1. Real-Time Estimation of Volcanic Ash/SO2 Cloud Height from Combined UV/IR Satellite Observations and Numerical Modeling Gilberto A. Vicente NOAA National Environmental Satellite, Data, and Information Service (NESDIS) Office of Satellite Data Processing and Distribution (OSDPD) Eric Hughes University of Maryland - College Park Cooperative Institute for Climate and Satellites (CICS) Wilfrid Schroeder University of Maryland - College Park Cooperative Institute for Climate and Satellites (CICS)

2. Overview Project Outline:  Volcano Monitoring (Review)  Volcanic Cloud Height Estimation Height Estimation Online System Applications: Eruption of Eyjafjallajokull Future Directions:  Additional Satellite Data  Forecast Generation

3. Volcano Monitoring Create a platform that allows users to view near real-time volcanic data products. Volcanic Cloud Height Estimation Construct a system which compares near real-time data with model simulation data. Project Overview Overall Goal: Create a set of tools that assist users in the near-real time (NRT) monitoring, modeling, and forecasting of volcanic emissions

4. Volcano Monitoring Volcanic Cloud Height Estimation Retrieve satellite data* SO2/AI Retrieval* Place SO2/AI maps and data files on our web server These project parts were developed independently, but work together as a set of tools for users. Model Initialization Run model simulations Post-Processing Compare to satellite observations Users * Processed at NASA GSFC Project Overview

5. Volcano Monitoring The NOAA/NESDIS OMISO2 product delivery and visualization user interface http://satepsanone.nesdis.noaa.gov/pub/OMI/OMISO2/ Global composites Volcano sectors Satellite orbit Digital images

6. SO 2 Cloud (Reflectivity)AI Volcanic Sector Imagery Volcano Monitoring

7. The final product is an operational system where a user provides minimal input and receives ash cloud height information (based on model results and satellite observations). Volcanic Cloud Height Estimation Run various dispersion model simulations and see which initial height conditions reconstruct satellite observations. The Approach:

8. Step 1: Run the dispersion model (PUFF) using various initial height conditions Step 2: Compare the results from the various simulations to satellite observations. – Generate Images for a visual analysis. – Compute a statistical image comparison. Volcanic Cloud Height Estimation Implementation:

9. Define initialization parameters (User): – Location or name of eruptive volcano – Estimated time of the eruption – Estimated duration of the eruption Run various simulations, iterating through various initial height assumptions Run the model assuming a 2 km injection height – Adjust the initial injection height by +  1 km – Re-run the model … – Adjust the initial injection height by +  1 km … – Re-run the model … Continue adjusting and re-running the model until the injection height has reached 20 km Implementation: Run the Dispersion Model Volcanic Cloud Height Estimation

10. Implementation: Compare the Results Input data Gridded data Compare overlap AIRS (Ash) PUFF (2km Simulation) Overlapping region:  Compare the results from the various simulations to satellite observations. Basic Concept Volcanic Cloud Height Estimation

11. A = Statistical comparison: A = Number of Coincident Satellite and Model points B = Number of Satellite points NOT coincident with model data C = Number of Model points NOT coincident with satellite data B = Compute two statistics: - Probability of Detection (PoD): PoD = A / (A+B) C = - False Alarm Rate (FAR): FAR = C / (A+C) Probability of Detection Simulation Height (km) Currently, only implementing the PoD statistics Implementation: Statistical Comparisons Volcanic Cloud Height Estimation

12. Putting it all together Constructing a system to perform the height analysis in the Near-real Time (NRT)

13. Observation Data Grid Observation data Eruption Parameters Observation Conditions PUFF model Grid model output Compare the model output to the observed data Change eruption parameters (height) Save/Output comparison images and statistics Inputs Observation Conditions: Observed Time, Threshold value Eruption Parameters: Height, Start Time, Duration System Construction Abstract Workflow Diagram

14. Web Server (satepsanone) Management Server Model Server Submit request (User) Check to see if a request has been submitted … Retrieve the request, then submit the request to PUFF Run the PUFF simulations and perform the height analysis. Generate output images w/ IDL Retrieve output images and data files. Submit them to the web Display the results Firewall … show the status of the analysis … System Construction Online Model Setup

15. System Demonstration

16. Application: An analysis of the April 2010 Eruption of Eyjafjallajokull

17. AI (Ash) SO2 Beginning of Eruption* 12:0013:30 Time (GMT) 18:00 OMI AIRS Ash *Estimated from satellite observations April 14 th April 15 th The Eyjafjallajokull eruption Observations from OMI and AIRS - (April 15th, 2010)

18. Input parameters Eruption Parameters:  Starting time of the eruption  Duration  Location (or volcano name) Satellite/Observation Parameters:  Satellite name  Orbit number  Time of the observation  Threshold value to distinguish between signal/noise Meteorological Model Parameters:  Region of the globe where the event occurred  Meteorological model data input User server interface

19. Input parameters about the eruptions: Volcano? Eruption time, duration? Input parameters about the satellite observations from the NOAA OMI NRT Volcanic Emissions site Mapping Limits/Region Observation time and orbit number http://satepsanone.nesdis.noaa.gov/pub/OMI/OMISO2/ OMI-AI April 15 th 12:00 UTC Input Data The Eyjafjallajokull eruption

20. Online Interface for volcanic cloud height estimation

27. PUFF Simulation OMI AI Online Interface for volcanic cloud height estimation

28. All profiles show two distinct peaks in height: 8-10 km and 5-4 km. OMI – AI/SO 2 AIRS-Ash The Eyjafjallajokull eruption Analysis Summary Observations from April 15 th 2010

29. 8 km 2 km OMI-AI Vertical profile Visual Analysis April 15 th 12:00 UTC  The statistical and visual analysis do not match exactly  The statistics predicts the 10km and 4km simulations heights  A visual analysis suggests the 8-7km and 2-3km heights  False Alarm Rate (FAR) analysis should improve the statistics The Eyjafjallajokull eruption Limitations

30. Future Directions  Incorporating data from other satellites  Forecast Generation

31. Future Directions Additional Satellites We are currently only working with OMI data. Plans to add data from other satellites, particularly from: AIRS and AVHRR Can we use measurements from different satellites, at different times, to build a stronger height profile? Combine the height profiles from various different simulations to build stronger height profile statistics

32. Future Directions Forecast Generation Overlapping region: Re-initialize trajectories at the locations where the PUFF model matched the satellite observations. Vertical profile A forecast will be run for every simulated height Combine all these forecasts into one overall forecast. Those simulation heights which most accurately match the observations will contribute more towards the forecast.

33. Description of an automated system to compare dispersion model outputs with Near-Real-Time (NRT) satellite observations of volcanic emission  Generate a series of maps overlaying various model simulations atop of satellite observations  Perform a statistical analysis on the simulation/satellite data to determine which simulation injection heights produce the best match to satellite observations  Perform these tasks quickly, requiring little input from the analyst Summary and Conclusions

34. Arlin Krueger, Simon Carn, and Keith Evans: JCET/UMBC George Serafino: NOAA/NESDIS Nick Krotkov and Kai Yang: GEST/UMBC Jerry Guo: Perot Systems Government Services Pieternel Levelt: KNMI - And thank you for your time! Acknowledgements