Dispersion Processes in Large-Scale Weather Prediction
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Lecture 5: Saturn, Neptune, … A. P. Ingersoll [email protected] 1
Lecture 5: Saturn, Neptune, … A. P. Ingersoll [email protected] 1. A lot a new material from the Cassini spacecraft, which has been in orbit around Saturn for four years. This talk will have a lot of pictures, not so much models. 2. Uranus on the left, Neptune on the right. Uranus spins on its side. The obliquity is 98 degrees, which means the poles receive more sunlight than the equator. It also means the sun is almost overhead at the pole during summer solstice. Despite these extreme changes in the distribution of sunlight, Uranus is a banded planet. The rotation dominates the winds and cloud structure. 3. Saturn is covered by a layer of clouds and haze, so it is hard to see the features. Storms are less frequent on Saturn than on Jupiter; it is not just that clouds and haze makes the storms less visible. Saturn is a less active planet. Nevertheless the winds are stronger than the winds of Jupiter. 4. The thickness of the atmosphere is inversely proportional to gravity. The zero of altitude is where the pressure is 100 mbar. Uranus and Neptune are so cold that CH forms clouds. The clouds of NH3 , NH4SH, and H2O form at much deeper levels and have not been detected. 5. Surprising fact: The winds increase as you move outward in the solar system. Why? My theory is that power/area is smaller, small‐scale turbulence is weaker, dissipation is less, and the winds are stronger. Jupiter looks more turbulent, and it has the smallest winds. -
ATMOSPHERIC and OCEANIC FLUID DYNAMICS Fundamentals and Large-Scale Circulation
ATMOSPHERIC AND OCEANIC FLUID DYNAMICS Fundamentals and Large-Scale Circulation Geoffrey K. Vallis Contents Preface xi Part I FUNDAMENTALS OF GEOPHYSICAL FLUID DYNAMICS 1 1 Equations of Motion 3 1.1 Time Derivatives for Fluids 3 1.2 The Mass Continuity Equation 7 1.3 The Momentum Equation 11 1.4 The Equation of State 14 1.5 The Thermodynamic Equation 16 1.6 Sound Waves 29 1.7 Compressible and Incompressible Flow 31 1.8 * More Thermodynamics of Liquids 33 1.9 The Energy Budget 39 1.10 An Introduction to Non-Dimensionalization and Scaling 43 2 Effects of Rotation and Stratification 51 2.1 Equations in a Rotating Frame 51 2.2 Equations of Motion in Spherical Coordinates 55 2.3 Cartesian Approximations: The Tangent Plane 66 2.4 The Boussinesq Approximation 68 2.5 The Anelastic Approximation 74 2.6 Changing Vertical Coordinate 78 2.7 Hydrostatic Balance 80 2.8 Geostrophic and Thermal Wind Balance 85 2.9 Static Instability and the Parcel Method 92 2.10 Gravity Waves 98 v vi Contents 2.11 * Acoustic-Gravity Waves in an Ideal Gas 100 2.12 The Ekman Layer 104 3 Shallow Water Systems and Isentropic Coordinates 123 3.1 Dynamics of a Single, Shallow Layer 123 3.2 Reduced Gravity Equations 129 3.3 Multi-Layer Shallow Water Equations 131 3.4 Geostrophic Balance and Thermal wind 134 3.5 Form Drag 135 3.6 Conservation Properties of Shallow Water Systems 136 3.7 Shallow Water Waves 140 3.8 Geostrophic Adjustment 144 3.9 Isentropic Coordinates 152 3.10 Available Potential Energy 155 4 Vorticity and Potential Vorticity 165 4.1 Vorticity and Circulation 165 -
Installing and Using the Hadley Centre Regional Climate Modelling System, PRECIS
Installing and using the Hadley Centre regional climate modelling system, PRECIS Version 1.9.4 www.metoffice.gov.uk/precis Simon Wilson, David Hassell, David Hein, Chloe Eagle, Simon Tucker, Richard Jones and Ruth Taylor April 12, 2012 Contents 1 Introduction 11 1.1 Background .............................. 11 1.2 Objectivesandstructureofthemanual . 12 2 Hardware, operating system and software environment 13 2.1 Recommended Hardware Configurations . 13 2.2 Multi-processorsystems . 14 2.3 InstallationofLinux ......................... 15 2.4 Compilers ............................... 16 2.5 System setup before installing PRECIS . 17 3 PRECIS software and installation 19 3.1 Introduction.............................. 