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How Does Convective Self-Aggregation Affect Precipitation Extremes?
EGU21-5216 https://doi.org/10.5194/egusphere-egu21-5216 EGU General Assembly 2021 © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License. How does convective self-aggregation affect precipitation extremes? Nicolas Da Silva1, Sara Shamekh2, Caroline Muller2, and Benjamin Fildier2 1Centre for Ocean and Atmospheric Sciences, School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom 2Laboratoire de Météorologie Dynamique (LMD)/Institut Pierre Simon Laplace (IPSL), École Normale Supérieure, Paris Sciences & Lettres (PSL) Research University, Sorbonne Université, École Polytechnique, CNRS, Paris, France Convective organisation has been associated with extreme precipitation in the tropics. Here we investigate the impact of convective self-aggregation on extreme rainfall rates. We find that convective self-aggregation significantly increases precipitation extremes, for 3-hourly accumulations but also for instantaneous rates (+ 30 %). We show that this latter enhanced instantaneous precipitation is mainly due to the local increase in relative humidity which drives larger accretion efficiency and lower re-evaporation and thus a higher precipitation efficiency. An in-depth analysis based on an adapted scaling of precipitation extremes, reveals that the dynamic contribution decreases (- 25 %) while the thermodynamic is slightly enhanced (+ 5 %) with convective aggregation, leading to lower condensation rates (- 20 %). When the atmosphere is more organized into a moist convecting region, and a dry convection-free region, deep convective updrafts are surrounded by a warmer environment which reduces convective instability and thus the dynamic contribution. The moister boundary-layer explains the positive thermodynamic contribution. The microphysic contribution is increased by + 50 % with aggregation. The latter is partly due to reduced evaporation of rain falling through a moister near-cloud environment (+ 30 %), but also to the associated larger accretion efficiency (+ 20 %). -
Cloud Characteristics, Thermodynamic Controls and Radiative Impacts During the Observations and Modeling of the Green Ocean Amazon (Goamazon2014/5) Experiment
Atmos. Chem. Phys., 17, 14519–14541, 2017 https://doi.org/10.5194/acp-17-14519-2017 © Author(s) 2017. This work is distributed under the Creative Commons Attribution 3.0 License. Cloud characteristics, thermodynamic controls and radiative impacts during the Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) experiment Scott E. Giangrande1, Zhe Feng2, Michael P. Jensen1, Jennifer M. Comstock1, Karen L. Johnson1, Tami Toto1, Meng Wang1, Casey Burleyson2, Nitin Bharadwaj2, Fan Mei2, Luiz A. T. Machado3, Antonio O. Manzi4, Shaocheng Xie5, Shuaiqi Tang5, Maria Assuncao F. Silva Dias6, Rodrigo A. F de Souza7, Courtney Schumacher8, and Scot T. Martin9 1Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA 2Pacific Northwest National Laboratory, Richland, WA, USA 3National Institute for Space Research, São José dos Campos, Brazil 4National Institute of Amazonian Research, Manaus, Amazonas, Brazil 5Lawrence Livermore National Laboratory, Livermore, CA, USA 6University of São Paulo, São Paulo, Department of Atmospheric Sciences, Brazil 7State University of Amazonas (UEA), Meteorology, Manaus, Brazil 8Texas A&M University, College Station, Department of Atmospheric Sciences, TX, USA 9Harvard University, Cambridge, School of Engineering and Applied Sciences and Department of Earth and Planetary Sciences, MA, USA Correspondence to: Scott E. Giangrande ([email protected]) Received: 12 May 2017 – Discussion started: 24 May 2017 Revised: 30 August 2017 – Accepted: 11 September 2017 – Published: 6 December 2017 Abstract. Routine cloud, precipitation and thermodynamic 1 Introduction observations collected by the Atmospheric Radiation Mea- surement (ARM) Mobile Facility (AMF) and Aerial Facility The simulation of clouds and the representation of cloud (AAF) during the 2-year US Department of Energy (DOE) processes and associated feedbacks in global climate mod- ARM Observations and Modeling of the Green Ocean Ama- els (GCMs) remains the largest source of uncertainty in zon (GoAmazon2014/5) campaign are summarized. -
