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 Introduction: DAOTOVS 1DVAR assimilation Assessment of cloud- and land-affected data in DAS Use of OPTRAN OSSE simulations Use of TOVS for land-surface analysis/assimilation 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

Joanna Joiner, DAO ITSC-12 DAOTOVS attributes DAOTOVS 1D-Var Assimilation of Radiances: Uses Level 1b 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 (fvDAS) currently running in parallel Joanna Joiner, DAO ITSC-12

Joanna Joiner, DAO ITSC-12 fvDAS 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 bi-directional reflectance, mw emissivity in 1DVAR 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 3x3 array of HIRS pixels R1-Rn ~95% of cases explained by two modes (cloud-formations) Joanna Joiner, DAO ITSC-12

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

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

Joanna Joiner, DAO ITSC-12 Forecast experiments, RMS error 500 hPa 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 fvCCM/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

Skin temperature biases over land *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

Control fvDAS Ts Bias ECMWF |top|-|mid|

Unbiased Analysis Equation Joanna Joiner, DAO ITSC-12

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

Joanna Joiner, DAO ITSC-12

New fvDAS Ts Bias Control fvDAS Ts Bias |top|-|mid|

More TOVS marked “clear” by internal 1DVAR QC Red in bottom panel means more TOVS 1DVAR passes internal cloud checks And determined to be “clear” 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 hPa 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 hPa 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 (6hrs-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, clear-scene 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

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