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citysyntaxlab/
campus-weather

Name: campus-weather
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variational-autoencoder

embeddings

cc-by-4.0

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🌤️ NUS-40: Dense Campus Weather Embeddings

40 weather stations · 1 year hourly · 6 variables · NUS Singapore
A single 6-dimensional VAE embedding supports spatial interpolation, forecasting, clustering, anomaly detection, and future prediction.

🔬 Overview

A variational autoencoder (VAE) that compresses 6-variable weather observations from 40 campus stations into a compact 6-dimensional embedding. The embedding achieves R² > 0.99 reconstruction and supports 5 downstream tasks with no retraining:

#	Task	Result
1️⃣	Spatial Interpolation — predict weather at unmeasured locations	AirTemp MAE = 0.39°C
2️⃣	Temporal Forecasting — predict future weather vs persistence baseline	+15.7% skill at T+6h
3️⃣	Microclimate Clustering — discover climate zones without labels	4 zones (silhouette=0.23)
4️⃣	Anomaly Detection — flag unusual weather from reconstruction error	5% flagged, storm-linked
5️⃣	24h Future Prediction — rolling forecast across full diurnal cycle	+42% peak skill at T+14h

📊 The NUS-40 Dataset

40 stations deployed across the National University of Singapore Kent Ridge campus (~2 km²), recording at hourly resolution for all of 2025.

Variables

Variable	Unit	Mean	Std	Range
🌡️ Air Temperature	°C	28.64	2.56	21.5 – 39.5
💧 Relative Humidity	%	80.49	10.11	37.6 – 99.5
🔵 Atmospheric Pressure	hPa	1006.4	2.72	994.6 – 1016.4
💨 Wind Speed	m/s	0.65	0.67	0.0 – 17.0
🧭 Wind Direction	°	185.4	108.0	0 – 360
☀️ Solar Radiation	W/m²	141.1	233.2	0 – 1500

At a Glance

40 stations, mean spacing ~100–200 m
8,760 hours (Jan–Dec 2025)
2.0 km × 1.4 km campus footprint
Tropical climate (Köppen Af) — minimal seasons, strong diurnal cycle
4.7% missing, with imputation flags provided
WS17 has no pressure sensor (filled with campus mean)

🏗️ Model

A standard VAE with MLP encoder and decoder.

Input (6 vars) → Encoder (3-layer MLP, 128 hidden) → z ~ N(μ, σ²)  [6 dims]
                                                          ↓
Output (6 vars) ← Decoder (3-layer MLP, 128 hidden) ←────┘

Property	Value
Parameters	70,930
Latent dimensions	6
Encoder/Decoder	3-layer MLP, LayerNorm, GELU
Loss	MSE + β·KL (β = 0.001)
Training	100 epochs, AdamW, cosine schedule
Training time	~20 min on CPU

📈 Results

Reconstruction (R² on held-out test set)

AirTemp	RelHum	AtmPress	WindSpeed	WindDir	GlobalRad
0.9997	0.9997	0.9995	0.9429	0.9994	0.9998

Spatial Interpolation (5 held-out stations, reconstructed from neighbours)

Variable	MAE	R²
🌡️ Air Temperature	0.39°C	0.949
💧 Relative Humidity	1.80%	0.944
🔵 Atmospheric Pressure	0.21 hPa	0.987
💨 Wind Speed	0.33 m/s	−0.52
☀️ Solar Radiation	35.5 W/m²	0.19

Temperature and humidity interpolate within sensor accuracy (±0.3°C). Wind and radiation depend too strongly on local building geometry.

Forecasting Skill (vs persistence baseline)

Horizon	AirTemp	RelHum
T+1h	−6.0% ❌	−8.0% ❌
T+6h	+15.7% ✅	+13.8% ✅
T+12h	+37.9% ✅	~+25% ✅
T+24h	+2.5%	+5.0%

Persistence wins at 1h in the tropics. Embeddings outperform at 6–15h horizons.

Anomaly Detection

438 hours flagged (5.0% of year)
Anomalous hours have 54% less solar radiation → storm/cloud association
Bimodal temporal pattern: peaks at 07:00 (sunrise) and 18:00 (sunset) transitions
Station WS17 flagged automatically (missing pressure sensor)

📁 Repository Structure

📦 citysyntaxlab/campus-weather
├── 📄 README.md
├── 📄 paper/paper.md               ← Full manuscript (~4,300 words, 16 references)
│
├── 💻 code/
│   ├── model.py                    ← VAE architecture (90 lines)
│   ├── train.py                    ← Data loading, training, embedding extraction
│   ├── evaluate.py                 ← All 5 downstream evaluations
│   └── figures.py                  ← Figure generation
│
├── 📊 figures/                     ← 6 figures (PDF + PNG)
│   ├── fig1_campus.{pdf,png}       ← Station map + discovered clusters
│   ├── fig2_reconstruction.{pdf,png} ← Reconstruction R² bar chart
│   ├── fig3_spatial.{pdf,png}      ← Spatial interpolation results
│   ├── fig4_forecasting.{pdf,png}  ← Forecast MAE comparison
│   ├── fig5_anomaly.{pdf,png}      ← Anomaly timeseries + hour distribution
│   └── fig6_future.{pdf,png}       ← 24h forecast skill curves
│
├── 🧪 results/
│   ├── all_results.json            ← All numerical results
│   ├── anomaly_errors.npy          ← Hourly reconstruction errors
│   └── checkpoints/
│       ├── best.pt                 ← Trained model weights
│       └── embeddings.npz          ← All embeddings: (8760, 40, 6) + data + coords
│
├── 📡 raw/                         ← 40 station CSVs (original measurements)
│   └── NUS_CAMPUS_WS{01-40}_2025_Hourly.csv
│
└── 📡 imputed/                     ← 40 station CSVs (gap-filled, with flags)
    └── NUS_CAMPUS_WS{01-40}_2025_Hourly_imputed.csv

🚀 Quick Start

Load pre-trained model and embeddings

python

import torch, numpy as np
from model import WeatherVAE

# Load checkpoint
ckpt = torch.load('results/checkpoints/best.pt', map_location='cpu')
model = WeatherVAE(**ckpt['config'])
model.load_state_dict(ckpt['model'])
model.set_normalisation(ckpt['mean'], ckpt['std'])
model.eval()

# Get embedding for a weather observation
# [WindSpeed, WindDir, AirTemp, RelHum, AtmPress, GlobalRad]
x = torch.tensor([[0.5, 180.0, 29.0, 80.0, 1007.0, 300.0]])
z = model.get_embedding(x)  # shape: (1, 6)

# Load pre-computed embeddings for all data
npz = np.load('results/checkpoints/embeddings.npz', allow_pickle=True)
embeddings = npz['embeddings']   # (8760, 40, 6)
data = npz['data']               # (8760, 40, 6)
coords = npz['coords']           # (40, 2) — [lat, lng]

Train from scratch

python code/train.py --data imputed/ --epochs 100 --config base

Run all evaluations

python code/evaluate.py

Generate figures

python code/figures.py

📝 Paper

Full manuscript at paper/paper.md.

Title: Learning Dense Weather Embeddings for Campus-Scale Microclimate Analysis
Target venue: Building and Environment
Words: ~4,300 | References: 16 (all verified)

📎 Citation

bibtex

@article{nus40weather2025,
  title={Learning Dense Weather Embeddings for Campus-Scale Microclimate Analysis},
  author={City Syntax Lab, National University of Singapore},
  year={2025}
}

📜 License

Dataset and code released under CC-BY-4.0. Please cite the paper if you use this work.