using Observed vs. Third-Party Weather Data Sets." Sustainability 12, no. 17 (August 21, 2020): 6788. http://dx.doi.org/10.3390/su12176788.
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The use of building energy models (BEMs) is becoming increasingly widespread for assessing the suitability of energy strategies in building environments. The accuracy of the results depends not only on the fit of the energy model used, but also on the required external files, and the weather file is one of the most important. One of the sources for obtaining meteorological data for a certain period of time is through an on-site weather station; however, this is not always available due to the high costs and maintenance. This paper shows a methodology to analyze the impact on the simulation results when using an on-site weather station and the weather data calculated by a third-party provider with the purpose of studying if the data provided by the third-party can be used instead of the measured weather data. The methodology consists of three comparison analyses: weather data, energy demand, and indoor temperature. It is applied to four actual test sites located in three different locations. The energy study is analyzed at six different temporal resolutions in order to quantify how the variation in the energy demand increases as the time resolution decreases. The results showed differences up to 38% between annual and hourly time resolutions. Thanks to a sensitivity analysis, the influence of each weather parameter on the energy demand is studied, and which sensors are worth installing in an on-site weather station are determined. In these test sites, the wind speed and outdoor temperature were the most influential weather parameters.
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Gutiérrez González, Vicente, Germán Ramos Ruiz, and Carlos Fernández Bandera. "Impact of Actual Weather Datasets for Calibrating White-Box Building Energy Models Base on Monitored Data." Energies 14, no. 4 (February 23, 2021): 1187. http://dx.doi.org/10.3390/en14041187.
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The need to reduce energy consumption in buildings is an urgent task. Increasing the use of calibrated building energy models (BEM) could accelerate this need. The calibration process of these models is a highly under-determined problem that normally yields multiple solutions. Among the uncertainties of calibration, the weather file has a primary position. The objective of this paper is to provide a methodology for selecting the optimal weather file when an on-site weather station with local sensors is available and what is the alternative option when it is not and a mathematically evaluation has to be done with sensors from nearby stations (third-party providers). We provide a quality assessment of models based on the Coefficient of Variation of the Root Mean Square Error (CV(RMSE)) and the Square Pearson Correlation Coefficient (R2). The research was developed on a control experiment conducted by Annex 58 and a previous calibration study. This is based on the results obtained with the study case based on the data provided by their N2 house.
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Tchamda, André Rodrigue, Merlain Boris Djousse K., Anselme Maffo Koumetio, Mathias Fru Fonteh, François Becau Pelap, Robert Tchitnga, and Martin Kom. "Low-cost Wireless Personal Weather Station for Rural Farmers." Transactions on Networks and Communications 9, no. 1 (February 28, 2021): 01–10. http://dx.doi.org/10.14738/tnc.91.9647.
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This document presents the design of a prototype of a low-cost personal weather station suitable for farmers in rural areas who are or may not be engaged in rudimentary agriculture. This prototype measure several weather data: temperature, relative humidity, wind speed, wind direction, rainfall. For further data analysis, these are transmitted for recording to a remote server via wireless communication. The server offers data extraction possibilities in multiple file formats. A prototyping of the personal weather station is designed and commissioned. An extract of the results over two days is presented in the results section of this document
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Giri, Pratit Raj, and Gajendra Sharma. "Apache Hadoop Architecture, Applications, and Hadoop Distributed File System." Semiconductor Science and Information Devices 4, no. 1 (May 18, 2022): 14. http://dx.doi.org/10.30564/ssid.v4i1.4619.
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The data and internet are highly growing which causes problems in management of the big-data. For these kinds of problems, there are many software frameworks used to increase the performance of the distributed system. This software is used for the availability of large data storage. One of the most beneficial software frameworks used to utilize data in distributed systems is Hadoop. This paper introduces Apache Hadoop architecture, components of Hadoop, their significance in managing vast volumes of data in a distributed system. Hadoop Distributed File System enables the storage of enormous chunks of data over a distributed network. Hadoop Framework maintains fsImage and edits files, which supports the availability and integrity of data. This paper includes cases of Hadoop implementation, such as monitoring weather, processing bioinformatics.
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Hutchison, Keith D., Barbara D. Iisager, Sudhakar Dipu, Xiaoyan Jiang, Johannes Quaas, and Randy Markwardt. "A Methodology for Verifying Cloud Forecasts with VIIRS Imagery and Derived Cloud Products—A WRF Case Study." Atmosphere 10, no. 9 (September 5, 2019): 521. http://dx.doi.org/10.3390/atmos10090521.
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A methodology is presented to evaluate the accuracy of cloud cover fraction (CCf) forecasts generated by numerical weather prediction (NWP) and climate models. It is demonstrated with a case study consisting of simulations from the Weather Research and Forecasting (WRF) model. In this study, since the WRF CCf forecasts were initialized with reanalysis fields from the North American Mesoscale (NAM) Forecast System, the characteristics of the NAM CCf products were also evaluated. The procedures relied extensively upon manually-generated, binary cloud masks created from VIIRS (Visible Infrared Imager Radiometry Suite) imagery, which were subsequently converted into CCf truth at the resolution of the NAM and WRF gridded data. The initial results from the case study revealed biases toward under-clouding in the NAM CCf analyses and biases toward over-clouding in the WRF CCf products. These biases were evident in images created from the gridded NWP products when compared to VIIRS imagery and CCf truth data. Thus, additional simulations were completed to help assess the internal procedures used in the WRF model to translate moisture forecast fields into layered CCf products. Two additional sets of WRF CCf 24 h forecasts were generated for the region of interest using WRF restart files. One restart file was updated with CCf truth data and another was not changed. Over-clouded areas in the updated WRF restart file that were reduced with an update of the CCf truth data became over-clouded again in the WRF 24 h forecast, and were nearly identical to those from the unchanged restart file. It was concluded that the conversion of WRF forecast fields into layers of CCf products deserves closer examination in a future study.
