HR: 1340h
AN: C33B-1135    [Abstracts]
TI: Comparison of Outputs From an Arctic Sea Ice Snow Melt Onset Date Algorithm Run for Brightness Temperature Data Sets From AMSR-E and SSM/I Sensors.
AU: * Jackson, B A
EM: jacksonb@bigred.unl.edu
AF: University of Nebraska - Lincoln, 214 Bessey Hall PO Box 880340, Lincoln, NE 68588 United States
AU: Molthan, A
EM: a_molthan@hotmail.com
AF: University of Nebraska - Lincoln, 214 Bessey Hall PO Box 880340, Lincoln, NE 68588 United States
AU: Anderson, M R
EM: mra@unl.edu
AF: University of Nebraska - Lincoln, 214 Bessey Hall PO Box 880340, Lincoln, NE 68588 United States
AB: The Advanced Microwave Scanning Radiometer for EOS (AMSR-E) is the newest conically scanning passive microwave sensor. The AMSR-E was launched in 2002 with notable advances in technology from previous sensors like the Special Sensor Microwave Imager (SSM/I). Data from SSM/I sensors, first launched in 1987, have enabled analysis of geophysical parameters in the cryosphere for a considerable length of time. Usage of geophysical parameters derived from brightness temperature (Tb) data sets from the AMSR-E and SSM/I sensors is expected to continue for the foreseeable future, so comparisons between the two sensors are beneficial. Also, the formation of multiple year data sets allows for climate studies. The AMSR-E and SSM/I both have cryospheric data presented in a 25 km resolution polar stereographic grid. The AMSR-E also has a 12.5 km resolution grid. This study will use the Advanced Horizontal Range Algorithm (AHRA), which computes snow melt onset dates (SMOD) over Arctic sea ice by flagging changes in the Tb's during melt due to moisture formation in snow crystals which leads to an increase in crystalline size. Comparison tests between the AMSR-E and SSM/I are performed for the melt seasons of 2003 and 2004. One test compares Tb's and SMOD from the 25 km AMSR-E and 25 km SSM/I Tb data sets. The other test compares the Tb's and SMOD from the 25 km and 12.5 km grids of the AMSR-E. Both tests will compare individual pixels and sub-regions of the Arctic between the two data sets by using simple statistical methods.
DE: 0700 CRYOSPHERE (4540)
DE: 0750 Sea ice (4540)
DE: 0758 Remote sensing
SC: Cryosphere [C]
MN: Fall Meeting 2005