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Journal articles on the topic "Future 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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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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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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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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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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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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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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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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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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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.
Dissertations / Theses on the topic "Future weather file"
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Casagrande, Bruna Gomes. "Cenários climáticos futuros: diagnóstico prospectivo do desempenho termoenergético de edifícios comerciais no Brasil para o século XXI." Universidade Federal do Espírito Santo, 2013. http://repositorio.ufes.br/handle/10/6178.
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Previous issue date: 2013-08-19Conselho Nacional de Desenvolvimento Científico e Tecnológico
Ao mesmo tempo em que foram desenvolvidos no Brasil programas com a meta de racionalização do sistema energético nacional, motivados principalmente pelas crises enfrentadas pelo país, como o racionamento de 2001, estudos a respeito do comportamento do clima em escala mundial apresentaram avanços expressivos, acilitados pela evolução tecnológica e computacional. Entre as estratégias para contenção do desperdício da energia produzida está o consumo pelas edificações, uma vez que a adoção de sistemas construtivos adequados pode reduzir o consumo final de eletricidade. Tal constatação constitui um dos preceitos da arquitetura bioclimática, que preconiza a necessidade de adaptação do edifício ao clima local, sendo, para isso, imprescindível a compreensão dos fenômenos climáticos. Desta forma, o princípio que conduziu esta pesquisa foi o comportamento variável do clima, consenso para grande parte dos climatologistas, e suas consequências para as demandas energéticas futuras, particularmente durante o ciclo de vida planejado para cada edifício. Investigar o impacto das mudanças projetadas para o clima ao longo do século XXI no desempenho termoenergético de edificações comerciais artificialmente climatizadas localizadas em diferentes cidades do Brasil foi o principal objetivo deste estudo. Os procedimentos metodológicos foram divididos em quatro etapas, iniciando-se por uma ampla revisão bibliográfica sobre a temática central mudanças climáticas bem como os temas correlacionados, com especial ênfase para a associação entre conforto térmico e a questão energética. Na segunda etapa foram estabelecidos os mecanismos para preparação de arquivos climáticos futuros, incluindo-se a seleção de cidades para representação das diferentes condições geográficas do território brasileiro. Posteriormente foi efetuado o recorte do objeto, com a indicação dos parâmetros de controle e das variáveis em análise, designandose as características do edifício que não serão afetadas por intervenções futuras: percentual de abertura nas fachadas, dispositivos de proteção solar e orientação das maiores fachadas. A etapa final foi dedicada às simulações, realizadas no programa DesignBuilder a partir da configuração dos 192 modelos paramétricos. Os resultados da aplicação da metodologia, analisados quantitativa e qualitativamente, reproduziram, de forma generalizada, um aumento no consumo de 10,7% em 2020, 16,9% em 2050 e 25,6% em 2080, em relação ao consumo atual. Apesar da significância desse aumento, inclusive para o planejamento energético nacional, aumentos mais expressivos foram registrados em estudos internacionais, reforçando a necessidade de consideração dos fenômenos regionais na preparação de dados climáticos futuros neste tipo de pesquisa. Em Recife, a variação de parâmetros construtivos não provocou diferenças tão significativas nas taxas de aumento do consumo quanto nas outras cinco localidades, sendo que Brasília apresentou as maiores taxas de aumento. Considerando-se os edifícios de todas as cidades, a presença de dispositivos de proteção solar foi a variável com maior impacto para diminuição do consumo, e o edifício orientado a Leste e Oeste, com grandes aberturas desprotegidas, apresentou consumo significativamente superior aos outros modelos, atual e futuramente. Por fim, ao contrário da maioria dos resultados mensais observados, em Porto Alegre ocorreu uma diminuição no consumo em alguns meses de 2020 e 2050, ocasionada possivelmente pela diminuição dos períodos de utilização da climatização artificial para aquecimento
At the same time that programs were developed in Brazil with the goal of the national energy system rationalization, mainly motivated by the crisis faced by the country as in the rationing of 2001 studies of the climate s behavior on a global scale showed significant advances, facilitated by technological and computational development. One of the strategies for containment the waste energy produced is the energy consumption by buildings, since the adoption of appropriate constructive systems can reduce the final electricity consumption. This was a principle of bioclimatic architecture, which recommends an adaptation of the building to the local climate conditions, and for that, it is essential to understand the climate system. Therefore, the principle that guided the development of this research was the variable behavior of the climate, which is consensus for most climatologists, and its consequences for the future energy demands of buildings, particularly along the planned life cycle for each building. The main objective of this study was to investigate the impact of projected changes to the climate over the twenty-first century in the thermo energetic performance of commercial buildings artificially acclimatized located in different cities of Brazil. The methodological procedures were divided into four stages, initiating with an extensive literature review on the central theme climate change as well as related topics, with special emphasis on the relationship between thermal comfort and energy issue. In the second step mechanisms for preparing future climate files were established, including the selection of cities for representation of different geo-climatic conditions of the Brazilian territory. After that the definition of the object was performed, indicating the control parameters and variables in the analysis, assigning the characteristics of the building that will not be affected by future interventions window wall ratio, solar shading and orientation of the largest facades. The final step was dedicated to the simulations, performed in the program DesignBuilder from the configuration of the 192 parametric models. The results of applying the methodology, analyzed quantitatively and qualitatively, reproduced in generalized way an increase in energy consumption in buildings by 10.7% in 2020, 16.9% in 2050 and 25.6% in 2080, compared to current consumption. Although the significance of this increase, including the national energy planning, most significant increases were recorded in international studies, reinforcing the need for consideration of regional climate events in the preparation of future climate data in this type of research. In Recife, the variation of constructive parameters did not cause as significant differences in the rates of increase in consumption as the other five locations, and Brasilia had the highest rates of increase. Considering the buildings of all the cities, the presence of solar shading was the variable with the greatest impact on reducing energy consumption, and the building oriented east and west, with large unprotected openings, showed energy consumption significantly superior to other models, in all cities and periods. Finally, unlike most of monthly results observed, in Porto Alegre occurred a decrease in energy consumption in some months of 2020 and 2050, possibly caused by the reduction in time use of artificial air conditioning heating
Books on the topic "Future weather file"
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Romanowska, Iza. Agent-Based Modeling for Archaeology. SFI Press, 2021. http://dx.doi.org/10.37911/9781947864382.
