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Thu 1/28/2016 WRF tendency terms WRF LSM options Water-surface parameterization Representation of turbulence Reminders/announcements: - SCM2 assignment due Tuesday - Upcoming: WRF real-data case assignment (will post, officially assign Tuesday)
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2016 L05 MEA716 1 28 PBL1 short · 2016. 5. 2. · Thu 1/28/2016 • WRF tendency terms • WRF LSM options • Water-surface parameterization • Representation of turbulence...

Feb 10, 2021

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  • Thu 1/28/2016• WRF tendency terms

    • WRF LSM options

    • Water-surface parameterization

    • Representation of turbulence

    Reminders/announcements:- SCM2 assignment due Tuesday- Upcoming: WRF real-data case assignment (will post,

    officially assign Tuesday)

  • cd to WRFV3/dyn_em directory. Examine solve_em.F (4558 lines):

    WRF Solver

  • cd to WRFV3/dyn_em directory. Examine solve_em.F:

    Solve_em.F:

    Large outer Runge-Kutta loop:non-timesplit physics (for state variables)acoustic time step (many per larger outer loop)advance variables (moist, chemistry scalars)time-split physics (cloud microphysics)

    End large outer Runge-Kutta loop

    Time filtering

    Lots of MPI issues along the way. 4558 lines of code, very well commented!

    WRF Solver

  • Solve_em.F:

    Large outer Runge-Kutta loop:non-timesplit physics

    CALL phy_prep ( config_flags, mut, u_2, v_2, p, pb, alt, ph_2, phb, t_2, tsk, moist, num_3d_m, mu_3d, rho, th_phy, p_phy, pi_phy, u_phy, v_phy, p8w, t_phy, t8w, z, z_at_w, dz8w, fnm, fnp, RTHRATEN, RTHBLTEN, RUBLTEN, RVBLTEN,

    RQVBLTEN, RQCBLTEN, RQIBLTEN, RTHCUTEN, RQVCUTEN, RQCCUTEN, RQRCUTEN, RQICUTEN, RQSCUTEN, RTHFTEN, RQVFTEN, ids, ide, jds, jde, kds, kde, ims, ime, jms, jme, kms, kme,

    grid%i_start(ij), grid%i_end(ij), grid%j_start(ij), grid%j_end(ij), k_start, k_end)

    e.g., TH = theta (potential temperature), RA = radiation, etc.

    WRF Solver

  • Physics Tendencies

    In namelist.input &time_control:iofields_filename = "myoutfields.txt"

    Added variables in myoutfields.txt:

    +:h:0:H_DIABATIC,RTHBLTEN,RTHRATEN

    “+:h” means add these variables to the “history” (output) file

    Otherwise to add variables, would modify Registry, re-compile

  • WRF LSM options

    Sources include Jimy Dudhia WRF presentation

  • WRF LSM namelist options (V3.5.1)

    4 choices

  • WRF LSM namelist options (V3.7.1)

    7 choices

  • sf_surface_physics=1

    5-layer thermal diffusion model from MM5

    • Predict ground temp and soil temps• Thermal properties depend on land use• No effect of water• Provides heat and moisture fluxes for PBL• Very simple treatment, not recommended for real-

    data cases

    • Set num_soil_layers = 5, re-run ideal.exe or real.exe

  • sf_surface_physics=2

    Noah Land Surface Model – formerly OSU LSM(Chen and Dudhia 2001, Ek et al. 2003)

    • Vegetation effects included• Predicts soil temperature and soil moisture in 4 layers• Predicts snow cover and canopy moisture• Handles fractional snow cover and frozen soil• Diagnoses skin temp and uses emissivity• Provides heat and moisture fluxes for PBL

    • Set num_soil_layers = 4, re-run ideal.exe or real.exe

  • Noah Land-Surface Model (LSM)

  • sf_surface_physics=3

    RUC Land Surface Model (Smirnova et al. 2000)

    • Vegetation effects included• Predicts soil temperature and moisture in 6 layers• Multi-layer snow model• Provides heat and moisture fluxes for PBL• Can also now run with 9 layers

    • Set num_soil_layers = 6, re-run ideal.exe or real.exe

  • sf_surface_physics=4

    Noah “MP” Land Surface Model (Niu et al. 2011)

    • New “multiparameterization” options: Improved– Vegetation canopy energy balance– Snowpack and frozen soil representation (snow skin T)– Groundwater interactions, runoff– Evidently, dynamic vegetation option (!)

    • Requires additional set of 12 namelist settings

  • sf_surface_physics=4

  • sf_surface_physics=5

    CLM4 [from NCAR Community Atmosphere Model (CAM) climate model] LSM (Lawrence et al. 2011)

    • Many additional processes included, biogeochemical model (Carbon-Nitrogen)

    • 10-layers• Urban effects• Dynamic – land-use change capability• Aerosol deposition for snow melt, snow aging, etc.

