Observations of aerosol and liquid-water clouds with Dual-Field-of-View Polarization Lidar: A ground-based view on aerosol-cloud interactions

The book presents my PhD thesis, which is about aerosol-cloud interactions by means of a dual-field-of-view polarization lidar. Aerosol-cloud interactions (ACI) are a big challenge to quantify the overall effect of human activities on the radiative, heat, and precipitation budgets of the atmosphere. New observational capabilities are demanded. To study the influence of aerosol particles on cloud microphysics an analysis scheme composed of newly-developed arrays is introduced. The retrieval of microphysical properties of liquid-water clouds and of the aerosol particles below the clouds from lidar observations, in a practical and replicable way, is the major challenge tackled in this work. A lidar-based approach to derive liquid-water cloud microphysical properties from dual-field-of-view (DFOV) depolarization measurements is introduced. In addition, a new method to accurately obtain the aerosol properties below cloud layers was developed and implemented into the analysis infrastructure. Comparisons with alternative observational and modeling approaches corroborate the accuracy of both methods. The number concentration of cloud condensation nuclei (CCN) is derived from the aerosol particle extinction coefficient below the cloud, and in combination with the cloud-microphysics retrieval, they provide an aerosol-cloud scene, which allow us to study ACI. Long-term observations at the pristine location of Punta Arenas (PA), Chile, and at the polluted site of Dushanbe (DB), Tajikistan, were analyzed for this purpose. On average, similar values of cloud droplet and below-cloud CCN number concentrations, in the range of 10--150~cm$^{-3}$, were observed at PA. At DB, larger cloud droplet number concentrations were observed, in the order of 200--400 cm-3 but much larger CCN concentrations of about 700--900 cm-3 were found. The so-called ACI index was assessed from the collected data sets. The most robust estimate of the index was obtained when calculating monthly averages over the whole measurement periods, fourteen months at PA and seven months at DB. Values of 0.83 +/- 0.20 and 0.57+/ 0.26 were derived at PA and DB, respectively, and they were used to estimate the radiative forcing due to the Twomey effect. A radiative cooling from -0.70 to -0.17 Wm-2 for PA and between -1.89 and -0.66 Wm-2 for DB is found. These results agree with global estimates of the cloud-mediated aerosol effect but are slightly larger than those values usually found at the specific locations considered. Furthermore, the results obtained at PA show the relevance of updraft movements to trigger ACI. When considering only updraft-dominated periods, the ACI index is up to 50% larger than when no wind information is considered. The new capabilities illuminated during this work may provide a big help for estimations of the cloud-mediated radiative effect and may provide a baseline to confront models dealing with cloud microphysics in future studies.:1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 2 Aerosol, clouds and their interaction - State of the art and research questions. . 7 2.1 Aerosol and clouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.1 Aerosol effect on liquid-water clouds . . . . . . . . . . . . . . . . . . . . . . . . .8 2.1.2 Aerosol effect on ice-containing clouds . . . . . . . . . . . . . . . . . . . . . . .9 2.1.3 Cloud processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 10 2.1.4 Modeling droplet number concentration Nd . . . . . . . . . . . . . . . . . . 10 2.2 Aerosol radiative effect via ACI in liquid-water clouds . . . . . . . . . . . . . .11 2.2.1 Aerosol-cloud-interaction index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2.2 Observational approaches for the ACI index. . . . . . . . . . . . . . . . . . . .14 2.2.3 Strategies to evaluate the ACI index from observations . . . . . . . . . . .16 2.2.4 ACI studies based on lidar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3 Lidar measurements of aerosol-cloud interaction – Overview of applied methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23 3.1 Multiple-scattering lidar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23 3.2 DFOV-Raman technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3 Single-FOV polarization lidar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25 3.3.1 Comparison between DFOV-Raman and SFOV-Depol methods . . . 27 3.4 Dual-FOV depolarization approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.4.1 Calibration of the lidar system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4.2 DFOV-Depol measurement cases . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.5 Implementation of the DFOV-Depol approach into the standardized lidar sys- tem Polly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33 4 Research results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37 4.1 First publication: Polarization lidar: an extended three-signal calibration approach . . . . . . .39 4.2 Second publication: The dual-field-of-view polarization lidar technique: A new concept in monitoring aerosol effects in liquid-water clouds – Theoretical framework . . . . . . . . .59 4.3 Third publication: The dual-field-of-view polarization lidar technique: A new concept in monitoring aerosol effects in liquid-water clouds – Case studies . . . . . . . . . . . . . . . .79 5 Discussion and further applications – Long-term observations of aerosol- cloud interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .101 5.1 Observations on cloud scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .102 5.2 Long-term results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.2.1 Comparison of DFOV-Depol products with available estimations and observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .108 5.3 Assessment of the ACI index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 5.4 Relevance of the ACI index for the radiative effect . . . . . . . . . . . . 112 6 Summary and outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .117 Appendix A: Aerosol properties with lidar . . . . . . . . . . . . . . . . . . . .125 A.1 Lidar principles of elastic and Raman lidar . . . . . . . . . . . . . . . .125 A.2 Raman lidar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 A.2.1 Retrieval of extinction coefficient . . . . . . . . . . . . . . . . . . . . . 128 A.2.2 Retrieval of backscattering coefficient. . . . . . . . . . . . . . . . . . 128 A.2.3 Bottom-up approximation for Raman Signals . . . . .. . . . . . . 129 A.2.4 Evaluation of Raman methods. . . . . . . . . . . . . . . . . . . . . . . 130 A.3 Elastic Lidar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .132 A.3.1 Klett-Fernald Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 A.3.2 Quasi-backscattering for high resolved retrievals. . . . . . . . . 133 A.3.3 Bottom-up approximation for elastic signals . . . . . . . . . . . . 135 A.3.4 Evaluation of methods based on elastic lidar. . . . . . . . . . . . 137 A.3.5 Microphysical properties from optical properties. . . . . . . . . . 139 Appendix B Characterization of DFOV-Depol lidar . . . . . . . . . . . . 143 B.1 Transmission ratio based on long-term analysis . . . . . . . . . . . 144 Appendix C: Author’s contributions to the three publications . . . . 149 Appendix D Lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .151 D.1 List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .151 D.2 List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 D.3 List of Symbols (excluding cumulative part) . . . . . . . . . . 156 D.4 List of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Experimental assessment of a micro-pulse lidar system in comparison with reference lidar measurements for aerosol optical properties retrieval

Simultaneous observations of a polarized micro-pulse lidar (P-MPL) system and two reference European Aerosol Research Lidar Network lidars running at the Leipzig site Germany, 51.4g gN, 12.4g gE; 125gmga.s.l.) were performed during a comprehensive 2-month field intercomparison campaign in summer 2019. An experimental assessment regarding both the overlap (OVP) correction of the P-MPL signal profiles and the volume linear depolarization ratio (VLDR) analysis, together with its impact on the retrieval of the aerosol optical properties, is achieved; the experimental procedure used is also described. The optimal lidar-specific OVP function is experimentally determined, highlighting that the one delivered by the P-MPL manufacturer cannot be used long. Among the OVP functions examined, the averaged function between those obtained from the comparison of the P-MPL observations with those of the other two reference lidars seems to be the best proxy at both near- and far-field ranges. In addition, the impact of the OVP function on the accuracy of the retrieved profiles of the total particle backscatter coefficient (PBC) and the particle linear depolarization ratio (PLDR) is examined. The VLDR profile is obtained and compared with that derived from the reference lidar, showing that it needs to be corrected by a small offset value with good accuracy. Once P-MPL measurements are optimally (OVP, VLDR) corrected, both the PBC and PLDR profiles can be accurately derived and are in good agreement with reference aerosol retrievals. Overall, as a systematic requirement for lidar systems, an adequate OVP function determination and VLDR testing analysis needs to be performed on a regular basis to correct the P-MPL measurements in order to derive suitable aerosol products. A dust event observed in Leipzig in June 2019 is used for illustration

