Journal
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume 10, Issue 5, Pages 2406-2414Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2017.2659220
Keywords
Measurement errors; microwave measurements; remote sensing; satellite applications; wind
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Scatterometer and altimeter wind data are very important for data assimilation and verification of numerical weather prediction models. Standard deviation of absolute random errors can be estimated using the triple collocation technique. However, error correlations between various wind sources (e.g., due to data assimilation) complicate the error estimation. A method is used to alleviate the impact of error correlations between the scatterometer and the model that assimilates such data. Using twenty-two datasets of triplet composed of Jason-2 altimeter, Metop-A/B scatterometers (ASCAT-A/B, respectively), and ECMWF model analysis and forecasts ( 1 altimeter x 2 scatterometers x 11 model analysis and forecasts = 22 datasets) covering a period of two years fromAugust 2013 to July 2015, the correlation coefficient between the errors of scatterometers and the model analysis was found to be about 0.33 for those datasets. This correlation reduces with forecast lead time until it almost vanishes at day seven. Altimeter and scatterometer errors are not correlated. The standard deviation of wind speed random errors of Jason-2, ASCAT-A/B, and the IFS analysis are estimated as 0.7, 0.8, and 0.9 m/s, respectively. As expected, there was no difference between ASCAT-A and ASCAT-B results.
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