DOCSLIB.ORG

Karen Blouin

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

University of Alberta Lightning prediction models for the province of Alberta, Canada by Karen Blouin A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of requirements for degree of Master of Science in Forest Biology and Management Department of Renewable Resources © Karen Blouin Spring 2014 Edmonton, Alberta Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission. ABSTRACT Lightning is widely acknowledged as a major cause of wildland fires in Canada. On average, 250,000 cloud-to-ground lightning strikes occur in Alberta every year. Lightning-caused wildland fires in remote areas have considerably larger suppression costs and a much greater chance of escaping initial attack. Geographic and temporal covariates were paired with Reanalysis and Radiosonde observations to generate a series of 6-hour and 24-hour lightning prediction models valid from April to October. These models, based on cloud-to-ground lightning from the CLDN, were developed and validated for the province of Alberta, Canada. The ensemble forecasts produced from these models were most accurate in the Rocky Mountain and Foothills Natural Regions achieving hits rates of ~85%. The Showalter index, convective available potential energy, Julian day, and geographic covariates were highly important predictors. Random forest classification is introduced as a viable modelling method to generate lightning forecasts. Limitations and recommendations are also discussed. ACKNOWLEDGEMENTS I am sincerely grateful to my Supervisor, Dr. Mike Flannigan. His continual support, encouragement, and genuine concern for all of his students did not go unnoticed. I appreciate the input and recommendation made by my committee members, Bob Kochtubajda, Dr. Scott Nielsen (chair), Dr. Gerhard Reuter, and Dr. Xianli Wang. I am extremely thankful for the numerous hours of programming support provided by Dr. Xianli Wang of the University of Alberta. Without his expertise this could not have been accomplished. I am also thankful to the Western Partnership for Wildland Fire Science and the University of Alberta for providing the opportunity for this research to take place. A big thank you to the Fire Lab Group for being a sounding board and continually providing new and interesting perspectives. I am very thankful for my family and their never ending support and love (even the tough love). Finally, I thank my husband Jerald for being my rock and always encouraging me to chase my dreams. The funding for this study was generously provided by Alberta Environment and Sustainable Resource Development. Lightning data were provided by Bob Kochtubajda of Environment Canada, and Radiosonde data were provided by Dr. Larry Oolman of the University of Wyoming. CONTENTS ABSTRACT ACKNOWLEDGEMENTS 3 LIST OF FIGURES LIST OF ACRONYMS CHAPTER 1. INTRODUCTION 1 1.1 PREAMBLE 1 1.2 LIGHTNING : BACKGROUND 2 1.3 CONVECTIVE BASICS 4 1.4 CLOUD ELECTRIFICATION 8 1.5 LIGHTNING 10 1.6 FIRE 14 1.7 LIGHTNING-FIRE INTERACTIONS 19 1.8 PREVIOUS WORKS 22 1.9 OBJECTIVES 25 CHAPTER 2. DATA AND METHODS 27 2.1 STUDY AREA 27 2.2 DATA 28 2.2.1 PREDICTAND 31 2.2.2 PREDICTORS 32 Geographic and Temporal Covariates 32 Reanalysis ІІ : Pressure Levels 32 Reanalysis ІІ : Surface Grid 33 Reanalysis І : Pressure Levels 34 Radiosonde Observations 34 Calculated Variables 38 2.3 DATA PROCESSING AND MODIFICATIONS 40 2.3.1 LIGHTNING DATA PROCESSING 42 2.3.2 REANALYSIS DATA PROCESSING 43 2.3.3 RADIOSONDE DATA PROCESSING 44 2.3.4 THIN-PLATE SPLINE 45 2.4 MODELLING METHODS 48 2.4.1 RANDOM FOREST: BACKGROUND 48 2.4.2 CONUNDRUM OF IMBALANCED DATA 49 2.4.3 RANDOM FOREST : MODELLING 51 Preliminary Runs 51 Creating Random Forest Models 53 CHAPTER 3. RESULTS 56 3.1 EXPLORATORY RUNS 56 3.2 DETERMINING TOP PREDICTORS 60 3.3 LIGHTNING PREDICTION MODELS 64 3.3.1 2.5° LATITUDE by 2.5° LONGITUDE 67 Daily Lightning Prediction 67 6-hour Lightning Prediction 68 3.3.2 1.25° LATITUDE by 2.5° LONGITUDE 70 Daily Lightning Prediction 71 6-hour Lightning Prediction 72 3.3.3 50KM BY 50KM 74 Boreal Forest : Daily Lightning Prediction 74 Boreal Forest : 6-hour Lightning Prediction 76 Parkland and Grassland : Daily Lightning Prediction 79 Parkland and Grassland : 6-hour Lightning Prediction 82 Mountain and Foothills : Daily Lightning Prediction 84 Mountain and Foothills : 6-hour Lightning Prediction 85 CHAPTER 4. DISCUSSION 91 4.1 TOP MODEL SELECTION 91 4.2 PREDICTOR IMPORTANCE AND CONTRIBUTION 93 4.2.1 PARTIAL DEPENDENCE PLOTS 93 4.3 RANDOM FOREST MODELLING 98 4.3.1 BENEFITS 99 4.3.2 DATA IMBALANCE 99 4.3.3 OOB ERROR ESTIMATE VS. INDEPENDENT PREDICTIONS 100 4.3.4 EFFECTS OF CORRELATION 101 4.3.5 NUMBER OF TREES 101 4.3.6 DISADVANTAGES 102 4.4 POSSIBLE SOURCES OF