Simplot Weather

Overview

Simplot needed a way to surface weather data from their growing network of field-based weather stations directly to agronomists. Existing weather apps provided regional forecasts, but lacked the hyper-local precision that agricultural decision-making requires. A farmer needs to know exactly what's happening at their specific field, not what's happening five miles away.

The work spanned the full product surface:

• Native mobile app for iOS
• Light and dark mode experiences
• Station selection and favoriting system
• Real-time weather data visualization for agricultural metrics
• Hyper-localized weather monitoring for multiple field locations

Team & Role

From Concept to Launch

This project moved fast. Working closely with one developer and one PM, I designed the core experience in under a month, gathering feedback from business stakeholders and field users with tight iteration loops. The short timeline meant prioritizing ruthlessly focusing on the core value proposition of hyper-localized data and station favoriting.

Designing for field conditions

I designed light and dark mode experiences optimized for outdoor use. Agronomists often check weather conditions in bright sunlight or early morning darkness, so both modes needed strong contrast and readability. I also kept the interface simple and glanceable, as users needed to quickly check conditions without navigating through complex menus.

Station selection and favoriting

The core interaction was allowing users to find nearby Simplot weather stations, save their preferred locations, and quickly switch between them. I designed a map-based station browser and a favoriting system that let users build a personalized dashboard of their most important fields.

Agricultural weather metrics

I worked with the team to determine which weather metrics mattered most for agricultural decisions: temperature, precipitation, wind speed, humidity, and UV index. I designed data visualizations that surfaced current conditions clearly while providing enough historical context for trend analysis.