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Mahesh

22/12/23 11:42 AM IST

AI tool for Weather forcasting

In News
  • At the recent COP28, NASA and IBM announced that an Artificial Intelligence (AI) tool called watsonx.ai would be available on the open-source AI platform Hugging Space.
AI in weather forcasting
  • In recent decades, weather prediction has improved rapidly with today’s six-day forecast as accurate as a five-day forecast 10 years ago.
  • Hurricane tracks can be predicted with more accuracy three days in advance than they could 24 hours in advance 40 years ago.
  • This achievement is due to improvements in atmosphere and ocean technology, and in parallel, the progress made in high-computing power.
  • Weather models today base their predictions on massive computing simulations that run on interpreted data. However, two key challenges remain.
  • By 2024, scientists will have 250,000 terabytes of climate data sets to work with. Climate data sets are massive and take significant time to collect, analyse and subsequently utilise to make informed decisions.
  • In the last year, the European Center for Medium-Range Weather Forecasting started using deep-learning models known as AI emulators to generate forecasts based on historical weather patterns.
  • According to IBM, while the laws of physics are not encoded into AI emulators, they can be inferred from the data, meaning that a forecast can be generated by a desktop computer in minutes instead of the hours typically taken by current systems.
Working of watsonx.ai
  • Like Microsoft’s Bing, OpenAI’s ChatGPT, and other chatbots, watsonx.ai is also built on a foundation model — it’s trained on a broad set of uncategorised data allowing the model to apply information about one situation to another.
  • In the case of watsonx.ai, NASA provides the datasets (in terms of satellite images instead of words,) and IBM created the foundation model to interpret them.
  • In order to train the model to comprehend visual sequences that unfold over time, scientists filled in blank areas in each image and asked the model to piece it back together.
  • It became increasingly adept at figuring out how the photos connected to one another as it reassembled additional images. The model was then adjusted for certain tasks like segmenting and categorising photos
  • The model is also designed to be extremely simple to use.
  • A user would merely need to select a location and a date, and the model will highlight changes in floodwater, reforestation efforts and other relevant factors.
Implications
  • This approach has the potential to minimise the amount of data cleaning and labelling needed to train a typical deep-learning model, and it could speed up geographical analysis by a factor of three to four.
  • Information from the visualisations may be used to lessen the effects of flooding, develop infrastructure, assist in disaster response, and safeguard the environment.
  • The technology could also apply to businesses, helping disaster response teams to prepare for fires impacting residential housing or helping supply chain logistics companies better understand macro weather patterns.
AI and Climate change
  • AI is already significantly impacting climate change strategies.
  • According to the Boston Consulting Group (BCG) AI survey report, 87 per cent of private and public sector CEOs believe that AI is an essential tool in the fight against climate change.
  • In the transportation industry, AI-enabled vehicles have the potential to minimise energy use by mapping and identifying the most efficient routes.
  • In agriculture, 40 per cent of freshwater usage is wasted on average but with AI technology, farmers can optimise crop irrigation, reducing water wastage and leading to more productive harvests.
  • In India, AI-equipped peanut farmers have already witnessed a 30 per cent increase in yield.
  • AI may also be used to assess emissions at the macro and micro levels, cut emissions and the impacts of greenhouse gases, and remove already-existing emissions from the environment.
  • According to BCG’s experience, AI may be utilised to help cut greenhouse gas emissions by five to 10 per cent of an organisation’s carbon footprint.
Source- Indian Express

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