Recent advances in RAMS cloud

microphysics and precipitation

over-prediction

By

William R. Cotton and Steve Saleeby

Dept of Atmospheric Science

Colorado State University

Outline

• Brief overview of RAMS physics including

recent additions/changes

• Brief summary of examples of aerosol-

cloud interaction studies

• Examples of precipitation over-prediction

biases

• Attempts to remove the bias

• Summary and conclusions.

RAMS microphysics with explicit

aerosol nucleation

Cloud Droplet Nucleation

Number nucleated obtained from lookup table as a function of

CCN number concentration

Vertical velocity

Temperature

Lookup table generated previously (offline) from detailed parcel-bin model

Nc1=Nccn

Nc2=Ngccn ; Sw > 0.0

b

wS

Ice Crystal Nucleation

Ice nucleation follows the approach described by Meyers et al. (1992):

Ni = NIN exp [12.96 (Si - 1)]

T < -5oC; rv > rsi (supersaturation with respect to ice), and T < -2oC ; rv > rsl(supersaturation with respect to liquid).

Secondary ice particle production model in RAMS is based on Mossop (1976).

In MKS units, the formula is:

where B increases linearly from 0 to 1 as ice temperature T increases from -8 C to -5 C, B decreases linearly from 1 to 0 as T increases from -5 C to -3 C, and B is zero at other ice temperatures. Ni is the number of ice particles produced per second, N24 is the number of cloud droplets larger than 24 �m in diameter that are collected by ice each second, N13 is the number of cloud droplets smaller than 13�m in diameter that are collected by ice each second.

.9324 13iN = 9.1e-10 B N N ( )× × ×

Ice Crystal Nucleation --continued

2. Contact nucleation

T < 0oCC IFN

3. Homogeneous freezing of cloud droplets T < -30oC

4. Homogeneous freezing of haze

Rate depends on T, rv, amount of haze present

Microphysical Processes

Represented in RAMS• Cloud droplet nucleation in one or two modes

• Ice nucleation

• Vapor deposition growth

• Evaporation/sublimation

• Heat diffusion

• Freezing/melting

• Shedding

• Sedimentation

• Collisions between hydrometeors

• Secondary ice production

Hydrometeor Types

Cloud droplets

Rain

Pristine ice (crystals)

Snow

Aggregates

Graupel

Hail H

G

S

A

C

P

R

RAMS Liquid Hydrometeor DistributionsRAMS Liquid Hydrometeor Distributions

Features of RAMS Microphysics:

� Uses generalized gamma distribution basis functions :

where n(D) is the number of particles of diameter D, Nt is the total number of particles, ν is the shape parameter, and Dn is some characteristic diameter of the distribution. The Marshall-Palmer (exponential) and Khrgian-Mazindistribution functions are special cases of this generalized function.

� Simulations can be done with one or two moments. When two-moments of a hydrometeor class is predicted, all that is needed to completely specify the distribution function given by (1) is the specification of ν.

1

1( ) exp

( )

t

n n n

N D Dn D

v D D D

ν −

= −

Γ

�� Collection is simulated using stochastic Collection is simulated using stochastic collection solutions rather than continuous collection solutions rather than continuous accretion approximations. Owing to the accretion approximations. Owing to the use of lookuse of look--up tables, it became apparent up tables, it became apparent that it is no longer necessary to constrain that it is no longer necessary to constrain the system to constant or average the system to constant or average collection efficiencies. Thus the formerly collection efficiencies. Thus the formerly ad hoc autoad hoc auto--conversion formulations in conversion formulations in RAMS was replaced with full stochastic RAMS was replaced with full stochastic collection solutions for selfcollection solutions for self--collection collection among cloud droplets and for rain among cloud droplets and for rain (drizzle) drop collection of cloud droplets. (drizzle) drop collection of cloud droplets. This approach is being extended to all This approach is being extended to all hydrometeor interactionshydrometeor interactions..

�� The philosophy of bin representation of collection was The philosophy of bin representation of collection was

also extended to calculations of drop sedimentation. also extended to calculations of drop sedimentation.

