Something I learned only recently: Weather forecasts don't actually directly use sensor data. Instead, a physically consistent model is first fitted to all the available sensor data, and then the forecast is made based on the values that model produces. Doing it this way has the benefit that physically implausible sensor readings are given less importance, and the fact that this model can be sampled in regular intervals, whereas the sensors are all over the place (and often moving, e.g. in aircraft, which contribute crucial data).
Of course, higher density of sensors would lead to a better fit of the model to the real world, but there would still be no guarantee that the model would reflect the measured values exactly. I found that pretty interesting.
(And it's kind of funny to think about our own consciousness in this way, which seems to work somewhat similarly: we don't experience the actual 'sensor values', but instead we experience the output of a model our brain fits to those inputs.)
Of course, higher density of sensors would lead to a better fit of the model to the real world, but there would still be no guarantee that the model would reflect the measured values exactly. I found that pretty interesting.
(And it's kind of funny to think about our own consciousness in this way, which seems to work somewhat similarly: we don't experience the actual 'sensor values', but instead we experience the output of a model our brain fits to those inputs.)