Bridging Observation and Modeling: Classroom Design Using Snow Crystal Growth Experiments and Excel-Based Simulations

In meteorological research, numerical simulations, laboratory experiments, and field observations are widely used as complementary approaches to investigate natural phenomena. Understanding their roles, advantages, and interconnections is essential in meteorology and science education.

This study presents the development and implementation of a lesson plan focusing on snow crystal formation. Snow is a familiar and visually engaging phenomenon, making it an effective entry point for learning meteorology. In regions where snowfall occurs, natural snow crystals can be directly observed outdoors; however, such observations are limited to specific geographical regions and seasons and are not always accessible in classroom settings. As a result, connecting observation with theoretical understanding remains challenging.

The formation of snow crystals involves diffusion of water vapor, phase change, and anisotropic growth. While these processes can be explained theoretically, students often struggle to relate abstract concepts to observable phenomena. Laboratory experiments using diffusion cloud chambers allow artificial growth and observation of snow crystals under controlled conditions, but they typically require waiting times of one to two hours and provide only limited crystal types with simple equipment.

To address these challenges, this study proposes a classroom design that integrates laboratory experiments with a simple numerical simulation to support conceptual understanding and foster computational thinking. This lesson plan was implemented in an undergraduate meteorology course. During the waiting time required for crystal growth in a dry-ice diffusion chamber, students engage in active learning using an Excel-based simulation developed in VBA.

The simulation represents snow crystal growth using a simplified diffusion field in which water vapor diffuses across a grid and is consumed at the ice interface. Additional rules incorporate local growth, instability-driven tip enhancement, and anisotropy reflecting hexagonal symmetry. A worksheet-based user interface allows students to modify parameters—such as diffusion intensity, growth rate, noise, and anisotropy—without interacting directly with the code. By systematically adjusting these parameters and observing the resulting structures, students explore how local rules and environmental conditions produce emergent patterns.

The lesson follows a structured sequence: (1) posing questions about snow crystal morphology, (2) setting up the experiment, (3) exploring the simulation through parameter experiments, (4) introducing theoretical concepts, and (5) comparing simulated results with observations. This design promotes active learning and supports the transition from descriptive observation to mechanistic understanding.

Furthermore, the model serves as a transparent “toy model,” illustrating key elements of numerical modeling such as discretization, iterative processes, and parameter sensitivity. This helps bridge the gap between simple rule-based models and more complex simulations.

This approach is not limited to snow crystal growth but can be extended to other experimental topics in atmospheric and oceanic sciences, such as rotating tank experiments.

 

This study contributes to innovative and practical approaches to meteorology education.