When we talk about a modal, some of the explanations you'll find on the web conflate modal with a mode, but actually, each normals comprising the bimodal has its own mode, median, and mean. If there is enough data points under each of the normals, there won't be any skew involved. So take those peaks to be the mode, median and mean.
This implies that the footprints are circles. The normals would be symmetric, so you have a center and a data value on the outer edge of the distribution related to its closest mean for each mean. That tells you how wide each normal would be. You would have enough information to determine the relative proportion of the total probability is contributed by each of the underlying normals.
When a normal does not yet have enough data points it exhibits skew. The footprint of a skewed normal is an ellipse. In a skewed normal, the median leans. It is not perpendicular. The median becomes perpendicular as the skew disappears. The angle between the baseline and the median drives the skew. The sin of that angle equals the height. The median would be centered in the ellipse, so the median to the edge would give us enough information about the ellipses.
In a standard normal, the mean, median, and mode are in the same place. In the skewed normal, the median pushes the mean and mode apart symmetrically across a vertical line centered between mean and mode. In multimodal distributions, each modal has its own normal and its own mean, median, and mode. The same analysis applies to each of those normals.
Sometimes a large normal has a sublimated normal inside or under it. These will be hidden. If the sublimated normal is kurtotic is will make the aggregated normal oscillate. There are whole books on this subject. Enjoy.