GSI developments and plans at NCAR/MMM Tom Auligné Aimé Fournier, Hans Huang, Andy Jones, Hui-Chuan Lin, Zhiquan Liu, Yann Michel, Arthur Mizzi, Thomas

GSI developments and plans at NCAR/MMM

Tom Auligné

Aimé Fournier, Hans Huang, Andy Jones, Hui-Chuan Lin, Zhiquan Liu, Yann Michel,

Arthur Mizzi, Thomas Nehrkorn, Syed Rizvi, Hongli Wang, Xin Zhang

National Center for Atmospheric Research

NCAR is supported by the National Science Foundation

GSI Data Assimilation Workshop - June 28, 2011

Focus at NCAR/MMM– Regional GSI– WRF-ARW model (NetCDF files)

Projects funded by AFWA– AFWA Coupled Analysis and Prediction System (ACAPS)– AFWA Data Assimilation– AFWA Aerosols

Collaboration with – GSI developers (EMC, GMAO, GSD, DTC)– JCSDA

Introduction

• Background and Observation Errors

• Variational/Ensemble Hybrid

• Displacement Pre-processing

• WRF Adjoint: 4DVar and Observation Impact

• Aerosol and Cloud Satellite Observations

• Verification

Outline






• Verification

Outline

Background and Obs Errors: Community tools

“Community GEN_BE” utility: https://svn-wrf-var.cgd.ucar.edu/branches/gen_be

– Includes all the features of WRFDA V3.2.2– Multi-variate humidity– Generation of WRF-ARW background errors for GSI

Extension of GEN_BE to include– Aerosol concentrations (univariate)– Cloud parameters (Qcloud, Qrain, Qice, Qsnow)

Expansion of GSI control variable

Observation error tuning with the diagnostic equations (Desroziers 2005)

E dba (db

o )T⎡⎣ ⎤⎦=HBHT E dao(db

o )T⎡⎣ ⎤⎦=R

E dba (da

o )T⎡⎣ ⎤⎦=HAH TE dbo(db

o )T⎡⎣ ⎤⎦=HBH T + R

https://svn-wrf-var.cgd.ucar.edu/branches/gen_be

Background Error Covariances: Masked Statistics

Michel et al. (MWR, 2011)

Background Error Covariances: Masked Statistics

Background Error Covariances: Wavelets

Background Error Covariances: Wavelets






• Verification

Outline

Variational/Ensemble Hybrid

• WRF/GSI Regional Hybrid• Testing package: https://svn-mmm-hybrid-testbed.cgd.ucar.edu/HYBRID_TRUNK

Cf. presentation by Arthur Mizzi

https://svn-mmm-hybrid-testbed.cgd.ucar.edu/HYBRID_TRUNK






• Verification

Outline

Conceptual view of using displacements to characterize errors

background error

displacements of coherent features

additive (residual) error

=> +

Displacement Pre-Processing

Initial time:08-28-05 06:00:00z

Vortex displaced forward along track

18 Hour forecast time:08-29-05 00:00:00z

18 hours later vortex maintains forward position

Collaboration between AER, MIT and NCAR

Integration of displacements– Build on the existing API, with enhancements to add:– Support for multiple displacement algorithms

Algorithmic developments– Constraints formulated and evaluated specifically for cloud-related fields

• Candidates: smoothness, non-divergence of displacements• Application in: grid point, spectral, or wavelet space

– Time evolution of displacements• Characterize and model the time evolution of displacements• Prepare for integration with 4D-Var

– Figures of Merit for cloud-related fields

Displacement Pre-Processing: Status and Plans






• Verification

Outline

WRF Adjoint: WRF/GSI 4DVar

New TL/AD code: WRFPLUS– Consistent with latest WRF-ARW (v3.3) – Includes simplified physics (surface drag, large-scale condensation,

cumulus scheme, Kessler microphysics)

WRF/GSI 4DVar– Based on GMAO 4DVar framework– New coupling between GSI and WRF/WRFPLUS

Initial testing looks good.

