Proceedings - C-SRNWP Project

expected to result in an improved representation of a number of processes and
phenomena, including non-local transport of heat, moisture and momentum due to
boundary-layer turbulence and shallow convection, triggering of deep cumulus
convection, and stratiform cloud cover. This in turn is expected to lead to an
improved forecast of several key quantities, such as the rate and timing of
precipitation and the 2m temperature.
For more details please see the report by D.Mironov on this Newsletter
7. Tackle deficiencies in precipitation forecasts - Project leader: Marco Arpagaus
(MeteoSwiss)
Quantitative precipitation forecasting (QPF) is one of the most important reasons to utilize
and pay for a numerical weather prediction model, both for forecasters and customers.
Unfortunately, it is also among the most difficult parameters to quantitatively forecast for
an NWP model, and the LM is not doing a particularly good job at it (other models may
not be much better, though). This project aims at looking into the LM deficiencies
concerning QPF by running sensitivity experiments on a series of well chosen cases which
have verified very poorly. If successful, the outcome of these sensitivity experiments will
suggest what parts of the model need to be reformulated and improved most urgently to
obtain better quantitative precipitation forecasts. At the present stage, the list of cases is
ready and the sensitivity runs are ongoing.
8. Development of Short Range ensemble based on LM - Project Leader: Chiara Marsigli
(ARPA-SIM)
Thanks to COSMO-LEPS, COSMO already has experience in ensemble forecasting but
mainly as a downscaling of the ECMWF ensemble with the main focus on the (early)
medium range. This COSMO SREPS (Short-Range Ensemble Prediction System) project
deals with the development and implementation of a short-range ensemble based on LM
to fulfil some needs arisen in the COSMO community:
(i) To have a short-range mesoscale ensemble to improve the support especially in
situations of high-impact weather.
(ii) To have a very short-range ensemble for data assimilation purposes (e.g. the SIR
filter project and the possibility to evaluate the flow dependent model error
statistics for the 1D-VAR system).
The strategy to generate the mesoscale ensemble members proposed by this project tries to
take into account all the possible sources of uncertainty and then to model many of the
possible causes of forecast error. The proposed system would benefit of perturbations in
the boundary conditions, perturbations of the model and, in the future, perturbations of the
initial conditions. This project is carried on in close cooperation with the INM SREPS
project. For more details please see the report by C. Marsigli on this Newsletter.
9. Advanced Interpretation of LM output - Project leader: Pierre Eckert (MeteoSwiss,
Genève)
This project investigates several methods that can be used to exploit high resolution model
output in an optimal way. Issues considered are the diagnosis of high impact weather, how
to extract the most valuable information out of high-density fields, how to produce the
best input for a forecast matrix or the driving of other models like hydrological models.
During the last period the scope of this project has been enlarged to cover also verification
methodologies for higher resolution models (1-3 km) which is strongly related to
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