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Passos Filho, José Aderson Araújo, Bruno de Payva y. Raviolo, Natasha Catunda, Nayana Helena Barbosa de Castro, Karoline Cordeiro de Andrade, and Daniel Ribeiro Cardoso. "Synthesizing test Reference Year files from known climate patterns of nearby cities." PARC Pesquisa em Arquitetura e Construção 10 (December 29, 2019): e019030. http://dx.doi.org/10.20396/parc.v10i0.8653706.
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The importance of an architecture adapted to its climatic context is often debated. In order to avoid future unexpected environmental behavior or failure of a building during its use, building simulation tools are used in the design and require complete and consistent weather data. However, such data are not always available for the locations where buildings are simulated, and the use of data from neighboring cities becomes usual. There are, though, several uncertainties involved in the behavior of environmental variables when the climate of large urban centers is attributed to nearby localities and areas with more significant vegetation cover, water bodies, different topography, among others. The present paper aims to present the process of preparing a weather file for the Pecém Industrial and Port Complex, located at 40 km from the capital Fortaleza, Brazil, in order to be used in simulations during the design process of buildings. The synthesis of the file was achieved through the collection and treatment of information measured in loco, the application of recommended models for the estimation of missing data, and the development of an alternative method for the estimation of a Test Reference Year of localities without weather data of several years.
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Chui, Timothy C. Y., David Siuta, Gregory West, Henryk Modzelewski, Roland Schigas, and Roland Stull. "On Producing Reliable and Affordable Numerical Weather Forecasts on Public Cloud-Computing Infrastructure." Journal of Atmospheric and Oceanic Technology 36, no. 3 (March 2019): 491–509. http://dx.doi.org/10.1175/jtech-d-18-0142.1.
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AbstractCloud-computing resources are increasingly used in atmospheric research and real-time weather forecasting. The aim of this study is to explore new ways to reduce cloud-computing costs for real-time numerical weather prediction (NWP). One way is to compress output files to reduce data egress costs. File compression techniques can reduce data egress costs by over 50%. Data egress costs can be further minimized by postprocessing in the cloud and then exporting the smaller resulting files while discarding the bulk of the raw NWP output. Another way to reduce costs is to use preemptible resources, which are virtual machines (VMs) on the Google Cloud Platform (GCP) that clients can use at an 80% discount (compared to nonpreemptible VMs), but which can be turned off by the GCP without warning. By leveraging the restart functionality in the Weather Research and Forecasting (WRF) Model, preemptible resources can be used to save 60%–70% in weather simulation costs without compromising output reliability. The potential cost savings are demonstrated in forecasts over the Canadian Arctic and in a case study of NWP runs for the West African monsoon (WAM) of 2017. The choice in model physics, VM specification, and use of the aforementioned cost-saving measures enable simulation costs to be low enough such that the cloud can be a viable platform for running short-range ensemble forecasts when compared to the cost of purchasing new computer hardware.
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Andrian, Kim, and Ju. "A Distributed File-Based Storage System for Improving High Availability of Space Weather Data." Applied Sciences 9, no. 23 (November 21, 2019): 5024. http://dx.doi.org/10.3390/app9235024.
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In space science research, the Indonesia National Institute of Aeronautics and Space (LAPAN) is concerned with the development of a system that provides actual information and predictions called the Space Weather Information and Forecast Services (SWIFtS). SWIFtS is supported by a data storage system that serves data, implementing a centralized storage model. This has some problems that impact to researchers as the primary users. The single point of failure and also the delay in data updating on the server is a significant issue when researchers need the latest data, but the server is unable to provide it. To overcome these problems, we proposed a new system that utilized a decentralized model for storing data, leveraging the InterPlanetary File System (IPFS) file system. Our proposed method focused on the automated background process, and its scheme would increase the data availability and throughput by spreading it into nodes through a peer-to-peer connection. Moreover, we also included system monitoring for real-time data flow from each node and information of node status that combines active and passive approaches. For system evaluation, the experiment was performed to determine the performance of the proposed system compared to the existing system by calculating mean replication time and the mean throughput of a node. As expected, performance evaluations showed that our proposed scheme had faster file replication time and supported high throughput.
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Maharjan, Ashish, and Ashish Shakya. "Enhancement of WRF Model Using CUDA." Interdisciplinary Journal of Innovation in Nepalese Academia 1, no. 1 (December 31, 2022): 16–22. http://dx.doi.org/10.3126/idjina.v1i1.51963.
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The main aim of this paper is to implement and run WRF (Weather Research and Forecasting) model on Graphical Processing Unit (GPU) with the help of NVidia’s CUDA (Compute Unified Device Architecture) in a normal machine. Without GPU, the model needs high-end systems to be executed smoothly. For this, CUDA code is executed for a particular microphysics module to create an object file which is then added to the WRF model. Later the object file is executed on the GPU with the help of CUDA.
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Smith, Amanda, Benjamin Stürmer, Travis Thurber, and Chris Vernon. "diyepw: A Python package for Do-It-Yourself EnergyPlus weather file generation." Journal of Open Source Software 6, no. 64 (August 31, 2021): 3313. http://dx.doi.org/10.21105/joss.03313.
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Hashem, Noor M., and Heba Kh Abbas. "Automatic Detection and Recognition of Car Plates Based on Cascade Classifier." Ibn AL-Haitham Journal For Pure and Applied Sciences 36, no. 1 (January 21, 2023): 130–38. http://dx.doi.org/10.30526/36.1.2895.
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The study consists of video clips of all cars parked in the selected area. The studied camera height is1.5 m, and the video clips are 18video clips. Images are extracted from the video clip to be used for training data for the cascade method. Cascade classification is used to detect license plates after the training step. Viola-jones algorithm was applied to the output of the cascade data for camera height (1.5m). The accuracy was calculated for all data with different weather conditions and local time recoding in two ways. The first used the detection of the car plate based on the video clip, and the accuracy was 100%. The second is using the clipped images stored in the positive file, based on the training file (XML file), where the accuracy was 99.8%.
