Improving surface mass balance estimation of the Greenland ice sheet through assimilation of multi-sensor satellite products and ground measurements into a regional climate model

该奖项是根据2009年《美国复苏和再投资法案》(公法111-5)提供资金的。格陵兰冰盖在北极气候中发挥着重要作用，是预测海平面上升的主要考虑因素。诊断地理信息系统的地表质量平衡是一项关键目标，它仍然涉及模拟降水误差和与次网格尺度过程表示有关的误差所带来的很大不确定性。迄今为止，在将遥感技术和地面数据与建模相结合方面的工作有限。在这个项目中，数据同化方法将提供一个严格的框架，以便以一致的方式(根据其相关的不确定性)合并这些不同的信息源，以获得包括空间和时间图在内的最佳地表质量平衡后验估计。该项目将侧重于实施一个协同模拟/观测框架，最终结果是改进对与表面质量平衡有关的表面状态和通量的平均值和不确定性的估计。更广泛的影响包括对了解地理信息系统对海平面上升的影响的重大贡献。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The Greenland Ice Sheet (GIS) plays a major role in Arctic climate and is a major consideration in projections of sea level rise. Diagnosing the surface mass balance of the GIS is a critical objective that continues to involve large uncertainties from errors in modeled precipitation and errors related to sub-grid-scale process representation. To date, there has been limited work in integrating remote sensing techniques and ground-based data with modeling. In this project, a data assimilation approach will provide a rigorous framework for merging these disparate sources of information in a consistent way (based on their associated uncertainty) to obtain an optimal surface mass balance posterior estimate comprising maps in space and time. This project will focus on implementing a synergistic modeling/observation framework with the final outcome being improved estimates of the mean and uncertainty of the surface states and fluxes associated with the surface mass balance. Broader impacts include a significant contribution to understanding of GIS influence on sea level rise.