|Title||The impact of moist singular vectors and horizontal resolution on short-range limited-area ensemble forecasts for two European winter storms.|
|Year of Publication||2005|
|Authors||Walser, A, Arpagus, M, Leutbecher, M, Appenzeller, C|
|Secondary Title||Technical Memorandum|
|Place Published||Shinfield Park, Reading|
This paper studies the impact of different initial condition perturbation methods and horizontal resolutions on short-range limited-area ensemble predictions for two severe winter storms. The methodology consists of 51 member ensembles generated with the global ensemble prediction system (EPS) of the European Centre for Medium-Range Weather Forecasts which are downscaled with the nonhydrostatic limited-area model LM. The resolution dependency is studied by comparing 3 different limited-area ensembles: a) 80 km gridspacing, b) 10 km grid-spacing, and c) 10 km grid-spacing with a topography coarse grained to 80 km resolution. The initial condition perturbations of the global ensembles are based on singular vectors (SVs), and the tendencies are not perturbed (i.e., no stochastic physics). Two configurations are considered for the initial condition perturbations: (i) the operational SV configuration: T42 truncation, 48-h optimization time, and dry tangent-linear model, and (ii) the "moist SV" configuration: TL95 truncation, 24-h optimization time, and moist tangent-linear model. LM ensembles are analyzed for the European winter storms Lothar and Martin, both occurring in December 1999, with particular attention paid to near-surface wind gusts. It is shown that forecasts using the moist SV configuration predict higher probabilities for strong wind gusts during the storm period compared to forecasts with the operational SV configuration. Similarly, the forecasts with increased horizontal resolution - even with coarse topography - lead to higher probabilities compared to the low resolution forecasts. Overall, the two case studies suggest that currently developed operational high-resolution limited-area EPSs have a great potential to improve early warnings for severe winter storms, particularly when the driving global EPS employs moist SVs.