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

高纬度大气水汽及地表性质微波卫星遥感

• 批准号: RGPIN-2020-05580
• 负责人: Duck, Thomas
• 金额: $ 2.19万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717302
• 关键词: Microwave; Satellite; Remote; Sensing; Latitude

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.

北极的气候变化速度是其他地方的两到三倍，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限制，原因是地面上的交通困难以及云层对大多数形式的卫星测量的干扰。 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潜力。 28年来，一系列极地轨道仪器的无源微波（183 GHz）测量是北极大气和地表遥感的一个重要、免费提供和利用不足的资源。 他们使用的一个障碍是缺乏完善的算法需要检索地球物理的利益，包括水蒸气柱，表面发射率，表面发射层温度的属性。 检索的水汽柱系统比模拟的历史再分析，这降低了地球物理研究的测量效用。 我提出了一个雄心勃勃的计划，以提高微波卫星测量所获得的信息的质量，通过开发创新的检索技术，分析错误的来源，并验证检索到的属性对其他数据源。 如果观测到的水蒸气偏差经得起进一步的审查，北极环境的模型将需要改变，这将对气候和天气预测产生影响。 充分利用现有的卫星资源将需要新的理论方法。 我们将使用最佳估计和机器学习，重点是人工神经网络（ANN），以创建新的检索。 将编写新的软件，以处理卫星测量结果并制作高质量的地球物理数据产品。 将探讨由表面假设、云和辐射传输模型的选择引起的误差的影响。 高级误差统计将与加拿大环境和气候变化局（ECCC）运营的同化系统一起获得。 这些计算将是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。 对研究界的惠益将包括微波检索能力的提高和地球物理研究公开可用的经验证的在线数据集。 对改善预测的贡献将通过更好的短期（即，天气）和长期（即，气候预测。 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。