Neural Network Applications in Temperature Forecasting

Recent advances in neural network methodologies have ushered in a new era for temperature forecasting, complementing and in some instances rivalling traditional numerical weather prediction models. By leveraging deep learning architectures, researchers can extract complex spatiotemporal features from large-scale observational and reanalysis datasets, enabling accurate short-term and mid-range temperature projections. Convolutional neural networks capture spatial dependencies across meteorological grids, while recurrent architectures such as long short-term memory units model temporal sequences and seasonal cycles. Hybrid models that combine feature extractors, probabilistic layers and meta-learning strategies further refine predictions and quantify forecast uncertainty. These approaches offer rapid, cost-effective forecasting solutions with global and regional applications in agriculture, energy management, public health warning systems and climate risk assessment.

Research from Nature Portfolio

An empirical study applied a suite of machine learning algorithms to 24 years of daily and monthly meteorological records at a tropical coastal station. Various neural network architectures—including multilayer perceptrons and radial basis function networks—were trained alongside tree-based and linear regressors to predict air temperature and humidity. The multilayer perceptron excelled in daily temperature forecasting (correlation coefficient ~0.71) and monthly temperature estimation (correlation ~0.85), while the radial basis network performed best for monthly humidity. Validation on independent data confirmed the robustness of these tailored architectures, underscoring the value of optimised neural designs for regional climate parameter prediction.

Research from all publishers

A probabilistic framework integrated convolutional autoencoders with Gaussian process regression to forecast one-step-ahead global temperature and pressure fields. By learning a low-dimensional latent representation, the model achieved mean temperature errors of 3.8 °C and delivered well-calibrated uncertainty estimates, with true values captured within predictive intervals over 95% of the time. Separately, a hybrid convolutional LSTM network was developed for hourly temperature forecasting at a mid-latitude station using multivariate inputs (temperature, dew point, pressure, wind, cloud cover). This model outperformed standalone CNN and LSTM variants, yielding a testing-stage mean absolute error of 1.02 °C and demonstrating enhanced capacity to capture both local spatial patterns and long-term temporal dependencies.

Neural Network Applications in Temperature Forecasting publication trend

The graph below shows the total number of articles in neural network applications in temperature forecasting across all publications each year (not limited to Nature Index journals).

Technical terms

Neural network: A layered computing model inspired by biological neurons, trained to map complex input–output relationships.

Convolutional neural network (CNN): A specialised network applying spatially local filters to detect patterns and features in grid-structured data.

Long short-term memory (LSTM): A recurrent neural network unit with internal gating mechanisms that capture long-range temporal dependencies.

Autoencoder: A neural network trained to compress input data into a latent representation and reconstruct it, facilitating feature learning.

Gaussian process: A non-parametric probabilistic model defining distributions over functions, used for regression and uncertainty quantification.

Latent space: A reduced-dimensional representation learned by a model that encapsulates essential data features.

Predictive uncertainty: A measure of confidence in model forecasts, expressed as probability distributions around predictions.

References

1. Forecasting global climate drivers using Gaussian processes and convolutional autoencoders. Engineering Applications of Artificial Intelligence (2024).
2. Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia. Scientific Reports (2021).
3. Prediction of hourly air temperature based on CNN–LSTM. Geomatics Natural Hazards and Risk (2022).