Tag: bertino

Hybrid covariance super-resolution data assimilation

Barthélémy, S., Counillon, F., Brajard, J., Bertino, L. 2024: Hybrid covariance super-resolution data assimilation. Ocean Dynamics. https://doi.org/10.1007/s10236-024-01643-6

Summary: The super-resolution data assimilation (SRDA) enhances a low-resolution (LR) model with a Neural Network (NN) that has learned the differences between high and low-resolution models offline and performs data assimilation in high-resolution (HR). The method enhances the accuracy of the EnKF-LR system for a minor computational overhead. However, performance quickly saturates when the ensemble size is increased due to the error introduced by the NN. We therefore combine the SRDA with the mixed-resolution data assimilation method (MRDA) into a method called “Hybrid covariance super-resolution data assimilation” (Hybrid SRDA). The forecast step runs an ensemble at two resolutions (high and low). The assimilation is done in the HR space by performing super-resolution on the LR members with the NN. The assimilation uses the hybrid covariance that combines the emulated and dynamical HR members. The scheme is extensively tested with a quasi-geostrophic model in twin experiments, with the LR grid being twice coarser than the HR. The Hybrid SRDA outperforms the SRDA, the MRDA, and the EnKF-HR at a given computational cost. The benefit is the largest compared to the EnKF-HR for small ensembles. However, even with larger computational resources, using a mix of high and low-resolution members is worth it. Besides, the Hybrid SRDA, the EnKF-HR, and the SRDA, unlike the MRDA, prevent the smoothing of dynamical structures of the background error covariance matrix. The Hybrid SRDA method is also attractive because it is customizable to available resources.

Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Earth System Reanalysis in Support of Climate Model Improvements

Stammer, D., Amrhein, D.E., Alonso Balmaseda, M., Bertino, L., Bonavita, M., Buontempo, C., Caltabiano, N., Counillon, F., Fenty, I., Ferrari, R., Fujii, Y., et al. 2024: Earth System Reanalysis in Support of Climate Model Improvements. Bull. Amer. Meteor. Soc.. https://doi.org/10.1175/BAMS-D-24-0110.1

Summary: A 3-day workshop took place from 12 to 14 June 2023, at the Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, focusing on data assimilation (DA) and machine learning (ML) in the context of Earth system reanalysis and climate model improvements.
The workshop, organized 25 years after the inception of the Estimating the Circulation and Climate of the Ocean (ECCO), was an effort to lay out the roadmap for future development of DA in support of climate modeling and climate knowledge improvements, or “climate DA.” The following is a summary of the workshop outcomes and recommendations arising to move the field of DA forward in the context of climate modeling.
Recent climate model developments, established through increased model resolution, have led to substantial improvements in model simulations of the time-evolving, coupled Earth system and its subcomponents. However, regardless of resolution, climate models will always produce climate features and variability that differ from the real world and will be prone to biases. This is due to many remaining uncertainties, such as in parametric and structural model uncertainty, in the initial conditions prescribed, and in the prescribed (scenario) forcing which varies on decadal to centennial time scales.
Further model improvements are expected to arise specifically from the improved representation of physical processes realized through model–data fusion. This will create an unprecedented opportunity to better exploit a large array of Earth observations, from in situ measurements to weather radars and satellite observations, as the resolved scales of the models approach those of the observations. For this, climate DA will be the central tool to bring models and observations into consistency, by improving initial conditions, inferring uncertain model parameters and structure, and quantifying uncertainty. Generally, there will be advantages and complementarities of adjoint-based smoother approaches, ensemble-based filter approaches, or new ML-inspired approaches. Yet the ever-increasing model resolution will present growing challenges arising from computational cost, calling for new ways of performing data assimilation and model optimization. Using the complementarity in a hybrid approach, blending tools and concepts from variational, ensemble, and ML methods might be what is required in the future. In this context, ML could be important to handle nonlinear responses and to better approximate non-Gaussian distributions.

Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Super-resolution data assimilation

Barthélémy, S., Brajard, J., Bertino, L., Counillon, F. 2022: Super-resolution data assimilation. Ocean Dyn. https://doi.org/10.1007/s10236-022-01523-x

Summary: Increasing model resolution can improve the performance of a data assimilation system because it reduces model error, the system can more optimally use high-resolution observations, and with an ensemble data assimilation method the forecast error covariances are improved. However, increasing the resolution scales with a cubical increase of the computational costs. A method that can more effectively improve performance is introduced here. The novel approach called “Super-resolution data assimilation” (SRDA) is inspired from super-resolution image processing techniques and brought to the data assimilation context. Starting from a low-resolution forecast, a neural network (NN) emulates the fields to high-resolution, assimilates high-resolution observations, and scales it back up to the original resolution for running the next model step. The SRDA is tested with a quasi-geostrophic model in an idealized twin experiment for configurations where the model resolution is twice and four times lower than the reference solution from which pseudo-observations are extracted. The assimilation is performed with an Ensemble Kalman Filter. We show that SRDA outperforms both the low-resolution data assimilation approach and a version of SRDA with cubic spline interpolation instead of NN. The NN’s ability to anticipate the systematic differences between low- and high-resolution model dynamics explains the enhanced performance, in particular by correcting the difference of propagation speed of eddies. With a 25-member ensemble at low resolution, the SRDA computational overhead is 55 percent and the errors reduce by 40 percent, making the performance very close to that of the high-resolution system (52 percent of error reduction) that increases the cost by 800 percent. The reliability of the ensemble system is not degraded by SRDA.

Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Predicting Arctic sea ice for the marine transport sector

Image credit: Copernicus Marine Service

Check out this nice article by Dr. Ellen Viste at the Bjerknes Centre for Climate Research, on the evaluation of sea ice models and how far we are to being able to provide reliable, near-term sea-ice predictions:

Read the article: Predicting Arctic sea ice.

In it, we hear from Tarkan Bilge, our BCPU data manager, and his recent paper on sea ice thickness forecasts to support Arctic marine transport, together with other collaborating scientists at our partner, NERSC, among others.

Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model.

Brajard, J., Carrassi, A., Bocquet, M., Bertino, L. 2020: Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model. Geoscientific Model Development. https://doi.org/10.1016/j.jocs.2020.101171 .

Summary: A novel method, based on the combination of data assimilation and machine learning is introduced. The new hybrid approach is designed for a two-fold scope: (i) emulating hidden, possibly chaotic, dynamics and (ii) predicting their future states. The method consists in applying iteratively a data assimilation step, here an ensemble Kalman filter, and a neural network. Data assimilation is used to optimally combine a surrogate model with sparse noisy data. The output analysis is spatially complete and is used as a training set by the neural network to update the surrogate model. The two steps are then repeated iteratively. Numerical experiments have been carried out using the chaotic 40-variables Lorenz 96 model, proving both convergence and statistical skill of the proposed hybrid approach. The surrogate model shows short-term forecast skill up to two Lyapunov times, the retrieval of positive Lyapunov exponents as well as the more energetic frequencies of the power density spectrum. The sensitivity of the method to critical setup parameters is also presented: the forecast skill decreases smoothly with increased observational noise but drops abruptly if less than half of the model domain is observed. The successful synergy between data assimilation and machine learning, proven here with a low-dimensional system, encourages further investigation of such hybrids with more sophisticated dynamics.

Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment.

Shah, A., Bertino, L., Counillon, C., El Gharamti, M., Xie, J. 2019: Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment. Tellus A: Dynamic Meteorology and Oceanography. https://doi.org/10.1080/16000870.2019.1697166

Summary: A newly introduced stochastic data assimilation method, the Ensemble Kalman Filter Semi-Qualitative (EnKF-SQ) is applied to a realistic coupled ice-ocean model of the Arctic, the TOPAZ4 configuration, in a twin experiment framework. The method is shown to add value to range-limited thin ice thickness measurements, as obtained from passive microwave remote sensing, with respect to more trivial solutions like neglecting the out-of-range values or assimilating climatology instead. Some known properties inherent to the EnKF-SQ are evaluated: the tendency to draw the solution closer to the thickness threshold, the skewness of the resulting analysis ensemble and the potential appearance of outliers. The experiments show that none of these properties prove deleterious in light of the other sub-optimal characters of the sea ice data assimilation system used here (non-linearities, non-Gaussian variables, lack of strong coupling). The EnKF-SQ has a single tuning parameter that is adjusted for best performance of the system at hand. The sensitivity tests reveal that the tuning parameter does not critically influence the results. The EnKF-SQ makes overall a valid approach for assimilating semi-qualitative observations into high-dimensional nonlinear systems.

Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.