The document discusses career opportunities in weather, climate, and atmospheric science. It outlines that these careers involve work in areas like meteorology, climatology, oceanography, hydrology, and related fields of research. Some key career paths discussed are working as a research scientist, pursuing an academic career through PhD and postdoc positions, or working in forecasting for the Met Office or private sector in jobs applying climate data and models.
This presentation introduces numerical methods and their applications. Numerical methods use numerical approximations to solve mathematical problems. They are used in various fields like engineering, scientific computing, weather forecasting, estimating ocean currents, modeling airplanes, solving heat equations, and crime detection. The document provides examples of how numerical methods are applied to structural analysis, hydrological forecasting, transportation modeling, integrating and solving differential equations, root finding, numerical optimization, modeling ocean circulation, simulating aircraft wings, and predicting crime patterns.
This document discusses applications of numerical methods. It provides examples of using numerical methods for weather forecasting, engineering problems like structural analysis, scientific computing, estimating ocean currents, modeling airplanes, solving heat equations, and crime detection. Numerical methods are widely used to solve mathematical problems in diverse fields like science, engineering, and operations research by approximating solutions.
Numerical methods are mathematical tools used to solve numerical problems. They are applied in engineering, crime detection, scientific computing, finding roots, and solving heat equations. Examples given include using numerical methods to find the velocity of a rocket at a certain time and in statistics. Numerical methods have various applications in different fields of science, mathematics, and engineering.
This presentation discusses numerical methods and their applications. It is presented by 4 students from Daffodil International University in Bangladesh. The presentation defines numerical methods as algorithms that use numerical approximations to solve mathematical problems. It provides examples of applying numerical methods to weather forecasting, engineering, scientific computing, estimating ocean currents, modeling airplanes, solving heat equations, and crime detection. It discusses specific techniques like numerical weather prediction and analyzing data to predict crime.
This presentation discusses the application of numerical methods in real-life scenarios. It provides examples such as estimating ocean currents, modeling combustion flow in coal power plants, and simulating airflow over airplane bodies. The presentation also examines modeling electromagnetics, shuttle/tank separation, and other applications involving differential equations, programming, control systems, and data fitting. In total, 16 real-world uses of numerical methods are outlined.
This document describes research using genetic programming (GP) and artificial neural networks (ANN) to develop short-term air quality forecast models for Pune, India. 36 models were developed using daily average meteorological and pollutant concentration data from 2005-2008 to predict concentrations of SOx, NOx, and particulate matter one day in advance. The models were designed to be robust in situations where complete input data is unavailable. Performance of the GP and ANN models was evaluated based on correlation, error, and other statistical measures. The research found that the GP models generally performed better than the ANN models, especially in cases with incomplete data, and had the advantage of generating equation-based forecasts.
CREATION OF A POSTGRADUATE COURSE FOR WIND ENERGY AT THE INTERNATIONAL HELLEN...Dimitrios Kanellopoulos
1) The International Hellenic University created an 18-hour postgraduate course on wind energy as part of their MSc in Energy Systems program, taught solely in English.
2) The course aims to provide quality wind engineering knowledge to students with diverse scientific backgrounds, within the time limitations.
3) It covers key wind energy topics in a specific order, and references accredited materials and major online sources for further reading. Technical videos and animations are used to effectively teach students.
The two main challenges of predicting the wind speed depend on various atmospheric factors and random variables. This paper explores the possibility of developing a wind speed prediction model using different Artificial Neural Networks (ANNs) and Categorical Regression empirical model which could be used to estimate the wind speed in Coimbatore, Tamil Nadu, India using SPSS software. The proposed Neural Network models are tested on real time wind data and enhanced with statistical capabilities. The objective is to predict accurate wind speed and to perform better in terms of minimization of errors using Multi Layer Perception Neural Network (MLPNN), Radial Basis Function Neural Network (RBFNN) and Categorical Regression (CATREG). Results from the paper have shown good agreement between the estimated and measured values of wind speed.
This presentation introduces numerical methods and their applications. Numerical methods use numerical approximations to solve mathematical problems. They are used in various fields like engineering, scientific computing, weather forecasting, estimating ocean currents, modeling airplanes, solving heat equations, and crime detection. The document provides examples of how numerical methods are applied to structural analysis, hydrological forecasting, transportation modeling, integrating and solving differential equations, root finding, numerical optimization, modeling ocean circulation, simulating aircraft wings, and predicting crime patterns.
This document discusses applications of numerical methods. It provides examples of using numerical methods for weather forecasting, engineering problems like structural analysis, scientific computing, estimating ocean currents, modeling airplanes, solving heat equations, and crime detection. Numerical methods are widely used to solve mathematical problems in diverse fields like science, engineering, and operations research by approximating solutions.
