Volcanic Ash Algorithm Intercomparison

Marco Fulle ‐ www.stromboli.net

Michael PavolonisNOAA/NESDIS

1

2

Before Ash Event During Ash Event

Why is volcanic ash monitoring important?

VAAC Best Practices Meeting (May 2015)

3

General consensus: The next generation of GEO satellites (HIMAWARI‐8/9, MSG/MTG, GOES‐R/S, GEO‐KOMPSAT, FY‐4, Electro‐L)  will allow the VAAC’S to produce more detailed information, especially at the t+0 timeframe

http://www.wmo.int/aemp/?q=node/65

Motivation for VA IntercomparisonValue of satellite-based volcanic ash products recognized by VAACs and airlines, especially during the eruptions in the last five years, but:

Quantifying volcanic ash parameters is difficult, but in demand;

There is no internationally-agreed validation protocol for such products;

Many products are available, and their strengths and weaknesses are not known or comparable,

Many products are produced on an ad-hoc basis and not sustained or operationally available,

There is no standard for volcanic cloud geophysical parameters endorsed by WMO.

Eyjafjallajokull (Arni Fridriksson, 17 Apr 2010)

4

VA Intercomparison: Need for Guidance

Satellite-based VA Intercomparison

WMO SCOPE-Nowcasting

WMO Commission

for Basic Systems

WMO/IUGG VASAG

WMO Commission

for AeronauticalMeteorology

ICAO Met Panel WG 2 Met Information and

Service DevelopmentSub-Group on VA

ICAO Met Panel

ICAO (Contracting States)

WMO (Member States and Territories)

Other users / benefits

WMO Commission

for Atmospheric

Science

GAW/WWRP

5

Intercomparison Meeting

29 June – 02 July, 2015

Madison, WI, USA

6

http://cimss.ssec.wisc.edu/meetings/vol_ash15/

7

http://www.wmo.int/pages/prog/sat/documents/SCOPE-NWC-PP2_VAIntercompWSReport2015.pdf

Algorithm Contributions (Total: 27 (22))Organization Algorithm(s)

NOAA SEVIRI_NOAAMODIS_NOAA

Oxford University IASI_OXFORDTERRA_MODIS_ORACAQUA_MODIS_ORAC

Université Libre de Bruxelles IASI_ULB

CMA SEVIRI_CMA

EUMETSAT METOP-A_PMAPMETOP-B_PMAPSEVIRI_EUMOP

Australian BOM MTSAT2_BOMMODIS_BOM

DLR Germany SEVIRI_VADUGS

SNM Argentina MODIS_CENZARG

INGV Italy MODIS_LUTMODIS_VPR

SRC Planeta, Russia METOP_PLANETA

University of Bristol BRISTOL_IASI

UK MetOffice SEVIRI_MOAVHRR_MO

Organization Algorithm(s)

JMA MTSAT2_JMAMTSATIR_JMA

STFC RAL, UK SEVIRI_ORAC_RALTERRA_MODIS_RALAQUA_MODIS_RAL

FMI AATSR_FMI

NASA MISR

“Validation” Sources• FAAM UK Airborne lidar

• CALIPSO CALIOP

• Ground-based Lidar

• Expert assessment

8

Primary Conclusions

The accuracy of satellite-based volcanic ash products is a strong function of the retrieval methodology, satellite sensor capability, and scene complexity.

Additional analyses are required to better understand differences and provide a consensus outlook on end-to-end capabilities for operational applications

9

Proposed Actions (12 month effort) Reaffirm commitment from algorithm contributors (contributors will have a

chance to update their data sets) Expand ash detection validation through comparison with expert analyses Gain detailed insight into differences in retrieved ash cloud properties:

Compare all retrieval inputs (satellite measurements and ancillary data) for a select number of common pixels, co-located with validation data, with different background conditions (water background, land background, meteorological cloud background, etc.). For the same common pixels, analyze all retrieval outputs.

Inter-compare volcanic ash products derived from the first of the next generation geostationary satellites (Himawari-8 AHI) for a single event from start to finish

Hold a workshop to document results, formulate best practices, and assess ability to meet demands of aviation community for more quantitative volcanic ash advisories

10

11

Very optically thin(far field)

Semi‐transparent(intermediate field)

Optically thick/Opaque(near field)

Height

PoorSensitivity

SomeSensitivity

GoodSensitivity

SomeSensitivity

PoorSensitivity 

(some UV/VIS exceptions)

Mass Loading

More consistent ash detection and characterization capabilities are needed across the spectrum of optical depth (down to detection limit) and height

Very optically thin(far field)

Semi‐transparent(intermediate field)

Optically thick/Opaque(near field)

Height

SomeSensitivity

PoorSensitivity

PoorSensitivity 

(some UV/VIS exceptions)

GoodSensitivity

SomeSensitivity