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To fully understand not only the past, but also the trajectories, of human societies, we need a more dynamic view of human social systems. Agent-based modeling (ABM), which can create fine-scale models of behavior over time and space, may reveal important, general patterns of human activity. Agent-Based Modeling for Archaeology is the first ABM textbook designed for researchers studying the human past. Appropriate for scholars from archaeology, the digital humanities, and other social sciences, this book offers novices and more experienced ABM researchers a modular approach to learning ABM and using it effectively. Readers will find the necessary background, discussion of modeling techniques and traps, references, and algorithms to use ABM in their own work. They will also find engaging examples of how other scholars have applied ABM, ranging from the study of the intercontinental migration pathways of early hominins, to the weather–crop–population cycles of the American Southwest, to the trade networks of Ancient Rome. This textbook provides the foundations needed to simulate the complexity of past human societies, offering researchers a richer understanding of the past—and likely future—of our species.
Book chapters on the topic "Future weather file"
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Karali, A., A. Roussos, C. Giannakopoulos, M. Hatzaki, G. Xanthopoulos, and K. Kaoukis. "Evaluation of the Canadian Fire Weather Index in Greece and Future Climate Projections." In Advances in Meteorology, Climatology and Atmospheric Physics, 501–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29172-2_71.
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Papavasileiou, Georgios, and Theodore M. Giannaros. "Validation of ERA5 fire weather conditions in Greece between 2007 and 2019: A preliminary analysis." In Advances in Forest Fire Research 2022, 1815–19. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_281.
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Accurate simulations of fire weather conditions for both the past and the future are of great importance for fire management and preparedness. With the advancement of numerical weather prediction models and data assimilation techniques, more accurate reanalysis products have been developed the recent years. Here we validate fire weather conditions in Greece which are computed based on ERA5 reanalysis data using surface observations from the automatic weather station network of the National Observatory of Athens (NOA). We assess the fire weather conditions in an application of the Canadian Forest Fire Weather Index (FWI) System in both datasets. Although, ERA5 FWI archive is available since 1979 here we limit our analysis during the period of 2007 to 2019, due to the limited data availability from the NOA network. The validation of FWI in ERA5 data shows good agreement with the NOA observations with a mean correlation of 0.87. Furthermore, FWI in ERA5 data is overall slightly underestimated compared to NOA observations, which is driven by an underestimation of the three moisture components of FWI, namely the Fine Fuel Moisture Code (FFMC), the Drought Code (DC) and the Duff Moisture Code (DMC). Preliminary results also indicate that the largest errors are found over the eastern and southern parts of Greece, which is the are that experiences the highest FWI values during the summer.
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Hillman, Samuel C., Luke Wallace, Thomas J. Duff, Tegan P. Brown, and W. Matt Jolly. "Generating a framework for fuel inputs to future fire behaviour models: reviews, recommendations and remote sensing." In Advances in Forest Fire Research 2022, 1128–33. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_171.
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A high-resolution meteorological model has enough spatial detail and physical meaning to be able to characterize climate at small administrative regions in mainland Portugal (NUTS III). The weather conditions associated with the Synoptic patterns (Weather Types) are better detailed with this model data. This is essential for a better understanding of the relations between climate and forest fires which will allow for better measures taken by the decision makers.
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Esther Babalola, Toju, Philip Gbenro Oguntunde, Ayodele Ebenezer Ajayi, and Francis Omowonuola Akinluyi. "Future Climate Change Impacts on River Discharge Seasonality for Selected West African River Basins." In Weather Forecasting [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.99426.
