The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development
Abstract
:1. Introduction
2. MSG/SEVIRI
3. VADUGS
4. Training Dataset
4.1. Input Data
4.1.1. Surface Emissivity
4.1.2. Atmospheric Data
4.1.3. Volcanic Ash Clouds
4.2. Radiative Transfer Calculations
4.3. Test of the Ash-Free Training Data
4.4. Training, Validation and Test Data
5. Training of the ANNs
6. Notes on the Application
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Dataset | Description | Samples | Ash Fraction |
---|---|---|---|
Training A | clear + ash | 8,725,531 | 32.1% |
Validation A | clear + ash | 2,493,719 | 32.1% |
Test A | clear + ash | 1,252,470 | 32.3% |
Training B | only ash | 2,798,004 | 100.0% |
Validation B | only ash | 800,117 | 100.0% |
Test B | only ash | 405,556 | 100.0% |
Classification | ||||
---|---|---|---|---|
Model setting | ||||
Input/output standardization | × | × | × | × |
LeCun normal distributed initialization | × | × | × | × |
Add Gaussian noise to input () | × | × | ||
Architecture | 19-100-100-100-4 | 19-100-100-100-1 | 23-100-100-100-1 | 23-100-100-100-1 |
Activation function (hidden neurons) | tanh | tanh | tanh | tanh |
Activation function (output neurons) | softmax | linear | linear | linear |
Loss function | cross entropy | mean squared error | mean squared error | mean squared error |
Sample weighting | × | |||
Nadam training algorithm | × | × | × | × |
Epochs trained | 60,000 | 2000 | 2000 | 2000 |
Feature (unit/range) | ||||
() | × | × | × | × |
() | × | × | × | × |
() | × | × | × | × |
() | × | × | × | × |
() | × | × | × | × |
() | × | × | × | × |
() | × | × | × | × |
Skin temperature () | × | × | × | × |
Binary land/sea mask | × | × | × | × |
Total column water vapor () | × | × | × | × |
Total column water () | × | × | × | × |
Total column ozone () | × | × | × | × |
Latitude (−90 to 90°) | × | × | × | × |
Longitude (−180 to 180°) | × | × | × | × |
Sine of day of year | × | × | × | × |
Cosine of day of year | × | × | × | × |
Sine of hour of day | × | × | × | × |
Cosine of hour of day | × | × | × | × |
Cosine of satellite zenith angle | × | × | × | × |
(retrieved) | × | × | ||
() | × | × | ||
() | × | × | ||
() | × | × |
/wt.% | /μm | ||||
---|---|---|---|---|---|
0.6 | 1.8 | 3.0 | 4.5 | 6.0 | |
45 | |||||
50 | |||||
55 | |||||
60 | |||||
65 | |||||
70 | |||||
75 |
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Piontek, D.; Bugliaro, L.; Schmidl, M.; Zhou, D.K.; Voigt, C. The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development. Remote Sens. 2021, 13, 3112. https://doi.org/10.3390/rs13163112
Piontek D, Bugliaro L, Schmidl M, Zhou DK, Voigt C. The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development. Remote Sensing. 2021; 13(16):3112. https://doi.org/10.3390/rs13163112
Chicago/Turabian StylePiontek, Dennis, Luca Bugliaro, Marius Schmidl, Daniel K. Zhou, and Christiane Voigt. 2021. "The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development" Remote Sensing 13, no. 16: 3112. https://doi.org/10.3390/rs13163112