Satellite Remote Sensing of Volcanic Ash

Transcription

1 Marco Fulle Satellite Remote Sensing of Volcanic Ash Michael Pavolonis NOAA/NESDIS/STAR SCOPE Nowcasting 1 Meeting November 19 22,

2 Outline Getty Images Volcanic ash satellite remote sensing challenges Photo: Photo/Jose Luis Pos Overview of advanced volcanic cloud remote sensing techniques Integrating passive satellite remote sensing products with other tools

3 Volcanic ash satellite remote sensing challenges

4 1). Ash dominated volcanic plumes Semitransparent clouds dominated by volcanic ash. Lightning is usually not present in these clouds. 2). Ice topped umbrella clouds These cloud are mostly observed during a major eruption. A spectral based volcanic ash signal is usually initially absent because the ash is encased in ice and/or the cloud is opaque. Lightning is often present in these clouds. 3). SO 2 clouds Sulfur dioxide clouds (SO 2 gas is invisible to the eye) that may or may not contain volcanic ash. Some eruptions produce large amounts of SO 2 and very little ash and vice versa. Nadeau and Dalton (2009)

5 1). Ash dominated volcanic plumes Semitransparent clouds dominated by volcanic ash. Lightning is usually not present in these clouds. Most important sensor attributes Cloud Tracking/Identification At least two channels in the μm window μm and/or 3.9 μm channel(s) High temporal refresh High spatial resolution Global coverage Large viewing angle and/or high spectral resolution Determining Cloud Properties Lidar and/or multiple IR window channels + IR CO 2 absorption channels and/or multi angle measurements High temporal refresh High spatial resolution Global coverage Large viewing angle and/or high spectral resolution No planned or current sensor has all of these attributes (e.g. best spectral and spatial info is generally available on LEO sensors; high temporal refresh is achieved with GEO sensors)

6 2). Ice topped umbrella clouds These cloud are mostly observed during a major eruption. A spectral based volcanic ash signal is usually initially absent because the ash is encased in ice and/or the cloud is opaque. Lightning is often present in these clouds. Cloud Tracking/Identification Measurements at UV and/or visible wavelengths High temporal refresh (at least 15 min; 5 min or less is much better) High spatial resolution Global coverage Lightning detection capability Most important sensor attributes Determining Cloud Properties Active sensor (lidar or radar) and/or multiple IR window channels + IR CO 2 absorption channels and/or multi angle measurements High temporal refresh High spatial resolution Global coverage No planned or current sensor has all of these attributes (e.g. best spectral and spatial info is generally available on LEO sensors; high temporal refresh is achieved with GEO sensors)

7 3). SO 2 clouds Sulfur dioxide clouds (SO 2 gas is invisible to the eye) that may or may not contain volcanic ash. Some eruptions produce large amounts of SO 2 and very little ash and vice versa. Nadeau and Dalton (2009) Cloud Tracking/Identification Most important sensor attributes High spectral resolution measurements in the nm range and/or 7 9 μm range High temporal refresh Global coverage Determining Cloud Properties High spectral resolution measurements in the nm range and/or 7 9 μm range High temporal refresh Global coverage No planned or current sensor has all of these attributes (e.g. best spectral and spatial info is generally available on LEO sensors; high temporal refresh is achieved with GEO sensors)

8 Summary of Primary Challenges No single sensor is ideally suited for detecting and characterizing all types of volcanic clouds in a timely manner Basic cloud retrieval challenges: multiple cloud layers (results in artifacts) with the same and/or different compositions, uncertainty in microphysical parameters (particle shape, index of refraction, etc ) Mitigating measurement errors and artifacts (calibration, noise, stray light, sensor degradation, striping, navigation errors, etc ) Interaction with operational users developing product displays, communicating product uncertainty and caveats A multi sensor approach is best, but more difficult to design and implement

9 Overview of advanced volcanic cloud remote sensing techniques

10 Possible attributes of advanced approaches Explicitly accounts for surface temperature, surface emissivity, atmospheric temperature, and major background absorbing gases (H 2 O, CO 2, O 3 ) Retrieves ash cloud temperature/pressure/height (uses absorption channels) Fully automated Utilizes a flexible (in terms of input and output) mathematical model that provides uncertainty estimates (e.g. optimal estimation) Does not rely on negative um BTD Utilizes all relevant spectral information Utilizes spatial and temporal information Multi sensor based approach **Volcanic cloud retrieval algorithms should be consistent with best practices established by the greater meteorological remote sensing community