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The changing climate is a concern to sustainable water resources. This study examined climate change impacts on river discharge seasonality in two West African river basins; the Niger river basin and the Hadejia-Jama’are Komadugu-Yobe Basin (HJKYB). The basins have their gauges located within Nigeria and cover the major climatic settings. Here, we set up and validated the hyper resolution global hydrological model PCR-GLOBWB for these rivers. Time series plots as well five performance evaluation metrics such as Kling–Gupta efficiency (KGE),); the ratio of RMSE-observations standard deviation (RSR); per cent bias (PBIAS); the Nash–Sutcliffe Efficiency criteria (NSE); and, the coefficient of determination (r2), were employed to verify the PCR-GLOBWB simulation capability. The validation results showed from satisfactory to very good on individual rivers as specified by PBIAS (−25 to 0.8), NSE (from 0.6 to 0.8), RSR (from 0.62 to 0.4), r2 (from 0.62 to 0.88), and KGE (from 0.69 to 0.88) respectively. The impact assessment was performed by driving the model with climate projections from five global climate models for the representative concentration pathways (RCPs) 4.5 and 8.5. We examined the median and range of expected changes in seasonal discharge in the far future (2070–2099). Our results show that the impacts of climate change cause a reduction in discharge volume at the beginning of the high flow period and an increase in discharge towards the ending of the high flow period relative to the historical period across the selected rivers. In the Niger river basin, at the Lokoja gauge, projected decreases added up to 512 m3/s under RCP 4.5 (June to July) and 3652 m3/s under RCP 8.5 (June to August). The three chosen gauges at the HJKYB also showed similar impacts. At the Gashua gauge, discharge volume increased by 371 m3/s (RCP8.5) and 191 m3/s (RCP4.5) from August to November. At the Bunga gauge, a reduction/increase of -91 m3/s/+84 m3/s (RCP 8.5) and -40 m3/s/+31 m3/s/(RCP 4.5) from June to July/August to October was simulated. While at the Wudil gauge, a reduction/increase in discharge volumes of −39/+133 m3/s (RCP8.5) and −40/133 m3/s (RCP 4.5) from June to August/September to December is projected. This decrease is explained by a delayed start of the rainy season. In all four rivers, projected river discharge seasonality is amplified under the high-end emission scenario (RCP8.5). This finding supports the potential advantages of reduced greenhouse gas emissions for the seasonal river discharge regime. Our study is anticipated to provide useful information to policymakers and river basin development authorities, leading to improved water management schemes within the context of changing climate and increasing need for agricultural expansion.
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Billing, Maik, Christopher Marrs, Matthias Forkel, Eike Sebode, and Kirsten Thonicke. "Present and future fire risk changes in Central Europe." In Advances in Forest Fire Research 2022, 1279–81. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_193.
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Fire risk is projected to increase under future climate change. Most projections focus on fire-prone regions, such as the Mediterranean-type ecosystems, whereas little attention has been paid to regions of low fire risk such as Central Europe. Here, future projections of fire risk which are tailor-made for its specific conditions are scarce. With our study we aim to fill this gap. We use meteorological station data and interpolated climate datasets to compute future fire risk for Central Europe (covering Germany, Poland, the Czech Republic) using the Fire Weather Index. In a next step, we analyse the spatial distribution of reported fire ignitions to identify additional drivers that can explain the spatial pattern of fire ignition and risk, or accelerate fire risk under climate extremes (drought or extreme heat). We analyse how transport infrastructure and proximity to settlements have influenced fire ignition in Central Europe and compare it against relationships known from fire-prone regions. We aim to build on recent adjustments of the FWI to account for respective increased fire risk and apply it to our study area. We conduct high-resolution analysis of fire-risk analysis by computing the FWI along the wildland-urban and wildland-rural interface in individual sites in the study region. In a next step, downscaled future climate scenarios (CMIP6) are applied to compute changes in future fire risk for the entire study area as well as the selected sites in Central Europe. Uncertainty ranges of future fire risk projections will be covered by using several climate scenarios for the entire study region. Detailed analysis will be conducted at the local scale by further refining changes in ignition potentials along the wildland-urban and the wildland-rural interface in selected sites in Central Europe under climate change conditions.
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Moreira, Nuno, Ilda Novo, Pedro Silva, Edna Cardoso, Álvaro Silva, João Ferreira, and Ricardo Ramos. "Joint Drought-Temperature conditions as factors contributing to the occurrence of forest fires in Portugal: a NUT III clustering perspective in a monthly/seasonal time scale." In Advances in Forest Fire Research 2022, 1182–88. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_179.
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The objective of the present work is to find a joint relation of drought and temperature regimes as factors contributing to the occurrence of forest fires in the period 1981-2018 in mainland Portugal. To meet this goal PDSI drought data in NUT III regions from automatic weather stations were matched with temperature regimes obtained from a 3 km downscaling using WRF model and ERA5 reanalysis as forcing data. Clustering analysis was used to aggregate seasons from June to October, according to the weather-climate conditions provided together by drought and temperature regimes. The method proposed in the present work can be used to check in which cluster every new future season fits better, thus providing a tool to, e.g, better assess the success of the implementation of forestry and forest fire management policies.
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Elsner, James B., and Thomas H. Jagger. "Classical Statistics." In Hurricane Climatology. Oxford University Press, 2013. http://dx.doi.org/10.1093/oso/9780199827633.003.0006.
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All hurricanes are different. Statistics helps you characterize hurricanes from the typical to the extreme. In this chapter, we provide an introduction to classical (or frequentist) statistics. To get the most out of it, we again encourage you to open an R session and type in the code as you read along. Descriptive statistics are used to summarize your data. The mean and the variance are good examples. So is correlation. Data can be a set of weather records or output from a global climate model. Descriptive statistics provide answers to questions like does Jamaica experience more hurricanes than Puerto Rico? In Chapter 2, you learned some functions for summarizing your data, let us review. Recall that the data set H.txt is a list of hurricane counts by year making landfall in the United States (excluding Hawaii). To input the data and save them as a data object, type . . . > H = read. Table ("H.txt", header=TRUE) . . . Make sure the data file is located in your working directory. To check your working directory, type getwd (). Sometimes all you need are a few summary statistics from your data. You can obtain the mean and variance by typing . . . > mean (H$All); var (H$All) [1] 1.69375 [1] 2.10059 . . . Recall that the semicolon acts as a return so you can place multiple functions on the same text line. The sample mean is a measure of the central tendency and the sample variance is a measure of the spread. These are called the first-and second-moment statistics. Like all statistics, they are random variables. A random variable can be thought of as a quantity whose value is not fixed; it changes depending on the values in your sample. If you consider the number of hurricanes over a different sample of years, the sample mean will almost certainly be different. Same with the variance. The sample mean provides an estimate of the population mean (the mean over all past and future years).
