Assessment of model uncertainty for soil moisture through ensemble verification

By G. De Lannoy, Paul R. Houser, Valentijn R. N. Pauwels, Niko E. C. Verhoest. 2006

118 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/7753810ca15a9758bb1cd4c63be4722201fbc08c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Valentijn R. N. Pauwels
2. G. De Lannoy
3. Paul R. Houser
4. Niko E. C. Verhoest
5. Rolf H. Reichle
6. William A. Lahoz
7. Philipp Schneider
8. Hoshin V. Gupta

In Environmental Engineering

1. Valentijn R. N. Pauwels
2. G. De Lannoy
3. Paul R. Houser
4. Niko E. C. Verhoest
5. Rolf H. Reichle
6. Patrick Matgen
7. Laura Giustarini
8. Robain de Keyser

In Water Science and Technology

1. Valentijn R. N. Pauwels
2. G. De Lannoy
3. Niko E. C. Verhoest
4. Paul R. Houser
5. Patrick Matgen

Citations in the corpus

• The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter (2011, methodology): “0 was adapted in order to be able to use the individual patches as ensemble members according to De Lannoy et al. (2006a). The meteorological forcings required by the model are the air temperature, wind speed, specific humidity, incoming solar radiation, and precipitation.”
• Correcting for forecast bias in soil moisture assimilation with the ensemble Kalman filter (2007, methodology): “The interface between the layers was halfway in between two consecutive nodes (except for the upper and lower layer) and hence the layer thickness was variable (schematic shown by De Lannoy et al. [2006a]).”
• Ensemble‐based assimilation of discharge into rainfall‐runoff models: A comparison of approaches to mapping observational information to state space (2009, methodology): “Further, if the truth is statistically indistinguishable from a member of the ensemble, the following should be true [De Lannoy et al., 2006]: h ffiffiffiffiffiffiffiffiffi ensk p i h ffiffiffiffiffiffiffiffi mse p i ffiffiffiffiffiffiffiffiffiffiffiffi N þ 1 2N r ð30Þ These verification measures…”
• State and bias estimation for soil moisture profiles by an ensemble Kalman filter: Effect of assimilation depth and frequency (2007, methodology): “A DA frequency of 1 or 2 weeks is slightly less than the temporal autocorrelation length found for soil moisture in the top layer of the OPE3 field [De Lannoy et al., 2006b].”