Characterization of cirrus clouds over Sao Paulo Metropolitan City (MSP) by Elastic Lidar

Este trabalho, sendo pioneiro no Brasil, teve o intuito de efetuar uma investigação das nuvens cirrus na região Metropolitana de São Paulo (23,33ºS / 46,44ºW), SP, através do sistema MSP-Lidar para o período de Junho à Julho de 2007. Durante este período, foi verificada uma ocorrência de cirrus de aproximadamente 54% sobre o total de medidas efetuadas pelo sistema Lidar. Medidas com Lidar nos forneceram uma alta resolução espacial e temporal destas nuvens, permitindo assim caracterizá-las e classificá-las de acordo com as suas propriedades macro- e microfísicas. Para obter tais parâmetros, uma metodologia própria foi desenvolvida na recuperação dos dados de Lidar e uma robusta estatística foi aplicada para determinar as diferentes classes de cirrus. A metodologia adotada se resumiu basicamente (a) na determinação de períodos estacionários (ou observações) durante a evolução temporal de detecção de cirrus, (b) determinação da base e topo através de um valor limiar para o cálculo das variáveis macrofísicas (altitudes, temperaturas, espessuras geométricas), (c) aplicação do método da transmitância para cada camada de nuvem e a determinação das variáveis microfísicas (profundidade óptica e razão de Lidar). Neste processo, a razão de Lidar é calculada iterativamente até que haja a convergência da mesma. Análises estatísticas de multivariáveis foram efetuadas para a determinação das classes de cirrus. Estas classes são baseadas na espessura geométrica, altitude média e sua respectiva temperatura, a altitude relativa (diferença entre a altura da tropopausa e topo da nuvem) e a profundidade óptica. O uso sucessivo da Análise de Componentes Principais (PCA), do Método de Cluster Hierárquico (MCH) e da Análise de Discriminantes (AD) permitiu a identificação de 4 classes. Vale ressaltar que tais métodos foram aplicados somente para os casos identificados como camadas únicas de nuvens, pois não se observou significativamente a ocorrência de nuvens com multicamadas. A origem de formação das classes de cirrus encontradas, embora apresentando propriedades macro- e microfísicas distintas, foi identificada basicamente como a mesma, isto é, provenientes da injeção de vapor dágua na atmosfera por meio de sistemas frontais e seu respectivo resfriamento para a formação dos cristais de gelo. O mesmo mecanismo de formação também é atribuído aos jatos subtropicais. Uma análise em relação ao perfil de temperatura e a comparação com a literatura mostrou que as cirrus classificadas apresentam possivelmente cristais em forma de placas e colunas hexagonais. As razões de lidar (RL) calculadas também estão de acordo com a literatura.This pioneer work in Brazil, aimed at investigating cirrus clouds in the metropolitan region of São Paulo (23.33 ºS / 46.44 ºW), SP, observed by the MSP-Lidar system in June and July 2007. During this period, cirrus clouds were observed during approximately 54% of the time of all Lidar measurements available. The Lidar provided measurements with high spatial and temporal resolution measurements of these clouds that allowed characterizing and classifying them according to their macro-and microphysical properties. For such parameters, a unique methodology was developed for the Lidar data retrieval and a robust statistic was applied to determine the different classes of cirrus. The following steps were adopted to characterize the observations: (a) the determination of stationary periods (or observations) during the time evolution of cirrus detection, (b) determination of the base and top of clouds through a so called threshold value to derive the macrophysical variables (altitude, temperature, geometrical thickness), (c) the application of the transmittance method for each layer and the determination of cloud microphysical variables (optical depth and Lidar ratio). In this process, the Lidar ratio is calculated iteratively until a convergence of this value is achieved. Multivariate statistical analyses were performed to determine the classes of cirrus. These classes are based on geometric thickness, average altitude and the respective temperature, relative altitude (difference between tropopause height and cloud top) and optical depth. The successive use of Principal Component Analysis (PCA), Hierarchical Clustering Method (HCM) and Discriminant Analysis (DA) allowed the identification of four classes of cirrus. It is important to point out here that such methods were applied only to cases identified as single layers of clouds, due to the rare occurrence of multilayered clouds. The origin of formation for the four cirrus classes, though they have distinct macro-and microphysical properties, was found to be basically the same, i.e., from the injection of water vapor in the atmosphere provided by frontal systems, followed by the cooling process to form ice crystals. The same formation mechanism is also attributed to the subtropical jet. An analysis of the temperature profile and comparison with the literature showed that the cirrus crystals possibly have the form of hexagonal plates and columns. The Lidar Ratio (LR) was also found to be in accordance with the literature

