Click Here: "Cloudy with a Chance of Algorithms"

Podcast: Click Here (Recorded Future News)
Host: Dena Temple Raston
Guest: Paris Perdicaris (Associate Professor, University of Pennsylvania)
Release Date: September 26, 2025
Summary by Section, with Quotes and Timestamps

Overview

This episode delves into the evolving role of artificial intelligence in weather forecasting. Host Dena Temple Raston and guest Paris Perdicaris discuss how AI could revolutionize predictions, surpass traditional meteorology in speed, scope, and accuracy, and even democratize access. They also confront the limitations and risks—including the impact of data loss due to political and funding changes—and emphasize that while AI offers promise, its accuracy and reliability still depend on high-quality human-provided data and expertise.

Key Discussion Points & Insights

The Promise—and Limits—of AI Forecasts

• AI as an Enhancement, Not a Replacement
  • Paris addresses the common fear that AI will replace human forecasters, referencing Hollywood's "Terminator" as a metaphor for public anxiety:

    "The general fear that those systems are kind of going to be like a Terminator movie and kind of replace humans and so forth. At least in the field of Earth system science and weather forecasting, they're just going to enhance our ability to better predict the Earth system rather than replacing a human expert."
    (Paris Perdicaris, 00:54)

  • Dena jokes, “So no killer robots, just better predictions.” (01:20)
  • AI's goal: Make forecasting tools accessible to everyone, “so people can run their own forecasts right on their laptops.”
    (Dena Temple Raston summarizing, 01:20-01:45; Paris Perdicaris, 01:37)

The Data Behind the Forecast

• Vast Historical Records Enable AI Progress

  • Data from satellites, weather stations, balloons, aircraft, and ships has accumulated since the 1950s, forming an “information goldmine.”
    (Dena Temple Raston, 03:27)
  • Paris' AI system, Aurora, was trained on this wealth of data.

• Aurora: How AI ‘Learns' the Weather

  • Aurora “doesn’t understand the atmosphere” in a human sense; rather, it recognizes and learns patterns:

    "The way a sailor predicts the weather is experience based, right? ...They kind of feel the wind and see the clouds, and from that information, they can make a prediction of what's about to happen."
    (Paris Perdicaris, 03:47)

  • Aurora, unlike a sailor, “just sees it in terms of patterns.”
    (Dena Temple Raston, 04:00)

• Strengths and Limitations

  • Aurora “outperforms all existing operational forecasts for hurricane tracking,” providing accurate tracks four to five days before land impact.
    (Paris Perdicaris, 04:06)
  • However, critics cite AI’s struggle with rapid, small-scale events like flash floods or microbursts, which are increasingly relevant.
    (Dena Temple Raston, 04:25)

Building a Smarter Model

• Going Beyond Weather: Integrating Diverse Data
  • Paris includes “data from the oceans, air chemistry and air pollution...things like fossil fuel emissions, ocean chemistry, air pollution, concentration of carbon dioxide in the atmosphere.” (Paris Perdicaris, 05:08, 05:26)
  • The model's training mimics human education: a “general education phase” followed by advanced specialization (e.g., hurricanes, air quality).
    (Paris Perdicaris and Dena Temple Raston, 05:38–06:15)

AI vs. Traditional Meteorology

• How AI Delivers Faster, Cheaper Forecasts

  • “AI models excel by learning patterns in actual data, rather than trying to solve complicated systems of mathematical or physics equations like the traditional tools are doing.”
    (Paris Perdicaris, 08:04)
  • Aurora can deliver forecasts from 6 hours to 10 days ahead.
    (Paris Perdicaris, 08:21)

• Human Expertise Remains Crucial

  • “If I give my model the wrong inputs and then it tells me that it’s going to snow in Greece in August, then obviously that is an erroneous prediction that I should filter out.”
    (Paris Perdicaris, 08:33)
  • Final forecasts still depend on “a human meteorologist...has synthesized many different forecasts...and based on their expertise, are issuing the official forecast.”
    (Paris Perdicaris, 08:47)

The Politics and Perils of Data

• AI Needs Data—Quality and Quantity

  • Funding and staffing cuts to NOAA (National Oceanic and Atmospheric Administration) threaten the “raw material” of AI models.

    "If those changes impact the quality of the data we have access to and the quality at which we are able to monitor the Earth system, that's going to have a direct impact on the AI models, which are only as good as the data that they've seen."
    (Paris Perdicaris, 10:11)

  • Paris expresses worry about the consequences, especially given political actions aiming to privatize or dismantle NOAA.

    “The funding cuts at NOAA shook up the community...if it continues in this direction, it is going to slow down progress.”
    (Paris Perdicaris, 10:35)

• The Risk of Stale Training Data

  • The first version of Aurora was trained on comprehensive data before major cuts, but future models could struggle as climate change accelerates and “less or outdated data is the last thing forecasting needs.” (Dena Temple Raston, 10:54)

Noteworthy Optimism Amid Uncertainty

• Paris closes with a note of hope, stressing there’s “still plenty of untapped data” for the next generation of AI models:

  "We're at the beginning and in fact, we have only scratched the surface when it comes to the data that is available out there for building those AI systems."
  (Paris Perdicaris, 11:14)

Final Reflection

• Dena sums up the episode with a reminder that AI’s real test will be how well it predicts rare or severe events:

  “The real test of AI won’t be the sunny day it gets right. It’ll be the storm it never saw coming.”
  (Dena Temple Raston, 11:23)

Timestamps for Important Segments

• 00:54 – Paris on AI as a tool, not a threat: “No Terminator movie for weather.”
• 03:12-03:47 – The scale and source of weather data, and how Aurora ‘learns.’
• 04:06 – Aurora’s strengths (hurricane tracking) and its current limitations.
• 05:08-05:32 – Incorporating emissions, chemistry, and diverse data into forecasts.
• 08:04 – AI’s efficiency advantage over traditional meteorological models.
• 08:47 – The enduring importance of human expertise in the forecasting process.
• 09:26-10:35 – Implications of NOAA cuts and political influences on data.
• 11:14 – Paris’s optimism about future data and AI’s potential.

Notable Quotes

• “We want to make those tools accessible to everyone around the world that can operate them on their own personal computers.”
  —Paris Perdicaris (01:37)

• “AI models excel by learning patterns in actual data, rather than trying to solve complicated systems of mathematical or physics equations like the traditional tools are doing.”
  —Paris Perdicaris (08:04)

• “The real test of AI won’t be the sunny day it gets right. It’ll be the storm it never saw coming.”
  —Dena Temple Raston (11:23)

Tone & Style

• The episode strikes a conversational, approachable tone. Technical concepts are made accessible, with references to pop culture (like “Terminator”) and relatable analogies (sailors, liberal arts education).
• The guest is optimistic but realistic about AI’s limitations and dependencies.

Summary

This episode paints a nuanced, accessible picture of AI’s transformative potential in weather forecasting. While AI models like Aurora can process vast datasets and vastly improve forecast speed and accuracy, their power is ultimately constrained by the quality and quantity of data available—much of it threatened by political actions and budget cuts. Human expertise remains integral, both to correct AI's odd errors and to provide regional guidance. AI’s future in meteorology is bright but depends not only on algorithms, but on sustaining the institutions that supply them with data. The real proof will come when AI faces unprecedented, extreme weather events—“the storm it never saw coming.”