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Fiorito, Francesco, Giandomenico Vurro, Francesco Carlucci, Ludovica Maria Campagna, Mariella De Fino, Salvatore Carlucci, and Fabio Fatiguso. "Adaptation of Users to Future Climate Conditions in Naturally Ventilated Historic Buildings: Effects on Indoor Comfort." Energies 15, no. 14 (July 7, 2022): 4984. http://dx.doi.org/10.3390/en15144984.
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User behaviour can significantly affect indoor thermal comfort conditions, as well as energy consumption, especially in existing buildings with high thermal masses where natural cross ventilation is the main strategy to reduce cooling loads. The aims of this paper were: (i) to compare how behavioural changes evaluated by means of rule-based and stochastic models lead to changes in indoor thermal comfort levels, and (ii) to define the patterns of indoor thermal comfort in historic residential buildings in future scenarios. To this end, a historic building located in Molfetta (Southern Italy) was analysed using a dynamic energy simulation engine in five weather scenarios (Typical Meteorological Year, current extreme weather file 2018, predicted weather files for 2020, 2050, and 2080 generated by morphing method), and stochastic and rule-based models for window openings were adopted and compared. The results showed that the stochastic model was more accurate than the rule-based one, resulting in a reduction of discomfort conditions during the summer period between 30% and 50% in all climate scenarios. However, although the differences between predicted discomfort levels using rule-based and stochastic models tended to increase, discomfort levels still appeared to be not acceptable in the 2050 and 2080 scenarios due to the rising temperature driven by climate change.
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SHIYANOVA, ANASTASIA A. "SEMANTICS OF THE KHANTY ADJECTIVES CHARACTERIZING WEATHER PHENOMENA." Theoretical and Applied Linguistics, no. 2 (2021): 169–78. http://dx.doi.org/10.22250/2410-7190_2021_7_2_169_178.
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The article considers the semantic structure of the Khanty adjectives characterizing weather phenomena. The study is carried out on the material of Western dialects of Khanty - Kazymsky, Shuryshkar and Priural. The material for the study was a card file composed of examples of bilingual dictionaries on the Khanty language, folklore collections and samples of spoken speech collected from Khanty native speakers. The result of the study was a description of the semantics of the language units characterizing weather events in all studied dialects of the Khanty language. Five word-building patterns were discovered for the adjectives: 1) «N+ =әӈ/=єӈ», 2) «N + =ԓы», 3) «N + =и/=ы», 4) «V + =ам/=әм», 5) «N + =ам». These adjectives form 4 lexico-semantic groups. Neither systematization of these lexico-semantic groups has been previously performed, nor the 5 word-building patterns have been previously reported in the literature devoted to the Khanty language. Another contribution was clarification of lexical units characterizing natural phenomena.
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Watkins, R., GJ Levermore, and JB Parkinson. "Constructing a future weather file for use in building simulation using UKCP09 projections." Building Services Engineering Research and Technology 32, no. 3 (March 8, 2011): 293–99. http://dx.doi.org/10.1177/0143624410396661.
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Kormos, Patrick R., Danny G. Marks, Mark S. Seyfried, Scott C. Havens, Andrew Hedrick, Kathleen A. Lohse, Micah Sandusky, Annelen Kahl, and David Garen. "31 years of hourly spatially distributed air temperature, humidity, and precipitation amount and phase from Reynolds Critical Zone Observatory." Earth System Science Data 10, no. 2 (June 28, 2018): 1197–205. http://dx.doi.org/10.5194/essd-10-1197-2018.
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Abstract. Thirty-one years of spatially distributed air temperature, relative humidity, dew point temperature, precipitation amount, and precipitation phase data are presented for the Reynolds Creek Experimental Watershed, which is part of the Critical Zone Observatory network. The air temperature, relative humidity, and precipitation amount data are spatially distributed over a 10 m lidar-derived digital elevation model at an hourly time step using a detrended kriging algorithm. This 21 TB dataset covers a wide range of weather extremes in a mesoscale basin (238 km2) that encompasses the rain–snow transition zone and should find widespread application in earth science modeling communities. Spatial data allow for a more holistic analysis of basin means and elevation gradients, compared to weather station data measured at specific locations. Files are stored in the NetCDF file format, which allows for easy spatiotemporal averaging and/or subsetting. Data are made publicly available through an OPeNDAP-enabled THREDDS server hosted by Boise State University Libraries in support of the Reynolds Creek Critical Zone Observatory (https://doi.org/10.18122/B2B59V).
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Carlos-Mancilla, Miriam A., Luis F. Luque-Vega, Héctor A. Guerrero-Osuna, Gerardo Ornelas-Vargas, Yehoshua Aguilar-Molina, and Luis E. González-Jiménez. "Educational Mechatronics and Internet of Things: A Case Study on Dynamic Systems Using MEIoT Weather Station." Sensors 21, no. 1 (December 29, 2020): 181. http://dx.doi.org/10.3390/s21010181.
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This paper presents the design and development of an IoT device, called MEIoT weather station, which combines the Educational Mechatronics and IoT to develop the required knowledge and skills for Industry 4.0. MEIoT weather station connects to the internet, measures eight weather variables, and upload the sensed data to the cloud. The MEIoT weather station is the first device working with the IoT architecture of the National Digital Observatory of Intelligent Environments. In addition, an IoT open platform, GUI-MEIoT, serves as a graphic user interface. GUI-MEIoT is used to visualize the real-time data of the weather variables, it also shows the historical data collected, and allows to export them to a csv file. Finally, an OBNiSE architecture application to Engineering Education is presented with a dynamic system case of study that includes the instructional design carried out within the Educational Mechatronics Conceptual Framework (EMCF) to show the relevance of this proposal. This work main contribution to the state of art is the design and integration of the OBNiSE architecture within the EMCF offering the possibility to add more IoT devices for several smart domains such as smart campus, smart cities, smart people and smart industries.
