Linearized Physics for Data Assimilation at ECMWF - Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications - page 245

Chapter 11
Linearized Physics for Data Assimilation
at ECMWF

Marta Janiskov a and Philippe Lopez

Abstract A comprehensive set of linearized physical parameterizations has been
developed for the global ECMWF Integrated Forecasting System. Implications
of the linearity constraint for any parametrization scheme, such as the need for
simplification and regularization, are discussed. The description of the methodology
to develop linearized parameterizations highlights the complexity of obtaining a
physics package that can be efficiently used in practical applications. The impact
of the different physical processes on the tangent-linear approximation and adjoint
sensitivities, as well as their performance in data assimilation are demonstrated.

11.1 Introduction

Adjoint models have several applications in numerical weather prediction (NWP).
In variational data assimilation (DA) for instance, they are used to efficiently
determine optimal initial conditions. Another application of the adjoint technique
is the computation of the fastest growing modes (i.e. singular vectors) over a finite
time interval, which can be used in Ensemble Prediction Systems (EPS). Adjoint
models can also be used for sensitivity studies since they enable the computation
of the gradient of a selected output parameter from a numerical model with respect
to all its input parameters. In practice, this is often used to obtain the sensitivity of
the analysis to model parameters, sensitivities of one aspect of the forecast to initial
conditions or sensitivities of the analysis to observations.