Characterization of cirrus clouds over Sao Paulo Metropolitan City (MSP) by Elastic Lidar

Este trabalho, sendo pioneiro no Brasil, teve o intuito de efetuar uma investigação das nuvens cirrus na região Metropolitana de São Paulo (23,33ºS / 46,44ºW), SP, através do sistema MSP-Lidar para o período de Junho à Julho de 2007. Durante este período, foi verificada uma ocorrência de cirrus de aproximadamente 54% sobre o total de medidas efetuadas pelo sistema Lidar. Medidas com Lidar nos forneceram uma alta resolução espacial e temporal destas nuvens, permitindo assim caracterizá-las e classificá-las de acordo com as suas propriedades macro- e microfísicas. Para obter tais parâmetros, uma metodologia própria foi desenvolvida na recuperação dos dados de Lidar e uma robusta estatística foi aplicada para determinar as diferentes classes de cirrus. A metodologia adotada se resumiu basicamente (a) na determinação de períodos estacionários (ou observações) durante a evolução temporal de detecção de cirrus, (b) determinação da base e topo através de um valor limiar para o cálculo das variáveis macrofísicas (altitudes, temperaturas, espessuras geométricas), (c) aplicação do método da transmitância para cada camada de nuvem e a determinação das variáveis microfísicas (profundidade óptica e razão de Lidar). Neste processo, a razão de Lidar é calculada iterativamente até que haja a convergência da mesma. Análises estatísticas de multivariáveis foram efetuadas para a determinação das classes de cirrus. Estas classes são baseadas na espessura geométrica, altitude média e sua respectiva temperatura, a altitude relativa (diferença entre a altura da tropopausa e topo da nuvem) e a profundidade óptica. O uso sucessivo da Análise de Componentes Principais (PCA), do Método de Cluster Hierárquico (MCH) e da Análise de Discriminantes (AD) permitiu a identificação de 4 classes. Vale ressaltar que tais métodos foram aplicados somente para os casos identificados como camadas únicas de nuvens, pois não se observou significativamente a ocorrência de nuvens com multicamadas. A origem de formação das classes de cirrus encontradas, embora apresentando propriedades macro- e microfísicas distintas, foi identificada basicamente como a mesma, isto é, provenientes da injeção de vapor dágua na atmosfera por meio de sistemas frontais e seu respectivo resfriamento para a formação dos cristais de gelo. O mesmo mecanismo de formação também é atribuído aos jatos subtropicais. Uma análise em relação ao perfil de temperatura e a comparação com a literatura mostrou que as cirrus classificadas apresentam possivelmente cristais em forma de placas e colunas hexagonais. As razões de lidar (RL) calculadas também estão de acordo com a literatura.This pioneer work in Brazil, aimed at investigating cirrus clouds in the metropolitan region of São Paulo (23.33 ºS / 46.44 ºW), SP, observed by the MSP-Lidar system in June and July 2007. During this period, cirrus clouds were observed during approximately 54% of the time of all Lidar measurements available. The Lidar provided measurements with high spatial and temporal resolution measurements of these clouds that allowed characterizing and classifying them according to their macro-and microphysical properties. For such parameters, a unique methodology was developed for the Lidar data retrieval and a robust statistic was applied to determine the different classes of cirrus. The following steps were adopted to characterize the observations: (a) the determination of stationary periods (or observations) during the time evolution of cirrus detection, (b) determination of the base and top of clouds through a so called threshold value to derive the macrophysical variables (altitude, temperature, geometrical thickness), (c) the application of the transmittance method for each layer and the determination of cloud microphysical variables (optical depth and Lidar ratio). In this process, the Lidar ratio is calculated iteratively until a convergence of this value is achieved. Multivariate statistical analyses were performed to determine the classes of cirrus. These classes are based on geometric thickness, average altitude and the respective temperature, relative altitude (difference between tropopause height and cloud top) and optical depth. The successive use of Principal Component Analysis (PCA), Hierarchical Clustering Method (HCM) and Discriminant Analysis (DA) allowed the identification of four classes of cirrus. It is important to point out here that such methods were applied only to cases identified as single layers of clouds, due to the rare occurrence of multilayered clouds. The origin of formation for the four cirrus classes, though they have distinct macro-and microphysical properties, was found to be basically the same, i.e., from the injection of water vapor in the atmosphere provided by frontal systems, followed by the cooling process to form ice crystals. The same formation mechanism is also attributed to the subtropical jet. An analysis of the temperature profile and comparison with the literature showed that the cirrus crystals possibly have the form of hexagonal plates and columns. The Lidar Ratio (LR) was also found to be in accordance with the literature

