Earth Science Informatics - CFP: Utilising High Performance Computing for Complex Weather Forecasting Models
Global weather and climate centres depend on high-performance computing (HPC) resources to monitor, assess, model, and forecast the behaviour of atmospheric phenomena such as climate trends and weather systems. High Performance computers can offer a more accurate depiction of the climate models because of its improved computer capacity. Predicting extreme weather conditions, as well as their causes and effects, can be made easier and more accurate with the use of HPC. Meteorologists use a supercomputer to read the weather. After that, the computer weather model uses this data regarding the state of the atmosphere right now to run it through physics formulas that forecast changes in the weather over time. The supercomputer is one of the most well-known categories of HPC systems. Thousands of compute nodes in a supercomputer cooperate to accomplish one or more tasks. Economists refer to this as parallel processing. It functions similarly to thousands of PCs connected over a network, pooling processing power to finish jobs more quickly. Computer simulation is the process of modelling a system's dynamic responses using the behaviour of another system that is patterned after it.
In a simulation, a computer program serves as a mathematical model or description of an actual system. The functional linkages found in the real system are replicated in this model through the use of equations. The dynamic behaviour of systems or objects in response to situations that are difficult or unsafe to apply in real life is studied using computer simulations. For instance, a mathematical model that considers variables like heat, velocity, and radioactive emissions can be used to explain a nuclear bomb. The model can then be modified using additional mathematical formulas to account for variations in specific factors, like the quantity of fissionable material that caused the explosion. Simulations are particularly helpful in allowing observers to quantify and forecast the potential effects of changing individual system components on the overall functioning of the system. Computers gather information from radar, weather stations, satellites, and other sources, then combine it with data from previous observations. By refining the baseline circumstances for weather models, a process known as data assimilation serves to increase the accuracy of short-term forecasts. The fact that weather alerts are used to safeguard people and property makes them crucial. Temperature and precipitation forecasts are crucial for agriculture and, consequently, for commodity market traders. Utility providers use temperature forecasts to project demand for the upcoming days. Extremely compute-intensive operations including quantum physics, weather forecasting, oil and gas exploration, climate change study, molecular modelling, and physical simulations have all been accomplished with supercomputers. Several workload types can be executed on HPC systems. Workloads that are tightly connected and parallel are two common categories. Computational issues are split up into discrete, stand-alone jobs in parallel workloads, which allow for extremely fast parallel execution. These tasks frequently don't communicate with one another.
Possible Topics include:
Weather analysis and predictions software model-based high-performance computing.
High-speed computing for numerical weather and climate prediction: resilience and fault tolerance.
Moving towards incredibly high-resolution weather and climate models.
Superb computational techniques utilised for forecasting space weather models.
The meteorological analysis and forecasting model's use of cloud computing.
Advances in high-performance computing recently for remote sensing applications.
Enhancing the performance of numerical weather prediction models using machine learning methods.
High-performance computing for weather and climate is the destination earth.
The limitations of weather forecasting in developing nations.
Development of an affordable mobile cluster for dynamical weather prediction.
Forecasts of dust storms can be supported by using cloud computing.
Weather forecasting expedited by near-memory changeable material.
Guest Editors:
Dr. Mohammad Nishat Akhtar
School of Aerospace Engineering,
Engineering Campus, Universiti Sains Malaysia,
14300 Nibong Tebal, Pulau Pinang, Malaysia
E-mail: nishat@usm.my (this opens in a new tab), iresearchertech2023@gmail.com (this opens in a new tab)
ResearcherID: S-7313-2018
ORCID ID: https://orcid.org/0000-0001-8592-5966 (this opens in a new tab)
Scopus Author ID: 55065002500
Google Scholar: https://scholar.google.com/citations?user=ie7z5hQAAAAJ&hl=en (this opens in a new tab)
Research Background:
Dr. Mohammad Nishat Akhtar received his B.E in Computer Science from VTU, India during the year 2010, MSc in Electrical and Electronics from Universiti Sains Malaysia during the year 2013 and PhD in the field of Parallel Image Processing from Universiti Sains Malaysia during the year 2018. Currently, He is a Lecturer at School of Aerospace Engineering in Universiti Sains Malaysia. His research interests include High Performance Computation, Image Processing, Control & Embedded Systems, System-on-Chip. Currently, He has several International Journals and International Conferences under his banner. He has been actively involved as a lead researcher and co-researcher in USM’s research grant project. He has imparted several programming-based trainings to internship students and technicians. In this regard, Dr. Mohammad Nishat Akhtar also serves as Liaison Officer for School of Aerospace Business Unit whereby He coordinates in conducting training to the employees of multinational companies. He has also been designated as one of the lead trainers for a professional certification program conducted by School of Aerospace Business Unit for the session 2022/23.
Dr. Muhammad Rafiq Khan Kakar
Department of Architecture, Wood and Civil Engineering,
Bern University of Applied Sciences (BFH),
Pestalozzistrasse 20, 3400 Burgdorf, Switzerland
E-mail: muhammad.kakar@bfh.ch (this opens in a new tab)
ORCID ID: https://orcid.org/0000-0001-8669-897X (this opens in a new tab)
Scopus Author ID: 55994095000
Google Scholar: https://scholar.google.com/citations?user=0YKlSqEAAAAJ&hl=en (this opens in a new tab)
Research Background:
Dr. Muhammad Rafiq Khan Kakar completed his Bachelor of Science (Civil Engineering) from University of Engineering & Technology, Taxila, Pakistan in 2006, Master of Engineering (Transportation Engineering) from NED University of Engineering & Technology, Karachi, Pakistan in 2010 and Doctor of Philosophy (Ph.D.) in Civil Engineering (Asphalt Technology) from Universiti Sains Malaysia during the year 2015. Currently, He is a Scientific Collaborator in Department of Architecture, Bern University of Applied Sciences (BFH), Switzerland. His research interests include Asphalt Technology, Pavement Engineering, and Highway Engineering Material. His other professional skills include Road Maintenance execution, Budgeting and Tendering process, Government policies for road infrastructure. His current responsibilities include Leading Science & Technology funded Projects, Supervision of MSE student’s thesis, conducting research, and Establishing and developing test procedures.
Dr. Asha Crastaa
Department of Mathematics, Centre for advanced learning,
Bejai, Mangalore
E-mail: hodmat@mite.ac.in (this opens in a new tab), ashacrasta81@gmail.com,
GoogleScholar: https://scholar.google.co.in/citations?user=xiyg5XIAAAAJ&hl=en (this opens in a new tab)
University Link: https://mite.ac.in/member/dr-asha-crasta/ (this opens in a new tab)
Research Background:
Dr. Asha Crastaa completed his B.Sc in Physics, Computer Science & Mathematics, St. Aloysius College, Mangalore in 2004. M.Sc in Mathematics Mangalore University, Mangalore in 2002. M.Phil in Mathematics Vinayaka Missions University, Salem in 2008. Ph.D in applied Mathematics Jain University, Bangalore in 2014. His research interests include Engineering Mathematics, Advanced Mathematics I and II, Discrete Mathematical Structures, Management Information systems, Statistics for management, Business research methods, Probability Statistics and Queuing theory.
Submission Deadline: 10th July, 2024
Authors Notification: 20th September, 2024
Revised Version Submission: 25th November, 2024
Final Decision Notification: 05th February, 2025