Notes for Team Creation:
Future benchmarks:
should be realistic
realistic outliers/random errors - assume a good QC has been undertaken
insert random missing data (which we will have masked from the real stations anyway)
study frequency and size of local trends (which will come from the climate models)
Adding the noise term – some of this will be uncorrelated with other stations – simple random errors, some of this would be the weather term although how this would play out on monthly timescales is unclear – persistent cold or hot events – these would be correlated across networks. Some kind of simple weather generator? Could this sort of thing be modelled from the real stations? Study periodicities in common or something like that? Could use geospatial statistics to get at spatial covariance and add 'weather' based on these underlying relationships?
May be worth storing some other information from the models to be used by team corruption – incoming solar radiation, windspeed? This wouldn't be public info but could help with 'realistic' error input.
1 comment:
Sounds good.
Storing information on the weather for team corruption is a very good idea. This would make the inhomogeneity perturbations a little bit different every month, which is something HOME did not test yet.
Next to solar radiation and windspeed for problems related to radiation errors, also rain, wind speed and humidity could be good for wetting problems and maybe freezing rain and snow for ventilation problems. Different measurement systems may have different compromises in protecting against radiation, wetting and ventilation errors.
We may want to simplify the problem by not including artificial outliers. The analysis of the HOME benchmark suggests that the influence of outliers on homogenisation is not strong. It would be a different matter if we would also like to validate the quality control methods, but I did not hear much about that yet. (Maybe something for the second cycle?)
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