The Advanced Research WRF (ARW) Dynamics Solver

Similar documents
Description of. Jimy Dudhia Dave Gill. Bill Skamarock. WPS d1 output. WPS d2 output Real and WRF. real.exe General Functions.

Split explicit methods

The WRF NMM Core. Zavisa Janjic Talk modified and presented by Matthew Pyle

Weather Research and Forecasting Model. Melissa Goering Glen Sampson ATMO 595E November 18, 2004

Mass Conserving Courant Number Independent Eulerian Advection of the Moisture Quantities for the LMK

HWRF Dynamics The WRF-NMM

A Time-Split Nonhydrostatic Atmospheric Model for Weather Research and Forecasting Applications. William C. Skamarock and Joseph B.

The Shallow Water Equations

Deutscher Wetterdienst

Stabilitätsanalyse des Runge-Kutta- Zeitintegrationsschemas für das konvektionserlaubende Modell COSMO-DE

Jacobians of transformation 115, 343 calculation of 199. Klemp and Wilhelmson open boundary condition 159 Kuo scheme 190

Advection / Hyperbolic PDEs. PHY 604: Computational Methods in Physics and Astrophysics II

CAM-SE: Lecture I. Peter Hjort Lauritzen

WRF Modeling System Overview

ICON. The Icosahedral Nonhydrostatic modelling framework

Deutscher Wetterdienst

A semi-implicit non-hydrostatic covariant dynamical kernel using spectral representation in the horizontal and a height based vertical coordinate

Formulation and performance of the Variable-Cubic Atmospheric Model

Earth System Modeling Domain decomposition

ρ x + fv f 'w + F x ρ y fu + F y Fundamental Equation in z coordinate p = ρrt or pα = RT Du uv tanφ Dv Dt + u2 tanφ + vw a a = 1 p Dw Dt u2 + v 2

Linear model for investigation of nonlinear NWP model accuracy. Marko Zirk, University of Tartu

WRF Modeling System Overview

Comparing the formulations of CCAM and VCAM and aspects of their performance

The Runge-Kutta method

ICON. The Icosahedral Nonhydrostatic model: Formulation of the dynamical core and physics-dynamics coupling

Finite Volume Method for Scalar Advection in 2D

The dynamics of high and low pressure systems

Idealized Nonhydrostatic Supercell Simulations in the Global MPAS

WRF Modeling System Overview

MODEL TYPE (Adapted from COMET online NWP modules) 1. Introduction

Time-stepping of the CROCO hydrostatic code and its impact on code structure (and on advection schemes)

Modeling, Simulating and Rendering Fluids. Thanks to Ron Fediw et al, Jos Stam, Henrik Jensen, Ryan

Daniel J. Jacob, Models of Atmospheric Transport and Chemistry, 2007.

The 'Conservative Dynamical Core' priority. developments in the COSMO model

Development of Yin-Yang Grid Global Model Using a New Dynamical Core ASUCA.

The Developmental Testbed Center WRFv3.2.1 QNSE Test Plan

A time-split nonhydrostatic atmospheric model for weather research and forecasting applications

A Description of the Advanced Research WRF Version 2

Vertical Coordinates and Upper Boundary Conditions. When selecting a vertical coordinate, there are three primary considerations to keep in mind:

Initialization for Idealized Cases

What is a flux? The Things We Does Know

WRF Modeling System Overview

3.4. Monotonicity of Advection Schemes

Lecture IV: Time Discretization

Francis X. Giraldo,

Using hybrid isentropic-sigma vertical coordinates in nonhydrostatic atmospheric modeling

6 Theoretical. Formulation Introduction Dynamic Equations and Numerical Formulations

Lecture 17: Initial value problems

Variable-Resoluiton Global Atmospheric Modeling Spanning Convective to Planetary Scales

Numerical Solutions for Hyperbolic Systems of Conservation Laws: from Godunov Method to Adaptive Mesh Refinement

1/18/2011. Conservation of Momentum Conservation of Mass Conservation of Energy Scaling Analysis ESS227 Prof. Jin-Yi Yu

How is balance of a forecast ensemble affected by adaptive and non-adaptive localization schemes?

