Efficient nonlinear data assimilation using synchronisation in a particle filter

Current data assimilation methods still face problems in strongly nonlinear cases. A promising solution is a particle filter, which provides a representation of the state probability density function (pdf) by a discrete set of particles. To allow a particle filter to work in high-dimensional systems, the proposal density freedom is explored.We used a proposal density from synchronisation theory, in which one tries to synchronise the model with the true evolution of a system using one-way coupling, via the observations. This is done by adding an extra term to the model equations that will control the growth of instabilities transversal to the synchronisation manifold. In this paper, an efficient ensemble-based synchronisation scheme is used as a proposal density in the implicit equal-weights particle filter, a particle filter that avoids filter degeneracy by construction. Tests using the Lorenz96 model for a 1000-dimensional system show successful results, where particles efficiently follow the truth, both for observed and unobserved variables. These first test show that the new method is comparable to and slightly outperforms a well-tuned Local Ensemble Transform Kalman Filter. This methodology is a promising solution for high-dimensional nonlinear problems in the geosciences, such as numerical weather prediction

Short Course SC1.1: Data Assimilation in the Geosciences - Practical Data Assimilation with the Parallel Data Assimilation Framework

Data assimilation combines observational data with a numerical model. It is commonly used in numerical weather prediction, but is't also applied also in oceanography and hydrology. The integrating observations with models in a quantitative way, data assimilation allows to estimate and improve the model states, e.g. to initialize model forecasts. Also it can estimate parameters that control processes in the model or fluxes, which can be difficult and even impossible to measure. As such data assimilation can use observations to provide information about unobservable quantities if the model represents those. The combination of model and observation requires to have error estimates of both information sources. In ensemble data assimilation the error in the model state is estimated by an ensemble of model state realizations. This ensemble not only provides estimates of uncertainties, but also of cross-correlations between different model variables or parameters. The ensemble information is then used by the assimilation method, whose most widely known is the ensemble Kalman filter. To simplify the implementation and use of ensemble data assimilation, the Parallel Data Assimilation Framework - PDAF - has been developed. PDAF is a freely-available open-source software (http://pdaf.awi.de) that provides ensemble-based data assimilation methods like the ensemble Kalman filter, but also support to perform ensemble simulations. PDAF is designed so that it can be used from small toy problems running on notebook computers up to high-dimensional Earth Systems models running on supercomputers. This short course aims at geoscientists who have a modeling application or observations and are interested in applying data assimilation, but haven't found a starting point yet. The course will first provide an introduction to the ensemble data assimilation methodology. Then, it will explain the implementation concept of PDAF and finally provide a hands-on example of building a data assimilation system based on a numerical model. This practical introduction will prepare the participants to build a data assimiliton system by combining their numerical model with PDAF, hence providing a quick starting point for apply the ensemble data assimilation

Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication

The beginning of the 21st century is marked by a rapid growth of land surface satellite data and model sophistication. This offers new opportunities to estimate multiple components of the water cycle via satellite-based land data assimilation (DA) across multiple scales. By resolving more processes in land surface models and by coupling the land, the atmosphere, and other Earth system compartments, the observed information can be propagated to constrain additional unobserved variables. Furthermore, access to more satellite observations enables the direct constraint of more and more components of the water cycle that are of interest to end users. However, the finer level of detail in models and data is also often accompanied by an increase in dimensions, with more state variables, parameters, or boundary conditions to estimate, and more observations to assimilate. This requires advanced DA methods and efficient solutions. One solution is to target specific observations for assimilation based on a sensitivity study or coupling strength analysis, because not all observations are equally effective in improving subsequent forecasts of hydrological variables, weather, agricultural production, or hazards through DA. This paper offers a perspective on current and future land DA development, and suggestions to optimally exploit advances in observing and modeling systems

Effects of species selection and management on forest canopy albedo

International audienceForest management is considered to be one of the key instruments available to mitigate climate change as it can lead to increased sequestration of atmospheric carbon dioxide. However, the changes in canopy albedo may neutralise or offset the climate benefits of carbon sequestration. Although there is an emerging body of literature linking canopy albedo to management, understanding is still fragmented. We make use of a generally applicable approach: we combine a stand-level forest gap model with a canopy radiation transfer model and satellite-derived model parameters to quantify the effects of forest management on canopy albedo for different forest species and management strategies. We find that the most intensive management measures lead to largest albedo change. The choice of species in combination with thinning dominates the variation in canopy albedo. In addition, the canopy albedo of forest stands changes with the latitude, i.e. forest stands with similar structure have different albedo depending on the latitude. The structural changes associated with forest management can be described by change in LAI in combination with crown volume. However, not only the removal of trees but also the type of understorey affects the canopy albedo. The lower the canopy cover, the larger the background albedo contributes to the canopy albedo. In summary, forest albedo is strongly altered by humans

Presentation and discussion of the high-resolution atmosphere–land-surface–subsurface simulation dataset of the simulated Neckar catchment for the period 2007–2015

Coupled numerical models, which simulate water and energy fluxes in the subsurface–land-surface–atmosphere system in a physically consistent way, are a prerequisite for the analysis and a better understanding of heat and matter exchange fluxes at compartmental boundaries and interdependencies of states across these boundaries. Complete state evolutions generated by such models may be regarded as a proxy of the real world, provided they are run at sufficiently high resolution and incorporate the most important processes. Such a simulated reality can be used to test hypotheses on the functioning of the coupled terrestrial system. Coupled simulation systems, however, face severe problems caused by the vastly different scales of the processes acting in and between the compartments of the terrestrial system, which also hinders comprehensive tests of their realism. We used the Terrestrial Systems Modeling Platform (TerrSysMP), which couples the meteorological Consortium for Small-scale Modeling (COSMO) model, the land-surface Community Land Model (CLM), and the subsurface ParFlow model, to generate a simulated catchment for a regional terrestrial system mimicking the Neckar catchment in southwest Germany, the virtual Neckar catchment. Simulations for this catchment are made for the period 2007–2015 and at a spatial resolution of 400 m for the land surface and subsurface and 1.1 km for the atmosphere. Among a discussion of modeling challenges, the model performance is evaluated based on observations covering several variables of the water cycle. We find that the simulated catchment behaves in many aspects quite close to observations of the real Neckar catchment, e.g., concerning atmospheric boundary-layer height, precipitation, and runoff. But also discrepancies become apparent, both in the ability of the model to correctly simulate some processes which still need improvement, such as overland flow, and in the realism of some observation operators like the satellite-based soil moisture sensors. The whole raw dataset is available for interested users. The dataset described here is available via the CERA database (Schalge et al., 2020): https://doi.org/10.26050/WDCC/Neckar_VCS_v1