Bottleneck

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Background: Data Science

Before talking about “AI for Earth observation and numerical weather prediction” by Sid-Ahmed Boukabara, I need to talk a little bit about data science. One of interesting distinction in the data science world is the separation of data and information: the former refers to the raw facts without context while the latter is a processed data to give it a meaning. In another words, raw data is not very useful in that state until it is distilled into information. This distinction formed the foundation of the article by Boukabara, the acting deputy director of the NOAA NESDIS Center for Satellite Applications and Research.

Bottleneck

Boukabara’s article describes a lop-sided advancement in technology: the human infrastructure to collect data, such as an array of satellites, has far surpassed our ability to process the data in a timely manner. This imbalance is so huge that “only 3-5 percent of satellite observations are actually used in preparing numerical weather forecasts” (Boukabara, 2019).

Proposition

Boukabara suggests that the advancement in AI will greatly reduce the computational challenge that we face in processing the data into information. He has observed its advantages and power in applications of other disciplines, and he urges the community to apply the same strength into the environmental satellite data so that the society can turn the wealth of data into wealth of information and rip the benefit.

References

  • Boukabara, S. (2019, April 17). AI for Earth observation and numerical weather prediction. Retrieved May 26, 2019, from https://spacenews.com/ai-for-earth-observation-and-numerical-weather-prediction/

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