Classification of lidar measurements using supervised and unsupervised machine learning methods

While it is relatively straightforward to automate the processing of lidar signals, it is more difficult to choose periods of good measurements to process. Groups use various ad hoc procedures involving either very simple (e.g. signal-to-noise ratio) or more complex procedures (e.g. Wing et al. 2018) to perform a task that is easy to train humans to perform but is time-consuming. Here, we use machine learning techniques to train the machine to sort the measurements before processing. The presented method is generic and can be applied to most lidars. We test the techniques using measurements from the Purple Crow Lidar (PCL) system located in London, Canada. The PCL has over 200 000 raw profiles in Rayleigh and Raman channels available for classification. We classify raw (level-0) lidar measurements as clear sky profiles with strong lidar returns, bad profiles, and profiles which are significantly influenced by clouds or aerosol loads. We examined different supervised machine learning algorithms including the random forest, the support vector machine, and the gradient boosting trees, all of which can successfully classify profiles. The algorithms were trained using about 1500 profiles for each PCL channel, selected randomly from different nights of measurements in different years. The success rate of identification for all the channels is above 95 %. We also used the t-SNE) method, which is an unsupervised algorithm, to cluster our lidar profiles. Because the t-SNE is a data-driven method in which no labelling of the training set is needed, it is an attractive algorithm to find anomalies in lidar profiles. The method has been tested on several nights of measurements from the PCL measurements. The t-SNE can successfully cluster the PCL data profiles into meaningful categories. To demonstrate the use of the technique, we have used the algorithm to identify stratospheric aerosol layers due to wildfires

P07. Characterizing the Purple Crow Lidar to investigate potential sources of wet bias

The Purple Crow Lidar is a large aperture lidar, capable of retrieving water vapor profiles into the stratosphere. Water vapor in the upper Troposphere-Lower Stratosphere (UTLS) region is of particular importance in understanding Earth\u27s radiative budget and atmospheric dynamics, making accurate UTLS measurements crucial. A comparison campaign with the NASA/GSFC ALVICE mobile lidar in the spring of 2012 showed PCL water vapor measurements were consistently larger than those of ALVICE in the lower stratosphere, prompting an investigation to characterize the system. The investigation looks into how changes to the data processing approach, as well as applying additional instrumental corrections, would affect the water vapor mixing ratio. We also look into a retrieval of the mixing ratio using optimal estimation method (OEM), which should provide greater insight into the associated data processing parameters and uncertainties

A Bayesian Neural Network Approach for Tropospheric Temperature Retrievals from a Lidar Instrument

We have constructed a Bayesian neural network able of retrieving tropospheric temperature profiles from rotational Raman-scatter measurements of nitrogen and oxygen and applied it to measurements taken by the RAman Lidar for Meteorological Observations (RALMO) in Payerne, Switzerland. We give a detailed description of using a Bayesian method to retrieve temperature profiles including estimates of the uncertainty due to the network weights and the statistical uncertainty of the measurements. We trained our model using lidar measurements under different atmospheric conditions, and we tested our model using measurements not used for training the network. The computed temperature profiles extend over the altitude range of 0.7 km to 6 km. The mean bias estimate of our temperatures relative to the MeteoSwiss standard processing algorithm does not exceed 0.05 K at altitudes below 4.5 km, and does not exceed 0.08 K in an altitude range of 4.5 km to 6 km. This agreement shows that the neural network estimated temperature profiles are in excellent agreement with the standard algorithm. The method is robust and is able to estimate the temperature profiles with high accuracy for both clear and cloudy conditions. Moreover, the trained model can provide the statistical and model uncertainties of the estimated temperature profiles. Thus, the present study is a proof of concept that the trained NNs are able to generate temperature profiles along with a full-budget uncertainty. We present case studies showcasing the Bayesian neural network estimations for day and night measurements, as well as in clear and cloudy conditions. We have concluded that the proposed Bayesian neural network is an appropriate method for the statistical retrieval of temperature profiles

The flow of plasma in the solar terrestrial environment

The overall goal of our NASA Theory Program was to study the coupling, time delays, and feedback mechanisms between the various regions of the solar-terrestrial system in a self-consistent, quantitative manner. To accomplish this goal, it will eventually be necessary to have time-dependent macroscopic models of the different regions of the solar-terrestrial system and we are continually working toward this goal. However, with the funding from this NASA program, we concentrated on the near-earth plasma environment, including the ionosphere, the plasmasphere, and the polar wind. In this area, we developed unique global models that allowed us to study the coupling between the different regions. These results are highlighted in the next section. Another important aspect of our NASA Theory Program concerned the effect that localized 'structure' had on the macroscopic flow in the ionosphere, plasmasphere, thermosphere, and polar wind. The localized structure can be created by structured magnetospheric inputs (i.e., structured plasma convection, particle precipitation or Birkland current patterns) or time variations in these input due to storms and substorms. Also, some of the plasma flows that we predicted with our macroscopic models could be unstable, and another one of our goals was to examine the stability of our predicted flows. Because time-dependent, three-dimensional numerical models of the solar-terrestrial environment generally require extensive computer resources, they are usually based on relatively simple mathematical formulations (i.e., simple MHD or hydrodynamic formulations). Therefore, another goal of our NASA Theory Program was to study the conditions under which various mathematical formulations can be applied to specific solar-terrestrial regions. This could involve a detailed comparison of kinetic, semi-kinetic, and hydrodynamic predictions for a given polar wind scenario or it could involve the comparison of a small-scale particle-in-cell (PIC) simulation of a plasma expansion event with a similar macroscopic expansion event. The different mathematical formulations have different strengths and weaknesses and a careful comparison of model predictions for similar geophysical situations provides insight into when the various models can be used with confidence