Observations of aerosol and liquid-water clouds with Dual-Field-of-View Polarization Lidar: A ground-based view on aerosol-cloud interactions

The book presents my PhD thesis, which is about aerosol-cloud interactions by means of a dual-field-of-view polarization lidar. Aerosol-cloud interactions (ACI) are a big challenge to quantify the overall effect of human activities on the radiative, heat, and precipitation budgets of the atmosphere. New observational capabilities are demanded. To study the influence of aerosol particles on cloud microphysics an analysis scheme composed of newly-developed arrays is introduced. The retrieval of microphysical properties of liquid-water clouds and of the aerosol particles below the clouds from lidar observations, in a practical and replicable way, is the major challenge tackled in this work. A lidar-based approach to derive liquid-water cloud microphysical properties from dual-field-of-view (DFOV) depolarization measurements is introduced. In addition, a new method to accurately obtain the aerosol properties below cloud layers was developed and implemented into the analysis infrastructure. Comparisons with alternative observational and modeling approaches corroborate the accuracy of both methods. The number concentration of cloud condensation nuclei (CCN) is derived from the aerosol particle extinction coefficient below the cloud, and in combination with the cloud-microphysics retrieval, they provide an aerosol-cloud scene, which allow us to study ACI. Long-term observations at the pristine location of Punta Arenas (PA), Chile, and at the polluted site of Dushanbe (DB), Tajikistan, were analyzed for this purpose. On average, similar values of cloud droplet and below-cloud CCN number concentrations, in the range of 10--150~cm$^{-3}$, were observed at PA. At DB, larger cloud droplet number concentrations were observed, in the order of 200--400 cm-3 but much larger CCN concentrations of about 700--900 cm-3 were found. The so-called ACI index was assessed from the collected data sets. The most robust estimate of the index was obtained when calculating monthly averages over the whole measurement periods, fourteen months at PA and seven months at DB. Values of 0.83 +/- 0.20 and 0.57+/ 0.26 were derived at PA and DB, respectively, and they were used to estimate the radiative forcing due to the Twomey effect. A radiative cooling from -0.70 to -0.17 Wm-2 for PA and between -1.89 and -0.66 Wm-2 for DB is found. These results agree with global estimates of the cloud-mediated aerosol effect but are slightly larger than those values usually found at the specific locations considered. Furthermore, the results obtained at PA show the relevance of updraft movements to trigger ACI. When considering only updraft-dominated periods, the ACI index is up to 50% larger than when no wind information is considered. The new capabilities illuminated during this work may provide a big help for estimations of the cloud-mediated radiative effect and may provide a baseline to confront models dealing with cloud microphysics in future studies.:1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 2 Aerosol, clouds and their interaction - State of the art and research questions. . 7 2.1 Aerosol and clouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.1 Aerosol effect on liquid-water clouds . . . . . . . . . . . . . . . . . . . . . . . . .8 2.1.2 Aerosol effect on ice-containing clouds . . . . . . . . . . . . . . . . . . . . . . .9 2.1.3 Cloud processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 10 2.1.4 Modeling droplet number concentration Nd . . . . . . . . . . . . . . . . . . 10 2.2 Aerosol radiative effect via ACI in liquid-water clouds . . . . . . . . . . . . . .11 2.2.1 Aerosol-cloud-interaction index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2.2 Observational approaches for the ACI index. . . . . . . . . . . . . . . . . . . .14 2.2.3 Strategies to evaluate the ACI index from observations . . . . . . . . . . .16 2.2.4 ACI studies based on lidar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3 Lidar measurements of aerosol-cloud interaction – Overview of applied methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23 3.1 Multiple-scattering lidar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23 3.2 DFOV-Raman technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3 Single-FOV polarization lidar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25 3.3.1 Comparison between DFOV-Raman and SFOV-Depol methods . . . 