Abstract
In this paper we discuss some observations of the Soil Moisture Active Passive (SMAP) mission’s high-resolution synthetic aperture radar (SAR) for extreme winds and tropical cyclones. We find that the cross-polarized backscatter is far more sensitive to wind speed at extreme winds than the copolarized backscatter and it is essential to observations of extreme winds with L-band SAR. We introduce a cyclone wind speed retrieval algorithm and apply it to the limited SMAP SAR dataset of cyclones. We show that the SMAP SAR instrument is capable of detecting extreme winds up to the category 5 wind speed regime providing unique capabilities as compared to traditional scatterometer with C and Ku-band radars.
Highlights
Soil Moisture Active Passive (SMAP) is a mission designed to observe the soil moisture over land using a combined active/passive L-band system [1]
L-band is not significantly affected by attenuation due to rain, and in this study we will show that the cross-polarization in particular remains sensitive to wind at the wind speeds of tropical cyclones/hurricanes, even up to category 5/70 m/s wind speeds
We do not think Faraday rotation is significant for the data considered here due to two main factors: synthetic aperture radar (SAR) data over ocean is only collected on the descending pass (6 am local time) when the total electron content (TEC) is minimal and even a Faraday rotation correction is performed on the SAR data using a model TEC data product
Summary
Soil Moisture Active Passive (SMAP) is a mission designed to observe the soil moisture over land using a combined active/passive L-band system [1]. The SAR data is generally only available over land, a small amount was collected over the ocean for the descending portion of the orbit, only on the fore look, and only within 1000 km of coast. These constraints severely limited the quantity of SAR mode data available over the ocean and were driven by downlink capacity considerations. The SMAP radar failed in early July 2015, after having provided only two months of data. Though short, this dataset is sufficient to explore the vast potential of this type of system for ocean remote sensing of extreme wind events
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