StormScope Meteosat FCI Nowcasting¶

StormScope inference workflow with MTG-I1 FCI satellite imagery.

This example will demonstrate how to run inference to generate nowcasts of Meteosat Third Generation (MTG-I1) FCI satellite imagery using StormScopeMeteosatEU, a generative diffusion model that predicts the next 10-minute FCI frame from a sliding window of recent observations.

In this example you will learn:

How to instantiate earth2studio.models.px.StormScopeMeteosatEU
Creating a earth2studio.data.MeteosatFCI data source
Fetching FCI observations as the model input
Running iterative nowcasting
Plotting an RGB composite of FCI channels with cartopy

Set Up¶

StormScopeMeteosatEU is a pure-observation nowcasting model: it requires no external NWP conditioning. The only input is a sliding window of L consecutive FCI frames ending at the analysis time.

The model operates on a rectangular sub-region of the full MTG disk defined in native FCI pixel coordinates by [StormScopeMeteosatEU.Model_FCI_BBox][]. We configure earth2studio.data.MeteosatFCI with the matching pixel_bbox so that data fetching returns exactly the pixels the model expects, with no interpolation needed.

EUMETSAT Data Store credentials must be available via the environment variables EUMETSAT_CONSUMER_KEY and EUMETSAT_CONSUMER_SECRET, or in the ~/.eumdac/credentials file.

import os
from datetime import datetime, timezone

os.makedirs("outputs", exist_ok=True)
from dotenv import load_dotenv

load_dotenv()

import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import numpy as np
import torch
from tqdm import tqdm

from earth2studio.data import fetch_data
from earth2studio.data.meteosat_fci import (
    PERSPECTIVE_POINT_HEIGHT,
    SEMI_MAJOR_AXIS,
    SEMI_MINOR_AXIS,
    MeteosatFCI,
)
from earth2studio.models.px.stormscope_meteosat import StormScopeMeteosatEU
from earth2studio.utils.coords import map_coords

Load Model¶

Load StormScopeMeteosatEU from a package.

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

package = StormScopeMeteosatEU.load_default_package()
model = StormScopeMeteosatEU.load_model(package=package)
model = model.to(device)
model.eval()
model.compile_model()

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Set Up Data Sources¶

MeteosatFCI is configured with pixel_bbox matching the model's domain (Model_FCI_BBox) so that the returned y/x scan-angle coordinates align with what the model expects.

Meteosat FCI visible and NIR channels are provided by the EUMETSAT data store at 1 km resolution while IR channels are 2 km. The StormScope MTG model uses the 2 km grid for all channels, so we need to pull data from both 1 km and 2 km sources and do a minimal regrid on the visible and NIR channels.

Here we will set up data sources for both grid resolutions. The 1 km grid is exactly 2 times the pixels of the 2 km grid, so we get the 1 km bounding box by doubling the 2 km bounding box.

bbox_2km = StormScopeMeteosatEU.Model_FCI_BBox
FCI_BBox = {"2km": bbox_2km}
FCI_BBox["1km"] = (
    (2 * bbox_2km[0][0], 2 * bbox_2km[0][1]),
    (2 * bbox_2km[1][0], 2 * bbox_2km[1][1]),
)
fci = {
    res: MeteosatFCI(resolution=res, pixel_bbox=bbox)
    for (res, bbox) in FCI_BBox.items()
}

Fetch Initial Condition¶

We fetch initial window of 6 consecutive FCI frames ending at the chosen analysis time. fetch_data calls the data source at start_time + lead_time for each lead-time offset defined by the model ([-50 min, -40 min, ..., 0 min] for a 6-frame window).

StormScope MTG uses the 2 km grid for all channels, so we use StormScopeMeteosatEU.combine_1km_2km_inputs to resize the channels retrieved from the 1 km data source.

# Start time; replace with any time for which MTG-I1 FCI data is available.
# StormScopeMeteosatEU was trained on November 2024 - May 2026 data; start times
# should generally be outside that period.
start_time = np.datetime64(datetime(2026, 6, 30, 12, 0, 0, tzinfo=timezone.utc))
in_coords = model.input_coords()
variables = in_coords["variable"]

with torch.no_grad():
    x_res = {}
    for res in ["2km", "1km"]:
        available_vars = fci[res].available_variables()
        if res == "1km":
            # use 2km version if variable available in both 1km and 2km
            available_vars -= fci["2km"].available_variables()
        variables_res = [var for var in variables if var in available_vars]
        x_res[res] = fetch_data(
            fci[res],
            time=np.array([start_time]),
            variable=variables_res,
            lead_time=in_coords["lead_time"],
            device=device,
        )

    # 2x downsample 1km data to common 2km grid
    x, coords = StormScopeMeteosatEU.combine_1km_2km_inputs(
        *x_res["1km"], *x_res["2km"]
    )
    del x_res
    # ensure data is on model grid (reorders variables if needed)
    x, coords = map_coords(x, coords, in_coords)

