[earthscienceman]

I disagree that my (extremely broad) diagnosis of the problems with forecasting in America is based on flawed premises. You've provided thorough, correct, and important details here but my comment was aimed at the broader HN audience, not on writing the central argument for a discussion on the historical timeline. I think what I said, "Horrible oversight by the federal govt (read, congress) of our technical/scientific forecasting resources means that our forecasting ability is extremely fragmented and poorly organized." is a very concise summary of the things you've laid out here.

As for the details in your comments, the only thing I disagree with strongly is the comment about investment in computing. While sufficient computing resources are central to good forecasting, the lack of investment by NOAA in computing (I sit at NOAA) is a red herring. ECMWF is significantly better than either of the two available American forecasts because they are just better at what they do, all around. In particular with respect to data assimilation. I've sat at meetings with ECMWF forecasters who have asked for access to my in-situ data products and their pipeline is as simple as "point us to the data please". Their data assimilation pipeline is so much more sophisticated and thorough that catching up on that alone would be a huge huge leap. Mind you, not just '4DVAR' the methodology, but literally the way that the community finds and integrates observational data.

ECMWF, the organization, is quite literally structured to strictly accomplish the goal of 'improve the forecast'. Whereas, again broadly, the American institutions are much more a congolemerate of associated researchers doing individual science projects while small teams work on specific segments of the forecast. Yes, we are attempting to fix this. No, we haven't fixed it yet.

This is not to say I don't think we should fund computing or that computing won't help. But we are quite literally 5-10 years of research behind on multiple fronts.

[counters]

The thing about the computing is that it has impacted the culture surrounding NWP model development within the American modeling community. At ECMWF, there is capacity in the primary systems to support R&D, so the total cost to the community to maintain this capability is much lower than in the US where everything is fragmented. If there was greater capacity for researchers to run GFS on the limited number of systems with first-class (or any) support, it may have helped consolidate the community.

Totally acknowledge that there are other takes here. And I have a bit of skepticism about how much EPIC will really achieve and what it can do to resolve these issues. But I don't necessarily agree that the science at EC is 5-10 years ahead of the American community's. What's matriculated R2O is definitely a few years ahead, of us, especially for medium-range forecasting. But the US likely still maintains a competitive edge in mesoscale/rapid refresh forecasting, and even though we've lost key developers to the private sector recently, the NBM seems (in my admittedly limited experience) to perform favorably to similar products out of ECMWF or other European national weather services.

Your point about ECMWF being fundamentally structured with the singular goal of improving the forecast is super important - I 100% agree with that, and the US has yet to do much of anything to address this.

[earthscienceman]

Extremely valid points. Thanks for sharing your perspective, counters. It's much appreciated. One thing I think is fascinating, every time this comes up on a place like HN, is how detached the conversation often is from the meat-and-potatoes of forecasting. Which is to say I've seen many a googler think that weather forecasting is a simple problem and handwave the discussion away with "throw compute at the problem". It's always great when there are people around to ground the discussion.

Last point to the specifics. You're very right that the American teams have nailed mesoscale/rr forecasting. Which is, if we wanted to really divert the discussion, interesting and arguably more advanced because of its industrial applications (wind farms, etc et al).

I think your most salient point is about how the extra compute resources foster a culture of improvement on the models themselves. I am on the compute task team and it's something I argue for every day. Are you a researcher? If so, send me a message. I used to have my NOAA email in my profile but it would probably be neat to connect professionally.