1. A New Ocean Suite Algorithm for AMSR2 David I. Duncan September 16 th, 2015 AMSR Science Team Meeting Huntsville, AL

2. Outline 1. Algorithm Description 2. Key Innovations 3. Validation & Comparison 4. Ongoing Work

3. Algorithm Description 1DVAR (Optimal Estimation) simultaneous retrieval of atmospheric and ocean surface properties (TPW, 10m wind, CLWP, SST) via a physical forward model, f, y = f(x,b) + ε, where y is the Tb vector and x is state vector. Iterate to find simulated Tbs that minimize the cost function: Φ = (x-x a ) T S a -1 (x-x a ) + [y-f(x,b)] T S y -1 [y- f(x,b)]

4. Algorithm Description  RT model employed is NOAA’s Community Radiative Transfer Model (CRTM) v2.2.1  Coded to allow different channel combinations, various emissivity models, various amounts of ancillary data  ECMWF Interim Reanalysis employed as constraints on the retrieval  Non-raining retrieval, so assumed non-scattering  Ability to run for AMSR2 and GMI, all channels  AMSR2: L1R Tbs at 23.8GHz resolution,12-channel and 9-channel versions  GMI: L1CR Tbs for 13 channel retrieval

5. Key Innovations: WV profile  Structure of water vapor profile is now retrieved explicitly  Coefficients of 3 EOFs of water vapor mixing ratio are retrieved parameters, indexed by SST  Mean profile and EOFs are derived from ECMWF analyses EOF1 EOF2 EOF3

6. Key Innovations: WV profile  Structure of water vapor profile is now retrieved explicitly  Coefficients of 3 EOFs of water vapor mixing ratio are retrieved parameters, indexed by SST  Mean profile and EOFs are derived from ECMWF analyses

7. Key Innovations: S y Matrix  Most satellite retrievals and DA systems assume uncorrelated channel errors  Via an innovative method for determining the impact of forward model assumptions (non-scattering, single cloud layer, 3 EOFs of water vapor, etc.), channel variances/covariances and offsets are calculated  Add published values of NEdT to diagonal terms to yield S y matrix  Must be calculated for all forward model and sensor combinations!  In practice, slight increase may be required in diagonal elements to achieve adequate convergence

8. Key Innovations: Ancillary data  Microwave imager retrievals are under-constrained due to finite channels and limited information content in the Tbs  To assess the impact of ancillary data, while ensuring that the retrieval is not model-dependent, the algorithm can be run in two modes: Initial Conditions ‘Climatology’‘Analysis’ SSTReynolds OI Wind Direction*/Magnit ude ECMWF / ClimatologyECMWF / ECMWF WV ProfileClimatological from SSTECMWF Temperature Profile* Global mean lapse rateECMWF Cloud LWPThin static cloudECMWF LWP Sfc Pressure*1000mbECMWF * not retrieved

9. Key Innovations: Posterior Errors  Uncertainty values from a retrieval product are very useful to users, yet many products do not provide uncertainties/error estimates  Posterior errors drop out of the mathematics of the 1DVAR approach for all retrieved parameters

10. Key Innovations: Posterior Errors  Uncertainty values from a retrieval product are very useful to users, yet many products do not provide uncertainties/error estimates  Posterior errors drop out of the mathematics of the 1DVAR approach for all retrieved parameters

11. Validation & Comparison  For GMI version, can use Dual Frequency Precipitation Radar (GPM DPR) to verify if retrieval screens out rain adequately  DPR data averaged into FOV of GMI footprint  Tricky for RR<1mm/hr, robust above 2mm/hr

12. Validation & Comparison  Preliminary results from matchups with radiosonde network (RAOB)  Matchup criteria not finalized  Difficulty at high latitudes  Green ‘+’ signifies higher quality convergence  Near-zero bias for high quality retrievals

13. Validation & Comparison  Comparison with RSS AMSR2 product  LWP maps very different due to rain screening  Many of the same features  Difficult/impossible to validate LWP retrievals

14. Validation & Comparison  Comparison with RSS AMSR2 product  TPW shows many similarities  Slightly higher values in Deep Tropics, lower in storm tracks, higher at high latitudes  Over most of the globe agreement to within 1mm

15. Validation & Comparison  Comparison with RSS AMSR2 product  Wind patterns are nearly identical  Retrieval is picking up smaller regional features  RSS values are lower (~20%) almost uniformly over the globe  Test sensitivity of result to emissivity model

16. Ongoing Work  Continue matchup/validation work with RAOB, Suominet, and floating buoys  Examine LWP/TPW biases and adjust assumed covariances in algorithm to help remove biases  Work more on minimizing Tb residuals (Tb Observed – Tb Simulated ) for each channel  Further experimentation with emissivity models and channel combinations  Upcoming paper will focus on the impact of using model data to constrain the retrieval, i.e. ‘climatological’ vs. ‘analysis’ versions

17. Thank you!