I was an atmospheric scientist, but I never got in too deep in the modeling — if I had, I’d have actually received the PhD lol. But yes, data assimilation very

I was an atmospheric scientist, but I never got in too deep in the modeling — if I had, I’d have actually received the PhD lol. But yes, data assimilation very important in weather models. You know what’s going on out in the oceans where instruments are sparse, because land measurements are advected (blown) over the ocean. You still need to assimilate to correct to IRL physics. The data itself is noisy. Bad readings aren’t uncommon. Common hygrometers (humidity sensors) have hysteresis, wind is turbulent, radiosondes stop transmitting in midair. Some data is really weird, like GPS occultation data, which gives temperature mixed with humidity along a 200km long cylinder. Suffice to say that while higher order approximations in modeling have helped, DA is super important because measurements are both sparse and flaky. But that’s why I asked: numerical errors are typically dwarfed by measurement errors. So it shouldn’t be worse than a member of an ensemble model, right?