Thu 4/7/2016Begin radiation section

- Why it matters: Fovell anvil self spreading example- Consideration of WRF radiation schemes

Representation of clouds and precipitation:- Finish with microphysics: Paper summaries (begin – 3 – James,

Masih, Dylan)

Also, Keith’s MT paper summary (Molinari and Dudek)

Reminders/announcements:• MP experiment assignment (due in 2 weeks)• Final presentations: 28 April, 1-4 pm (final exam period)

• One more progress report will be assigned. Do you have any input on this?

Molinari and Dudek (1992)Dept. of Atmospheric Science, University at Albany

Parameterization of Convective Precipitation in Mesoscale Numerical Models: A Critical ReviewMonthly Weather Review

• Provides a general review on three philosophies regarding cumulus parameterization– Traditional: Implicit (i.e., subgrid-scale) convection at convectively unstable points; explicit

(i.e., grid-scale) convection at convectively stable points– Fully explicit: Explicit convection for both– Hybrid: Hybrid approach at convectively unstable points; explicit convection at convectively

stable points• Cumulus parameterization provides vertical cloud/precipitation particle distribution• Some particles then detrained and predicted explicitly

• Case for and against cumulus parameterization– For: Environmental response to convective instability, in terms of heat, moisture, and

momentum fluxes both within and outside of convection, below resolvable scale of many operational models

• As a result, although attempting to more accurately represent the atmosphere by excluding CP, solution could be less realistic

– Against: At some finer scales, the same process may essentially be represented twice; additionally, some closures/aspects of parameterizations are “ad hoc due to lack of observations” explicit approach generally preferred, when possible

Mesoscale models must adequately represent both subgrid-scale and grid-scale processes!However, if we can simulate heating/cooling profiles properly, should be able to get the rest right…

Explicit convection example:

• Increased RH saturation• Rainfall initially delayed (did not occur until

grid saturated)

• Strong localized heating superadiabaticlapse rates, adjusted to near dry adiabatic by Richardson number constraint

• Rapid overturning intense precipitation, near-neutral lapse rate

Ultimately: Delayed but overestimated precipitation due to unrealistic treatment of

vertical motion and heat/moisture fluxes

t = 0 t = 3h

t = 6h t = 9h

Authors propose hybrid approach, as noted previously…• Offers a smoother transition from implicit to explicit convection• Better at timing (i.e., reduces “spin-up” time problem above)• Some issues:

• Closures, results sensitive to convective parameterization parameters, some unrealistic instability, vertical motion oversimplified, observations in detrained precipitation sparse...

• Appropriate for upright convection, maybe not slantwise

Grayzone

Control Run No Convective Parameterization

Generally smoother fieldGenerally lower maxima

More localized “tracks”Higher maxima, typically

Note: Very little difference in non-convective regions

Take-home portion of exam

Extra credit

Diabatic heating tendency

z0

Comments:• Because I was not assigned a CP

scheme, I was asked to assess the heating tendencies from the microphysics scheme.

• This is the vertical heating profile I would expect from convective microphysical processes.

• Below maximum heating, cooling resulting from melting of frozen hydrometeors likely counteracts some of the heating.

Heating from condensation/freezing

Cooling from evaporation/sublimation?

Cooling from evaporation

• Values range from -0.005 to 0.05 K s-1

• Immediately surrounding convective updrafts (i.e., locations of maximum diabatic heating), see a deeper zone of cooling below the heating… perhaps attributable to increased evaporation/melting here?

Example from a localized zone of maximum mid-level heating (i.e., convective updraft)

Re-Cap from TuesdayWhat are some challenges associated with interpretation of

model-simulated radar?

- Does computation include contribution from sub-grid scale (CP) precipitation?

- Are details of the effective reflectivity calculations in the postprocessor consistent with those of the MP scheme?

- Real radar has errors such as bright bands, which are not present in model output (where we have accurate information about which hydrometeors are present)

Differences in Sim Radar (TN case)WPP/UPP

NCL/ WRF2GEM

Re-Cap from TuesdayWhat is the sign of the historical bias in model-computed

shortwave radiation reaching the Earth’s surface?

What are some possible explanations for this bias?

What is the “two stream” assumption, and in what situations does it become problematic?

To what extent does the choice of radiation scheme matter for atmospheric modeling and prediction?

