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Whole-lake heat budget vs observations: net deficit/surplus, or just surface/bottom redistribution?

Source: R/diag_aeme.R
diag_heat_budget.Rd

Uses HYD_temp (not HYD_nrgcnt directly - there is no independent observation of energy density to compare against, only temperature) for the bias itself, volume-weighted via the lake's own hypsography, then reports the implied total heat-content bias in Joules for an intuitive magnitude (rho * cw * volume-weighted-bias * lake_volume).

Usage

diag_heat_budget(
  aeme,
  model,
  depth_breaks = c(-0.01, 2, 6, 10, 15, 20, 100),
  bias_ok_degC = 0.5,
  redistribution_ratio = 0.5
)

Arguments

aeme

Aeme object; already built and run for model.

model

character; single model code to diagnose (see check_model).

depth_breaks

numeric vector; passed to cut for the by-depth-band summary.

bias_ok_degC

numeric; below this absolute volume-weighted bias, classified "ok".

redistribution_ratio

numeric; if the volume-weighted bias's magnitude is below this fraction of the largest single-band absolute bias, the bands are judged to be substantially cancelling each other out (heat misplaced, not missing/excess) rather than agreeing (a genuine net deficit or surplus).

Value

list with volume_weighted_bias_degC, total_bias_J, lake_volume_m3, by_depth_band (data frame), classification (one of "deficit", "surplus", "redistribution", "ok", "no_data"), detail.