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Sommer, Philipp S., and Jed O. Kaplan. "A globally calibrated scheme for generating daily meteorology from monthly statistics: Global-WGEN (GWGEN) v1.0." Geoscientific Model Development 10, no. 10 (October 16, 2017): 3771–91. http://dx.doi.org/10.5194/gmd-10-3771-2017.
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Abstract. While a wide range of Earth system processes occur at daily and even subdaily timescales, many global vegetation and other terrestrial dynamics models historically used monthly meteorological forcing both to reduce computational demand and because global datasets were lacking. Recently, dynamic land surface modeling has moved towards resolving daily and subdaily processes, and global datasets containing daily and subdaily meteorology have become available. These meteorological datasets, however, cover only the instrumental era of the last approximately 120 years at best, are subject to considerable uncertainty, and represent extremely large data files with associated computational costs of data input/output and file transfer. For periods before the recent past or in the future, global meteorological forcing can be provided by climate model output, but the quality of these data at high temporal resolution is low, particularly for daily precipitation frequency and amount. Here, we present GWGEN, a globally applicable statistical weather generator for the temporal downscaling of monthly climatology to daily meteorology. Our weather generator is parameterized using a global meteorological database and simulates daily values of five common variables: minimum and maximum temperature, precipitation, cloud cover, and wind speed. GWGEN is lightweight, modular, and requires a minimal set of monthly mean variables as input. The weather generator may be used in a range of applications, for example, in global vegetation, crop, soil erosion, or hydrological models. While GWGEN does not currently perform spatially autocorrelated multi-point downscaling of daily weather, this additional functionality could be implemented in future versions.
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Życzkowski, Marcin, Joanna Szłapczyńska, and Rafał Szłapczyński. "Review of Weather Forecast Services for Ship Routing Purposes." Polish Maritime Research 26, no. 4 (December 1, 2019): 80–89. http://dx.doi.org/10.2478/pomr-2019-0069.
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Abstract Weather data is nowadays used in a variety of navigational and ocean engineering research problems: from the obvious ones like voyage planning and routing of sea-going vessels, through the analysis of stability-related phenomena, to detailed modelling of ships’ manoeuvrability for collision avoidance purposes. Apart from that, weather forecasts are essential for passenger cruises and fishing vessels that want to avoid the risk associated with severe hydro-meteorological conditions. Currently, there is a wide array of services that offer weather predictions. These services include the original sources – services that make use of their own infrastructure and research models – as well as those that further postprocess the data obtained from the original sources. The existing services also differ in their update frequency, area coverage, geographical resolution, natural phenomena taken into account and finally – output file formats. In the course of the ROUTING project, primarily addressing ship weather routing accounting for changeable weather conditions, the necessity arose to prepare a report on the state-of-the-art in numerical weather prediction (NWP) modelling. Based on the report, this paper offers a thorough review of the existing weather services and detailed information on how to access the data offered by these services. While this review has been done with transoceanic ship routing in mind, hopefully it will also be useful for a number of other applications, including the already mentioned collision avoidance solutions.
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Pouriya, Jafarpur, and Berardi Umberto. "Building energy demand within a climate change perspective: The need for future weather file." IOP Conference Series: Materials Science and Engineering 609 (October 23, 2019): 072037. http://dx.doi.org/10.1088/1757-899x/609/7/072037.
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Yassaghi, Hamed, and Simi Hoque. "Impact Assessment in the Process of Propagating Climate Change Uncertainties into Building Energy Use." Energies 14, no. 2 (January 11, 2021): 367. http://dx.doi.org/10.3390/en14020367.
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Buildings are subject to significant stresses due to climate change and design strategies for climate resilient buildings are rife with uncertainties which could make interpreting energy use distributions difficult and questionable. This study intends to enhance a robust and credible estimate of the uncertainties and interpretations of building energy performance under climate change. A four-step climate uncertainty propagation approach which propagates downscaled future weather file uncertainties into building energy use is examined. The four-step approach integrates dynamic building simulation, fitting a distribution to average annual weather variables, regression model (between average annual weather variables and energy use) and random sampling. The impact of fitting different distributions to the weather variable (such as Normal, Beta, Weibull, etc.) and regression models (Multiple Linear and Principal Component Regression) of the uncertainty propagation method on cooling and heating energy use distribution for a sample reference office building is evaluated. Results show selecting a full principal component regression model following a best-fit distribution for each principal component of the weather variables can reduce the variation of the output energy distribution compared to simulated data. The results offer a way of understanding compound building energy use distributions and parsing the uncertain nature of climate projections.
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Yassaghi, Hamed, and Simi Hoque. "Impact Assessment in the Process of Propagating Climate Change Uncertainties into Building Energy Use." Energies 14, no. 2 (January 11, 2021): 367. http://dx.doi.org/10.3390/en14020367.
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Buildings are subject to significant stresses due to climate change and design strategies for climate resilient buildings are rife with uncertainties which could make interpreting energy use distributions difficult and questionable. This study intends to enhance a robust and credible estimate of the uncertainties and interpretations of building energy performance under climate change. A four-step climate uncertainty propagation approach which propagates downscaled future weather file uncertainties into building energy use is examined. The four-step approach integrates dynamic building simulation, fitting a distribution to average annual weather variables, regression model (between average annual weather variables and energy use) and random sampling. The impact of fitting different distributions to the weather variable (such as Normal, Beta, Weibull, etc.) and regression models (Multiple Linear and Principal Component Regression) of the uncertainty propagation method on cooling and heating energy use distribution for a sample reference office building is evaluated. Results show selecting a full principal component regression model following a best-fit distribution for each principal component of the weather variables can reduce the variation of the output energy distribution compared to simulated data. The results offer a way of understanding compound building energy use distributions and parsing the uncertain nature of climate projections.
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Rinio, Markus. "PVcheck—A Software to Check Your Photovoltaic System." Energies 14, no. 20 (October 17, 2021): 6757. http://dx.doi.org/10.3390/en14206757.
