Multi-Model Ensemble Approaches to Data Assimilation Using the 4D-Local Ensemble Transform Kalman Filter
Abstract
Uncertainties in the numerical prediction using a computational model of a physical system arise from two primary sources: i) errors within the model itself; and ii) imperfect knowledge of (a) the initial conditions to start the model and (b) boundary conditions and the forcing that is required to run the model. One way to examine these uncertainties is the multi-model approach, i.e., to compare results from multiple models. However, the multi-model approach cannot completely address either (i) or (ii) due to lack of knowledge of the real state. Another way is to compare the results with the observations that sample the real state. However, the observations introduce another source of uncertainties, i.e., iii) imperfect knowledge and/or improper assumptions within the observations including sampling error.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 30, 2013
- Accession Number
- ADA601440
Entities
People
- Kayo Ide
Organizations
- University of Maryland