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Exercise 10: Cumulus Cloud with Bulk Cloud Physics
Exercise Hints Tasks Results Exercise 10: Cumulus Cloud With Bulk Cloud Physics PALM group Institute of Meteorology and Climatology, Leibniz Universität Hannover last update: 21st September 2015 PALM group PALM Seminar 1 / 16 I Initialize the simulation with a marine, cumulus-topped, trade-wind region boundary layer. I Trigger the cloud by a bubble of rising warm air. I Parameterize condensation using a simple bulk cloud physics scheme. I Learn how to carry out conditional averages. Exercise Hints Tasks Results Exercise Exercise 10: Cumulus Cloud With Bulk Cloud Physics Simulate a cumulus cloud: PALM group PALM Seminar 2 / 16 I Trigger the cloud by a bubble of rising warm air. I Parameterize condensation using a simple bulk cloud physics scheme. I Learn how to carry out conditional averages. Exercise Hints Tasks Results Exercise Exercise 10: Cumulus Cloud With Bulk Cloud Physics Simulate a cumulus cloud: I Initialize the simulation with a marine, cumulus-topped, trade-wind region boundary layer. PALM group PALM Seminar 2 / 16 I Parameterize condensation using a simple bulk cloud physics scheme. I Learn how to carry out conditional averages. Exercise Hints Tasks Results Exercise Exercise 10: Cumulus Cloud With Bulk Cloud Physics Simulate a cumulus cloud: I Initialize the simulation with a marine, cumulus-topped, trade-wind region boundary layer. I Trigger the cloud by a bubble of rising warm air. PALM group PALM Seminar 2 / 16 I Learn how to carry out conditional averages. Exercise Hints Tasks Results Exercise Exercise 10: Cumulus Cloud With Bulk Cloud Physics Simulate a cumulus cloud: I Initialize the simulation with a marine, cumulus-topped, trade-wind region boundary layer. -
Exam 2: Cloud Physics April 16, 2008 Physical Meteorology 3440
Name ____________________________ Exam 2: Cloud Physics April 16, 2008 Physical Meteorology 3440 Questions 1-10 are worth 5 points each. Questions 11-15 are worth 10 points each. 1. Rank the concentrations of the following from lowest (1) to highest (3): cloud condensation nuclei (3) cloud droplets (2) raindrops (1) 2. Match the following particles with their typical size cloud condensation nuclei 10 μm cloud droplets 0.1 μm raindrops 1000 μm 3. Why do ice crystals grow at the expense of supercooled water droplets? The saturation vapor pressure over liquid is higher than the saturation vapor pressure over ice. Therefore, the environment will be more supersaturated with respect to ice than with respect to liquid, and the ice crystals will grow more quickly. At some point, the ice crystals may bring S (with respect to ice) down to 1, in which case Sl (with respect to liquid) is less than 1, causing the liquid drops to evaporate. 4. Match the following particles with their most likely means of growth 5 μm cloud drop accretion 5 μm ice crystal aggregation 100 μm dendritic ice crystals in ice cloud depositional growth 500 μm ice crystal in mixed-phase cloud condensational growth 100 μm cloud drop collision-coalescence 5. Fill in the blanks: Not all clouds with temperatures below the freezing point of water contain ice, because of the scarcity of ice nuclei in the atmosphere, and the temperature at which they nucleate ice. As the cloud temperature decreases, the probability of ice increases to the 1 temperature of -40 °C, at which point homogeneous freezing occurs. -
Introduction to Climatology
© Jones & Bartlett Learning, LLC. NOT FOR SALE OR DISTRIBUTION © Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC NOT FOR SALE Inc. © Eyewire, OR DISTRIBUTION NOT FOR SALE OR DISTRIBUTION © Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC NOT FOR SALE OR DISTRIBUTION NOT FOR SALE OR DISTRIBUTION CHAPTER 1Introduction to Climatology © Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC NOT FOR SALE OR DISTRIBUTION those specializingNOT FOR in glaciology, SALE OR as wellDISTRIBUTION as special- Chapter at a Glance ized physical geographers, geologists, and ocean- Meteorology and Climatology ographers. The biosphere, which crosscuts the Scales in Climatology lithosphere, hydrosphere, cryosphere, and atmo- Subfields of Climatology sphere, includes the zone containing all life forms © Jones & BartlettClimatic Learning, Records andLLC Statistics © Joneson the& Bartlettplanet, including Learning, humans. LLC The biosphere NOT FOR