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Having a photovoltaic (PV) system raises the question of whether it runs as expected. Measuring its energy yield takes a long time and the result still contains uncertainties from varying weather conditions and possible shading of the modules. Here, a free software PVcheck to measure the peak power of the system is announced, using the power data of a single sunny day. The software loads a data file of the generated power as a function of time from this day. This data file is provided by typical inverters. The software then simulates this power curve using known parameters like angle and location of the PV system. The assumed peak power of the simulation can then be adjusted so that the simulated curve matches the measured one. The software runs under Microsoft Windows™ and makes use of the free library pvlib python. The simulation can be refined by importing weather data like temperature, wind speed, and insolation. Furthermore, curves describing the nominal module efficiency as a function of the illumination intensity as well as the power-dependent inverter efficiency can be included in the simulation. First results reveal a good agreement of the simulation with experimental data. The software can be used to detect strong problems in PV systems after installation and to monitor their long-time operation.
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Ciancio, Virgilio, Serena Falasca, Iacopo Golasi, Pieter de Wilde, Massimo Coppi, Livio de Santoli, and Ferdinando Salata. "Resilience of a Building to Future Climate Conditions in Three European Cities." Energies 12, no. 23 (November 27, 2019): 4506. http://dx.doi.org/10.3390/en12234506.
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Building energy need simulations are usually performed using input files that contain information about the averaged weather data based on historical patterns. Therefore, the simulations performed are not able to provide information about possible future scenarios due to climate change. In this work, future trends of building energy demands due to the climate change across Europe were studied by comparing three time steps (present, 2050, and -2080) in three different European cities, characterized by different Köppen-Geiger climatic classes. A residential building with modern architectural features was taken into consideration for the simulations. Future climate conditions were reached by applying the effects of climate changes to current hourly meteorological data though the climate change tool world weather file generator (CCWorldWeatherGen) tool, according to the guidelines established by the Intergovernmental Panel on Climate Change. In order to examine the resilience of the building, the simulations carried out were compared with respect to: peak power, median values of the power, and energy consumed by heating and cooling system. The observed trend shows a general reduction in the energy needs for heating (–46% for Aberdeen, –80% for Palermo, –36% for Prague in 2080 compared to the present) and increase (occurrence for Aberdeen) in cooling requirements. These results imply a revaluation of system size.
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Kimambo, Offoro Neema, Hector Chikoore, and Jabulani Ray Gumbo. "Understanding the Effects of Changing Weather: A Case of Flash Flood in Morogoro on January 11, 2018." Advances in Meteorology 2019 (April 21, 2019): 1–11. http://dx.doi.org/10.1155/2019/8505903.
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Floods are the leading cause of hydrometeorological disasters in East Africa. Regardless of where, when, and how the event has happened, floods affect social security as well as environmental damages. Understanding floods dynamics, their impacts, and management is thus critical, especially in climate risk assessment. In the present study, a flash flood (a case of an episodic hydrological event) which happened on January 11, 2018, in Morogoro, Tanzania, is examined and synthesized. Data were courtesy of the National Oceanic and Atmospheric Administration Global Forecasting System (NOAA GFS) (forecast data), Tanzania Meteorological Agency (TMA), and Sokoine University of Agriculture (for the automatic weather data). With the help of ZyGRIB-grib file visualization software (version 8.01, under General Public License (GNU GPL v3)), the forecast data and patterns of the observation from the automatic weather station (temperatures, wind speed and directions, rainfall, humidity, and pressure) and the long-term rainfall data analysis in the study area made it possible. This study contributes to the knowledge of understanding the changing weather for planning and management purposes. Both forecasts and the observations captured the flash flood event. The rain was in the category of heavy rainfall (more than 50 mm per day) as per the regional guidelines. The synergy between the forecasts and the 30-minute weather observation interval captured the fundamental weather patterns that describe the event. For studying the nature and impacts of flash floods in the region, the integration of automatic weather observation into the systems of national meteorological centers is inevitable. Additionally, as part of an integrated disaster risk reduction effort, there is a need for a review on catchment management strategies.
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Demir, Saniye, and İrfan Oğuz. "Validation of The Weather Generator CLIGEN with Season Precipitation Data in Tokat Province." Turkish Journal of Agriculture - Food Science and Technology 7, no. 10 (October 12, 2019): 1589. http://dx.doi.org/10.24925/turjaf.v7i10.1589-1596.2633.
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In models that predict hydrologic, ecological and product-yield relationship, the climate interface file is widely used. Today, CLIGEN is the most widely used climate model. While this model is extensively used in many countries around the world, it is not used commonly in our country. In this study, daily data belonging to Tokat climate conditions were simulated with CLIGEN. Observed and simulated precipitation was classified as the wet/dry period. The performance of the CLIGEN climate model was evaluated in both wet and dry periods according to the seasons. The relationship between the obtained precipitation data was statistically determined by Kolmogorov-Smirnov and t-test. Tokat province is located in the arid and semi-arid climate zone. The performance of the model in predicting precipitation in all seasons during the dry period was found to be quite successful.
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Dannenberg, Valentin, Robert Schüler, and Achill Schürmann. "A Data Processing Framework for Polar Performance Diagrams." Applied Sciences 12, no. 6 (March 17, 2022): 3085. http://dx.doi.org/10.3390/app12063085.
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Polar performance diagrams are commonly used to predict the performance of a sailing vessel under given wind conditions. They are, in particular, an essential part of robotic sailing vessels and a basis for weather routing algorithms. In this paper we introduce a new framework for scientific work with such diagrams, which we make available as an open source Python package. It contains a model for the creation of polar performance diagrams from measurement data and supports different representations of polar performance diagrams for different tasks. The framework also includes several methods for the visualisation of polar performance diagrams, for example for scientific publications. Additionally, the presented framework solves basic tasks for the future development of weather-routing algorithms in a far more general manner than other methods did previously: it provides the calculation of costs of a sailing trip using custom cost functions, suggestions of optimal steering using convex hull calculations and a more flexible calculation of isochrone points, using custom weather models. Altogether, the presented framework allows future researchers to more easily handle polar performance diagrams. The corresponding Python package is compatible with various established file formats.
