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2015 | OriginalPaper | Buchkapitel

12. Mathematical Skills Required to Fully Understand SEGMENT-Landslide

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Abstract

In this book, we suppose the reader has basic vector concept and for the convenience, listed the basic vector operations in the Appendix D. In the following discussion, we stick to the right-hand system. Suppose the unit directional vectors of a Cartesian system is \( {\widehat{e}}_1,\kern0.5em {\widehat{e}}_2\ \mathrm{and}\ {\widehat{e}}_3 \). Position vector \( \overrightarrow{x} \) (of a granular element), can be decomposed into the \( {\widehat{e}}_1,\kern0.5em {\widehat{e}}_2\ \mathrm{and}\ {\widehat{e}}_3 \) directions, so that \( \overrightarrow{x}={x}_1{\widehat{e}}_1+{x}_2{\widehat{e}}_2+{x}_3{\widehat{e}}_3 \). Now introduce a new Cartesian coordinate system, it is obtained by rotation of the original one. Unit vectors in the rotated system called \( {{\widehat{e}}_1}^{\hbox{'}},\kern0.5em {{\widehat{e}}_2}^{\hbox{'}}\ \mathrm{and}\ {{\widehat{e}}_3}^{\hbox{'}}. \) Then the same vector can be write in the new (rotated system) as: \( \overrightarrow{x}={x}_1^{\hbox{'}}{\widehat{e}}_1^{\hbox{'}}+{x}_2^{\hbox{'}}{\widehat{e}}_2^{\hbox{'}}+{x}_3^{\hbox{'}}{\widehat{e}}_3^{\hbox{'}}={\displaystyle \sum_{i=1}^3{x}_i^{\hbox{'}}{\widehat{e}}_i^{\hbox{'}}} \). Generally speaking, x i  ≠ x i ' , i = 1,2,3. A natural question is how are the components of \( \overrightarrow{x} \) in the new system related to its components in the original one? Take x 2 ' for an example, \( {x}_2^{\hbox{'}}=\overrightarrow{x}\bullet {\widehat{e}}_2^{\hbox{'}}={\displaystyle \sum_{i=1}^3{x}_i{\widehat{e}}_i}\cdot {\widehat{e}}_2^{\hbox{'}}={\displaystyle \sum_{i=1}^3{x}_i\left|{\widehat{e}}_i\right|}\left|{\widehat{e}}_2^{\hbox{'}}\right| \cos \left({\widehat{e}}_i,{\widehat{e}}_2^{\hbox{'}}\right)={\displaystyle \sum_{i=1}^3{x}_i}{C}_{i,2} \)

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Fußnoten
1
Proof of the equivalent of an adjoint based 4D-Var and a Kalman filter.
The most important advantage of the 4D-Var is that if we assume that the forward model is perfect, and that the a priori error covariance at the initial time B 0 is correct, it can be shown that the 4D-Var analysis at the final time is identical to that of the extended Kalman Filter (Lorenc, 1986; Daley, 1991). This means that implicitly 4D-Var is able to evolve the forecast error covariance from B 0 to the final time.
Daley, R., 1991. Atmospheric data analysis. Cambridge University Press, Cambridge, P457.
Lorenc, A., 1986. Analysis methods for numerical weather prediction. Quart. J. Roy. Meteor. Soc., 112, 11771194.
 
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Metadaten
Titel
Mathematical Skills Required to Fully Understand SEGMENT-Landslide
verfasst von
Diandong Ren
Copyright-Jahr
2015
DOI
https://doi.org/10.1007/978-3-319-08518-0_12