# Presentation: 477796

## Classification of Solar Imagery and Prediction for Nonlinear Systems

**Michael Turmon (PI)**** Mike Chin, Jeff Jewell (JPL)**** Michael Ghil (UCLA/ENS Paris)**
**May 5 – 7, 2008**

## Classification of Solar Imagery

*Classification of Solar Imagery*

**Objective:**
- Develop and validate algorithms for object identification, tracking, and track analysis

**Mission Customers: ****	**Solar Dynamics Observatory (HMI), SoHO/MDI

**Enabling Technical Innovations:**
- Markov Random Field (MRF) classifiers
- Single-link most-likely tracker
- Kalman filter object-behavior modeling

**Expected Impact:**
- Address data overload problem by concentrating attention on objects of interest
- Develop models for objects via their intrinsic behavior

|  | Milestones |
| --- | --- |
| FY07 | Integrated and tested improved MRF optimizer |
|  | Calibrated tracker to agree with NOAA catalog |
| FY08 | Port to HMI pipeline, develop models for proxy HMI data |
|  | Greater tracking automation and coverage |

**PI: ****	**Michael Turmon

## Core Functions

- Work has focused on two of three components:
- _Identification_:  Find objects in (magnetogram,photogram) pairs
- Gaussian mixture models and Markov random fields
- _Tracking_:  Link identified objects in series of images
- Single-link most likely tracker
- _Trajectory Analysis_:  Model and classify object tracks
- Kalman filter models
- Original motivation: Link visible activity (sunspots, faculae from MDI images) to irradiance from VIRGO
- Other uses: Image masksActivity catalogsFocus of attention for analysts or expensive inversions

## Identification:  Finding the Best Labeling

- Bayesian: maximize posterior probability having *two terms*
- Trade off fidelity to data vs. spatial coherence
*Likelihood:*  Probability of some observed (field,intensity) given quiet, facula, sunspot
- Use a Gaussian mixture model, learned from data, to parameterize this term
*Prior:*  Enforces spatial smoothness of labeling to disambiguate cases near the class boundary
- Spatial coherence by Markov random field (MRF) model
- General, mathematical foundation: discrete optimization of classification given observation

**N**

**Q**

- _Key_:
- S(pot)
- F(acula)
- Q(uiet sun)

**Q**

**S**

**F**

## Identification: Image Screening Interface

- Active region models, and tracks, are very sensitive to erroneous data
- Most bad data can be found automatically
- But manual screening is another layer in the stack
- Developed a browser interface (Javascript) for manual screening
- Identify bad guysand exclude themfrom future labeling
- Screening possibleat all points inprocessing chain

## Object Tracking:  Sunspots

**Magnetogram**

**Labeling**

## Identification:  Software and Infusion

- Core *identification* code is in C with thin data access API
- Command line interface uses fitsio for data access
- Matlab interface uses the same code with different Makefile
- Bourne shell drivers for metadata maintenance
- Propagation of original metadata, derived quality indicators, preprocessing information like provenance
- Simplify pipeline, where possible, for use with SDO

## Existing Applications

- MDI: automatic classification of all available full-disk (magnetogram,photogram) pairs from 1996-2004 (going on 2008)
- Models and extensive results already exist for MDI data
- Ported into a runnable module on the MDI data pipeline (“PUI”)
- Kitt Peak: calibration and test of classifier on SPM/VSM data
- NASA SR&T, with Harry Jones, Kitt Peak
- SPM data taken 1992-2003
- In its time, the premier terrestrial magnetograph
- Five observables (field, intensity + equivalent width, line depth, velocity)
- SPM’s successor instrument is an upgraded vector spectromagnetograph (VSM)

## Identification: Recent Activities with HMI/SDO

- SDO data volume is unprecedented in Solar Physics
- HMI’s data volume is unprecedented in Solar Physics
- 4096x4096 pixel images every 90 seconds; data products ~ TB/day
- These data volumes make it more important to focus attention
- HMI/SDO is the successor to MDI/SoHO
- Presented at SDO science team meetings
- Feb. 2006 (Monterey); Sept. 2007 (Boulder)
- March 2008, Napa, CA
- SDO nominally launches December 2008
- Porting to HMI data pipeline at Stanford
- Science team (Todd Hoeksema, Rock Bush, Rick Bogart) enthusiastic about integration
- Discussed specific API interactions and CPU usage
- Meeting in June at Stanford for integration and test

## Object Tracking:  Status

- Adapted tracker parameters to reproduce NOAA sunspot groups
- Algorithm is in mixed C/Matlab
- Sufficient interest to implement in C/Python for portability to HMI
- Tracked all sunspots over 1996-2002 with some human intervention
- 2200 days, 10000 frames, 2100 regions
- Better than 1% error rate
- Regions erroneously combined or regions missed
- Code allows merging of small groups and follows remnants
- Have four, three-month periods of high-cadence data from MDI
- Full-disk images every minute
- Near-HMI temporal resolution: 1 image/min

## Tracking: Current and Upcoming Activities

- Interest of HMI team in tracked objects came faster than expected
- Motivated by computational demands of local helioseismology and vector magnetic fields
- Validation requires the largest possible time interval
- We will extend coverage and screening from 2004-2007
- This summer, track 1996-2007 with no human in loop
- Porting tracking code from Matlab-and-C to Python-and-C to allow data center operations
- Will still use same underlying tracker technology

## Predicting the Perfect Storm:  Project Summary

*Predicting the Perfect Storm:  Project Summary*

**Methods:**
- Extensions of ensembles for representing competing hypotheses about system state.  Learning dynamical rules for features from data.

