|FORECAST BUST: HUMAN ERROR
METEOROLOGIST JEFF HABY
This series of Haby Hints investigates problems that cause a forecast to bust. A bust occurs when a
certain weather parameter is expected but one or more factors cause the forecast to be wrong. This
particular Haby Hint will focus on how human error causes forecast problems.
Here are some common ways that human error produces forecast busts. For each of these think of ways
that you can prevent and reduce these problems from occurring:
1. Misreading the forecast data. This can occur from not spending enough time at examining various weather data
or looking over the weather data too quickly.
2. Not having enough analysis skills to interpret the weather data correctly. Not having enough meteorology
education- including dynamic, thermodynamic, and physical meteorology.
3. Biasing the data toward "weather wishes". Common weather wishes include a
severe weather event or
winter storm event. Wanting a particular type of weather and that weather occurring are two
independent issues. Look at the weather data objectively.
4. Relying too heavily on someone else's forecast instead of drawing your conclusions directly
from the weather data.
5. Lack of personal experience. It helps to know the local effects within a forecast region. Experience
by learning from forecast mistakes produced by local effects will make you a better forecaster for
that region over time. Also, those experienced with knowing model biases will have an edge over those who do
not. The memory of weather patterns in the past is often beneficial to know when a similar pattern sets up.
6. Drawing illogical or meteorologically unsound conclusions from the weather data.
7. Not recognizing what the major forecast challenge for the day will be. For example, coming up with a
finely turned temperature forecast is not as important as determining if severe storms will occur.
8. Not recognizing bad or irrelevant weather data.
9. Reading the valid time for weather data incorrectly. For example, thinking the 0Z forecast data is for Wednesday
evening local time when it is actually for Tuesday evening local time.
10. Making the forecast too specific that it does not properly account for uncertainty or making the
forecast too general that is does not have enough meaningful information.
11. Not communicating the forecast clearly to the target audience. A forecast to others is only as good
as who hears it and who understands it.
12. A forecast that goes out of limb for the sole basis of being different from others.