SALE SummaryOR DISTRIBUTION NOT FORis examined SALE by OR specialists DISTRIBUTION in the wide array of life Key Terms sciences, along with physical geographers, geolo- Review Questions gists, and other environmental scientists. Questions for Thought The atmosphere is the component of the system © Jones & Bartlett Learning, LLCstudied by climatologists ©and Jones meteorologists. & Bartlett Ho- Learning, LLC NOT FOR SALE OR DISTRIBUTIONlistic interactions betweenNOT the FORatmosphere SALE and OR DISTRIBUTION Climatology may be described as the scientific each combination of the “spheres” are important study of the behavior of the atmosphere—the contributors to the climate (Table 1.1), at scales thin gaseous layer surrounding Earth’s surface— from local to planetary. Thus, climatologists must integrated over time. Although this definition is draw on knowledge generated in several natural © Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC certainly acceptable, it fails to capture fully the and sometimes social scientific disciplines to un- NOTscope FOR of SALE climatology. -
Twelve Lectures on Cloud Physics
Twelve Lectures on Cloud Physics Bjorn Stevens Winter Semester 2010-2011 Contents 1 Lecture 1: Clouds–An Overview3 1.1 Organization...........................................3 1.2 What is a cloud?.........................................3 1.3 Why are we interested in clouds?................................4 1.4 Cloud classification schemes..................................5 2 Lecture 2: Thermodynamic Basics6 2.1 Thermodynamics: A brief review................................6 2.2 Variables............................................8 2.3 Intensive, Extensive, and specific variables...........................8 2.3.1 Thermodynamic Coordinates..............................8 2.3.2 Composite Systems...................................8 2.3.3 The many variables of atmospheric thermodynamics.................8 2.4 Processes............................................ 10 2.5 Saturation............................................ 10 3 Lecture 3: Droplet Activation 11 3.1 Supersaturation over curved surfaces.............................. 12 3.2 Solute effects.......................................... 14 3.3 The Kohler¨ equation and its properties............................. 15 4 Lecture 4: Further Properties of an Isolated Drop 16 4.1 Diffusional growth....................................... 16 4.1.1 Temperature corrections................................ 18 4.1.2 Drop size effects on droplet growth.......................... 20 4.2 Terminal fall speeds of drops and droplets........................... 20 5 Lecture 5: Populations of Particles 22 5.1 -
Lightning Dr
ESCI 340 - Cloud Physics and Precipitation Processes Lesson 12 - Lightning Dr. DeCaria References: The Lightning Discharge, Uman The Electrical Nature of Storms, MacGorman and Rust Fundamentals of Lightning, Rakov `Runaway Breakdown and the Mysteries of Lightning', Gurevich and Zybin, Physics Today, 2005 Mechanisms of Charge Separation • The top of a thunderstorm (cumulonimbus) cloud becomes positively charged, while the middle-to-lower portions of the cloud becomes negatively charged. • Often there is also a smaller pocket of positive charge near the bottom of the cloud. • The reason for this charge separation is not completely understood, but some of the more prominent theories are described below. • Those mechanisms requiring a preexisting electric field are called inductive charging mechanisms, while those not requiring a preexisting electric field are called nonin- ductive charging mechanisms. Inductive ion capture: In a preexisting electrical field, there will be a separation of charge across a hydrometeor. As the hydrometeor falls, gaseous ions will either be attracted or repelled from the underside of the hydrometeor, depending on their sign. Thus, the hydrometeor will gain an increasing charge of whatever sign it has on its topside. In Fig. 1 the positive ions are repelled as the hydrometeor falls, but the negative ions are attracted. Thus, the hydrometeor gains a net negative charge as it falls. Figure 1: Inductive ion capture. 1 Ion capture may play a role in weakly electrified storms, but cannot be used to explain the amount of charge separation seen in a thunderstorm without the presence of other mechanisms. Inductive particle rebound: Two hydrometeors in a preexisting electric field, falling at different speeds, will exchange charge during collision as shown in Fig. -
