Forschungszentrum Jülich GmbH, Institute of Bio- and Geosciences: Agrosphere, Jülich 52425, Germany.
Sensors (Basel). 2012 Nov 26;12(12):16291-333. doi: 10.3390/s121216291.
More and more terrestrial observational networks are being established to monitor climatic, hydrological and land-use changes in different regions of the World. In these networks, time series of states and fluxes are recorded in an automated manner, often with a high temporal resolution. These data are important for the understanding of water, energy, and/or matter fluxes, as well as their biological and physical drivers and interactions with and within the terrestrial system. Similarly, the number and accuracy of variables, which can be observed by spaceborne sensors, are increasing. Data assimilation (DA) methods utilize these observations in terrestrial models in order to increase process knowledge as well as to improve forecasts for the system being studied. The widely implemented automation in observing environmental states and fluxes makes an operational computation more and more feasible, and it opens the perspective of short-time forecasts of the state of terrestrial systems. In this paper, we review the state of the art with respect to DA focusing on the joint assimilation of observational data precedents from different spatial scales and different data types. An introduction is given to different DA methods, such as the Ensemble Kalman Filter (EnKF), Particle Filter (PF) and variational methods (3/4D-VAR). In this review, we distinguish between four major DA approaches: (1) univariate single-scale DA (UVSS), which is the approach used in the majority of published DA applications, (2) univariate multiscale DA (UVMS) referring to a methodology which acknowledges that at least some of the assimilated data are measured at a different scale than the computational grid scale, (3) multivariate single-scale DA (MVSS) dealing with the assimilation of at least two different data types, and (4) combined multivariate multiscale DA (MVMS). Finally, we conclude with a discussion on the advantages and disadvantages of the assimilation of multiple data types in a simulation model. Existing approaches can be used to simultaneously update several model states and model parameters if applicable. In other words, the basic principles for multivariate data assimilation are already available. We argue that a better understanding of the measurement errors for different observation types, improved estimates of observation bias and improved multiscale assimilation methods for data which scale nonlinearly is important to properly weight them in multiscale multivariate data assimilation. In this context, improved cross-validation of different data types, and increased ground truth verification of remote sensing products are required.
越来越多的陆地观测网络正在建立，以监测世界不同地区的气候、水文和土地利用变化。在这些网络中，状态和通量的时间序列以自动化的方式记录，通常具有较高的时间分辨率。这些数据对于理解水、能量和/或物质通量及其与陆地系统的相互作用以及与陆地系统的相互作用的生物和物理驱动因素非常重要。同样，可由星载传感器观测到的变量的数量和精度也在增加。数据同化（DA）方法利用这些观测值在陆地模型中，以增加对过程的了解，并提高对所研究系统的预测。观测环境状态和通量的广泛自动化使得操作计算变得越来越可行，并为陆地系统的短期预测开辟了前景。在本文中，我们回顾了 DA 的最新技术，重点是从不同空间尺度和不同数据类型联合同化观测数据先例。介绍了不同的 DA 方法，如集合卡尔曼滤波（EnKF）、粒子滤波（PF）和变分方法（3/4D-VAR）。在本综述中，我们将 DA 方法分为四大类：（1）单变量单尺度 DA（UVSS），这是大多数已发表的 DA 应用中使用的方法；（2）单变量多尺度 DA（UVMS），指的是一种方法，该方法承认至少有一些同化数据是在与计算网格尺度不同的尺度上测量的；（3）多变量单尺度 DA（MVSS），涉及同化至少两种不同的数据类型；（4）多变量多尺度联合 DA（MVMS）。最后，我们讨论了在模拟模型中同化多种数据类型的优缺点。如果适用，现有方法可用于同时更新几个模型状态和模型参数。换句话说，多变量数据同化的基本原理已经存在。我们认为，重要的是更好地了解不同观测类型的测量误差，改进对观测偏差的估计，以及改进非线性标度数据的多尺度同化方法，以便在多尺度多变量数据同化中对其进行适当加权。在这种情况下，需要改进不同数据类型的交叉验证，并增加对遥感产品的实地验证。
