sEddyProc_sSetUstarScenarios: Set Ustar Processing Scenarios
In grahamstewart12/tidyflux:

Original name: sEddyProc_sSetUstarScenarios

1 2	sEddyProc_sSetUstarScenarios(ustar_thr, suffixes = colnames(ustar_thr)[-1])

`ustar_thr`	Data frame returned by `get_annual_map` or `get_seasonal_map`. First column, season names, and remaining columns different estimates of ustar threshold. If `ustar_thr` has only one row, then each ustar threshold estimate is applied to the entire dataset. Entries in first column must match levels in argument `sf` of `est_ustar_dist`.
`suffixes`	Suffixes appended to result column names; by default the column names of ustar_thr unless its first season column.

The eddy covariance method does not work with low turbulence conditions, and periods with low turbulence indicated by low ustar must be filtered out and gap-filled (see fill_mds). The threshold value of a sufficient ustar causes one of the largest uncertainty components within the gap-filled data. Hence, it is good practice to compare derived quantities based on gap-filled data using different ustar threshold values.

For example a user could provide the the following columns in argument ustar_thr

season: identifier for which season this row is used.
Ustar: estimate on original series
U05: 5
U50: median of bootstrap
U95: 95

Usually, Scenarios are retrieved from resuls of estimating the distribution by est_ustar_dist and then taking values for either:

annual aggregate over all seasons (get_annual_map) or
seasonal estimates (get_seasonal_map).

The following functions apply a processing step to all of the scenarios.

est_ustar_scens: gap-filling
sEddyProc_sMRFluxPartitionUStarScens: flux-partitioning

The generated output columns are distinguished by appending a suffix to the respective column name. Then the spread across those columns is an estimate of the uncertainty introduced by not knowing the exact value of the ustar threshold.

grahamstewart12/tidyflux documentation built on June 4, 2019, 7:44 a.m.