Relating West Antarctic Ice Cores to Climate with Artificial Neural Networks

0087380Alley该奖项提供三年的支持，以使用广泛，适应性强，多参数的方法，使用一系列技术，包括人工神经网络，以寻求气象条件与雪坑和冰芯记录之间的关系，他们产生。 多参数，高分辨率，冰芯数据已经在手或正在收集反映积雪，大气化学，同位素分馏和其他过程，往往与亚年度的分辨率。 可获得此类数据的南极西部站点将用作再分析数据产品中的反向轨迹分析的起点，以确定数据流的气象条件。 然后，人工神经网络将被用来寻找这些气象条件和它们的产品之间的最佳关系。 以前的工作已经证明了再分析产品的价值，在确定积雪，了解冰川化学的反向轨迹分析，人工神经网络在连接气象条件和他们的产品。 初步工作表明，神经网络成功地将再分析产品降尺度到南极洲西部的自动气象站数据，从而能够对特定地点的数据进行插值，以提高对南极洲西部气候近期变化的理解。

0087380AlleyThis award provides three years of support to use a broad, adaptable, multi-parameter approach, using a range of techniques including artificial neural networks to seek the relations between meteorological conditions and the snow pit and ice core records they produce. Multi-parameter, high resolution, ice core data already in hand or now being collected reflect snow accumulation, atmospheric chemistry, isotopic fractionation, and other processes, often with subannual resolution. The West Antarctic sites from which such data are available will be used as starting points for back-trajectory analyses in reanalysis data products to determine the meteorological conditions feeding the data stream. The artificial neural nets will then be used to look for optimal relations between these meteorological conditions and their products. Previous work has demonstrated the value of reanalysis products in determining snow accumulation, of back trajectory analyses in understanding glaciochemistry, and of artificial neural nets in linking meteorological conditions and their products. Preliminary work shows that neural nets are successful in downscaling from reanalysis products to automatic weather station data in West Antarctica, enabling interpolation of site-specific data to improve understanding of recent changes in West Antarctic climate.