How to measure the agreement between to curves? I have values (plotted below) of expected RSSI values over time that I would like to compare with my measured RSSI values.  What I was looking for was a way to quantify it so I can change parameters and be able to compare/contrast different approaches.

It is a hard problem in my mind because I don't know how to compare the signals and yet take into account the large-scale (overall shape) and small-scale (individual fluctuations) of signal.  

For instance, here is a plot of one set of signals:


In the image I can see that the red measure signal roughly follows the model, but it also does an OK job of simulating some of the sinusoidal qualities of the model (in some places).  Any thoughts?

&lt;>
In response to pichenettes' comments (which seem reasonable), I took a diff of the two values and plotted the abs(fft(diff)) and got this:


I am not sure what to make of that though.  Since we don't have any actual freqs, I am not sure how to scale the axis, and then if I did, what metric would you use?
If the signals are not aligned, yet you have a clue that they more or less "mean" the same thing, or refer to similar data, you could use dynamic time warping (DTW) algorithm to obtain a better correspondence (then simply taking the value at the same location). You could measure RMS, MSE or whatever you like, using those correspondences. For DTW, you might want to check:
http://en.wikipedia.org/wiki/Dynamic_time_warping

A good way to elaborate this idea is utilized in Earth Mover Distance (EMD), which computes the minimal effort to bring the signals to alignment, as a distance measure. EMD is presented here:
http://ai.stanford.edu/~rubner/emd/default.htm

EMD gives you a direct distance, which you might use for further analysis.