Overview of the Numerics of the ECMWF. Atmospheric Forecast Model

Ordinary Differential Equations II

TWO PROBLEMS IN COMPUTATIONAL WAVE DYNAMICS: KLEMP-WILHELMSON SPLITTING AT LARGE SCALES AND WAVE-WAVE INSTABILITIES IN ROTATING MOUNTAIN WAVES

Lecture V: The game-engine loop & Time Integration

Multistep Methods for IVPs. t 0 < t < T

WRF Modeling System Overview

Evaluation of mountain drag schemes from regional simulation Steve Garner GFDL

X i t react. ~min i max i. R ij smallest. X j. Physical processes by characteristic timescale. largest. t diff ~ L2 D. t sound. ~ L a. t flow.

The Fifth-Generation NCAR / Penn State Mesoscale Model (MM5) Mark Decker Feiqin Xie ATMO 595E November 23, 2004 Department of Atmospheric Science

A Description of the Advanced Research WRF Version 2

Finite volume method for CFD

Conservation of Mass Conservation of Energy Scaling Analysis. ESS227 Prof. Jin-Yi Yu

Chapter 3. Stability theory for zonal flows :formulation

B O S Z. - Boussinesq Ocean & Surf Zone model - International Research Institute of Disaster Science (IRIDeS), Tohoku University, JAPAN

q t = F q x. (1) is a flux of q due to diffusion. Although very complex parameterizations for F q

Chapter 5. Methods for Solving Elliptic Equations

1 Introduction to Governing Equations 2 1a Methodology... 2

Lecture 3. Turbulent fluxes and TKE budgets (Garratt, Ch 2)

Numerical Hydraulics

Physics-Based Animation

PDE Solvers for Fluid Flow

A Global Atmospheric Model. Joe Tribbia NCAR Turbulence Summer School July 2008

Discretisation of the pseudo-incompressible Equations with implicit LES for Gravity Wave Simulation DACH2010. Felix Rieper, Ulrich Achatz

A Simple Turbulence Closure Model

Chapter 1. Governing Equations of GFD. 1.1 Mass continuity

A Simple Turbulence Closure Model. Atmospheric Sciences 6150

Numerical Modelling in Fortran: day 10. Paul Tackley, 2016

IMPACTS OF SIGMA COORDINATES ON THE EULER AND NAVIER-STOKES EQUATIONS USING CONTINUOUS/DISCONTINUOUS GALERKIN METHODS

Chapter 5. Shallow Water Equations. 5.1 Derivation of shallow water equations

Goal: Use understanding of physically-relevant scales to reduce the complexity of the governing equations

A Discussion on The Effect of Mesh Resolution on Convective Boundary Layer Statistics and Structures Generated by Large-Eddy Simulation by Sullivan

Flux Diagnosis in the COSMO model and their problems

MOX EXPONENTIAL INTEGRATORS FOR MULTIPLE TIME SCALE PROBLEMS OF ENVIRONMENTAL FLUID DYNAMICS. Innsbruck Workshop October

Fluid Animation. Christopher Batty November 17, 2011

Solving the Euler Equations!

Developmental Testbed Center (DTC) Project for the Air Force Weather Agency (AFWA) Final Report of Annual Baseline GSI Experiments (Appendix Task)

Mathematical Concepts & Notation

Weather Forecasting Models in Met Éireann. Eoin Whelan UCD Seminar 3 rd April 2012

Chapter 5. Fundamentals of Atmospheric Modeling

Candidates must show on each answer book the type of calculator used. Log Tables, Statistical Tables and Graph Paper are available on request.

WRF Model Simulated Proxy Datasets Used for GOES-R Research Activities

A recovery-assisted DG code for the compressible Navier-Stokes equations

WRF Advanced Usage and Best Prac3ces. Jimy Dudhia Wei Wang NCAR/MMM

Physics/Dynamics coupling

A Description of the Advanced Research WRF Version 3

Ordinary Differential Equations II

Enabling Multi-Scale Simulations in WRF Through Vertical Grid Nesting

MASSACHUSETTS INSTITUTE OF TECHNOLOGY DEPARTMENT OF MECHANICAL ENGINEERING CAMBRIDGE, MASSACHUSETTS NUMERICAL FLUID MECHANICS FALL 2011

Transcription:

Dynamics: Introduction The Advanced Research WRF (ARW) Dynamics Solver 1. What is a dynamics solver? 2. Variables and coordinates 3. Equations 4. Time integration scheme 5. Grid staggering 6. Advection (transport) and conservation 7. Time step parameters 8. Filters 9. Map projections and global configuration 10. Boundary condition options WRF ARW Tech Note A Description of the Advanced Research WRF Version 3 (June 2008, 2012 update) http://www.mmm.ucar.edu/wrf/users/pub-doc.html

Dynamics: 1. What is a dynamics solver? A dynamical solver (or a dynamical core, or dycore) performs a time (t) and space (x,y,z) integration of the equations of motion. Given the 3D atmospheric state at time t, S(x,y,z,t), we integrate the equations forward in time from t T, i.e. we run the model and produce a forecast. The equations cannot be solved analytically, so we discretize the equations on a grid and compute approximate solutions. The accuracy of the solutions depend on the numerical method and the mesh spacing (grid).

Dynamics: 2. Variables and coordinates Vertical coordinates: (1) Traditional terrain-following mass coordinate Dry hydrostatic pressure π d π t η Column mass (per unit area) µ d = π s π t Vertical coordinate Layer mass (per unit area) ( η = π π ) d t µ d π s µ d Δη = Δπ d = gρ d Δz, Pressure π d ( η)= ηµ d + π t

Dynamics: 2. Variables and coordinates Vertical coordinates: (2) Hybrid terrain-following mass coordinate Isobaric coordinate (constant pressure): η = π d π 0 π t Hybrid terrain-following coordinate: π t η π d ( η)= B(η)µ d + π t +[η B(η)](π 0 π t ) (Terrain-following) (Isobaric) η c η c level at which B 0, i.e. transition between isobaric and terrain-following coordinate. π s

Dynamics: 2. Variables and coordinates Variables: Grid volume mass (per unit area): µ d = π d η = B η (π s π t ) + (1 B η )(π 0 π t ) π t η Conserved state (prognostic) variables: µ d, U = µ d u, V = µ d v, W = µ d w, Θ = µ d θ Non-conserved state variable: φ = gz η c π s

Dynamics: 2. Variables and coordinates Vertical momentum eqn. Subscript d denotes dry, and ρ = ρ d covariant (u, ω) and contravariant w velocities u = dx dt, w = dz dt, ω = dη dt U = µu, W = µw, Ω = µω ( 1+ q v + q c + q r + ) α = ( 1+ q α v + q c + q r + ) 1 d

Dynamics: 3. Equations U t = Uu V t = Uv W = Uw t µ d = U t t = µ d q j t t = U = Uq j u Vu y u Vv v Vw w V V Vq j q j v y! p µ d p µ d g d d y µ d d + R + Q + R qj + Q qj + gw + R u + Q u y + R v + Q v + R w + Q w

Dynamics: 3. Equations transport U t = Uu V t = Uv W = Uw t µ d = U t t = µ d q j t t = U = Uq j u Vu y u Vv v Vw w V V Vq j q j v y! p µ d p µ d g d d y µ d d + R + Q + R qj + Q qj + gw + R u + Q u y + R v + Q v + R w + Q w

Dynamics: 3. Equations U t = Uu V t = Uv W = Uw t µ d = U t t = µ d q j t t = U = Uq j u transport Vu y u Vv v Vw w V V Vq j q j v y! pressure gradient p µ d p µ d g d d y µ d d + R + Q + R qj + Q qj + gw + R u + Q u y + R v + Q v + R w + Q w

Dynamics: 3. Equations U t = Uu V t = Uv W = Uw t µ d = U t t = µ d q j t t = U = Uq j u transport Vu y u Vv v Vw w V V Vq j q j v y! pressure gradient p µ d p µ d g d d y µ d d + R + Q + R qj + Q qj + gw + R u + Q u y + R v + Q v + R w + Q w numerical filters, physics, projection terms geopotential eqn term

Dynamics: 3. Equations U t = Uu V t = Uv W = Uw t µ d = U t t = µ d q j t t = U = Uq j u transport Vu y u Vv v Vw w V V Vq j q j v y! pressure gradient p µ d p µ d g d d y µ d d + R + Q + R qj + Q qj + gw + R u + Q u y + R v + Q v + R w + Q w numerical filters, physics, projection terms geopotential eqn. term Diagnostic relations: p = R d Θ m p o µ d α d γ, Θ m = Θ 1+ R v q v R d

