Meeting room nr. 235C
29 rue d'Ulm
Seminar: Data assimilation for the detection and attribution of weather and climate-related events
by Alberto Carrassi , Nansen Environmental and Remote Sensing Center - NERSC, Bergen, Norway
Data Assimilation (DA) is the procedure through which observations and model are
merged to get an improved and homogeneous estimate of a system of interest. In
numerical weather and oceanic predictions DA has dramatically contributed to
enhance the forecast skill and its application to coupled Earth System Simulators is
nowadays regarded with strong interest as a unified initialization approach across all
forecast horizons from weeks to seasonal and decadal.
The talk will provide an introduction of the context and rationale behind DA theory
and will describe the main methodological options arisen in geosciences in the last
decades with a focus on the theoretical and mathematical challenges encountered.
As an example of a new promising field of application of DA a new approach
allowing for near real time, systematic causal attribution of weather and climaterelated
events is described. The method is purposely designed to allow its operability
at meteorological centers by synergizing causal attribution with DA. It is shown how
casual attribution can be obtained as a by-product of the statistical inference
undergone when doing the assimilation of data. The theoretical rationale of this
approach is explained along with the most prominent features of a DA-based
detection and attribution procedure. The proposal is illustrated in the context of the
3-variables Lorenz model and is compared with standard methods for detection and
attribution showing promising performance.
The method stresses on the concept of model evidence, and open questions on how to
compute and interpret the response to forcings whose effects one wants to contrast. A
discussion on the lines taken to address these issues is provided. Practical obstacles
that need to be addressed to make the proposal readily operational within weather
forecasting centers are finally laid out.
For more information: A. CARRASSI ()
Link to presentation: