Ideas for a pattern-oriented approach towards a VERA analysis ensemble

Autor(en)

T. Gorgas, M. Dorninger

For many applications in meteorology and especially for verification

purposes it is important to have some information about the

uncertainties of observation and analysis data. A high quality of these

"reference data" is an absolute necessity as the uncertainties are

reflected in verification measures. The VERA (Vienna Enhanced Resolution

Analysis) scheme includes a sophisticated quality control tool which

accounts for the correction of observational data and provides an

estimation of the observation uncertainty. It is crucial for

meteorologically and physically reliable analysis fields. VERA is based

on a variational principle and does not need any first guess fields. It

is therefore NWP model independent and can also be used as an unbiased

reference for real time model verification. For downscaling purposes

VERA uses an a priori knowledge on small-scale physical processes over

complex terrain, the so called "fingerprint technique", which transfers

information from rich to data sparse regions. The enhanced Joint D-PHASE

and COPS data set forms the data base for the analysis ensemble study.

For the WWRP projects D-PHASE and COPS a joint activity has been started

to collect GTS and non-GTS data from the national and regional

meteorological services in Central Europe for 2007. Data from more than

11.000 stations are available for high resolution analyses. The usage of

random numbers as perturbations for ensemble experiments is a common

approach in meteorology. In most implementations, like for NWP-model

ensemble systems, the focus lies on error growth and propagation on the

spatial and temporal scale. When defining errors in analysis fields we

have to consider the fact that analyses are not time dependent and that

no perturbation method aimed at temporal evolution is possible. Further,

the method applied should respect two major sources of analysis errors:

Observation errors AND analysis or interpolation errors. With the

concept of an analysis ensemble we hope to get a more detailed sight on

both sources of analysis errors. For the computation of the VERA

ensemble members a sample of Gaussian random perturbations is produced

for each station and parameter. The deviation of perturbations is based

on the correction proposals by the VERA QC scheme to provide some

"natural" limits for the ensemble. In order to put more emphasis on the

weather situation we aim to integrate the main synoptic field structures

as weighting factors for the perturbations. Two widely approved

approaches are used for the definition of these main field structures:

The Principal Component Analysis and a 2D-Discrete Wavelet Transform.

The results of tests concerning the implementation of this

pattern-supported analysis ensemble system and a comparison of the

different approaches are given in the presentation.

Organisation(en)

Institut für Meteorologie und Geophysik

Seiten

409

Anzahl der Seiten

Publikationsdatum

09-2010

ÖFOS 2012

105206 Meteorologie

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/b8c6432d-ee69-432a-b24e-36d55712c94b