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 small scale influences cause forecast problems.
The weather can be remarkably different over a small distance. Examples:
a. Lake-effect snow where one location gets a foot of snow while a location 10 miles away gets less than an inch.
b. Convective precipitation producing a couple inches of rain at one location while a location 10 miles away gets no precipitation.
c. A location receiving paralyzing freezing rain while 50 miles to the south it is all rain.
d. Precipitation and temperature gradients over mountain areas
e. One location receiving golfball size hail while a location 10 miles away has a rainbow in the sky with light rain.
f. A hurricane producing 100 knots winds along the coast while a location 50 miles away has only 30 knot winds.
g. A tornado leveling a home to the foundation while a home 100 meters away has no major damage.
h. A valley or coastal area having dense fog all day while a location 20 miles away has sunny skies.
The forecast models will not be very reliable at predicting the exact location of the events above. However, the models can give you insight into the potential of the event occurring. This is why weather forecasting is full of predictions that include a probability of occurrence. Weather features often occur on too small of a scale to be realistically forecasted over a particular location. Probability forecasting basically eliminates this problem. For example, giving a probability of thunderstorms for a city is a more realistic prediction than a forecast saying there will or will not be a thunderstorm at a city. If a thunderstorm misses a city by a few miles when there was a probability of thunderstorms in the forecast it is not a busted forecast. However, if there is a high probability of thunderstorms in the area and nothing happens in or near the forecast area then this would be a bust.
The smaller the scale of a weather feature the more difficulty the forecast models will have in resolving that feature. This is important because smaller scale features influence larger scale features more significantly as time passes. This fact is what causes the forecast models to tend to be less accurate as time moves forward. This is termed "the butterfly effect". Just a person breathing can have an influence on the weather over time. The smallest of events can have the largest of outcomes over time.