Previously, bulk microphysics schemes have treated Previously, bulk microphysics schemes have treated

sedimentation of hydrometeors by integrating over the sedimentation of hydrometeors by integrating over the

entire particle sizeentire particle size--spectra and obtaining a massspectra and obtaining a mass--

weighted fall speed. Bin sedimentation is simulated by weighted fall speed. Bin sedimentation is simulated by

dividing the gamma distribution into discrete bins and dividing the gamma distribution into discrete bins and

then building lookthen building look--up tables to calculate how much up tables to calculate how much

mass and number in a given grid cell fall into each cell mass and number in a given grid cell fall into each cell

beneath a given level in a given time step.beneath a given level in a given time step.

Recent improvements in RAMS

Microphysics• Binned collection approach now mimics the technique used for

collection of cloud droplets by rain. New hydrodynamic collection kernels were created using state-of-the-art collection efficiencies for the riming of droplets by snow and aggregates (Wang and Ji 2000), graupel (Cober and List 1993), and hail (Greenan and List 1995).

• In the droplet auto-conversion process, the hydrodynamic collection kernel is broken up in 36 mass-doubling bins. Cloud droplets are represented by bins with a diameter less than 80 microns, and all larger bins represent the rain category.

• The bulk riming approach that uses a single collection efficiency to represent collection is replaced with entire distributions of ice particles and droplets and realistic efficiencies.

Improvements continued

• Power law relations based on the work of Mitchell (1996) and adapted to RAMS by Rob Carver of Penn State University, have been applied to RAMS 4.3 microphysics.

• Mitchell (1996), and Andy Heymsfield have shown that RAMS fall speeds for pristine ice and snow are too slow. So, more realistic fall speed power laws were applied, as well as a revised density variation for fall speeds in the sedimentation routines.

• The original density variation for fall distance used a multiplier of:

0.5

1.0

a

x

ρ

=

continued

• the new application of fall speed power

laws uses a multiplier of:

0.36

0.7

a

x

ρ

=

Continued

• the new power laws plus the density factor

for sedimentation tends to allow for

increased presence of cloud water, due to faster fall speeds for pristine ice and snow,

and a general reduction of surface

precipitation by several percent.

continued

• Aerosol/droplet nucleation now allows for nucleation in conditions of high supersaturation but low vertical velocity such as due to cloud top radiational cooling.

• A sedimentation limitation has been imposed to deal with the large end tails of the distributions and with power laws that do not become asymptotic when they should in reality.

• The method of transferring pristine ice to snow has been improved to limit artificial production of pristine ice number concentration when the pristine ice distribution mean diameter passes beyond the 125 micron cutoff limit.

Radiation Direct Effects

• The Harrington radiation scheme has been

modified to include direct radiative heating

of aerosols assuming they are in log-normal size-distribution functions

• User must supply the single scattering

albedo for specific aerosols and

appropriate size parameters

Some examples of aerosol-cloud

simulations

Pollution impacts on winter

orographic clouds

Summary

• In supercooled winter orographic clouds variations in both CCN and GCCN can have an appreciable influence on accumulated precipitation

• High concentrations of CCN can not only suppress riming growth by producing smaller droplets but also reduce supercooled liquid water contents via the Bergeron-Feindeisen process

Simulations of Florida

Thunderstorms during a Saharan

Dust Event over South Florida

Summary

• Dust which can enhance CCN, GCCN, and IN concentrations can alter the dynamics of convective storms, the optical thickness of the anvils they produce, and surface rainfall.

• However, the impact of rainfall is complicated by cold-pool dynamics which in turn respond to the amounts of rainfall and the size-spectrum of raindrops.

• Thus we saw that rainfall increased due to dust at first whereas after several hours the accumulated rainfall decreased.

Urban aerosol influences—

METROMEX revisited

Location of grids 1 through 3

CCN and GCCN Concentrations for

Model Initialization

• Rural and urban concentrations of CCN and GCCN were estimated using observations made during METROMEX.

• Upwind and downwind summer CCN concentrations made between 1971 and 1973 at 0.7% supersaturation were averaged (Braham, 1974; Spyers-Duran, 1974; Auer, 1975).