Numerical methods are mathematical tools used to solve numerical problems. They are applied in engineering, crime detection, scientific computing, finding roots, and solving heat equations. Examples given include using numerical methods to find the velocity of a rocket at a certain time and in statistics. Numerical methods have various applications in different fields of science, mathematics, and engineering.
This presentation discusses numerical methods and their applications. It is presented by 4 students from Daffodil International University in Bangladesh. The presentation defines numerical methods as algorithms that use numerical approximations to solve mathematical problems. It provides examples of applying numerical methods to weather forecasting, engineering, scientific computing, estimating ocean currents, modeling airplanes, solving heat equations, and crime detection. It discusses specific techniques like numerical weather prediction and analyzing data to predict crime.
This presentation discusses the application of numerical methods in real-life scenarios. It provides examples such as estimating ocean currents, modeling combustion flow in coal power plants, and simulating airflow over airplane bodies. The presentation also examines modeling electromagnetics, shuttle/tank separation, and other applications involving differential equations, programming, control systems, and data fitting. In total, 16 real-world uses of numerical methods are outlined.
This document describes research using genetic programming (GP) and artificial neural networks (ANN) to develop short-term air quality forecast models for Pune, India. 36 models were developed using daily average meteorological and pollutant concentration data from 2005-2008 to predict concentrations of SOx, NOx, and particulate matter one day in advance. The models were designed to be robust in situations where complete input data is unavailable. Performance of the GP and ANN models was evaluated based on correlation, error, and other statistical measures. The research found that the GP models generally performed better than the ANN models, especially in cases with incomplete data, and had the advantage of generating equation-based forecasts.
CREATION OF A POSTGRADUATE COURSE FOR WIND ENERGY AT THE INTERNATIONAL HELLEN...Dimitrios Kanellopoulos
1) The International Hellenic University created an 18-hour postgraduate course on wind energy as part of their MSc in Energy Systems program, taught solely in English.
2) The course aims to provide quality wind engineering knowledge to students with diverse scientific backgrounds, within the time limitations.
3) It covers key wind energy topics in a specific order, and references accredited materials and major online sources for further reading. Technical videos and animations are used to effectively teach students.
The two main challenges of predicting the wind speed depend on various atmospheric factors and random variables. This paper explores the possibility of developing a wind speed prediction model using different Artificial Neural Networks (ANNs) and Categorical Regression empirical model which could be used to estimate the wind speed in Coimbatore, Tamil Nadu, India using SPSS software. The proposed Neural Network models are tested on real time wind data and enhanced with statistical capabilities. The objective is to predict accurate wind speed and to perform better in terms of minimization of errors using Multi Layer Perception Neural Network (MLPNN), Radial Basis Function Neural Network (RBFNN) and Categorical Regression (CATREG). Results from the paper have shown good agreement between the estimated and measured values of wind speed.
The document discusses a methodology for improving wind speed forecasts through synergizing outputs from two numerical weather prediction (NWP) models - the Global Environmental Multiscale model (GEM) and the North American Mesoscale model (NAM). Wind speed measurements from four meteorological towers are used to evaluate the individual NWP models and their combined forecasts. Results show the combined GEM-NAM forecasts reduce root mean square error by up to 20% compared to the individual models, indicating improved forecast accuracy through optimal combination of the two NWP models.
This document lists and describes 24 sources of climate data from various organizations. It includes global datasets from the IPCC and NOAA, as well as regional datasets for Europe, the US, and other parts of the world. The sources provide raw and processed climate data, paleoclimate reconstructions, and model output. The datasets contain temperature, precipitation, and other climate variables and can be used for mapping, analysis, and modeling.
This document provides a summary of Hani Alnakeeb's background and qualifications. It includes his contact information, educational history, previous work experience, publications, associations, and extracurricular activities. Alnakeeb has a Ph.D. in Mechanical Engineering and over 30 years of experience in energy planning, management, and engineering in Egypt, Canada, and internationally with organizations like the United Nations. He has authored numerous publications on energy topics and served on technical committees and boards.
This document discusses load forecasting methods for deregulated electricity markets. It covers the importance of load forecasting, types of forecasting like long-term and short-term, and factors that influence loads such as weather, time of day, and customer class. Mathematical methods for load forecasting include regression models, similar day approaches, and neural networks. The author's research group has developed statistical learning models for long-term forecasting 2-3 years ahead and short-term forecasting 48 hours ahead. Their long-term model uses weather and time variables to forecast annual peak demand, while their short-term model provides load pocket forecasts up to 48 hours in advance.