Manuscript Was Written Jointly by YL, CK, and MZ
A simplified method for the detection of convection using high resolution imagery from GOES-16 Yoonjin Lee1, Christian D. Kummerow1,2, Milija Zupanski2 1Department of Atmospheric Science, Colorado state university, Fort Collins, CO 80521, USA 5 2Cooperative Institute for Research in the Atmosphere, Colorado state university, Fort Collins, CO 80521, USA Correspondence to: Yoonjin Lee ([email protected]) Abstract. The ability to detect convective regions and adding heating in these regions is the most important skill in forecasting severe weather systems. Since radars are most directly related to precipitation and are available in high temporal resolution, their data are often used for both detecting convection and estimating latent heating. However, radar data are limited to land areas, 10 largely in developed nations, and early convection is not detectable from radars until drops become large enough to produce significant echoes. Visible and Infrared sensors on a geostationary satellite can provide data that are more sensitive to small droplets, but they also have shortcomings: their information is almost exclusively from the cloud top. Relatively new geostationary satellites, Geostationary Operational Environmental Satellites-16 and -17 (GOES-16 and GOES-17), along with Himawari-8, can make up for some of this lack of vertical information through the use of very high spatial and temporal 15 resolutions. This study develops two algorithms to detect convection at vertically growing clouds and mature convective clouds using 1-minute GOES-16 Advanced Baseline Imager (ABI) data. Two case studies are used to explain the two methods, followed by results applied to one month of data over the contiguous United States. -
Assessing and Improving Cloud-Height Based Parameterisations of Global Lightning Flash Rate, and Their Impact on Lightning-Produced Nox and Tropospheric Composition
https://doi.org/10.5194/acp-2020-885 Preprint. Discussion started: 2 October 2020 c Author(s) 2020. CC BY 4.0 License. Assessing and improving cloud-height based parameterisations of global lightning flash rate, and their impact on lightning-produced NOx and tropospheric composition 5 Ashok K. Luhar1, Ian E. Galbally1, Matthew T. Woodhouse1, and Nathan Luke Abraham2,3 1CSIRO Oceans and Atmosphere, Aspendale, 3195, Australia 2National Centre for Atmospheric Science, Department of Chemistry, University of Cambridge, Cambridge, UK 3Department of Chemistry, University of Cambridge, Cambridge, UK 10 Correspondence to: Ashok K. Luhar ([email protected]) Abstract. Although lightning-generated oxides of nitrogen (LNOx) account for only approximately 10% of the global NOx source, it has a disproportionately large impact on tropospheric photochemistry due to the conducive conditions in the tropical upper troposphere where lightning is mostly discharged. In most global composition models, lightning flash rates used to calculate LNOx are expressed in terms of convective cloud-top height via the Price and Rind (1992) (PR92) 15 parameterisations for land and ocean. We conduct a critical assessment of flash-rate parameterisations that are based on cloud-top height and validate them within the ACCESS-UKCA global chemistry-climate model using the LIS/OTD satellite data. While the PR92 parameterisation for land yields satisfactory predictions, the oceanic parameterisation underestimates the observed flash-rate density severely, yielding a global average of 0.33 flashes s-1 compared to the observed 9.16 -1 flashes s over the ocean and leading to LNOx being underestimated proportionally. We formulate new/alternative flash-rate 20 parameterisations following Boccippio’s (2002) scaling relationships between thunderstorm electrical generator power and storm geometry coupled with available data. -
Effect of Wind on Near-Shore Breaking Waves
