Brajard Julien, Jamet Cédric, Moulin Cyril, Thiria Sylvie
IPSL/LOCEAN (ex LODyC), BC 100, T45-55, 4 Place Jussieu, 75252 Paris Cedex, France.
Neural Netw. 2006 Mar;19(2):178-85. doi: 10.1016/j.neunet.2006.01.015.
This paper presents a new development of the NeuroVaria method. NeuroVaria computes relevant atmospheric and oceanic parameters by minimizing the difference between the observed satellite reflectances and those computed from radiative transfer simulations modelled by artificial neural networks. Aerosol optical properties are computed using the Junge size distribution allowing taking into account highly absorbing aerosols. The major improvement to the method has been to implement an iterative cost function formulation that makes the minimization more efficient. This implementation of NeuroVaria has been applied to sea-viewing wide field-of-view sensor (SeaWiFS) imagery. A comparison with in situ measurements and the standard SeaWiFS results for cases without absorbing aerosols shows that this version of NeuroVaria remains consistent with the former. Finally, the processing of SeaWiFS images of a plume of absorbing aerosols off the US East coast demonstrate the ability of this improved version of NeuroVaria to deal with absorbing aerosols.
本文介绍了NeuroVaria方法的新进展。NeuroVaria通过最小化观测到的卫星反射率与由人工神经网络建模的辐射传输模拟计算出的反射率之间的差异，来计算相关的大气和海洋参数。气溶胶光学特性使用容格粒径分布来计算，从而能够考虑高吸收性气溶胶。该方法的主要改进在于实施了一种迭代成本函数公式，使最小化过程更高效。NeuroVaria的这种实现方式已应用于海景宽视场传感器（SeaWiFS）图像。对于无吸收性气溶胶的情况，将其与现场测量结果及标准SeaWiFS结果进行比较表明，这个版本的NeuroVaria与前者保持一致。最后，对美国东海岸外一股吸收性气溶胶羽流的SeaWiFS图像进行处理，证明了这个改进版的NeuroVaria处理吸收性气溶胶的能力。
