AeroWF

AeroWF is a multi-airport aerodrome meteorology dataset designed for self-supervised representation learning and downstream evaluation. It contains aligned runway observations, exogenous meteorological variables, weather labels, timestamps, normalization statistics, and airport graph structures stored as NumPy dictionaries.

Dataset subsets

Aero-Pre2020 — Pre-training Subset

139,878 samples from ZBAA, ZSPD, and ZSSS, collected in 2020 and used for the self-supervised pre-training of AeroWF and other self-supervised learning baselines.

• Repository directory: processed_2020/
• Airports: ZBAA, ZSPD, ZSSS
• Intended use: self-supervised pre-training
• ZBAD is intentionally excluded so that it can be used to evaluate transfer to an unseen airport.

Aero-Task — Downstream Subset

108,404 samples used for downstream evaluation, measuring adaptation to diverse operational scenarios beyond the pre-training distribution.

• Repository directory: processed/
• Airports: ZBAA, ZBAD, ZSPD, ZSSS
• Intended use: downstream training and evaluation

Repository structure

.
├── processed/                       # Aero-Task
│   ├── ZBAA_train.npy
│   ├── ZBAD_train.npy
│   ├── ZSPD_train.npy
│   ├── ZSSS_train.npy
│   └── global_weather_config.json
└── processed_2020/                  # Aero-Pre2020
    ├── ZBAA_train.npy
    ├── ZSPD_train.npy
    ├── ZSSS_train.npy
    └── global_weather_config.json

Each airport .npy file stores a serialized Python dictionary. In the table below, split is one of train, val, or test; N is the number of samples, R is the number of runways, T is the window length, and E is the number of graph edges.

Data schema

Key	Shape / type	Description
{split}_runway	(N, R, T, 11), float32	Runway-level meteorological time series.
{split}_exo_categorical	dict[str, ndarray(N)]	Categorical exogenous variables: weather_code_id, sky_condition, has_gust, and is_cavok.
{split}_exo_continuous	(N, 3), float32	Standardized visibility, cloud_height, and gust_speed, in that order.
{split}_weather_label	(N,), int64	Global weather-class ID. This is the same target represented by weather_code_id.
{split}_timestamps	(N,), datetime64[ns]	Start timestamp of each sliding window.
num_weather_classes	int	Number of global weather classes (21 in the published files).
num_sky_classes	int	Number of sky-condition classes (8 in the published files).
class_weights	(num_weather_classes,), float32	Class-weight metadata. The published files currently contain unit weights.
exo_scaler	dict	Training-split transformation parameters for continuous exogenous variables.
graph_structure	dict	Airport/runway graph metadata described below.
runway_feature_names	list[str]	Ordered names of the 11 runway features.
exo_categorical_names	list[str]	Ordered categorical exogenous feature names.
exo_continuous_names	list[str]	Ordered continuous exogenous feature names.
window_size	int	Number of one-minute observations in each window (96 in the published files).
stride	int	Sliding-window stride (1 in the published files).
num_runways	int	Number of runway nodes for the airport.

Runway features

The last dimension of {split}_runway follows this order:

Index	Feature	Description
0	cloud_base	Runway cloud-base observation; stored in the source-data unit.
1	wind_x	Wind vector component: wind_speed * cos(wind_direction).
2	wind_y	Wind vector component: wind_speed * sin(wind_direction).
3	pressure	Atmospheric pressure; stored in the source-data unit.
4	temperature	Air temperature; stored in the source-data unit.
5	humidity	Relative humidity; stored in the source-data unit.
6	dewpoint	Dew-point temperature; stored in the source-data unit.
7	hour_sin	sin(2*pi*hour/24).
8	hour_cos	cos(2*pi*hour/24).
9	month_sin	sin(2*pi*month/12).
10	month_cos	cos(2*pi*month/12).

The published artifacts preserve the physical runway variables before runtime Min-Max scaling. Their exact physical units follow the upstream AWOS source and should not be inferred from the NumPy files alone.

Exogenous features

Feature	Type	Description
weather_code_id	categorical	ID in global_weather_config.json.
sky_condition	categorical	Encoded METAR sky condition.
has_gust	binary	Whether a gust was reported.
is_cavok	binary	Whether CAVOK was reported.
visibility	continuous	METAR visibility; source unit: miles.
cloud_height	continuous	Lowest reported METAR cloud height; source unit: feet.
gust_speed	continuous	Reserved continuous gust feature. It is zero-valued in the current published files; use has_gust for gust presence.

Intended use

AeroWF is intended for research on aerodrome meteorological representation learning, time-series forecasting, missing-value imputation, weather-condition classification, anomaly detection, and cross-airport adaptation. It is a research benchmark and is not a replacement for certified operational weather or aviation systems.