# Presentation: 845413

## 

Peter MacNeice (NASA/GSFC)
Joel Allred (Drexel Univ.)
Kevin Olson (Drexel Univ.)
Sandro Taktakishvilli (NPP)
Marlo Maddox (NASA/GSFC)

AISRP Workshop – May 5-7, 2008
Peter.J.MacNeice@nasa.gov

**Peter MacNeice (NASA/GSFC)**
**Joel Allred (Drexel Univ.)**
**Kevin Olson (Drexel Univ.)**
**Sandro Taktakishvilli (NPP)**
**Marlo Maddox (NASA/GSFC)**
**AISRP Workshop – May 5-7, 2008**
**Peter.J.MacNeice@nasa.gov**

**Notes:**
I am going to tell you about a project we have just begun to build a tool to synthesize solar magnetograms. This is a vital, and so far, neglected part of the infra-structure required for space weather forecasting. I have a number of collaborators listed here. While I get to stand up here and collect the glory, most of the work is being done at Drexel  by Joel Allred and Kevin Olson, who is in fact giving the next talk .  
Yesterday when I counted the slides I had prepared, I realised I had way too many for 15 minutes, so naturally, I solved the problem by adding one more. So in case I don’t finish, here is my talk distilled to one slide.

## My Talk in One Slide

- Space Weather is hot!
- Forecasting model chains start with solar magnetograms
- New generation of models will demand much more from the magnetogram data
- No single magnetogram source can satisfy them
- Need to synthesize magnetograms from multiple sources !
- Who is going to create the infra-structure for this ? Modelers ? Observatories ?  No !  -  We will !
- Developing GUI driven CAD-like tool
- First check arrived two weeks ago.

**Notes:**
..... That then is my talk. The rest is just the details, and the most important of which is that the first check arrived two weeks ago, so technically ours may be the youngest of all the projects being presented here. 

## Space Weather Primer

- Sun is the source of all transients driving space weather
- Most severe cases - Highly stressed coronal magnetic fields relax explosively – Flares/Coronal mass ejection

- Credit : SOHO Project

- Resulting hazards
- Prompt radiation (8 minutes)
- Fast particles (e- > 30 mins, p+ > 60mins)
- CME shock driven particles ( > 12 hours)
- Mass Ejecta (>18 hours)
- Resulting magnetic storm

**Notes:**
The sun is the source of all transients driving space weather. It spits out a bunch of stuff on different timescales, some of which leave us with some hope of forecasting their arrival. These hazards have real societal consequences.

## Societal Impact of Space Weather

- Power Grid failures
- Blackouts (eg Quebec, Mar 13 1989)
- long term, if  high voltage transformers damaged
- Satellite failures (and over long term, reduced lifetimes)
- Communication and GPS blackouts
- Particle hazards to astronauts and polar flight passengers

**Notes:**
The produce induced currents in long power transmission lines which can lead to widespread system failures. They compromise satellite functions and lead to earlier reentry tmes, they cause communication and GPS blackouts and produce radiation hazards for astronauts.

## Space Weather Primer (contd)

- Worst Case Scenario – Carrington event, Sept 1,1859
- Aurora in Havana
- No solar event of comparable magnitude in the technological era, by factor of 4 !
- Ice core records suggest one ‘Carrington like’ event or bigger impacts Earth every 500 years.

