Multiscale Data Assimilation
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1 Multiscale Data Assimilation Multiscale Dimensionality Reduction for Rainfall Fields Eulerian vs. Lagrangian Perspectives
description
Multiscale Data Assimilation. Multiscale Dimensionality Reduction for Rainfall Fields. Eulerian vs. Lagrangian Perspectives. Some Difficulties in Rainfall Assimilation. truth. truth. model. precipitation. model. y. x. time. Rainfall Errors at a Point: - PowerPoint PPT Presentation
Transcript of 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
truth
model
time
truth
model
prec
ipita
tion
x
y
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
x
y
c1 c4
c2c3
cluster1
• 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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