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Surface Products Status

Surface Products Status. C. Grassotti, and S.-A. Boukabara. Ocean Surface Wind Speed. Ocean surface wind speed algorithm development: preliminary results from SSMIS F16 Approach Real and simulated data: comparisons w/GDAS Unresolved issues. Ocean Surface Wind Speed: General Approach.

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Surface Products Status

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  1. Surface Products Status C. Grassotti, and S.-A. Boukabara

  2. Ocean Surface Wind Speed • Ocean surface wind speed algorithm development: preliminary results from SSMIS F16 • Approach • Real and simulated data: comparisons w/GDAS • Unresolved issues

  3. Ocean Surface Wind Speed: General Approach Approach follows philosophy used for other MIRS emissivity-based products (SIC, SWE) 1) Offline physical (or semi-empirical) modelling to generate catalog of emissivity spectra as a function of most relevant parameters (including desired EDRs) 2) MIRS retrievals: post-processing of retrieved emissivities to determine catalog emissivity spectrum (and associated geophysical state) closest to retrieved

  4. Ocean Surface Wind Speed: Specifics Emissivity modelling uses FASTEM-3 (English et al.) already used within MIRS to develop means/covariances for constraint in 1d-var, NWP-based simulated TBs for bias correction generation over ocean, simulated retrievals Catalog emissivity: function of frequency, polarization, SST, incidence angle, wind speed; one catalog file/sensor Polarizations stored as V and H: mixing of catalog values for cross-track sensors done on the fly based on current scene incidence angle All code in Fortran95, using ascii files: allows very easy extension to other sensor configurations (already created for SSMIS, N18, N19, MetopA)

  5. Emissivity: dependence on angle and wind speed

  6. Retrieved and catalog emissivity for F16 Mean values (~2 revs) of MIRS retrieved vs. best match from catalog 19V 37V 19H 37H Only 19 and 37 GHz measurements used in catalog search

  7. cor=0.066 bias=2.3 stdv=7.0 Real Data Retrieval GDAS WSPD MIRS WSPD MIRS vs. GDAS MIRS-GDAS

  8. Real Data Retrieval: WSPD, Em19H, TPW, CLW Em19H (MIRS-GDAS) WSPD (MIRS-GDAS) CLW TPW (MIRS-GDAS)

  9. cor=0.646 bias=0.5 stdv=3.4 Simulated Data Retrieval: Nominal Em constraints GDAS WSPD MIRS WSPD MIRS vs. GDAS MIRS-GDAS

  10. Simulated Data Retrieval: WSPD, Em19H, TPW, CLW Em19H (MIRS-GDAS) WSPD (MIRS-GDAS) CLW TPW (MIRS-GDAS)

  11. cor=0.066 cor=0.646 cor=0.996 cor=0.329 cor=0.897 cor=0.968 bias=0.011 bias=0.5 bias=-0.8 bias=0.017 bias=2.3 bias=-1.4 stdv=0.013 stdv=0.348 stdv=7.0 stdv=3.4 stdv=1.4 stdv=4.1 WSPD, TPW, EM19H: Real data vs. Simulated WSPD (real) Em19H (real) TPW (real) GDAS WSPD TPW (sim) WSPD (sim) Em19H (sim)

  12. Simulated Data 2: Emissivity constraints loosened Constraint: Em uncorrelated w/other EDRs + uncertainty in BG=nominal diagonal matrix*100 Em19H (MIRS-GDAS) WSPD (MIRS-GDAS) CLW TPW (MIRS-GDAS)

  13. cor=0.833 cor=0.944 cor=0.996 bias=-0.6 bias=-0.003 bias=1.7 stdv=2.3 stdv=0.009 stdv=1.5 Simulated Data 2: Emissivity constraints loosened WSPD Em19H Constraint: Em uncorrelated w/other EDRs + uncertainty in BG=nominal diagonal matrix*100 Improved WSPD and Emissivity, at the expense of TPW TPW

  14. Summary • Wind speed algorithm development continuing (f16): • Problems of overestimation (wrt GDAS) in tropics, underestimation in mid-latitudes • Cross-talk with TPW, CLW: ΔW+ ΔEm+ΔTPW- ΔCLW- • Bias correction, CRTM, 1d-var constraints? • Eventually applied other sensors (n18, n19, metopA) • Similar approach to be followed for recoding snow and ice products algorithms

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