Robotics

Open-source GZ Weather System adds realistic rain & snow to Gazebo Sim

No custom rendering plugins—just ParticleEmitter and Gazebo Transport

Deep Dive

GZ Weather System is a new open-source project from developers Aachal Sharma and Rahul Gupta that brings realistic rain and snow environments to Gazebo Sim (Harmonic). Built using Gazebo Sim's native ParticleEmitter system, the project offers lightweight, reusable weather worlds designed specifically for robotics simulation. Unlike other weather simulators that require custom rendering plugins or heavy external dependencies, this system leverages the built-in ParticleEmitter framework, making it easy to drop into existing simulation setups without modifying core rendering architectures.

The system currently provides two out-of-the-box environments: Rain World and Snow World, both featuring realistic particle behavior for their respective precipitation types. A standout feature is runtime particle emission control through Gazebo Transport, enabling developers to dynamically adjust weather intensity and parameters while a simulation is running. This means robot testing can include sudden downpours or heavy snowfalls without restarting the environment. Future plans include wind effects and dynamic weather transitions, which will allow more complex environmental scenarios. Inspired by the official ParticleEmitter (Fog Generator) example, the project aims to give the ROS and Gazebo community a simple yet powerful way to validate perception, navigation, and planning algorithms in adverse weather, a critical need for autonomous systems operating outdoors.

Key Points
  • Built entirely with Gazebo Sim's built-in ParticleEmitter system—no custom rendering plugins required
  • Supports runtime weather control via Gazebo Transport, letting users adjust rain/snow intensity live
  • Currently offers Rain World and Snow World; planned additions include wind effects and dynamic weather transitions

Why It Matters

Enables robotics engineers to validate perception and navigation in adverse weather without expensive real-world testing.

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