How WeatherNext Handles Different Geographical Regions: Geodesic-Aware Cyclone Tracking Across the Globe

WeatherNext tracks cyclones globally by building local bounding boxes around candidate centers, with longitude wrap-around handling and hard latitude limits to ensure consistent performance across all geographical regions.

The WeatherNext model from Google DeepMind performs cyclone tracking on a global latitude-longitude grid. Its regional handling strategy, implemented in the DirectTracker class, combines geodesic calculations with defensive constraints to operate reliably from the tropics to the poles. This article examines the specific mechanisms that enable robust tracking across diverse geographical regions.

Core Regional Handling in DirectTracker

The DirectTracker class in weathernext/cyclones/direct_tracker.py serves as the primary entry point for region-aware cyclone processing. When analyzing a track or cyclogenesis candidate, the tracker constructs a local bounding box around the current cyclone center coordinates (latlon).

Latitude-Longitude Bounding Box Construction

The _get_bounding_box_sides_in_degrees() method converts a geodesic disc radius into degree-based box dimensions:

# From weathernext/cyclones/direct_tracker.py (lines 87-94)

def _get_bounding_box_sides_in_degrees(
    self, latlon: np.ndarray, disc_radius_km: float
) -> Tuple[float, float]:
    """Compute lat/lon sides of bounding box in degrees."""
    lat_side = 2 * disc_radius_km / KM_PER_DEGREE_LAT
    # Scale longitude by cosine of extreme latitudes

    cos_lat = np.cos(np.deg2rad(np.abs(latlon[0]) + lat_side/2))
    lon_side = min(2 * disc_radius_km / (KM_PER_DEGREE_LAT * cos_lat), 360.0)
    return lat_side, lon_side

The longitude span calculation caps at 360° to prevent overflow, while the cosine scaling accounts for meridian convergence at higher latitudes.

Longitude Wrap-Around Safety

For boxes that straddle the 0°/360° seam, the tracker delegates to utils.slice_data_array_latlon_box_with_lon_wraparound():

# From weathernext/cyclones/direct_tracker.py (lines 574-580)

local_region = slice_data_array_latlon_box_with_lon_wraparound(
    data_array=gridded_predictions[variable],
    center_lat=lat,
    center_lon=lon,
    lat_side_degrees=lat_side,
    lon_side_degrees=lon_side,
)

This utility ensures seamless operation across the International Date Line—critical for Pacific basin cyclone tracking.

Geographical Constraints and Validation

Hard Latitude Ceiling

WeatherNext enforces a global latitude boundary to filter spurious high-latitude artifacts:

# From weathernext/cyclones/direct_tracker.py (lines 78-81)

MAX_ABSOLUTE_LATITUDE = 80.0  # degrees

def _is_valid_latitude(self, lat: float) -> bool:
    return abs(lat) <= self.MAX_ABSOLUTE_LATITUDE

Any candidate center beyond ±80° latitude is automatically discarded. This prevents the model from chasing unrealistic polar disturbances while preserving coverage of extratropical transition events.

Position Refinement with Spherical Geometry

Once a local region is isolated, the tracker computes refined positions using geodesically-aware methods:

Mean Position (Probability-Weighted)

The _get_mean_latlon_of_grid_points_within_disc() method calculates a probability-weighted average:

# Simplified representation of lines 96-105

weights = probabilities / probabilities.sum()
weighted_lat = np.average(lats, weights=weights)
weighted_lon = np.average(lons, weights=weights)

# Project onto sphere via 3D averaging

mean_position = _average_in_three_dimensions_and_project_on_sphere(
    weighted_lat, weighted_lon
)

Mode Position (Maximum Probability)

The _get_mode_latlon_of_grid_points_within_disc() method finds the maximum probability point:

# Simplified representation of lines 124-131

max_idx = np.unravel_index(
    np.argmax(probabilities), probabilities.shape
)
mode_lat = lats[max_idx]
mode_lon = lons[max_idx]

Both methods leverage cyclone_utils.geodesic_distance for accurate distance calculations on the Earth's surface.

Cyclogenesis Regional Handling

For initial storm detection, the tracker implements a coarse-to-fine regional strategy:

1. Grid sampling: Builds a coarse grid of candidate bounding boxes across the global domain
2. Mode-then-mean refinement: Applies the mode method first, then refines with the mean method
3. Spatial pruning: Removes candidates within min_disc_radius_between_cyclogenesis_candidates_km (default 250 km) of existing tracks

The same latitude limits and wrap-around handling apply at each stage.

Practical Configuration

Regional behavior is controlled via constructor parameters:

from weathernext.cyclones.direct_tracker import DirectTracker

tracker = DirectTracker(
    # Regional extraction radii for different operations

    disc_radius_mean_latlon_km=300.0,          # Mean position calculation

    disc_radius_mode_latlon_km=200.0,          # Mode position calculation

    disc_radius_mean_probability_of_existence_km=400.0,  # Existence check

    disc_radius_cyclogenesis_refinement_km=150.0,        # Cyclogenesis refinement

    
    # Strategy and validation

    tracking_mode="mode_then_mean",            # Or "mean_only", "mode_only"

    min_disc_radius_between_cyclogenesis_candidates_km=250.0,  # Pruning distance

    temporal_resolution_hours=6,
)

Key Supporting Files

• weathernext/cyclones/tracker_utils.py: Grid slicing with longitude wrap-around
• weathernext/cyclones/constants.py: Global constants including MAX_ABSOLUTE_LATITUDE
• weathernext/utils/typed_graph.py: Geodesic distance calculations

Summary

• Geodesic awareness: All regional calculations use spherical geometry, not planar approximations
• Longitude wrap-around: Dedicated utilities handle 0°/360° seam crossing for Pacific basin coverage
• Latitude bounds: Hard limit at ±80° prevents spurious polar artifacts
• Configurable radii: Disc radius parameters control local region size per operation type
• Dual refinement: Mode-then-mean strategy balances precision and robustness across varying storm structures

Frequently Asked Questions

How does WeatherNext handle cyclones near the International Date Line?

The tracker uses slice_data_array_latlon_box_with_lon_wraparound() in tracker_utils.py to correctly extract sub-grids that cross the 0°/360° longitude seam. This ensures continuous tracking as storms move between the western and eastern Pacific basins.

What prevents WeatherNext from detecting false cyclones near the poles?

A hard-coded MAX_ABSOLUTE_LATITUDE = 80 constant in direct_tracker.py automatically discards any candidate center beyond this threshold. This boundary eliminates spurious high-latitude artifacts that can arise from model bias or orographic effects.

Why does the longitude bounding box use cosine scaling?

The cosine scaling in _get_bounding_box_sides_in_degrees() accounts for meridian convergence—longitude degrees represent smaller physical distances at higher latitudes. Without this adjustment, the geodesic disc would become elliptical, distorting probability calculations.

Can the regional extraction radius be customized per storm?

Yes. The DirectTracker accepts separate disc_radius_*_km parameters for mean position, mode position, probability-of-existence, and cyclogenesis refinement operations. Smaller radii improve precision for compact storms; larger radii capture broader systems.

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