1. Volcanic ash retrieval from IR multispectral measurements by means of Neural Networks: an analysis of the Eyjafjallajokull eruption Matteo Picchiani1, Marco Chini2, Stefano Corradini2, Luca Merucci2, Pasquale Sellitto3,Fabio Del Frate1, Alessandro Piscini2 and Salvatore Stramondo2 1Earth Observation Laboratory – Tor Vergata University, Rome, Italy 2Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy 3Laboratoire Inter-universitaire des SystèmesAtmosphériques (LISA), Universités Paris-Est et Paris Diderot, CNRS, Créteil, France

3. The procedure for the ash mass computation [Prata et al., 1989; Wen & Rose, 1994] requires many input parameters and it can be so time consuming that could prevent the utilization during the crisis phases.

4. A novel technique [1] based on the synergic use of MODTRAN simulations and Neural Network has shown good potentiality in the automatic development of Ash detection and Ash mass retrievals from Moderate resolution Imager Spectroradiometer (MODIS) data.[1] Picchiani, M., Chini, M., Corradini, S., Merucci, L., Sellitto, P., Del Frate, F. and Stramondo, S., “Volcanic ash detection and retrievals from MODIS data by means of Neural Networks”, Atmos. Meas. Tech. Discuss., 4, 2567-2598, 2011.

5. Scenario The methodology has been developed considering several eruption of Mt. Etna [37.73°N, 15.00°E], a massive stratovolcano (3330 m a.s.l.) located in the eastern part of Sicily (Italy), showing interesting results: BTD Ash Retrieval BTD Ash Retrieval NN Ash Retrieval NN Ash Retrieval

8. If the NN is properly trained new data can be inverted in a few minutes (instead of some hours of MODTRAN based procedure).

10. ModisSpectralBands MODIS is a multi-spectral instrument that covers 36 spectral bands, from visible (VIS) to thermal infrared (TIR) with a global coverage in 1 to 2 days. The spatial resolution ranges from 250 m to 1000 m, depending on the acquisition mode.

11. Artificial Neural Networks Artificial Neural Networks (ANNs) can be seen as mathematical models for multivariate nonlinear regression or functional approximation. Functional mapping: a relationship between an input space (the space of the data) and an output space is searched : y= Ψw (x) x : vector of independent variables w : free adjustable parameters In ANNs Ψ is a linear combination of a large number of non-linear functions (sigmoid functions).

13. One or more hidden layers, consisting of non linear neurons.

14. One output layer, which produces the output signal.MLP are feedforwardNeural Network: the signal is propagated forward through the layers (no recurrent connections).

16. During the training phase, the free parameters of the ANN (weights, biases) are adjusted in order to minimize a cost function, e.g. p=number of training patterns, M=number of output units Problem: We cannot directly measure the ash quantity in the atmosphere. A forward models is needed.

17. BTD < 0 volcanic ash BTD > 0 meteo clouds Pixel Area Ash Density Extinction Efficiency Factor Ash retrieval in the TIR spectral range The cloud discrimination is based on Brightness Temperature Difference algorithm [Prata et al., 1989] (+ water vapor correction) BTD = Tb(11m) - Tb(12m) The retrieval is based on computing the simulated inverted arches curves “BTD vs Tb(11m)” varying the AOD (t) and the particles effective radius (re) [Wen and Rose, 1994; Prata et al., 2001] The TOA simulated Radiances LUT has been computed using MODTRAN RTM

20. Neural Networks Training E Training time When to stop Training? E on Training set E on Test set Training: 65% Test: 20% Validation: 15% A trade off between accuracyandgeneralization capability of the networks are reached when the error function on the test set reaches the global minimum.

22. Training (Tr), Test (Ts) and Validation (V) sets have been extracted from the data to train the NNs.

24. Methodology: NN forAsh Detection Ch 28 0 1: NotAsh 1 0 : Ash CH 31 CH 32 Uniform Sampling Neural Network forAsh Detection Tr, Ts an V sets have been extracted from the ash plume (Ash class) and the remaining zone of the images (Not Ash class). Inputs:CH 28 CH 31 CH 32 Output: Ash Detection Map

25. Methodology: NN for Ash Retrieval NN -Inputs Ch. 32 Ch. 28 Ch. 31 BTD - MODTRAN NN – Target Outputs

26. Methodology: NN forAshRetrieval CH 28 CH 31 CH 32 Uniform Sampling Neural Network forAsh Mass Retrieval Tr, Ts an V sets have been extracted from the ash plume. Output: Ash Mass Map Inputs:CH 28 CH 31 CH 32

27. Methodology: Processing Chain The two NNs have been insert in an automatic chain, processing the MODIS data to produce the ash detection and ash mass retrieved maps. The second NN is applied only where the ash is detected by the first NN. To improve the results a region growing algorithm is applied after the NN for the detection. NN forAsh Detection NN for Ash Mass Retrieval Inputs:CH 28 CH 31 CH 32 A region growing approach can be further applied to avoid the false positive ash pixels, due to high meteorological clouds. Ch 28 CH 31 CH 32

28. Ash Detection Results Confusion Matrix computed onto the V sets: April 19th 2010

29. Ash Retrieval Results Scatter plots computed onto the V sets:

30. NN procedure – MODTRAN based procedure results comparison April 19th 2010 BTD – MODTRAN Ash Retrieval NN Ash Retrieval

31. NN procedure – MODTRAN based procedure results comparison May 6th 2010 BTD – MODTRAN Ash Retrieval NN Ash Retrieval

32. NN procedure – MODTRAN based procedure results comparison May 7th 2010 BTD – MODTRAN Ash Retrieval NN Ash Retrieval

33. The Grismvotn Eruption The eruption events of the Icelandic Grismvotn volcano have offered an interesting opportunity to test the NN procedure. The NNs trained onto Eyjafjallajokull have been used to retrieve the Ash mass of the May 22nd 2011eruption. NN Ash Retrieval BTD – MODTRAN Ash Retrieval

34. The Grismvotn Eruption BTD – MODTRAN Ash Retrieval NN Ash Retrieval

36. A minimum set of MODIS channels have been used.

37. The obtained results show that the trained NNs can be used on new area under particular conditions (sea surface temperature, atmospheric profile) and can replace the BTD retrieval procedure in the crisis phase management.