On Thursday, May 21, the publication of the annual forecasts from the US National Oceanic and Atmospheric Administration (NOAA) for the Atlantic hurricane season is highly anticipated. However, these forecasts may lose precision and reliability due to a lack of data. Many specialists are concerned about the budget cuts imposed by the Trump administration, which impact various research areas, particularly climate research.
Despite this, in December, NOAA introduced brand new weather forecast models based on artificial intelligence (AI), touting attractive features: they are said to be faster, more efficient, and more precise models. However, these promising models require data to train.
“Reducing funding allocated to climate research has a negative impact on weather forecasts and hinders their development,” explained Craig McLean, former NOAA research director, in an interview with The Guardian.
Context: The NOAA annual hurricane season forecasts are crucial for preparing and responding to potential natural disasters, providing valuable information to the public and policymakers.
Fact Check: The budget cuts affecting NOAA research were a significant concern during the Trump administration, impacting various scientific fields, including climate research.
Data More Essential Than Ever
The British journal points out the challenges facing the observation systems of the planet — reductions in launches of scientific satellites, weather balloons, and ocean buoys, as well as a decrease in researchers analyzing data. These reductions are happening at a time when forecasts “will be most necessary,” as extreme weather events are expected to increase in frequency and intensity in the future due to climate change.
Meteorologist Chris Gloninger warns that if the government continues to rely more on AI-based models while reducing the amount of data feeding into them, government forecasts could be compromised.
The new AI-based models are still insufficient to predict extreme weather events, as they rely on historical data from a time when such events were rare. Therefore, they are more likely to “predict weather phenomena similar to those observed in the past,” as explained by The Guardian, emphasizing the importance of acquiring new data.