ERROR 102 4.4.1 THIN-PLATE SPLINE 103 4.4.2 LIGHTNING MISCLASSIFICATION 103 4.5 LOOKING FORWARD 104 CHAPTER 5. CONCLUSIONS 106 BIBLIOGRAPHY 109 LIST OF TABLES Table 1: List of input variables. Abbreviations, full names, units of measure, and some additional information about data sources and resolution are given for all of the predictors initially considered. __________________________________________________________________ 30 Table 2: List of the 10 data sets created for the two time frames (daily and 6-hour) and three spatial scales. The finest resolution scale (50km by 50km) is further subdivided into three separate zones. The number of observations remaining after all rows with NAs present were removed are also provided for all data sets. These 10 data sets are the basis for the lightning prediction models. _____________________________________________________________________ 47 Table 3: Contingency matrix for model forecasts. The A and D cells (highlighted in grey) show the correctly forecasted events and non-events, respectively. ______________________________ 55 Table 4: List of the forecast skill criterion used to analyses the lightning prediction models. The inputs for the formulas can be found in the contingency matrix shown in Table 3. ___________ 55 Table 5: Level of imbalance between the positive (events) and negative class (non-events) for each data set. The number of observations are for the training sets, however, the proportions hold true for both the training and validation sets. ___________________________________________ 58 Table 6: Variable importance for a 1:1 BRF model with ntree=300 generated for the 6-hour Boreal Forest data set. The higher the MeanDecreaseGini value, the greater the variable importance and thus the rank. ___________________________________________________ 62 Table 7: List of top 15 predictors for each data set. Based on models built with a Balanced Random Forest (BRF) approach. The various data sets are listed. _______________________ 63 Table 8: Six predictions models were generated with the top 15 and top 12 variables from each data set. Let n be the number of observations in the minority class. The second and third columns show how the sampsize arguments for each model were specified to change the number of observations sampled from each class with replacement. The unbalanced model was run under default conditions making the imbalance similar to the original data imbalance outlined in Table 5. __________________________________________________________________________ 64 Table 9: Optimum daily ensemble models (0.6:1) produced with 15 variables for the 2.5° latitude by 2.5° longitude scale. The 15 variable balanced models are included for comparison. The three event forecast thresholds of ≥ 0.9, ≥ 0.7, and ≥ 0.5 are shown for each model. As H increases, the FAR and F increase, and the PAG and PC decrease. _________________________________ 68 Table 10: Comparison of the two top ensemble models (0.6:1) produced with the top 12 and 15 variables for the 2.5° latitude by 2.5° longitude 6-hour forecast. The ensemble variations for the three event forecast thresholds of ≥ 0.9, ≥ 0.7, and ≥ 0.5 are shown for each model. _________ 69 Table 11: Comparison of the top two daily ensembles lightning forecast models for the 1.25° latitude by 2.5° longitude scale. The three event forecast thresholds of ≥ 0.9, ≥ 0.7, and ≥ 0.5 are shown for each model. _________________________________________________________ 72 Table 12: Forecast skill comparison for the top two 6-hour ensembles lightning forecast models for the 1.25° latitude by 2.5° longitude scale. The three event forecast thresholds of ≥ 0.9, ≥ 0.7, and ≥ 0.5 are shown for each model. ______________________________________________ 73 Table 13: Ensemble forecast skill comparison of the optimum forecast model (five variable 0.6:1) generated for daily lightning prediction in the Boreal Forest. The optimum model is compared to a model variation often selected as a top forecast model for the coarser spatial scales (12 variable 0.6:1). The three event forecast thresholds of ≥ 0.9, ≥ 0.7, and ≥ 0.5 are shown for each model. 76 Table 14: Ensemble forecast skill comparison of the 0.6:1 BRF models generated for 6-hour lightning prediction in the Boreal Forest. Ensemble models created from the top 12, 10, eight and five variables are included. The three event forecast thresholds of ≥ 0.9, ≥ 0.7, and ≥ 0.5 are shown for each model. _________________________________________________________ 79 Table 15: Comparison of the daily Parkland and Grassland ensemble forecasts generated from the 0.6:1 BRF models for the top 12, 10, eight and five variables.
Recommended publications
  • Correlating the Electrification of Volcanic Plumes With

