Infrared Sounder Data Processing
Infrared sounders represent some of the most sophisticated instruments used in Earth and planetary observation. These instruments measure thermal infrared radiation emitted by Earth's atmosphere and surface to obtain vertical temperature and humidity profiles, composition of various atmospheric gases, cloud properties, and surface characteristics. This article provides an overview of infrared sounder data processing techniques and their applications.
Introduction to Infrared Sounders
Infrared sounders measure the upwelling thermal radiation from the Earth-atmosphere system in multiple spectral bands. These measurements contain information about the vertical structure of atmospheric temperature and humidity profiles, as well as concentrations of trace gases such as ozone, carbon monoxide, carbon dioxide, and methane.
Satellite infrared sounders have been operational since the 1970s, starting with the Vertical Temperature Profile Radiometer (VTPR) on NOAA satellites. Modern infrared sounders, such as the Atmospheric Infrared Sounder (AIRS) on NASA's Aqua satellite, the Infrared Atmospheric Sounding Interferometer (IASI) on MetOp satellites, and the Cross-track Infrared Sounder (CrIS) on NOAA's Suomi NPP and NOAA-20 satellites, provide thousands of spectral channels with high spectral resolution, enabling more accurate retrievals.
Instrument Characteristics
Infrared sounders typically operate in spectral ranges from approximately 660 to 2,800 cm (3.57-15.15 m), covering both shortwave and thermal infrared regions. Modern hyperspectral sounders like IASI and CrIS are based on Fourier transform interferometry, providing thousands of spectral channels with fine spectral resolution (0.25-0.5 cm).
Key instrument parameters affecting data processing include:
- Spectral resolution: Determines the ability to resolve individual absorption features of atmospheric gases
- Signal-to-noise ratio: Affects radiometric accuracy and detection limits
- Spatial resolution: Typically ranges from kilometers to tens of kilometers at nadir
- Calibration accuracy: Impacts absolute radiance quality
- Spectral bandwidth: Determines the range of atmospheric parameters that can be retrieved
Raw Data Acquisition and Preprocessing
The first step in infrared sounder data processing is the acquisition of raw digital counts from the instrument. These raw measurements are converted to calibrated radiances through a series of preprocessing steps:
Calibration Process
1. Dark signal subtraction: Remove instrument self-emission and detector dark current
2. Nonlinearity correction: Account for detector response nonlinearities
3. Radiometric calibration: Convert digital counts to radiances using onboard blackbody targets and space views
4. Spectral calibration: Correct for spectral registration and resolution effects
5. Geolocation: Assign precise latitude, longitude, and viewing geometry to each measurement
Modern sounders perform most of these calibrations onboard, but additional refinements are often applied in ground processing to improve accuracy.
Radiative Transfer Modeling
The foundation of infrared sounder data processing is radiative transfer modeling, which describes how infrared radiation propagates through the atmosphere. The radiative transfer equation for infrared sounding can be expressed as:
R(v) = (v) B(v,T_s) (v,s) + B(v,T(z)) (v,z)/z dz
Where R(v) is the measured radiance at wavenumber v, (v) is surface emissivity, B(v,T) is the Planck function at temperature T, T_s is surface temperature, is atmospheric transmission, and the integral represents atmospheric contribution.
Accurate radiative transfer models must account for:
- Absorption and emission by atmospheric gases
- Scattering by particles (aerosols, clouds)
- Surface reflection and emission
- Multiple scattering effects
Laboratory-measured spectroscopic parameters from databases like HITRAN (High-Resolution Transmission Molecular Absorption Database) feed into radiative transfer calculation codes such as LBLRTM (Line-by-Line Radiative Transfer Model) and MODTRAN (Moderate Resolution Atmospheric Transmittance and Radiance Code).
Cloud Detection and Clearing
Clouds significantly affect infrared sounder measurements, challenging the retrieval of atmospheric profiles. Cloud detection identifies measurements contaminated by clouds, while cloud clearing techniques attempt to extract clear-column radiances for partially cloudy fields of view.
