TY - GEN
KW - NWP
KW - lecture notes
AU - Martin Leutbecher
AB - <p>
  Techniques for planning adaptive observations that are based on tangent-linear models and their adjoints are discussed. The emphasis is on the validation of techniques that predict the statistically expected impact of additional non-routine observations on the forecast error. The concepts are illustrated using the Lorenz-95 system, which is a low-dimensional system that has similar error growth characteristics as operational NWP systems. The objective of a consistent approach todata assimilation and adaptive observations is formulated and illustrated for an extended Kalman filter and for an OI/3DVar system. A reduced rank technique is introduced. It predicts forecast error variance in a singular vector subspace. The reduced rank predictions of forecast error variance are evaluated for both assimilation systems. Furthermore, a few examples are given of possible applications of the reduced rank estimate in the context of an operational variational assimilation scheme.</p>

<h6>Contents</h6>
<p>
  &nbsp;</p>

<ol><li>Introduction</li><li>Adaptive observations in the Lorenz 95 system - Methodology</li><li>Adaptive observations in the Lorenz 95 system - Results</li><li>Reduced rank prediction of forecast error variance reductions in an operational NWP context</li><li>Discussion</li><li>Conclusions</li><li>References</li></ol>
<p>
  &nbsp;</p>

C1 - Learning
DA - 2003
LA - eng
N2 - <p>
  Techniques for planning adaptive observations that are based on tangent-linear models and their adjoints are discussed. The emphasis is on the validation of techniques that predict the statistically expected impact of additional non-routine observations on the forecast error. The concepts are illustrated using the Lorenz-95 system, which is a low-dimensional system that has similar error growth characteristics as operational NWP systems. The objective of a consistent approach todata assimilation and adaptive observations is formulated and illustrated for an extended Kalman filter and for an OI/3DVar system. A reduced rank technique is introduced. It predicts forecast error variance in a singular vector subspace. The reduced rank predictions of forecast error variance are evaluated for both assimilation systems. Furthermore, a few examples are given of possible applications of the reduced rank estimate in the context of an operational variational assimilation scheme.</p>

<h6>Contents</h6>
<p>
  &nbsp;</p>

<ol><li>Introduction</li><li>Adaptive observations in the Lorenz 95 system - Methodology</li><li>Adaptive observations in the Lorenz 95 system - Results</li><li>Reduced rank prediction of forecast error variance reductions in an operational NWP context</li><li>Discussion</li><li>Conclusions</li><li>References</li></ol>
<p>
  &nbsp;</p>

PY - 2003
TI - Adaptive observations, the Hessian metric and singular vectors
ER -