Wrong aggregation without correlations

With neglecting correlations among records, the uncertainty of the mean annual flux is computed by adding the variances. The mean is computed by \(m = \sum{x_i}/n\). And hence its standard deviation by \(sd(m) = \sqrt{Var(m)}= \sqrt{\sum{Var(x_i)}/n^2} = \sqrt{n \bar{\sigma^2}/n^2} = \bar{\sigma^2}/\sqrt{n}\). This results in an approximate reduction of the average standard deviation \(\bar{\sigma^2}\) by \(\sqrt{n}\).

results %>% summarise(
  nRec = sum(is.finite(NEE_orig_sd))
  , NEEagg = mean(NEE_uStar_f)
  , varSum = sum(NEE_orig_sd^2, na.rm = TRUE)
  , seMean = sqrt(varSum) / nRec
  , seMeanApprox = mean(NEE_orig_sd, na.rm = TRUE) / sqrt(nRec)
  ) %>% select(NEEagg, nRec, seMean, seMeanApprox)

##      NEEagg  nRec     seMean seMeanApprox
## 1 -1.657303 10901 0.02988839   0.02650074

Due to the large number of records, the estimated uncertainty is very low.

Considering correlations

When observations are not independent of each other, the formulas now become \(Var(m) = s^2/n_{eff}\) where \(s^2 = \frac{n_{eff}}{n(n_{eff}-1)} \sum_{i=1}^n \sigma_i^2\), and with the number of effective observations \(n_{eff}\) decreasing with the autocorrelation among records (Bayley 1946, Zieba 2011).

The average standard deviation \(\sqrt{\bar{\sigma^2_i}}\) now approximately decreases only by about \(\sqrt{n_{eff}}\):

\[ Var(m) = \frac{s^2}{n_{eff}} = \frac{\frac{n_{eff}}{n(n_{eff}-1)} \sum_{i=1}^n \sigma_i^2}{n_{eff}} = \frac{1}{n(n_{eff}-1)} \sum_{i=1}^n \sigma_i^2 \\ = \frac{1}{n(n_{eff}-1)} n \bar{\sigma^2_i} = \frac{\bar{\sigma^2_i}}{(n_{eff}-1)} \]

First we need to quantify the error terms, i.e. model-data residuals. For all the records of good quality, we have an original measured value NEE_uStar_orig and modelled value from MDS gapfilling, NEE_uStar_fall.

For computing autocorrelation, equidistant time steps are important. Hence, instead of filtering, the residuals of non-observation, i.e. gap-filled, data are set to missing.

results <- EProc$sExportResults() %>% 
  mutate(
    resid = ifelse(NEE_uStar_fqc == 0, NEE_uStar_orig - NEE_uStar_fall, NA)
    ,NEE_orig_sd = ifelse(
      is.finite(.data$NEE_uStar_orig), .data$NEE_uStar_fsd, NA)
  )

Now we can inspect the the autocorrelation of the errors.

acf(results$resid, na.action = na.pass, main = "")

The empirical autocorrelation function shows strong positive autocorrelation in residuals up to a lag of 10 records.

Computation of effective number of observations is provided by function computeEffectiveNumObs from package lognorm based on the empirical autocorrelation function for given model-data residuals. Note that this function needs to be applied to the series including all records, i.e.  not filtering quality flag before.

autoCorr <- lognorm::computeEffectiveAutoCorr(results$resid)
nEff <- lognorm::computeEffectiveNumObs(results$resid, na.rm = TRUE)
c(nEff = nEff, nObs = sum(is.finite(results$resid)))

##      nEff      nObs 
##  4230.522 10901.000

We see that the effective number of observations is only about a third of the number of observations.

Now we can use the formulas for the sum and the mean of correlated normally distributed variables to compute the uncertainty of the mean.

resRand <- results %>% summarise(
  nRec = sum(is.finite(NEE_orig_sd))
  , NEEagg = mean(NEE_uStar_f, na.rm = TRUE)
  , varMean = sum(NEE_orig_sd^2, na.rm = TRUE) / nRec / (!!nEff - 1)
  , seMean = sqrt(varMean) 
  #, seMean2 = sqrt(mean(NEE_orig_sd^2, na.rm = TRUE)) / sqrt(!!nEff - 1)
  , seMeanApprox = mean(NEE_orig_sd, na.rm = TRUE) / sqrt(!!nEff - 1)
  ) %>% select(NEEagg, seMean, seMeanApprox)
resRand

##      NEEagg     seMean seMeanApprox
## 1 -1.657303 0.04798329   0.04254471

The aggregated value is the same, but its uncertainty increased compared to the computation neglecting correlations.

Note, how we used NEE_uStar_f for computing the mean, but NEE_orig_sd instead of NEE_uStar_fsd for computing the uncertainty.

Daily aggregation

When aggregating daily respiration, the same principles hold.

However, when computing the number of effective observations, we recommend using the empirical autocorrelation function estimated on longer time series of residuals (autoCorr computed above) in computeEffectiveNumObs instead of estimating them from the residuals of each day.

First, create a column DoY to subset records of each day.

results <- results %>% mutate(
  DateTime = EddyDataWithPosix$DateTime     # take time stamp form input data
  , DoY = as.POSIXlt(DateTime - 15*60)$yday # midnight belongs to the previous
)

Now the aggregation can be done on data grouped by DoY. The notation !! tells summarise to use the variable autoCorr instead of a column with that name.

aggDay <- results %>% 
  group_by(DoY) %>% 
  summarise(
    DateTime = first(DateTime)
    ,nEff = lognorm::computeEffectiveNumObs(
       resid, effAcf = !!autoCorr, na.rm = TRUE)
    , nRec = sum(is.finite(NEE_orig_sd))
    , NEE = mean(NEE_uStar_f, na.rm = TRUE)
    , sdNEE = if (nEff <= 1) NA_real_ else sqrt(
      mean(NEE_orig_sd^2, na.rm = TRUE) / (nEff - 1)) 
    , sdNEEuncorr = if (nRec <= 1) NA_real_ else sqrt(
       mean(NEE_orig_sd^2, na.rm = TRUE) / (nRec - 1))
    , .groups = "drop_last"
  )
aggDay

## # A tibble: 365 × 7
##      DoY DateTime             nEff  nRec      NEE  sdNEE sdNEEuncorr
##    <int> <dttm>              <dbl> <int>    <dbl>  <dbl>       <dbl>
##  1     0 1998-01-01 00:30:00 11.0     21  0.124    0.760       0.536
##  2     1 1998-01-02 00:30:00  3.66     7  0.00610  1.56        1.04 
##  3     2 1998-01-03 00:30:00  0        0  0.0484  NA          NA    
##  4     3 1998-01-04 00:30:00  0        0  0.303   NA          NA    
##  5     4 1998-01-05 00:30:00 10.9     28  0.195    0.861       0.521
##  6     5 1998-01-06 00:30:00 18.0     48  0.926    0.615       0.370
##  7     6 1998-01-07 00:30:00 18.0     48 -0.337    0.566       0.340
##  8     7 1998-01-08 00:30:00 17.7     46 -0.139    0.525       0.320
##  9     8 1998-01-09 00:30:00 17.5     45  0.614    0.474       0.290
## 10     9 1998-01-10 00:30:00 15.4     36  0.242    0.641       0.411
## # ℹ 355 more rows

The confidence bounds (+-1.96 stdDev) computed with accounting for correlations in this case are about twice the ones computed with neglecting correlations.