A interação universidade-empresa na indústria de petróleo brasileira: o caso da Petrobras

This paper examines research collaboration between the Brazilian state-controlled oil company, Petrobras, and universities from 1980 to 2014. Despite the importance of universityindustry research collaboration in Brazilian oil industry, there are few comprehensive and long-time spam studies on this topic. This paper helps to fill a gap in the academic literature by providing comparative historical data on research collaboration between Petrobras and Brazilian universities. Based on the co-authored publications by Petrobras we analyze changes in intensity of this collaboration and its geographical orientation, inter-organizational level and scientific knowledge base. Furthermore, we address the issue of whether changes in Brazilian R&D funding policy have affected trends in collaboration. Our findings show an increasing collaboration between Petrobras and Brazilian universities, resulting in an enlargement of the company’s network collaboration and reinforcing its knowledge base162325350CAPES - COORDENAÇÃO DO APERFEIÇOAMENTO DE PESSOAL DE NIVEL SUPERIORCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOBEX 10715/14-2sem informaçãoEste artigo analisa a colaboração em pesquisa entre a empresa estatal petrolífera brasileira, Petrobras, e universidades no período de 1980 a 2014. Apesar da importância da interação universidade-empresa na indústria de petróleo brasileira, há poucos estudos temporalmente abrangentes sobre o tema. Este trabalho ajuda a preencher uma lacuna na literatura, provendo dados comparativos de longo prazo sobre a colaboração em pesquisa entre a Petrobras e universidades. Baseando-se nas publicações da Petrobras em coautoria com universidades, são analisadas as mudanças na intensidade e orientação geográfica da colaboração, no nível de relação interorganizacional e na base de conhecimentos da empresa. Além disso, o trabalho também aborda os efeitos da recente política de financiamento à pesquisa e desenvolvimento na interação. Os resultados mostram uma crescente interação entre a Petrobras e as universidades brasileiras, levando a um alargamento da rede de colaborações científicas da empresa e reforçando sua base de conhecimento

Recommendations for myeloid-derived suppressor cell nomenclature and characterization standards

Myeloid-derived suppressor cells (MDSCs) have emerged as major regulators of immune responses in cancer and other pathological conditions. In recent years, ample evidence supports key contributions of MDSC to tumour progression through both immune-mediated mechanisms and those not directly associated with immune suppression. MDSC are the subject of intensive research with >500 papers published in 2015 alone. However, the phenotypic, morphological and functional heterogeneity of these cells generates confusion in investigation and analysis of their roles in inflammatory responses. The purpose of this communication is to suggest characterization standards in the burgeoning field of MDSC research

Expert range maps of global mammal distributions harmonised to three taxonomic authorities

AimComprehensive, global information on species' occurrences is an essential biodiversity variable and central to a range of applications in ecology, evolution, biogeography and conservation. Expert range maps often represent a species' only available distributional information and play an increasing role in conservation assessments and macroecology. We provide global range maps for the native ranges of all extant mammal species harmonised to the taxonomy of the Mammal Diversity Database (MDD) mobilised from two sources, the Handbook of the Mammals of the World (HMW) and the Illustrated Checklist of the Mammals of the World (CMW).LocationGlobal.TaxonAll extant mammal species.MethodsRange maps were digitally interpreted, georeferenced, error-checked and subsequently taxonomically aligned between the HMW (6253 species), the CMW (6431 species) and the MDD taxonomies (6362 species).ResultsRange maps can be evaluated and visualised in an online map browser at Map of Life (mol.org) and accessed for individual or batch download for non-commercial use.Main conclusionExpert maps of species' global distributions are limited in their spatial detail and temporal specificity, but form a useful basis for broad-scale characterizations and model-based integration with other data. We provide georeferenced range maps for the native ranges of all extant mammal species as shapefiles, with species-level metadata and source information packaged together in geodatabase format. Across the three taxonomic sources our maps entail, there are 1784 taxonomic name differences compared to the maps currently available on the IUCN Red List website. The expert maps provided here are harmonised to the MDD taxonomic authority and linked to a community of online tools that will enable transparent future updates and version control

Expert range maps of global mammal distributions harmonised to three taxonomic authorities