EFFECT OF WIND ON NEAR-SHORE BREAKING WAVES by Faydra Schaffer A Thesis Submitted to the Faculty of The College of Engineering and Computer Science in Partial Fulfillment of the Requirements for the Degree of Master of Science Florida Atlantic University Boca Raton, Florida December 2010 Copyright by Faydra Schaffer 2010 ii ACKNOWLEDGEMENTS The author wishes to thank her mother and family for their love and encouragement to go to college and be able to have the opportunity to work on this project. The author is grateful to her advisor for sponsoring her work on this project and helping her to earn a master’s degree. iv ABSTRACT Author: Faydra Schaffer Title: Effect of wind on near-shore breaking waves Institution: Florida Atlantic University Thesis Advisor: Dr. Manhar Dhanak Degree: Master of Science Year: 2010 The aim of this project is to identify the effect of wind on near-shore breaking waves. A breaking wave was created using a simulated beach slope configuration. Testing was done on two different beach slope configurations. The effect of offshore winds of varying speeds was considered. Waves of various frequencies and heights were considered. A parametric study was carried out. The experiments took place in the Hydrodynamics lab at FAU Boca Raton campus. The experimental data validates the knowledge we currently know about breaking waves. Offshore winds effect is known to increase the breaking height of a plunging wave, while also decreasing the breaking water depth, causing the wave to break further inland. Offshore winds cause spilling waves to react more like plunging waves, therefore increasing the height of the spilling wave while consequently decreasing the breaking water depth. -
HS Science Distance Learning Activities
HS Science (Earth Science/Physics) Distance Learning Activities TULSA PUBLIC SCHOOLS Dear families, These learning packets are filled with grade level activities to keep students engaged in learning at home. We are following the learning routines with language of instruction that students would be engaged in within the classroom setting. We have an amazing diverse language community with over 65 different languages represented across our students and families. If you need assistance in understanding the learning activities or instructions, we recommend using these phone and computer apps listed below. Google Translate • Free language translation app for Android and iPhone • Supports text translations in 103 languages and speech translation (or conversation translations) in 32 languages • Capable of doing camera translation in 38 languages and photo/image translations in 50 languages • Performs translations across apps Microsoft Translator • Free language translation app for iPhone and Android • Supports text translations in 64 languages and speech translation in 21 languages • Supports camera and image translation • Allows translation sharing between apps 3027 SOUTH NEW HAVEN AVENUE | TULSA, OKLAHOMA 74114 918.746.6800 | www.tulsaschools.org TULSA PUBLIC SCHOOLS Queridas familias: Estos paquetes de aprendizaje tienen actividades a nivel de grado para mantener a los estudiantes comprometidos con la educación en casa. Estamos siguiendo las rutinas de aprendizaje con las palabras que se utilizan en el salón de clases. Tenemos una increíble -
Extratropical Cyclones and Anticyclones
© Jones & Bartlett Learning, LLC. NOT FOR SALE OR DISTRIBUTION Courtesy of Jeff Schmaltz, the MODIS Rapid Response Team at NASA GSFC/NASA Extratropical Cyclones 10 and Anticyclones CHAPTER OUTLINE INTRODUCTION A TIME AND PLACE OF TRAGEDY A LiFE CYCLE OF GROWTH AND DEATH DAY 1: BIRTH OF AN EXTRATROPICAL CYCLONE ■■ Typical Extratropical Cyclone Paths DaY 2: WiTH THE FI TZ ■■ Portrait of the Cyclone as a Young Adult ■■ Cyclones and Fronts: On the Ground ■■ Cyclones and Fronts: In the Sky ■■ Back with the Fitz: A Fateful Course Correction ■■ Cyclones and Jet Streams 298 9781284027372_CH10_0298.indd 298 8/10/13 5:00 PM © Jones & Bartlett Learning, LLC. NOT FOR SALE OR DISTRIBUTION Introduction 299 DaY 3: THE MaTURE CYCLONE ■■ Bittersweet Badge of Adulthood: The Occlusion Process ■■ Hurricane West Wind ■■ One of the Worst . ■■ “Nosedive” DaY 4 (AND BEYOND): DEATH ■■ The Cyclone ■■ The Fitzgerald ■■ The Sailors THE EXTRATROPICAL ANTICYCLONE HIGH PRESSURE, HiGH HEAT: THE DEADLY EUROPEAN HEAT WaVE OF 2003 PUTTING IT ALL TOGETHER ■■ Summary ■■ Key Terms ■■ Review Questions ■■ Observation Activities AFTER COMPLETING THIS CHAPTER, YOU SHOULD BE ABLE TO: • Describe the different life-cycle stages in the Norwegian model of the extratropical cyclone, identifying the stages when the cyclone possesses cold, warm, and occluded fronts and life-threatening conditions • Explain the relationship between a surface cyclone and winds at the jet-stream level and how the two interact to intensify the cyclone • Differentiate between extratropical cyclones and anticyclones in terms of their birthplaces, life cycles, relationships to air masses and jet-stream winds, threats to life and property, and their appearance on satellite images INTRODUCTION What do you see in the diagram to the right: a vase or two faces? This classic psychology experiment exploits our amazing ability to recognize visual patterns. -