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Johnson, Jesse V., Anthony Marcozzi, Frederick Bunt, Jacob Bova, and John Hogland. "Predicting fire severity in Montana using a random forest classification scheme." In Advances in Forest Fire Research 2022, 323–28. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_51.
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Fire managers often make decisions about wildfire incidents on a landscape scale. While several well developed models can predict fire behaviour at these scales, the paucity of data they draw upon can limit their range of validity. Other models explicitly represent the physical complexities of a fire environment, but at increased computational costs and increased sensitivity to boundary conditions. In this paper, we explore a middle ground between landscape level, data-driven fire behaviour predictions and physics-based, computationally expensive models. As a first step, we employ machine learning to predict fire severity from a set of well recognized covariates such as weather, fuel, and topography. A gradient boosted regression tree is used for the classification task, and sets of model hyperparameters tested to determine their role in the classification. The model demonstrates skill in prediction of the burn severity, but is limited by the dynamically evolving set of features; especially the weather. Results were found to be biassed towards under-predicting the severity of wildfires. We conclude with a discussion of approaches available to improve model performance and future applications of the predicted burn severity.
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Lovejoy, Shaun. "Macroweather predictions and climate projections." In Weather, Macroweather, and the Climate. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190864217.003.0011.
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“Does the Flapping of a Butterfly’s Wings in Brazil Set off a Tornado in Texas?” This was the provocative title of an address given by Edward Lorenz, the origin for the (nearly) household expression “butterfly effect.” It was December 1972 and it had been nearly ten years since he had discovered it,1 yet its significance was only then being recognized. Lorenz explained: “In more technical language, is the behavior of the atmosphere unstable to small perturbations?” His answer: “Although we cannot claim to have proven that the atmosphere is unstable, the evidence that it is so is overwhelming.” Imagine two planets identical in every way except that on one there is a butterfly that flaps its wings. The butterfly effect means that their future evolutions are “sensitively dependent” on the initial conditions, so that a mere flap of a wing could perturb the atmosphere sufficiently so that, eventually, the weather patterns on the two planets would evolve quite differently. On the planet with the Brazilian butterfly, the number of tornadoes would likely be the same. But on a given day, one might occur in Texas rather than Oklahoma. This sensitive dependence on small perturbations thus limits our ability to predict the weather. For Earth, Lorenz estimated this predictability limit to be about two weeks. From Chapters 4 and 5 and the discussion that follows, we now understand it as the slightly shorter weather– macroweather transition scale. In Chapter 1, we learned that the ratio of the nonlinear to linear terms in the (deterministic) equations governing the atmosphere is typically about a thousand billion. The nonlinear terms are the mathematical expressions of physical mechanisms that can blow up microscopic perturbations into large effects. Therefore, we expect instability. Chapter 4, we examined instability from the point of view of the higher level statistical laws— the fact that, at weather scales, the fluctuation exponents H for all atmospheric fields are positive (in space, up to the size of the planet; in time, up to the weather– macroweather transition scale at five to ten days).
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Pimont, François, Julien Ruffault, Thomas Opitz, Hélène Fargeon, Jorge Castel-Clavera, Nicolas Martin-StPaul, Eric Rigolot, Renaud Barbero, and Jean-Luc Dupuy. "Lengthening, expansion and intensification of future fire activities in South-Eastern France." In Advances in Forest Fire Research 2022, 1198–203. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_181.
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Anticipating future fire activity at global and regional scales is critical in a changing climate. Indeed, fire seasons are expected to lengthen and fire prone areas are expected to extend, but the magnitude, location and timing of such increases remain uncertain. Moreover, an intensification is expected during the core of the fire season of already fire-prone regions. However, quantifying seasonal and spatial impacts of climate change on fire activity is challenging. Here, we projected future fire activities in Southern France using the Firelihood model. This Bayesian probabilistic model operates on a daily basis in 8-km pixels, allowing to analyze both seasonal and spatial distributions of fire activities in a framework integrating stochasticity. Projections were computed for 13 GCM-RCM couples under two RCP scenarios (4.5 and 8.5), assuming that the only factor of change in future fire activity was the daily fire weather. The fire season was defined as the period with fire-activity level higher than the level of the 15th of July of the present period. The fire prone region corresponded to locations with fire-activity levels higher than the 2nd level of a 5-level fire-activity scale derived from numbers of fires larger than 1ha, 100ha (N1ha and N100ha) and burnt areas (BA). Simulations under RCP8.5 show that large increases in fire activity should be expected from the mid-century and that the rate of increase should then accelerate, leading to up to three-fold increases for number of fires larger than 100ha by the end of the century. In particular, all metrics except N1ha increased faster than the mean FWI and even the mean DSR. Such increases were partly caused by a massive seasonal lengthening from 45-50 days to up to 125 days, equally distributed between spring and autumn. However, the intensification during the present fire season was found to contribute slightly more to the overall increase than the lengthening itself. For example, for N100ha, the intensification would represent a 280 % increase in fire activity with respect to the present seasonal reference, whereas the lengthening outside of the present season would represent +230%. The fire prone area would increase by 168%, shifting from 22 to 56% of region total area. However, the intensification inside the already fire-prone region was found to contribute more to the increase than the spatial extension. For example, for N100ha, the intensification would represent a 190% increase with respect to the present fire-prone regional reference, whereas the extension outside of this area would represent +110%. These drastic increases provide a good indication of the potential lengthening of the fire season, spatial extension and intensification of future fire activities under RCP 8.5, all three being importantly concerned, but dominated by intensification. Extending and lengthening suppression policies may allow to mitigate projected increases, but the intensification of fire activity during the core of the fire season overwhelm current fire suppression capacities.
