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

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Presentation on theme: "Estimation of clouds in atmospheric models Tomislava Vukicevic CIRA/CSU and PAOS/CU."— Presentation transcript:

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

2 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

3 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

4 4D cloud data assimilation Satellite radiance CRM with bulk cloud microphysics + Sensitive to atmospheric hydrology High spatial and possibly temporal resolution

5 GOES Wavelength Central Detector Channel (µm) Wavelength Resolution (µm) (km) ___________________________________________ G G GOES imager 15 minute data VIS Near IR Diff between ice and water clouds IR water vapor IR clouds and surface IR clouds, surface and low level vapor

6 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

7 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

8 Mapping from CRM to GOES VIS and IR operator Greenwald et al Gas absorption: OPTRAN (McMillin et al., 1995) Cloud properties: Anomalous Diffraction Theory Solar: SHDOM (Evans, 1998) IR: Eddington two-stream (Deeter and Evans 1998)

9 4D assimilation of GOES imager IR error statistics (model – observation) mean = 0.3 K sd = 5.9 K mean = 33 K sd = 8.2 K prior posterior Brightness Temperature Vukicevic et al, 2004, 2005

10 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 Height km 1 hour Thick ice cloud Thin ice cloud

11 More observations better result Single channel assimilations, 30 min frequency 2-channel assimilation, 30 min frequency 2-channel assimilation, 15 min frequency Guess Worst Best Tb errors

12 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

13 Linear model error added as 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

14 Comparison of state and parameter estimation Lorenz 3 component system Estimation technique: Markov Chain Monte Carlo (MCMC)

15 Estimation of parameters State solutions within estimation period PDFs of parameters after estimation Estimation period Forecast period

16 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

17 Estimation of state Observations without errors X Y Erroneous observations

18 Derivation of suitable form of parameterization for estimation


20 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

21 Sensitivity to reducing frequency of observations mean = -0.6 K sd = 9.7 K observationsPosterior all obsPosterior less obs mean = 0.3 K sd = 5.9 K Less observations flat distribution less accuracy

22 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 Ch 4 prior Ch 5 prior Model – Observations brightness temperature Yes Complementary information more accuracy

23 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

24 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

25 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

26 models Observational operators States and parameters Adjoint models

27 VIS and IR information content analysis Example for case with mixed phase clouds Visible Near IR IR Vertical and horizontal variability Sensitivity to multiple cloud layers Greenwald et al, 2004

28 Sensitivity by optical properties and hydrometeor type

29 priorObservationsposterior + = Model 3D cloud 2D Tb Sequence every 15 minEnd time shown 4D assimilation of GOES imager IR multi-layered non-convective case

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