GOES-12 (Channel Radiometer)

• Channels are typically independent of each other• Need to know each channel’s

– Spectral response function– Noise characteristics

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

AERI Interferometer AssemblyAERI Interferometer Assembly

BomemInterferometer

ABB

HBB

OpticsOpticsBenchBench

ShockShockMounts (4)Mounts (4)

Interferometer / AERIInterferometer / AERIElectronics Interface BoxElectronics Interface Box

IR DetectorIR DetectorDewar withDewar withCooler Cold FingerCooler Cold Finger

Stirling CoolerStirling CoolerCompressorCompressor

Front End AssemblyFront End AssemblyBlackbodiesBlackbodiesScene Mirror AssemblyScene Mirror AssemblyForced Air InletForced Air InletRain SensorRain SensorSun SensorSun Sensor

Front-endFront-endCloseoutCloseout(thermal)(thermal)

Knuteson et al., JTECH, 2004

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)

Weighting Function

This would be ideal This would

be nice This usually what we get

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

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

Ground-based IR Profiling Capability

Warm & humid mid-lat clear sky case

Cold & dry mid-lat clear sky case

Example Results from Mid-latitude Site

winter: cold and dry summer: hot and humid