Console output65 lines

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2026-09-03 11:34:18.421 | INFO     | earth2studio.data.meteosat_fci:_fetch_product:629 - Downloading MTG FCI product: W_XX-EUMETSAT-Darmstadt,IMG+SAT,MTI1+FCI-1C-RRAD-FDHSI-FD--x-x---x_C_EUMT_20260630111317_IDPFI_OPE_20260630111007_20260630111935_N__O_0068_0000

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Add Ensemble Dimension¶

The model expects input of shape (ensemble, time, lead_time, variable, y, x). Up to GPU memory limits, ensemble_size can be increased to produce multiple independent ensemble members in a single forward pass.

ensemble_size = 1
x = x.expand(ensemble_size, -1, -1, -1, -1, -1)
coords["ensemble"] = np.arange(ensemble_size)
coords.move_to_end("ensemble", last=False)

Execute the Nowcast¶

create_iterator handles the autoregressive rollout for us: the first yielded value is the (unchanged) initial condition, and each subsequent value advances the prediction by one 10-minute interval.

n_steps = 12  # 2 hours of 10-minute forecast steps

for step, (x_pred, coords_pred) in enumerate(
    tqdm(model.create_iterator(x, coords), total=n_steps + 1)
):
    if step == n_steps:
        break

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Post Processing¶

Plot the final predicted FCI frame as a true-color RGB composite (VIS 0.6 / 0.5 / 0.4 µm) in geostationary projection. Off-Earth pixels are already set to NaN by denormalize.

rgb_channels = ["fci06vis", "fci05vis", "fci04vis"]
ch_idx = [list(model.variables).index(ch) for ch in rgb_channels]

# x_pred has shape (batch, time, 1, variable, y, x)
# here we approximately replicate EUMETSAT's recipe for "true-colour" RGB
rgb = (x_pred[0, 0, 0, ch_idx] / 24.0).clamp(min=0, max=1) * 255
rgb = rgb.permute(1, 2, 0).cpu().numpy()
rgb = np.interp(rgb, [0, 30, 60, 120, 190, 255], [0, 110, 160, 210, 240, 255]) / 255.0

proj = ccrs.Geostationary(
    central_longitude=0.0,
    satellite_height=PERSPECTIVE_POINT_HEIGHT,
    globe=ccrs.Globe(semimajor_axis=SEMI_MAJOR_AXIS, semiminor_axis=SEMI_MINOR_AXIS),
)
extent = MeteosatFCI.projection_extent(resolution="2km", pixel_bbox=bbox_2km)

aspect = (extent[1] - extent[0]) / (extent[3] - extent[2])
fig_height = 8
fig = plt.figure(figsize=(fig_height * aspect, fig_height))
ax = fig.add_subplot(1, 1, 1, projection=proj)
ax.set_xlim(extent[0], extent[1])
ax.set_ylim(extent[2], extent[3])

ax.coastlines(color="white", linewidth=0.8)

im = ax.imshow(rgb, transform=proj, extent=extent, origin="lower")

valid_time = coords_pred["time"][0]
valid_time = datetime.fromisoformat(str(valid_time)).strftime("%Y-%m-%d %H:%M")
lead_min = coords_pred["lead_time"][0].astype("timedelta64[m]").item()
lead_min = int(lead_min / np.timedelta64(1, "m"))
ax.set_title(
    f"StormScopeMeteosatEU — RGB\n" f"Valid {valid_time} UTC  (+{lead_min} min)"
)

fig.tight_layout()
fig.savefig("outputs/04_stormcast_meteosat_example.png", dpi=150)
print("Saved outputs/04_stormcast_meteosat_example.png")

Console output1 line

Saved outputs/04_stormcast_meteosat_example.png

Output from StormScope Meteosat FCI Nowcasting

Download Python source Download Jupyter notebookexecuted in 1404.2s

Execution profile

Runtime telemetry

Profiled phases

Total runtime23m 24s

Setup12m 49s · 727 samples

Duration12m 49sTagged cells

CPU load2%Average · Peak 28%

Process memory14.9 GiBPeak resident set

Network received8.1 GiBHost-wide estimate

Network sent30.0 MiBHost-wide estimate

GPU utilization0%Peak 0%

GPU memory3.5 GiBof 79.6 GiB

GPU power97 WPeak draw

Inference10m 8s · 574 samples

Duration10m 8sTagged cells

CPU load8%Average · Peak 25%

Process memory13.2 GiBPeak resident set

Network received0 BHost-wide estimate

Network sent0 BHost-wide estimate

GPU utilization98%Peak 100%

GPU memory13.7 GiBof 79.6 GiB

GPU power349 WPeak draw

Plotting2.3 s · 2 samples

Duration2.3 sTagged cells

CPU load8%Average · Peak 8%

Process memory7.8 GiBPeak resident set

Network received0 BHost-wide estimate

Network sent132 BHost-wide estimate

GPU utilization0%Peak 0%

GPU memory13.7 GiBof 79.6 GiB

GPU power132 WPeak draw

Execution environment

CPUAMD EPYC 7313P 16-Core Processor

GPUNVIDIA H100 PCIe · 79.6 GiB

System RAM58.5 GiB

PlatformLinux 6.8.0-137-generic

Python3.13.13

GPU driver / CUDADriver 595.84 · CUDA support 13.2