The solution shows that the model would diverge if it was not constantly
corrected. This shows how a wrong model can be lead to give an acceptable
solution by assimilating the observations during the simulation.
The solution obtained using data assimilation is quite close to the
real solution, although the model is completly false. This can help a model
converge, but only allows simulation of states on wich data has been collected.
Even though this case shows the use of data assimilation as a powerful
tool, the scientific interest of this use is very small. The second model,
being closer to the given data can be used as a better way to use data
assimilation.
Using data assimilation on the same observations as on the first model leads to the following solution :
The solution is now really close to what the real solution is, usin the base of a model that would diverge.. The aim of data assimilation is to have a model able to predict what will happen, using disponble data. With as many observations as this, the predictions are not done on long term. The case is studied with 4 observation, on one revolution around the earth :
The solution still is quite close to what it should be.A study of what the trajectories would give without the correction shows the predictions made, and the evolution of these predictions as observations arrive.
This allows to have quite good prevision for short and middle term studies.
but it also shows, that somtimes the model without correction can be more
precise than with the given corrections. The importance given to each observed
parameter should be very carefully studied, as well as the influence of
different parameters on eachothers.