Note
Go to the end to download the full example code.
Temporal Interpolation#
Temporal Interpolation inference using InterpModAFNO model.
This example demonstrates how to use the InterpModAFNO model to interpolate forecasts from a base model to a finer time resolution. Many of the existing prognostic models have a step size of 6 hours which may prove insufficient for some applications. InterpModAFNO provides a AI driven method for getting hourly resolution given 6 hour predictions.
In this example you will learn:
How to load a base prognostic model
How to load the InterpModAFNO model
How to run the interpolation model
How to visualize the results
Set Up#
First, import the necessary modules and set up our environment and load the models. We will use SFNO as the base prognostic model and the InterpModAFNO model to interpolate its output to a finer time resolution. The prognostic model must predict the needed variables in the interpolation model.
This example needs the following:
Interpolation Model:
earth2studio.models.px.InterpModAFNO.Prognostic Base Model: Use SFNO model
earth2studio.models.px.SFNO.Datasource: Pull data from the GFS data api
earth2studio.data.GFS.
import os
import matplotlib.pyplot as plt
from earth2studio.data import GFS
from earth2studio.io import ZarrBackend
from earth2studio.models.px import SFNO, InterpModAFNO
# Create output directory
os.makedirs("outputs", exist_ok=True)
sfno_package = SFNO.load_default_package()
base_model = SFNO.load_model(sfno_package)
# Load the interpolation model
interp_package = InterpModAFNO.load_default_package()
interp_model = InterpModAFNO.load_model(interp_package)
interp_model.px_model = base_model # Set the base model
# Create the data source
data = GFS()
# Create the IO handler
io = ZarrBackend()
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Run the Interpolation Model#
Now run the interpolation model to get forecasts at a finer time resolution. The base model (SFNO) produces forecasts at 6-hour intervals, and the interpolation model will interpolate to 1-hour intervals.
# Define forecast parameters
forecast_date = "2024-01-01"
nsteps = 5 # Number of interpolated forecast steps
# Run the model
from earth2studio.run import deterministic
io = deterministic([forecast_date], nsteps, interp_model, data, io)
print(io.root.tree())
2025-05-15 03:23:33.404 | INFO | earth2studio.run:deterministic:75 - Running simple workflow!
2025-05-15 03:23:33.405 | INFO | earth2studio.run:deterministic:82 - Inference device: cuda
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2025-05-15 03:23:34.477 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 329739828-930772
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2025-05-15 03:23:34.499 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 323061199-895080
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2025-05-15 03:23:34.544 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 386359113-963363
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2025-05-15 03:23:34.567 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 204839116-753955
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2025-05-15 03:23:35.011 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 204118947-720169
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2025-05-15 03:23:35.034 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 268878207-724790
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2025-05-15 03:23:35.056 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 347812101-849637
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2025-05-15 03:23:35.079 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 210217024-602993
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2025-05-15 03:23:35.101 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 169090127-920171
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2025-05-15 03:23:35.124 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 354558976-950206
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2025-05-15 03:23:35.147 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 190027998-577814
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2025-05-15 03:23:35.169 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 189450163-577835
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2025-05-15 03:23:35.191 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 268038708-839499
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2025-05-15 03:23:35.214 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 144283406-743766
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2025-05-15 03:23:35.237 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 270785488-1177846
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2025-05-15 03:23:35.260 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 406629528-962408
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2025-05-15 03:23:35.288 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 350072351-1232650
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2025-05-15 03:23:35.312 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 402321768-876246
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2025-05-15 03:23:35.335 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 143527711-755695
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2025-05-15 03:23:35.358 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 232400432-586007
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2025-05-15 03:23:35.380 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 193737151-726758
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2025-05-15 03:23:35.404 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 176434139-1041731
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2025-05-15 03:23:35.426 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 149106074-462399
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2025-05-15 03:23:35.448 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 228474082-1138597
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2025-05-15 03:23:35.471 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 292088625-1244938
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2025-05-15 03:23:35.494 | DEBUG | earth2studio.data.gfs:fetch_array:352 - Fetching GFS grib file: noaa-gfs-bdp-pds/gfs.20240101/00/atmos/gfs.t00z.pgrb2.0p25.f000 249098202-1262404
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Fetching GFS data: 1%|▏ | 1/73 [00:01<01:59, 1.66s/it]
Fetching GFS data: 100%|██████████| 73/73 [00:01<00:00, 44.03it/s]
2025-05-15 03:23:35.691 | SUCCESS | earth2studio.run:deterministic:106 - Fetched data from GFS
2025-05-15 03:23:35.692 | WARNING | earth2studio.io.zarr:add_array:192 - Datetime64 not supported in zarr 3.0, converting to int64 nanoseconds since epoch
2025-05-15 03:23:35.694 | WARNING | earth2studio.io.zarr:add_array:198 - Timedelta64 not supported in zarr 3.0, converting to int64 nanoseconds since epoch
2025-05-15 03:23:35.748 | INFO | earth2studio.run:deterministic:136 - Inference starting!