    Correlating the Electrification of Volcanic Plumes With

    Earth and Planetary Science Letters 492 (2018) 47–58 Contents lists available at ScienceDirect Earth and Planetary Science Letters www.elsevier.com/locate/epsl Correlating the electrification of volcanic plumes with ashfall textures at Sakurajima Volcano, Japan ∗ Cassandra M. Smith a, , Alexa R. Van Eaton b, Sylvain Charbonnier a, Stephen R. McNutt a, Sonja A. Behnke c, Ronald J. Thomas d, Harald E. Edens d, Glenn Thompson a a University of South Florida, School of Geosciences, Tampa, FL, United States of America b U.S. Geological Survey, Cascades Volcano Observatory, Vancouver, WA, United States of America c Los Alamos National Laboratory, Los Alamos, NM, United States of America d New Mexico Institute of Mining and Technology, Department of Physics, Socorro, NM, United States of America a r t i c l e i n f o a b s t r a c t Article history: Volcanic lightning detection has become a useful resource for monitoring remote, under-instrumented Received 7 September 2017 volcanoes. Previous studies have shown that the behavior of volcanic plume electrification responds to Received in revised form 26 March 2018 changes in the eruptive processes and products. However, there has not yet been a study to quantify the Accepted 27 March 2018 links between ash textures and plume electrification during an actively monitored eruption. In this study, Available online 11 April 2018 we examine a sequence of vulcanian eruptions from Sakurajima Volcano in Japan to compare ash textural Editor: T.A. Mather properties (grain size, shape, componentry, and groundmass crystallinity) to plume electrification using Keywords: a lightning mapping array and other monitoring data.
  • CURRÍCULUM VITAE Centro De Ciencias Aplicadas Y Desarrollo Tecnológico

