Data Assimilation
Overview |
People and Interests |
Research Topics |
Related Projects |
Data assimilation (DA) is a technique by which numerical model data and observations are combined to obtain an analysis that best represents the state of the atmospheric phenomena of interest. At HRD, the focus is on the utilization of a wide range of observations for the state analysis of tropical systems and their near environments to study their structure and physical/dynamical processes, and to improve numerical forecasts. Research includes the development and application of a state-of-the-art ensemble-based data assimilation system (the Hurricane Ensemble Data Assimilation System - HEDAS) with the operational Hurricane Weather Research and Forecast (HWRF) model, using airborne, satellite and other observations. In parallel, Observing System Simulation Experiments (OSSEs) are conducted for the systematic evaluation of proposed observational platforms geared toward the better sampling of tropical weather systems.
Name
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Interests
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Altug Aksoy | Ensemble Kalman filters, model error, parameter estimation, storm-relative data assimilation
| Lisa R. Bucci | Regional Observing System Simulation Experiments, Observing System Evaluations, airborne observation simulation
| Kathryn J. Sellwood | Optimal sampling strategies, vortex-scale and satellite data assimilation, thermodynamic processes, tropical cyclone secondary circulation, tropical cyclone intensity change, vortex-scale observations
| Tomislava Vukicevic | Data assimilation theory and techniques, satellite data assimilation, model error estimation,vortex-scale data assimilation, Regional Observing System Simulation Experiments, Observing System Evaluations
| Javier Delgado | Workflow automation for data assimilation and observing system simulation software, software performance optimization, diagnostic tool kits
| Hui Christophersen | Data assimilation, tropical cyclones, remote sensing, observation techniques
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Development of high-resolution ensemble data assimilation technology for tropical cyclones (TCs)
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Impacts of TC inner-core observations on state analysis and forecast
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Storm-relative data assimilation
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Satellite radiance assimilation in all-sky conditions
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Model error representation in ensemble-based TC data assimilation and forecasting
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Observing System Simulation Experiments for TCs
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Reducing hurricane model errors using parameter estimation
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Optimization of observation sampling strategies for airborne observations
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Observing System Evaluations
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