  • sf_surface_physics=5

    Lawrence et al. (2011);

    Fig. 1

  • sf_surface_physics=7Pleim-Xiu LSM (Pleim and Xiu 1995)

    • 2-layer model, aim towards air-quality applications

    • “Force-restore”, T forcing is surface energy budget, including radiation, SH flux, LH flux, and ground flux

    • Moisture forcing includes surface precipitation, dew, evaporation, and soil moisture flux

    • Accompanies P-X PBL package (asymmetrical convective model, ACM2); probably best to run together

  • sf_surface_physics=8SSiB (Xue et al. 1991, Sun and Xue 2001)

    • 3-layer model, aim towards climate applications

    • Simple Biosphere Model (SiB) was predecessor

    • Now, Simplified Simple Biosphere model (SSiB)

  • WRF SCM• Greensboro, NC from 1 / 18 / 2016, 12 UTC (default)

    • Changed only LSM choice

    • For some, had to link additional files from ../../run dir

    • Compared variation of PBLH (all with YSU PBL scheme)

  • A few SCM tests… same PBL & surface layer

    Thermal Diffusion (1)

    Noah (2)

    RUC (3)

    CLM4 (5)

    PBLH max ~ 1790 m

    PBLH max ~ 2100 m

    PBLH max ~ 2260 m

    PBHL max ~ 2050 m

  • Water Surface RepresentationGiven that 70% of Earth’s surface is covered by water, very important specification in model

    Strong influence on cyclones, climate, coastal processes, boundary currents, planetary energy budget, etc.

    Latest generation of models are coupled (AOGCMs, CFS, WRF, MPAS)

    Full coupling includes wave model (Drs. Xie, He in MEAS)

  • Water Surface RepresentationWRF features several options for dealing with sea surface:

    - Static (hold initial values constant)

    - SST update (use analyses to supply evolving SST analyses)

    - Ocean Mixed-Layer Model (OML): Only mixing, no currents or upwelling

    - 3-D Ocean Model (new): Based on Price (1981)

  • Water Surface RepresentationWhere do SST initial conditions come from?

    - Satellite observations crucial for SST analysis (Reynolds and Smith 1994) – Optimum Interpolation technique to blend available observations

    - Higher resolution now available, 14-km grid or better

    - NOAA AVHRR (advanced Very-High Resolution Radiometer) sensor is used, 8 km grid, 2 IR channels. Must be cloud-free

    - Satellite observations combined with in-situ ship, buoy obs

    - Supplemental data needed near ice edge (SSMI)

  • Analysis and Prediction of High-Impact Weather Events

    WRF Simulations (Megan Mallard)• Advanced Research WRF,

    version 3.0.1.1• 4-member ensemble with

    varying parameterization schemes

    – Microphysics: Morrison & WSM6– PBL: YSU (TC fluxes on) & MYJ

    • Kain-Fritsch convective scheme (on 54- & 18-km grids)

    • Initial & boundary conditions:– 1 GFS - FNL & 0.5 RTG SST

    • Results shown are from 18- & 6-km nests

    Δx=54 km Δx=18 km

    Δx=6 km

  • Analysis and Prediction of High-Impact Weather Events

    OML – default settingsNo OML

    Ocean Mixed Layer Model

    72-hour Lead Time

    939 mb 947 mb

  • Analysis and Prediction of High-Impact Weather Events

    Sea Surface Temperatures• TCs interact with ocean

    – Intensify over warm water– Leave cold water in their wake

    • Ocean mixed layer model used to account for cold wakes– Cold wakes persist

    • Modified source code, SST updated with observed temperatures daily with memory of cold wakes from previous day

    SST at initialization2 months later…

    Cold wake

  • Micrometeorology and Turbulence Parameterization

  • Outline1.) Review of turbulence and properties

    - Characteristics, worksheet

    - Definitions

    - Regimes and sources

    - Turbulence kinetic energy (TKE)

    - Flux and flux divergence

    2.) Closure problem- Bulk aerodynamic

    - K-theory (mixing length)

    - Higher-order closures

    - WRF schemes, examples

  • Planetary Boundary Layer: Processes & Parameterization

    In-class worksheet to familiarize with processes and set up parameterization problem

    First, a little background on turbulence

  • Turbulence

    http://www.efluids.com/efluids/gallery/gallery_pages/iso_turbulence_page.htmhttp://arstechnica.com/science/news/2008/11/is-turbulence-a-chaotic-repeller-or-attracter.ars

    Chaotic, isotropicEnergy cascade, dissipation

  • Turbulence…• Contains spatial scales not resolvable by observations

    • Characterized by downscale energy cascade; viscosity transforms turbulent to thermal energy at smallest scales

    • Thus, energy source is needed to maintain turbulence

    • Generally acts to mix fluid properties

    • Turbulent flux divergence or convergence is a first-order term in governing equations, must be accounted for

  • Reynolds Averaging

    s

    t

    Rule #1:

    1

    1 Nn

    is s

    N

    0

    1 Ts s dtT

    ' at any instants s s

    0 by definitions

    0 etcs s Any single-prime quantity = 0

  • What are turbulent fluxes of heat, moisture, & momentum?