Overview of MediaEval 2020 Insights for Wellbeing: Multimodal Personal Health...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper11.pdf
YouTube: https://youtu.be/fBPuacAZkxs
Minh-Son Dao, Peijiang Zhao, Thanh Nguyen, Thanh Binh Nguyen, Duc Tien Dang Nguyen and Cathal Gurrin : Overview of MediaEval 2020 Insights for Wellbeing: Multimodal Personal Health Lifelog Data Analysis. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This paper provides a description of the MediaEval 2020 “Multimodal personal health lifelog data analysis". The purpose of this task is to develop approaches that process the environment data to obtain insights about personal wellbeing. Establishing the association between people’s wellbeing and properties of the surrounding environment which is vital for numerous research. Our task focuses on the internal associations of heterogeneous data. Participants create systems that derive insights from multimodal lifelog data that are important for health and wellbeing to tackle two challenging subtasks. The first task is to investigate whether we can use public/open data to predict personal air pollution data. The second task is to develop approaches to predict personal air quality index(AQI) using images captured by people (plus GAQD). This task targets (but is not limited to) researchers in the areas of multimedia information retrieval, machine learning, AI, data science, event-based processing and analysis, multimodal multimedia content analysis, lifelog data analysis, urban computing, environmental science, and atmospheric science.
Presented by: Peijiang Zhao
Applications of numerical methods in civil engineeringওমর ফারুক
Numerical methods provide approximations that are useful for solving problems in engineering and sciences. They can be used for structural analysis, traffic simulations, weather prediction, analyzing groundwater and pollutant movement, and estimating water flow. Numerical methods allow engineers to create mathematical models of systems, solve those models using computer programs, and check the results to predict behavior. They are applied to calculate loads on structures, collision avoidance in traffic, temperature and precipitation predictions, and tracking groundwater movement.
This document discusses using satellite data and meteorological variables to estimate monthly average daily solar radiation (SR) over Turkey using artificial neural network (ANN) and multiple linear regression (MLR) models. Land surface temperature, altitude, latitude, longitude and month were used as inputs to the ANN and MLR models to estimate SR, which was then evaluated against meteorological measurements. The results showed that the ANN model produced more accurate estimates of SR compared to the MLR model. The ANN model was also able to generate a yearly average SR map for Turkey using satellite data.
This document discusses the importance of mathematics in various fields of engineering. It outlines several key areas of mathematics and provides examples of their applications in electrical, civil, mechanical, biomedical and other engineering domains. The areas described include complex numbers, matrices and determinants, Laplace transforms, statistics and probability, vectors and trigonometry, differentiation, integration, and functions, polynomials and linear equations. Across these areas, mathematics is essential for modeling and solving real-world engineering problems involving areas like circuit analysis, structural design, fluid mechanics, biomedical device development, and more.
Insights for wellbeing: Predicting Personal Air Quality Index using Regressio...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper51.pdf
Amel Ksibi, Amina Salhi, Ala Alluhaidan and Sahar A. El-Rahman : Insights for wellbeing: Predicting Personal Air Quality Index using Regression Approach. Proc. of MediaEval 2020, 14-15 December 2020, Online.
Providing air pollution information to individuals enables them to understand the air quality of their living environments. Thus, the association between people’s wellbeing and the properties of the surrounding environment is an essential area of investigation. This paper proposes Air Quality Prediction through harvesting public/open data and leveraging them to get the Personal Air Quality index. These are usually incomplete. To cope with the problem of missing data, we applied the KNN imputation method. To predict Personal Air Quality Index, we apply a voting regression approach based on three base regressors which are Gradient Boosting regressor, Random Forest regressor, and linear regressor. Evaluating the experimental results using the RMSE metric, we got an average score of 35.39 for Walker and 51.16 for Car.
The document summarizes an analysis of methane emissions from oil and gas production in Romania using atmospheric transport modeling and measurements from an aircraft campaign called ROMEO. Three mesoscale models were used to simulate methane transport and evaluate emissions from different oil and gas production clusters. Preliminary results found meteorology impacted model accuracy and emissions varied between production sites. Further analysis will quantify emissions and compare to mass balance estimates to improve methane emission estimates from the Romanian oil and gas sector.
Harry Coumnas is a meteorologist working in Ireland's top research institute. He is proficient in using computerized & mathematical models for making short as well as long range forecasts concerning climate patterns.
SWOT is a satellite mission to measure global water storage on land and ocean topography with unprecedented resolution and coverage. It will observe lakes as small as 250m x 250m, rivers 100m wide, and ocean eddies with 10km wavelengths. This high resolution data will provide key insights into water budgets on land and energy transport in oceans, enabling better climate predictions and fresh water management in response to climate change. Other applications include ocean bathymetry, sea ice thickness, and ice sheet topography.