Common cloud detection approaches include:
- Threshold tests: Compare measured radiances to expected clear-sky values
- Spatial coherence tests: Assess variability within neighborhoods of pixels
- Spectral tests: Exploit differences in cloud absorption across spectral channels
- Near-infrared and visible tests: Utilize additional spectral regions when available
For partially cloudy scenes, cloud clearing algorithms such as the Atmospheric Infrared Sounder cloud clearing (NCE) and the Minimum Residual method combine information from multiple spectral channels and fields of view to estimate what the radiance would be in clear conditions.
Retrieval Algorithms
Retrieval algorithms invert the measured radiances to estimate geophysical parameters of interest. This inversion problem is mathematically ill-posed, requiring regularization to stabilize the solution. Common retrieval approaches include:
- Direct physical retrieval: Iteratively adjust atmospheric state until simulated radiances match measurements
- Regression-based retrieval: Use statistical relationships between radiances and derived parameters
- Neural network retrieval: Train machine learning models to map radiances to atmospheric states
- Optimal estimation: Combine measurements with prior information, minimizing a cost function
Most operational infrared sounder retrievals use optimal estimation, which formally balances measurement information with prior expectations and provides uncertainty estimates:
x = x_a + KRK + R_a)KR(y - F(x_a))
Where x is the retrieved state, x_a is the a priori state, K is the Jacobian (sensitivity of radiances to state changes), R is the measurement error covariance, R_a is the a priori covariance, y is the measurement vector, and F(x) is the radiative transfer forward model.
Retrieval Products
Typical products derived from infrared sounder measurements include:
- Atmospheric temperature profiles: Usually at 100 vertical levels from surface to ~0.1 hPa
- Water vapor profiles: Specific humidity or relative humidity at multiple levels
- Trace gas concentrations: Ozone, carbon monoxide, methane, and other gases
- Surface properties: Surface temperature, emissivity, and sometimes surface pressure
- Cloud properties: Cloud top height, optical thickness, and effective particle size
- Radiative fluxes: Shortwave and longwave radiative fluxes at various atmospheric levels
Quality Control and Validation
Quality control (QC) identifies and flags retrieval products that may have poor reliability. QC metrics include:
- Chi-square residual: Indicates how well the solution fits the measurements
- Information content: Measures the contribution of measurements versus prior information
- Convergence indicator: Whether the iterative solution reached a stable state
- Cloud flag: Indication of potential cloud contamination
Validation compares retrieved products with independent measurements from radiosondes, aircraft, ground-based instruments, and other satellites to assess accuracy and identify systematic biases.
Data Assimilation Applications
Infrared sounder data has revolutionized numerical weather prediction (NWP) through data assimilation. Rather than using retrieved profiles directly, modern NWP systems assimilate either calibrated radiances or retrieved products. The assimilation of clear-sky radiances from hyperspectral sounders like AIRS, IASI, and CrIS is estimated to have significantly improved forecast skill, particularly in the Southern Hemisphere where conventional observations are sparse.
Climate and Environment Monitoring
Beyond weather forecasting, infrared sounders contribute to climate monitoring and environmental science:
- Climate trend analysis: Long-term monitoring of temperature and water vapor changes
- Air quality monitoring: Tracking of pollution gases like carbon monoxide and ozone
- Greenhouse gas studies: Measurements of methane and carbon dioxide distributions
- Volcanic ash detection: Identification of volcanic ash clouds and estimation of their properties
- Dust storm tracking: Detection and characterization of mineral dust aerosols
Future Developments
The future of infrared sounding will focus on:
- Improved spatial resolution: Higher resolution measurements to resolve smaller-scale features
- Expanded spectral coverage: Additional spectral regions to retrieve new parameters
- Advanced algorithms: More sophisticated retrieval methods including machine learning
- Joint retrievals: Simultaneous retrieval of atmospheric, surface, and cloud properties
- All-sky capability: Improved retrievals under cloudy conditions
- Constellation approaches: Multiple satellites providing improved temporal and spatial coverage
Conclusion
Infrared sounder data processing encompasses a complex chain of operations from raw measurements to geophysical parameters. Through advances in instrument technology, radiative transfer modeling, and retrieval algorithms, these systems provide invaluable information for weather forecasting, climate monitoring, and environmental science. As satellite technology and computational methods continue to evolve, we can expect even more accurate and comprehensive atmospheric information from future infrared sounders.
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