Weather and Climate Intelligence Platform

Web development for the world’s leading weather decision support platform for energy trading

ABOUT the project

Client:: Financial Services Company
Location:: USA
Company Size:: 30+ Employees
Industry:: Financial Risk Management Services
Solution:: Legacy Software Modernization

Services:

Web Development

Technologies:

.NET

Python

Angular.js (API -C#)

AWS Lambda

ECS

Highcharts

AG Grid

Leobit helped the customer enhance their advanced weather intelligence software tailored for energy trading, asset management, and risk analysis. Their flagship tools provide highly accurate, real-time forecasts and historical weather data across global markets, enabling users to anticipate energy demand and optimize their trading strategies.

The platform also features a proprietary FRisk index to assess forecast uncertainty and probabilistic data streams for deeper risk analysis. With API integration and partnerships like IBM’s GRAF model, the customer ensures clients receive fast, reliable, and business-focused weather intelligence.

This project is a perfect example of how trust and technical expertise go hand in hand. From day one, this project is built on close collaboration and mutual trust. Our team works hand-in-hand with the client to understand their needs and consistently deliver.

Olga Maslova

Project Manager at Leobit

Customer

Our customer is a specialized weather intelligence company that provides high-accuracy forecasting and data analytics for energy trading, asset management, and risk evaluation. Founded over two decades ago, they have built a reputation for delivering market-leading weather forecasts and historical climate data tailored to business needs.

Business Challenge

The client operated with a small in-house development team that lacked the specialized expertise needed to scale and evolve their complex weather intelligence platform. As the platform grew in functionality and user demand increased, maintaining performance and reliability became a serious challenge.

The client required a reliable technology partner to help stabilize the platform, eliminate technical debt, modernize the codebase, and establish a sustainable foundation for future development and growth.

Why Leobit

The foundation of this partnership was laid long before the project officially began. One of Leobit’s senior developers had previously worked directly with the client on earlier iterations of their weather intelligence platform. When that developer later joined Leobit, the client saw a clear opportunity to continue the collaboration — this time with Leobit’s full support team behind it.

Project
in detail

Leobit team performed major front-end improvements and restructured key components of the user interface to align with modern design standards and usability expectations. They implemented a cleaner layout, optimized performance for data-heavy pages, and introduced interactive elements that made complex weather data easier to navigate and interpret.

Interface and user experience enhancements

Map visualizations and forecast analysis

Leobit enhanced dynamic global map visualizations by implementing multi-continent views with temperature and humidity overlays, using region-specific color coding for instant clarity. This allowed users to zoom into any region and assess weather conditions at a glance.

Improving forecast evolution and tracking

To deepen analytical capabilities, Leobit developed a “forecast evolution” feature that tracks how predictions change across multiple model runs. This tool helps users identify shifts in forecast reliability over time, which is critical for high-stakes, time-sensitive energy trading decisions. By surfacing predictive model variations, the platform now gives traders better visibility into forecast uncertainty and model behavior.

Boosting performance and real-time capabilities

Leobit significantly improved the platform’s performance by implementing real-time data updates through web sockets. This enabled incremental graph rendering, eliminating the need for full page reloads and reducing latency. To further enhance responsiveness, the team wrote custom JavaScript to optimize event handling and extend the functionality of UI components, particularly for interactive sliders used to control timeframes and data ranges.

Architecture and storage modernization

To support future scalability, Leobit restructured the platform’s data architecture. MySQL now manages user data, site configuration, and core application logic, while PostgreSQL handles complex forecasting computations and processes meteorological data. Leobit also began preparing the platform for Snowflake integration to enable long-term archival of historical weather data. This feature will lay the groundwork for future data monetization and advanced analytics.

Security and identity management

To strengthen platform security, Leobit integrated a custom Identity Server. This replaced the outdated multi-user login system and introduced a secure, centralized authentication framework with fine-grained access control. The new system ensures scalability and compliance while improving the user management experience.

Partial front-end rewrite and fine-tuning

To align the platform with its new design system, Leobit performed a partial frontend rewrite. The team fine-tuned key UI components and interactions to reflect the updated visual identity while ensuring consistency across devices and browsers. This effort not only modernized the user experience but also laid a flexible foundation for future UI enhancements.

Interface and user experience enhancements

Leobit led a complete UI overhaul to modernize the platform’s interface and improve usability. The redesigned layout made complex data easier to navigate, with cleaner visuals and intuitive user flows. The team integrated AG Grid to display dense, structured weather data in interactive tables, allowing users to filter, sort, and manipulate data with ease.

For charting and analysis, Leobit used Highcharts to power advanced visualizations of time-series forecasts, solar energy production, and historical climate trends. Thanks to backend tuning and frontend performance enhancements, these charts rendered smoothly—even with high-frequency, real-time data.

Widget integration and summary view

Leobit enhanced the platform’s summary view by configuring multiple data-rich widgets on a single page. Using Highcharts, the team delivered responsive visualizations for time-series weather forecasts, solar energy output projections, and historical climate trends. Back-end and front-end optimizations ensured these graphs remained smooth and interactive, even under heavy data loads.

Custom graph rendering

Leobit developed custom JavaScript logic to dynamically render graphs stored on AWS S3. This allowed the platform to efficiently retrieve and display large sets of pre-processed forecast data without overloading the front end, improving load times and reducing client-side rendering overhead.

Technology Solutions

Adopted Highcharts and AG Grid to visualize time-series forecasts, solar production estimates, and tabular weather data with high performance and interactivity.
Used MySQL and PostgreSQL to separate application data from high-load forecast computations, optimizing both system performance and scalability.
Replaced legacy code with clean, maintainable architecture, eliminating bottlenecks left by previous contractors and enabling faster iteration on new features.
Used web sockets and custom JavaScript for real-time graph rendering and seamless data updates without full page reloads.
Integrated a custom Identity Server for secure, scalable user authentication and role-based access control.

Value Delivered

Leobit accelerated development velocity and boosted sprint completion rates from 60–70% (with previous teams) to a consistent 90–100%.
A fully redesigned interface and improved front-end performance made the platform faster, more intuitive, and easier to navigate.
Through forecast evolution tracking, departure-from-norms analysis, and advanced visualization tools, users gained deeper insight into weather variability, risk factors, and energy production patterns.