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f4a39ee | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 | #!/usr/bin/env python3
"""Generate OneScience-compatible synthetic ERA5 HDF5 data."""
from __future__ import annotations
import argparse
from pathlib import Path
import h5py
import numpy as np
try:
from common import PROJECT_ROOT, SYNTHETIC_GENERATOR_VERSION, channel_order, load_config, resolve_path, write_json
except ModuleNotFoundError: # supports ``python -m scripts.fake_data``
from scripts.common import PROJECT_ROOT, SYNTHETIC_GENERATOR_VERSION, channel_order, load_config, resolve_path, write_json
def make_field(name: str, step: int, year: int, lat: np.ndarray, lon: np.ndarray, rng: np.random.Generator) -> np.ndarray:
latr, lonr = np.deg2rad(lat)[:, None], np.deg2rad(lon)[None, :]
wave = np.cos(latr) * np.sin(lonr + step * 0.05)
noise = rng.normal(0, 1, wave.shape).astype(np.float32)
if name == "sea_ice_cover":
return np.clip(0.55 - 0.45 * np.cos(latr) + 0.03 * noise, 0, 1).astype(np.float32)
if name == "sea_surface_temperature":
return (273.15 + 26 * np.cos(latr) + 1.5 * wave + 0.1 * noise).astype(np.float32)
base, _, suffix = name.rpartition("_")
level = int(suffix)
ratio = level / 1000.0
if base == "geopotential":
# Hydrostatic log-pressure profile calibrated against the bundled ERA5
# statistics (about 464 km2/s2 at 1 hPa and 0.7 km2/s2 at 1000 hPa).
vertical = 733.0 + 67000.0 * np.log(1000.0 / level)
return (vertical + 1800.0 * (1.0 - ratio) * np.sin(latr) ** 2 + 120.0 * wave + noise).astype(np.float32)
if base == "temperature":
# Piecewise standard-atmosphere profile captures stratospheric warming;
# a monotone 220->288 K profile is not physically valid above 10 hPa.
anchors_hpa = np.asarray([1.0, 10.0, 100.0, 500.0, 1000.0])
anchors_k = np.asarray([261.0, 229.0, 207.0, 253.0, 281.0])
vertical = np.interp(np.log(level), np.log(anchors_hpa), anchors_k)
return (vertical + 4.0 * wave + 0.1 * noise).astype(np.float32)
if base == "specific_humidity":
vertical = 4.0e-6 + 7.0e-3 * ratio**3
return np.maximum((vertical * (0.8 + 0.2 * np.cos(latr)) + 1e-7 * noise), 1e-8).astype(np.float32)
if base == "u_component_of_wind":
return (12 * (1 - ratio) * wave + 0.2 * noise).astype(np.float32)
if base == "v_component_of_wind":
return (8 * (1 - ratio) * np.sin(2 * latr) * np.cos(lonr) + 0.2 * noise).astype(np.float32)
if name.startswith("specific_cloud_ice_water_content_"):
profile = 8e-6 * np.exp(-0.5 * ((level - 450.0) / 180.0) ** 2)
return np.maximum(profile * (0.7 + 0.3 * np.cos(latr)) + 1e-7 * noise, 0).astype(np.float32)
if name.startswith("specific_cloud_liquid_water_content_"):
profile = 1.2e-5 * np.exp(-0.5 * ((level - 750.0) / 160.0) ** 2)
return np.maximum(profile * (0.7 + 0.3 * np.cos(latr)) + 1e-7 * noise, 0).astype(np.float32)
raise ValueError(f"Unsupported channel: {name}")
def generate_year(
path: Path,
channels: list[str],
timesteps: int,
height: int,
width: int,
year: int,
seed: int,
time_step_hours: int,
) -> dict:
lat = np.linspace(90, -90, height, dtype=np.float32)
lon = np.linspace(0, 360, width, endpoint=False, dtype=np.float32)
path.parent.mkdir(parents=True, exist_ok=True)
sums = np.zeros(len(channels), dtype=np.float64)
sq = np.zeros(len(channels), dtype=np.float64)
with h5py.File(path, "w") as handle:
fields = handle.create_dataset("fields", shape=(timesteps, len(channels), height, width), dtype="f4", chunks=(1, 1, min(height, 32), min(width, 64)), compression="gzip", compression_opts=1)
fields.attrs["variables"] = np.asarray(channels, dtype=h5py.string_dtype("utf-8"))
fields.attrs["time_step"] = time_step_hours
fields.attrs["synthetic"] = True
fields.attrs["generator_version"] = SYNTHETIC_GENERATOR_VERSION
for t in range(timesteps):
rng = np.random.default_rng(seed + year * 1009 + t)
for i, name in enumerate(channels):
value = make_field(name, t, year, lat, lon, rng)
fields[t, i] = value
sums[i] += value.sum(dtype=np.float64)
sq[i] += np.square(value, dtype=np.float64).sum(dtype=np.float64)
count = timesteps * height * width
means = sums / count
stds = np.sqrt(np.maximum(sq / count - means**2, 1e-12))
handle.create_dataset("global_means", data=means[None, :, None, None].astype("f4"))
handle.create_dataset("global_stds", data=stds[None, :, None, None].astype("f4"))
return {"path": str(path), "shape": [timesteps, len(channels), height, width], "mean_std_min": float(stds.min())}
def generate_static(path: Path, height: int, width: int) -> dict:
"""Generate deterministic ERA5-like static topography and land mask.
