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Related Experiment Video

Updated: Sep 30, 2025

Using Generative Art to Convey Past and Future Climate Transitions

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

Embedded Temporal Convolutional Networks for Essential Climate Variables Forecasting.

Maria Myrto Villia¹, Grigorios Tsagkatakis¹, Mahta Moghaddam²

¹Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 70013 Heraklion, Greece.

Sensors (Basel, Switzerland)

|March 10, 2022

Summary

This summary is machine-generated.

Forecasting land surface temperature and soil moisture is crucial for climate change impact studies. A new deep learning model, Embedded Temporal Convolutional Network (E-TCN), shows improved prediction accuracy with fewer parameters than existing methods.

Keywords:

climate variables deep learning remote sensing soil moisture surface temperature time-series forecasting

Related Experiment Videos

Last Updated: Sep 30, 2025

Using Generative Art to Convey Past and Future Climate Transitions

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

Area of Science:

Climate science
Earth system science
Environmental modeling

Background:

Accurate forecasting of climate variables like land surface temperature and soil moisture is vital for understanding climate change impacts.
Spatiotemporal data analysis is essential for climate research, often relying on satellite and model-based data assimilation.

Purpose of the Study:

To develop and evaluate a novel deep learning model for analyzing and predicting spatial time series of climate variables.
To compare the performance of the proposed model against the state-of-the-art ConvLSTM model for climate variable forecasting.

Main Methods:

Development of the Embedded Temporal Convolutional Network (E-TCN) architecture, integrating encoder, temporal convolutional, and decoder networks.
Inputting satellite-derived or model-based assimilation data (land surface temperature, soil moisture) with monthly periodicity over fifteen years.
Comparative analysis with the ConvLSTM model, exploring variations in spatial resolution, region extension, training data, and prediction windows.

Main Results:

The proposed E-TCN model demonstrated superior prediction accuracy compared to the ConvLSTM model.
The E-TCN architecture achieved better performance using a significantly smaller number of parameters.
Performance was quantified across various experimental settings, including different spatial resolutions and dataset sizes.

Conclusions:

The E-TCN model offers a more efficient and accurate approach for forecasting essential climate variables.
The methodology is adaptable for predicting other environmental variables beyond land surface temperature and soil moisture.
This advancement contributes to improved climate change impact assessment and prediction capabilities.