Methodology for a robust retrieval of the extinction-to-backscatter ratio of cirrus clouds based on lidar measurements at Sao Paulo, Brazil

The extinction-to-backscatter ratio (also called lidar ratio-LR) is an important parameter which provides information on the transmission and reflection properties of cirrus clouds and also on the ice crystal properties due to their dependence on the particle shape, size and orientation for the particles. In this study is showed an innovative method to obtain theLR for each cloud layer through iterative processes, applying a numerical routine developed at the Center for Lasers and Applications (CLA/IPEN-Brazil) in cooperation with the Laboratoire Amosphères, Milieus, Observations Spatiales, Institut Pierre Simon Laplace/Versailles-Saint Quentin University (LATMOS/IPSL-France). The resulting LR values were obtained based on measurements of the MSP (Metropolitan City of São Paulo) - lidar system, performed on 11th June 2007, comprehending 298 minutes separated in 7 distinct so called stationary periods. For the first four periods, was observed two distinct layers of clouds with LR values varying between 28±15 and 35 ± 18 sr for the first layer and 37±11srand 74±14 sr for the second, indicating the presence of both small and large ice crystals composed by solid and hollow columns in majority. The last 3 periods of measurements in turn presented a mono¬layer cloud with LR about 19±04 sr, what corresponds to a relatively small solid needles, plates and column crystals

Towards an automatic lidar cirrus cloud retrieval for climate studies

In the present study, a methodology to calculate lidar ratios for distinct cirrus clouds has been implemented for a site located in the Southern Hemisphere. The cirrus cloud lidar data processing has been developed to consider a large cloud variability with the final aim of cirrus cloud monitoring through a robust retrieval process. Among the many features lidar systems can extract for cirrus detection, we highlight: cloud geometrical information and extinction-to-backscatter ratio (also called lidar ratio - LR). LR's can, in general, provide important information on cirrus cloud microphysics due to the presence of ice crystals and their properties such as shape, size, composition and orientation of particles and their effect on LR values. Conditions for LR calculations and their resulting uncertainty have been improved as their analysis requires identifying cirrus cloud stationary periods through the use of a specific statistical approach well-established in the literature and employed here with good results, allowing for the study of specific cases with multi-layer cirrus cloud occurrence. The results from the measurements taken in the region of the Metropolitan City of São Paulo - MSP have been used to implement and test the methodology developed herein. In addition to the geometrical parameters extracted, improved values of LR's were calculated and showed significantly different values for the different layers inspected, varying between 19 ± 01 sr and 74 ± 13 sr. This large value interval allowed us to indirectly verify the presence of different ice crystal sizes and shapes and those associated with different air mass sources for the cirrus cloud formation

South American Land Data Assimilation System (SALDAS) 5-yr retrospective atmospheric forcing datasets

The rain gauge data available in South America are very sparse and strongly biased towards more populated areas near the edge of the continent or near inland cities along the main river courses. Results of the study show the South American Land Data Assimilation System (SALDAS) dataset has a positive bias in temperature typically between 0 and 4 K. This paper describes the creation and validation of the meteorological forcing datasets used with the SALDAS System. Land surface models (LSMs) are an important component of numerical weather prediction (NWP) and global climate models, which can also be used to assess surface hydrology

Long-Range Transport of Water Channelized through the Southern Subtropical Jet

In this study, an air mass (containing a cirrus cloud) was detected by light detection and ranging (lidar) above São Paulo (Brazil) in June 2007 and tracked around the globe, thanks to Lagrangian calculations as well as ground-based and satellite observations. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data were also used to provide locations of occurrence of cirrus around the globe and extract their respective macro physical parameters (altitude and temperature). An analysis of the air mass history based on Lagrangian trajectories reveals that water coming from the Equator is channelized through the southern subtropical jet for weeks. In this case, the back-trajectories showed that the cirrus cloud detected at São Paulo was a mixture of air masses from two different locations: (1) the active convective area located around the Equator, with transport into the upper troposphere that promotes cirrus cloud formation; and (2) the South Pacific Ocean, with transport that follows the subtropical jet stream (STJ). Air masses coming from equatorial convective regions are trapped by the jet, which contributes to maintaining the lifetime of the cirrus cloud for a few days. The cloud disappears near the African continent, due to a southern excursion and warmer temperatures, then reappears and is detected again by the lidar system in São Paulo after 12 days. The observed cloud is located at a similar altitude, revealing that sedimentation is small or compensated by radiative uplift

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