Geostatistical radar-raingauge combination with nonparametric correlograms: methodological considerations and application in Switzerland
R Schiemann, R Erdin, M Willi, C Frei… - Hydrology and Earth …, 2011 - hess.copernicus.org
Modelling spatial covariance is an essential part of all geostatistical methods. Traditionally,
parametric semivariogram models are fit from available data. More recently, it has been
suggested to use nonparametric correlograms obtained from spatially complete data fields.
Here, both estimation techniques are compared. Nonparametric correlograms are shown to
have a substantial negative bias. Nonetheless, when combined with the sample variance of
the spatial field under consideration, they yield an estimate of the semivariogram that is …
parametric semivariogram models are fit from available data. More recently, it has been
suggested to use nonparametric correlograms obtained from spatially complete data fields.
Here, both estimation techniques are compared. Nonparametric correlograms are shown to
have a substantial negative bias. Nonetheless, when combined with the sample variance of
the spatial field under consideration, they yield an estimate of the semivariogram that is …
[PDF][PDF] Geostatistical radar-raingauge combination with nonparametric correlograms methodological considerations and application in Switzerland
R Schiemann, R Erdin, M Willi, C Frei… - Hydrology and Earth …, 2010 - hess.copernicus.org
Modelling spatial covariance is an essential part of all geostatistical methods. Traditionally,
parametric semivariogram models are fit from available data. More recently, it has been
suggested to use nonparametric correlograms obtained from spatially complete data fields.
Here, both estimation techniques are compared. Nonparametric correlo-5 grams are shown
to have a substantial negative bias. Nonetheless, when combined with the sample variance
of the spatial field under consideration, they yield an estimate of the semivariogram that is …
parametric semivariogram models are fit from available data. More recently, it has been
suggested to use nonparametric correlograms obtained from spatially complete data fields.
Here, both estimation techniques are compared. Nonparametric correlo-5 grams are shown
to have a substantial negative bias. Nonetheless, when combined with the sample variance
of the spatial field under consideration, they yield an estimate of the semivariogram that is …