@inproceedings{a4d1f2fc536649f69c04e20108ae6572,
title = "NOAA scatterometer wind retrievals from the SCATSAT-1 mission",
abstract = "In this paper, we present the SCATSAT-1 wind data processor developed by NOAA. The sigma0 from L1B produced by ISRO was used as an input to our processor. Ocean surface wind vector products are produced at the grid resolutions of 12.5 km and 25 km. We experimented with different objective functions and the number of solutions. The ambiguity removal method utilized in our processor is the Two-Dimensional Variational Ambiguity Removal (2DVAR). The rain flag algorithm was developed based on the Bayes{\textquoteright} theorem by calculating the rain probability given the rain sensitive parameter threshold. Finally, we validated our Scatsat-1 wind retrievals by both statistical analyses and visual inspection of the wind field for meteorological consistency to determine which objective function produced the best results. The performance of the Scatsat-1 wind retrievals compared to the Global Data Assimilation System (GDAS) winds shows reasonable wind speed and wind direction biases and standard deviation differences.",
keywords = "Ocean Surface Winds, Scatsat-1, Scatterometer",
author = "Seubson Soisuvarn and Zorana Jelenak and Faozi Said and Jeonghwan Park and Qi Zhu and Chang, \{Paul S.\}",
note = "Publisher Copyright: {\textcopyright}2019 IEEE; 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference date: 28-07-2019 Through 02-08-2019",
year = "2019",
doi = "10.1109/IGARSS.2019.8900305",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "8039--8042",
booktitle = "2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings",
address = "United States",
}