Sky Watch Heard Most Weekdays on WFWM, FSU's Public Supported
Night Highlights – Dec.2014 through Dec.2015 by Dr. Bob Doyle, Frostburg State Planetarium Dr. Doyle’s email is: [email protected]: His office phone number is (301) 687-7799 MOON – Earth’s companion both orbits Earth and rotates in 27.32 E. days so one side of moon always faces Earth (while other side is turned away from us). Moon’s cycle of lighted shapes (phases) lasts 29.53 E. days, as the phases also depend on direction of sun (appears to move 30 degrees eastward each month along zodiac). The moon is seen about 13 days growing in the evening from a slender crescent ( ) ) to Full, followed by an equal time shrinking (mainly seen in the a.m. sky) and then 3 days hidden in sun’s glare. Key Moon Phases (D) moon ½ full in evening (best for crater & mountain viewing ) & (O) full moon (see all lava plains) (Dec. ’14, 6 -O, 28 – D)) // (Jan. ’15, 4 - O, 26 - D), // (Feb. ’15, 3 – O, 25 - D) // (Mar. 5 – O, 27 – D) (Apr. 4 – O, 25 – D), // ( May 3 – O, 25 - D) // (Jun., 2- O, 27 – D)) // (Jul., 1 – O, 24 –D, 31 –O (Blue Moon)) (Aug., 22 – D, 29 – O) // (Sep., 20 – D, 27 – O (Harvest Moon)) // (Oct. 20 – D, 27 – O (Hunters’ Moon)) // (Nov., 19 – D, 25 – O) // (Dec., 18 – D, 25 – O (Long Night Moon)) (D = ½ full, O = full) THE 5 BRIGHT PLANETS (Mercury, Venus, Mars, Jupiter & Saturn): Uranus, Neptune are much dimmer. When high above horizon, planets appear as points of light that shine steadily. -
NWS Unified Surface Analysis Manual
Unified Surface Analysis Manual Weather Prediction Center Ocean Prediction Center National Hurricane Center Honolulu Forecast Office November 21, 2013 Table of Contents Chapter 1: Surface Analysis – Its History at the Analysis Centers…………….3 Chapter 2: Datasets available for creation of the Unified Analysis………...…..5 Chapter 3: The Unified Surface Analysis and related features.……….……….19 Chapter 4: Creation/Merging of the Unified Surface Analysis………….……..24 Chapter 5: Bibliography………………………………………………….…….30 Appendix A: Unified Graphics Legend showing Ocean Center symbols.….…33 2 Chapter 1: Surface Analysis – Its History at the Analysis Centers 1. INTRODUCTION Since 1942, surface analyses produced by several different offices within the U.S. Weather Bureau (USWB) and the National Oceanic and Atmospheric Administration’s (NOAA’s) National Weather Service (NWS) were generally based on the Norwegian Cyclone Model (Bjerknes 1919) over land, and in recent decades, the Shapiro-Keyser Model over the mid-latitudes of the ocean. The graphic below shows a typical evolution according to both models of cyclone development. Conceptual models of cyclone evolution showing lower-tropospheric (e.g., 850-hPa) geopotential height and fronts (top), and lower-tropospheric potential temperature (bottom). (a) Norwegian cyclone model: (I) incipient frontal cyclone, (II) and (III) narrowing warm sector, (IV) occlusion; (b) Shapiro–Keyser cyclone model: (I) incipient frontal cyclone, (II) frontal fracture, (III) frontal T-bone and bent-back front, (IV) frontal T-bone and warm seclusion. Panel (b) is adapted from Shapiro and Keyser (1990) , their FIG. 10.27 ) to enhance the zonal elongation of the cyclone and fronts and to reflect the continued existence of the frontal T-bone in stage IV. -
The Blue Hours Dusk, Early September, Just Beneath the Arctic
