|Titel:||Improving an Atmosphere General Circulation model through Parameter Optimization||Sonstige Titel:||Verbesserung eines atmosphaerischen Modells durch Parameteroptimierung||Sprache:||Englisch||Autor*in:||Agarwal, Reema||Schlagwörter:||Atmosphere; Assimilation; Optimization; Cost function; Sensitivity||Erscheinungsdatum:||2016||Tag der mündlichen Prüfung:||2017-01-17||Zusammenfassung:||
This thesis presents an implementation and evaluation of two different multivariate
data assimilation techniques for the optimization of parameters of a global primitive
equation Atmospheric General Circulation Model (AGCM), the Planet Simulator
(PlaSim). The hypothesis used is that the source of uncertainty in the model is
related to parameters from the cloud parameterizations, vertical and horizontal dif-
fusion time scales in the model. The results are evaluated by comparing basic physi-
cal state variables of the atmosphere such as surface temperature, precipitation, net
heat flux, winds and sea level pressure predicted by the model with observations.
Initially, sensitivity analysis of PlaSim with respect to various parameters used in
its different parameterizations is carried out. The variation of the cost function
with respect to changes in each control parameter is studied and the most sensitive
parameters are identified. The results of the sensitivity analysis serve as a guideline
for identifying sensitive model parameters optimization procedures.
Green’s function (GF) method of parameter optimization is applied on two differ-
ent model configurations, with and without moisture related processes (wet and
dry configurations, respectively) in an identical twin model framework. The results
are inter-compared with existing results from 4D-variational (4D-var) assimilation
scheme in PlaSim. GF procedure successfully estimates model parameters for both
shorter (30 days) and longer time (365 days) scales using 3, 5 and 6 control param-
eters. However, when using real world observations, the GF method is unable to
minimize cost function even using a single control parameter.
Another optimization procedure based on stochastic approximation, the simulta-
neous perturbation stochastic approximation (SPSA) method is implemented in
PlaSim and the results are discussed. The advantage of using SPSA method is
its ease of implementation and its robustness to noise in cost function. In identical
twin experimental framework SPSA method reliably recovers the control parame-
ters. When real observations are used, the errors in optimized state, for example
in surface temperature and net heat flux are reduced by 16% and 30% respectively.
In addition, the optimized state of PlaSim shows improvement in sea level pressure,
zonal winds at 500 hPa and surface precipitation. This study demonstrates the use-
fulness of a simple data assimilation scheme in a highly non-linear chaotic system
and its potential application in tuning of the climate models.
|URL:||https://ediss.sub.uni-hamburg.de/handle/ediss/7038||URN:||urn:nbn:de:gbv:18-83059||Dokumenttyp:||Dissertation||Betreuer*in:||Stammer, Detlef (Prof. Dr.)|
|Enthalten in den Sammlungen:||Elektronische Dissertationen und Habilitationen|