Microwave Satellite Remote Sensing of High -Latitude Atmospheric Water Vapour and Surface Properties

北极地区气候变化的速度是其他地区的两到三倍，导致9月份北极海冰面积每十年减少13%。这些变化是由人为的二氧化碳排放驱动的，并受到反馈效应的影响，特别是来自水蒸气的反馈效应。我们对高纬度地区水蒸气的了解受到地面上难以接近的困难和大多数卫星测量方式受到云的干扰的限制。微波卫星测量在填补数据空白和帮助提高预测能力方面具有很大的潜力。28年来，一系列极轨仪器进行的被动微波（183 GHz）测量为北极大气和地表遥感提供了重要的、可免费获得的、未充分利用的资源。使用它们的一个障碍是缺乏检索地球物理特性所需的成熟算法，包括水蒸汽柱、地表发射率和地表发射层温度。回收的水蒸汽柱比模拟的历史再分析系统地湿润，这降低了测量对地球物理研究的效用。我提出了一个雄心勃勃的计划，通过开发创新的检索技术，分析错误来源，并根据其他数据源验证检索属性，来提高从微波卫星测量中获得的信息的质量。如果观测到的水汽偏倚经得起进一步的审查，北极环境模型就需要改变，这将对气候和天气预测产生影响。充分利用现有卫星资源将需要新的理论方法。我们将使用最优估计和机器学习，重点是人工神经网络（ann），来创建新的检索。将编写新的软件来处理卫星测量并产生高质量的地球物理数据产品。我们将探讨由地表假设、云和辐射传输模式的选择所引起的误差的影响。先进的误差统计数据将与加拿大环境和气候变化（ECCC）运行的同化系统一起获得。这些计算将代表着同化陆地和海冰表面敏感微波辐射的重要一步，这将有助于提高预测能力。测量验证将包括广泛的地面站和飞机数据。对研究界的好处将包括微波检索能力的进步和地球物理研究中公开可用的验证在线数据集。通过更好的短期（如天气）和长期（如气候）预测，对改进预报的贡献将有利于提高生活质量。将培养准备在加拿大环境监测和空间科学界从事职业的受训人员和科学家。

Climate change is proceeding two to three times faster in the Arctic than elsewhere, leading to a 13% per decade reduction in September Arctic sea ice extent. The changes are driven by anthropogenic carbon dioxide emissions, and are subject to feedback effects, especially from water vapour. Our understanding of water vapour at high latitudes is limited by accessibility difficulties on the ground and interference by clouds with most forms of satellite measurement. Microwave satellite measurements have strong potential for filling data gaps, and for helping to improve predictive capabilities. Twenty-eight years of passive microwave (183 GHz) measurements from a series of polar-orbiting instruments represents a significant, freely available, and under-utilized resource for remote sensing of the Arctic atmosphere and surface. An impediment to their use is the lack of well-developed algorithms needed to retrieve properties of geophysical interest including water vapour column, surface emissivity, and surface emitting layer temperature. Retrieved water vapour columns are systematically moister than modeled historical reanalyses, which decreases the utility of the measurements for geophysical research. I propose an ambitious plan to increase the quality of information obtained from microwave satellite measurements by developing innovative retrieval techniques, analyzing sources of error, and validating retrieved properties against other data sources. Should the observed water vapour bias hold up to further scrutiny, models of the Arctic environment would need to change, with consequences for climate and weather prediction. Full use of available satellite resources will require new theoretical approaches. We will use optimal estimation and machine learning, with an emphasis on artificial neural networks (ANNs), to create new retrievals. New software will be written to process satellite measurements and produce high-quality geophysical data products. The impact of errors induced by surface assumptions, clouds, and choice of radiative transfer model will be explored. Advanced error statistics will be obtained in conjunction with the assimilation system operated by Environment and Climate Change Canada (ECCC). The calculations will represent an important step toward the assimilation of surface-sensitive microwave radiances over land and sea ice that will help improve predictive capabilities. Measurement validation will include a wide range of surface station and aircraft data. Benefits to the research community will include advancements in microwave retrieval capabilities and publicly available validated online data sets for geophysical research. Contributions toward improved forecasts will benefit quality of life through better short-term (i.e., weather) and long-term (i.e., climate) predictions. Trainees and scientists ready for careers in Canada's environmental monitoring and space sciences communities will be developed.