27 3.4 Dual-FOV depolarization approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.4.1 Calibration of the lidar system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4.2 DFOV-Depol measurement cases . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.5 Implementation of the DFOV-Depol approach into the standardized lidar sys- tem Polly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33 4 Research results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37 4.1 First publication: Polarization lidar: an extended three-signal calibration approach . . . . . . .39 4.2 Second publication: The dual-field-of-view polarization lidar technique: A new concept in monitoring aerosol effects in liquid-water clouds – Theoretical framework . . . . . . . . .59 4.3 Third publication: The dual-field-of-view polarization lidar technique: A new concept in monitoring aerosol effects in liquid-water clouds – Case studies . . . . . . . . . . . . . . . .79 5 Discussion and further applications – Long-term observations of aerosol- cloud interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .101 5.1 Observations on cloud scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .102 5.2 Long-term results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.2.1 Comparison of DFOV-Depol products with available estimations and observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .108 5.3 Assessment of the ACI index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 5.4 Relevance of the ACI index for the radiative effect . . . . . . . . . . . . 112 6 Summary and outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .117 Appendix A: Aerosol properties with lidar . . . . . . . . . . . . . . . . . . . .125 A.1 Lidar principles of elastic and Raman lidar . . . . . . . . . . . . . . . .125 A.2 Raman lidar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 A.2.1 Retrieval of extinction coefficient . . . . . . . . . . . . . . . . . . . . . 128 A.2.2 Retrieval of backscattering coefficient. . . . . . . . . . . . . . . . . . 128 A.2.3 Bottom-up approximation for Raman Signals . . . . .. . . . . . . 129 A.2.4 Evaluation of Raman methods. . . . . . . . . . . . . . . . . . . . . . . 130 A.3 Elastic Lidar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .132 A.3.1 Klett-Fernald Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 A.3.2 Quasi-backscattering for high resolved retrievals. . . . . . . . . 133 A.3.3 Bottom-up approximation for elastic signals . . . . . . . . . . . . 135 A.3.4 Evaluation of methods based on elastic lidar. . . . . . . . . . . . 137 A.3.5 Microphysical properties from optical properties. . . . . . . . . . 139 Appendix B Characterization of DFOV-Depol lidar . . . . . . . . . . . . 143 B.1 Transmission ratio based on long-term analysis . . . . . . . . . . . 144 Appendix C: Author’s contributions to the three publications . . . . 149 Appendix D Lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .151 D.1 List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .151 D.2 List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 D.3 List of Symbols (excluding cumulative part) . . . . . . . . . . 156 D.4 List of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Arbuscular mycorrhizal fungi colonization of Jatropha curcas roots and its impact on growth and survival under green-house-induced hydric stress.

Abstract: Arbuscular mycorrhizal fungi (AMF) provide benefits to host plants by enhancing nu-trition and overall fitness. In this study, AMF species were isolated from the soil rhizosphere of Jatropha curcas and were identified and evaluated for their potential in fostering the development of Jatropha seedlings within a controlled greenhouse environment. The first experiment assessed the interplay between hydric stress and AMF inoculation on mycorrhizal colonization. The next ex-periment examined the impact of quercetin on mycorrhizal colonization. Out of 204 glomero-spores corresponding to 28 species spanning 10 genera, Acaulospora (14) and Scutellospora (5) were the most abundant taxa. Strikingly, nine species are new records for Costa Rica. Mycorrhizal colonization was observed in 43.2% of Jatropha plants (34.7% by AMF typical hyphae; arbuscules 8.9%; coils 5.6% and vesicles 5.4%). Significant survival effects due to AMF inoculation under hydric stress were observed. On day 85, non-mycorrhizal plants subjected to hydric stress showed a mere 30% survival rate, whereas their mycorrhizal counterparts under hydric stress exhibited survival rates of 80% and 100% with and without irrigation, respectively. Furthermore, plants with irri-gation and mycorrhizas showed greater hydric stress tolerance and superior growth. The inocu-lated plants, irrespective of irrigation, demonstrated mycorrhizal colonization rates of 63% and 72%, respectively. Quercetin did not affect Jatropha's growth, but there were differences in AMF root colonization. In summary, these findings accentuate the viability of a native consortium in augmenting Jatropha survival, warranting consideration as a potent biofertilizer within green-house settings. The AMF described can be used for Jatropha propagation programs.Universidad de Costa Rica/[111-B6-194]/UCR/Costa RicaUCR::Vicerrectoría de Docencia::Ciencias Básicas::Facultad de Ciencias::Escuela de BiologíaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigación en Biodiversidad y Ecología Tropical (CIBET

