final_program_abstracts[1]

11 IMSC Session Program
A Bayesian method for the comparison of trend data and
application to water vapour
Monday - Parallel Session 9
S. Mieruch 1 , S. Noël1, M. Reuter 1 , H. Bovensmann 1 , J. P. Burrows 1 , M.
Schröder 2 and J. Schulz 2
1
Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany
2
Satellite Application Facility on Climate Monitoring, German Weather Service
(DWD), Offenbach, Germany
Global total column water vapour trends from 1996 to 2007 have been derived from
GOME (Global OzoneMonitoring Experiment) and SCIAMACHY (SCanning
Imaging Absorption spectrometer for Atmospheric CHartographY) satellite data by
IUP and from globally distributed radiosonde measurements, archived and quality
controlled by DWD.
In this presentation we address the question if observed water vapour trends from
independent instruments are equal or not. This is in principle the Behrens-Fisher
problem (comparison of samples with different means and different standard
deviations) applied to trends from time series.
First we solve the Behrens-Fisher problem approximately using frequentist standard
hypothesis testing by performing the Welch-test. Second, a Bayesian model selection
is applied to solve the Behrens-Fisher problem exactly by integrating the posterior
probabilities numerically by the algorithm Differential Evolution Markov Chain
(DEMC). Additionally we derive an analytical approximative solution of the Bayesian
posterior probabilities by a quadratic Taylor series expansion, which can be applied
computationally efficient to large data sets.
The results from the frequentist and Bayesian approaches are discussed from a
statistical view and a climatic view.
Abstracts 65