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Atmospheric Profile Retrievals From GIFTS-IOMI

Atmospheric Profile Retrievals From GIFTS-IOMI. Jun Li (UW MURI Co-I) Allen H.-L. Huang (UW MURI PI) and CIMSS collaborators Cooperative Institute for Meteorological Satellite Studies Space Science and Engineering Center University of Wisconsin-Madison

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Atmospheric Profile Retrievals From GIFTS-IOMI

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  1. Atmospheric Profile Retrievals From GIFTS-IOMI Jun Li (UW MURI Co-I) Allen H.-L. Huang (UW MURI PI) and CIMSS collaborators Cooperative Institute for Meteorological Satellite Studies Space Science and Engineering Center University of Wisconsin-Madison UW MURI Workshop, 14 - 15 May 2002, Madison, Wisconsin UW-Madison

  2. (~1600) Geostationary Sounders (# of channels) GOES Advanced Baseline Sounder (2012, operational) (~1600) GIFTS-IOMI (2005, experimental) (18) GOES Sounder (1994, operational) (12) VAS (1981, experimental) time

  3. GIFTS-IOMI Sounding Characteristics Coverage Rate Variable Horizontal Resolution 4 km Spectral Coverage (band1) 685 - 1150cm-1 (8.7 - 14.6μm)Spectral Coverage (band2) 1650 - 2250cm-1 (4.4 - 6.1μm) Vertical resolution 1 km Accuracy Vertical Temperature 1 K Vertical WV mixing ratio 10 ~20 %

  4. Physical retrieval algorithm is developed, two retrieval schemes are used (1) Maximum Likelihood method (data assimilation in NWP) which needs Radiance measurements and observation errors Atmospheric background info. (from forecast or climatology) First guess from regression or background Error covariance matrix of background RTM, linearized RTM and adjoint (2) Regularization Method (retrieval processing) which needs Radiance measurements and observation errors First guess from regression or other sources RTM, linearized RTM and adjoint

  5. Regularization Inversion Equations Unknowns Solutions Newton Iteration 1. Empirical (Susskind 1984; Smith 1985; Hayden 1988) 2. Discrepancy Principle (Li and Haung 1999)

  6. Methods for Jacobian calculations (1) Linear Model (1 BT calculation for T/q/O3) (under progress) (2) Brute force perturbation (2L BT calculation for T/q/O3) (Yes) (3) Transmittance ratio method (2 BT calculation for T/q/O3) (Yes) (4) Approx. Analytical form (0.2 BT calculation for T/q/O3) (Yes) 1 2 3 4 More accurate Less accurate

  7. GIFTS LW 650 - 1150 cm**-1

  8. GIFTS SMW 1650 - 2250 cm-1

  9. (Ts-Ta)=5K (Ts-Ta)=0K

  10. Texas Spikes down - Cooling with height (No inversion) Brightness Temperature (K) Spikes up - Heating with height Ontario (low-level inversion) Detection of Temperature Inversions Possible with Interferometer GOES GOES Wavenumber (cm-1) The detection of inversions is critical for severe weather forecasting. Combined with improved low-level moisture depiction, critical ingredients for night-time severe storm development over the Plains can be monitored. Knowing if there is an inversion can also help improve the profiles estimates.

  11. Simulated GIFTS observations, 9 January 2001, Lat=44.50, Lon=93.43, Time=12UTC

  12. 850 hPa temperature time 1

  13. 850 hPa temperature time 2

  14. 100 hPa temperature time 1

  15. 100 hPa temperature time 2

  16. Surface skin temp time 1

  17. Surface skin temp time 2

  18. 700 hPa moisture (g/kg) time 1

  19. 700 hPa moisture (g/kg) time 2

  20. Summary and Future Work • GIFTS-IOMI will resolve high temporal and vertical fluctuations of moisture that are not resolved by current in-situ or satellite measurements. • Cube study shows GIFTS-IOMI is capable of depicting the gradient of temperature and moisture field. • Only geostationary high spectral resolution interferometer observes critical meteorological parameters (temperature, moisture, clouds, winds) with necessary temporal, spatial and vertical resolutions to support future • Nowcasting, • Short-range weather forecasting, and • Longer-range numerical weather prediction. • Future work includes further cube study, using newest forward model, profile retrieval under cloudy skies, using AIRS data for algorithm validation.

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