How Big Data Can Boost Weather Forecasting
According to the
article"Big data has brought significant changes to many aspects of
education. According to a study, published by the Publications Office of the
European Union, the most significant change brought by the big data to
education, is the ability to monitor educational systems."
Students create a
lot of data information per day. Through the Social Network Adapting
Pedagogical Practice (SNAPP) into the teaching process, studying students’
blogs, and measuring how much they are interested in a specific course.
Spending time
tracking students ’daily social circles helps improve students’ learning
The achievement of
the big data on education is the creation of a learning management system. It
allows teachers to use automated information online to create assignments and
send information.
They set a test bed in the New York City metropolitan area, with
three-dimensional grid of thousands of blocks. That makes it easier and
possible for them to run the sophisticated data calculations that
produce very precise weather forecasts for a particular locale.
Based on the increasing
number of accidents that occurred by climate change, scientists take action to
protect people and property from the disaster caused by climate change. But, to
take effective action, they need to know more deeper about the weather.
source: https://www.wired.com/insights/2013/02/how-big-data-can-boost-weather-forecasting/
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ReplyDeleteThe significant increase in data collection and processing capabilities has greatly improved the ability of weather forecasters to pinpoint the time and severity of hurricanes, floods, snowstorms and other weather events. Globally, more and more evidence of climate change is prompting governments and scientists to take action to protect people and property from its effects. But to take effective measures, they need to know more about the weather-from everything that will happen tomorrow to what will happen next year. Considering the number of variables involved and the complex interactions between these variables, weather forecasting has always been extremely challenging. According to the professor of economics and finance, Alan Anderson, PhD, “Example of an application of big data to weather forecasting is IBM’s Deep Thunder. Unlike many weather forecasting systems, which give general information about a broad geographical region, Deep Thunder provides forecasts for extremely specific locations, such as a single airport, so that local authorities can get critically important information in real time.” One of the interesting results of collecting and processing more weather data is the sale of custom insurance to prevent the emergence of weather damaged companies. The Climate Company sells weather forecast services and professional insurance to farmers seeking to hedge their crop damage risks. The company uses large amounts of data based on large amounts of water, soil types, and past crop yields to determine the types of risks associated with specific areas. After I finished nine article reviews, I finally found one article that did not said a lot of business of big data, it shows that how we can use this tool so diversely.
ReplyDeleteGood article, the content tells us how the weather forecast is calculated using big data. Can be divided into the following. Meteorological data is the basis for carrying out weather forecast and early warning, climate prediction and forecast, various meteorological services and scientific research, and is the initial input of the meteorological business system.
ReplyDeleteIt can be said without hesitation that without these meteorological data, the computer will have fewer initial values when calculating "model data", and the weather forecast will not be able to proceed normally. Even in the artificial age without computers, data is essential.
There are many meteorological data, such as temperature, air pressure, air humidity, wind direction, wind speed, cloud, visibility, weather phenomenon, precipitation, evaporation, sunshine, snow depth, ground temperature, frozen soil, frozen wire, etc.
But no matter how complicated it is, it can also be divided into two categories: live data and model data.
Live data belongs to the most basic meteorological data. In a simple understanding, it is past data, mainly including data directly detected by instruments and equipment.
The data model is calculated by the computer program to solve the physical equations, including the reanalysis data of the historical weather data fusion and assimilation, and the numerical forecast and prediction data output for the future weather and climate calculation.
It can be simply visually considered that there is such a huge set of programs for calculating weather forecasts, and inputting currently known weather phenomena can output weather phenomena that have not yet occurred in the future.