Package 'DatAssim'

Data Assimilation

Description

This function estimates a variable of interest through Data Assimilation technique by incorporating results from previous assessments.

Usage

datassim(X, Var, Corr)

Arguments

X	Matrix of predictions, with n number of rows as the number of observations, and t number of columns as the number of time points from which data were collected.
Var	Matrix of corresponding prediction variances, same dimension as X.
Corr	Matrix or value of correlations between observations from different time points, by default Corr = 0.

Value

$weights	Estimated Kalman gain according to Eq.[7] in Ehlers et al. (2017).
$PreDA	Predicted values through Data Assimilation according to Eq.[5] in Ehlers et al. (2017).
$VarDA	Corresponding estimated variances according to Eq.[6] in Ehlers et al. (2017).
$Correlation	Correlation matrix.

References

Ehlers, S., Saarela, S., Lindgren, N., Lindberg, E., Nyström, M., Grafström, A., Persson, H., Olsson, H. & Ståhl, G. (2017). Assessing error correlations in remote sensing-based predictions of forest attributes for improved data assimilation. DOI

Examples

Pred1 = rnorm(10, mean = 50, sd = 100);
Pred2 = rnorm(10, mean = 50, sd = 30);
Pred3 = rnorm(10, mean = 50, sd = 80);
Pred4 = rnorm(10, mean = 50, sd = 100);
# Predictions based on ten observations, at four different time points
Prediction = cbind(Pred1, Pred2, Pred3, Pred4); 
Var1 = matrix(10000, 10);
Var2 = matrix(900, 10);
Var3 = matrix(1600, 10);
Var4 = matrix(10000, 10);
# Corresponding prediction variances
Variance = cbind(Var1, Var2, Var3, Var4);
# Corr = 0 by default
datassim(X = Prediction, Var = Variance);
# Corr = 0.5
datassim(Prediction, Variance, 0.5); 
Corr = cor(Prediction);
datassim(Prediction, Variance, Corr);

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