SACRUS

00Z UKMT running

1/24 00z GFS Au AIGFS Total QPF TOtal Now:

1/24 00z GFS total QPF storm Total Snow (10:1)

Updated

RGEM Total QPF storm Total snow (10:1)

RGM

1/24 00z RGEM

Cold push

The soon to replace NAM (q2?) RRFS - Rapid Refresh Forecast System 1/24 00z

Updated

1/24 00z ICON total storm QPF Total snow / Frz (10:1)

1/24 00z ICON run v

1/24 00Z NYC QPF / Snow (Frz) SREF (mean): 1.3 / 9.7 NAM: 1.2 / 4.7 ICON: 1.4 / 8.1 RGEM: 1.2 / 9.5 GFS: 1/3 / 11.1 GFS AI AIGFS: 1.1 / 9.8 GEFS: 1.5 / 10.3 UKMET: 0.9 / 7.4 GGEM: 1.3 / 9.1 Euro : 1.2 / 10.2 Euro AI AIFS: 1,2 / 11.0

1/24 00z NAM total QPF storm Total snow / sleet (10:1)

GRAF refers to an AI-based global weather forecasting model developed by NOAA (GSL) and collaborators, and the name stands for: GRAF = Global Regression AI Forecast model It’s part of the new generation of machine-learning weather models, similar in spirit to GraphCast or Pangu, but developed inside the NOAA ecosystem. What is the GRAF model? GRAF is a pure machine-learning global forecast model that: Learns atmospheric evolution from historical reanalysis data Produces global forecasts without explicitly solving physical equations Uses regression-based deep learning to predict future atmospheric states Think of it as: “AI learning how the atmosphere usually evolves, then extrapolating forward.” Key characteristics Global coverage AI / ML-based (no traditional physics core) Predicts large-scale fields (e.g., 500 mb heights, winds, temps, MSLP) Extremely fast compared to physics models Designed primarily for pattern and flow prediction What GRAF is good at Large-scale synoptic pattern recognition Jet stream placement Ridge / trough evolution Medium-range guidance (Days 3–7) This makes it useful for: Pattern forecasting Ensemble support Early signal detection What GRAF is NOT good at Precipitation amounts Snowfall totals Precipitation type Mesoscale features (banding, fronts) Boundary-layer processes