    CURRÍCULUM VITAE Centro De Ciencias Aplicadas Y Desarrollo Tecnológico

    Nombre Completo (Apellidos y Nombres): Sobral, Hugo Martín Fecha de Actualización: marzo de 2014 CURRÍCULUM VITAE Centro de Ciencias Aplicadas y Desarrollo Tecnológico 1. DATOS PERSONALES Nombre completo: Sobral, Hugo Martín Domicilio particular completo: Luis Enrique Erro # 14, Col. Copilco El Alto, C.P. 04360, Coyoacán, México DF, México. Domicilio laboral completo: Centro de Ciencias Aplicadas y Desarrollo Tecnológico, UNAM; Circuito Exterior s/n, C.P. 04510, Ciudad Universitaria; México D.F., México. Teléfono(s), fax: (+5255) 5622-8602 ext 1177; (+5255) 5622-8651 Correo electrónico: [email protected] Página Electrónica de WEB http://www.cinstrum.unam.mx/grupos/laboratorio.jsp?id=19 Lugar y fecha de nacimiento: La Plata, Bs. As., Argentina, 11 de noviembre de 1967 Estado civil: Soltero Nacionalidad: Argentina Registro federal de causantes: SOHU671111658 Clave única de registro de población: SOXH671111HNEBXG07 2. FORMACIÓN ACADÉMICA 2.1 Licenciatura Licenciatura en Física, Universidad Nacional de La Plata, Argentina, Título de la Tesis: “Estudio Resuelto en Tiempo de la Emisión de un Láser Iónico de Xenón”, Director de Tesis: Jorge Tocho, 1994. 2.4 Doctorado Doctorado de la Facultad de Ciencias Exactas, Física, Universidad Nacional de La Plata, Argentina, 1995-1999; Título de la Tesis: “Mecanismos de excitación de la emisión láser en un plasma de xenón múltiplemente ionizado”, Director de Tesis: Mario Gallardo, Fecha de obtención del grado: 3 de mayo de 1999. 2.5 Posdoctorado Universidad Nacional Autónoma de México, Centro de Instrumentos, Laboratorio de Óptica Aplicada, Investigador responsable: Mayo Villagrán Muniz, Proyecto realizado: Diagnóstico de Plasmas Inducidos por Láser, julio de 1999 a diciembre de 2000.
  • Äikesega) Kaasnevad Ohtlikud Ilmanähtused

    Äikesega) Kaasnevad Ohtlikud Ilmanähtused

    TALLINNA TEHNIKAÜLIKOOL Eesti Mereakadeemia Merenduskeskus Veeteede lektoraat Raldo Täll RÜNKSAJUPILVEDEGA KAASNEVAD OHTLIKUD ILMANÄHTUSED LÄÄNEMEREL Lõputöö Juhendajad: Jüri Kamenik Lia Pahapill Tallinn 2016 SISUKORD SISUKORD ................................................................................................................................ 2 SÕNASTIK ................................................................................................................................ 4 SISSEJUHATUS ........................................................................................................................ 6 1. RÜNKSAJUPILVED JA ÄIKE ............................................................................................. 8 1.1. Äikese tekkimine ja areng ............................................................................................. 10 1.1.1. Äikese arengustaadiumid ........................................................................................ 11 1.2. Äikeste klassifikatsioon ................................................................................................. 14 1.2.1. Sünoptilise olukorra põhine liigitus ........................................................................ 14 1.2.2. Äikese seos tsüklonitega ......................................................................................... 15 1.2.3. Organiseerumispõhine liigitus ................................................................................ 16 2. RÜNKSAJUPILVEDEGA (ÄIKESEGA) KAASNEVAD OHTLIKUD ILMANÄHTUSED
  • Advances in MS-Based Analytical Methods: Innovations and Future Trends

