School of Meteorology (Defense)

"Optimal design of a multi-scale ensemble system for convective scale probabilistic forecasts: Data assimilation and IC perturbation methods"

Aaron Johnson
OU School of Meteorology

01 December 2014, 2:00 PM

National Weather Center, Room 4140
120 David L. Boren Blvd.
University of Oklahoma
Norman, OK

This study aims to better understand the optimal design of the data assimilation (DA) and initial condition (IC) perturbation methods for convection-permitting multi-scale ensemble forecast systems. A GSI-based DA system is implemented in a multi-scale scenario with observations ranging from synoptic scale rawinsonde to convective scale radar observations. The GSI-based 3DVar and EnKF techniques are also compared to each other in the multi-scale context. The impacts of different IC perturbation methods are then evaluated using perfect model Observation System Simulation Experiment (OSSE) and real-data frameworks.

Precipitation forecasts initialized using GSI-based EnKF are more skillful than those using GSI-based 3DVar, both with and without radar DA. The better EnKF-initialized forecast is attributed to more accurate analyses of both the mesoscale environment and the storm scale features. The inferior analysis at meso- and storm-scales for the 3DVar is primarily due to the lack of flow-dependence and coherent cross-variable correlation, respectively, in the 3DVar static background error covariance.

Perfect model OSSEs are used to compare multi-scale flow-dependent IC perturbations (MULTI) to downscaled IC perturbations from a larger scale grid (LARGE). Mesoscale precipitation forecasts from MULTI are systematically more skillful than LARGE at 1h and ~5-9h lead times. This is a result of smaller magnitude mesoscale IC perturbations near analyzed convective systems for MULTI that are more consistent with the analysis uncertainty than for LARGE. This difference also leads to advantages for short-term storm-scale reflectivity forecasts. Small scale flow-dependent IC perturbations, resolved only by MULTI, lead to significant advantages for storm-scale reflectivity in neighborhoods <8km during the first hour and for mesoscale precipitation after ~5h.

Real-data experiments with model error show that the excessive magnitude of mesoscale IC perturbations in LARGE compensates for unrepresented model errors, leading to more skillful forecasts than MULTI. This shows the importance of a holistic approach to optimal ensemble design that also includes model and physics diversity.

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