Stensrud et al. (2006) BAMS: Positive model radiation bias

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

Microphysics Section Outline• Basics of microphysics schemes

– MP scheme “responsibilities”– Distinguishing characteristics: Classes, Distribution, & Processes– Why classes matter: Hurricane example– Representation of number concentration: Bin vs. Bulk– Single, double, and triple moment schemes

• The WRF schemes– Calling sequence– Defining characteristics: Warm and cold-cloud processes– Scheme details: CCN, process representation

• Model simulated radar

• Case-study examples:– Winter storm (lake effect)– Convective storm– Tropical cyclone

Hong et al. (2010): Evaluation of the WRF Double-Moment 6-Class Microphysics Scheme for

Precipitating Convection

Summary by James RussellMEA716

Improved representation of MCSObserved WSM6 WDM6Composite Reflectivity

from 3km simulation with gust fronts labeled

• Convection more focused with continuous line in WDM6 – larger rain number concentrations at smaller size - causes rain to fall to the ground slower.

• Substantially faster movement of the WDM6 simulation than in WSM6 due to increased organization of downdrafts.

00Z

03Z

06Z

Reduced Spurious RainfallAccumulated rainfall and SLP from 10km simulation

• Significantly reduces spurious convection over ocean and also near Japan

Analysis WSM6

WDM6Equitable threat score and bias for light and heavy precipitation periods

Precipitation Uncertainty Due to Variations in Precipitation Particle Parameters within a Simple

Microphysics Scheme

Gilmore et al. (2004) Monthly Weather Review

Motivation and Importance:

1) Ice hydrometeors can greatly influence precipitation distribution, fallout velocity and resulting downdraft intensity.

2) Hail can cause roof destruction, damage to crops, injury to unsheltered livestock and people

Masih Eghdami April 12, 2016

Methods

• Straka Atmospheric Model (SAM) is used for simulating idealized multicell and supercell thunderstorms

• A simple liquid-ice microphysics scheme is used (3-ICE) that is a single-moment bulk mixing ratio for each precipitating class.

• Similar to Lin et al. (1983) scheme in ARPS, WRF, and RAMS

Accumulated precipitation

900 kg/m3 400 900 400

U=30 m/s U=50 m/s

Conclusions

Results• Accumulated precipitation produced by a system of simulated midlatitude

multicelll and supercell storms varies by a factor of about 3 or 4.• Fall velocity, mass growth and loss rate equations for qh are sensitive to

model parameters.• Snow and rain are not as sensitive as hail/graupel.• The use of three-class ice microphysics schemes such as Lin et al. (1983) is

not advised due to the high uncertainty.

Collection efficiencies

Testing different environments and storm systems

Improving terminal velocities

Future work

Cloud Microphysics Impact on Hurricane Track as Revealed in Idealized 

ExperimentsFovell, Corbosiero, & Kuo, 2009

Presented by Dylan White

Introduction• Studies have shown tropical cyclone and hurricane dependence on microphysics parameterization (MP) choices

• Few have examined • track sensitivity• why MP schemes would affect  storm motion

Methods• WRF (2.2.1) simulations – Hurricane Rita case study 

• 3 MP choices• Kessler scheme• Lin‐Farley‐Orville 5 class scheme (LFO) • WRF 3 class single moment scheme (WSM3)

• Sensitivity tests• Tweak some of the MP schemes

Results

• Two ice schemes (Lin‐Farley‐Orville 5 class scheme and WSM3) are similar and closer to observation

• Kessler scheme very different• Prominent warm regions below and cool regions above the anvil

Shaded; condensate

Contoured; Virtual temperature perturbations 

12 hourly positions

ResultsSymmetric  

|V850| 

•Vortex motion substantially dependent on the strength of symmetric flow far from storm core• This flow is in balance that reflects the gradient of mean virtual temperature. 

• This horizontal temperature profile is heavily modulated by the CP scheme!

Outline for radiation parameterization section

Radiative transfer- Review of radiation basics- Atmospheric radiation- Model representation strategies- An example of physics interactions (MP-RA)- WRF radiation schemes- Cloud-radiation interactions: Thompson/RRTMG

Fovell and Su 2007: MP and TC track

Interesting result: TC track sensitive to MP choice: • Outer rainbands, wind field crucial• See also Fovell et al. (2009) JAS

MP and TC track

Was it only latent heating profile that led to track differences?

Fovell et al. 2011: Cloud-radiative feedback at work

Full physics No Cloud-radiative feedback

MP and TC track: Fovell et al. 2011

Longwave temperature tendency gives cooling above, inward of warming bracketing anvil

This diabatic tendency drives outflow, further advecting hydrometeors radially: “Anvil self-spreading”

Idealized Rotunno-Emanuel

model

WRF Radiation Options

• For a tropical cyclone, how might the radiation scheme and microphysics scheme relate, physically, to storm intensity?

• What determines thermodynamic efficiency of a hurricane “heat engine”?