Conference papers on the topic "Future weather file"
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Kamal, Athar, Ibrahim Hassan, Liangzhou (Leon) Wang, and Mohammad Azizur Rahman. "Estimating Combined Impact of Urban Heat Island Effect and Climate Change on Cooling Requirements of Tall Residential Buildings in Hot-Humid Locations." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-94272.
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Abstract Climate change estimates are critical in developing long-term solutions to the dwelling problems that we currently face. This study combines the impact of climate change and the urban heat island effect to study the outcomes of future weather conditions on the cooling of tall residential buildings in hot and humid climates. For the year 2050, we calculate the impact of urban characteristics through the urban weather generator and climate change through the world weather gen tool on the micro-climatic condition of a district in a newly constructed city near Doha, Qatar, the Lusail City. A total of four weather files are compared to the weather data gathered from the established weather station in the city (two for the year 2020 and three for the year 2050). Results reveal that once the open weather map file has been processed through the urban weather generator (UWG) first and then the climate change model, the MAE increases to 3.30, and the RMSE goes to 3.8 with a maximum deviation of 11.4°c occurring. If the process is done the other way around, the climate change model is applied first, and then the UWG file is applied, the MAE of 3.46 is with RMSE of 3.94 with a maximum deviation of 11.3°c occurring. The impact of these weather files is then assessed on a tall residential building in Lusail. A significant increase of 777197 kwh or 20% is seen in the openweather map file that has been processed first through the climate change model and then through the urban weather generator (as compared to the rural weather file); an increase of 739983 kwh or 19% is seen in the openweather map file that has been processed first through the UWG and then through the climate change model; finally close to 22.6 percent increase or 874088 kwh is seen in the openweather map file that has been processed first through the climate change model and then through the climate change model.
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Naranjo-Mendoza, Carlos, Jesús López-Villada, Gabriel Gaona, and Jerko Labus. "Performance Analysis With Future Predictions of Different Solar Cooling Systems in Guayaquil, Ecuador." In ASME 2014 8th International Conference on Energy Sustainability collocated with the ASME 2014 12th International Conference on Fuel Cell Science, Engineering and Technology. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/es2014-6594.
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This paper presents a comparative analysis of three different solar cooling system configurations developed for a case study building in Guayaquil, Ecuador. Guayaquil is a city located at the Ecuadorian coast with an average annual temperature of 25°C. The city’s need for air conditioning throughout the year and the relatively intense solar radiation provide a great opportunity for implementation of solar cooling systems. The first cooling system includes a 175 kWc single-effect absorption chiller powered by evacuated tubes solar thermal collectors. This system was compared with two 140 kWc compression chiller systems (air-cooled (AC) and water-cooled (WC)) powered by grid-connected photovoltaics. Both constant flow rate (CFR) and variable flow rate (VFR) of chilled water were analyzed. The three systems have to satisfy a cooling demand of the top floor in one governmental building (app. 1296 m2) which was selected as case study. Additionally, two 140 kWc conventional compression chiller systems (AC and WC) were included in the comparison as reference systems. Cooling demand of the building was simulated in EnergyPlus and coupled with the appropriate system configurations developed in TRNSYS. The weather file (TMY) was developed based on real meteorological data collected in the last decade. The present analysis was extended with the prediction scenarios for the years 2020, 2050 and 2080 using climate change adapted weather files.
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Tibana, Yehisson, Estatio Gutierrez, Sashary Marte, and J. E. Gonzalez. "Modeling Building HVAC Energy Consumption During an Extreme Heat Event in a Dense Urban Environment." In ASME 2014 8th International Conference on Energy Sustainability collocated with the ASME 2014 12th International Conference on Fuel Cell Science, Engineering and Technology. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/es2014-6315.
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Dense urban environments are exposed to the combined effects of rising global temperatures and urban heat islands, a thermal gradient between the urban centers and the less urbanized surroundings suburbs. This combination is resulting in increasing trends of energy consumption in cities, associated mostly to air conditioning to maintain indoor human comfort conditions. The energy demand is further magnified during extreme heat events to a point where the electrical grid may be at risk. Given the anticipated increased frequency of extreme heat events for the future, it is imperative to develop methodologies to quantify energy demands from buildings during extreme heat events. The purpose of this study is to precisely quantify thermal loads of buildings located in the very dense urban environment of New York City under an extreme heat event that took place in the summer of 2010 (July 4–8). Two approaches were used to quantify thermal loads of buildings for these conditions; a single building energy model (SBEM), such as the US Department of Energy eQUEST and EnergyPlus™, and an urbanized weather forecasting model (uWRF) coupled to a building energy model. The SBEM was driven by Typical Meteorological Year (TMY) weather file and by a customized weather file built from uWRF’s weather data for the specific days of the heat wave. A series of simulations were conducted with both SBEM software to model building energy consumption data due to air conditioning for two locations in Uptown and Midtown Manhattan, NY, which represented a low density and a high density building area within the city. Assumptions were made regarding the building’s floor plans and operation schedule to simplify the model and provide a close comparison to uWRF. Results of the ensemble of SBEM indicate there was an increase in energy consumption during the July 2010 heat-wave when compared with the central park TMY case. The uptown location consumed 137% more energy during the heat wave event, while the midtown location showed an increased in energy consumption of 125% when compared to a typical July three day period, reaching total loads of close to 9812 kWh for a 20 m height building. Comparison of the results directly from uWRF for the energy consumption for same locations, indicate that for the midtown location both SBEMs underestimated the total energy consumption within a factor of three. This may be due to the fact that uWRF energy model takes into account urban microclimate parameters such as anthropogenic sources and waste heat interactions between surrounding buildings.
