In his article “How Big Data Can Boost Weather Forecasting” for Wired, author Steve Hamm discusses how Big Data analysis techniques are being used to more accurately predict weather patterns. As his first example, Hamm discusses how the Korean Metrological Administration (KMA) is working to upgrade its predictive systems in order to better prepare the Korean peninsula for storms that “[carry] dense clouds of yellow dust from China’s Gobi Desert that are sometimes loaded with heavy metals and carcinogens”. As part of their upgrades, the KMA is dramatically increasing the agency’s storage capabilities in hopes of more accurately forecast weather patterns through an increased ability to quickly analyze large amounts of information.
Such efforts to increase predictive capabilities are also being made in other parts of the world. Following the destruction caused by Hurricane Sandy, “leaders of the city of Hoboken…are considering building a wall around the city to keep the tidal Hudson River at bay”, but such efforts will be in vain if scientists are unable to predict how the changing climate will affect the river’s future depth and behavior. Due to the scale of the issue, IBM is assisting in researching more accurate predictive methods through a project called Deep Thunder, a “long-term weather analysis project”.
Currently, Deep Thunder has been used to accurately predict “the snowfall totals in New York City during the mammoth snowstorm” in February, and was also able to predict “when the snowfall would start and stop”. IBM is currently working to implement Deep Thunder in Rio de Janeiro for the 2016 Olympics, and provide attendees access to the predictive information through “iPad and cloud applications”. The accuracy and speed of Deep Thunder have great implications for the future of climate prediction; if the planet’s weather can be consistently be predicted, the damages and injuries caused by catastrophic weather could be greatly mitigated during future events.