Seasonal forecasting is like predicting the outcome of a baseball game.
There are so many statistics, facts, and figures to analyze. The next step would be to decide which factors are most important. Is it an overpowering pitcher with a low ERA and high strikeouts? Conditions which favor the batters to excel against such a Pitcher. Does a team become error-prone in the second half of the season? Could one team be unbeatable on Sunday afternoons? If you want some really cool (or convoluted) ways to understand Baseball then learn about Sabermetrics.
Weather forecasting casts the same analytical spiderwebs. What rules the day? Is it a warmer than average Atlantic Ocean (AMO), tendency for blocking highs in the North Atlantic (NAO) or over the Pole (AO)? How about the ENSO (El Nino/La Nina? ). Then there is historical data and solar cycles. Caterpillars, Grounhogs, Squirrels gathering nuts ..... ENOUGH INFORMATION TO DRIVE ANY SANE PERSON CRAZY!
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