    Advances in MS-Based Analytical Methods: Innovations and Future Trends

    Journal of Analytical Methods in Chemistry Advances in MS-Based Analytical Methods: Innovations and Future Trends Lead Guest Editor: Federica Bianchi Guest Editors: Leopold Ilag, Veronica Termopoli, and Lucia Mendez Advances in MS-Based Analytical Methods: Innovations and Future Trends Journal of Analytical Methods in Chemistry Advances in MS-Based Analytical Methods: Innovations and Future Trends Lead Guest Editor: Federica Bianchi Guest Editors: Leopold Ilag, Veronica Termopoli, and Lucia Mendez Copyright © 2018 Hindawi. All rights reserved. This is a special issue published in “Journal of Analytical Methods in Chemistry.” All articles are open access articles distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Editorial Board Mohamed Abdel-Rehim, Sweden Karoly Heberger, Hungary Pablo Richter, Chile Hassan Y. Aboul Enein, Egypt A. V. Herrera-Herrera, Spain Fábio Rodrigo Piovezan Rocha, Brazil Silvana Andreescu, USA Eliseo Herrero-Hernández, Spain Erwin Rosenberg, Austria Aristidis N. Anthemidis, Greece Bernd Hitzmann, Germany Jose Vicente Ros-Lis, Spain Alessandro Buccolieri, Italy Chih-Ching Huang, Taiwan Giuseppe Ruberto, Italy Antony C. Calokerinos, Greece Jaroon Jakmunee, Thailand Antonio Ruiz Medina, Spain Luca Campone, Italy Christos Kontoyannis, Greece Bradley B. Schneider, Canada Ricardo Jorgensen Cassella, Brazil Radosław Kowalski, Poland Jesus Simal-Gandara, Spain Angela Chambery, Italy Eulogio J. Llorent-Martínez, Spain Hana Sklenarova, Czech Republic Igor Chourpa, France Mercedes G. Lopez, Mexico Beate Strehlitz, Germany Filomena Conforti, Italy Miren Lopez de Alda, Spain Luca Tortora, Italy Guido Crisponi, Italy Larisa Lvova, Italy Marek Trojanowicz, Poland Eduardo Dellacassa, Uruguay Jose Carlos Marques, Portugal Bengi Uslu, Turkey Ana María Díez-Pascual, Spain Christophe A.
  • Was the Eruption of Ferdinandea, Near Sicily, One of the Largest Volcanic Climate Forcing Events of the Nineteenth Century?

    Was the Eruption of Ferdinandea, Near Sicily, One of the Largest Volcanic Climate Forcing Events of the Nineteenth Century?

    https://doi.org/10.5194/cp-2021-78 Preprint. Discussion started: 5 August 2021 c Author(s) 2021. CC BY 4.0 License. The blue suns of 1831: was the eruption of Ferdinandea, near Sicily, one of the largest volcanic climate forcing events of the nineteenth century? 5 Christopher Garrison1, Christopher Kilburn1, David Smart 1, Stephen Edwards 1. 1 UCL Hazard Centre, Department of Earth Sciences, University College London, Gower Street, London, WC1E 6BT, UK. Correspondence to: Christopher Garrison ([email protected]) Abstract. One of the largest climate forcing eruptions of the nineteenth century was, until recently, 10 believed to have taken place at Babuyan Claro volcano, in the Philippines, in 1831. However, a recent investigation found no reliable evidence of such an eruption, suggesting that the 1831 eruption must have taken place elsewhere. A newly compiled dataset of reported observations of a blue, purple and green sun in August 1831 is here used to reconstruct the transport of a stratospheric aerosol plume from that eruption. The source of the aerosol plume is identified as the eruption of Ferdinandea, which took place 15 about 50 km off the south-west coast of Sicily (lat. 37.1o N., long. 12.7o E.), in July and August 1831. The modest magnitude of this eruption, assigned a Volcanic Explosivity Index (VEI) of 3, has commonly caused it to be discounted or overlooked when identifying the likely source of the stratospheric sulphate aerosol in 1831. It is proposed, however, that convective instability in the troposphere contributed to aerosol reaching the stratosphere and that the aerosol load was enhanced by addition of a sedimentary 20 sulphur component to the volcanic plume.
  • International Atlas of Clouds and of States of the Sky

