Probability is a way of expressing uncertainty. This is the core insight of Bayesian methods. That uncertainty is not an objective universal fact, though. It is specific to your state of knowledge.

If I flip a fair coin and don't look at it as it sits in my hand, the probability of heads is 50%. Your estimate should be the same. Before or after the flip doesn't matter because our state of knowledge is the same.

If I look (and you don't), the probability that it is heads from my state of knowledge is 100% or 0%. Your estimate would be unchanged because you didn't get any more information.

If somebody looks at the coin and tells you what they say, you estimate should be somewhat less than 100% or more than 0% depending on how much you trust them.

All of this is the long way around the barn to say that after the hurricane, the probability that Sandy hit Manhattan is definitely 100%. That's past tense probability, however. It is still also true that the best estimate a year before it hit would be much, much lower. Using words like "probably that a hurricane would hit Manhattan" makes interpretation much harder, of course, because of the uncertain time frame.