Insofar as operational weather models are updated four times daily, this choice roughly corresponds to the atmospheric region that has the greatest impact on the weather at a given point during a typical 6-hour period.
If achieved, the real-time prediction of the K index will contribute significantly to improving regional ionospheric modelling as well as other regional space weather models that consider the locally measured magnetic activity as input.
Although SSWs only happen at most a few times per year, they nevertheless provide an excellent opportunity to test current theories of neutral-plasma coupling on a global scale and for testing the predictive capabilities of space weather models, as SSWs can be forecasted on a 1-week timescale (Fuller Rowell et al. 2011; Wang et al. 2014).
They do, though, stop buildings and aeroplanes from suffering the fate, as well as helping predict weather, model various other physical phenomena, search for oil or gas reserves, encrypt and decrypt communications and simulate all manner of processes.
Many different parameters measured from the Sun to the Earth's magnetosphere are used as inputs to the space weather models.
Most of the above track models (except CLIPER) require data from global weather models, such as the GFS, which produce output about four hours after the synoptic times of 0000, 0600, 1200, and 1800 Universal Coordinated Time (UTC).
Unless the initial conditions are updated sufficiently often, numerical weather models produce forecasts that are only as accurate as an almanac's.
But even as living conditions are becoming denser, catastrophic weather events are growing in frequency and intensity, to the point that weather models based on historical patterns can no longer accurately forecast the future.
Interestingly, weather models also outperformed climate models for sedentary species.
The reverse was true for tropical species, as climate models were most influenced by temperature seasonality, and mean temperature was the highest contributor for weather models.
