Lesson 1. Forecasting Fundamentals

This is a sample lesson page from the Certificate of Achievement in Weather Forecasting offered by the Penn State Department of Meteorology. Any questions about this program can be directed to: Steve Seman

Motivate...

In your coursework so far, you have focused on learning how the atmosphere works and how to analyze synoptic and mesoscale weather. But, in this course, it's time to put that knowledge to work and make some forecasts! I believe that "learning by doing" represents the highest form of learning, and in this course, you'll be "getting your hands dirty" as you apply many of the concepts from past courses to real weather situations in order to make actual forecasts. 

As you've learned, weather forecasters today have a wide array of computer model guidance to work with. Therefore, being able to correctly interpret this guidance is a critical part of forecasting. Sounds easy enough, right? Anyone can read the models with a bit of training, but being a really good forecaster is much more difficult. It requires solid conceptual models of how the atmosphere works and years of experience, because models are far from perfect. Even as computing power increases and model performance improves, models produce highly-detailed, but still erroneous simulations of future weather. Having sound conceptual models and years of experience keeps us from being completely at the mercy of model predictions and gives us a chance to create better forecasts than a computer model can.

30-hour forecast of simulated radar reflectivity
A 30-hour computer model forecast for simulated radar reflectivity and 10-meter winds valid at 18Z on August 10, 2020 (from the 12Z run on August 9). Was this model prediction exactly right? Spoiler alert: no. 

As a quick example, take the 30-hour computer model forecast valid at 18Z on August 10, 2020 (above), and focus on Iowa. Now compare it to the 18Z radar image for verification. The handful of isolated thunderstorms predicted to be over western Iowa at 18Z actually turned out to be a wicked derecho racing across the Midwest, which spawned more than 30 reports of wind gusts 75 miles per hour or more and a few wind gusts in excess of 100 miles per hour (SPC filtered storm reports). Two weeks later, thousands of Iowans were still without power! That's a pretty significant model forecast bust only a day in advance! As a forecaster, if you live solely by the models, you'll die by the models.

How much experience makes for a good forecaster? Well, no specific amount of time makes a forecaster an "expert," but practicing your forecasting skills over time will make you a better forecaster. If we apply the "10,000-hour rule" that Malcom Gladwell talks about in his 2008 book, Outliers: The Story of Success, to really become good at forecasting, you need 10,000 hours of practice. Think about that: If you made weather forecasts as a full-time job, eight hours a day, five days a week, 50 weeks a year (only two weeks of vacation!), it would take five full years to get to 10,000 hours. While you won't approach your "10,000 hours" this semester, this course will hopefully help you establish good forecasting habits and set you on the path of lifelong learning about forecasting.

All the practice in the world won't really help you become a better forecaster, though, if you don't have sound conceptual models of how the atmosphere works. This is where your knowledge from the previous three courses comes into play, especially the basics of synoptic and mesoscale weather. These concepts will play a pivotal role in your experience in this course. If you are feeling a bit rusty, don't get nervous. Throughout the lessons, I'll include some review material, as well as try to make targeted suggestions for review reading from previous courses.

Before we get into more advanced controllers of forecast variables and the strategies used in forecasting, we have to answer some basic questions first. What are we forecasting? How are forecasts verified? How do prudent forecasters go about making a forecast? 

We'll look at these issues in this lesson.  Let's get started!