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Duarte, Luis, Jesus Revollo, Daniel Betancur, Gabriel Lopez, Idi Isaac, Hugo Cardona, and Sebastian Ortega. "Placement of weather stations in Colombia for future applications in solar and wind energy forecasting models." In 2019 FISE-IEEE/CIGRE Conference - Living the energy Transition (FISE/CIGRE). IEEE, 2019. http://dx.doi.org/10.1109/fisecigre48012.2019.8984983.
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Dubrovsky, Martin, Michele Salis, Petr Stepanek, Pierpaolo Duce, Pavel Zahradnicek, Jan Meitner, and Martin Mozny. "Modelling Present and Future Wildfire Risk with Use of a Fire Weather Index, Spatial Weather Generator and Regional Climate Models." In The Third International Conference on Fire Behavior and Risk. Basel Switzerland: MDPI, 2022. http://dx.doi.org/10.3390/environsciproc2022017130.
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Salvati, Agnese, and Maria Kolokotroni. "Generating future-urban weather files for building performance simulations: case studies in London." In 2021 Building Simulation Conference. KU Leuven, 2021. http://dx.doi.org/10.26868/25222708.2021.30315.
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"Exploring the future of fuel loads in Tasmania. Shifts in vegetation in response to changing fire weather, productivity, and fire frequency." In 22nd International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2017. http://dx.doi.org/10.36334/modsim.2017.h10.harris.
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KAZEMIAN, MAHYAR, SAJAD NIKDEL, MEHRNAZ MOHAMMADESMAEILI, VAHID NIK, and KAMYAB ZANDI. "KALIX BRIDGE DIGITAL TWIN—STRUCTURAL LOADS FROM FUTURE EXTREME CLIMATE EVENTS." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36323.
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Environmental loads, such as wind and river flow, play an essential role in the structural design and structural assessment of long-span bridges. Climate change and extreme climatic events are threats to the reliability and safety of the transport network. This has led to a growing demand for digital twin models to investigate the resilience of bridges under extreme climate conditions. Kalix bridge, constructed over the Kalix river in Sweden in 1956, is used as a testbed in this context. The bridge structure, made of posttensioned concrete, consists of five spans, with the longest one being 94 m. In this study, aerodynamic characteristics and extreme values of numerical wind simulation such as surface pressure are obtained by using Spalart-Allmaras Delayed Detached Eddy Simulation (DDES) as a hybrid RANS-LES turbulence approach which is both practical and computationally efficient for near-wall mesh density imposed by the LES method. Surface wind pressure is obtained for three extreme climate scenarios, including extreme windy weather, extremely cold weather, and design value for a 3000-year return period. The result indicates significant differences in surface wind pressure due to time layers coming from transient wind flow simulation. In order to assess the structural performance under the critical wind scenario, the highest value of surface pressure for each scenario is considered. Also, a hydrodynamic study is conducted on the bridge pillars, in which the river flow is simulated using the VOF method, and the water movement process around the pillars is examined transiently and at different times. The surface pressure applied by the river flow with the highest recorded volumetric flow is calculated on each of the pier surfaces. In simulating the river flow, information and weather conditions recorded in the past periods have been used. The results show that the surface pressure at the time when the river flow hit the pillars is much higher than in subsequent times. This amount of pressure can be used as a critical load in fluid-structure interaction (FSI) calculations. Finally, for both sections, the wind surface pressure, the velocity field with respect to auxiliary probe lines, the water circumferential motion contours around the pillars, and the pressure diagram on them are reported in different timesteps.
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ZHU, Mingya, Yiqun PAN, Zhizhong HUANG, Peng XU, and Huajing SHA. "Future Hourly Weather Files Generation For Studying The Impact Of Climate Change On Building Energy Demand In China." In 2017 Building Simulation Conference. IBPSA, 2013. http://dx.doi.org/10.26868/25222708.2013.2102.
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Choe, Do-Eun, Gary Talor, and Changkyu Kim. "Prediction of Wind Speed, Potential Wind Power, and the Associated Uncertainties for Offshore Wind Farm Using Deep Learning." In ASME 2020 Power Conference collocated with the 2020 International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/power2020-16557.
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Abstract Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size.
Reports on the topic "Future weather file"
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Aalto, Juha, and Ari Venäläinen, eds. Climate change and forest management affect forest fire risk in Fennoscandia. Finnish Meteorological Institute, June 2021. http://dx.doi.org/10.35614/isbn.9789523361355.