This document defines key vocabulary terms related to oceanography and ocean features and processes. It includes definitions for abyssal plains, continental rises, continental shelves, continental slopes, Coriolis effect, deep currents, El Niño, La Niña, longshore currents, mid-ocean ridges, ocean currents, ocean trenches, rift valleys, salinity, seamounts, storm surges, surface currents, swells, tsunamis, undertow, upwelling, and whitecaps.
This document provides instructions for assembling a printable weather station. It includes cutting guidelines and terms to label the weather station parts such as the pivot, cover, and temperature scale. Additional cuts and folds are indicated to shape the weather station assembly.
Monitoring systems and metrology stations which accompany the energy stationsAli Abass
I am talking about the applications of monitoring systems and metrology stations which are use in renewable power stations and renewable heating cooling system such heat pump.
Basics of Wind Meteorology - Dynamics of Horizontal FlowTuong Do
This document provides an overview of wind energy meteorology research topics at the Institute of Physics / ForWind, including:
1) Forecasting of wind power and offshore wind energy meteorology using numerical weather prediction and physical/statistical models.
2) Numerical modelling of atmospheric boundary layer flow and wakes in wind farms using mesoscale modelling, Large Eddy Simulation (LES), and Reynolds-averaged Navier-Stokes (RANS) models.
3) Research on offshore-specific wind conditions, turbulence, vertical wind profiles, and air-sea interactions using measurements from platforms like FINO-1.
The document outlines the basic meteorological concepts required to study these topics, such as atmospheric dynamics,
The document provides an overview of water resources management and hydrology. It discusses the goals of understanding hydrologic processes and solving water-related problems. Key topics covered include the water cycle, what hydrologists study and do, examples of ancient hydrologic history like the Nile River, major global water usage, water scarcity issues, and the shrinking of the Aral Sea as an example of poor water management.
eMAST aims to integrate data from TERN and other sources to model ecosystems at all scales in Australia from 2013-2015. This will be done using data assimilation, model evaluation and optimization tools to further ecosystem science and help address questions about topics like carbon, water, climate change, fire, and biodiversity. Key products being delivered include high resolution climate and productivity datasets as well as tools for data analysis, interpolation and modeling. Progress includes the development and delivery of ANUClimate climate datasets and the ePiSaT model for estimating primary productivity across Australia using flux tower and satellite data.
The document discusses a methodology for improving wind speed forecasts through synergizing outputs from two numerical weather prediction (NWP) models - the Global Environmental Multiscale model (GEM) and the North American Mesoscale model (NAM). Wind speed measurements from four meteorological towers are used to evaluate the individual NWP models and their combined forecasts. Results show the combined GEM-NAM forecasts reduce root mean square error by up to 20% compared to the individual models, indicating improved forecast accuracy through optimal combination of the two NWP models.
This document lists and describes 24 sources of climate data from various organizations. It includes global datasets from the IPCC and NOAA, as well as regional datasets for Europe, the US, and other parts of the world. The sources provide raw and processed climate data, paleoclimate reconstructions, and model output. The datasets contain temperature, precipitation, and other climate variables and can be used for mapping, analysis, and modeling.
This document provides a summary of Hani Alnakeeb's background and qualifications. It includes his contact information, educational history, previous work experience, publications, associations, and extracurricular activities. Alnakeeb has a Ph.D. in Mechanical Engineering and over 30 years of experience in energy planning, management, and engineering in Egypt, Canada, and internationally with organizations like the United Nations. He has authored numerous publications on energy topics and served on technical committees and boards.
This document discusses load forecasting methods for deregulated electricity markets. It covers the importance of load forecasting, types of forecasting like long-term and short-term, and factors that influence loads such as weather, time of day, and customer class. Mathematical methods for load forecasting include regression models, similar day approaches, and neural networks. The author's research group has developed statistical learning models for long-term forecasting 2-3 years ahead and short-term forecasting 48 hours ahead. Their long-term model uses weather and time variables to forecast annual peak demand, while their short-term model provides load pocket forecasts up to 48 hours in advance.
Overview of MediaEval 2020 Insights for Wellbeing: Multimodal Personal Health...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper11.pdf
YouTube: https://youtu.be/fBPuacAZkxs
Minh-Son Dao, Peijiang Zhao, Thanh Nguyen, Thanh Binh Nguyen, Duc Tien Dang Nguyen and Cathal Gurrin : Overview of MediaEval 2020 Insights for Wellbeing: Multimodal Personal Health Lifelog Data Analysis. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This paper provides a description of the MediaEval 2020 “Multimodal personal health lifelog data analysis". The purpose of this task is to develop approaches that process the environment data to obtain insights about personal wellbeing. Establishing the association between people’s wellbeing and properties of the surrounding environment which is vital for numerous research. Our task focuses on the internal associations of heterogeneous data. Participants create systems that derive insights from multimodal lifelog data that are important for health and wellbeing to tackle two challenging subtasks. The first task is to investigate whether we can use public/open data to predict personal air pollution data. The second task is to develop approaches to predict personal air quality index(AQI) using images captured by people (plus GAQD). This task targets (but is not limited to) researchers in the areas of multimedia information retrieval, machine learning, AI, data science, event-based processing and analysis, multimodal multimedia content analysis, lifelog data analysis, urban computing, environmental science, and atmospheric science.