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Running inference: 17%|█▋ | 1/6 [00:08<00:42, 8.58s/it]
Running inference: 33%|███▎ | 2/6 [00:19<00:39, 9.95s/it]
Running inference: 50%|█████ | 3/6 [00:31<00:32, 10.97s/it]
Running inference: 67%|██████▋ | 4/6 [00:45<00:24, 12.04s/it]
Running inference: 83%|████████▎ | 5/6 [00:59<00:12, 12.97s/it]
Running inference: 100%|██████████| 6/6 [01:15<00:00, 13.92s/it]
Running inference: 100%|██████████| 6/6 [01:15<00:00, 12.62s/it]
2025-05-15 03:24:51.469 | SUCCESS | earth2studio.run:deterministic:146 - Inference complete
/
├── lat (720,) float64
├── lead_time (6,) int64
├── lon (1440,) float64
├── msl (1, 6, 720, 1440) float32
├── q100 (1, 6, 720, 1440) float32
├── q1000 (1, 6, 720, 1440) float32
├── q150 (1, 6, 720, 1440) float32
├── q200 (1, 6, 720, 1440) float32
├── q250 (1, 6, 720, 1440) float32
├── q300 (1, 6, 720, 1440) float32
├── q400 (1, 6, 720, 1440) float32
├── q50 (1, 6, 720, 1440) float32
├── q500 (1, 6, 720, 1440) float32
├── q600 (1, 6, 720, 1440) float32
├── q700 (1, 6, 720, 1440) float32
├── q850 (1, 6, 720, 1440) float32
├── q925 (1, 6, 720, 1440) float32
├── sp (1, 6, 720, 1440) float32
├── t100 (1, 6, 720, 1440) float32
├── t1000 (1, 6, 720, 1440) float32
├── t150 (1, 6, 720, 1440) float32
├── t200 (1, 6, 720, 1440) float32
├── t250 (1, 6, 720, 1440) float32
├── t2m (1, 6, 720, 1440) float32
├── t300 (1, 6, 720, 1440) float32
├── t400 (1, 6, 720, 1440) float32
├── t50 (1, 6, 720, 1440) float32
├── t500 (1, 6, 720, 1440) float32
├── t600 (1, 6, 720, 1440) float32
├── t700 (1, 6, 720, 1440) float32
├── t850 (1, 6, 720, 1440) float32
├── t925 (1, 6, 720, 1440) float32
├── tcwv (1, 6, 720, 1440) float32
├── time (1,) int64
├── u100 (1, 6, 720, 1440) float32
├── u1000 (1, 6, 720, 1440) float32
├── u100m (1, 6, 720, 1440) float32
├── u10m (1, 6, 720, 1440) float32
├── u150 (1, 6, 720, 1440) float32
├── u200 (1, 6, 720, 1440) float32
├── u250 (1, 6, 720, 1440) float32
├── u300 (1, 6, 720, 1440) float32
├── u400 (1, 6, 720, 1440) float32
├── u50 (1, 6, 720, 1440) float32
├── u500 (1, 6, 720, 1440) float32
├── u600 (1, 6, 720, 1440) float32
├── u700 (1, 6, 720, 1440) float32
├── u850 (1, 6, 720, 1440) float32
├── u925 (1, 6, 720, 1440) float32
├── v100 (1, 6, 720, 1440) float32
├── v1000 (1, 6, 720, 1440) float32
├── v100m (1, 6, 720, 1440) float32
├── v10m (1, 6, 720, 1440) float32
├── v150 (1, 6, 720, 1440) float32
├── v200 (1, 6, 720, 1440) float32
├── v250 (1, 6, 720, 1440) float32
├── v300 (1, 6, 720, 1440) float32
├── v400 (1, 6, 720, 1440) float32
├── v50 (1, 6, 720, 1440) float32
├── v500 (1, 6, 720, 1440) float32
├── v600 (1, 6, 720, 1440) float32
├── v700 (1, 6, 720, 1440) float32
├── v850 (1, 6, 720, 1440) float32
├── v925 (1, 6, 720, 1440) float32
├── z100 (1, 6, 720, 1440) float32
├── z1000 (1, 6, 720, 1440) float32
├── z150 (1, 6, 720, 1440) float32
├── z200 (1, 6, 720, 1440) float32
├── z250 (1, 6, 720, 1440) float32
├── z300 (1, 6, 720, 1440) float32
├── z400 (1, 6, 720, 1440) float32
├── z50 (1, 6, 720, 1440) float32
├── z500 (1, 6, 720, 1440) float32
├── z600 (1, 6, 720, 1440) float32
├── z700 (1, 6, 720, 1440) float32
├── z850 (1, 6, 720, 1440) float32
└── z925 (1, 6, 720, 1440) float32
Visualize Results#
Let’s visualize the total column water vapour (tcwv) at each time step and save them as separate files.
# Get the number of time steps
n_steps = io["tcwv"].shape[1]
# Create a single figure with subplots
fig, axs = plt.subplots(2, 3, figsize=(15, 6))
axs = axs.ravel()
# Create plots for each time step
for step in range(min([n_steps, 6])):
im = axs[step].imshow(
io["tcwv"][0, step], cmap="twilight_shifted", aspect="auto", vmin=0, vmax=85
)
axs[step].set_title(f"Water Vapour - Step: {step} hrs")
fig.colorbar(im, ax=axs[step], label="kg/m^2")
plt.tight_layout()
# Save the figure
plt.savefig("outputs/12_tcwv_steps.jpg")
Total running time of the script: (2 minutes 14.926 seconds)