Dynamics: 4. Time integration scheme 3 rd Order Runge-Kutta time integration advance Amplification factor

Dynamics: 4. Time integration scheme time splitting U t = L fast (U) + L slow (U) 3rd order Runge-Kutta, 3 steps L s (U t ) U * t t+dt/3 t+dt L s (U * ) U ** t t+dt/2 t+dt L s (U ** ) U t+dt t t+dt fast: acoustic and gravity wave terms. slow: everything else. RK3 is 3rd order accurate for linear eqns, 2nd order accurate for nonlinear eqns. Stable for centered and upwind advection schemes. Stable for Courant number Udt/dx < 1.43 (5 th order adv.) Three L slow (U) evaluations per timestep.

Dynamics: 4. Time integration scheme acoustic step Forward-backward differencing on U, Θ, and μ equations Vertically implicit differencing on W and φ equations

Dynamics: 4. Time integration scheme - implementation Begin time step End time step Runge-Kutta loop (steps 1, 2, and 3) (i) advection, p-grad, buoyancy using (φ t, φ *, φ **) (ii) physics if step 1, save for steps 2 and 3 (iii) mixing, other non-rk dynamics, save (iv) assemble dynamics tendencies Acoustic step loop (i) advance U,V, then μ, Θ, then w, φ (ii) time-average U,V, Ω End acoustic loop Advance scalars using time-averaged U,V, Ω End Runge-Kutta loop Adjustment physics (currently microphysics)

Dynamics: 5. Grid staggering horizontal and vertical y C-grid staggering h V Ω,W, φ U µ,θ,q v,q l U U µ,θ,q v,q l U V x Ω,W, φ x horizontal vertical

Dynamics: 6. Advection (transport) and conservation dry-air mass U t = Uu V t = Uv W = Uw t µ d = U t t = µ d q j t t = U = Uq j u transport Vu y u Vv v Vw w V V Vq j q j v y! pressure gradient p µ d p µ d g d d y µ d d + R + Q + R qj + Q qj + gw + R u + Q u y + R v + Q v + R w + Q w Next: Dry-air mass conservation in WRF Diagnostic relations: p = R d Θ m p o µ d α d γ, Θ m = Θ 1+ R v q v R d

Dynamics: 6. Advection (transport) and conservation dry-air mass control volume (2D example) Mass in a control volume is proportional to ( ΔxΔη) ( µ d ) t since µ d Δη = Δπ d = gρ d Δz

Dynamics: 6. Advection (transport) and conservation dry-air mass Mass in a control volume 2D example Mass conservation equation Change in mass over a time step mass fluxes through control volume faces

Dynamics: 6. Advection (transport) and conservation dry-air mass Mass in a control volume Mass conservation equation Horizontal fluxes through the vertical control-volume faces

Dynamics: 6. Advection (transport) and conservation dry-air mass Mass in a control volume Mass conservation equation Vertical fluxes through the horizontal control-volume faces

Dynamics: 6. Advection (transport) and conservation dry-air mass The same mass fluxes are used for neighboring grid cells - hence mass is conserved locally and globally.

Dynamics: 6. Advection (transport) and conservation U t = Uu V t = Uv W = Uw t µ d = U t t = µ d q j t t = U = Uq j u transport Vu y u Vv v Vw w V V Vq j q j v y! pressure gradient p µ d p µ d g d d y µ d d + R + Q + R qj + Q qj + gw + R u + Q u y + R v + Q v + R w + Q w Entropy and scalar mass conservation in WRF Diagnostic relations: p = R d Θ m p o µ d α d γ, Θ m = Θ 1+ R v q v R d

Dynamics: 6. Advection (transport) and conservation scalars Mass conservation equation: Mass in a control volume Scalar mass change in mass over a time step mass fluxes through control volume faces Scalar mass conservation equation: change in tracer mass over a time step tracer mass fluxes through control volume faces

