Use of ATOVS at DAO Joanna Joiner Donald

Use of ATOVS at DAO Joanna Joiner, Donald Frank, Arlindo da Silva, Emily Liu, Clark Weaver Data Assimilation Office, NASA/GSFC ITSC-12 Joanna Joiner, DAO ITSC-12

Outline 1. 2. 3. 4. 5. Introduction: DAOTOVS 1 DVAR assimilation Assessment of cloud- and land-affected data in DAS Use of OPTRAN OSSE simulations Use of TOVS for land-surface analysis/assimilation • • 6. 7. 8. Off-line skin temperature analysis (including bias correction) Off-line skin temperature assimilation AIRS dynamic channel selection based on cloud height Incorporate effects of aerosol Summary and Future Plans Joanna Joiner, DAO ITSC-12

DAOTOVS attributes • DAOTOVS 1 D-Var Assimilation of Radiances: – – – – Uses Level 1 b data HIRS, MSU, SSU and AMSU-A radiances Variational cloud-clearing (Joiner and Rokke, 2000) Eigenvector FOV determination (AIRS ATBD) Physically-based systematic error correction GLATOVS, MIT -> OPTRAN (JCSDA) Running in operational GEOS-DAS and next-generation Finite-volume DAS (fv. DAS) currently running in parallel Joanna Joiner, DAO ITSC-12

fv. DAS Data Flow (PED coeff) Joanna Joiner, DAO ITSC-12

DAOTOVS: What makes it different? • Uses cloud- and land-affected data (CERES land-emissivity data set based on satellite/laboratory measurements). • Uses all channels except HIRS 16, 17 AMSU 1, 2 (IR bidirectional reflectance, mw emissivity in 1 DVAR state vector) • Variational cloud-clearing (done simultaneously with retrieval); allows for internal quality control, consistency • Tuning using collocated radiosondes (not background). Updated daily via Kalman filter. • Errors in assimilation system include separate components with and without vertical/horizontal correlations Joanna Joiner, DAO ITSC-12

How many cloud formations are seen in NOAA-K data? Answer: ~2 Look at eigenvectors of 3 x 3 array of HIRS pixels R 1 -Rn ~95% of cases explained by two modes (cloudformations) Joanna Joiner, DAO ITSC-12

O-F Statistics NW NE Tropics SW SE • Fit to Rawinsondes • Obs – 6 h Forecast • Bias (spatial RMS, time mean) • Standard Deviations Joanna Joiner, DAO ITSC-12

Cloud clearing has positive impact on 6 hour forecast, verified with radiosondes in finite-volume DAS green: NESDIS TOVS, red: DAOTOVS w/cloudcleared, blue: DAOTOVS, no cloudy Joanna Joiner, DAO ITSC-12

Forecast experiments, RMS error 500 h. Pa height red: cloud-cleared, blue: no cloudy Joanna Joiner, DAO ITSC-12

Impact of land-affected data (red-includes land, blue-no land) Joanna Joiner, DAO ITSC-12

OPTRAN significantly reduces ATOVS radiance biases note: a) scale b) large reduction in channel 1 and 12 biases OPTRAN GLATOVS Joanna Joiner, DAO ITSC-12

Observing System Simulation Experiments (OSSE) • Use fv. CCM/Optran to simulate cloudy radiance • Use GEOSDAS/ GLATOVS for assimilation • Model has reasonable simulation of cloud/upper tropospheric humidity (use maximum overlap assumption) Joanna Joiner, DAO ITSC-12

*The problem: Skin temperature biases over land (especially desert) causing clear-sky Outgoing Longwave Radiation (OLR) biases as compare with CERES; *Problem caused by emissivity used in land-surface model (LSM) and inconsistent definition of ground temperature Joanna Joiner, DAO ITSC-12

Control fv. DAS Ts Bias ECMWF Ts Bias |top|-|mid| Joanna Joiner, DAO ITSC-12

Unbiased Analysis Equation Joanna Joiner, DAO ITSC-12

Ts Bias and Anal. Increments Joanna Joiner, DAO ITSC-12

Joanna Joiner, DAO ITSC-12

New fv. DAS Ts Bias Control fv. DAS Ts Bias |top|-|mid| Joanna Joiner, DAO ITSC-12

More TOVS marked “clear” by internal 1 DVAR QC Red in bottom panel means more TOVS 1 DVAR passes internal cloud checks And determined to be “clear” Joanna Joiner, DAO ITSC-12

Joanna Joiner, DAO ITSC-12

AIRS initial channel selection Joanna Joiner, DAO ITSC-12

Channel selection based on retrieved cloud height Cloud: 50% at 200 h. Pa Yellow: Clear. Cloudy Green: Add noise, background errors 17 channels unaffected by cloud Joanna Joiner, DAO ITSC-12

Channel selection based on retrieved cloud height Cloud: 10% at 700 h. Pa Yellow: Clear. Cloudy Green: Add noise, background errors 77 channels unaffected by cloud (If retrieve pressure of 525, get 58 channels) Joanna Joiner, DAO ITSC-12

Using model-simulated aerosol in DAOTOVS (Weaver poster) Top: O-F HIRS 8 no dust in calculations Bottom: O-F HIRS 8 dust from transport model included in radiative transfer Joanna Joiner, DAO ITSC-12

Summary and Future Work • Cloud- and land-affected data has positive impact on forecasts (6 hrs-5 days) • OPTRAN reduces biases, but little overall impact due to tuning • OSSE simulations show reasonable model cloud • TOVS Ts analysis (including bias correction) improves OLR, clearscene identification over land • AIRS channel selection good for cloudy situations (sharp weighting functions); Dynamic channel selection in cloudy scenes, cloud slicing-like approaches worthwhile • Aerosol effects are significant (see Weaver poster) Joanna Joiner, DAO ITSC-12

In the future… • GOES sounder (JCSDA) • AMSU-B • Analyze pseudo-relative humidity instead of ln(q) in 1 DVAR • Partial eigen-value decomposition/radiance assimilation • AIRS – more from Don Frank Joanna Joiner, DAO ITSC-12