19 3.2 Disklayout .............................. 20 3.3 Main steps in installation process . 21 3.4 Installation of PRECIS software and data . 21 3.5 InstallationofMetOfficedata . 25 3.5.1 Boundary data supplied on hard drive . 25 3.6 Installation verification . 27 3.7 InstallationofCDAT ......................... 28 4 Experimental design and setup 30 4.1 Experimentaldesign ......................... 30 4.1.1 Regional climate model . 30 2 4.1.2 Choice of driving model and forcing scenario . 30 4.1.3 Simulationlength. 36 4.1.4 Initial condition ensembles . 37 4.1.5 Choice of land surface scheme . 38 4.1.6 Outputdata.......................... 38 4.1.7 Spinup............................. 38 4.1.8 Choice of region . 39 4.1.9 Land-seamask ........................ 39 4.1.10 Altitude ............................ 40 4.1.11 Altitude of inland waters . 41 4.1.12 Soil and land cover . 41 4.1.13 RCM calendar and clock . 42 4.1.14 RCM Resolution . 42 4.1.15 Outputformat ........................ 43 4.1.16 Checklist............................ 43 5 Configuring an experiment with PRECIS 45 5.1 Introduction............................. -
Chapter 7 Balanced Flow
Chapter 7 Balanced flow In Chapter 6 we derived the equations that govern the evolution of the at- mosphere and ocean, setting our discussion on a sound theoretical footing. However, these equations describe myriad phenomena, many of which are not central to our discussion of the large-scale circulation of the atmosphere and ocean. In this chapter, therefore, we focus on a subset of possible motions known as ‘balanced flows’ which are relevant to the general circulation. We have already seen that large-scale flow in the atmosphere and ocean is hydrostatically balanced in the vertical in the sense that gravitational and pressure gradient forces balance one another, rather than inducing accelera- tions. It turns out that the atmosphere and ocean are also close to balance in the horizontal, in the sense that Coriolis forces are balanced by horizon- tal pressure gradients in what is known as ‘geostrophic motion’ – from the Greek: ‘geo’ for ‘earth’, ‘strophe’ for ‘turning’. In this Chapter we describe how the rather peculiar and counter-intuitive properties of the geostrophic motion of a homogeneous fluid are encapsulated in the ‘Taylor-Proudman theorem’ which expresses in mathematical form the ‘stiffness’ imparted to a fluid by rotation. This stiffness property will be repeatedly applied in later chapters to come to some understanding of the large-scale circulation of the atmosphere and ocean. We go on to discuss how the Taylor-Proudman theo- rem is modified in a fluid in which the density is not homogeneous but varies from place to place, deriving the ‘thermal wind equation’. Finally we dis- cuss so-called ‘ageostrophic flow’ motion, which is not in geostrophic balance but is modified by friction in regions where the atmosphere and ocean rubs against solid boundaries or at the atmosphere-ocean interface. -
Climate Risk Management Xxx (2016) Xxx–Xxx
Climate Risk Management xxx (2016) xxx–xxx Contents lists available at ScienceDirect Climate Risk Management journal homepage: www.elsevier.com/locate/crm Regional climate change trends and uncertainty analysis using extreme indices: A case study of Hamilton, Canada ⇑ Tara Razavi a,c, , Harris Switzman b,1, Altaf Arain c, Paulin Coulibaly a,c a McMaster University, Department of Civil Engineering, 1280 Main Street West, Hamilton, Ontario L8S 4L7, Canada b Ontario Climate Consortium/Toronto and Region Conservation, Toronto, Ontario, Canada c McMaster University, School of Geography and Earth Sciences, 1280 Main Street West, Hamilton, Ontario L8S 4L7, Canada article info abstract Article history: This study aims to provide a deeper understanding of the level of uncertainty associated Received 17 December 2015 with the development of extreme weather frequency and intensity indices at the local Revised 13 June 2016 scale. Several different global climate models, downscaling methods, and emission scenar- Accepted 14 June 2016 ios were used to develop extreme temperature and precipitation indices at the local scale Available online xxxx in the Hamilton region, Ontario, Canada. Uncertainty associated with historical and future trends in extreme indices and future climate projections were also analyzed using daily Keywords: precipitation and temperature time series and their extreme indices, calculated from grid- Climate change ded daily observed climate data along with and projections from dynamically downscaled Uncertainty Trend datasets of -