• GCCN concentrations were obtained from volume distributions of large aerosol particles (diameters between 1 and 55 microns) upwind and downwind of St. Louis (Johnson, 1976; Braham, 1977; Komp and Auer, 1978).

Summary and Conclusions

• Urban land-use has the biggest control on locations and amounts of precipitation

• Overall both CCN and GCCN concentrations are important to cloud responses to varying aerosols.

• In supercooled clouds and deep convective clouds variations in IN concentrations can also play an important role in governing storm dynamics and rainfall.

• Even in shallow boundary layer clouds once aerosols effect the precipitation process, the cloud radiative properties can be modified in nonlinear ways.

• For deep convective clouds the nonlinear interactions between varying aerosol amounts and cloud dynamics can lead to responsesin terms of rainfall amounts that are quite unpredictable. Short term responses may increase rainfall whereas longer-term responses can decrease rainfall.

Simulations of Seeding of Winter

Orographic Clouds in Colorado

RAMS EXTENDED TO INCLUDE SEEDING EFFECTS

Post-season RAMS seed simulations were preformed for each of the

86 days on which seeding operations were conducted during

November 2003 - March 2004.

Simulated sources of silver iodide (AgI) were at specified low-level

model grid points in accordance with the timing and magnitude of

AgI release at each seeding generator as recorded in WWC’s

operational seeding logs.

The AgI was treated as a second predictive IFN field with its own

activation characteristics.

All other aspects of the seed simulation runs were identical to the

control (no-seed) simulation design.

Small differences were found between RAMS seed and no-seed simulated precipitation.

• This could be due to:

• Actual small seeding responses• The background CCN and IFN concentrations are unknown; therefore,

the results are at the mercy of specified background concentrations.• The model under-predicts supercooled liquid water content in the

lower portion of clouds over the target area, thereby reducing seedability.

• An unforeseen dynamic response that appears to result in large areas of slightly suppressed precipitation in the target area and small regions of slightly enhanced precipitation.

• The low-level warm temperature bias results in delayed AgI nuclei activation, fewer activated nuclei, and less time for crystals to grow and snow to fall in the target area.

• The transport and diffusion of seeding material from generator sites is getting into the clouds too far downwind of the generator sites.

EFFECTS OF AEROSOL ENTRAINMENT

ON SEA-ICE FREEZING AND MELTING

RATES

MELTING SEASON MULTI-

MONTH SIMULATIONS • Domain: 5X4 Km, �x = 50m, � z = 30m, � t =

2sec. • Simulation time: 92 days from 1 May 1998. • Cyclic boundary conditions and nudging using 2-3

daily SHEBA soundings. • Two-moment microphysics.• Sub-grid thickness distribution: six categories with

a linear remapping scheme (Lipscomb, 2001).• Initial mean thickness of the distribution: 2.41m.

(Average of seven closest gauges for 1 May.)

SUMMARY

• Results indicate that increasing the IFN concentrations within the upper layer:– increases total condensate path,

– decreases the liquid water fraction,

– decreases the relative importance of ice sedimentation by reducing their free fall speed,

– increases net radiative forcing,

– enhances sea-ice melting rates when mixed phase clouds are present.

• Results suggest that CCN entrainment has an opposite although less important effect.

• Six-month cloud resolving simulations results suggest lower freezing rates for more polluted aerosol profiles above the inversion.

Precipitation over-prediction

Evaluation of Winter Orographic

Precipitation for the 2003-2004

season• Simulated 24-hr precipitation from the daily control

runs was used to establish precipitation for individual events and monthly and seasonal totals.

• 30 operational cloud seeding days were selected from November 2003 through March 2004 for evaluation studies.

• Comparison with 61 Snotel observations shows that the forecast runs generally simulated the spatial distribution well, but with an over-prediction bias for precipitation amounts (factor of 1.88).

RAMS 3-km grid with target area boundary, towns, seeding generator

locations, Snotel sites

SIMULATED PRECIPITATION OVER-PREDICTION BIAS

Plot of 30-day total control-run precipitation extracted at

61 Snotel sites vs. the 30-day total observed precipitation.

Possible sources of model precipitation biases

• Inadequate resolution of atmospheric

dynamics and terrain, especially when

embedded convection is prevalent.