Latin American Lidar Network (LALINET): a diagnostic on networking instrumentation

LALINET (Latin American Lidar Network), previously known as ALINE, is the first fully operative lidar network for aerosol research in South America, probing the atmosphere on regular basis since September 2013. The general purpose of this network is to attempt to fill the gap in the knowledge on aerosol vertical distribution over South America and its direct and indirect impact on weather and climate by the establishment of a vertically-resolved dataset of aerosol properties. Similarly to other lidar research networks, most of the LALINET instruments are not commercially produced and, consequently, configurations, capabilities and derived-products can be remarkably different among stations. It is a fact that such un-biased 4D dataset calls for a strict standardization from the instrumental and data processing point of view. This study has been envisaged to investigate the ongoing network configurations with the aim of highlighting the instrumental strengths and weaknesses of LALINET.Fil: Guerrero Rascado, Juan Luis. Instituto de Pesquisas Energéticas e Nucleares; Brasil. Instituto Interuniversitario de Investigación del Sistema Tierra en Andalucía; España. Universidad de Granada; EspañaFil: Landulfo, Eduardo. Instituto de Pesquisas Energéticas e Nucleares; BrasilFil: Antuña, Juan Carlos. Instituto de Meteorología de Cuba; CubaFil: Barbosa, Henrique de Melo Jorge. Universidade de Sao Paulo; BrasilFil: Barja, Boris. Instituto de Meteorología de Cuba; Cuba. Universidade de Sao Paulo; BrasilFil: Bastidas, Álvaro Efrain. Universidad Nacional de Colombia; ColombiaFil: Bedoya, Andrés Esteban. Universidad Nacional de Colombia; ColombiaFil: da Costa, Renata Facundes. Instituto de Pesquisas Energéticas e Nucleares; BrasilFil: Estevan, René. Instituto de Meteorología de Cuba; CubaFil: Forno, Ricardo. Universidad Mayor de San Andrés; BoliviaFil: Gouveia, Diego Alvés. Universidade de Sao Paulo; BrasilFil: Jiménez, Cristofer. Universidad de Concepción; ChileFil: Larroza, Eliane Gonçalves. Instituto de Pesquisas Energéticas e Nucleares; BrasilFil: da Silva Lopes, Fábio Juliano. Instituto de Pesquisas Energéticas e Nucleares; Brasil. Universidade de Sao Paulo; BrasilFil: Montilla Rosero, Elena. Universidad de Concepción; ChileFil: de Arruda Moreira, Gregori. Instituto de Pesquisas Energéticas e Nucleares; BrasilFil: Nakaema, Walker Morinobu. Instituto de Pesquisas Energéticas e Nucleares; BrasilFil: Nisperuza, Daniel. Universidad Nacional de Colombia; ColombiaFil: Alegria, Dairo. Universidad Nacional de Colombia; ColombiaFil: Múnera, Mauricio. Universidad Nacional de Colombia; ColombiaFil: Otero, Lidia Ana. k División Lidar, CEILAP (UNIDEF-CONICET); ArgentinaFil: Papandrea, Sebastián Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Científicas y Técnicas para la Defensa. Centro de Investigación en Láseres y Aplicaciones; ArgentinaFil: Pallota, Juan Vicente. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Científicas y Técnicas para la Defensa. Centro de Investigación en Láseres y Aplicaciones; ArgentinaFil: Pawelko, Ezequiel Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Científicas y Técnicas para la Defensa. Centro de Investigación en Láseres y Aplicaciones; ArgentinaFil: Quel, Eduardo Jaime. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Científicas y Técnicas para la Defensa. Centro de Investigación en Láseres y Aplicaciones; ArgentinaFil: Ristori, Pablo Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Científicas y Técnicas para la Defensa. Centro de Investigación en Láseres y Aplicaciones; ArgentinaFil: Rodrigues, Patricia Ferrini. Instituto de Pesquisas Energéticas e Nucleares; BrasilFil: Salvador, Jacobo Omar. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Científicas y Técnicas para la Defensa. Centro de Investigación en Láseres y Aplicaciones; ArgentinaFil: Sánchez, Maria Fernanda. Universidad Mayor de San Andrés; BoliviaFil: Silva, Antonieta. Universidad de Concepción; Chile. Universidad de La Frontera; Chil