After running the synthesis for AGU 2019 it became clear that the raw AMF data available from Berkeley has a lot of sites that are missing specific key variables (e.g. LH, SH, NEE, GPP, Reco etc). We would like to use the longest time-series possible with the newest data and as many northern temperate/boreal locations as possible for the first manuscript. How best to deal with missing data/variables? Calculate on-the-fly in the AMF parsing code? If so, how best to do this and what variables will be required? Do we leverage existing code to do this such as EddyProc?

Some examples of what we would like to enable for diurnal data and use the standard scoring system in ILAMB

Feature request:

If we would like compare ensemble model outputs to model benchmarks, we should consider how to score ensemble runs. That is account for uncertainty in observations and variance in model output such that we score based on overlap region vs a mean comparison.

We need to develop a methodology to calculate the growing season (which we do for many variables already) that is then applied to variables (e.g. SH) which may not have a clear on/off period. We would then apply the ranges to these variables so that the scoring can be done either just on growing season or broken up by growing and dormant seasons. As it stands, some variable scoring is incorrect because the seasonal timing isnt properly captured