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At least 397 records · Page 22

Aquarius Radiometer and Scatterometer Weekly-Polar-Gridded Products to Monitor Ice Sheets, Sea Ice, and Frozen Soil

Space-based microwave sensors have been available for several decades, and with time more frequencies have been offered. Observations made at frequencies between 7 and 183 GHz were often used for monitoring cryospheric properties (e.g. sea ice concentration, snow accumulation, snow melt extent and duration). Since 2009, satellite observations are available at the low frequency of 1.4 GHz. Such observations are collected by the Soil Moisture and Ocean Salinity (SMOS) mission, and the AquariusSAC-D mission. Even though these missions have been designed for the monitoring of soil moisture and sea surface salinity, new applications are being developed to study the cryosphere. For instance, L-band observations can be used to monitor soil freezethaw (e.g. Rautiainen et al., 2012), and thin sea ice thickness (e.g. Kaleschke et al., 2010, Huntemann et al., 2013). Moreover, with the development of satellite missions comes the need for calibration and validation sites. These sites must have stable characteristics, such as the Antarctic Plateau (Drinkwater et al., 2004, Macelloni et al., 2013). Therefore, studying the cryosphere with 1.4 GHz observations is relevant for both science applications, and remote sensing applications.

Aquarius↗

Aquarius Radiometer and Scatterometer Weekly Polar-Gridded Products to Monitor Ice Sheets, Sea Ice, and Frozen Soil

Space-based microwave sensors have been available for several decades, and with time more frequencies have been offered. Observations made at frequencies between 7 and 183 GHz were often used for monitoring cryospheric properties (e.g. sea ice concentration, snow accumulation, snow melt extent and duration). Since 2009, satellite observations are available at the low frequency of 1.4 GHz. Such observations are collected by the Soil Moisture and Ocean Salinity (SMOS) mission, and the Aquarius/SAC-D mission. Even though these missions have been designed for the monitoring of soil moisture and sea surface salinity, new applications are being developed to study the cryosphere. For instance, L-band observations can be used to monitor soil freeze/thaw (e.g. Rautiainen et al., 2012), and thin sea ice thickness (e.g. Kaleschke et al., 2010, Huntemann et al., 2013). Moreover, with the development of satellite missions comes the need for calibration and validation sites. These sites must have stable characteristics, such as the Antarctic Plateau (Drinkwater et al., 2004, Macelloni et al., 2013). Therefore, studying the cryosphere with 1.4 GHz observations is relevant for both science applications, and remote sensing applications.

Sea ice↗

Surface Stress in Tropical Cyclone Observed by Scatterometer

Ocean surface wind (U) is air in motion and stress (τ) is the turbulent transport of momentum between the ocean and the atmosphere. While the strong wind of a tropical cyclone (TC) causes destruction at landfall, it is the surface stress that drags down the TC. There was almost no stress measurement except in dedicated field campaigns and the stress we used was almost entirely derived from wind through a drag coefficient (C(sub D)), as defined by C(sub D) = τ / ( ρ U(exp 2)). In TC, there is difficulty in measuring strong wind and large uncertainty in the drag coefficient.

Liu, W. Timothy↗

Examining the Constellation of Scatterometers and Radiometers for Diurnal and Sub-Diurnal Wind Vector Variability

The constellation of satellite-based ocean surface wind and precipitation observations since 1999 consists of a diverse collection of both sunsynchronous and asynchronous orbiting satellite platforms, both wind vector-capable (e.g., RapidScat, QuikSCAT, ASCAT) and speed-only radiometers (e.g., TRMM and GPM imagers, AMSR-E) The surface wind varies widely throughout the day, owing to various meteorological forcings, such as land/sea temperature differences, coastal forcings, or possible variations associated with tropical convection Where are regions of strongest time-of-day variability?

Turk, F. Joseph (Joe)↗