Atmospheric and Environmental Research (AER) has extensive expertise in developing state-of-art algorithms to retrieve atmospheric and surface properties from ground- and space-based sensors. We also support end-to-end modeling studies used in the design of sensors and measurement systems. Working closely with radiative transfer experts at AER, our approach takes a comprehensive view, starting with advancing knowledge of radiative transfer phenomena followed by the development of appropriate modeling methods and algorithms and leading to system calibration and algorithm validation/verification.
In work spanning a wide variety of sensor types, our experts are experienced in validating retrievals against independent measurements and evaluating models and algorithms using simulations. Our test environment includes the capability for end-to-end simulation of all the processes that affect remote sensing measurements—from emission by realistic Earth scenes to antenna/channel response and system calibration. With these simulation tools we are able to evaluate the effects of sensor design decisions on the bottom-line performance of environmental retrievals.
Our radiative transfer modeling and retrieval approaches are consistent across the full electromagnetic spectrum, providing a foundation for a uniform approach to remote sensing, applicable to individual sensors and required by multi-spectral data fusion. Our scientists developed and continue to validate AER's Optimal Spectral Sampling (OSS) model.
Our remote sensing experts develop state-of-the-art algorithms to support end-to-end modeling studies used in the design of sensors to measure the distribution of trace gases in the atmosphere. These data provide key information for the characterization of air pollution and atmospheric change on a global scale. Much of our work also leverages AER’s chemical modeling expertise to provide realistic profiles of atmospheric composition and to build our overall understanding of the needs of the data user community through use of satellite and aircraft sensor data in other geophysical models.
Infrared Remote Sensing - AER has extensive expertise in retrieving atmospheric and surface properties from infrared sensors, with a special focus on the development of geophysical parameter algorithms and ultra-fast radiative transfer models for various sensor-specific applications.
Microwave Remote Sensing - Our experts develop algorithms to retrieve a broad range of environmental variables, from soil moisture to cirrus cloud properties to mesospheric temperatures. While the microwave spectrum is central to this work, data from infrared sensors and numerical weather prediction systems also play an integral part in our algorithms and analyses.
To learn more about AER's Remote Sensing expertise, please contact us.
Approximations of the Planck Function for Models and Measurements Into the Submillimeter Range
Fast and accurate radiative transfer in the microwave with optimum spectral sampling
Infrared radiance modeling by optimal spectral sampling
Testing the CMIS core retrieval algorithm with AMSU data and comparison with NOAA and AMSR products
Application of a testbed for validating remote sensor data and retrieval algorithms
Satellite sounding channel optimization in the microwave spectrum
Retrieval of water vapor over land surfaces from microwave measurements
Polarization of measurement for microwave temperature sounding of the mesosphere
Algorithm Theoretical Basis Documents (ATBDs) for the Conically-scanning Microwave Imager/Sounder