Climate Multi-model Regression Using Spatial Smoothing
Climate Multi-model Regression Using Spatial Smoothing
复制标题
使用空间平滑的气候多模型回归
DOI：
10.1137/1.9781611972832.36
复制
发表时间：
2013
影响因子：
5.5
通讯作者：
A. Banerjee
中科院分区：
文献类型：
--
作者：
Karthik Subbian;A. Banerjee
有几个全球气候模式（GCM）由不同的国家向政府间气候变化专门委员会（IPCC）报告。由于大气环流模型假设的不同性质，大气环流模型的未来预测显示出很大的可变性，这使得很难对未来做出有信心的预测。气候科学家联合收割机将这些多个GCM模型结合起来，以最大限度地减少变异性和预测误差。这些模型组合中的大多数是专门针对某个位置或全球范围的。它们不考虑区域或局部平滑（包括IPCC模型）。在本文中，我们解决这个问题，结合多个GCM模型的输出与空间平滑作为一个重要的期望标准。该问题的制定采取的形式，多个最小二乘回归的每个地理位置与图形拉普拉斯平滑之间的相邻位置。与现有的拉普拉斯回归框架不同，我们的公式具有系数矩阵的内积和外积，并将西尔维斯特方程作为其特例。我们讨论了几种方法来解决这个问题，包括通过求解一个大型线性系统的封闭形式，以及梯度下降法，这是更有效的。我们建立的优越性，我们的方法在模型的精度和平滑相比，几个流行的基线真实的GCM气候数据集。
There are several Global Climate Models (GCM) reported by various countries to the Intergovernmental Panel on Climate Change (IPCC). Due to the varied nature of the GCM model assumptions, the future projections of the GCMs show high variability which makes it difficult to come up with confident projections into the future. Climate scientists combine these multiple GCM models to minimize the variability and the prediction error. Most of these model combinations are specifically for a location, or at a global scale. They do not consider regional or local smoothing (including the IPCC model). In this paper, we address this problem of combining multiple GCM model outputs with spatial smoothing as an important desired criterion. The problem formulation takes the form of multiple least squares regression for each geographic location with graph Laplacian based smoothing amongst the neighboring locations. Unlike the existing Laplacian regression frameworks, our formulation has both inner and outer products of the coefficient matrix, and has Sylvester equations as its special case. We discuss a few approaches to solve the problem, including a closed-form by solving a large linear system, as well as gradient descent methods which turn out to be more efficient. We establish the superiority of our approach in terms of model accuracy and smoothing compared to several popular baselines on real GCM climate datasets.