Presented by: Peijiang Zhao
Applications of numerical methods in civil engineeringওমর ফারুক
Numerical methods provide approximations that are useful for solving problems in engineering and sciences. They can be used for structural analysis, traffic simulations, weather prediction, analyzing groundwater and pollutant movement, and estimating water flow. Numerical methods allow engineers to create mathematical models of systems, solve those models using computer programs, and check the results to predict behavior. They are applied to calculate loads on structures, collision avoidance in traffic, temperature and precipitation predictions, and tracking groundwater movement.
This document discusses using satellite data and meteorological variables to estimate monthly average daily solar radiation (SR) over Turkey using artificial neural network (ANN) and multiple linear regression (MLR) models. Land surface temperature, altitude, latitude, longitude and month were used as inputs to the ANN and MLR models to estimate SR, which was then evaluated against meteorological measurements. The results showed that the ANN model produced more accurate estimates of SR compared to the MLR model. The ANN model was also able to generate a yearly average SR map for Turkey using satellite data.
This document discusses the importance of mathematics in various fields of engineering. It outlines several key areas of mathematics and provides examples of their applications in electrical, civil, mechanical, biomedical and other engineering domains. The areas described include complex numbers, matrices and determinants, Laplace transforms, statistics and probability, vectors and trigonometry, differentiation, integration, and functions, polynomials and linear equations. Across these areas, mathematics is essential for modeling and solving real-world engineering problems involving areas like circuit analysis, structural design, fluid mechanics, biomedical device development, and more.
Insights for wellbeing: Predicting Personal Air Quality Index using Regressio...multimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper51.pdf
Amel Ksibi, Amina Salhi, Ala Alluhaidan and Sahar A. El-Rahman : Insights for wellbeing: Predicting Personal Air Quality Index using Regression Approach. Proc. of MediaEval 2020, 14-15 December 2020, Online.
Providing air pollution information to individuals enables them to understand the air quality of their living environments. Thus, the association between people’s wellbeing and the properties of the surrounding environment is an essential area of investigation. This paper proposes Air Quality Prediction through harvesting public/open data and leveraging them to get the Personal Air Quality index. These are usually incomplete. To cope with the problem of missing data, we applied the KNN imputation method. To predict Personal Air Quality Index, we apply a voting regression approach based on three base regressors which are Gradient Boosting regressor, Random Forest regressor, and linear regressor. Evaluating the experimental results using the RMSE metric, we got an average score of 35.39 for Walker and 51.16 for Car.
The document summarizes an analysis of methane emissions from oil and gas production in Romania using atmospheric transport modeling and measurements from an aircraft campaign called ROMEO. Three mesoscale models were used to simulate methane transport and evaluate emissions from different oil and gas production clusters. Preliminary results found meteorology impacted model accuracy and emissions varied between production sites. Further analysis will quantify emissions and compare to mass balance estimates to improve methane emission estimates from the Romanian oil and gas sector.
Harry Coumnas is a meteorologist working in Ireland's top research institute. He is proficient in using computerized & mathematical models for making short as well as long range forecasts concerning climate patterns.
SWOT is a satellite mission to measure global water storage on land and ocean topography with unprecedented resolution and coverage. It will observe lakes as small as 250m x 250m, rivers 100m wide, and ocean eddies with 10km wavelengths. This high resolution data will provide key insights into water budgets on land and energy transport in oceans, enabling better climate predictions and fresh water management in response to climate change. Other applications include ocean bathymetry, sea ice thickness, and ice sheet topography.
This document defines key vocabulary terms related to oceanography and ocean features and processes. It includes definitions for abyssal plains, continental rises, continental shelves, continental slopes, Coriolis effect, deep currents, El Niño, La Niña, longshore currents, mid-ocean ridges, ocean currents, ocean trenches, rift valleys, salinity, seamounts, storm surges, surface currents, swells, tsunamis, undertow, upwelling, and whitecaps.
This document provides instructions for assembling a printable weather station. It includes cutting guidelines and terms to label the weather station parts such as the pivot, cover, and temperature scale. Additional cuts and folds are indicated to shape the weather station assembly.