Dynamics: 6. Advection (transport) and conservation U t = Uu V t = Uv W = Uw t µ d = U t t = µ d q j t t = U = Uq j u transport Vu y u Vv v Vw w V V Vq j q j v y! pressure gradient p µ d p µ d g d d y µ d d + R + Q + R qj + Q qj + gw + R u + Q u y + R v + Q v + R w + Q w Transport schemes: flux divergence (transport) options in WRF Diagnostic relations: p = R d Θ m p o µ d α d γ, Θ m = Θ 1+ R v q v R d

Dynamics: 6. Advection (transport) and conservation 2 nd, 3 rd, 4 th, 5 th and 6 th order centered and upwind-biased schemes are available in the ARW model. Example: 5 th order scheme where F i 1 2 ( Uψ) = U i 1 2 ( Uψ) x sign ( 1,U ) 1 60 = 1 Δx F i + 1 2 37 60 ψ +ψ i i 1 ( ) 2 ( Uψ) F i 1 2 ( Uψ) ( ) + 1 ( 60 ψ +ψ i +2 i 3 ) 15 ψ i +1 +ψ i 2 ( ψ i +2 ψ i 3 ) 5 ψ i +1 ψ i 2 ( ) +10 ( ψ i ψ i 1 ) { }

Dynamics: 6. Advection (transport) and conservation For constant U, the 5 th order flux divergence tendency becomes Δt δ ( Uψ ) Δx 5th =Δt δ ( Uψ ) Δx 6th UΔt 1 ( Δx 60 ψ + 6ψ 15ψ + 20ψ 15ψ + 6ψ ψ i 3 i 2 i 1 i i +1 i +2 i +3 ) Cr 6 ψ 60 x +H.O.T 6 The odd-ordered flux divergence schemes are equivalent to the next higher ordered (even) flux-divergence scheme plus a dissipation term of the higher even order with a coefficient proportional to the Courant number.

Dynamics: 6. Advection (transport) and conservation shape preserving 1D advection overshoot undershoot ARW transport is conservative, but not positive definite nor monotonic. Removal of negative q results in spurious source of q.

Dynamics: 6. Advection (transport) and conservation shape preserving Scalar update, last RK3 step (1) (1) Decompose flux: f i = f i upwind + f i c (2) Renormalize high-order correction fluxes f i c such that solution is positive definite or monotonic: f i c = R(f ic ) (3) Update scalar eqn. (1) using f i = f i upwind + R(f ic )

Dynamics: 6. Advection (transport) and conservation examples 1D Example: Top-Hat Advection tracer mixing ratio + 0 - x tracer mixing ratio + 0 - x

Dynamics: 6. Advection (transport) and conservation Where are the transport-scheme parameters? The namelist.input file: &dynamics h_mom_adv_order v_mom_adv_order h_sca_adv_order v_sca_adv_order scheme order (2, 3, 4, or 5) defaults: horizontal (h_*) = 5 vertical (v_*) = 3 momentum_adv_opt moist_adv_opt scalar_adv_opt chem_adv_opt tracer_adv_opt tke_adv_opt = 1 standard scheme = 3 5 th order WENO default: 1 options: = 0 : no limiter, = 1 : positive definite (PD), = 2 : montonic = 3 : 5 th order WENO = 4 : 5 th order PD WENO

Dynamics: 7. Time step parameters 3 rd order Runge-Kutta time step Δt RK UΔt Courant number limited, 1D: C r = <1.73 1.43 Δ x Generally stable using a timestep approximately twice as large as used in a leapfrog model. (5 th order adv.) Where? The namelist.input file: &domains time_step (integer seconds) time_step_fract_num time_step_fract_den

Dynamics: 7. Time step parameters 3 rd order Runge-Kutta time step Δt RK (&domains time_step) Acoustic time step 2D horizontal Courant number limited: Δτ sound = Δt RK C ( number of acoustic steps) r = CsΔτ < Δh 1 2 Where? The namelist.input file: &dynamics time_step_sound (integer)

Dynamics: 7. Time step parameters 3 rd order Runge-Kutta time step Δt RK (&domains time_step) Acoustic time step [&dynamics time_step_sound (integer)] Guidelines for time step Δt RK in seconds should be about 6*Δx (grid size in kilometers). Larger Δt can be used in smaller-scale dry situations, but time_step_sound (default = 4) should increase proportionately if larger Δt is used. If ARW blows up (aborts) quickly, try: Decreasing Δt RK (that also decreases Δt sound ), Or increasing time_step_sound (that decreases Δt sound but does not change Δt RK )

