S2S4E - Prediction tool

Overview

S2S4E (Subseasonal to Seasonal Forecasting for Energy) is a decision support tool designed to help expert users explore and interpret subseasonal and seasonal climate forecasts for energy-related use cases. The platform provides advanced visualizations to support analysis, comparison, and interpretation of probabilistic climate data.

Project context: https://s2s4e-dst.bsc.es/#/ / Institution: Barcelona Supercomputing Center 

Type: Visualization  tool

My role: UX & Data Visualization Lead

Duration: 24 months

Challenge 

The project was developed within a research and operational context at BSC, targeting expert users such as climate scientists, energy analysts, and technical stakeholders. UX activities were carried out in collaboration with an external UX team, integrating user-centered design practices into a highly technical, data-driven environment.

The main challenge was to design an interface capable of supporting expert analytical workflows without oversimplifying the data or limiting flexibility. Users needed to interpret probabilistic forecasts, compare scenarios, and explore multiple variables across time and geography.

From a UX perspective, the challenge was to:

1. Support expert mental models while maintaining clarity and consistency.

2. Avoid cognitive overload in a tool with high data density and complex interactions.

3. Integrate UX improvements without disrupting established scientific workflows.

Methodology

A traditional, iterative UX process was applied, adapted to expert users and technical constraints:

User Research

1. Conducted research with expert users to understand analytical goals, workflows, terminology, and pain points.

2. Identified key tasks such as comparison across forecasts, interpretation of uncertainty, and temporal navigation.

Co-creation with Domain Experts

1. Ran co-creation sessions with climate and energy experts to align interaction patterns and visual encodings with real analytical practices.

2. Ensured that design decisions respected scientific rigor while improving usability.

Usability Testing

1. Tested prototypes with expert users to validate navigation, comprehension of visual encodings, and interaction logic.

2. Identified friction points related to information density, hierarchy, and interaction complexity.

Evidence-Based Redesign

1. Iteratively redesigned parts of the interface based on test findings.

2. Improved visual hierarchy, interaction affordances, and layout consistency to support more efficient expert analysis.

User research, expert co-creation, and iterative usability testing—conducted in collaboration with an external UX team—improved clarity and usability in a highly complex, expert-oriented climate forecasting tool.

Final outcome

The final tool provides expert users with a more usable and coherent interface for exploring complex climate forecast data. UX validation and iterative redesign improved clarity and reduced unnecessary friction, allowing users to focus on analysis rather than interface interpretation, while preserving the depth and flexibility required for expert work.

Link: S2S4E