Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 MAP (Maximum A Posteriori) x is reduced state vector [SST(x), TCWV(w)] T y o is observation vector, y b from CRTM+NWP K is Jacobian matrix (∂y i /∂x, ∂y i /∂w) ← CRTM S ε = cov (RTM+instr err), S b cov background (x, w err) δx= [K T S ε -1 K + S b -1 ] -1 K T S ε -1 (y o - y b ) →δx= a 1 b δy a 2 b δy 11 Thus retrieval is a local linearization based on “first- guess” (or “background”) state of SST and atmospheric state, combined with uncertainty of first guess propagated to top-of-atmosphere modeled brightness temperatures and uncertainty in observed brightness temperatures ML (Maximum Likelihood) Similar to MAP but without background error constraint Physical SST Retrieval Algorithms Andy Harris 1, Jonathan Mittaz 1, Christopher Merchant 2 and Eileen Maturi 3 (GOVERNMENT PRINCIPAL INVESTIGATOR) 1 NOAA-CICS, University of Maryland, 2 University of Edinburgh, 3 NOAA/NESDIS/STAR Requirement: The objective of satellite product research and development is to make the NOAA operational satellite data more valuable to our user community Science: Can physical retrieval methods improve quality of the SST product? Benefit : Optimal SST product, including point-by-point assessment of retrieval accuracy. Enabling NOAA to meet NOAA’s Mission Goals specifically ecosystems -Develop integrated ecosystem assessments for regional management weather and water-improve forecast capability in coasts, estuaries, and oceans Climate- Understand impacts of climate variability and change on marine ecosystems Science Challenges: These fall into three main areas: Optimization of error covariances & exploitation of correlation length scales to reduce noise Incorporation of aerosols into state vector & incorporation of diurnal warming into first guess At-source removal of radiance bias due to deficiencies in instrument calibration and characterization Next Steps: Implement and test various improvements on development system in STAR and transfer code to OSDPD operational system Transition Path: Implementation, updates and improvements via the OSDPD and SPSRB Approval processes Physical retrieval for GOES Current GOES SST retrieval is “physical-statistical” – linear retrieval coefficients are derived by regression on simulated data: We already do physically-based Bayesian cloud detection for geostationary SST Can we use the same RT calculations to perform fully physical retrieval of SST? Physical retrieval Apply to GOES data MAP ML Radiance biases Physical retrieval assumes that there are no systematic biases or trends in the difference between modeled and observed radiances, i.e. that differences occur because of uncertainty in the first-guess state vector, radiative transfer modeling “noise” and instrumental noise. GOES radiance biases display temporal variability and have a diurnal cycle (see Mittaz et al. poster) After removal of hourly mean bias, remaining errors are reduced by determining a multi- parameter fit to residual bias: δT = a 0 + a 1 ΔT a + a 2 T b + a 3 S + a 4 ΔT a T b + a 5 ΔT a S where ΔT a = SST b - T b In practice, the coefficients are determined for each channel for every hour and smoothed with a half-gaussian (σ=3½ days, cutoff=5 days) Before bias correction After bias correction MAP retrieval has less noise than ML but slope of δSST change from first guess SST is less than 1 because retrieval is constrained by background error covariance. ML retrieval does not have this constraint but the penalty is increased noise Diurnal variation Long-term variation