- Shea et al 2006

## Space Weather Primer (contd)

- Carrington Event, Sept 1,1859
- Telegraph Disruption
-        Observations made at Pittsburgh, Pa., communicated by E.W. CULGAN, Telegraph manager.
-       “During the Aurora of Aug. 28th the intensity of the current evolved from it varied very much, being at times no stronger than an ordinary battery, and then suddenly changing the poles of the magnets it would sweep through them, charging them to their utmost capacity, and compelling a cessation of work while it continued.     On the morning of Sept. 2nd, at my request the Philadelphia operator detached his battery, mine being already off. We then worked with each other at intervals as long as the auroral current continued, which varied from thirty to ninety seconds. During these working intervals we exchanged messages with much satisfaction, and _***we worked more steadily when the batteries were off than when they were attached***_.   On the night of Aug. 28th the batteries were attached, and on breaking the circuit there were seen not only _***sparks***__*** ***_(that do not appear in the normal condition of a working line) but _***at intervals regular streams of fire, which, had they been permitted to last more than an instant, would certainly have fused the platinum points of the key***_, and the helices became so hot that the hand could not be kept on them. These effects could not have been produced by the batteries.”   Boteler(2006)
- Estimates ~$70 billion impact on satellite industry (Odenwald et al 2006)  - (equiv 6 months in Iraq, or, quarterly profits for the 3 biggest oil companies, or 1 Bill Gates)
-  more than 80 satellites would be disabled
- Approx 100 LEO would reenter prematurely

## Current ‘Forecasting Models’

- Typical model chain
- model coronal field
- Coronal solution sets inner boundary to heliospheric model
- CCMC has 4 model chains (WSA, WSA/ENLIL, CORHEL, SWMF SC/IH)
- Principal role – to model ambient corona and heliosphere
- Only beginning to dabble in transients

- Models begin with synoptic maps of photospheric  magnetic field – static field for a given solar rotation.

- GONG

## SWMF – SC/IH (Univ. Mich)

- Corona

- Inner Heliosphere

- Solution at 1AU

- CCMC ‘Realtime’ run for April 30, 2008 using GONG magnetogram

##  WSA/ENLIL

- Cone model approximation for CME

##  ENLIL-WSA Fieldline Tracing

## Advances in Space Weather Models

- Current Generation:
- use ‘static’ synoptic magnetograms at 1o resolution
- create ambient corona and heliosphere solutions
- Next Generation:
- will benefit from much better
-  data sources,
-  model algorithms
- computer hardware
- will create time dependent coronal models
- will use time dependent vector magnetogram data at 0.05o resolution
- the model’s physics, not the data, will define the resolution.

## Next Generation Space Weather Models

- Coronal models will be
- Global
- Time dependent
- 3D MHD with adaptive mesh refinement
- Driven by observed surface flows
- Models will need to support both forecasting and research
- Function with latest data and archived data regardless of data limitations
- Models will define spatial resolution and cadence of magnetogram data at inner boundary
-  eg global vector fields with maximum resolution of  ~ 1”, cadence of 1 second

- Where will these models get their data ?

## Magnetogram Problems

- Magnetogram source limitations include
- Cadence and duty cycle
- Resolution
- Field of view
- Quality, particularly horizontal components of vector data
- Systematic errors associated with line fittings
- No coverage of far side
- Very poor polar fields
- No single source provides enough coverage !
-  eg SDO – 1” resolution data
- Limited FOV Vector data every 10 minutes
- Full Disk Line of Sight data every 10 minutes
- Full disk vector data every 6 hours

- Need a CAD-like tool to synthesize magnetograms from multiple sources

## A Hypothetical  Modeling Challenge

- Active Region evolution Model
- Suppose we need a model for slow evolution of Active Region A
- There is a second active region B on disk
- Synoptic vector magnetogram data is available from Kitt Peak along with individual vector magnetograms taken 3 times per day. However data for region B is poorly sampled due to instrument problems.
- Marshall Vector Magnetogram has data for B but at different times and resolution than Kitt Peak.
- Also have LOS magnetograms at selected times from Kitt Peak, Mt Wilson and MDI.

- How do we provide global surface vector fields and flow fields to an active region model at the spatial and temporal resolution required by the model?

