The unnecessary decline of U.S. numerical weather prediction
U.S. numerical weather forecasting is under scrutiny as many see federal models lagging behind European and other national systems, despite steady technical improvements. Commenters point to fragmented responsibilities across agencies, rigid federal hiring and HR rules, and growing political pressure to downsize or privatize NOAA as key risks to talent retention and public access to free forecast data. Others highlight the rapid rise of private and AI-based models, questioning whether they should complement or replace traditional physics-based approaches and what that shift would mean for accuracy, research, and public safety.
Overall assessment of U.S. forecast quality and the article’s framing
- Several commenters argue U.S. global models (e.g., GFS/UFS) have improved over time; the issue is lagging peers, not an actual decline.
- Some think the blog overstates the crisis: its own plots show gradual convergence with leading European models and no obvious worsening.
- Others accept that U.S. models are still behind top centers (especially the European center), and that the U.S. should aim to lead given its resources.
- One critic dislikes the nationalistic framing (“U.S. should be best at everything”), seeing it as political rather than scientific.
Politics, privatization, and NOAA’s future
- Multiple comments raise concern about efforts to shrink or break up NOAA and commercialize forecasting (e.g., Project 2025 language about downsizing and “fully commercializing” forecasts).
- There is repeated mention of private weather firms lobbying to limit public-domain data and shift value to paid services.
- Some fear political interference, loyalty tests, and replacement of career civil servants (including scientific roles) with political appointees.
Hiring, bureaucracy, and institutional dysfunction
- A detailed anecdote describes a highly qualified applicant rejected by NOAA HR for not listing “hours worked per week,” despite a director’s encouragement.
- Other federal employees confirm the process is rigid, compliance-driven, and favors insiders and veterans; hiring managers themselves often feel constrained.
- Some see this as necessary legalism; others portray HR as power-preserving and anti-meritocratic.
AI/ML vs traditional numerical weather prediction
- One side claims major centers are “stuck” in traditional NWP and not embracing AI; others directly refute this with examples of active AI forecast systems.
- Consensus in the thread: AI emulators are very cheap to run at inference time but depend on physics-based reanalysis and NWP outputs for training.
- NWP is described as irreplaceable research infrastructure, providing rich 3D physical fields that current AI models do not.
Forecast performance in practice
- Sailors and glider pilots report that high-resolution niche products (commercial or hobbyist, including ML-based) can be “mind-blowingly” accurate for micro-scale effects like island wind shadows or mountain waves.
- Others say global models like GFS are very good if interpreted with meteorological knowledge (e.g., sea-breeze, CAPE/CIN, convection not explicitly resolved).
- Some Europeans and Californians perceive worsening day-ahead rain and temperature forecasts, possibly due to fast-changing climate or loss of local observing infrastructure; this remains anecdotal and flagged as unclear.