Evaluating 2017 Hurricane Season Using Aerosols Simulation

Recently, NASA released its 2017 Hurricanes and Aerosols Simulation video footage. This simulation shows how cyclones like Harvey, Irma, Jose, Maria, Ophelia can be tracked using aerosols. Observations sent by NASA satellites were factored into mathematical models to visualize the storms. By following the tiny aerosol particles such as smoke, dust and sea salt being carried by storms across the globe, one can see how the hurricanes of 2017 were formed. It is possible to get a good idea of how complex the global nature of hurricane formation is and how hurricanes are influenced by global trends in weather.

Hurricane Harvey turned out to be one of the costliest cyclone causing catastrophic flooding in the Houston area. By tracking Harvey, one can see how it developed as a tropical wave to the east of Lesser Antilles, moved on to the Windward Islands, entered the Caribbean Sea and weakened out as a tropical wave north of Colombia. But later by the end of August, its remnants redeveloped as a tropical storm becoming a major hurricane. These hurricanes leave behind a trail of destruction that amounts to billions of dollars worth damage to life and property in United States(US) itself.

We can see how one after another hurricanes with their vicious forces are getting generated, with Irma forming of the west coast of Africa, followed by Jose and Maria. Finally, hurricane Ophelia formed in eastern Atlantic, which ended up spreading desert dust from Sahara over western Europe. The simulation also shows smoke from the wildfire season in the western US.

Meteorologists have observed that the changing atmospheric currents make it difficult to visualize hurricanes. Until recently, with NASA releasing the new simulation that shows how these hurricanes can be observed by following the path of aerosol formation. One can see the weather parameters that lead to such extreme hurricanes by tracking how these aerosol particles move over time, applying advanced mathematical models and projecting it over satellite imagery for that period. As aerosol particles spread over long distances their simulation provides a complete picture of how these hurricanes move and intensify.

A multitude of parameters is required for the development of a super hurricane. However, it is often seen that severity of Hurricanes is influenced by high ocean temperatures. There are numerous ways to measure the severity of hurricanes, by following the weather patterns associated with each storm, analyzing the number of storms, and measuring it in terms of accumulated cyclone energy. Scientists hope to use the data from these simulations to improve forecasting in the future.

Weather Forecast

The accuracy of weather forecasts is critical when it comes to the performance of Macrosoft’s Mine-Weather platform. Mine-Weather is an analytical database and visualization system that allows companies probe the accuracy of the weather forecasts they use to plan their activities and preparedness during storm conditions. Mine-Weather provides weather forecast verification in case of weather events like winter storms, hurricanes, or other weather events (high wind gusts, etc.).

Data generated with advanced simulations will improve the accuracy of the weather forecast. This in turn will improve the verification capabilities of Mine-Weather allowing users to evaluate vendor forecast performance as storm event approaches. Please go through our White Papers to learn more about what Mine-Weather offers and how the quality of weather forecast impacts its evaluation process.

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About the Author

Dr. Ronald Mueller

Ron is CEO and Founder of Macrosoft, Inc.. Ron heads up all company strategic activities, and directs day-to-day work of the Leadership Team at Macrosoft. Ron is also Macrosoft’s Chief Scientist, defining and structuring Macrosoft’s path forward on new technologies and products, such as Cloud; Big Data; and AI. Ron has a Ph.D. in Theoretical Physics from New York University, and worked in physics for over a decade at Yale University, The Fusion Energy Institute in Princeton, NJ, and at Argonne National Laboratory. Ron also worked at Bell Laboratories in Murray Hill, NJ., where he managed a group on Big Data, including very early work on neural networks. Ron has a career-long passion in ultra-large-scale data processing and analysis including: predictive analytics; data mining, machine learning and neural networks.

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