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Yashiro, Hisashi, Koji Terasaki, Takemasa Miyoshi, and Hirofumi Tomita. "Performance evaluation of a throughput-aware framework for ensemble data assimilation: the case of NICAM-LETKF." Geoscientific Model Development 9, no. 7 (July 5, 2016): 2293–300. http://dx.doi.org/10.5194/gmd-9-2293-2016.
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Abstract. In this paper, we propose the design and implementation of an ensemble data assimilation (DA) framework for weather prediction at a high resolution and with a large ensemble size. We consider the deployment of this framework on the data throughput of file input/output (I/O) and multi-node communication. As an instance of the application of the proposed framework, a local ensemble transform Kalman filter (LETKF) was used with a Non-hydrostatic Icosahedral Atmospheric Model (NICAM) for the DA system. Benchmark tests were performed using the K computer, a massive parallel supercomputer with distributed file systems. The results showed an improvement in total time required for the workflow as well as satisfactory scalability of up to 10 K nodes (80 K cores). With regard to high-performance computing systems, where data throughput performance increases at a slower rate than computational performance, our new framework for ensemble DA systems promises drastic reduction of total execution time.
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Maria Sacht, Helenice, Andréa de Oliveira Cardoso, and Victor Figueiredo Roriz. "Influence of the surroundings areas in the microclimates of Santo André City – Brazil and Indication of Bioclimatic Strategies for Buildings." E3S Web of Conferences 111 (2019): 06005. http://dx.doi.org/10.1051/e3sconf/201911106005.
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The characterization of the climate of Santo André and its weather data are essential instruments for the study of implantation, both of new buildings and solutions of retrofit, as well as for the elaboration of projects of urban interventions or degraded areas occupation. In this study, meteorological weather data were obtained by meteorological stations in Santo André, a municipality located in the region of ABC Paulista (São Paulo State), for five different points of the city in order to evaluate the different micro climates and the influence of the surroundings in microclimates and an climate file in .epw format was developed for analysed bioclimatic strategies for buildings. Results show temperature and humidity variations among the five microclimates analyzed, as a consequence of the following actions: replacement of vegetation by constructions, asphalt, concrete and other impermeable surfaces, increased air pollution which cause a great absorption of solar radiation, forming a barrier for air circulation and of pollutant gases into the atmosphere creating urban heat island effects.
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Gowan, Taylor A., John D. Horel, Alexander A. Jacques, and Adair Kovac. "Using Cloud Computing to Analyze Model Output Archived in Zarr Format." Journal of Atmospheric and Oceanic Technology 39, no. 4 (April 2022): 449–62. http://dx.doi.org/10.1175/jtech-d-21-0106.1.
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Abstract Numerical weather prediction centers rely on the Gridded Binary Second Edition (GRIB2) file format to efficiently compress and disseminate model output as two-dimensional grids. User processing time and storage requirements are high if many GRIB2 files with size O(100 MB, where B = bytes) need to be accessed routinely. We illustrate one approach to overcome such bottlenecks by reformatting GRIB2 model output from the High-Resolution Rapid Refresh (HRRR) model of the National Centers for Environmental Prediction to a cloud-optimized storage type, Zarr. Archives of the original HRRR GRIB2 files and the resulting Zarr stores on Amazon Web Services (AWS) Simple Storage Service (S3) are available publicly through the Amazon Sustainability Data Initiative. Every hour, the HRRR model produces 18- or 48-hourly GRIB2 surface forecast files of size O(100 MB). To simplify access to the grids in the surface files, we reorganize the HRRR model output for each variable and vertical level into Zarr stores of size O(1 MB), with chunks O(10 kB) containing all forecast lead times for 150 × 150 gridpoint subdomains. Open-source libraries provide efficient access to the compressed Zarr stores using cloud or local computing resources. The HRRR-Zarr approach is illustrated for common applications of sensible weather parameters, including real-time alerts for high-impact situations and retrospective access to output from hundreds to thousands of model runs. For example, time series of surface pressure forecast grids can be accessed using AWS cloud computing resources approximately 40 times as fast from the HRRR-Zarr store as from the HRRR-GRIB2 archive. Significance Statement The rapid evolution of computing power and data storage have enabled numerical weather prediction forecasts to be generated faster and with more detail than ever before. The increased temporal and spatial resolution of forecast model output can force end users with finite memory and storage capabilities to make pragmatic decisions about which data to retrieve, archive, and process for their applications. We illustrate an approach to alleviate this access bottleneck for common weather analysis and forecasting applications by using the Amazon Web Services (AWS) Simple Storage Service (S3) to store output from the High-Resolution Rapid Refresh (HRRR) model in Zarr format. Zarr is a relatively new data storage format that is flexible, compressible, and designed to be accessed with open-source software either using cloud or local computing resources. The HRRR-Zarr dataset is publicly available as part of the AWS Sustainability Data Initiative.
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Vallati, Andrea, Luca Mauri, and Chiara Colucci. "How the urban environment affects the microclimate and the building energy demand for the City of Rome." Thermal Science 23, Suppl. 4 (2019): 1035–42. http://dx.doi.org/10.2298/tsci19s4035v.
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Urban heat island has significant impacts on buildings? energy consumption. The phenomenon is associated with increased urban air temperatures compared to the air temperature of the surrounding rural or suburban areas. The ambient air temperature growth due to climate changes and the urban heat island phenomenon are dramatically increasing the cooling demand in buildings. This is worsened by irradiation conditions, construction technologies, and subjective comfort expectations. This paper examines the impact of the urban environment on the energy demand of buildings, considering the case of two districts of the city of Rome, Italy: one is representative of a central zone, the other of a rural zone. Weather data were then used to calculate the thermal demand of a typical Italian building, ideally located in the monitored areas of the city. Standalone building with modified weather file was modeled in TRNSYS. Results show that urban heat island intensity causes an increase in cooling demand up to +33% for the urban area (+20% for the rural area) compared to the demand calculated using weather data from airportual areas. On the other hand, urban heat island intensity has a positive effect on heating demand which turns out to decrease up to -32% for the urban area (-14% for the rural area).
