Summary:
This module highlights why every skier or rider benefits from starting to build the skill of daily forecasting. We’ll explore how to make forecasting a personal habit, and the role of uncertainty, confidence, and bias in shaping decisions. You’ll see how common traps like recency or confirmation bias sneak in, and how creating your own snapshot helps you shift from reactive choices to proactive risk management.

We’ll begin to break down the forecasting process into manageable steps: gathering inputs, identifying likely avalanche problems, and translating them into trip-specific decisions.

You’ll learn how to scale down from broad forecast zones to your own trip zone, and how to use frameworks like the Avalanche Problem types and the Conceptual Model of Avalanche Hazard (CMAH) to stay systematic. By the end, you’ll understand how to connect big-picture data to the actual slopes you plan to ride.

Weather drives avalanche problems. To understand what’s going on in the mountains, we can break it into three main ways to observe and anticipate conditions: the key variables, the models, and the weather stations. Looking at all three gives a more complete picture.

Models show what is going to happen. Weather stations show what is happening right now. Combining both with your understanding of key variables helps predict avalanche risk more reliably.  By the end, you’ll know how to connect weather data directly to avalanche risk.

We’ll cover the basics of snowpack layering, metamorphism, and simple observations that matter most for recreational forecasting. You’ll learn to keep a basic snow journal, recognize persistent weak layers, and separate “useful” observations from noise. We’ll connect weather patterns to snowpack evolution and highlight red flags like collapsing, cracking, and recent avalanches.

Finally, we’ll show how sharing even simple observations strengthens the whole forecasting community.

In this module, we’ll introduce frameworks for classifying terrain by trigger likelihood, consequence, and exposure. You’ll learn the Avalanche Terrain Exposure Scale (ATES) and how to recognize when terrain itself amplifies risk — like wind-loaded bowls or sun-baked slopes. Through case studies, we’ll practice matching terrain choices to levels of snowpack uncertainty. The key takeaway: when conditions are unclear, conservative terrain keeps you alive.

This module teaches you to synthesize the key ingredients into a trip-specific forecast. You’ll learn to write avalanche problem statements (where, likelihood, size, and trend), assign a simple field-based hazard rating with confidence, and link it directly to terrain choices.

We’ll cover how to make forecasts when no bulletin exists, how to manage uncertainty with conservative terrain, and how documenting your process sharpens both communication and decision-making for your group.

Here we’ll apply the DIY forecasting framework to real-world case studies: remote trips with no bulletin, shoulder season missions, and backyard tours. You’ll practice building forecasts with limited data, managing uncertainty when it’s maxed out, and seeing how guides create private forecasts for expeditions. This module is about making the leap from theory to practice, with plenty of reps.

This module introduces practical resources to support your forecasting: downloadable forecast worksheets, curated links to weather and snowpack data sources, and easy systems for organizing your inputs. You’ll walk away with a ready-to-use toolkit for building forecasts anywhere in the world.

In the final module, we’ll focus on building forecasting into your routine and making it a group practice, not just a solo exercise. You’ll revisit bias traps, practice consistency checks against official bulletins, and learn how to clearly share your forecast with partners. We’ll also point you toward next steps — advanced courses, mentorship, and continued field practice — so you can keep growing your forecasting skills.