Collaborative Research: Flexible Statistical Models to Blend Massive Geostationary-Derived Climate Data Records

陆地表面反射率是入射太阳辐射被陆地表面反射的分数。它是全球气候观测系统确定的一个重要气候变量。来自美国、欧盟、日本、瑞士和韩国的机构参与了一项国际努力，致力于从五颗不同的卫星获取地球静止图像。这些卫星有重叠的区域，可以检索两种不同的测量方法。该项目将开发综合全球可持续发展产品，以说明不同检索之间的差异，并使用概率量化所得可持续发展值的不确定性。该项目将利用两名专业人员的专门知识，开发最先进的方法，以捕捉从地球静止卫星星座观测到的气候变量的时空变异性，如云的特征、风速和风向、积雪和降水等。该项目将通过基于模型的方法促进地质统计方法，以分析、内插和推断多元空间场，其特点是在连续空间中索引的非平稳空间过程。可扩展性是通过局部邻域调节结构实现的。计算将快速并行地进行，通过层次结构满足全局一致性。所提出的方法的基于模型的性质，可以考虑复杂的观测误差，执行非齐次多元空间回归，并结合联合收割机在不同层次的空间聚集获得的意见。扩展到时空模型无缝地获得纳入一个简约的层次模型的时空相互作用。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Land surface albedo is the fraction of incoming solar radiation reflected by the land surface. It is an essential climate variable as identified by the Global Climate Observing System. An international effort involving institutions from the USA, European Union, Japan, Switzerland and Korea is dedicated to obtaining geostationary images from five different satellites. Those satellites have overlapping areas for which two different measures of albedo are retrieved. The project will develop blended global albedo products that account for the discrepancies between the different retrievals and quantify the uncertainty in the resulting albedo values using probabilities. The project will leverage the expertise of two PIs to develop state of the art methods that can capture the variability in time and space of climate variables that are observed from a constellation of geostationary satellites, like cloud characteristics, wind speed and direction, snow cover, and precipitation, among others. The project will contribute geostatistical methods with a model-based approach to analyze, interpolate and make inferences for a multivariate spatial field, featuring a non-stationary spatial process, indexed in a continuous space. The scalability is achieved thanks to a local neighborhood conditioning structure. Computations will be performed fast and concurrently, satisfying global coherence through a hierarchical structure. The model-based nature of the proposed approach allows to account for complex observational errors, perform non-homogeneous multivariate spatial regression, and combine observations obtained at different levels of spatial aggregation. Extensions to spatio-temporal models are seamlessly obtained incorporating space-time interactions within a parsimonious hierarchical model.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.