Correction of upstream flow and hydraulic state with data assimilation in the context of flood forecasting

The present study describes the assimilation of river water level observations and the resulting improvement in flood forecasting. The Kalman Filter algorithm was built on top of a one-dimensional hydraulic model which describes the Saint-Venant equations. The assimilation algorithm folds in two steps: the first one was based on the assumption that the upstream flow can be adjusted using a three-parameter correction; the second one consisted of directly correcting the hydraulic state. This procedure was applied using a four- day sliding window over the flood event. The background error covariances for water level and discharge were repre- sented with anisotropic correlation functions where the cor- relation length upstream of the observation points is larger than the correlation length downstream of the observation points. This approach was motivated by the implementation of a Kalman Filter algorithm on top of a diffusive flood wave propagation model. The study was carried out on the Adour and the Marne Vallage (France) catchments. The correction of the upstream flow as well as the control of the hydraulic state during the flood event leads to a significant improve- ment in the water level and discharge in both analysis and forecast modes

Estimation of lateral inflows using data assimilation in the context of real-time flood forecasting for the Marne catchment in France

Water Qualit

Benchmarking Regridding Libraries Used in Earth System Modelling

Components of Earth system models (ESMs) usually use different numerical grids because of the different environments they represent. Therefore, a coupling field sent by a source model has to be regridded to be used by a target model. The regridding has to be accurate and, in some cases, conservative, in order to ensure the consistency of the coupled model. Here, we present work done to benchmark the quality of four regridding libraries currently used in ESMs, i.e., SCRIP, YAC, ESMF and XIOS. We evaluated five regridding algorithms with four different analytical functions for different combinations of six grids used in real ocean or atmosphere models. Four analytical functions were used to define the coupling fields to be regridded. This benchmark calculated some of the metrics proposed by the CANGA project, including the mean, maximum, RMS misfit, and global conservation. The results show that, besides a few very specific cases that present anomalous values, the regridding functionality in YAC, ESMF and XIOS can be considered of high quality and do not present the specific problems observed for the conservative SCRIP remapping. The evaluation of the computing performance of those libraries is not included in the current work but is planned to be performed in the coming months. This exercise shows that benchmarking can be a great opportunity to favour interactions between users and developers of regridding libraries

Multithreaded or thread safe OASIS version including performance optimisations to adapt to many‐core architectures (D2.3)

<p><strong>Abstract</strong></p> <p>The developments realised in OASIS3‐MCT to improve its parallel efficiency are detailed. These will be available in the next release OASIS3‐MCT_4.0 planned for spring 2018. The most important improvements concern the communication scheme and the hybrid MPI+OpenMP parallelisation of theSpherical Coordinate Remapping and Interpolation Package (SCRIP) library. The new communication method, which can now use the mapping weights to define the intermediate mapping decomposition, takes longer to initialise but offers significant gain at run time, especially for high‐resolution cases running on a high number of tasks. The parallelisation introduced in the SCRIP library for the mapping weight calculation allows a reduction in the weight calculation time of 2 to 3 orders of magnitude for high‐resolution grids. Also, significant gains are obtained in the initialisation phase by updating the MCT library from version 2.8 to 2.10.beta1 and additional debugging. New methods introduced in the CONSERV post‐processing operation ensuring the global conservation of the coupling fields lower the calculation costs by one order of magnitude while still ensuring good level of reproducibility. Finally, additional results obtained with IS‐ENES2 coupling technology benchmarks show that OASIS3‐MCT performs as well as, and even better at very high number of cores, than other coupling technologies and<br> that its behaviour on Marconi KNL is fully satisfactory.</p> <p><strong>About this document</strong></p> <ul> <li>Work package in charge: WP2 Scalability</li> <li>Actual delivery date for this deliverable: 28 March 2018</li> <li>Dissemination level: PU (for public use)</li> <li>Lead author: Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS): Sophie Valcke</li> <li>Other contributing authors: Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS): Laure Coquart, Anthony Craig, Gabriel Jonville, Eric Maisonnave, Andrea Piacentini</li> <li>Internal reviewers: Sveriges Meteorologiska och Hydrologiska Institut (SMHI): Uwe Fladrich,  BULL/ATOS (BULL): David Guibert, Erwan Raffin, Deutsches Klimarechenzentrum (DKRZ): Philipp Neumann</li> </ul

Ensemble-based algorithm for error reduction in hydraulics in the context of flood forecasting

Over the last few years, a collaborative work between CERFACS, LNHE (EDF R&D), SCHAPI and CE-REMA resulted in the implementation of a Data Assimilation (DA) method on top of MASCARET in the framework of real-time forecasting. This prototype was based on a simplified Kalman filter where the description of the background error covariances is prescribed based on off-line climatology constant over time. This approach showed promising results on the Adour and Marne catchments as it improves the forecast skills of the hydraulic model using water level and discharge in-situ observations. An ensemble-based DA algorithm has recently been implemented to improve the modelling of the background error covariance matrix used to distribute the correction to the water level and discharge states when observations are assimilated from observation points to the entire state. It was demonstrated that the flow dependent description of the background error covariances with the EnKF algorithm leads to a more realistic correction of the hydraulic state with significant impact of the hydraulic network characteristic

Ensemble-based algorithm for error reduction in hydraulics in the context of flood forecasting

Over the last few years, a collaborative work between CERFACS, LNHE (EDF R&D), SCHAPI and CE-REMA resulted in the implementation of a Data Assimilation (DA) method on top of MASCARET in the framework of real-time forecasting. This prototype was based on a simplified Kalman filter where the description of the background error covariances is prescribed based on off-line climatology constant over time. This approach showed promising results on the Adour and Marne catchments as it improves the forecast skills of the hydraulic model using water level and discharge in-situ observations. An ensemble-based DA algorithm has recently been implemented to improve the modelling of the background error covariance matrix used to distribute the correction to the water level and discharge states when observations are assimilated from observation points to the entire state. It was demonstrated that the flow dependent description of the background error covariances with the EnKF algorithm leads to a more realistic correction of the hydraulic state with significant impact of the hydraulic network characteristic