Dynamics of Jupiter's Atmosphere
6 Dynamics of Jupiter's Atmosphere Andrew P. Ingersoll California Institute of Technology Timothy E. Dowling University of Louisville P eter J. Gierasch Cornell University GlennS. Orton J et P ropulsion Laboratory, California Institute of Technology Peter L. Read Oxford. University Agustin Sanchez-Lavega Universidad del P ais Vasco, Spain Adam P. Showman University of A rizona Amy A. Simon-Miller NASA Goddard. Space Flight Center Ashwin R . V asavada J et Propulsion Laboratory, California Institute of Technology 6.1 INTRODUCTION occurs on both planets. On Earth, electrical charge separa tion is associated with falling ice and rain. On Jupiter, t he Giant planet atmospheres provided many of the surprises separation mechanism is still to be determined. and remarkable discoveries of planetary exploration during The winds of Jupiter are only 1/ 3 as strong as t hose t he past few decades. Studying Jupiter's atmosphere and of aturn and Neptune, and yet the other giant planets comparing it with Earth's gives us critical insight and a have less sunlight and less internal heat than Jupiter. Earth broad understanding of how atmospheres work that could probably has the weakest winds of any planet, although its not be obtained by studying Earth alone. absorbed solar power per unit area is largest. All the gi ant planets are banded. Even Uranus, whose rotation axis Jupiter has half a dozen eastward jet streams in each is tipped 98° relative to its orbit axis. exhibits banded hemisphere. On average, Earth has only one in each hemi cloud patterns and east- west (zonal) jets. -
ATM 500: Atmospheric Dynamics End-Of-Semester Review, December 6 2017 Here, in Bullet Points, Are the Key Concepts from the Second Half of the Course
ATM 500: Atmospheric Dynamics End-of-semester review, December 6 2017 Here, in bullet points, are the key concepts from the second half of the course. They are not meant to be self-contained notes! Refer back to your course notes for notation, derivations, and deeper discussion. Static stability The vertical variation of density in the environment determines whether a parcel, disturbed upward from an initial hydrostatic rest state, will accelerate away from its initial position, or oscillate about that position. For an incompressible or Boussinesq fluid, the buoyancy frequency is g dρ~ N 2 = − ρ~dz For a compressible ideal gas, the buoyancy frequency is g dθ~ N 2 = θ~dz (the expressions are different because density is not conserved in a small upward displacement of a compressible fluid, but potential temperature is conserved) Fluid is statically stable if N 2 > 0; otherwise it is statically unstable. Typical value for troposphere: N = 0:01 s−1, with corresponding oscillation period of 10 minutes. Dry adiabatic lapse rate g −1 Γd = = 9:8 K km cp The rate at which temperature decreases during adiabatic ascent (for which Dθ Dt = 0) \Dry" here means that there is no latent heating from condensation of water vapor. Static stability criteria for a dry atmosphere Environment is stable to dry con- vection if dθ~ dT~ > 0 or − < Γ dz dz d and otherwise unstable. 1 Buoyancy equation for Boussinesq fluid with background stratification Db0 + N 2w = 0 Dt where we have separated the full buoyancy into a mean vertical part ^b(z) and 0 2 d^b 2 everything else (b ), and N = dz . -
Technical Manual for PRECIS
