Estimation of clouds in atmospheric models Tomislava Vukicevic CIRA/CSU and PAOS/CU.

Estimation of clouds in atmospheric models

Tomislava VukicevicCIRA/CSU and PAOS/CU

Motivation for accurate information on cloud

properties • GCM, NWP and CRM

– Development and validation of cloud parameterizations

– Initialization in NWP

• Assessment of current climate – Hydrologic trends– Interaction with other climate system

components

Observation sources

• Special site measurements (ARM)• Field experiments• Satellite remote sensing • Ground based remote sensing

Mostly indirect Retrieval content limited relative to desired

information Spatial distribution Model quantities not observable

4D cloud data assimilation

Satellite radiance

CRM with bulk cloud

microphysics

+

Sensitive to atmospheric hydrology

High spatial and possibly temporal resolution

GOES Wavelength Central Detector

Channel (µm) Wavelength Resolution (µm)

(km)___________________________________________

1 0.52-0.72 0.7 12 3.78-4.03 3.9 4 3 6.47-7.02 6.7 8

3 G12 5.77-7.33 6.5 4 4 10.2-11.2 10.7 4 5 11.5-12.5 12.0 4

6 G12 12.9-13.7 13.3 8

GOES imager

15 minute data

VISNear IR

Diff between ice and water

clouds

IR water vapor

IR clouds and surface

IR clouds, surface and low level

vapor

CRM RAMS• Bulk, 2 moment cloud microphysics for ice:

pristine ice, aggregates, snow, graupel and hail• 1 moment for liquid: cloud droplets and and rain• Prognostic mixing ratio and number

concentration in 3D• Assumed Gamma distribution with prescribed

width• Nonhydrostatic dynamics • Regional simulations with initial and boundary

conditions from weather analysis

Technique

• Nonlinear 4DVAR • Full physics nonlinear forward

model • No approximations in adjoint of

RAMS with cloud microphysics• Quasi-Newton minimization of cost

function with preconditioning

Mapping from CRM to GOES VIS and IR operator

ytXHy )( Greenwald et al. 2003

Gas absorption: OPTRAN (McMillin et al., 1995) Cloud properties: Anomalous Diffraction Theory

Solar: SHDOM (Evans, 1998)

IR: Eddington two-stream (Deeter and Evans 1998)

4D assimilation of GOES imager IRerror statistics

(model – observation)

mean = 0.3 K

sd = 5.9 K

mean = 33 K

sd = 8.2 K

prior posterior

Brightness Temperature

Brightness Temperature

Vukicevic et al, 2004, 2005

Verification of the estimate in 4D cloud study against independent obs

ARM Cloud Radar reflectivity

Before assimilation

After assimilation

observations

Time

Thick ice cloud

Liquid cloud

Heig

ht

km

1 hour

Thick ice cloud

Thin ice cloud

More observations better result

Single channel assimilations, 30 min frequency

2-channel assimilation, 30 min frequency

2-channel assimilation, 15 min frequency

GuessWorst

Best

m7.10 m0.12

Tb errors

Conclusions• Successful estimation:

– Information content in the model enhanced consistent with the the observation information content

– Stronger observational constraint narrower error distribution

• But, model was applied as strong constraint

Linear model error addedas in other NDVAR studies

Did not work : no convergence

• Conclusion Linear generic model error not appropriate

in cloud estimation

• Suggested approach Physically based model error model

parameter estimation

Comparison of state and parameter estimation

Lorenz 3 component system

Estimation technique: Markov Chain Monte Carlo (MCMC)

Estimation of parameters

State solutions within estimation period

PDFs of parameters after estimation

Estimation period

Forecast period

Estimation of initial condition (state)

PDFs of initial condition components after estimation

State solutions in forecast using mean of distribution as best estimate

State solutions in forecast using maxima of distribution as best estimate

X Y

X forecast forecast

Estimation of state

Observations without errors

XY

Erroneous observations

Derivation of suitable form of parameterization for estimation

Possible solution Extend information from the

measurements into 3D+time

CRM information is not accurate but has skill

CRM simulation in 600 by 17

domain started from crude 4D weather analysis

Mixed phase

Pristine ice

Liquid cloud

rain

Horizontal circulation

Vertical circulation

Ground based

Satellite

Sensitivity to reducing frequency of observations

mean = -0.6 K

sd = 9.7 K

observations

Posterior all obsPosterior less obs

mean = 0.3 K

sd = 5.9 K

Less observations

flat distribution

less accuracy

Sensitivity to channels

Sensitivity to clouds in 10.7nm and 12.0 nm is very similar.

Are both channels needed?

Ch 4 alone

Ch 5 alone

4 and 5 together

4 and 5 together

Ch 4 prior

Ch 5 prior

Model – Observations brightness temperature

Yes

Complementary information

more accuracy

Study conclusions• Ice cloud well specified by GOES imager IR

channels 4 and 5 and CRM when all observations were used

• Weaker observational constraint wider error distribution, less accuracy

• Modeled liquid cloud not improved below ice cloud– No observational constraint: need other

measurements, different channels

• Modeled cloud environment only slightly improved– Weak observational constraint: need other

observations

What next?• Add more satellite observations

– Visible channel– GOES sounder – Microwave for precipitation– Other IR

• Add ground based measurements – ARM

• Goal is to test how much constraint is there in the observations for variety of cloud cases

Back to original motivation Problems

1. Retrievals from any of the measurements cannot fully verify parameterizations

Solution: Assimilation of satellite and other observations into CRM(s) to represent 4D cloudy atmosphere

2. Current cloud climate trends and role of clouds inconclusive because the current retrievals are not accurate enough or the observation information content is insufficient

Solution: Systematic assimilation of satellite and other observations into future NWP with CRM resolution OR cloud properties 1D retrievals with multi

channel measurements in high spatial resolution

modelsObservational

operators

States and

parameters

Adjoint models

VIS and IR information content analysis Example for case with mixed phase clouds

VisibleVisible

Near IRNear IR

IR IR

•Vertical and horizontal variability

•Sensitivity to multiple cloud layers

Greenwald et al, 2004

Sensitivity by optical properties and hydrometeor type

prior Observations

posterior

+ =

Model 3D

cloud

2D

Tb

Sequence every 15 min End time shown

4D assimilation of GOES imager IRmulti-layered non-convective case