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Forest and wildland fires are a natural part of ecosystems worldwide, but large fires in particular can cause societal, economic and ecological disruption. Fires are an important source of greenhouse gases and black carbon that can further amplify and accelerate climate change. In recent years, large forest fires in Sweden demonstrate that the issue should also be considered in other parts of Fennoscandia. This final report of the project “Forest fires in Fennoscandia under changing climate and forest cover (IBA ForestFires)” funded by the Ministry for Foreign Affairs of Finland, synthesises current knowledge of the occurrence, monitoring, modelling and suppression of forest fires in Fennoscandia. The report also focuses on elaborating the role of forest fires as a source of black carbon (BC) emissions over the Arctic and discussing the importance of international collaboration in tackling forest fires. The report explains the factors regulating fire ignition, spread and intensity in Fennoscandian conditions. It highlights that the climate in Fennoscandia is characterised by large inter-annual variability, which is reflected in forest fire risk. Here, the majority of forest fires are caused by human activities such as careless handling of fire and ignitions related to forest harvesting. In addition to weather and climate, fuel characteristics in forests influence fire ignition, intensity and spread. In the report, long-term fire statistics are presented for Finland, Sweden and the Republic of Karelia. The statistics indicate that the amount of annually burnt forest has decreased in Fennoscandia. However, with the exception of recent large fires in Sweden, during the past 25 years the annually burnt area and number of fires have been fairly stable, which is mainly due to effective fire mitigation. Land surface models were used to investigate how climate change and forest management can influence forest fires in the future. The simulations were conducted using different regional climate models and greenhouse gas emission scenarios. Simulations, extending to 2100, indicate that forest fire risk is likely to increase over the coming decades. The report also highlights that globally, forest fires are a significant source of BC in the Arctic, having adverse health effects and further amplifying climate warming. However, simulations made using an atmospheric dispersion model indicate that the impact of forest fires in Fennoscandia on the environment and air quality is relatively minor and highly seasonal. Efficient forest fire mitigation requires the development of forest fire detection tools including satellites and drones, high spatial resolution modelling of fire risk and fire spreading that account for detailed terrain and weather information. Moreover, increasing the general preparedness and operational efficiency of firefighting is highly important. Forest fires are a large challenge requiring multidisciplinary research and close cooperation between the various administrative operators, e.g. rescue services, weather services, forest organisations and forest owners is required at both the national and international level.
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Huntley, D., D. Rotheram-Clarke, R. Cocking, J. Joseph, and P. Bobrowsky. Current research on slow-moving landslides in the Thompson River valley, British Columbia (IMOU 5170 annual report). Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331175.
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Interdepartmental Memorandum of Understanding (IMOU) 5170 between Natural Resources Canada (NRCAN), the Geological Survey of Canada (GSC) and Transport Canada Innovation Centre (TC-IC) aims to gain new insight into slow-moving landslides, and the influence of climate change, through testing conventional and emerging monitoring technologies. IMOU 5107 focuses on strategically important sections of the national railway network in the Thompson River valley, British Columbia (BC), and the Assiniboine River valley along the borders of Manitoba (MN) and Saskatchewan (SK). Results of this research are applicable elsewhere in Canada (e.g., the urban-rural-industrial landscapes of the Okanagan Valley, BC), and around the world where slow-moving landslides and climate change are adversely affecting critical socio-economic infrastructure. Open File 8931 outlines landslide mapping and changedetection monitoring protocols based on the successes of IMOU 5170 and ICL-IPL Project 202 in BC. In this region, ice sheets, glaciers, permafrost, rivers and oceans, high relief, and biogeoclimatic characteristics contribute to produce distinctive rapid and slow-moving landslide assemblages that have the potential to impact railway infrastructure and operations. Bedrock and drift-covered slopes along the transportation corridors are prone to mass wasting when favourable conditions exist. In high-relief mountainous areas, rapidly moving landslides include rock and debris avalanches, rock and debris falls, debris flows and torrents, and lahars. In areas with moderate to low relief, rapid to slow mass movements include rockslides and slumps, debris or earth slides and slumps, and earth flows. Slow-moving landslides include rock glaciers, rock and soil creep, solifluction, and lateral spreads in bedrock and surficial deposits. Research efforts lead to a better understanding of how geological conditions, extreme weather events and climate change influence landslide activity along the national railway corridor. Combining field-based landslide investigation with multi-year geospatial and in-situ time-series monitoring leads to a more resilient railway national transportation network able to meet Canada's future socioeconomic needs, while ensuring protection of the environment and resource-based communities from landslides related to extreme weather events and climate change. InSAR only measures displacement in the east-west orientation, whereas UAV and RTK-GNSS change-detection surveys capture full displacement vectors. RTK-GNSS do not provide spatial coverage, whereas InSAR and UAV surveys do. In addition, InSAR and UAV photogrammetry cannot map underwater, whereas boat-mounted bathymetric surveys reveal information on channel morphology and riverbed composition. Remote sensing datasets, consolidated in a geographic information system, capture the spatial relationships between landslide distribution and specific terrain features, at-risk infrastructure, and the environmental conditions expected to correlate with landslide incidence and magnitude. Reliable real-time monitoring solutions for critical railway infrastructure (e.g., ballast, tracks, retaining walls, tunnels, and bridges) able to withstand the harsh environmental conditions of Canada are highlighted. The provision of fundamental geoscience and baseline geospatial monitoring allows stakeholders to develop robust risk tolerance, remediation, and mitigation strategies to maintain the resilience and accessibility of critical transportation infrastructure, while also protecting the natural environment, community stakeholders, and Canadian economy. We propose a best-practice solution involving three levels of investigation to describe the form and function of the wide range of rapid and slow-moving landslides occurring across Canada that is also applicable elsewhere. Research activities for 2022 to 2025 are presented by way of conclusion.
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Thoma, David. Landscape phenology, vegetation condition, and relations with climate at Capitol Reef National Park, 2000–2019. Edited by Alice Wondrak Biel. National Park Service, March 2023. http://dx.doi.org/10.36967/2297289.