**Unique Innovations:**
- Rather than repeated simulation (“dart  throwing”) to uncover high-risk scenarios, our system allows experts to pose them via high-level features and directly compute their relative odds.

|  | $K | Milestones |
| --- | --- | --- |
| FY08 | 200 | Identify & track objects (sim. data) |
|  |  | Identify & track objects (obs. data) |
| FY09 | 200 | Learn forward object dynamics |
|  |  | Identify dynamic path to end state |
| FY10 | 200 | Uncover low-probability storm path |

**PI: ****	**M. Turmon (JPL)
**Team:**	Jewell, Chin, Ghil (UCLA/ENS)

**Sponsor:****	****  NASA SMD (AISR)**

**Customers: **Ocean/atmosphere forecastingand NWP, mission simulation

**Objective:**
- Provide a conditional forecasting capability, enabled by posing scenarios using human-interpretable features, not at the level of grid cells.

**Maturity:**
- Beginning (2007) TRL is 2-3; exit (2010) TRL is 4.

*Measure the Disparate Outcomes*
*Due to Nonlinear State Evolution*

## Problem Context

- We target problems in ocean and atmosphere prediction with:
- 	Known, gridded nonlinear dynamics...
- 	... producing complex emergent features...
- 	...where much physical intuition guides the behavior of the features
- _Core problem_:  System evolution is subject to chance.  Small perturbations can imply huge differences in outcome and risk (Lorenz 1963 studied weather).
- This problem are conventionally addressed in data assimilation using ensembles of *independent*, parallel evolution paths
- 	Advantage: simplicity
- 	Disadvantage: does not scale to highly-unlikely outcomes
- We propose to use a *learned *object-level dynamical model to guide these large-scale gridded simulations.

## Approach

- Define parameterized templates for high-level objects, such as vortices or advancing fronts. Track those features through time.
- Using examples from simulations and real data, learn the (nonlinear) rules telling how such parameterized high-level features evolve over time.
- Use the learned rules as a high-level predictive model.  This sparse model constrains the likely grid-level dynamics, providing the computational advantage necessary to explore likely outcomes.

## Feature Dynamics Constrain Grid Dynamics

- Conventional dynamics are grid-level (Eulerian)
- Alternately, an object-level (Lagrangian) dynamic parallels the Eulerian dynamical system
- Tools like MCMC and Sequential Monte Carlo now exist for distributions on Lagrangian spaces
- Faster computations due to fewer variables in object-level space
- More intuitive domain for end users (fronts, vortices)
*Develop object**templates* and*learn dynamics*for thesefeatures

## Example:  Lagrangian Dynamics

- Two vortices tracked in ocean velocity/temperature (top)
- Lagrangian state (position, size) of V1 and V2, bottom left
- Two object families plainly visible, bottom right

## Specific 2008 Activities

- Identify and track objects (simulation data)
- Shallow-water equation simulation exhibiting mid-latitude meso-scale vortices
- Similar vortices are visible in satellite data at the sea surface
- Moving to SeaWinds (QuickScat, ADEOS-II) in 2009
- Learn forward dynamics (simulation data)
- Model object dynamics based on tracks above
- Perform data assimilation on tracked objects

## References:  Feature Classification

- H. Jones, G. Chapman, K. Harvey, J. Pap, D. Preminger, M. Turmon, S. Walton, “A comparison of feature classification methods for modeling solar irradiance variation”, accepted to *Solar Physics*, 2007.
- Posters at the 2006 AGU Fall Meeting, AAS Solar Physics Division Meeting (May 2007), LWS Workshop (September 2007), and the 2007 AGU Fall Meeting.Several upcoming posters.
- The mixture modeling work appeared in:
- Mixtures-2001, “Recent Developments in Mixture Modelling,” Hamburg
- Compstat-2004, Prague, as “Symmetric Normal Mixtures”
- Earlier work:
- Turmon, Pap, Mukhtar, “Statistical Pattern Recognition for Labeling Solar Active Regions: Application to SoHO/MDI Imagery,” *ApJ*, 2002, 396-407.
- J. Pap, H. Jones, M. Turmon & L. Floyd, “Study of the SOHO/VIRGO Irradiance Variations using MDI and Kitt Peak images,” *Proc. SOHO-11 Workshop*, Davos, 2002.

## References:  Object Dynamics

- Chin, Turmon, Jewell, and Ghil paper in *Monthly Weather Review*, January 2007:  “An ensemble-based smoother with retrospectively-updated weights for highly nonlinear systems”
- Turmon (AGU 2005):  “﻿Ensemble Smoothing and Markov Chain Monte Carlo for Data Assimilation in Highly Nonlinear Systems.”  Chin (AGU 2006).
- Related work is being done by Doug Nychka (NCAR), Greg Eyink (Johns Hopkins), Juan Restrepo (U. Arizona).