11 Francis et al., 2012 Pavolonis, 2010 and Pavolonis et al., 2013 Also see Clarisse et al., 2010 and Watts et al., 2011 for examples of optimal estimation schemes

12 Benefits of Optimal Estimation and CO 2 Absorption Channels Unless the cloud is really optically thin, the results are not particularly sensitive to the first guess Tri spectral optimal estimation schemes that robustly account for background conditions have demonstrated skill in retrieving ash cloud loading and height Pavolonis et al., 2013

13 Benefits of Optimal Estimation and CO 2 Absorption Channels Francis et al., 2012 Tri spectral optimal estimation schemes that robustly account for background conditions have demonstrated skill in retrieving ash cloud loading and height

14 Prata and Prata (2012) assembled a multi sensor (ground and aircraft based) mass loading validation data set and validated their split window approach. Is this data set available for other algorithm developers to use? The establishment of common validation data sets is an important steps towards harmonization. Prata and Prata, 2012

15 Hyperspectral IR algorithms can now differentiate different aerosol types with good skill Clarisse et al., 2013

16 Reliance on manual analysis of satellite images from a single sensor means that volcanic ash advisories and volcanic SIGMETS are not always timely and accurate! Nabro volcano in Eritrea erupted for the first time in recorded history on June 12, 2011 at ~20:30 UTC, injecting volcanic ash and dangerous concentrations of SO 2 high into the atmosphere The first VAA was issued at 04:00 UTC on June 13 (~7.5 hours after the start of the eruption!) Nabro The height estimate for this cloud was also severely underestimated

17 Automated alert would have been issued after 20:45 UTC image on June 12, 2011

18 Subscriptions to the experimental NOAA volcanic cloud alerting system will be more broadly available in 2014

19 Brenot et al., 2013

20 Example Procedure for Combining Measurements from Different Sensors on the Same Spacecraft 1). Perform volcanic cloud retrieval using hyperspectral sounder (e.g. CrIS, IASI, AIRS) EUMETSAT 2). Perform retrieval using high spatial resolution imager (e.g. VIIRS, AVHRR, MODIS) a.use hyperspectral retrieval as first guess b.fill in important spectral gaps (e.g. LW CO 2 ) needed for high quality retrieval by interpolating from sounder spatial resolution to imager spatial resolution The end result is a high spatial resolution product that is more accurate because hyperspectral information was incorporated into the retrieval!

21 Example Procedure for Combining Measurements from Different Sensors on Different Spacecraft (e.g. LEO/GEO) High quality retrieval results from LEO sensors can be used as a first guess into GEO sensor retrievals, which generally have less spectral information to work with. The end result is a high temporal resolution product with an accuracy similar to that achievable from LEO sensors (information from LEO is transferred to GEO). A relatively recent LEO overpass of the cloud is required though!

22 Integrating passive satellite remote sensing products with other tools

23 Eruptive source term inversion modeling Source term parameters that minimize the difference between the satellite derived mass loadings and Lagrangian model predicted mass loadings are chosen. Effective automation of this process requires highly accurate satellite products (low false alarms, high detection, consistently reasonable mass loading estimates Stohl et al., 2011

24 Using satellite retrievals to initialize forward trajectories (collaboration between BoM and NOAA) NOAA/ARL and NOAA/NESDIS are working to automate this process with HYSPLIT

25 MER can be estimated from the geometric properties of volcanic clouds this process can potentially be automated using advanced satellite algorithms Pouget et al., 2013

26 Ash probability determined from satellite observations alone

27 Ash probability determined from PUFF simulations alone (Bursik et al., 2012)

28 Ash probability determined from satellite + PUFF simulations alone

29 Getty Images Summary Photo: Photo/Jose Luis Pos Harmonization of volcanic ash products is a daunting task, as achieving consistency through time using a single sensor is, itself, a challenge. A multi sensor approach may be the best path to harmonization Harmonization of product display practices and user training are also important

30 Merapi ash plume/cloud The 24 hour animation of 1 hour MTSAT 1R on November 11, 2010.