    International Atlas of Clouds and of States of the Sky

    INTERNATIONAL METEOROLOGICAL COMMITTEE COMMISSION FOR THE STUDY OF CLOUDS International Atlas of Clouds and of States of the Sky ABRIDGED EDITION FOR THE USE OF OBSERVERS PARIS Office National Meteorologique, Rue de I'Universite, 176 193O International Atlas of Clouds and of States of the Sky THIS WORK FOR THE USE OF OBSERVERS CONSISTS OF : 1. This volume of text. 2. An album of 41 plates. It is an abreviation of the complete work : The International Atlas of Clouds and of States of the Sky. It is published thanks to the generosity of The Paxtot Institute of Catalonia. INTERNATIONAL METEOROLOGICAL COMMITTEE COMMISSION FOR THE STUDY OF CLOUDS International Atlas of Clouds and of States of the Sky ABRIDGED EDITION FOR THE USE OF OBSERVERS Kon. Nad. Metoor. Intl. De Bilt PARIS Office National Meteorologique. Rue de I'Universite. 176 193O In memory of our Friend A. DE QUERVAIN Member of the International Commluion for the Study of Cloudt INTRODUCTION Since 1922 the International Commission for the Study of Clouds has been engaged in studying the classification of clouds for a new International Atlas. The complete work will appear shortly, and in it will be found a history of the undertaking. This atlas is only a summary of the complete work, and is intended for the use of observers. The necessity for it was realised by the Inter- national Conference of Directors, in order to elucidate the new inter- national cloud code; this is based on the idea of the state of the sky, but observers should be able to use it without difficulty for the separate analysis of low, middle, and high clouds.
  • First in Situ Observations of Gaseous Volcanic Plume Electrification

    First in Situ Observations of Gaseous Volcanic Plume Electrification

    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Central Archive at the University of Reading First in‐ situ observations of gaseous volcanic plume electrification Article Published Version Creative Commons: Attribution 4.0 (CC-BY) Open access Nicoll, K., Airey, M., Cimarelli, C., Bennett, A., Harrison, G., Gaudin, D., Aplin, K., Koh, K. L., Knuever, M. and Marlton, G. (2019) First in‐ situ observations of gaseous volcanic plume electrification. Geophysical Research Letters. ISSN 0094-8276 doi: https://doi.org/10.1029/2019GL082211 Available at http://centaur.reading.ac.uk/82977/ It is advisable to refer to the publisher's version if you intend to cite from the work. See Guidance on citing . Published version at: http://dx.doi.org/10.1029/2019GL082211 To link to this article DOI: http://dx.doi.org/10.1029/2019GL082211 Publisher: Wiley All outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders. Terms and conditions for use of this material are defined in the End User Agreement . www.reading.ac.uk/centaur CentAUR Central Archive at the University of Reading Reading's research outputs online RESEARCH LETTER First In Situ Observations of Gaseous Volcanic 10.1029/2019GL082211 Plume Electrification Key Points: Keri Nicoll1,2 , Martin Airey1 , Corrado Cimarelli3 , Alec Bennett2,4, Giles Harrison1, • We present the first in situ profiles of 3 5 6 3 thermodynamic, microphysical, and Damien Gaudin
  • Volcanology: Electric Eruption

