Madalina Surcel and Dominik Jacques have combined efforts to bring you a
special student seminar tomorrow at 14:35. Please join us for cookies and coffee before at
2:15 preceding the talk.
For our student seminar, we have prepared something special. When
discussing our respective talks, we realized that we were touching many
similar concepts from different points of view. Instead of working out a
bridge between our presentation, we have interlaced them in order to
make a story. We think this will make it easier (and more interesting)
to understand the concepts presented. For us, it make a nice change from
the more formal conference presentations that we are accustomed to.
In the same spirit, here is our common abstract,
*Mesoscale prediction of precipitation: current status and future work*
It is widely accepted that whereas the performance of Numerical Weather
Prediction (NWP) models is continuously improving, precipitation still
remains very difficult to forecast and quantitative precipitation
forecasting (QPF) skill is generally low. This talk discusses the
ability of current generation mesoscale models (with dx~1km) to forecast
rainfall. Emphasis is put on the main factors affecting forecast quality
and on the methods that could improve QPF.
Through the evaluation of high-resolution ensemble
precipitation forecasts it is shown that models have generally very
little skill in forecasting rainfall at scales lower than 100km.
Furthermore, while ensemble methods can increase predictability at
scales larger than 100km, for small scales, the spread is too large to
provide useful forecasts.
At storm scales, assimilation of radar observations has the potential to
improve model predictions. So far, demonstrating the improvements
brought by assimilation has proven very challenging as forecasts show
great sensitivity to small errors in initial conditions. This is
especially true for humidity, which is not corrected significantly
through assimilation. As a solution to this problem, an alternative
method for the assimilation of radar observations based on a combination
of variational techniques and statistical analysis of model output is
Another pathway for the improvement of forecasts is through the use of
more accurate model physics. However, sensitivity tests show that
despite large dependence of results on various model parameters at small
scales, no single parameter explains the largest forecast errors.
The work presented here seems to indicate the existence of
a critical spatial scale situated around 100km. Above this scale,
forecasting results are satisfactory, while below it QPF skill is poor.
The effect of radar data assimilation is limited to scales smaller than
the critical scale such that improvements due to assimilation are
expected to be short-lived.