Data Assimilation in the Age of Cloud-Resolving NWP

In the more than half century since its proper birth, Numerical Weather Prediction (NWP) has developed considerably in means and sophistication. So, too, have the problems which beset numerical modelers, not the least of which are the related issues of obtaining optimal initial and boundary conditions for model forecasts as well as a means for quantifying the error growth inherent in such forecasts. These problems are only multiplied as the resolution of the model is increased, making their consideration a matter of great importance since the age of cloud-resolving NWP is upon us. This talk will present a review of the developments in NWP and data assimilation (DA), both historically and contemporarily, and argue for the importance of high-resolution observation networks and a probabilistic approach to doing both NWP and DA. As a consequence, ensemble-based methods (Kalman filters, particle filters, etc.) emerge as a natural choice for the combined NWP/DA problem, and a summ ary of these techniques will be presented along with prospects for future development.