Monitoring systems and metrology stations which accompany the energy stationsAli Abass
I am talking about the applications of monitoring systems and metrology stations which are use in renewable power stations and renewable heating cooling system such heat pump.
Basics of Wind Meteorology - Dynamics of Horizontal FlowTuong Do
This document provides an overview of wind energy meteorology research topics at the Institute of Physics / ForWind, including:
1) Forecasting of wind power and offshore wind energy meteorology using numerical weather prediction and physical/statistical models.
2) Numerical modelling of atmospheric boundary layer flow and wakes in wind farms using mesoscale modelling, Large Eddy Simulation (LES), and Reynolds-averaged Navier-Stokes (RANS) models.
3) Research on offshore-specific wind conditions, turbulence, vertical wind profiles, and air-sea interactions using measurements from platforms like FINO-1.
The document outlines the basic meteorological concepts required to study these topics, such as atmospheric dynamics,
The document provides an overview of water resources management and hydrology. It discusses the goals of understanding hydrologic processes and solving water-related problems. Key topics covered include the water cycle, what hydrologists study and do, examples of ancient hydrologic history like the Nile River, major global water usage, water scarcity issues, and the shrinking of the Aral Sea as an example of poor water management.
eMAST aims to integrate data from TERN and other sources to model ecosystems at all scales in Australia from 2013-2015. This will be done using data assimilation, model evaluation and optimization tools to further ecosystem science and help address questions about topics like carbon, water, climate change, fire, and biodiversity. Key products being delivered include high resolution climate and productivity datasets as well as tools for data analysis, interpolation and modeling. Progress includes the development and delivery of ANUClimate climate datasets and the ePiSaT model for estimating primary productivity across Australia using flux tower and satellite data.
This document summarizes a presentation on climate data and projections focusing on limiting global warming to less than 2 degrees Celsius. It discusses the work of GERICS (the Climate Service Center Germany) in developing solutions for regional climate modeling, impacts analysis, and climate adaptation toolkits. Key points covered include:
- GERICS' interdisciplinary approach to regional climate modeling, impacts assessment, and stakeholder engagement.
- The development of adaptation toolkits for cities, companies, and other sectors to facilitate climate risk assessment and planning.
- An overview of the presentation, covering topics like climate modeling techniques, accessing climate projections data, and visualizing and analyzing climate information.
April 2010, Tri-State EPSCoR Meeting, Incline VillageJeff Dozier
This document discusses improving water management and predicting water availability given climate change and human impacts. It proposes integrating different types of data and modeling to better understand changes to the water cycle. Key challenges include closing knowledge gaps, developing scalable and robust forecasting tools, and providing usable information to decision-makers dealing with non-stationary conditions and greater uncertainties.
This document summarizes the key differences between weather and climate prediction and seasonal climate prediction methodology. Weather refers to short-term conditions while climate describes long-term trends and variability. Weather is unpredictable beyond 10 days due to atmospheric sensitivity, but climate can be predicted to some degree based on external forcing factors like sea surface temperatures (SSTs). Seasonal climate predictions use both empirical and dynamical models to provide probabilistic forecasts of climate statistics over the coming season, with the El Niño Southern Oscillation being a major source of predictability. Forecasts are verified using reliability and resolution metrics on many samples, and improvements rely on advancing models, observations, data assimilation, and understanding of seasonal variability.
Scott McIntosh, Director, High Altitude Observatory, National Center for Atmospheric Research, Boulder, Colorado
June 2016 - UCAR Congressional Briefing on Predicting Space Weather
Video of this presentation will be available soon.
The document discusses the challenges of long-duration human spaceflight and the need to understand human health risks over periods of 1000 days in space. The NASA Human Research Program aims to provide countermeasures, knowledge, and tools to enable safe space exploration by minimizing risks to human health and performance from hazards like altered gravity, isolation, closed environments, and distance from Earth. While six-month ISS missions provide some data, longer missions are needed to assess physiological and behavioral changes over time and validate countermeasures for medical conditions, deconditioning, and performance issues over multi-year missions like a journey to Mars.
Future guidelines on solar forecasting the research view - David Pozo (Univer...IrSOLaV Pomares
The document discusses solar radiation forecasting research conducted by the MATRAS solar radiation and atmosphere modelling group at the University of Jaen. The group has developed facilities for measuring and forecasting direct normal irradiance (DNI) using sky cameras, ceilometers and numerical weather prediction models. Their research aims to improve short-term DNI nowcasting and forecasting up to 72 hours ahead for applications such as solar power plant operation and electricity market participation. They are also investigating how to optimally balance solar and wind power resources to reduce production variability.