Technical Manual for PRECIS The Met Office Hadley Centre regional climate modelling system Version 2.0.0 www.metoffice.gov.uk/precis Simon Wilson, David Hassell, David Hein, Changgui Wang, Simon Tucker Richard Jones and Ruth Taylor November 16, 2015 Contents 1 Introduction 11 1.1 Background .............................. 11 1.2 Objectivesandstructureofthemanual . 12 2 Hardware, operating system and software environment 13 2.1 Recommended Hardware Configurations . 13 2.2 Multi-processorsystems . 14 2.3 InstallationofLinux ......................... 14 2.4 Compilers ............................... 16 2.5 System setup before installing PRECIS . 17 3 PRECIS software and installation 19 3.1 Introduction.............................. 19 3.2 Disklayout .............................. 20 3.3 Main steps in installation process . 21 3.4 Installation of PRECIS software and data . 22 3.5 InstallationofMetOfficedata . 26 3.5.1 Boundary data supplied on hard drive . 26 3.6 Installation verification . 28 3.7 InstallationofCDAT ......................... 29 4 Experimental design and setup 30 4.1 Experimentaldesign ......................... 30 4.1.1 Regional climate model . 30 2 4.1.2 Choice of driving model and forcing scenario . 30 4.1.3 CMIP5 Driving GCMs and Representative Concentration Pathways ........................... 37 4.1.4 Simulationlength. 40 4.1.5 Initial condition ensembles . 41 4.1.6 Choice of land surface scheme . 42 4.1.7 Outputdata.......................... 42 4.1.8 Spinup............................. 42 4.1.9 Choice of region . 43 4.1.10 Land-seamask ........................ 43 4.1.11 Altitude ............................ 45 4.1.12 Altitude of inland waters . 45 4.1.13 Soil and land cover . 45 4.1.14 RCM calendar and clock . 46 4.1.15 RCM Resolution . 46 4.1.16 Outputformat ....................... -
A Diagnostic Model for Initial Winds in Primitive
1 1 A DIAGNOSTIC MODEL FOR INITIAL WINDS IN PRIMITIVE EQUATIONS FORECASTS by Richard Asselin Department of Meteorology, Ph. D. ABSTRACT The set of primitive meteorologicai equations reduces to a single second order partial differential cquation when the accel- eration is replaced by the derivative of the geostrophic wind in the equation.of motion. From this equation a diagnostic three-dimensional wind field can be computed when the pressure distribution is known. Calculations for a barotropic atmosphere are performed fir st; the rotational part of the wind is found to be similar to that from the classical balance equation and the divergent part agrees well with synoptic experience. A forecast thus initialized contaills Lese; gravi- tational activity than one initialized with the non-divergent wind of the balance equation. With a five-level baroclinic atmosphere. the effects of mountains, surface drag, and release of latent heat are studi~d separately; the results arc consistent wlth classical concepts and other interesting features are revealed. T!;e diagnostic surfa:::e pres sure tendency is fo~nd to be in very good agreement with the observed pressure changes. A DIAGNOSTIC MODEL FOR INITIAL WINDS IN PRIMITIVE EQUATiONS FORECASTS by RI CHARD ASSE LIN . A the sis submitted to the Faculty of Graduate Studies and Re search in partial fulfillrnent of the requirernents for the degree of Doctor of Philosophy. Departrnent of Meteorology McGill Univer sity Montreal, Canada July. 1970 1971 A DL~GNOSTIC MODEL FOR INITIAL WINDS IN PRIMITIVE EQUATIONS FORECASTS by RI CHARD ASSE LIN . A thesis submitted ta the Faculty of Graduate Studies and Research in partial fulfillment of the requirements. -
Ch4: Basics of Stratified Fluid ∂
AOS611Ch.4, Z. Liu, 02/16/2016 1 Ch4: Basics of Stratified Fluid Sec. 4.1: Basic Equations Stratification will introduce new physics. We first derive the equations. Denoting rotation vector as Ω , gravity potential , u u,v, w, i x j y k z . The momentum equations are du 2Ω u p F . (4.1.1) dt The mass equation is d u 0 or u 0 (4.1.2) dt t The equation of state in general is p, T, S... (4.1.3) In the ocean, neglecting salinity, the equation of state is m 1T T0 (4.1.4) 1 where is the coefficient of thermal expansion and is a constant m T P reference density, which can be chosen as the average density. In the atmosphere, using the perfect gas, the