    Measuring Turbulence

    Ground

    Inversion

    (z)

    Turbulent air motions

    w’(m/s) ’ (K)0.21 1.02

    - 0.42 -0.350.03 -0.120.13 1.22

    - 0.23 -1.450.08 0.971.02 0.81

    Sensor, measuring w’, ’

  • Mathematical form of fluxes: Correlations (covariance) between turbulent fluctuations is what matters

    In general, do we expect terms like to be positive, negative, or zero? Why? Under what conditions?

    How about the momentum flux

    How about moisture flux?

    Turbulent Fluxes

    θw

    uw

    qw

    These are the vertical turbulent fluxes

    w’(m/s) ’ (K)0.21 1.02

    - 0.42 -0.350.03 -0.120.13 1.22

    - 0.23 -1.450.08 0.971.02 0.81

  • Distribution of Atmospheric Turbulence

    Turbulence – generation primarily from buoyancy, shear

    “Free troposphere”- generally little turbulence, but near jets some clear-air turbulence (CAT); also turbulence in/near convection

    Stratosphere- very stable, limited turbulence, good place for commercial aircraft to fly!

    ~1 km

    Inversion layer, entrainment zone

    CATtropopause

  • SURFACE

    PBL, mixed layer, Ekman layer, or “outer layer”

    surface layer(constant flux layer)

    Model-defined PBL top

    Lowest Model level

    z0

    Free atmosphere

    Viscous sub-layer, or molecular boundary layer

    Model PBL Components

  • PBL Overview/Review• Structure of the PBL

    – Molecular boundary layer: lowest few mm of atmosphere, molecular diffusion dominates

    – Surface layer: lowest 10-30 meters over which turbulent momentum flux may be assumed constant, logarithmic wind profile (neutral)

    – Mixed layer: From top of surface layer to inversion base marking lower boundary of inversion layer

    – “Free” atmosphere: portion of troposphere not directly affected by surface-based turbulent fluxes, frictional effects

    • Properties of PBL:– Turbulent flow– Depth ranges from a few meters at night with light winds to several

    km over intensely heated, dry terrain; very strong diurnal signal

  • Yamada and Mellor (1975)

    Wangaraexperiment.

  • TurbulencePBL regimes:

    – Unstable: Free convection, buoyant turbulence production– Neutral: Forced convection, mechanical production– Stable: Forced convection, mechanical production

    How is turbulence generated?1.) Mechanical production – forced convection, dynamic instability

    Shear – frictional near surfaceWake turbulenceShear – clear air in free atmosphere

    2.) Buoyant – free convection, static instabilityPlumes, order 100 m in scale, may merge to form thermals, order 1 km in scaleCharacterized by static instability

  • Turbulent Kinetic Energy (TKE)

    mechanical thermalTKE Advect P P TurbTrans diss

    t

    Equation available for prediction of TKE per unit mass:

    Buoyant term can be + or – depending on static stability

    22221 wvu

    mTKE

  • PBL Overview/ReviewRichardson number (Ri)

    – Energy required to maintain/generate turbulence related to Ri

    – Richardson # is ration of buoyant to shear turbulence production

    – Two common forms: Gradient and Bulk Richardson number

    22

    22

    vu

    zgRi

    zv

    zu

    zg

    Ri

    v

    vb

    v

    vgr

  • Some fairly recent resultsQJ papers (2007, 2008) challenge old ideas on Ri significance

  • Some fairly recent results

  • T

    Td

    PBL Review Worksheet4.) Consider the vertical profile of temperature and dew point shown below. This

    hypothetical profile could represent conditions over land on a sunny spring afternoon in the mid-latitudes.

    5.) Suppose that the profile shown above remains quasi-steady until sunset. Sketch the expected evolution of this profile for a clear, calm evening andovernight period using a series of dashed lines to show the temperature profile evolution (using the same diagram). What controls the slope of these lines?

  • T What physical processesare at work in bringing about these temperature changes?

    Which model physics parameterization packages are involved?

    Radiation… Land surface model… PBL scheme… maybe CP

    PBL Review Worksheet

    6.) Consider the temperature profile below, which is observed at sunrise for the same general situation as the profile on the previous page. Sketch on this diagram how the profile would evolve after the sun has risen, into the afternoon hours.

  • Turbulence parameterization in models is crucially important

    PBL/turbulence parameterization involves interactions between several other model physics schemes

    Important to gain an intuitive feel for how turbulence might affect a given simulation; check tendencies to confirm

    In the coming weeks, will spend considerable time and effort to familiarize ourselves with turbulence parameterization

    Prepare to read, discuss PBL journal papers!

    Summary