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• W3039058024 abstract "This dissertation research was undertaken to better understand the optimal design of convection-permitting storm-scale ensemble forecast (SSEF) systems that resolve features ranging from synoptic to convective scales. The focus of the research is on the data assimilation (DA) and initial condition (IC) perturbation methods, both of which are uniquely affected by the multi-scale interactions inherent in an SSEF system. There are four components to this research. First, a GSI-based DA system is implemented in the 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 this multi-scale context. Second, the systematic sensitivities of convection forecasts to different simple methods of IC perturbation are evaluated. Third, Observation System Simulation Experiments (OSSES) are conducted using ensemble analyses generated with the GSI-based EnKF to understand the impacts of different methods of generating more complex flow-dependent multi-scale IC perturbations. Fourth, the impacts of inconsistencies between the initial and lateral boundary condition (LBC) perturbations are evaluated as well as the impacts of model and physics errors in non-OSSE real-data experiments. In the first part of this research, the multi-scale GSI-based EnKF and 3DVar techniques are systematically compared to each other to better understand the impacts of their differences on the analyses at multiple scales and the subsequent convective scale probabilistic forecasts. Averaged over ten diverse cases, 8h forecasts of hourly accumulated precipitation initialized using GSI-based EnKF are more skillful than those initialized using GSI-based 3DVar, both with and without storm-scale radar DA. The advantage from radar DA persists for ~5h using EnKF, but only ~1h using 3DVar. A case study of an upscale growing MCS is also examined. The better EnKF-initialized forecast is attributed to more accurate analyses of both the mesoscale environment and the storm scale features. The mesoscale location and structure of a warm front is more accurately analyzed using EnKF than 3DVar. Furthermore, storms in the EnKF multi-scale analysis are maintained during the subsequent forecast period. However, storms in the 3DVar multi-scale analysis are not maintained and generate excessive cold pools. Therefore, while the EnKF forecast with radar DA remains better than the forecast without radar DA throughout the forecast period, the 3DVar forecast quality is degraded by radar DA after the first hour. Diagnostics revealed that 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. In the second part of this research, multi-scale precipitation forecast sensitivities are examined for two events and systematically over 34 events out to 30-h lead time using Haar Wavelet…" @default.
• W3039058024 created "2020-07-10" @default.
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• W3039058024 date "2014-12-12" @default.
• W3039058024 modified "2023-09-24" @default.
• W3039058024 title "Optimal Design of a Multi-scale Ensemble System for Convective Scale Probabilistic Forecasts: Data Assimilation and Initial Condition Perturbation Methods" @default.
• W3039058024 hasPublicationYear "2014" @default.
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