Big data and remote sensing: A new software of ingestion IJECEIAES
The document describes a new software for ingesting big remote sensing data. The software developed an efficient ingestion layer that acquires, filters, and preprocesses large volumes of satellite data. It discarded 86% of unnecessary daily files and cleaned 20% of erroneous data. The preprocessed output was integrated into the Hadoop system for further processing using HDFS, HBase and Hive. The results showed the ingestion layer efficiently handled remote sensing big data with high accuracy, low data volume and reasonable execution time.
This document discusses mountain hydrology and the role of snow in the water cycle. It notes that snowmelt is a major contributor to annual precipitation and runoff in many regions. The document outlines the shift from experimental to theoretical to computational to data-intensive science. It provides examples of using satellite data and modeling to map snow cover, reconstruct snow water content, and better understand the impacts of dust and other impurities on snowmelt runoff.
application of artificial intelligence in meteorology (1).pptxKarthikkingK1
The document discusses how artificial intelligence can improve meteorology. It explains that AI has the potential to more accurately forecast weather, predict extreme events, and optimize renewable energy production by analyzing large amounts of weather data. However, there are also challenges to applying AI in meteorology like data quality issues and the complexity of weather systems. Addressing these challenges is important to realizing the full benefits of AI for forecasting and renewable energy.
The document discusses the impacts of extreme space weather and NOAA's Space Weather Prediction Center's efforts to improve space weather forecasting and warnings. It describes three varieties of space weather - solar flares, coronal mass ejections, and solar wind. It outlines SWPC's customers in electric power, communications, aviation, spacecraft operations and navigation who rely on its products. It then discusses potential impacts like widespread power outages and the need to partner with NASA to develop new models to extend forecast lead times and improve regional predictions in order to help mitigate risks to critical infrastructure from space weather events.
This document discusses trends in cloudiness and challenges in detecting trends from observational data. It notes that precision in monitoring cloud cover has increased over time but accuracy of long-term trends is difficult to determine due to changes in observation methods, locations, and instruments. Global satellite data shows small or no statistically significant changes in total cloud cover over various time periods. Trends over the Netherlands cannot be reliably assessed due to gaps in surface observer records when instrumentation changed. Reconstructing aerosol and fog trends requires long records of relative humidity and visibility. The public may have difficulty distinguishing demonstrated climate trends from spurious trends caused by changes in observations. Successful experts in climate monitoring require persistence, attention to detail, and focus on continuity of measurements over
Dr. Thomas Zurbuchen discusses research frontiers in space weather. He notes that space weather is as important to space researchers as cancer is to biologists. His presentation covers the role of universities in fundamental research, modeling challenges like the University of Michigan's model, new distributed sensor network architectures using fleets of nanosatellites, and the educational challenge of training students in developing new technologies and analyzing data.
Key messages from the AR5 WGI with focus on Saudi Arabia and the regionJesbin Baidya
The document discusses future climate change in Southeast Asia and extreme events according to the IPCC. It notes that human influences on the climate system are clear based on multiple lines of evidence. If greenhouse gas emissions continue, warming and changes will affect all parts of the climate system. Limiting climate change will require substantial reductions in emissions. The region will likely see increased warming, changes in precipitation patterns including more variable rainfall, and more frequent extreme weather events.
Learning new climate science by thinking creatively with machine learningZachary Labe
Presentation for: GFDL/AOS Summer Internship Lecture Series
The popularity of machine learning is rapidly growing in nearly all areas of Earth science. However, there is also some hesitancy in adopting the use of these methods due to concerns about their reliability, reproducibility, and interpretability – thus, they are often described as “black boxes.”
In this talk, I will introduce a few simple examples from climate science that leverage new visualization methods to peer into the machine learning “black box,” which help us to better understand their predictions while also learning new science. These same machine learning visualization tools can be easily adapted for a wide variety of applications and other scientific fields of study.
This document provides an overview of the topics that will be covered in the course CE 5500: Stochastic Hydrology. The course aims to teach students about modeling uncertainty in hydrologic processes and methods for doing so. Topics that will be covered include flood frequency analysis, drought frequency analysis, predictions in ungauged basins, time series analysis, and modeling the potential impacts of climate and land use change. The course objectives and tentative schedule are also outlined.
Similar to Careers in weather, climate and atmospheric science (20)
Careers in weather, climate and atmospheric science
1. Head in the clouds: Careers in Weather, Climate and Atmospheric Science Andy Russell [email_address] @dr_andy_russell
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3. “ Suppose you went to your careers advisor in Year 10, what would you have wanted them to tell you?”
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7.
