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GOES-12 (Channel Radiometer) Channels are typically independent of each other Need to know each channel’s – Spectral response function – Noise characteristics.

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Presentation on theme: "GOES-12 (Channel Radiometer) Channels are typically independent of each other Need to know each channel’s – Spectral response function – Noise characteristics."— Presentation transcript:

1 GOES-12 (Channel Radiometer) Channels are typically independent of each other Need to know each channel’s – Spectral response function – Noise characteristics

2 How an Interferometer Works 1. Move one mirror slowly back-and- forth to create an interference pattern (interferogram) at the detector 2. Record the inteferogram as a function of time (or mirror position) 3. Apply a FFT to the interferogram to yield the spectrum

3 AERI Interferometer Assembly Bomem Interferometer ABB HBB OpticsBenchShock Mounts (4) Interferometer / AERI Electronics Interface Box IR Detector Dewar with Cooler Cold Finger Stirling Cooler Compressor Front End Assembly Blackbodies Scene Mirror Assembly Forced Air Inlet Rain Sensor Sun Sensor Front-endCloseout(thermal) Knuteson et al., JTECH, 2004

4 Passive IR Satellites In Space Wave of future is high-spectral resolution IR remote sensing Fourier transform spectrometers (FTS) Examples: – Infrared Atmospheric Sounding Interferometer (IASI) on METOP – Cross-track Infrared Sounder (CrIS) on NPOESS – Geostationary Imaging Fourier Transform Spectrometer (GIFTS)

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6 Weighting Function This would be ideal This would be nice This usually what we get

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10 Retrieving Temperature Profiles Signal in different channels is highly correlated due to vertical spread in the weighting functions – Typically have only a few “independent pieces of information” in the observations Multiple temperature profiles (solutions) yield the same observed radiance – Underdetermined, or ill-defined, problem Instrument noise further complicates matters

11 Retrieval Algorithms Algorithm must be able to handle ill-conditioned problem with noise Two general approaches: – Statistical: use a priori data to generate regressions to relate radiance to T(z) profile Easy to develop Handles noise well Computationally fast – Physical: iterative approach whereby a forward RT model is used to derive T(z) profile Need a priori data to help constrain solution Computationally slow Provides error bars as part of the retrieval

12 Ground-based IR Profiling Capability Warm & humid mid-lat clear sky case Cold & dry mid-lat clear sky case

13 Example Results from Mid-latitude Site winter: cold and dry summer: hot and humid


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