equation of state is P (4.1.5) RT where R is the gas constant. The thermodynamic equation describes the internal energy change. For the ocean, which is incompressible, the thermodynamic equation is dT ~ C Q k 2T (4.1.6) p dt ~ where k is the thermal conductivity, Q is the heating rate per unit mass. This can be rewritten as dT J k 2T (4.1.6a) dt ~ Q k where J and k . Cp Cp Copyright 2014, Zhengyu Liu AOS611Ch.4, Z. Liu, 02/16/2016 2 The atmosphere is compressible, so the thermodynamic equation is dT 1 dp J k 2T (4.1.7) dt C p dt p 0 R , Define the potential temperature as T , where C we have p p p0 ln lnT ln lnT ln p const p d dT dp T p T p0 T d dT dp dT dp T p p p d p0 2 J k T (4.1.8) dt p T RT 1 where we have used (4.1.7) and the ideal gas law (4.1.5), such that . -
Computing Challenges for Weather and Climate Modelling at the Met Office
Computing Challenges for Weather and Climate Modelling at the Met Office Paul Selwood © Crown copyright Met Office Current Status © Crown copyright Met Office The Unified Model • The same model formulation is used for all models from climate scale to mesoscale Climate modelling: input into IPCC reports (Coupled Atmosphere-Ocean models) Seasonal forecasting: For commercial and business customers NWP: Public Weather Service WAFC, Commercial …… © Crown copyright Met Office Dynamics – aka CFD • Lat-Long Grid Departure point • Advection Arrival Point • Semi-lagrangian scheme • Variable order interpolation • Adjustment • Semi-implicit scheme • 3D Helmoltz equation • Diffusion : Removing noise • Variable order © Crown copyright Met Office Physical Parameterizations Convection Clouds Short-wave radiation Vegetation Model Long-wave radiation Precipitation Surface Processes © Crown copyright Met Office Parallel Implementation • Regular, Static, Lat-Long Decomposition • Mixed mode MPI/OpenMP • Asynchronous I/O servers • Communications on demand for advection • Multiple halo sizes © Crown copyright Met Office Atmospheric In transition to Production grid length Production system 1.5km UKV 4km UK4 NAE HadGEM3-RA 12km MOGREPS-R ensemble Global regional HadGEM3 24km HadGEM2 TIGGE 40km GloSea4 ensemble HadGEM1 Earth System 80km DePreSys HadCM3 ty xi le PRECIS p m o Coupled atmos/ocean 150km C Global atmosphere-only 300km Regional atmosphere-only 36hrs 48hrs 5 days 15 days 6 months 10 years 30 years >100 years Timescale © Crown copyright Met Office Current -
Science in the Met Office, Written Evidence
Science & Technology Committee: Written evidence Science in the Met Office This volume contains the written evidence accepted by the Science & Technology Committee for the Science in the Met Office inquiry. No. Author No. Author 00 Met Office 00a Supplementary 01 Anthony John Power 02 Malcolm Shykles 03 Professor John Pyle 04 European Centre for Medium-Range Weather Forecasts 05 Prospect 06 Professor Sir Brian Hoskins 07 U.S. Department of Commerce National Oceanic and Atmospheric Administration National Weather Service 08 Australian Bureau of Meteorology 09 National Oceanography Centre 10 Rowan Douglas, Willis Research Network 11 Royal Meteorological Society 12 Committee on Climate Change 13 Research Councils UK 14 Government 15 Department of Meteorology, University of Reading 16 Stephen Burt 17 18 19 20 21 22 23 24 25 26 27 28 As at 12 October 2011 Written evidence submitted by the Met Office (MO 00) 1 Introduction 1.1 The aim of the Met Office is to provide the UK and its citizens with the best weather and climate service in the world, measured by the usefulness and quality of its products and services and the value for money it delivers. The quality of its services has a direct impact on public safety and national security and resilience. 1.2 International benchmarking of global weather forecasting skill is supported through the UN World Meteorological Organization (WMO). A range of metrics are used which all show that the Met Office is consistently within the top three centres globally. This position has been achieved through sustained investment in research, observations and supercomputing.