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De Masi, Rosa Francesca, Valentino Festa, Antonio Gigante, Margherita Mastellone, Silvia Ruggiero, and Giuseppe Peter Vanoli. "Effect of Climate Changes on Renewable Production in the Mediterranean Climate: Case Study of the Energy Retrofit for a Detached House." Sustainability 13, no. 16 (August 6, 2021): 8793. http://dx.doi.org/10.3390/su13168793.
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One of the strategies of the European Green Deal is the increment of renewable integration in the civil sector and the mitigation of the impact of climate change. With a statistical and critical approach, the paper analyzes these aspects by means of a case study simulated in a cooling dominated climate. It consists of a single-family house representative of the 1980s Italian building stock. Starting from data monitored between 2015 and 2020, a weather file was built with different methodologies. The first objective was the evaluation of how the method for selecting the solar radiation influences the prevision of photovoltaic productivity. Then, a sensitivity analysis was developed, by means of modified weather files according to representative pathways defined by the Intergovernmental Panel on Climate Change Fifth Assessment Report. The results indicate that the climate changes will bring an increment of photovoltaic productivity while the heating energy need will be reduced until 45% (e.g., in March) and the cooling energy need will be more than double compared with the current conditions. The traditional efficiency measures are not resilient because the increase of the cooling demand could be not balanced. The maximization of installed photovoltaic power is a solution for increasing the resilience. Indeed, going from 3.3 kWp to 6.9 kWp for the worst emission scenario, in a typical summer month (e.g., August), the self-consumption increases until 33% meanwhile the imported electricity passes from 28% to 17%.
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Atthaillah, Atthaillah, and Andik Bintoro. "USEFUL DAYLIGHT ILLUMINANCE (UDI) PADA RUANG BELAJAR SEKOLAH DASAR DI KAWASAN URBAN PADAT TROPIS (STUDI KASUS: SD NEGERI 2 DAN 6 BANDA SAKTI, LHOKSEUMAWE, ACEH, INDONESIA)." LANGKAU BETANG: JURNAL ARSITEKTUR 6, no. 2 (December 13, 2019): 72. http://dx.doi.org/10.26418/lantang.v6i2.33940.
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Studi ini melakukan evaluasi pencahayaan alami pada ruang belajar Sekolah Dasar Negeri 2 dan 6 Banda Sakti, Lhokseumawe. Sekolah tersebut berada pada lokasi urban padat dan merupakan sekolah dengan bangunan terluas di kecamatan Banda Sakti. Ada 36 (tiga puluh enam) ruang kelas yang dilakukan simulasi pencahayaan alami pada objek studi ini. Simulasi pencahayaan alami dilakukan dengan metrik useful daylight illuminance (UDI). Metrik ini menggunakan data cuaca lokasi objek penelitian dalam hal ini data cuaca Kota Lhokseumawe dalam format energyplus weather (EPW) file. Pemodelan untuk simulasi dilakukan dengan piranti Rhinoceros, sementara algoritma simulasi dibuat menggunakan Grasshopper dengan tambahan plugin Ladybug Tools. Ladybug Tools memberikan akses ke engine simulasi Daysim yang merupakan perangkat simulasi pencahayaan alami yang tervalidasi. Pada penelitian ini ketegori UDI terpenuhi dialterasi sesuai dengan standar pencahayaan alami disarankan dalam SNI 03-6197 yaitu 250-750 Lux untuk ruangan yang digunakan pada bangunan pendidikan dalam hal ini sekolah dasar. Hasil menunjukkan tidak ada ruang kelas yang dikategorikan baik pada SDN 2 dan 6 Banda Sakti. Hanya terdapat 29 (dua puluh sembilan) kelas dengan kategori cukup dan 7 (tujuh) ruang dengan kategori kurangUSEFUL DAYLIGHT ILLUMINANCE (UDI) IN ELEMENTARY SCHOOL CLASSROOMS IN TROPICAL HIGH DENSITY URBAN AREA (CASE STUDY: SD NEGERI 2 AND 6 BANDA SAKTI, LHOKSEUMAWE, ACEH, INDONESIA) This study evaluated the daylight distribution within classrooms at Sekolah Dasar Negeri 2 and 6 Banda Sakti, Lhokseumawe. The school is located in high-density urban area, and it was the largest state elementary school building in Banda Sakti. 36 (thirty-six) classrooms were simulated for its daylight performance. The simulation utilized useful daylight illuminance (UDI) metric. The metric used the local weather file for Lhokseumawe in energyplus weather (EPW) format. Modeling for simulation utilized Rhinoceros; further, the simulation algorithm was created using Grasshopper with the extension of Ladybug Tools. Ladybug Tools opened access to a Daysim, a validated daylight simulation engine. In this study, the useful daylight was referenced to SNI 03-6197, which was 250-750 Lux for educational spaces at this study classrooms for the elementary school. The result showed there was none of the classroom fallen under a good category. 29 (twenty-nine) classrooms was under insufficient category and 7 (seven) classrooms under a bad category
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Cho, Younghyun. "Application of NEXRAD Radar-Based Quantitative Precipitation Estimations for Hydrologic Simulation Using ArcPy and HEC Software." Water 12, no. 1 (January 17, 2020): 273. http://dx.doi.org/10.3390/w12010273.
