nested-EAGLE

EAGLE currently includes a prototype nested-EAGLE model trained with global GFS data together with HRRR data over the Contiguous United States (CONUS).

This model builds on previous work from Met Norway (Nipen et al., 2024, arXiv:2409.02891) by combining lower-resolution global data with higher-resolution data over an area of interest.

Overview of the nested-EAGLE domain

Overview of the nested-EAGLE domain

nested-EAGLE configurations were provided by Tim Smith at NOAA Physical Sciences Laboratory.

Training Data

The nested-EAGLE training dataset combines regridded global and regional forecast data.

At a glance:

  • GFS is conservatively regridded to 1 degree.

  • HRRR is conservatively regridded to 15 km.

  • The training period spans 2015-02-01T06 through 2023-01-31T18.

  • The validation period spans 2023-02-01T06 through 2024-01-31T18.

  • The testing period spans 2024-02-01T06 through 2025-01-31T18.

Table 1 nested-EAGLE input variables by category

Category

Fields

Prognostic

gh, u, v, w, t, q, sp, u10, v10, t2m, t_surface, sh2

Diagnostic

u80, v80, accum_tp using fhr=6

Forcing

lsm, orog, cos_latitude, sin_latitude, cos_longitude, sin_longitude, cos_julian_day, sin_julian_day, cos_local_time, sin_local_time, insolation

The vertical levels used in the dataset are 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, and 1000.

Model Architecture

The nested-EAGLE model uses the following architecture:

  • Encoder and Decoder: Graph Transformer

  • Processor: Sliding Window Transformer

  • Latent space is a 4x coarsened data space

The graph configuration connects targets to nodes through nearest neighbors in the encoder and decoder, with encoder_knn=12 and decoder_knn=3.

The latent mesh is four times coarser than the native data resolution.