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Can we improve our first guess estimate of clear?

This study explores methods to improve the initial estimate of clear conditions using microwave down-welling secant ratio retrieval. The results show improved agreement between microwave and IR-derived skin temperature in clear ocean night scenes.

timothyawilliams
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Can we improve our first guess estimate of clear?

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  1. Can we improve our first guess estimate of clear? March 30, 2004 Chris Barnet Mitch Goldberg Lihang Zhou Walter Wolf NOAA/NESDIS/ORA

  2. Microwave down-welling secant ratio retrieval is a positive step. • Used clear ocean night ±40o latitude cases on Jan. 3, 2003. • Phil’s secant ratio retrieval improves agreement between microwave and IR derived Tskin in these scenes. • Secant ratio has reasonable relationship with wind. • Microwave Tskin product still tends to be colder than the AIRS product (-2 K). BEFORE AFTER

  3. Derived down-welling secant ratio versus AVN wind speed.

  4. Is the Infrared Down-welling Term Correct? • The down-welling radiance in our current forward model is given by • L(n) = specified level for each channel • F = a0 + a1*SEC(Θ) + Bν(T(L(n))*[a2+a3*SEC(Θ)] + a4*Bv(T(L(n)))/Bv(T(Lbot)) • Rd = (1-ε)/pi*tau(Ps)*[π*(1-tau(Ps))*Bv(T(L(n)))*f] • We need to investigate new forms of the down-welling term (e.g., Nalli, Smith, and Huang, 2001) • Thermal reflectivity: • Over land continue to use Lambertian • Over ocean we need an angle dependent term. • Over ocean, develop a secant ratio term as a function of humidity and angle.

  5. Motivation for some new experiments. • While number of outliers are small, we still tend to miss low clouds. • Exploring new approaches to improve the information content in the lower 2 km’s of the atmosphere (i.e., detect low clouds). • Explore methods to increase yield. • This presentation is a status report. None of the methods presented here are viable at this time.

  6. Current QA may be over-tuned to the existing system. • Ad-hoc adjustment of rejection thresholds are difficult to justify. • Residual (i.e., convergence) tests in the cloud clearing, T(p), q(p) and surface retrieval residuals catch some bad cases; however, the thresholds for these remain much too high. • Tskin(Final) – Tskin(NOAA) test is required because of regression a-priori issue. • Some of the remaining outliers I would call “embarassments.” Failure of cloud clearing/ surface retrieval can still go undetected and result in 8K errors.

  7. Added the following QA for a comparison baseline

  8. Experiment #1: Cloudy Regression • This set of eigenvectors and regression coefficients are trained on cloud contaminated AIRS radiances selected via Mitch’s “diff” test. • BT(2390)-f(AMSU Ch.4,5,6, angles) <= 5 • Cloudy regression is applied prior to the first cloud clearing using the warmest cloudy FOV. • The cloudy regression state is used for the determination of eta and the 1st CCR’s. • The normal regression coefficients, trained on CCR’s, is applied to the CCR’s.

  9. Cloudy Regression versus MIT Physical Algorithm: RMS Cloudy regression better than the MIT physical in the lower atmosphere.

  10. Cloudy Regression versus MIT Physical Algorithm: BIAS And less BIAS

  11. Cloudy Regression RMS(ECMWF), Ocean, ±50 lat

  12. Cloud RegressionBIAS(ECMWF), Ocean, ±50 lat.

  13. Cloud RegressionRMS(ECMWF), all cases

  14. Cloud ReressionBIAS(ECMWF), all cases

  15. Experiment #2: SW correction to cloud cleared radiance • Utilize the non-linear sensitivity of the 4.3 micron band to temperature to correct the lower boundary temperature. • Start with a fg T(p) and Tskin (MIT) • Retrieve eta from computed 15 μm radiances and compute CCR’s. • Adjust T(p) using 4.3 μm CCR’s. • If not converged, goto 2

  16. Results of PRELIMINARYIterative Correction to CCR’s

  17. Experiment #2: Sidebar • Another variation of Experiment #2 was explored: Predict 15 μm clear radiances from 4.3 μm cloud cleared radiances via regression trained from AIRS clear ocean nightime radiances. • Moisture and ozone sensitivity of 15 μm band makes the regression operator problematic in practice. • Needs more work.

  18. Can we predict 15 μm channels from the 4.3 μm band? • LW(n,k,Θ) = A(n,m,Θ)*SW(m,k,Θ) • Solve A for each AIRS view angle, Θusing K=2200 ocean, clear, nightime AIRS FOV’s • Use 2386.2 to 2394.03 cm-1 as predictors, SW(m,k,Θ). • Standard deviation of ±1 K is probably due to moisture variability as well as variability induced by the hot bands in the 15 μm.

  19. Can we predict 4.3 μm ν3 R-branch from the 15 μm region? • SW(m,k,Θ) = B(m,n,Θ)*LW(n,k,Θ) • Solve for B(m,n) using ≈ 35 cloud clearing channels from the 15 μm band.

  20. Experiment #3: Use Model as cloud clearing clear estimate • Utilize the AVN model fields for T(p) and Tskin for the first cloud clearing step. • After 1st cloud clearing, AVN state is completely replaced with regression state. • Forecasts agree better with each other, than with any retrieval state. • AIRS provides new information. • Retrieval is degrading the result.

  21. AVN forecast state has bestagreement with ECMWF

  22. fg=AVN: RMSall cases

  23. fg=AVNBIAS, all cases

  24. Future Directions • Optimize cloudy regression system. • Continue to work towards an interative 4.3 μm correction algorithm. • Test some new ideas involving iterative improvements to Tsurf, and emissivity within the 1st cloud clearing. • Will begin comparisons with NOAA’s 19 months (200/d, 6000/month, ≈114,000) of sonde matchups soon.

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