Cloud Microphysics
Cloud microphysics Claudia Emde Meteorological Institute, LMU, Munich, Germany WS 2011/2012 Overview of cloud physics lecture Atmospheric thermodynamics gas laws, hydrostatic equation 1st law of thermodynamics moisture parameters adiabatic / pseudoadiabatic processes stability criteria / cloud formation Microphysics of warm clouds nucleation of water vapor by condensation growth of cloud droplets in warm clouds (condensation, fall speed of droplets, collection, coalescence) formation of rain, stochastical coalescence Microphysics of cold clouds homogeneous nucleation heterogeneous nucleation contact nucleation crystal growth (from water phase, riming, aggregation) formation of precipitation Observation of cloud microphysical properties Parameterization of clouds in climate and NWP models Cloud microphysics November 24, 2011 2 / 35 Growth rate and size distribution growing droplets consume water vapor faster than it is made available by cooling and supersaturation decreases haze droplets evaporate, activated droplets continue to grow by condensation growth rate of water droplet dr 1 = G S dt l r smaller droplets grow faster than larger droplets sizes of droplets in cloud become increasingly uniform, approach monodisperse distribution Figure from Wallace and Hobbs Cloud microphysics November 24, 2011 3 / 35 Size distribution evolution q 2 r = r0 + 2Gl St 0.8 1.4 t=0 t=0 0.7 t=10 t=10 1.2 t=30 t=30 0.6 t=50 t=50 1.0 0.5 0.8 0.4 0.6 0.3 n [normalized] n [normalized] 0.4 0.2 0.2 0.1 0.0 0.0 0 2 4 6 8 10 12 14 16 0 2 4 6 8 10 12 14 16 radius [arbitrary units] radius [arbitrary units] Cloud microphysics November 24, 2011 4 / 35 Growth by collection growth by condensation alone does not explain formation of larger drops other mechanism: growth by collection Cloud microphysics November 24, 2011 5 / 35 Terminal fall speed r 40µ . -
Potential Impacts of Climate Change on Habitat Suitability of Fagus Sylvatica L. Forests in Spain
Plant Biosystems - An International Journal Dealing with all Aspects of Plant Biology Official Journal of the Societa Botanica Italiana ISSN: 1126-3504 (Print) 1724-5575 (Online) Journal homepage: http://www.tandfonline.com/loi/tplb20 Potential impacts of climate change on habitat suitability of Fagus sylvatica L. forests in spain Sara del Río, Ramón Álvarez-Esteban, Eusebio Cano, Carlos Pinto-Gomes & Ángel Penas To cite this article: Sara del Río, Ramón Álvarez-Esteban, Eusebio Cano, Carlos Pinto-Gomes & Ángel Penas (2018): Potential impacts of climate change on habitat suitability of Fagus sylvatica L. forests in spain, Plant Biosystems - An International Journal Dealing with all Aspects of Plant Biology, DOI: 10.1080/11263504.2018.1435572 To link to this article: https://doi.org/10.1080/11263504.2018.1435572 View supplementary material Published online: 22 Feb 2018. Submit your article to this journal Article views: 10 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=tplb20 PLANT BIOSYSTEMS - AN INTERNATIONAL JOURNAL DEALING WITH ALL ASPECTS OF PLANT BIOLOGY, 2018 https://doi.org/10.1080/11263504.2018.1435572 Potential impacts of climate change on habitat suitability of Fagus sylvatica L. forests in spain Sara del Ríoa,b , Ramón Álvarez-Estebanc , Eusebio Canod , Carlos Pinto-Gomese and Ángel Penasa,b aDepartment of Biodiversity and Environmental Management (Botany), University of León, León, Spain; bFaculty of Biological and Environmental Sciences, Mountain Livestock Institute (CSIC-ULE), University of León, León, Spain; cFaculty of Economics and Business, Department of Economics and Statistics (Statistics and Operational Research), University of León, León, Spain; dDepartment of Animal and Plant Biology and Ecology, Botanical Section, University of Jaén, Jaén, Spain; eDepartamento de Paisagem, Ambiente e Ordenamento/Instituto de Ciências Agrárias e Ambientais Mediterrânicas (ICAAM). -
Thunderstorm Anatomy and Dynamics
