I'm working with a distributed snow model which simulates Snow Depth and Snow Cover area. It takes in input some atmospherics variables : Temperature / Precipitation / Wind speed ... I want to assimilate the output : Snow Depth retrieving remote sensing data ( I have snow cover indices for each 10 days ) and by using Kalman Filter. But some ambiguities that prevent me to do it :
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1$\begingroup$ $M$ is the forward model you are using. It is the dynamical model that you are using to solve whatever discrete equation evolves your field (d?/dt=). $\endgroup$– arkaiaAug 9, 2016 at 13:32
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$\begingroup$ @aretxabaleta why not put that down as an answer? $\endgroup$– BarocliniCplusplusAug 10, 2016 at 22:50
1 Answer
$M$ is the forward model you are using. It is the dynamical model used to solve whatever discrete equation evolves the field ($d?/dt=$).
The best way to start with data assimilation and understand its nomenclature is to read Ide et al. (1997). In their work, they explain it as:
A discrete model for the evolution of an atmospheric, oceanic, or coupled system from time $t_i$ to time $t_{i+1}$ is governed by an equation
$x^f(t_{i+1})=M_i[x^f(t_{i})]$, (1)
where $x$ and $M$ are the model's state vector and its corresponding dynamic operator, respectively. The state vector $x$ has dimension $n$. The dynamics $M$ of the model evolution (1) in a computer simulation or prediction is commonly nonlinear and deterministic, while the true geofluid might differ from (1) by random or systematic errors.
- Ide, K., Courtier, P., Ghil, M., Lorenc, A.C., 1997. Unified notation for data assimilation: operational, sequential and variational. J. Met. Soc. Jpn. 75, 181– 189.
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$\begingroup$ the link in the answer is broken $\endgroup$– user1066Nov 2, 2016 at 1:46
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$\begingroup$ I have changed the link. Let me know if it works for you $\endgroup$– arkaiaNov 2, 2016 at 14:18