The Blue Hours Dusk, early September, just beneath the Arctic Circle, by a tideline glacier in East Greenland. The cusp of the seasons, the cusp of the globe, the cusp of the land, and the day’s cusp too: twilight, the blue hours. At this latitude, at this time of year, dusk lasts for two or three hours. We have returned from a long mountain day: pitched climbing up steep slabs and over snow slopes to a towered summit, from which height we could see the great inland ice-cap itself. Then down, late in the day, the darkness thickening around us, and the sun dropping fast behind the western peaks. So we sit together back at camp as the last light gathers on the water of the fjord, on icebergs, on the quartz seams in the white boulder-field above our tents. Twilight specifies the landscape in this way – but it also disperses it. Relations between objects are loosened, such that shape-shifts occur. Just before full night falls, and the aurora borealis begins, a powerful hallucination occurs. My tired eyes start to see every pale stone around our tent not as boulder but as bear, polar bear, pure bear, crouched for the spring. Across the Northern hemisphere, twilight is known as the trickster-time: breeder of delusion, feeder of fantasies, zone of becomings. In Greek, dusk is called lykophos, ‘wolf-light’. In Austria, too, it is Wolflicht. In French it is the phase entre chien et loup, ‘between dog and wolf’: the time when, as Chrystel Lebas has written, ‘it is nearly impossible to tell the difference between the howling sound coming from the two animals, when the domestic and familiar transform into the wild.’ I do not know the Greenlandic word for dusk, but perhaps it would translate as ‘bear-light’. -
Downloaded 09/29/21 11:17 PM UTC 4144 MONTHLY WEATHER REVIEW VOLUME 128 Ferentiate Among Our Concerns About the Application of TABLE 1
DECEMBER 2000 NOTES AND CORRESPONDENCE 4143 The Intricacies of Instabilities DAVID M. SCHULTZ NOAA/National Severe Storms Laboratory, Norman, Oklahoma PHILIP N. SCHUMACHER NOAA/National Weather Service, Sioux Falls, South Dakota CHARLES A. DOSWELL III NOAA/National Severe Storms Laboratory, Norman, Oklahoma 19 April 2000 and 30 May 2000 ABSTRACT In response to Sherwood's comments and in an attempt to restore proper usage of terminology associated with moist instability, the early history of moist instability is reviewed. This review shows that many of Sherwood's concerns about the terminology were understood at the time of their origination. De®nitions of conditional instability include both the lapse-rate de®nition (i.e., the environmental lapse rate lies between the dry- and the moist-adiabatic lapse rates) and the available-energy de®nition (i.e., a parcel possesses positive buoyant energy; also called latent instability), neither of which can be considered an instability in the classic sense. Furthermore, the lapse-rate de®nition is really a statement of uncertainty about instability. The uncertainty can be resolved by including the effects of moisture through a consideration of the available-energy de®nition (i.e., convective available potential energy) or potential instability. It is shown that such misunderstandings about conditional instability were likely due to the simpli®cations resulting from the substitution of lapse rates for buoyancy in the vertical acceleration equation. Despite these valid concerns about the value of the lapse-rate de®nition of conditional instability, consideration of the lapse rate and moisture separately can be useful in some contexts (e.g., the ingredients-based methodology for forecasting deep, moist convection). -
Large-Eddy Simulations of Dust Devils and Convective Vortices
Large-Eddy Simulations of Dust Devils and Convective Vortices Aymeric Spiga, Erika Barth, Zhaolin Gu, Fabian Hoffmann, Junshi Ito, Bradley Jemmett-Smith, Martina Klose, Seiya Nishizawa, Siegfried Raasch, Scot Rafkin, et al. To cite this version: Aymeric Spiga, Erika Barth, Zhaolin Gu, Fabian Hoffmann, Junshi Ito, et al.. Large-Eddy Simulations of Dust Devils and Convective Vortices. Space Science Reviews, Springer Verlag, 2016, 203, pp.245 - 275. 10.1007/s11214-016-0284-x. hal-01457992 HAL Id: hal-01457992 https://hal.sorbonne-universite.fr/hal-01457992 Submitted on 6 Feb 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Large-Eddy Simulations of dust devils and convective vortices Aymeric Spiga∗1, Erika Barth2, Zhaolin Gu3, Fabian Hoffmann4, Junshi Ito5, Bradley Jemmett-Smith6, Martina Klose7, Seiya Nishizawa8, Siegfried Raasch9, Scot Rafkin10, Tetsuya Takemi11, Daniel Tyler12, and Wei Wei13 1 Laboratoire de M´et´eorologieDynamique, UMR CNRS 8539, Institut Pierre-Simon Laplace, Sorbonne Universit´es,UPMC Univ Paris 06, Paris, France 2SouthWest