Aim Comprehensive, global information on species' occurrences is an essential biodiversity variable and central to a range of applications in ecology, evolution, biogeography and conservation. Expert range maps often represent a species' only available distributional information and play an increasing role in conservation assessments and macroecology. We provide global range maps for the native ranges of all extant mammal species harmonised to the taxonomy of the Mammal Diversity Database (MDD) mobilised from two sources, the Handbook of the Mammals of the World (HMW) and the Illustrated Checklist of the Mammals of the World (CMW). Location Global. Taxon All extant mammal species. Methods Range maps were digitally interpreted, georeferenced, error-checked and subsequently taxonomically aligned between the HMW (6253 species), the CMW (6431 species) and the MDD taxonomies (6362 species). Results Range maps can be evaluated and visualised in an online map browser at Map of Life (mol.org) and accessed for individual or batch download for non-commercial use. Main conclusion Expert maps of species' global distributions are limited in their spatial detail and temporal specificity, but form a useful basis for broad-scale characterizations and model-based integration with other data. We provide georeferenced range maps for the native ranges of all extant mammal species as shapefiles, with species-level metadata and source information packaged together in geodatabase format. Across the three taxonomic sources our maps entail, there are 1784 taxonomic name differences compared to the maps currently available on the IUCN Red List website. The expert maps provided here are harmonised to the MDD taxonomic authority and linked to a community of online tools that will enable transparent future updates and version control.Output Status: Forthcoming/Available Online Output Type: Data Article Additional co-authors: Kira McCall, Ajay Ranipeta, Anna Schuerkmann, Michael A. Torselli, Thomas Lacher Jr, Russell A. Mittermeier, Anthony B. Rylands, Wes Sechrest, Don E. Wilson, Agustín M. Abba, Luis F. Aguirre, Joaquín Arroyo-Cabrales, Diego Astúa, Andrew M. Baker, Gill Braulik, Janet K. Braun, Jorge Brito, Peter E. Busher, Santiago F. Burneo, M. Alejandra Camacho, Paolo Cavallini, Elisandra de Almeida Chiquito, Joseph A. Cook, Tamás Cserkész, Gábor Csorba, Erika Cuéllar Soto, Valeria da Cunha Tavares, Tim R. B. Davenport, Thomas Deméré, Christiane Denys, Christopher R. Dickman, Mark D. B. Eldridge, Eduardo Fernandez-Duque, Charles M. Francis, Greta Frankham, William L. Franklin, Thales Freitas, J. Anthony Friend, Elizabeth L. Gadsby, Guilherme S. T. Garbino, Philippe Gaubert, Norberto Giannini, Thomas Giarla, Jason S. Gilchrist, Jaime Gongora, Steven M. Goodman, Sharon Gursky-Doyen, Klaus Hackländer, Mark S. Hafner, Melissa Hawkins, Kristofer M. Helgen, Steven Heritage, Arlo Hinckley, Stefan Hintsche, Mary Holden, Kay E. Holekamp, Rodney L. Honeycutt, Brent A. Huffman, Tatyana Humle, Rainer Hutterer, Carlos Ibáñez Ulargui, Stephen M. Jackson, Jan Janecka, Mary Janecka, Paula Jenkins, Rimvydas Juškaitis, Javier Juste, Roland Kays, C. William Kilpatrick, Tigga Kingston, John L. Koprowski, Boris Kryštufek, Tyrone Lavery, Thomas E. Lee Jr, Yuri L. R. Leite, Roberto Leonan M. Novaes, Burton K. Lim, Andrey Lissovsky, Raquel López-Antoñanzas, Adrià López-Baucells, Colin D. MacLeod, Michael A. Mares, Helene Marsh, Stefano Mattioli, Erik Meijaard, Ara Monadjem, F. Blake Morton, Grace Musser, Tilo Nadler, Ryan W. Norris, Agustina Ojeda, Nicté Ordóñez-Garza, Ulyses F. J. Pardiñas, Bruce D. Patterson, Ana Pavan, Michael Pennay, Calebe Pereira, Joyce Prado, Helder L. Queiroz, Matthew Richardson, Erin P. Riley, Stephen J. Rossiter, Daniel I. Rubenstein, Dennisse Ruelas, Jorge Salazar-Bravo, Stéphanie Schai-Braun, Cody J. Schank, Christoph Schwitzer, Lori K. Sheeran, Myron Shekelle, Georgy Shenbrot, Pipat Soisook, Sergio Solari, Richard Southgate, Mariella Superina, Andrew B. Taber, Maurício Talebi, Peter Taylor, Thong Vu Dinh, Nelson Ting, Diego G. Tirira, Susan Tsang, Samuel T. Turvey, Raul Valdez, Victor Van Cakenberghe, Geraldine Veron, Janette Wallis, Rod Wells, Danielle Whittaker, George Wittemyer, John Woinarski, Dietmar Zinner, Nathan S. Upham, Walter Jet