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A Review - JuSER - Forschungszentrum Jülich

Sensors 2012, 12 16292

(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.

Keywords: data assimilation; multiscale; multivariate; modeling; Ensemble Kalman Filter;

Particle Filter; variational methods

1. Introduction

The basic idea behind data assimilation (DA) is to combine complementary information from

measurements and models of the Earth system and thus optimally estimate geophysical fields of

interest [1]. It allows model simulations to be updated with observation data, for example in the carbon

cycle [2], plant phenology [3] or hydrologic remote sensing [4]. The theory of DA in the Earth

sciences rests on the mathematical framework of estimation theory [1,5]. More advanced DA methods

also provide a framework for incorporating model errors and for quantifying prediction uncertainties [6]

or updating model parameters [7].

In the context of climate change and land-use change, more and more terrestrial observational

networks are being established to monitor states and fluxes in an effort to understand water, energy, or

matter fluxes, as well as their biological and physical drivers and interactions with and within the

terrestrial system. Examples of these networks include the global FLUXNET [8], the US Soil Climate

Analysis Network (SCAN) [9], the US Snowpack Telemetry Network (SNOTEL) [10], the European

Integrated Carbon Observation System (ICOS), and the German Terrestrial Environmental

Observatories (TERENO) [11]. Within these networks, a huge amount of data from different sensors is

recorded on different temporal and spatial scales. Moreover, a large number of Earth observation

satellites have been launched, and products are being delivered for use in terrestrial models. Examples

are the leaf area index (LAI), the fraction of absorbed photosynthetic active radiation (FPAR) and the

land surface temperature (LST) retrieved by the Moderate Resolution Imaging Spectroradiometer

(MODIS) [12,13], the soil moisture retrieved by the Soil Moisture and Ocean Salinity (SMOS)

Mission [14], and the snow water equivalent as retrieved by the Advanced Microwave Scanning

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