Dynamics: 8. Filters divergence damping Purpose: filter acoustic modes (3-D divergence, ) From the pressure equation: γ d = 0.1 recommended (default) (&dynamics smdiv) (Illustrated in height coordinates for simplicity)

Dynamics: 8. Filters time off-centering the vertical acoustic modes Purpose: damp vertically-propagating acoustic modes Slightly forward centering the vertical pressure gradient damps 3-D divergence as demonstrated for the divergence damper β = 0.1 recommended (default) [&dynamics epssm]

Dynamics: 8. Filters external mode filter Purpose: filter the external mode Vertically integrated horizontal divergence, Continuity equation: γ e = 0.01 recommended (default) [&dynamics emdiv] (Primarily for real-data applications)

Dynamics: 8. Filters vertical velocity damping Purpose: damp anomalously-large vertical velocities Additional term: (usually associated with anomalous physics tendencies) Cr β = 1.0 typical value (default) [share/module_model_constants.f w_beta] γ w = 0.3 m/s 2 recommended (default) [share/module_model_constants.f w_alpha] [&dynamics w_damping 0 (off; default) 1 (on)]

Dynamics: 8. Filters 2D Smagorinsky 2nd-Order Horizontal Mixing, Horizontal-Deformation-Based K h Purpose: mixing on horizontal coordinate surfaces (real-data applications) [&dynamics diff_opt=1, km_opt=4] where C s = 0.25 (Smagorinsky coefficient, default value) [&dynamics c_s]

Dynamics: 8. Filters gravity-wave absorbing layer Implicit Rayleigh w Damping Layer for Split-Explicit Nonhydrostatic NWP Models (gravity-wave absorbing layer) R w (η)- damping rate (t -1 ) z d - depth of the damping layer γ r - damping coefficient [&dynamics damp_opt = 3 (default = 0)] [&dynamics damp_coef = 0.2 (recommended, = 0. default)] [&dynamics zdamp = 5000. (z d (meters); default); thickness of absorbing layer beneath model top]

Dynamics: 8. Filters gravity-wave absorbing layer example Model Initialized 04 Dec 2007 00 UTC w (cm/s) t = 12 h t = 30 h w (cm/s) t = 12 h t = 30 h

Dynamics: 9. Map projections and global configuration ARW Model: projection options 1. Cartesian geometry: idealized cases 2. Lambert Conformal: mid-latitude applications 3. Polar Stereographic: high-latitude applications 4. Mercator: low-latitude applications 5. Latitude-Longitude global, regional Projections 1-4 are isotropic (m x = m y ) Latitude-longitude projection is anistropic (m x m y )

Dynamics: 9. Map projections and global configuration Global ARW Polar filters Converging gridlines severely limit timestep. The polar filter removes this limitation. Filter procedure - Along a grid latitude circle: 1. Fourier transform variable. 2. Filter Fourier coefficients. 3. Transform back to physical space.

Dynamics: 9. Map projections and global configuration An alternative to global ARW Global, nonhydrostatic, C-grid Voronoi mesh Numerics similar to WRF; WRF-NRCM physics No pole problems Variable-resolution mesh no nested BC problems Available at: http://mpas-dev.github.io/

Dynamics: 10. Boundary condition options ARW Model: Boundary Condition Options Lateral boundary conditions 1. Specified BCs (from coarse grid, real-data applications). 2. Nested BCs (within coarse grid, real-data applications). 3. Open lateral boundaries (gravity-wave radiative). 4. Symmetric lateral boundary condition (free-slip wall). 5. Periodic lateral boundary conditions. Top boundary conditions 1. Constant pressure. Bottom boundary conditions 1. Free slip. 2. Various B.L. implementations of surface drag, fluxes.

Dynamics: Where are things? WRFV3 real test main idealized cases dyn_em phys (physics) share (b.c routines) (model constants) lots of other stuff Initialization code + dynamics solver code WRF ARW Tech Note A Description of the Advanced Research WRF Version 3 (June 2008, 2012 update) http://www.mmm.ucar.edu/wrf/users/pub-doc.html