MegaMike

12z GEFS consolidated a bit and is east of its 06z position. For now, I'm still ignoring AI.

Definitely! I'll never live this one down if AI outperforms NWP.

I never imagined this forum diverge into a discussion about AI Admittedly, your last sentence is difficult to accept. I'm sure most of us feel the same too. To add to the discussion, here's the 12z ensemble spread and diagnostic prate/type at 90hr... Still uncertainty amongst the ensembles/models so we're not entirely dead yet. If I had to stratify it as is (for measurable precipitation), I'd call it the AIs+ICON+CMC vs. GFS+ECMWF+UKMET.

That's a fair point. I didn't want to get technical, but I'll restate it as, "the ceiling for AI should be that of current NWP + bias correction." I've mentioned in the past that AI should be used to bias-correct ic/bcs, so I don't disagree. On top of bias-correction, I imagine the analysis datasets already incorporate 'nudging.' This is only done for the ic/bcs prior to initialization though, so you'd still need to do gridded bias correction post-simulation.

Thanks, dude. I always think of Ian Malcolm's quote from Jurassic Park when AI models are mentioned: Data scientists are so "preoccupied with whether or not they could, that they didn't stop to think if they should." I think they're more useful for climatological/ensemble purposes. Its resolution is too course for nowcasting, and whether people like it or not, the best real-time product we have is the HRRR (only model to update every hour not considering the RRFS). Users just need to understand its limitations... Within a few hours = good ||| outside a few hours = meh ||| beyond a PBL cycle = ignore... I've been thinking; theoretically, the ceiling for AI should be that of current NWP... I don't think it's possible to outperform the dataset its trained on, so to improve AI, you must improve NWP <OR> increase the size of your training dataset. As a result, NWP will never be phased out. :fist bump: If I remember correctly, the evaluation was conducted wrt an analysis dataset (not in-situ locations). To me, that implies they're evaluating its efficacy (can it 'hang' with a traditional modeling system?) and not its accuracy. I did this too when I compressed assimilation data and reran CMAQ simulations when I worked with the EPA. I won't trust AI until evaluations are conducted at remote sensing stations. Analysis datasets aren't entirely accurate.

In my opinion, there's too much trust in AI for weather prediction. I've mentioned this a few times, but It was made operational recently There's nothing wrong with current NWP excluding (a) it takes longer to run and (b) it requires a lot of resources vs. AI There is no significant evidence the EC-AIFS/AIGFS outperforms NWP for sensible weather at the surface during inclement weather (please provide a source if I'm wrong). For AI, nobody knows how forcing(x,y,z,t) is calculated (doesn't rely on traditional methods). ie... what is 1+1? Human = 1 + 1 == 2 ||| AI = :performs multi-dimensional math on 'n' fields: == 2. Do you trust that? Given the initial state of the atmosphere is captured flawlessly, there's no guarantee AI will perform well. AI is great when there is no known relationship/correlation between a predictor and many predictands. Weather is relatively predictable so I don't find AI useful unless the fields are bias-corrected then ingested back into data assimilation grids. If the AIs outperform NWP for this, *** and it evaluates well ***, I'll take it a little more seriously. Who knows... Maybe truncating/rendering certain fields may increase its accuracy for this one event <AND/OR> data assimilation is poor at the current location(s) where the disturbance(s) is/are, and AI could use historic events to predict this event with some level of accuracy.

Good observation. For the ECMWF/ECMWF-AI, the operational ECMWF has a horizontal resolution of ~9km. In comparison, the ECMWF-AI is trained on the ERA5 reanalysis dataset which has a resolution of ~31km (its operational product must be run on the same grid specifications). On top of resolution discrepancies, to my understanding, the ECMWF-AI uses its own, statistical relationship (AI and not traditional microphysical/physical schemes) to determine precipitation too. That likely compounds the resolution issue you mentioned, as well. I'm heavily leaning away from AI models for this event (and likely, for all events until it proves its accuracy for sensible weather).

Before 12z gets roll'n, here's what the 06z ensembles depict for QPF-mean and SFC MSLP: Will this trend into something like yesterday's 12z GFS? Probably not (as other mets mentioned), but at least the 0.5" QPF-mean contour is nearby (~Cape Cod; trying to be optimistic for the pessimistic weenies) for each ensemble. Given there's still a decent amount of spread at 96hrs, a lighter/moderate event may still be possible for eastern areas. I would like to see the EPS tick west at 12z, otherwise, I'd lower expectations if you haven't already.

I completely agree. It was recently made operational (Feb. 2025) and its primary purpose (imo at the current moment) is to provide an efficient (few resources and fast to simulate), medium/long range ensemble... I consider it a less accurate version of the CFS, honestly. They're years, if not decades, away from making the AIFS comparable to any traditional NWP modeling system. I'm not even fully sold on that being a possibility either... I'll take it seriously when the AIFS outperforms the IFS at the surface and not 500-50mb Vendors will provide any modeling system to stand out, unfortunately... At this range, I'd primarily consider the ECMWF, GFS, CMC, ICON, and UKMET (with more emphasis on their ensembles). Maybe look at trends of the AIFS for S&Gs.

I respect the effort. It takes a long time doing an analysis on one storm. You did it for 200+ events and manually conducted/plotted an interpolation. That's wild.

Pretty cool looking! Consensus is, that's the exhaust plume from the European Space Agency's Ariane 6 rocket (launched at Kourou, French Guiana).

Absolutely not. Maybe it performed well for this one event, but that doesn't mean it's better than traditional NWP. You really need to conduct a thorough evaluation at the surface and aloft (for forcing variables) to make such conclusions. As an example, it's possible something can be right for the wrong reason. You wouldn't know unless you evaluated it... So, if AI did well with forcing, wrt NWP, over a duration of 1 year, then you can entertain the idea. This is just imo, but we're years, if not decades, away from this. We likely need to significantly improve data assimilation for this to occur.

Narragansett is look'n pretty wavey: https://northeastsurfing.com/narragansett-cam/

More! (Tip's writing ability) x (Wiz's excitement over New England, severe weather) I'm not a fan of heat, so I ran script to figure out the median date of the max. (Summer) daily temperature via GHCND .csv files (TMAX field). Based on what I ran (32 different records/1 per-year from 1994-2025), the median date is ~Jul. 13th for KBOX (labeled, 'NWS BOSTON/NORTON' at https://www.ncei.noaa.gov/pub/data/ghcn/daily/ghcnd-stations.txt). After July 24th, there's a good chance (75%) KBOX experienced their warmest day of the year. Just for S&Gs.

Definitely! If CM1 missed this one (Reno), it likely can't resolve tornadoes unless (maybe) you beef up the model specs. The amount of resources to even run that simulation still gets me... A quarter of a trillion grid points, for a 42 minute simulation (time steps = 0.2s), that spans an area of ~5,600 miles^2 (~6x size of RI), and it took their cluster 3 days to run. That's crazy. Imagine running that for the entire U.S.?