THUNDERSTORM ANATOMY AND DYNAMICS An Overview Prepared by LCDR Bill Nisley MR 3421 • Cloud Physics Naval Postgraduate School • Monterey, California [email protected] Photo credits: Thunderstorm Cell and Mammatus Clouds by: Michael Bath; Tornado by Daphne Zaras / NSSL; Supercell Thunderstorm by: AMOS; Wall Cloud by: Greg Michels 1. Introduction The purpose of this paper is to present a broad overview of the various cloud structures displayed during the life cycle of a thunderstorm and the atmospheric dynamics associated with each. Knowledge of atmospheric dynamics provides for a keener understanding of the physical processes related to the “why and how” certain cloud features form. Accordingly, observation of cloud features presents visual queuing of changes in the atmosphere. 2. Thunderstorm Formation and Stages of Development Thunderstorm development is dependent on three basic components: moisture, instability, and some form of lifting mechanism. 2.1 Moisture – As air near the surface is lifted higher in the atmosphere and cooled, available water vapor condenses into small water droplets which form clouds. As condensation of water vapor occurs, latent heat is released making the rising air warmer and less dense than its surroundings (figure 1). The added heat allows the air (parcel) to continue to rise and form an updraft within the developing cloud structure. 2.1.1 In general1, low level moisture increases instability simply by making more latent heat available to the lower atmosphere. Increasing mid level moisture can decrease instability in the atmosphere because moist air is less dense than dry air and therefor is unable to evaporate • Figure 1 – Positive buoyancy / instability as a result of precipitation and cloud droplets as condensation and release of latent heat. -
Charge Accumulation in Thunderstorm Clouds: Fractal Dynamic Model
E3S Web of Conferences 127, 01001 (2019) https://doi.org/10.1051/e3sconf /201912701001 Solar-Terrestrial Relations and Physics of Earthquake Precursors Charge accumulation in thunderstorm clouds: fractal dynamic model Tembulat Kumykov* Institute of Applied Mathematics and Automation KBSC RAS, 360004 st. Shortanova, 89-а Nalchik, Russia Abstract. The paper considers a fractal dynamic charge accumulation model in thunderstorm clouds in view of the fractal dimension. Analytic solution to the model equation has been found. Using numerical calculations we have shown the relationship between the charge accumulation and the medium with the fractal structure. A comparative study of thunderstorm electrification mechanisms have been performed. 1 Introduction Convective clouds inside which thunderstorm processes mainly develop are natural objects with nontrivial fractal structure [1]. One of the most important thunderstorm parameters is the electric field intensity. Once a lightning strike have occurred, the electric field weakens and then recovers. The process of restoring neutralized charges and respectively the electric field after the thunderstorm discharge is the matter of animated debate among scientists. As is known, a central place in thunderstorm electricity occupies charge generation and separation processes inside convective clouds. About two dozen current mechanisms are known that result in charges generation inside the thunderclouds [2]. They can be divided into two groups. The first group is associated with elementary processes in the clouds:, the electrification within the ionic environment, the electrification caused by frictional contact between two icicles, the electrification accompanying the freezing of water and its solutions, the electrification upon destruction of crystallizing droplets, etc., The second group includes the induction electrification mechanism stipulated by the electric field, whose origin is not yet clear. -
Evaluation of ISKA Forecast Performance
Evaluation of ISKA forecast performance An investigation on binary and probabilistic forecast skill scores Björn Claremar, PhD Department of Earth Sciences Uppsala University Villavägen 16 SE-75236 Uppsala Sweden Uppsala, 12 March 2019 ii Evaluation of ISKA forecast performance Abstract The company Ignitia provides rainfall forecasts through SMS to subscribers in Ghana, Mali and some other countries in the West African region south of Sahara. Their ensemble-based probabilistic forecast product iska, based on the high-resolution weather model WRF with modifications, has been developed to better simulate tropical conditions where rainfall is driven by convection. This external investigation determined the forecast skill of iska by validating against NOAA satellite rainfall product. As comparison, rainfall probability forecasts from the global model GFS and from Weather Underground were evaluated. The iska forecasts outperformed the GFS forecasts and the Weather Underground