2016 l17 mea716 3 17 mp1 short - nc state university › ... › gary › mea716 ›...

Thu 3/17/2016Briefly discuss exam resultsRemaining hypothesis presentations – TuesdayModel capabilities:

• Resolution terminology• Domains and nesting• Moving nests• Digital Filter Initialization (DFI)

Begin cloud/precipitation microphysics (MP) section

Reminders/announcements:• Take-home portion of exam due

Semester OutlineModel Physics:

1.) Land-Surface Models (LSM)2.) Turbulence parameterization & the planetary boundary layer (PBL)3.) Convective parameterization (CP)4.) Cloud and precipitation microphysics (MP)5.) Parameterization of radiation

Project:1.) Topic selection, case identification2.) Hypothesis development3.) Control simulation, hypothesis presentation4.) Experiments and final presentation

Technical:1.) Running SCM2.) Running WPS, WRF, postprocessing for real-data cases3.) Model experiments: Terrain and physics modifications4.) Analysis and diagnosis of model output

DoneDoingNot yet

On the Term “Resolution”

Grasso (2000), and many others:• The terms “resolution” and “grid

spacing” are not equivalent

• Generally filter 2x and 3x waves, so at very least, “resolution” is 4x

• Most estimates are between 5 and 10 x (e.g., Walters comment)

• Safe terminology: Refer to “grid length” or “grid spacing” rather than “resolution”

WRF Resolution (Skamarock and Klemp 2008)

• Model initial conditions typically have little kinetic energy in mesoscale

• Requires 6-12 hours for development of mesoscale energy spectrum

• Skamarock and Klemp (2008): WRF “resolves” features ~6 to 8 x (with 5th order advection scheme)

On WRF Resolution (Skamarock and Klemp)

Some important considerations:1) Resolution and grid spacing. What phenomena must be resolved,

and what grid length will accomplish that?2) Domain size and “residence time”. What is the prevailing wind speed,

and how long must air reside in a given domain to benefit from the resolution?

3) One versus two-way nesting (not yet discussed: “feedback” in NL)4) Sources of initial, boundary condition data

Worksheet

Analysis and Prediction of High-Impact Weather Events

Circulations in eyewall

TC flow can be divided into… Primary: azimuthal

Maintains inertial stability of the warm-core

Secondary: radial and vertical Supplies thermodynamic energy in

the form of turbulent fluxes & releases latent heat aloft

Ooyama ’82: TC a “mesoscale power plant with a synoptic-scale support system”

Megan Gentry (now Mallard)


Eye diameter ~ 37 km

4x partially resolved x 9 km

10x fully resolved x 4 km

Scale of a hurricane

• What grid spacing is necessary to “resolve” a hurricane vortex?

• Even coarse grids can resolve basic aspects of vortex

RMW ~ 50 km, diameter ~ 100 km

4x partially resolved x 25 km

10x fully resolved x 10 km

Walters 2000 & comments by Grasso, The Differentiation between Grid Spacing and Resolution and Their Application to Numerical Modeling


Convection in eyewall• Observations: Updrafts and downdrafts in eyewall

are much smaller in scale than vortex itself

Core Diameter (km)

90% of updrafts ~ 4 km or less

4x partially resolved, x = 1 km

10x fully resolved, x = 0.4 km

Eastin 2005


Eyewall processes• Vortex Rossby waves

(VRWs) break in eyewall, forming pools of high PV

• Warm, low-momentum air in eye mixes out into eyewall, additional heat source for convection (Persing and Montgomery 2003)

Schubert et al. 1999


Experimental setup• Hurricane Ivan (2004) simulated at 27, 9, 8, 6, 4, 3, 2, 1 km

• See Gentry and Lackmann (2010), MWR for details


Vortex-tracking nest

Model simulated

radar

Aside about moving nests…

Nested domains on the fly…

“Easy way” to spawn additional nests, but

terrain resolution won’t match grid length

Real-time WRF forecast of Hurricane Earl, initialized 00 UTC 2 September 2010

Real-time WRF forecast of Hurricane Earl, initialized 00 UTC 2 Sept 2010 (with vortex-tracking moving nest feature)


