Simply stated, predicting the future is hard work, and meteorologists should never be expected to get things exactly right everywhere at once, but they often get close.

Since ancient times, weather prediction has used observations to forecast changes. For many generations, almanacs that contained the end-product of many years of observations were used to predict climate conditions on a given day. Even though they became more sophisticated over time (tracking temperature, pressure, wind, and humidity measurements at different times of day, in addition to sky conditions and moon phases), they weren't perfect; rain still fell when sunny and fair conditions were expected, and cold snaps and heat waves turned up unexpectedly. Later, observations made over wide geographic areas were reported to regional weather station networks and national weather bureaus. Weather maps collected and displayed all this timely information, but they were still imperfect tools. Since the mid-20th century, digital computers have made it possible to calculate changes in atmospheric conditions mathematically and objectively using weather models that continue to evolve, and, though forecasting has improved, even these tools could not bring it to perfection.

Why is predicting the weather so difficult for meteorologists? Well, meteorologists need to track multiple conditions and variables at the same time across vast areas, and many of these variables interact and affect one another. Weather prediction is more than just a moving target. It’s a prediction made from a swirling cloud of data. Information-age weather prediction tools and expertise are very good, and they continue to get better, but they will likely never produce absolutely perfect weather forecasts. However, all things being equal, they are generally pretty good these days.