Download - Multiscale Data Assimilation
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Multiscale Data Assimilation
Multiscale Dimensionality Reduction for Rainfall Fields
Eulerian vs. Lagrangian Perspectives
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Some Difficulties in Rainfall Assimilation
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Rainfall Errors at a Point:
• Non-Gaussian, Non-smooth (Atomic Probability Mass)
• Non-stationary
(1) Mis-located rainfall cells/clusters; (2) Mis-timed events; (3) Missing/excessive cells/events.
Chatdarong’s Approach from a Lagrangian Perspective
• Position Errors (shift detection by MRA)
• Scale (Intensity) Errors
• Timing Errors
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Eulerian and Lagrangian Representations
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• Sequence of raster images (time series of points)
• High-dimensional, sparse
• Complicated errors
• Implicit multiscale structures
• Most data available in this framework
Eulerian Perspective Lagrangian Perspective
• Clusters/cells, and their locations, shapes, sizes, intensities, life cycles, ...
• Low-dimensional, compact
• Less complicated errors
• Explicit multiscale structures
• No observation data in this format so far
Storm cell/cluster identification/ tracking (Quantization) – Difficult!
Rasterization – Easy!
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Assimilation on an Implicit Multiscale Structure
Implicit Multiscale Structure (from Chatdarong’s Thesis)
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Assimilation on an Explicit Multiscale Structure
Large Scale Features
Storm Cells
Radar Resolution
Explicit Multiscale Structure
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Available Storm Identification and Tracking Techniques
NOAA:Storm Cell Identification and Tracking algorithm (SCIT)
UCAR:Thunderstorm Identification
Tracking Analysis and Nowcasting (TITAN)
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RCR Model Developed at MIT
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Storm Cell/Cluster Identification/Tracking
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Storm Cell/Cluster Identification/Tracking
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Storm Cell/Cluster Indentification/Tracking
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Storm Cell/Cluster Indentification/Tracking
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In Progress
• Low dimensional representation and restoration.
• Unsupervised algorithms.
• Construction of likelihood function (error measure) for data assimilation.
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End
Thank You!
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