Quality assurance algorithm for NASA scatterometer wind retrieval

Amy E. Gonzales; David G. Long

doi:10.1117/12.278916

18 September 1997 Quality assurance algorithm for NASA scatterometer wind retrieval

Amy E. Gonzales, David G. Long

Author Affiliations +

Proceedings Volume 3117, Earth Observing Systems II; (1997) https://doi.org/10.1117/12.278916
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

Abstract

The recently launched NASA Scatterometer (NSCAT) estimates the wind speed and direction of near-surface ocean wind. Several possible wind vectors are identified for that location of the earth known as a cell. Typically, the speeds of the possible wind vectors are the same, but the directions are very different. The correct wind must be distinguished from these in a step called ambiguity removal. Unfortunately, ambiguity removal algorithms are subject to error. Because the true wind is not known, where these errors occur is difficult to determine, and there is little information about how to detect the errors in this removal step. One method we have developed to assess the accuracy of the ambiguity removal algorithm is to compare the point-wise retrieved wind to wind retrieved using a model. The model is fit to the point-wise retrieved wind to determine if the observed wind is realistic or containing possible ambiguity removal errors The algorithms' performance achieves its goal to identify at least 95 percent of the regions that contain errors. The algorithm provides a very simple tool to indicate regions of possible ambiguity removal errors in the point-wise retrieved winds for NSCAT data. This paper describes this algorithm and its performance for real NSCAT data. It also outlines an algorithm to correct some of the errors. This is done by either choosing the alias closest to the model-fit or by simply replacing the erroneous wind vectors with those produced by the model-fit.

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Amy E. Gonzales and David G. Long "Quality assurance algorithm for NASA scatterometer wind retrieval", Proc. SPIE 3117, Earth Observing Systems II, (18 September 1997); https://doi.org/10.1117/12.278916

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KEYWORDS

Error analysis

Wind measurement

Fourier transforms

Algorithm development

Data modeling

Statistical modeling

Chromium

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