Data assimilation

Data assimilation is a mathematical discipline that seeks to optimally combine theory (usually in the form of a numerical model) with observations. There may be a number of different goals sought – for example, to determine the optimal state estimate of a system, to determine initial conditions for a numerical forecast model, to interpolate sparse observation data using (e.g. physical) knowledge of the system being observed, to set numerical parameters based on training a model from observed data. Depending on the goal, different solution methods may be used. Data assimilation is distinguished from other forms of machine learning, image analysis, and statistical methods in that it utilizes a dynamical model of the system being analyzed. Data assimilation initially developed in the field of numerical weather prediction. Numerical weather prediction models are equations describing the dynamical behavior of the atmosphere, typically coded into a computer program. In order to use these mo

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Numerical weather prediction

Numerical weather prediction (NWP) uses mathematical models of the atmosphere and oceans to predict the weather based on current weather conditions. Though first attempted in the 1920s, it was not

Weather forecasting

Weather forecasting is the application of science and technology to predict the conditions of the atmosphere for a given location and time. People have attempted to predict the weather informally fo

National Weather Service

The National Weather Service (NWS) is an agency of the United States federal government that is tasked with providing weather forecasts, warnings of hazardous weather, and other weather-related prod

Coupled inverse modeling of a controlled irrigation experiment using multiple hydro-geophysical data

Damiano Pasetto, Matteo Rossi

Geophysical surveys can provide useful, albeit indirect, information on vadose zone processes. However, the ability to provide a quantitative description of the subsurface hydrological phenomena requires to fully integrate geophysical data into hydrological modeling. Here, we describe a controlled infiltration experiment that was monitored using both electrical resistivity tomography (ERT) and ground-penetrating radar (GPR). The experimental site has a simple, well-characterized subsoil structure: the vadose zone is composed of aeolic sand with largely homogeneous and isotropic properties. In order to estimate the unknown soil hydraulic conductivity, we apply a data assimilation technique based on a sequential importance resampling (SIR) approach. The SIR approach allows a simple assimilation of either or both geophysical datasets taking into account the associated measurement uncertainties. We demonstrate that, compared to a simpler, uncoupled hydro-geophysical approach, the coupled data assimilation process provides a more reliable parameter estimation and better reproduces the evolution of the infiltrating water plume. The coupled procedure is indeed much superior to the uncoupled approach that suffers from the artifacts of the geophysical inversion step and produces severe mass balance errors. The combined assimilation of GPR and ERT data is then investigated, highlighting strengths and weaknesses of the two datasets. In the case at hand GPR energy propagates in form of a guided wave that, over time, shows different energy distribution between propagation modes as a consequence of the evolving thickness of the wet layer. We found that the GPR inversion procedure may produce estimates on the depth of the infiltrating front that are not as informative as the ERT dataset.

2015

A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1

Damien Bouffard, Camille Roland Marie Minaudo, Firat Ozdemir, Cintia Luz Ramon Casanas

We present a Bayesian inference for a three-dimensional hydrodynamic model of Lake Geneva with stochastic weather forcing and high-frequency observational datasets. This is achieved by coupling a Bayesian inference package, SPUX, with a hydrodynamics package, MITgcm, into a single framework, SPUX-MITgcm. To mitigate uncertainty in the atmospheric forcing, we use a smoothed particle Markov chain Monte Carlo method, where the intermediate model state posteriors are resampled in accordance with their respective observational likelihoods. To improve the uncertainty quantification in the particle filter, we develop a bi-directional long short-term memory (BiLSTM) neural network to estimate lake skin temperature from a history of hydrodynamic bulk temperature predictions and atmospheric data. This study analyzes the benefit and costs of such a state-of-the-art computationally expensive calibration and assimilation method for lakes.

COPERNICUS GESELLSCHAFT MBH2022

Assimilation of POLDER aerosol optical thickness into the LMDz-INCA model: Implications for the Arctic aerosol burden

Isabelle Bey, Sylvia Generoso

The large spatial and temporal variability of atmospheric aerosol load makes it a challenge to quantify aerosol effect on climate. This study is one of the first attempts to apply data assimilation for the analysis of global aerosol distribution. Aerosol optical thickness (AOT) observed from the Polarization and Directionality of the Earth Reflectances (POLDER) spaceborne instrument are assimilated into a three-dimensional chemistry model. POLDER capabilities to distinguish between fine and coarse AOT are used to constrain them separately in the model. Observation and model errors are a key component of such a system and are carefully estimated on a regional basis using some of the high-quality surface observations from the Aerosol Robotic Network (AERONET). Other AERONET data provide an independent evaluation of the a posteriori fields. Results for the fine mode show improvements, in terms of reduction of root-mean-square errors, in most regions with the largest improvements found in the Mediterranean Sea and Eurasia. We emphasize the results for the Arctic, where there is growing evidence of a strong aerosol impact on climate, but a lack of regional and continuous aerosol monitoring. The a posteriori fields noticeably well reproduce the winter-spring “Arctic Haze” peak measured in Longyearbyen (15°E, 78°N) and typical seasonal variations in the Arctic region, where AOT increase by up to a factor of three between a posteriori and a priori. Enhanced AOT are found over a longer period in spring 2003 than in 1997, suggesting that the large Russian fires in 2003 have influenced the Arctic aerosol load.

2007

CIVIL-410: Fluvial hydraulics and river training works

Le cours donne aux étudiants des solides connaissances théoriques en hydraulique fluviale, et enseigne les bases de l'ingénierie fluviale dans le but de concilier la protection contre les crues et la protection de l'environnement. Le cours est illustré par des projets concrets.

CIVIL-441: Water ressources management

L'ensemble des problématiques liées à l'eau est présenté afin d'avoir une vue globale de la gestion et de l'économie de cet élément essentiel à toute vie et toute activité. L'analyse des ressources et des besoins est présentée ainsi que les outils d'optimisation et d'évaluation économique.