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June 02,2018
SIGNAL TO NOISE RATIO ESTIMATIONS FOR A VOLCANIC ASH DETECTION LIDAR. CASE STUDY: MET OFFICE
Dr George Georgousis,Dr. George Avdikos

In this paper we calculate the Signal-to-Noise (SNR) ratio of a 3-channel commercial volcanic ash detection system, (Raymetrics lidar model LR111-D300), already in operation for the Met Office organization. The methodology for the accurate estimation is presented for day and nighttime conditions. The results show that SNR values are higher than 10 for ranges up to 13 km for both nighttime and daytime conditions. This is a favourable good result compared with other values observed in the literature and proves that such a system is able to detect volcanic ash over a range of 20 km.

Abstract

In this paper we calculate the Signal-to-Noise (SNR) ratio of a 3-channel commercial volcanic ash detection system, (Raymetrics lidar model LR111-D300), already in operation for the Met Office organization. The methodology for the accurate estimation is presented for day and nighttime conditions. The results show that SNR values are higher than 10 for ranges up to 13 km for both nighttime and daytime conditions. This is a favourable good result compared with other values observed in the literature and proves that such a system is able to detect volcanic ash over a range of 20 km.

Introduction

The Eyjafjallajokull eruption started on 20 March 2010, resulting in large amounts of volcanic ash being injected into the atmosphere for almost 30 days[1]. This event strongly affected the European and global air transport industry since volcanic ash (VA) plumes were subjected to long-range transport and were carried over large areas in Central Europe due to strong westerly winds. Although volcanic ash is composed of materials with very small dimensions, the damages it may cause can be significant because, after being expelled in the atmosphere to a very high altitude, the ash is generally scattered over a large area. In all cases, volcanic ash at high altitudes in the atmosphere can pose a serious hazard to aircraft engines [2]. Monitoring and detecting VA particles remains a difficult task since aerosol particles are highly inhomogeneous and vary in time and space. Consequently, aerosol observations and measurements have to be global, continuous and systematic. Ground-based aerosol remote sensing instruments such as lidars can contribute significantly to the understanding of aerosol properties and potential associated impacts, caused by their distribution. Among lidar systems, those which have the capability of detecting aerosol type (depolarization lidar systems) are appropriate for detecting and distinguishing volcanic ash [4]. In several European countries, meteorological agencies decided to develop lidar networks (e.g. Met Office) in order to support decision-making in the case of volcanic ash events [5]. In our paper we aim to show that automatic, depolarization lidar systems with routine operation can be powerful tools for detecting volcanic ash over a range of 20 km or more.

DESCRIPTION OF THE LIDAR SYSTEM – METHODOLOGY FOR SNR

Raymetrics joins the global network of Endeavor, non-profit supporting high-impact entrepreneurship in 30 countries. The companies were selected during Endeavor Global 75th International Selection Panel, held in Sofia (October 23-25).

Raymetrics, represented by Nikos Kontos, designs and builds LIDAR (Light Detection And Ranging) systems that are used for identification, early warning and analysis of meteorological information, such as temperature and humidity, as well as air pollution, dust storms and volcanic eruptions. Raymetrics sells its products all across the world, including in the US, and throughout Africa, South America, Southeast Asia and Europe. The company has built up an extensive client list, which includes such prestigious organizations as the European Space Agency, the UK Met Office, Meteo France, Changi Airport and the German Aerospace Centre.

CONCLUSIONS

By processing daytime and nighttime routine lidar measurements, calculation for the Signal-to-Noise Ratio was feasible. According to our analysis, the SNR can reach the value of 10 at the range of 11.5 km and 13.0 km for both 355 nm channels leading to a very promising result. We showed that the system is capable of detecting volcanic ash (VA) aerosols for ranges up to 20 km or more. A network of 10 lidar systems across UK could provide very useful information in case an event similar to that of 2010 takes place

References

[1] Sigmundsson, F., Hreinsdottir, S., Hooper, A., Arnadottir, T., Pedersen, R., Roberts, M. J., O’skarsson, N., Auriac, A., Decriem, J., Einarsson, P., Geirsson, H.,Hensch, M., O´ feigsson, B. G., Sturkell, E., Sveinbjornsson, H., and Feigl, K. L. 2010: Intrusion triggering of the 2010 Eyjafjallajokull explosive eruption, Nature, 468, 426–430. [2] A. Mortier, P. Goloub, T. Podvin, C. Deroo, A. Chaikovsky, N. Ajtai, L. Blarel, D. Tanre1, and Y. Derimian 2013: Detection and characterization of volcanic ash plumes over Lille during the Eyjafjallajokull eruption, Atmos. Chem. Phys., 13, 3705–3720. [3] Ajtai, N., Stefanie, H., Stoian, L. C., and Oprea, M. G.: The volcanic ash and its impact on European air transport industry 2010: A case study on the detection and impact of the the Eyjafjallajokull volcanic ash plume over North-Western Europe between 14 and 21 April 2010, AES Bioflux, 2, 57–68. [4] Muller, D., Ansmann, A., Mattis, I., Tesche, M., Wandinger, U., Althausen, D., and Pisani, G. 2007: Aerosol-type-dependent lidar ratios observed with Raman lidar, J. Geophys. Res., 112, D16202, 1-11. [5] Adam, M. et al. 2015: From operational ceilometer network to operational lidar network, 27 ILRC – S14 Advances in LIDAR technologies and Techniques II (pt. 2). [6] B. Heese, H. Flentje, D. Althausen, A. Ansmann, and S. Frey 2010: Ceilometer lidar comparison: backscatter coefficient retrieval and signal-to-noise ratio determination, Atmos. Meas. Tech., 3, 1763–1770.

Acknowledgments
Detection And Ranging systems that are used for identification, early warning and analysis of meteorological information.
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