@Article{M-10014, AUTHOR = {patel, nikit}, TITLE = {Journal resiger in scopus2}, JOURNAL = {Scientific Research Journal Of Engineering and Technology}, VOLUME = {1}, YEAR = {2021}, NUMBER = {2}, ARTICLE-NUMBER = {M-10014}, URL = {https://staging.isrdo.org/journal/SRJET/currentissue/journal-resiger-in-scopus2}, ISSN = {XXXX-XXXX}, ABSTRACT = {hurricane model, climate models, weather hazards, hazard models, typical hurricane, hurricanes develop"information "Fifteen years ago, Kerry Emanuel developed a simple hurricane model. It was based on physical equations rather than statistics and could operate in real time, making it useful for modeling risk assessment. Emanuel wondered if similar models could be used for long-term risk assessment of other things, such as changes in extreme weather patterns due to climate change.</p><p>"I discovered, to my surprise and dismay, that almost all existing estimates of long-term weather hazards in the United States are based not on physical models, but on historical hazard statistics," says Emanuel. “The problem with relying on historical documents is that they are too short; Although they can help estimate common events, they do not contain enough information to make predictions for rarer events.Another limitation of weather hazard models that rely heavily on statistics: They have a built-in assumption that the weather is static.</p><p>“Historical records are based on the weather at the time they were recorded; they can't say anything about how hurricanes develop in warmer weather,” says Emanuel. The models are based on fixed relationships between events; they assume that hurricane activity will remain the same, although science shows that warmer temperatures will likely drive typical hurricane activity beyond the tropics and into a much wider band of latitudes.</p><p>As a flagship project, the goal is to eliminate this reliance on historical records by emphasizing physical principles (for example, the laws of thermodynamics and fluid mechanics) in the new generation models. The disadvantage is that many variables need to be included. Not only do we have to consider planetary-scale systems, such as the global circulation of the atmosphere, but there are also small-scale and extremely localized events, such as thunderstorms, that influence the predictive results.Trying to calculate all of this at once is expensive and time consuming, and the results often can't tell you the risk in a specific location. But there is a way to fix that: "What you do is use a global model and then use a method called downscaling, which tries to infer what would happen at very small scales that don't get resolved correctly by the global model." says O'Gorman. The team hopes to improve downscaling techniques so they can be used to calculate the risk of very rare but impactful weather events.</p><p>Global climate models, or general circulation models (GCMs), explains Emanuel, are built like a jungle gym. Like the bars in a playground, the Earth is sectioned into an interconnected three-dimensional framework, only it is divided 100 to 200 square kilometers at a time. Each node includes a set of calculations for features such as wind, precipitation, atmospheric pressure, and temperature within its limits; the outputs of each node are connected to its neighbor. This framework is useful for creating an overview of the Earth's climate system, but if you're trying to zoom in on a specific location, such as to see what's happening in Miami or Mumbai, the connecting nodes are too far away to do. predictions about something specific for these areas.Scientists get around this problem by using downscaling. They use the same blueprint as the Jungle Gym, but at the nodes they weave a mesh of smaller elements, incorporating equations for things like topography and vegetation or regional weather patterns to fill in the blanks. By creating a finer mesh over smaller areas, they can predict local effects without having to run the entire global model.</p><p>Of course, even this finer resolution solution has its pros and cons. While we can get a clearer picture of what is happening in a specific region by nesting models within models, it can still be computationally challenging to analyze all of this data at once, the trade-off being cost and time. , or forecasts limited to shorter duration windows: Where GCMs can be run considering decades or centuries, a particularly complex local model may be limited to forecasts on time scales of a few years at a time.}, DOI = {} }