- A

- B

## Modeler Requirements

**Using this data requires,**
- Ability to synthesize datasets into a greater whole
- More than just stitching images together
- Nobody knows what it will take to make synthesized maps accurate
-  _this really is a separate modeling challenge._
- We are not solving this problem – just building the infra-structure to support it
- Also incorporates determination of surface flows to drive coronal field evolution
- Why? – is it better to determine velocities from each magnetogram and then ‘combine’ velocities, or combine magnetograms and determine a single velocity map? Not clear yet!

## Tool Requirements

- Ability to interpolate in space (on a sphere) and in time
- Ability to handle many file formats
- Data – usually fits, sometimes ascii
- Model – customized at whim of developer
- Graphics – IDL, TecPlot, OpenDx etc

- Ability to support complex processing algorithms (with many yet to be defined)
- Our tool is called MAGIC (MAGnetogram Integration and Composition)

## MAGIC Design

- Modular design – 6 components
- GUI (Python/TkL)
- A magnetogram database manager (MySQL)
- VSO interaction ?
- Lightweight magnetogram processing layer, executing interactive single line calls to Kameleon functions and simple canned Python routines for frequently used processing tasks (eg monopole subtraction)
- A third-party program execution interface
- Small suite of basic visualization tools (IDL and OpenDx)
- A command recorder function to facilitate batch processing.
- Open Source Linux Application

## Typical Basic Use

- User requests a menu of all available data for time frame from database
- User selects their preferred data for each time
- User imports their model surface grids for all required times
- MAGIC does default (***x****,t*) interpolation for each dataset to the appropriate grid
- User calls basic composition function for first grid - at prompt they input requested dataset weights or weighting rule
- MAGIC returns a composed surface vector field with a set of default images (*B**r**, B****, B****, ****J***)
- MAGIC asks if this synthesized magnetogram is acceptable
- No - go back and rework
- Yes - move on to next grid
- MAGIC reads in second grid
- Etc
- MAGIC outputs synthesized magnetograms in KAMELEON format files

## MAGIC Design

- Backbone already in hand in CCMC’s  Kameleon Tool.
- KAMELEON (Maddox)
- two components, a file formater and an interpolator
- handles many file formats
- handles many model coordinate systems
- has both spatial and temporal interpolation functions
- portable – an interactive interface and a callable library (from C, Fortran and IDL).

## STATUS

- First funds only arrived in last few weeks
- Initial focus on defining a generic magnetogram format inside Kameleon
- Kameleon can now read, reformat, and interpolate on
- LOS data from Kitt Peak, Mt Wilson, SOHO/MDI
-  vector data from Kitt Peak and Marshall Vector Magnetograph.
- Have begun initial GUI construction
- Added first python processing widget – a monopole removal function
- Added graphics calls to compare initial data, interpolated data and data after processing.

## Importing and Converting Datafiles 

## Processing Data

- Example
- Interpolation of synoptic map onto test model grid followed by removal of any monopole signal.

## Summary

- Developing a magnetogram synthesis tool
- using KAMELEON as the low-level manager of the data structures, I/O interfaces and basic interpolation layer
- Upon this foundation we add two processing layers (lightweight and heavyweight)
- Have added ability to ingest and interpolate most current magnetogram files
- Have begun building GUI and lightweight processing layer
- Have begun developing visualization tools to display different stages of data processing.

## Modeler Requirements

- A tool is needed to do this synthesis.
- Who will develop it ?
- Not the data providers
- Not the modelers
- This is middleware – but every bit as important as the data acquisition and the modeling.
- This infrastructure element is very much IT in nature.
- What will it take to eliminate this need?
- _Full globe_ high resolution high cadence vector magnetograms in both photosphere and chromosphere! – not within the next decade (or two?) !
- Even this does not facilitate future scientific analysis of historical events or older archived data.

##  ENLIL Heliosphere Model (Odstrcil)

- 3D MHD equations solved from 21.5*r*s  to 2 AU
- Input at rotating inner boundary
- MHD parameters
- Output
- Magnetic field
- Velocity
- Density
- Temperature
- Two operating modes
- Ambient solution
- CME modeling using Cone model approximation