    Volcanology: Electric Eruption

    news & views VOLCANOLOGY Electric eruption On the morning of 24 August, ad 79, Mount Vesuvius in the Bay of Naples, Italy, exploded into activity. The eruption caught the roman occupants of Pompeii and Herculaneum utterly unawares. The cities were rapidly buried by thick ash fall and pyroclastic surges, killing thousands of people. In a letter to the historian Tacitus, Pliny the Younger, an eyewitness and survivor of the eruption, described “frightening dark clouds, rent by lightning twisted and hurled” (http://go.nature.com/gkcLgn). Volcanic lightning has since been documented during many historical eruptions. More recently, spectacular lightning was observed during the eruptions of Mount Redoubt in Alaska, USA, in 2009 and Eyjafjallajökull Volcano in Iceland in 2010. The lightning generally occurs within the volcanic plume — a spreading column of ash, rock and hot gases blasted from the volcano vent. Although the details of the processes are poorly understood, volcanic lightning is thought to be triggered by the fragmentation of, and high-speed collisions between, volcanic rocks and ash as they exit the vent, as well as by the interactions between super-cooled water / ALAMY IMAGES © ARCTIC droplets and ice particles in higher, cooler parts of the column. The collisions create positive and negative electrically charged analysed ash deposits collected from The glass spherules mostly formed regions within the volcanic plume that are the Mount Redoubt and Eyjafjallajökull from melting of the smallest individual ash equalized by an electrical discharge that eruptions and found tiny glass spherules grains or from the fusion of several melted creates a lightning flash. (less than 100 μm across), which could ash grains.
  • Convection Questions.Cdr

    Convection Questions.Cdr

    Name: _________________________ Sketch the four stages of thunderstorm development in the boxes below. This is not an art project, but give it a decent effort. For each cloud sketch, include the arrows showing the direction of the air movement going on - both in the cloud and near the ground (include both updrafts and downdrafts). Illustrate any processes going on inside the cloud. Cumulus mediocris Cumulus congestus (prior to ice formation) Cumulonimbus calvus Cumulonimbus incus Questions: 1. In the morning, you see some cumulus humilus. A few hours later (around noon), you still see cumulus humilus, with a few cumulus mediocris here and there. Later that afternoon, you look again and the sky is still littered with cumulus humilus and mediocris. Although the clouds are clearly moving around and changing shape, they really don't look any different than they did this morning. What can you conclude from this observation? A. The air is mostly Stable / Unstable (pick one) B. Thunderstorms are Likely / Unlikely (pick one) 2. In the morning, you see some cumulus humilus. A few hours later (around noon) you see a lot of cumulus congestus and some cumulus castellanus. What can you conclude from this observations? A. The air is mostly Stable / Unstable (pick one) B. Thunderstorms are Likely / Unlikely (pick one) 3. You are watching the local news and the weather reporter says that "Unstable air is moving into New Mexico for the next few days." What kind of weather do you expect in the next few days? A. Clear skies. B. Maybe some scattered cumulus but nothing else.
  • Anak Krakatau Triggers Volcanic Freezer in the Upper Troposphere A

    Anak Krakatau Triggers Volcanic Freezer in the Upper Troposphere A

    www.nature.com/scientificreports Corrected: Author Correction OPEN Anak Krakatau triggers volcanic freezer in the upper troposphere A. T. Prata 1*, A. Folch1, A. J. Prata2,3, R. Biondi4, H. Brenot5, C. Cimarelli 6, S. Corradini7, J. Lapierre8 & A. Costa 9 Volcanic activity occurring in tropical moist atmospheres can promote deep convection and trigger volcanic thunderstorms. These phenomena, however, are rarely observed to last continuously for more than a day and so insights into the dynamics, microphysics and electrifcation processes are limited. Here we present a multidisciplinary study on an extreme case, where volcanically-triggered deep convection lasted for six days. We show that this unprecedented event was caused and sustained by phreatomagmatic activity at Anak Krakatau volcano, Indonesia during 22–28 December 2018. Our modelling suggests an ice mass fow rate of ~5 × 106 kg/s for the initial explosive eruption associated with a fank collapse. Following the fank collapse, a deep convective cloud column formed over the volcano and acted as a ‘volcanic freezer’ containing ~3 × 109 kg of ice on average with maxima reaching ~1010 kg. Our satellite analyses reveal that the convective anvil cloud, reaching 16–18 km above sea level, was ice-rich and ash-poor. Cloud-top temperatures hovered around −80 °C and ice particles produced in the anvil were notably small (efective radii ~20 µm). Our analyses indicate that vigorous updrafts (>50 m/s) and prodigious ice production explain the impressive number of lightning fashes (~100,000) recorded near the volcano from 22 to 28 December 2018. Our results, together with the unique dataset we have compiled, show that lightning fash rates were strongly correlated (R = 0.77) with satellite-derived plume heights for this event.
  • Facing the Challenges of the International Airways Volcano