Cf. presentation by Xin Zhang

WRF Adjoint: Observation Impact

Observation(y)

WRFDA/GSIData

Assimilation

WRF-ARWForecast

Model

Forecast(xf)

DeriveForecastAccuracy

Background(xb)

Analysis(xa)

Adjoint of WRF-ARW

ForecastTL Model

(WRF+)

ObservationSensitivity(F/ y)

BackgroundSensitivity(F/ xb)

AnalysisSensitivity

(F/ xa)

Observation Impact<y-H(xb)> (F/ y)

Adjoint of WRFDA/GSI

Data Assimilation

Obs Error Sensitivity(F/ ob)

Gradient of F

(F/ xf)

DefineForecastAccuracy

ForecastAccuracy

(F)

Bias CorrectionSensitivity

(F/ k)

Figure adapted from Liang Xu (NRL)






• Verification

Outline

Aerosol Satellite Observations

Assimilation of MODIS Aerosol Optical Depth in GSI– Process MODIS AOD data (HDF to BUFR converter)– Use CRTM-AOD (Quanhua Liu)– Couple with WRF-Chem GOCART (14 aerosol species)

Status and plans– Assimilate surface PM2.5 (ongoing)– Assimilate MODIS Visible/NIR radiances (planned, pending)

Cf. presentation by Zhiquan Liu

Cloud Satellite Observations: Retrievals

MODIS cloud retrieval products– Cloud liquid/ice water path, cloud optical depth, particle effective radius

(1km resolution observations)– Cloud top properties: pressure, temperature, fraction/emissivity

(5km resolution observations)

Assimilation of MODIS Cloud Water Path– Process MODIS CWP data (HDF to BUFR converter)– Observation Operator (+ TL & AD)

Status and plans– Assimilate MODIS CWP at convective scale (ongoing)– Assimilate MODIS Cloud Optical Depth (planned)

Very first shot at cloudy radiances, still needs a lot more work…

Cloud parameters from WRF-ARW first-guess

CRTM forward model and Jacobian

Inclusion of cloud (microphysical) parameters in control variable (implemented in both WRFDA and GSI)

Cloud Satellite Observations: Radiance Assimilation

ObservationAIRS (12micron)

Background(WRF-DART)

Observation – Background

Simple B Matrix forcloud parameters copied from humidity

Ensemble assimilation usingthe alpha control variable(no tuning)


Clear observations only


Simple B Matrix forcloud parameters copied from humidity

Remaining issues include:

- Bias Correction

- Quality Control

- Non-linearities in the observation operator

- Representativeness Error

Cloud Satellite Observations: Radiances

Pixel

Nk1

Nk2 Nk3

No

Cloud Top Pressure (hPa)

MODIS Level2

AIRS MMR

with

[ ]nkN k ,0,10 ∈∀≤≤

∑=

=+n

k

kNN1

1o

Cloud fractions Nk are ajusted variationally to fit observations:


CloudSat Reflectivity

AIRS MMR Effective Cloud Fraction


RνObs −Rν

CldRνObs −Rν

CldRνObs −Rν

oRνObs −Rν

o

Towards Cloudy Radiance Assimilation

Pixel

Nk1

Nk2 Nk3

No


31


Simulated mismatch in resolution:

- Perfect observations (high resolution)- Perfect Background (lower resolution)

Innovations

Background

Cloud Satellite Observations: Representativeness

32


New interpolation scheme:

1. Automatic detection of sharp gradients 2. New “proximity” for interpolation

Innovations

Background

New Innovations



The raw yo− yb (left) includes errors due to yo and yb coming from completely different representations, that (hypothetically) have been reconciled by the foregoing wavelet-coefficient selection procedure.







• Verification

Outline

Period: 4-17 June 2009Analyses and 6 hr forecasts from 50-member ensembles using

Data Assimilation Research Testbed (DART) system

Verification: Test Case (courtesy Glen Romine)

15 km mesoscale, 3 km storm-scale

Verification: Validation Data

World-Wide MergedCloud Analysis (WWMCA)

Main Archive: •Quality-controlled, GOES East and GOES West over CONUS•Covers January 1998 – December 2009 •Resolution – 4x4 km for all channels except #3 which is 4x6 km•Monthly/hourly cloud cleared background for all visible hours•Monthly/hourly Cloud % using visible threshold•Monthly/every other hour Cloud % using IR threshold since 2003•Addition hours of QC’d GOES West for May-Sept 1999-2009

Example of GOES 8 background image

• New WRF Adjoint for GSI 4DVar and Observation Impact

• Community tool for Background Error calculation (GEN_BE)

• Specific developments for Cloud and Aerosol assimilation

• Opportunity for inter-comparison

Conclusion

Documents

GSI developments and plans at NCAR/MMM Tom Auligné Aimé Fournier, Hans Huang, Andy Jones, Hui-Chuan Lin, Zhiquan Liu, Yann Michel, Arthur Mizzi, Thomas