Our strategy of making recommendations based on the gaps between observational capabilities and requirements seems convincing and bulletproof. After all, if we need a certain level of measurement accuracy for an application, then how could any funding agency turn down a reasonable proposal to close the gap?
Well, that’s not enough. We need to be clear about the impact of more and/or better measurements. Will weather or climate predictions be improved? By how much? Will lives or infrastructure be saved? Will agricultural practices benefit? This is an obvious extension of what I said in an earlier blog entry (“Requirements”), that if an observation doesn’t have a requirement, then we shouldn’t be making it. Similarly, if improving an observation doesn’t have an impact, we shouldn’t be pursuing it. We need to tell the funding agencies what that impact is.
Of course, there’s always the possibility that we don’t yet know why we need a better measurement, but we’re pretty sure that we do. Unfortunately, “If you build it, they will come” is a harder approach to sell.