• Repeat: Ivan (2004) simulated using 27, 9, 8, 6, 4, 3, 2, 1 km grid length

• First, let’s visit “no man’s land”, examine 9-km output

• Purpose here: Examine with, without CP scheme on

• Note, with 1-way feedback, nested grids can be examined independently

• 2 runs

• KK -> Kain-Fritsch CP scheme on all domains

• K0 -> no CP in inner domain, KF outer

• Similar nomenclature for BMJ scheme


Azimuthal averages (9 km)• Good way to look at averaged structure of TC in profile

R

R

ZY

X


Updraft velocity (9 km)• Makes significant difference in

updraft velocity

• ~ 0.25 m/s difference with updraft of 1 – 1.5 m/s

• Think about vortex stretching…

KK

K0


Downdraft velocity (9 km)• Downdraft velocities are more

intense with explicit convection

KK

K0


No CP vs. CP• Weakening of vertical motions with CP… expected since CP

handles convection implicitly

• Overall, secondary circulation, spiral bands strongly influenced by whether or not CP used

• Influence of CP scheme is felt by balanced vortex… no clear scale separation between vortex and convective scale on which CPS is working…. what Molinari paper warns about


Implications of using CP• Use of CP weakens vertical velocities in/around eyewall,

affects intensification

• Without CP: Stronger updrafts, greater compensating subsidence in core… more compressional warming, hydrostatic lowering of surface pressure

• From vorticity perspective, weaker vortex stretching with CP

• Intensity differences (between CP vs. no CP with all else equal): ~ 5 to 10 hPa in central pressure


Outline• Hurricane simulations with & without the use of a

convective parameterization scheme (CPS)

• Sensitivity of hurricane structure & intensity to horizontal resolution


• What changes in TC structure and intensity occur when horizontal grid spacing is reduced?

• What physical processes might be responsible for these changes?

30 hPa of deepening

between 8 & 1 km grid spacing


Model-simulated

• Smaller vortex, RMW shrinks by 40%

• Development of spiral bands

• Convection becomes more axisymmetric, with more & smaller maxima in eyewall

8km 4km

2km 1km


Vertical motion

8km 6km

4km 2km


Eyewall convection• “Dumbell” behavior

(seen in coarser) simulations vanishes at small grid length

• Eyewall has interspersed up/downdrafts

850-hPa vertical velocity (w)


Eyewall Updraft• As resolution increases, eyewall shrinks and vertical velocity

increases, even when interpolated to same grid

8 6 4

3 2 1


VRWs and PV mixing• Straight-line PV segments and PV pools appear in 4-

km and finer simulations


As grid length decreases…– Up/downdrafts in eyewall stronger, more numerous, and

smaller in spatial extent

– Eyewall less elliptical, more likely to transition from circular to polygonal eyewall appearance

– Spiral bands develop

– VRWs phase-lock & break, eye/eyewall exchange of entropy & momentum

• Overall, much better representation of eyewallprocesses & intensity with 4-km and finer grid spacing

DFI

• Another important WRF capability (that should probably be used for most simulations): Digital Filter Initialization

• Following is extracted from WRF workshop talks by Peckham et al. and Huang et al. (2008)

• DFI is a way to filter noise and ensure dynamical consistency at model start-up: “Initialization”

• It does not involve interpolation, bogusing, nudging, or data assimilation

DFI

• Puts momentum and mass fields into dynamical balance

• Spins up cloud, precipitation fields at model start (unlike usual clear-sky start)

• Easy to use, a namelist option

• Drawback: Adds some computational expense

DFI

I often reduce forward and backstop time to 1 hour or even 30 mins –still derive benefit

DDFI

I often reduce forward and backstop time to 1 hour or even 30 mins –still derive benefit

Microphysics Outline

• Basics of microphysics schemes– Why classes matter– Representation of number concentration