These variables are auxiliary model inputs rather than flattened dynamic
channels. Values use the physical units expected by the official Gin
profiles: geopotential at surface in m² s⁻² and land-sea mask in [0, 1].
"""
import xarray as xr
lat = np.linspace(90.0, -90.0, height, dtype=np.float32)
lon = np.linspace(0.0, 360.0, width, endpoint=False, dtype=np.float32)
latr, lonr = np.deg2rad(lat)[:, None], np.deg2rad(lon)[None, :]
# Smooth continent-like mask and non-negative terrain height. This is
# intentionally synthetic; real ERA5 static fields should be preferred.
mask = (0.5 + 0.5 * np.sin(2.0 * latr) * np.cos(3.0 * lonr) > 0.52).astype(np.float32)
elevation_m = np.maximum(
0.0,
2200.0 * mask * (0.35 + 0.65 * np.cos(latr) ** 2)
+ 250.0 * np.sin(latr) ** 2,
).astype(np.float32)
geopotential = (9.80665 * elevation_m).astype(np.float32)
ds = xr.Dataset(
{
"geopotential_at_surface": (("longitude", "latitude"), geopotential.T),
"land_sea_mask": (("longitude", "latitude"), mask.T),
},
coords={"latitude": lat, "longitude": lon},
attrs={"synthetic": "true", "source": "neuralgcm_develop.fake_data"},
)
ds["geopotential_at_surface"].attrs["units"] = "m**2 s**-2"
ds["land_sea_mask"].attrs["units"] = "dimensionless"
path.parent.mkdir(parents=True, exist_ok=True)
ds.to_netcdf(path)
return {"path": str(path), "shape": [height, width], "variables": list(ds.data_vars)}
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--config", default=str(PROJECT_ROOT / "conf/config.yaml"))
parser.add_argument("--output-dir")
parser.add_argument("--years", nargs="*", type=int)
parser.add_argument("--timesteps", type=int)
parser.add_argument(
"--forecast-steps",
type=int,
help="number of future 6-hour frames; defaults to data.virtual.forecast_steps",
)
parser.add_argument("--height", type=int)
parser.add_argument("--width", type=int)
args = parser.parse_args()
config = load_config(args.config)
out = resolve_path(args.output_dir or config["data"]["data_dir"], args.config)
virtual = config["data"]["virtual"]
time_step_hours = int(config["data"].get("time_step_hours", 6))
if time_step_hours <= 0:
raise ValueError("data.time_step_hours must be positive")
years = args.years or sorted(set(config["data"]["train_years"] + config["data"]["val_years"] + config["data"]["test_years"]))
channels = channel_order(config)
# The explicit value always wins, followed by the virtual-data setting,
# then the inference default. The project defaults to a compact two-day
# window; 60 six-hour steps exercise the full official 15-day capability.
forecast_steps = int(
args.forecast_steps
if args.forecast_steps is not None
else virtual.get("forecast_steps", config.get("inference", {}).get("prediction_steps", 8))
)
if forecast_steps <= 0:
raise ValueError("--forecast-steps must be positive")
input_steps = int(config["data"].get("input_steps", 1))
configured_timesteps = int(virtual["timesteps_per_year"])
# The synthetic file contains one initial frame plus the requested future
# frames. Explicit --timesteps remains available for tiny smoke tests.
explicit_timesteps = args.timesteps is not None
timesteps = int(args.timesteps) if explicit_timesteps else max(
configured_timesteps, input_steps + forecast_steps
)
if timesteps <= 0:
raise ValueError("--timesteps must be positive")
if not explicit_timesteps and timesteps < input_steps + forecast_steps:
raise ValueError(
f"timesteps={timesteps} is too short for input_steps={input_steps} "
f"and forecast_steps={forecast_steps}; need at least {input_steps + forecast_steps}"
)
complete_window = timesteps >= input_steps + forecast_steps
forecast_horizon_days = forecast_steps * time_step_hours / 24
if explicit_timesteps and not complete_window:
print(
f"Warning: timesteps={timesteps} provides only a smoke window; "
f"{input_steps + forecast_steps} frames are required for the full "
f"{forecast_horizon_days:g}-day configured forecast."
)
records = [
generate_year(
out / "data" / f"{year}.h5",
channels,
timesteps,
args.height or virtual["height"],
args.width or virtual["width"],
year,
int(virtual["seed"]),
time_step_hours,
)
for year in years
]
static_record = generate_static(out / "static.nc", args.height or virtual["height"], args.width or virtual["width"])
write_json(out / "metadata" / "dataset_card.json", {"name": "neuralgcm-synthetic-era5", "format": "OneScience ERA5Dataset HDF5", "channels": channels, "files": records, "static_file": static_record, "native_model_grid": config["model"]["grid_shape"], "input_steps": input_steps, "forecast_steps": forecast_steps, "time_step_hours": time_step_hours, "forecast_horizon_hours": forecast_steps * time_step_hours, "forecast_horizon_days": forecast_horizon_days, "official_forecast_capability_days": [2, 15], "official_15_day_window_complete": forecast_horizon_days >= 15, "forecast_window_complete": complete_window})
print(f"Generated {len(records)} years under {out}")
if __name__ == "__main__":
main()
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