In every professional field, practitioners may come home at the end of the day and say "Yeah! I knocked it out of the park!", or alternatively "Oh, man. Gotta do better tomorrow." What about meteorologists?
When a meteorologist makes a weather forecast, she will strive to "get it right", so that when the date in question arrives her prediction will have matched the actual temperature, dew point, wind velocity and direction, precipitation, etc. But, when that date arrives, and the actual conditions have been measured and compared to the prediction, how is the prediction graded?
Precipitation seems to be the most problematic aspect, as it's quite nonlinear and predicted statistically. What does it mean to have "gotten it right" by predicting a 30% chance of precipitation? Yes, it rained 30% of the time? Yes, it rained in 30% of the forecast area? Yes, 30% of the public will answer "Yes" if asked whether it rained?
There's complexity in the other metrics as well. For instance, how are errors in the different values compared? Would it be better to get the temperature spot on but the wind velocity 5MPH high, or the wind exactly right but the temperature 5°F high? What about timing: what if that front came through two hours after it was predicted?
Edit: part of the difficulty is getting clarity about the consumer of the weather information. Fred the Farmer wants to know how much rain he can expect in next day or so, but the exact timing probably isn't important. Pete the Party Planner wants to herd his guests into the bar before the front comes through, but if it's going to rain he probably doesn't care whether it drops a quarter inch or a half. And Francine the Fisherman doesn't really care about the precipitation at all (within limits); her world centers around wind and waves.
I may want to post a second question that is explicitly from the consumer viewpoint: given weather data and meteorologists' historical forecasts, how would I choose a meteorologist?