    Facing the Challenges of the International Airways Volcano

    FEBRUARY 2007 T U P P E R E T A L . 175 Facing the Challenges of the International Airways Volcano Watch: The 2004/05 Eruptions of Manam, Papua New Guinea ϩ ANDREW TUPPER,* IMA ITIKARAI, MICHAEL RICHARDS,# FRED PRATA,@ SIMON CARN,& AND DANIEL ROSENFELD** *Bureau of Meteorology, Casuarina, Northern Territory, and School of Mathematical Sciences, Monash University, Victoria, Australia ϩRabaul Volcanological Observatory, Rabaul, Papua New Guinea #Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin @Norwegian Institute for Air Research, Kjeller, Norway &Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, Maryland **Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel (Manuscript received 17 March 2006, in final form 30 May 2006) ABSTRACT Devastating eruptions occurred at Manam, Papua New Guinea, from October 2004 to January 2005. An unprecedented set of pilot reports were obtained; ground-, air-, and satellite-observed eruption heights differed greatly. Satellite postanalysis and satellite CO2 slicing techniques give consistent heights. The climactic eruption, on 27 January 2005, reached 21–24 km MSL; four other eruptions reached 16.5–19 km. Tracking of these ice-rich clouds was done by monitoring strong “ice” signatures on 11–12-␮m infrared imagery (for two eruptions), by using reflectance-based techniques (during the daytime), and by using SO2 detection (available only in postanalysis). A remote lightning detection network could not detect the eruption clouds, despite detecting lightning from thunderstorms in the area. The eruptions appeared to enhance the nocturnal cycle of (ash contaminated) deep convection above the island, consistent with previous work on diurnal volcanic cumulonimbus at Mount Pinatubo.
  • The Ten Different Types of Clouds

    The Ten Different Types of Clouds

    THE COMPLETE GUIDE TO THE TEN DIFFERENT TYPES OF CLOUDS AND HOW TO IDENTIFY THEM Dedicated to those who are passionately curious, keep their heads in the clouds, and keep their eyes on the skies. And to Luke Howard, the father of cloud classification. 4 Infographic 5 Introduction 12 Cirrus 18 Cirrocumulus 25 Cirrostratus 31 Altocumulus 38 Altostratus 45 Nimbostratus TABLE OF CONTENTS TABLE 51 Cumulonimbus 57 Cumulus 64 Stratus 71 Stratocumulus 79 Our Mission 80 Extras Cloud Types: An Infographic 4 An Introduction to the 10 Different An Introduction to the 10 Different Types of Clouds Types of Clouds ⛅ Clouds are the equivalent of an ever-evolving painting in the sky. They have the ability to make for magnificent sunrises and spectacular sunsets. We’re surrounded by clouds almost every day of our lives. Let’s take the time and learn a little bit more about them! The following information is presented to you as a comprehensive guide to the ten different types of clouds and how to idenify them. Let’s just say it’s an instruction manual to the sky. Here you’ll learn about the ten different cloud types: their characteristics, how they differentiate from the other cloud types, and much more. So three cheers to you for starting on your cloud identification journey. Happy cloudspotting, friends! The Three High Level Clouds Cirrus (Ci) Cirrocumulus (Cc) Cirrostratus (Cs) High, wispy streaks High-altitude cloudlets Pale, veil-like layer High-altitude, thin, and wispy cloud High-altitude, thin, and wispy cloud streaks made of ice crystals streaks