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Quantitatively linking satellite observations of vegetation condition and climate data over time provides insight to climate influences on primary production, phenology (timing of growth), and sensitivity of vegetation to weather and longer-term patterns of weather referred to as climate. This in turn provides a basis for understanding potential climate impacts to vegetation—and the potential to anticipate cascading ecological effects, such as impacts to forage, habitat, fire potential, and erosion, as climate changes in the future. This report provides baseline information about vegetation production and condition over time at Capitol Reef National Park (NP), as derived from satellite remote sensing. Its objective is to demonstrate methods of analysis, share findings, and document historic climate exposure and sensitivity of vegetation to weather and climate as a driver of vegetation change. This report represents a quantitative foundation of vegetation–climate relationships on an annual timestep. The methods can be modified to finer temporal resolution and other spatial scales if further analyses are needed to inform park planning and management. The knowledge provided in this report can inform vulnerability assessments for Climate Smart Conservation planning by park managers. Patterns of pivot points and responses can serve as a guide to anticipate what, where, when, and why vegetation change may occur. For this analysis, vegetation alliance groups were derived from vegetation-map polygons (Von Loh et al. 2007) by lumping vegetation types expected to respond similarly to climate. Relationships between vegetation production and phenology were evaluated for each alliance map unit larger than a satellite pixel (~300 × 300 m). We used a water-balance model to characterize the climate experienced by plants. Water balance translates temperature and precipitation into more biophysically relevant climate metrics, such as soil moisture and drought stress, that are often more strongly correlated with vegetation condition than temperature or precipitation are. By accounting for the interactions between temperature, precipitation, and site characteristics, water balance helps make regional climate assessments relevant to local scales. The results provide a foundation for interpreting weather and climate as a driver of changes in primary production over a 20-year period at the polygon and alliance-group scale. Additionally, they demonstrate how vegetation type and site characteristics, such as soil properties, slope, and aspect, interact with climate at local scales to determine trends in vegetation condition. This report quantitatively defines critical water needs of vegetation and identifies which alliance types, in which locations, may be most susceptible to climate-change impacts in the future. Finally, this report explains how findings can be used in the Climate Smart Conservation framework, with scenario planning, to help manage park resources through transitions imposed by climate change.
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Gregow, Hilppa, Antti Mäkelä, Heikki Tuomenvirta, Sirkku Juhola, Janina Käyhkö, Adriaan Perrels, Eeva Kuntsi-Reunanen, et al. Ilmastonmuutokseen sopeutumisen ohjauskeinot, kustannukset ja alueelliset ulottuvuudet. Suomen ilmastopaneeli, 2021. http://dx.doi.org/10.31885/9789527457047.
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The new EU strategy on adaptation to climate change highlights the urgency of adaptation measures while bringing forth adaptation as vitally important as a response to climate change as mitigation. In order to provide information on how adaptation to climate change has been promoted in Finland and what calls for attention next, we have compiled a comprehensive information package focusing on the following themes: adaptation policy, impacts of climate change including economic impacts, regional adaptation strategies, climate and flood risks in regions and sea areas, and the availability of scientific data. This report consists of two parts. Part 1 of the report examines the work carried out on adaptation in Finland and internationally since 2005, emphasising the directions and priorities of recent research results. The possibilities of adaptation governance are examined through examples, such as how adaptations steering is organised in of the United Kingdom. We also examine other examples and describe the Canadian Climate Change Adaptation Platform (CCAP) model. We apply current information to describe the economic impacts of climate change and highlight the related needs for further information. With regard to regional climate strategy work, we examine the status of adaptation plans by region and the status of the Sámi in national adaptation work. In part 2 of the report, we have collected information on the temporal and local impacts of climate change and compiled extensive tables on changes in weather, climate and marine factors for each of Finland's current regions, the autonomous Åland Islands and five sea areas, the eastern Gulf of Finland, the western Gulf of Finland, the Archipelago Sea, the Bothnian Sea and the Bay of Bothnia. As regards changes in weather and climate factors, the changes already observed in 1991-2020 are examined compared to 1981-2010 and future changes until 2050 are described. For weather and climate factors, we examine average temperature, precipitation, thermal season duration, highest and lowest temperatures per day, the number of frost days, the depth and prevalence of snow, the intensity of heavy rainfall, relative humidity, wind speed, and the amount of frost per season (winter, spring, summer, autumn). Flood risks, i.e. water system floods, run-off water floods and sea water floods, are discussed from the perspective of catchment areas by region. The impacts of floods on the sea in terms of pollution are also assessed by sea area, especially for coastal areas. With regard to marine change factors, we examine surface temperature, salinity, medium water level, sea flood risk, waves, and sea ice. We also describe combined risks towards sea areas. With this report, we demonstrate what is known about climate change adaptation, what is not, and what calls for particular attention. The results can be utilised to strengthen Finland's climate policy so that the implementation of climate change adaptation is strengthened alongside climate change mitigation efforts. In practice, the report serves the reform of the National Climate Change Adaptation Plan and the development of steering measures for adaptation to climate change both nationally and regionally. Due to its scale, the report also serves e.g. the United Nations’ aim of protecting marine life in the Baltic Sea and the national implementation of the EU strategy for adaptation to climate change. As a whole, the implementation of adaptation policy in Finland must be speeded up swiftly in order to achieve the objectives set and ensure sufficient progress in adaptation in different sectors. The development of binding regulation and the systematic evaluation, monitoring and support of voluntary measures play a key role.