• The WRF schemes

• Model simulated radar

• Case-study analysis:– Winter storm– Convective storm– Tropical cyclone– Other…

Microphysics References

Microphysics Outline

• Basics of microphysics schemes– MP scheme responsibilities– Distinguishing characteristics: Classes, Distribution, & Processes– Why classes matter– Representation of number concentration

• The WRF schemes

• Model simulated radar

• Case-study analysis:– Winter storm– Convective storm– Tropical cyclone– Other…

Microphysics – cloud interactions

Water Vapor

evap

orat

ion

OceanLANDev

apor

atio

n

evap

otra

nspi

ratio

n

RunoffPrecipitation

Condensation, Deposition

CLOUDS

Long-Wave radiation

Solar shortwave

Adapted from F. Carr

evaporation

shallow Cu,downdrafts from

deep Cu

Grid-Scale Precipitation• Also known as “explicit” precipitation… why might this be a

misnomer?

– Still “parameterization” of very small-scale processes (cloud physics)! – But, the *motions* needed to supersaturate are represented on grid

• Precipitation produced in model as result of grid-scale ascent, removal of supersaturation

• Scheme that handles grid-scale precipitation dubbed “microphysics” or “cloud microphysics” scheme

Microphysics Background

• As model grid spacing decreases, it becomes easier to saturate a grid box

The role of microphysics schemes in NWP becomes increasingly important at higher resolution

Sources:Jason Millbrandt, McGill, excellent microphysics materialsHong et al. 2004Jimy Dudhia (NCAR)WRF technical description, WRF web pageMilbrandt and Yau papersStensrud parameterization text

Microphysics

Ascent, saturation, and condensation

Grid-scale precipitation: – Requires grid box RH to

exceed a specified threshold

– Excessive moisture eliminated through condensation or deposition

What must MP schemes do?• Account for change in vapor, condensate (fill 3-D arrays for

advection), latent heat release/absorption – compute mixing ratio

• Account for falling precipitation (different fall speeds for different size hydrometeors) – must know size distribution, number concentration

• Determine all phase change processes, compute changes in hydrometeor class (e.g., autoconversion, melting, riming, accretion, aggregation, etc.)

• Provide cloud information to radiation scheme

• Determine amount, type of precipitation reaching surface (interact with Land-Surface Model)

• Interact with CP scheme

What are MP distinguishing characteristics?

1.) Classes: # of different water species (cloud, precipitation, vapor):

Cloud liquid waterCloud iceSnowRainGraupelHailVapor…. Could also have different degrees of rimed snow crystal, different crystal habit, wet versus dry hail, melting (aggregate snow), etc.

2.) Specification of particle size distribution:

BinBulkSingle, double, triple moment

What are MP distinguishing characteristics?

3.) Processes

Specification of classes and size distribution dictates which processes may be represented in a given scheme

Braham and Squires (1974, BAMS)

Ice Processes: Habit as f(T) (Caltech photos)

http://www.caltech.edu/content/snowflake-science

When is it no longer snow?

http://www.inscc.utah.edu/~tgarrett/Snowflakes/WASHARX.html

Graupel vs Hail

http://www.komonews.com/weather/blogs/scott/112648409.html & Witchita NWS

Many WRF schemes include only one or other (mainly graupel)

High-end schemes include both

Worksheet example

Katrina, WSM6 run, Hour 18, Simulated radar, SLP

Hour 18, Cross section, cloud liquid water, cloud ice

Hour 18, cross section: rain water

What is approximate fall velocity of rain?

What about snow?

What is typical vertical velocity in eyewall of a mature hurricane?

Why does this matter?

Hour 60, Cross section, vertical velocity (m/s)

Not a bad updraft for 20 km grid with CP!

Hour 18, cross section: rain water, snowWhy is the snow so far above freezing level?

Hour 18, cross section: rain water, snow, graupel

2016 l17 mea716 3 17 mp1 short - nc state university › ... › gary › mea716 ›...

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