Yes, Congress passed the Weather Research and Forecasting Innovation Act back in 2017 which not only gave massive funding injection to model development activities, data procurement, and more, but also formalized the new next-generation community modeling initiatives which are focused on tackling the dominance of the EC models in the 2-5 day forecast space.
The diagnosis of the problems of the American forecast modeling community here is based on flawed premises. There are three major factors which led to the ECMWF leap-frogging the US in day-ahead forecasting capability. The first is the consolidated investment in supercomputing resources; the WRFIA tackles this by earmarking a much larger allocation of funding for NOAA's next gen supercomputer, but this still pales in comparison to ECMWF investments.
The second factor is the fragmentation of the research and operational weather modeling communities due to the divergent investment from NOAA and USAF in the 90's and 2000's; USAF in conjunction with NCAR sponsored the development of the WRF model which was widely adopted by the research community. NOAA continued investing the GFS lineage of models. The bifurcation of these communities slowed down the ability to matriculate advances in model developments to operations, and this was exacerbated by an old, closed-off approach by NOAA which made it extraordinarily difficult to run and develop the GFS on anything other than NOAA hardware.
Finally, the ECMWF went all-in on 4DVAR data assimilation in the late 90's, whereas the American community pursued a diversity of other approaches ranging from 4DVAR to ensemble Kalman filters. 4DVAR necessitates advances to core weather model software (e.g. you need to write a model's adjoint or its tangent linear in order to actually use 4DVAR) adn the US' failure to adopt it led, imo to a "double edged sword" effect of (a) failing to provide impetus to greatly improve the US modeling software suite and supporting tools, and (b) being a worse assimilation technique unless advanced EnsKF techniques are employed using very large ensembles of models (expensive).
The other problem as others have pointed out is that there is no accountability in the US private sector weather market. Virtually every player is re-transmitting raw model guidance or statistical post-processed forecasts using DiCast, _maybe_ with a some manual tweaking of forecast fields by humans. But this is not transparent, and many companies - if we're being charitable, here - are not honest about what they're actually doing to produce their forecasts. Put another way - there's a lot of BS claims out there, and it seems that investors have been more than happy to fund it over the past few years.