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Recent availability of various spatial data, especially for gridded rainfall amounts, provide a great opportunity in hydrological modeling of spatially distributed rainfall–runoff analysis. In order to support this advantage using gridded precipitation in hydrological application, (1) two main Python script programs for the following three steps of radar-based rainfall data processing were developed for Next Generation Weather Radar (NEXRAD) Stage III products: conversion of the XMRG format (binary to ASCII) files, geo-referencing (re-projection) with ASCII file in ArcGIS, and DSS file generation using HEC-GridUtil (existing program); (2) eight Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) models of ModClark and SCS Unit Hydrograph transform methods for rainfall–runoff flow simulations using both spatially distributed radar-based and basin-averaged lumped gauged rainfall were respectively developed; and (3) three storm event simulations including a model performance test, calibration, and validation were conducted. For the results, both models have relatively high statistical evaluation values (Nash–Sutcliffe efficiency—ENS 0.55–0.98 for ModClark and 0.65–0.93 for SCS UH), but it was found that the spatially distributed rainfall data-based model (ModClark) gives a better fit regarding observed streamflow for the two study basins (Cedar Creek and South Fork) in the USA, showing less requirements to calibrate the model with initial parameter values. Thus, the programs and methods developed in this research possibly reduce the difficulties of radar-based rainfall data processing (not only NEXRAD but also other gridded precipitation datasets—i.e., satellite-based data, etc.) and provide efficiency for HEC-HMS hydrologic process application in spatially distributed rainfall–runoff simulations.
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Zarnikau, Jay, and Shuangshuang Zhu. "The Identification of Peak Period Impacts When a TMY Weather File Is Used in Building Energy Use Simulation." Open Journal of Energy Efficiency 03, no. 01 (2014): 25–33. http://dx.doi.org/10.4236/ojee.2014.31003.
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Muntean, N., R. A. Chawdhery, V. Potopová, and L. Tűrkott. "The ability of CROPGRO-Tomato model to simulate the growth characteristics of Thomas F1 tomato cultivar grown under open field conditions." Journal of Agricultural Science 159, no. 7-8 (September 2021): 473–87. http://dx.doi.org/10.1017/s0021859621000770.
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Abstract There are few studies about the ability of CROPGRO-Tomato model to simulate tomato growth under field conditions as a function of both local weather and soil conditions. The aim of this work was to calibrate the CROPGRO-Tomato model, included in the Decision Support System for Agrotechnology Transfer (DSSAT) software, for the Thomas F1 indeterminate tomato cultivar grown under open field conditions at two locations in the Czech Republic with different soil and climate conditions. Additionally, this paper focuses on modelling the impact of compound weather events (CEs) on the growth characteristics of the hybrid field tomato variety. The genotype file, including the main parameters of crop phenology and plant growth, was adapted to the Thomas F1 indeterminate tomato cultivar. The CROPGRO-Tomato model was calibrated by inputting the soil characteristics, weather data and crop management data and then by adjusting the genetic coefficients to simulate the observed Leaf Area Index (LAI) and Above Ground Biomass (AGB) from transplanting to harvest under the farmers' field conditions. The comparison of the LAI simulated by the model and measured under field conditions showed adequate representation with the root mean square error of 0.86 and 1.11 m2/m2. Although there was a good fit for LAI and AGB between the simulated and measured data during the first part of the growing season, increasing differences were found in the growing season with cool-wet and/or hot-dry thresholds of CEs.
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Pulkkinen, Seppo, Daniele Nerini, Andrés A. Pérez Hortal, Carlos Velasco-Forero, Alan Seed, Urs Germann, and Loris Foresti. "Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0)." Geoscientific Model Development 12, no. 10 (October 7, 2019): 4185–219. http://dx.doi.org/10.5194/gmd-12-4185-2019.
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Abstract. Pysteps is an open-source and community-driven Python library for probabilistic precipitation nowcasting, that is, very-short-range forecasting (0–6 h). The aim of pysteps is to serve two different needs. The first is to provide a modular and well-documented framework for researchers interested in developing new methods for nowcasting and stochastic space–time simulation of precipitation. The second aim is to offer a highly configurable and easily accessible platform for practitioners ranging from weather forecasters to hydrologists. In this sense, pysteps has the potential to become an important component for integrated early warning systems for severe weather. The pysteps library supports various input/output file formats and implements several optical flow methods as well as advanced stochastic generators to produce ensemble nowcasts. In addition, it includes tools for visualizing and post-processing the nowcasts and methods for deterministic, probabilistic and neighborhood forecast verification. The pysteps library is described and its potential is demonstrated using radar composite images from Finland, Switzerland, the United States and Australia. Finally, scientific experiments are carried out to help the reader to understand the pysteps framework and sensitivity to model parameters.
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Rodríguez, Oriol, Joan Bech, Juan de Dios Soriano, Delia Gutiérrez, and Salvador Castán. "A methodology to conduct wind damage field surveys for high-impact weather events of convective origin." Natural Hazards and Earth System Sciences 20, no. 5 (May 29, 2020): 1513–31. http://dx.doi.org/10.5194/nhess-20-1513-2020.
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Abstract. Post-event damage assessments are of paramount importance to document the effects of high-impact weather-related events such as floods or strong wind events. Moreover, evaluating the damage and characterizing its extent and intensity can be essential for further analysis such as completing a diagnostic meteorological case study. This paper presents a methodology to perform field surveys of damage caused by strong winds of convective origin (i.e. tornado, downburst and straight-line winds). It is based on previous studies and also on 136 field studies performed by the authors in Spain between 2004 and 2018. The methodology includes the collection of pictures and records of damage to human-made structures and on vegetation during the in situ visit to the affected area, as well as of available automatic weather station data, witness reports and images of the phenomenon, such as funnel cloud pictures, taken by casual observers. To synthesize the gathered data, three final deliverables are proposed: (i) a standardized text report of the analysed event, (ii) a table consisting of detailed geolocated information about each damage point and other relevant data and (iii) a map or a KML (Keyhole Markup Language) file containing the previous information ready for graphical display and further analysis. This methodology has been applied by the authors in the past, sometimes only a few hours after the event occurrence and, on many occasions, when the type of convective phenomenon was uncertain. In those uncertain cases, the information resulting from this methodology contributed effectively to discern the phenomenon type thanks to the damage pattern analysis, particularly if no witness reports were available. The application of methodologies such as the one presented here is necessary in order to build homogeneous and robust databases of severe weather cases and high-impact weather events.