FN Archimer Export Format
PT J
TI An Updated Geophysical Model for AMSR-E and SSMIS Brightness Temperature Simulations over Oceans
BT 
AF ZABOLOTSKIKH, Elizaveta
   MITNIK, Leonid
   CHAPRON, Bertrand
AS 1:1;2:2;3:3;
FF 1:;2:;3:PDG-ODE-LOS;
C1 Russian State Hydrometeorol Univ, Satellite Oceanog Lab, St Petersburg 195196, Russia.
   VI Ilichev Pacific Oceanol Inst, Vladivostok 690041, Russia.
   IFREMER, Ctr Brest, F-29280 Plouzane, France.
C2 UNIV RUSSIAN STATE HYDROMETEOROL, RUSSIA
   VI ILICHEV PACIFIC OCEANOL INST, RUSSIA
   IFREMER, FRANCE
SI BREST
SE PDG-ODE-LOS
IN WOS Ifremer jusqu'en 2018
   copubli-int-hors-europe
IF 3.18
TC 16
UR https://archimer.ifremer.fr/doc/00190/30101/28628.pdf
LA English
DT Article
DE ;calibration;geophysical model;numerical simulation;SSMIS;AMSR-E;satellite passive microwave
AB In this study, we considered the geophysical model for microwave brightness temperature (BT) simulation for the Atmosphere-Ocean System under non-precipitating conditions. The model is presented as a combination of atmospheric absorption and ocean emission models. We validated this model for two satellite instruments-for Advanced Microwave Sounding Radiometer-Earth Observing System (AMSR-E) onboard Aqua satellite and for Special Sensor Microwave Imager/Sounder (SSMIS) onboard F16 satellite of Defense Meteorological Satellite Program (DMSP) series. We compared simulated BT values with satellite BT measurements for different combinations of various water vapor and oxygen absorption models and wind induced ocean emission models. A dataset of clear sky atmospheric and oceanic parameters, collocated in time and space with satellite measurements, was used for the comparison. We found the best model combination, providing the least root mean square error between calculations and measurements. A single combination of models ensured the best results for all considered radiometric channels. We also obtained the adjustments to simulated BT values, as averaged differences between the model simulations and satellite measurements. These adjustments can be used in any research based on modeling data for removing model/calibration inconsistencies. We demonstrated the application of the model by means of the development of the new algorithm for sea surface wind speed retrieval from AMSR-E data.
PY 2014
PD MAR
SO Remote Sensing
SN 2072-4292
PU Mdpi Ag
VL 6
IS 3
UT 000334797000028
BP 2317
EP 2342
DI 10.3390/rs6032317
ID 30101
ER

EF