forecasts in terms of accuracy. The statistical skill decreased towards the Sahel region and also towards the coast, the latter probably an artefact of the NOAA product having problems in identifying local warm-cloud rain, meaning that the forecast skill by the coast might be better than indicated. Keywords: West Africa, Ghana, Mali, Tropical weather, Weather forecast, Meteorology, WRF, rainfall, forecast skill, probabilistic, statistics, iska iii Executive summary Background Ignitia was seeking to evaluate the performance of its daily probabilistic rainfall forecasts through external party. The validation included making a comparison against baseline forecasts from NCEP (GFS), which is the most widely used source due to its free and public availability for commercial use, as well as data from a common online provider; forecasts from Weather Underground which is a subsidiary of The Weather Company, owned by IBM. -
A Physically Based Parameter for Lightning Prediction and Its Calibration in Ensemble Forecasts
4.3 A Physically Based Parameter for Lightning Prediction and its Calibration in Ensemble Forecasts David R. Bright* NOAA/NWS/NCEP/Storm Prediction Center, Norman, Oklahoma Matthew S. Wandishin CIMMS/University of Oklahoma, Norman, Oklahoma Ryan E. Jewell and Steven J. Weiss NOAA/NWS/NCEP/Storm Prediction Center, Norman, Oklahoma 1. INTRODUCTION that explicitly predict moist convection do not predict the occurrence of lightning. Thus, after The Storm Prediction Center (SPC) issues determining where moist convection is plausible, forecasts for the contiguous United States related the potential for thunderstorms must be deduced to hazardous convective weather including through further interrogation of the data. In thunderstorms, severe thunderstorms, tornadoes, addition to deterministic NWP output, the and elements critical to fire weather such as dry NCEP/EMC Short Range Ensemble Forecast lightning. In addition to severe weather products (SREF) is available to the SPC and attempts to such as watches and outlooks, the SPC also account for initial condition, model, and convective issues “general” thunderstorm outlooks for the physics uncertainty (Du et al. 2004). The SPC contiguous United States and adjacent coastal post-processes the NCEP SREF to create a suite waters. These outlooks delineate a > 10% chance of customized ensemble products specifically for of cloud-to-ground (CG) lightning within thunderstorm and severe thunderstorm prediction approximately 15 miles of a point. (For the (Bright et al. 2004). A subset of these products is purposes of this paper, a thunderstorm is defined available in real-time on the SPC web site at as deep convection producing at least one CG http://www.spc.noaa.gov/exper/sref/. -
Positive Feedback Between Future Climate Change and the Carbon Cycle
GEOPHYSICAL RESEARCH LETTERS, VOL. 28, NO. 8, PAGES 1543-1546, APRIL 15 , 2001 Positive feedback between future climate change and the carbon cycle Pierre Friedlingstein, Laurent Bopp, Philippe Ciais, IPSL/LSCE, CE-Saclay, 91191, Gif sur Yvette, France Jean-Louis Dufresne, Laurent Fairhead, Herv´eLeTreut, IPSL/LMD, Universit´e Paris 6, 75252, Paris, France Patrick Monfray, and James Orr IPSL/LSCE, CE-Saclay, 91191, Gif sur Yvette, France Abstract. Future climate change due to increased atmo- two carbon simulations. In the “constant climate” simula- sphericCO 2 may affect land and ocean efficiency to absorb tion the carbon models are forced by a CO2 increase of 1% atmosphericCO 2. Here, using climate and carbon three- per year and the control climate from the OAGCM. In the dimensional models forced by a 1% per year increase in at- “climate change” simulation, the carbon models are forced mosphericCO 2, we show that there is a positive feedback by the same 1%/yr CO2 increase as well as the climate from between the climate system and the carbon cycle. Climate the transient climate run. change reduces land and ocean uptake of CO2,respectively In this experiment, atmosphericCO 2 and monthly aver- by 54% and 35% at 4 × CO2 . This negative impact im- aged climate fields from the IPSL OAGCM [Braconnot et al., plies that for prescribed anthropogenic CO2 emissions, the 2000] are used to drive both terrestrial and oceanic carbon atmosphericCO 2 would be higher than the level reached if cycle models. These two models allow one to translate an- climate change does not affect the carbon cycle.