A document outlines a computational method that can be incorporated into two prior methods used to invert Global Positioning System (GPS) occultation data [signal data acquired by a low-Earth-orbiting satellite as either this or the GPS satellite rises above or falls below the horizon] to obtain information on altitude-dependent properties of the atmosphere. The two prior inversion methods, known as back propagation and canonical transform, are computationally expensive because for each occultation, they involve numerical evaluation of a large number of diffraction-like spatial integrals. The present method involves an angular-spectrum-based phase-extrapolation approximation in which each data point is associated with a plane-wave component that propagates in a unique direction from the orbit of the receiving satellite to intersect a straight line tangent to the orbit at a nearby point. This approximation enables the use of fast Fourier transforms (FFTs), which apply only to data collected along a straight-line trajectory. The computation of the diffraction-like integrals in the angular-spectrum domain by use of FFTs takes only seconds, whereas previously, it took minutes.
This work was done by Chi Ao of Caltech for NASA's Jet Propulsion Laboratory. For further information, access the Technical Support Package (TSP) free on-line at www.techbriefs.com/tsp under the Information Sciences category.
The software used in this innovation is available for commercial licensing. Please contact Don Hart of the California Institute of Technology at (818) 393-3425. Refer to NPO-30791.
This Brief includes a Technical Support Package (TSP).
Faster Processing for Inverting GPS Occulation Data
(reference NPO30791) is currently available for download from the TSP library.
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Overview
The document titled "Faster Processing for Inverting GPS Occultation Data" is a Technical Support Package from NASA's Jet Propulsion Laboratory, aimed at addressing the increasing demand for GPS occultation data due to new missions and improved data acquisition techniques. The motivation behind this development stems from the necessity for rapid retrieval of data, particularly for applications such as weather prediction, where timely information is crucial.
The document outlines a solution that significantly reduces the computational cost and data latency associated with the processing of GPS occultation data. Traditional methods of processing this data can take several minutes, but the proposed fast method reduces this time to mere seconds. This efficiency is achieved through the use of fast Fourier transform (FFT) techniques to compute diffraction-like integrals in the spectral domain.
Two key ideas underpin the fast method:
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Spectral Method: The diffraction-like integrals are reformulated using a spectral or angular spectrum approach. This allows each plane wave component to propagate in a straightforward manner, making it suitable for FFT implementation.
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Data Preprocessing: Since the FFT approach requires data to be collected along a straight-line trajectory, which is not the case in real occultations, a preprocessing step is introduced. This step involves extrapolating the data from the actual nearly spherical orbit to a nearby straight-line orbit by associating each data point with an approximate propagation direction. This method effectively reduces any errors in the propagation direction by a factor of 100 or more in the bending angle, which is critical for the retrieval products.
The document emphasizes the broader implications of this technology, suggesting that the advancements in processing GPS occultation data could have significant applications beyond weather prediction, potentially benefiting various fields that rely on accurate atmospheric data.
In summary, the Technical Support Package presents a groundbreaking approach to processing GPS occultation data, highlighting the importance of speed and efficiency in data retrieval. By leveraging advanced computational techniques, NASA aims to enhance the capabilities of GPS data applications, ultimately contributing to improved weather forecasting and other scientific endeavors.