8. Reading: BSc Meteorology and Climate 300 points overall including at least BB in physics and mathematics (both A2 levels) and 100 points from another A2 level or other AS levels Leeds: MEnv, BSc Meteorology and Climate Science (International) Normally three A-Levels, or 2 A-levels and 2 AS-levels, at grades AAA (or equivalent), including Mathematics and Physics or Chemistry but excluding General Studies UEA: BSc Meteorology and Oceanography ABB-BBB (including Maths)
9. Reading: BSc Meteorology and Climate 300 points overall including at least BB in physics and mathematics (both A2 levels) and 100 points from another A2 level or other AS levels Leeds: MEnv, BSc Meteorology and Climate Science (International) Normally three A-Levels, or 2 A-levels and 2 AS-levels, at grades AAA (or equivalent), including Mathematics and Physics or Chemistry but excluding General Studies UEA: BSc Meteorology and Oceanography ABB-BBB ( including Maths )
13. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Hydrology
14. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Hydrology Pure and applied research
20. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Hydrology Pure and applied research
21. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research
23. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research
24. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research Computing: models and hardware
25.
26. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research Computing: models and hardware
27. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research Observations: making and development Computing: models and hardware
28.
29. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research Observations: making and development Computing: models and hardware
30. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research Observations: making and development Data management Computing: models and hardware
32. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research Observations: making and development Data management Computing: models and hardware
33. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research Observations: making and development Data management Insurance, re-insurance, derivatives market Computing: models and hardware
35. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research Observations: making and development Data management Insurance, re-insurance, derivatives market Computing: models and hardware
36. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research Observations: making and development Data management Insurance, re-insurance, derivatives market Flood forecasting Computing: models and hardware
37.
38. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research Observations: making and development Data management Insurance, re-insurance, derivatives market Flood forecasting Computing: models and hardware
39. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research Observations: making and development Data management Insurance, re-insurance, derivatives market Flood forecasting Earth systems science Computing: models and hardware
40.
41. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research Observations: making and development Data management Insurance, re-insurance, derivatives market Flood forecasting Earth systems science Computing: models and hardware
42. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research Observations: making and development Data management Insurance, re-insurance, derivatives market Flood forecasting Earth systems science Data collection and analysis Computing: models and hardware
43.
44. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research Observations: making and development Data management Insurance, re-insurance, derivatives market Flood forecasting Earth systems science Data collection and analysis Computing: models and hardware
45. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research Observations: making and development Data management Insurance, re-insurance, derivatives market Flood forecasting Earth systems science Data collection and analysis Climate projections Computing: models and hardware
46.
47. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research Observations: making and development Data management Insurance, re-insurance, derivatives market Flood forecasting Earth systems science Data collection and analysis Climate projections Computing: models and hardware
48. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research Observations: making and development Data management Insurance, re-insurance, derivatives market Flood forecasting Earth systems science Data collection and analysis Climate projections Government policy Computing: models and hardware
49. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research Observations: making and development Data management Insurance, re-insurance, derivatives market Flood forecasting Earth systems science Data collection and analysis Climate projections Government policy Climate change mitigation & adaptation Computing: models and hardware
50.
51. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research Observations: making and development Data management Insurance, re-insurance, derivatives market Flood forecasting Earth systems science Data collection and analysis Climate projections Government policy Climate change mitigation & adaptation Computing: models and hardware
52. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research Observations: making and development Data management Insurance, re-insurance, derivatives market Flood forecasting Earth systems science Data collection and analysis Climate projections Government policy Climate change mitigation & adaptation Communication: for and against Computing: models and hardware
53.
54. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Hydrology Pure and applied research Observations: making and development Data management Insurance, re-insurance, derivatives market Flood forecasting Earth systems science Data collection and analysis Climate projections Government policy Climate change mitigation & adaptation Communication: for and against Computing: models and hardware
55. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Observations: making and development Earth systems science Communication: for and against Climate projections Climate change mitigation & adaptation Insurance, re-insurance, derivatives market Hydrology Flood forecasting Pure and applied research Data collection and analysis Government policy Data management Energy, health, travel, engineering, food Computing: models and hardware
56.
57. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Forecasting (MetOffice and private sector) Observations: making and development Earth systems science Communication: for and against Climate projections Climate change mitigation & adaptation Insurance, re-insurance, derivatives market Hydrology Flood forecasting Pure and applied research Data collection and analysis Government policy Data management Energy, health, travel, engineering, food Computing: models and hardware
58. Meteorology (small spatial scales, short time scales) Climatology (large spatial scales, long time scales) Oceanography Observations: making and development Earth systems science Communication: for and against Climate projections Post graduate? School leaver? Post doctoral? Climate change mitigation & adaptation Insurance, re-insurance, derivatives market Hydrology Flood forecasting Pure and applied research Data collection and analysis Government policy Data management Energy, health, travel, engineering, food Computing: models and hardware Forecasting (MetOffice and private sector)