• Some bugs/logical errors in the

microphysics

• Errors in RAMS core dynamics/numerics

Steve Saleeby’s attempts to solve

the problem

• Over-estimation of sedimentation of

hydrometeor species

• Over-estimation of collection efficiencies

• General over-saturation of RAMS and

resulting over-production of condensate

Sedimentation

• The power law relationships in RAMS over-estimate the fall speed of the largest hydrometeors in the tails of the distributions. This can impact the binned sedimentation.

• However there is very little mass or number concentration at the ends of the distributions.

• Examining the power law relations, all but pristine ice were found to be satisfactory, even for the largest size hydrometeors

• Pristine ice fall speed is strongly over-estimated for large sizes; however, with the current size cutoff of 125 microns diameter for pristine ice, this is not a serious issue.

• Despite rather significant changes to the

sedimentation routine concerning

displacement of the distribution tails, it appears that the accumulation of the

relatively small amount of mass from the

large sized bins has only a minimal

impact. The RAMS sedimentation routine is not the key source of the over-prediction

of precipitation problem.

Collision and Coalescence

• Over-prediction of collection among

hydrometeor types and/or over-riming

• Collection efficiencies were reduced

• A binned approach to riming was implemented-riming is reduced, especially that by graupel

• There is not a great difference in accumulated precipitation fields

• Precipitation is still strongly over-predicted along crests of highest mountain ranges

• Tweaking the microphysics processes

made about ~10% differences in

precipitation

Diabatic term in P-tendency

• Adding the diabatic terms to the pressure

tendency equation had no impact on

precipitation.

Chris Castro’s regional Simulations

• These simulations were performed in a

regional context using the Kain-Fritsch

cumulus parameterization scheme and no explicit microphysics

Comments:

In regional climate model simulations, the precipitation overestimation

problem can be alleviated by introducing interior nudging to the

simulation. For our experiments, we used a weak nudging of a

timescale of 1 day. The nudging weakens the vertical motion at all

scales, which reduces moisture flux convergence and rainfall (there is

a figure in R-276 which shows but I’ve omitted it here). The nudging

also helps the simulation better correspond to observations. Similar

results have been obtained other regional climate models.

There are also very large sensitivities to:

The choice of convection scheme. For example, the KF scheme tends

to overpredict in RAMS and the Kuo scheme underpridicts.

The choice of lateral boundary forcing. RAMS downscaling ECMWF

tends to produce more while RAMS downscaling NCEP predicts less.

This has generally been the case in model simulations throughout the

world, for example in North and South America and India.

Saleeby on moistening

RAMS Over-saturation

• Assuming ETA is truth, we see that RAMS

is much more saturated and over the

extent of the domain.

• This occurs in less than 10min of model

time

Level-1 Microphysics Only

• With only Level-1 microphysics RAMS

exhibits large areas of RH>90% but ETA

does not.

Vertically-Integrated Moisture/Condensate Using

Level-1 and Level-3 Microphysics

Internally Nudging only the Moisture Field to the Eta

analysis.

• Internal nudging to ETA analyses

significantly reduced precipitation.

• Internal nudging of winds, temperature,

and geopotential heights but not moisture

did not reduce precipitation

Charlie Cohen Slides

St. Louis area precipitation from

ordinary Cbs

Accumulated surface

precipitation (mm liq)

for the RURAL

experiment at grid 3

at 00Z (24 hours of

simulation time).

Observed Precipitation

• The observations in St. Louis for that day

were only 0.58” which is consistent with

what RAMS has for that specific location(~0.4”).

• However, we are doubtful that a 7”

rainstorm occurred SW of town!!

Summary

• RAMS microphysics with explicit cloud

nucleating aerosol is able to capture the

essence of aerosol cloud interactions and aerosol effects on precipitation

• RAMS exhibits a significant precipitation

bias that is not related to microphysics

• The main cause seems to be related to

over-production in condensate—Why?