I. Matthew Watson, MTU and University of Bristol, UK Remote sensing of volcanic emissions using MODIS.

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Presentation transcript:

I. Matthew Watson, MTU and University of Bristol, UK Remote sensing of volcanic emissions using MODIS

Talk structure Why quantify volcanic emissions? MODIS channels of interest Pre-MODIS algorithms Recent developments using MODIS –7.34  m channel e.g. Hekla eruption (2000) –Forward modeling Water vapor correction Future work –Species interaction (spectral) Conclusions

Why quantify volcanic emissions: Indicators of volcanic activity Hazardous to population, wildlife, environment and infrastructure Long-lived and climatologically active Aircraft hazard mitigation

Why quantify volcanic emissions: Indicators of volcanic activity Hazardous to population, wildlife, environment and infrastructure Long-lived and climatologically active Aircraft hazard mitigation

MODIS bands of interest

MODIS bands of interest SO 2 band (8.6  m)

MODIS bands of interest SO 2 band (7.3  m)

MODIS bands of interest ash band (11  m)

MODIS channel applications

Previous (pre-EOS) work SpeciesOriginal Sensor [and channels] ReferenceYearMODIS channels ASTER channels AIRS channel sets AshAVHRR [4,5]Prata1989 a,b31, 3213, AshAVHRR [4,5] GOES [4,5] Wen and Rose , 3213, IceAVHRR [4,5] GOES [4,5] Rose et al.,199531, 3213, SO 2 TIMS [1-6]Realmuto (et al.) (1994), 1997, N/A SO 4 2- HIRS/2 [5-10]Yu and Rose N/A10-15 SO 2 TOVS 1 [8,11,12]Prata et al.,200327, 28, 31 N/A5

Previous (pre-EOS) work SpeciesOriginal Sensor [and channels] ReferenceYearMODIS channels ASTER channels AIRS channel sets AshAVHRR [4,5]Prata1989 a,b31, 3213, AshAVHRR [4,5] GOES [4,5] Wen and Rose , 3213, IceAVHRR [4,5] GOES [4,5] Rose et al.,199531, 3213, SO 2 TIMS [1-6]Realmuto (et al.) (1994), 1997, N/A SO 4 2- HIRS/2 [5-10]Yu and Rose N/A10-15 SO 2 TOVS 1 [8,11,12]Prata et al.,200327, 28, 31 N/A5

Applications of different retrievals for SO 2 RetrievalApplicationAdvantagesDisadvantages TOMS  m Large scale eruptions Spectrally independent/long archive Spatial resolution AIRS 7.34  m Large scale eruptions High spectral resolution Spatial resolution MODIS/ TOVS 7.34  m Mid scale eruptions Climatologically active SO 2 High altitude required MODIS/ ASTER 8.6  m Mid-scale – passive degassing Monitoring possible Ash and sulfate interference

Applications of different retrievals for SO 2 RetrievalApplicationAdvantagesDisadvantages TOMS  m Large scale eruptions Spectrally independent/long archive Spatial resolution AIRS 7.34  m Large scale eruptions High spectral resolution Spatial resolution MODIS/ TOVS 7.34  m Mid scale eruptions Climatologically active SO 2 High altitude required MODIS/ ASTER 8.6  m Mid-scale – passive degassing Monitoring possible Ash and sulfate interference

Hekla volcano, Iceland, erupted Feb 26, 2000

Hekla MODIS rgb image (7.3, 11 and 12  m)

7.34  m SO 2 retrieval - Prata et al, 2004

MODIS retrieval for Hekla, Feb eruption Approximate aircraft track

SOLVE (NASA DC-8) in situ SO 2 measurements and MODIS retrievals show good agreement Integrated across plume: SOLVE=248 ppmV MODIS=241 ppmV

Water vapor interference The slopes of these lines are in opposition. The ‘split window’ algorithm BTD (11-12  m) is used operationally by VAACs to detect ash and by climatologists to map water vapor. Hence water vapor masks the ash signal.

Atmospheric effects of water

Forward model justification User specifies ‘external’ parameters: ground emissivity, ground temperature, atmospheric profile (water vapor, pressure and temperature as a function of height). User specifies ‘plume’ parameters: plume (top and base) altitude, effective radius, variance, refractive index and number density of particles The forward model uses MODTRAN to calculate ‘external’ effects and Mie scattering code to calculate ‘plume’ effects Can be used to –investigate atmospheric effects on IR retrievals –generate a LUT for multi-species spectra –genrate transmission spectra to be used to correct SO 2 maps for ash/sulftae

Forward model rationale

Forward model results – example spectra

Mt. Spurr in tropical atmosphere Brightness temperature change  BTD

Change in cloud area caused by water vapor interference

Species Interference 3  m andesite 5  m ice 0.5  m 75 % H 2 SO 4 10 g m -2 SO 2 Mid-lat atmosphere

Multi-species algorithm development Volcanic emissions group (3 professors, 5 PhD students and 2 MS students at two universities) working on various parts of the problem. Empircal and theoretical approaches used –Looking at examples of ash-SO 2 separation (e.g. Anatahan 2003) –Forward modeling of multispecies clouds –Correction of SO 2 for ash and sulfate –Multi-sensor comparisons (TOMS, AIRS, TOVS, ASTER) Several eruptions targeted, work in progress

Conclusions The development of the 7.3  m algorithm has been a significant advance in quantifying volcanic SO 2 (limited species interaction, works at night). Hekla-DC8 interaction provides unprecedented ground- truthing opportunity. Forward modeling can be used to quantitatively determine the effects of different water vapor concentrations on the ‘split-window’ signal. Water vapor more strongly affects clouds that are optically thinner as relative proportions of signal from the underlying ground (and water vapor) increase. A multi-species algorithm is the holy grail of volcanic emission remote sensing. Only through MODIS’s ability to detect and quantify more than one species has the problem been (a) illuminated and (b) potentially solvable.