• Are there other ways in which radiation might alter tropical cyclone track or intensity?

ra_lw_physics=1Rapid Radiative Transfer Model (RRTM) scheme – from AER Inc. (Mlawer et al.

1997, 2003)

• Spectral scheme (based on line-by-line (LBL) transfer model) – 16 LW bands

• Look-up tables to draw on accurate LBL calculations (absorption as function of pressure and temperature)

• Interacts with explicit clouds

• Ozone/CO2 from climatology in WRF-ARW

• Namelist default (what we’ve been running, unless you changed it)

• Accounts for water vapor, CO2, O3, N2O, CH4, halocarbons (CFCs)

• Validated for wide range of conditions, seasons, locations

• Designed for versatile applications, including climate and mesoscale models; used in ECHAM5 GCM

• Serious bug fixed in V3.2 (Cavallo) – major cold bias in upper stratosphere

ra_lw_physics=1: V3.4 and earlier

CO2 = 330 PPMv

ra_lw_physics=1= 1, rrtm scheme

(Default for GHG in V3.5: co2vmr=379.e-6, n2ovmr=319.e-9, ch4vmr=1774.e-9; Values used in previous versions: co2vmr=330.e-6, n2ovmr=0., ch4vmr=0.)

= 4, rrtmg scheme

(Default for GHG in V3.5: co2vmr=379.e-6, n2ovmr=319.e-9, ch4vmr=1774.e-9)

ra_lw_physics=3Community Atmosphere Model (CAM) radiation scheme,

see Collins et al. (2004)

• Requires additional namelist variables (see next slide)

• More sophisticated scheme from climate model, useful for long WRF runs (on order weeks or more)

• Use with CAM SW scheme

ra_lw_physics=3

e_vert

ra_lw_physics=3 (CAM)

CAM scheme includes CO2 data going back to 1869, and going ahead to 2101

Future CO2 determined from IPCC/UN A2scenario

(bad: 828.6 ppmv)

module_ra_cam_support.F

From module_ra_cam.F

Quick CAM experiment

Does WRF running with CAM radiation scheme really use time-dependent CO2 values seen in code?

How can we easily test this? What parameters should we examine?

Check LW down at surface with SCM, run for 2011, also 2100

Parameter LWDNB: Inst. downwelling LW flux at model bottom

Quick CAM experimentCheck LW down at surface (SCM), run for 2011, also for 2100

Parameter LWDNB is Instantaneous downwelling LW flux at model bottom

1869 2011

2100

ra_lw_physics=4RRTMG scheme:

Similar to RRTM: Look-up tables, K-distribution method

More sophisticated cloud treatment than RRTM; RRTMG handles cloud fractions whereas RRTM is 1/0

More interactions with WRF-chem, uses optical depth

Better suited for climate applications

Stephens, 1984; Mlawer et al. 1997

CO2 as f() (a) and “ordered” (b)

Radiation in Atmospheric Models

RRTM, RRTMG: Use very accurate, expensive line-by-line radiative transfer model to compute absorption coefficients across wide range of atmospheric conditions, create “lookup tables” in scheme

ra_lw_physics=5Updated Goddard scheme, added 2011 (Chou and

Suarez):

Fewer bands than RRTM schemes (10), also uses look-up tables

Handles cloud fractions? Designed to handle aerosols

Also well-suited for climate applications

ra_lw_physics=7New UCLA scheme (FLG)

Added 2012 (V3.4), based on Gu et al. 2011, JGR

Another k-distribution scheme

12 bands, look-up table

Cloud fraction 0/1

Designed for aerosol and trace gas interactions

Lots of work on cirrus problem (?)

ra_lw_physics=99GFDL longwave scheme (“semi-supported”)• Used in Eta/NMM; compared by Tarasova et al.

• Should only be used with Ferrier microphysics (?)

• Spectral scheme from global model

• Also uses lookup tables

• Interacts with explicit clouds

• Ozone/CO2 from climatology

ra_sw_physics=1MM5 shortwave (Dudhia)• Simple downward calculation, a wide-band method

• Clear-sky scattering, tunable, and can include aerosols

• Water vapor absorption, but not ozone(!)

• Computationally inexpensive, cloud reflection & absorption

• This is namelist default (what we’ve been using, unless you changed it)

• Seems to consistently produce less SWRAD at surface relative to Goddard Scheme

ra_sw_physics=2

Goddard shortwave (Chou and Suarez 1999, as mentioned in Tarasova et al. (2006) study)

• Spectral method

• Interacts with grid-scale clouds

• Does include ozone absorption, but uses climatological distributions

• Better account of water vapor absorption, oxygen absorption line in SW, aerosols (as discussed)

• Seems to consistently yield greater SWRAD than Dudhiascheme… consider this for convective initiation?