Deep Shared Representation Learning for Weather Elements Forecasting [Knowledge-Based Systems, 2019. PDF]

Published: 1 February 2026

Accurate weather forecasting plays a crucial role in many sectors, including transportation, agriculture, energy management, and disaster prevention. While numerical weather prediction models rely on complex physical equations, they often struggle with short- and medium-term forecasting due to computational cost and sensitivity to initial conditions.

In this post, I explain the key ideas behind deep shared representation learning for weather elements forecasting — a data-driven approach that learns hidden patterns directly from historical observations. 

🌍 Why forecasting multiple weather elements is difficult

Weather variables such as:

• temperature

• wind speed

• pressure

• humidity
  
  

are strongly interdependent. Traditional forecasting models often treat each weather station or each variable separately. This ignores important relationships such as:

• how wind influences temperature changes

• how nearby stations affect each other

• how spatial and temporal dependencies evolve together
  
  

As a result, valuable information is lost before prediction even begins.

🧠 What is shared representation learning?

Shared representation learning is based on one key idea:

Instead of predicting each weather variable independently, we allow the model to learn common latent features shared across stations, variables, and time.

In other words, the model learns hidden representations that capture:

• spatial correlations between weather stations

• temporal dynamics over time

• interactions between multiple weather elements
  
  

These shared features act as a compact and informative encoding of the atmospheric system.

🔬 Deep learning as a representation learner

Deep learning models, especially convolutional neural networks (CNNs), are particularly suitable for this task because they:

• automatically learn features from raw data

• do not require handcrafted predictors

• can model nonlinear spatiotemporal relationships
  
  

In this work, several CNN-based architectures are explored, including:

• 1D CNNs — learning temporal patterns per station

• 2D CNNs — learning joint station–time relationships

• 3D CNNs — learning full spatiotemporal representations
  
  

Each architecture provides a different level of abstraction for capturing weather dynamics.

🧩 Learning from multiple stations simultaneously

A key contribution of this approach is forecasting multiple weather stations at the same time.

Instead of training separate models per location, the network receives data from all stations jointly and learns:

• which stations are correlated

• how information propagates spatially

• how patterns repeat over time
  
  

This leads to better generalization and more stable predictions.

📊 Experimental setup

The models were evaluated using real meteorological datasets collected from:

• Weather Underground stations in the Netherlands and Belgium

• National Climatic Data Center (NCDC) stations in Denmark
  
  

Two forecasting tasks were studied:

• Temperature prediction (1–10 days ahead)

• Wind speed prediction (6–12 hours ahead)
  
  

The results consistently showed that models learning shared spatiotemporal representations outperform traditional neural networks.

✅ Key findings

The experiments revealed several important insights:

• Learning shared representations improves prediction accuracy

• Joint modeling of stations outperforms independent forecasting

• 2D and 3D CNNs capture richer spatiotemporal patterns

• Feature coupling between weather variables significantly boosts performance
  
  

In short: letting the model discover its own weather representations leads to better forecasts.