NP5.2 | Inverse problems, Predictability, and Uncertainty Quantification in the Earth System using Data Assimilation and its combination with Machine Learning
EDI
Inverse problems, Predictability, and Uncertainty Quantification in the Earth System using Data Assimilation and its combination with Machine Learning
Co-organized by AS5/BG9/CL5/CR2/G3/HS13/OS4
Convener: Javier Amezcua | Co-conveners: Harrie-Jan Hendricks Franssen, Lars Nerger, Guannan HuECSECS, Olivier Talagrand, Natale Alberto Carrassi, Yvonne RuckstuhlECSECS
Orals
| Wed, 26 Apr, 16:15–18:00 (CEST)
 
Room -2.31
Posters on site
| Attendance Tue, 25 Apr, 14:00–15:45 (CEST)
 
Hall X4
Posters virtual
| Attendance Tue, 25 Apr, 14:00–15:45 (CEST)
 
vHall ESSI/GI/NP
Orals |
Wed, 16:15
Tue, 14:00
Tue, 14:00
Inverse Problems are encountered in many fields of geosciences. One class of inverse problems, in the context of predictability, is assimilation of observations in dynamical models of the system under study. Furthermore, objective quantification of the uncertainty during data assimilation, prediction and validation is the object of growing concern and interest.
This session will be devoted to the presentation and discussion of methods for inverse problems, data assimilation and associated uncertainty quantification throughout the Earth System like in ocean and atmosphere dynamics, atmospheric chemistry, hydrology, climate science, solid earth geophysics and, more generally, in all fields of geosciences.
We encourage presentations on advanced methods, and related mathematical developments, suitable for situations in which local linear and Gaussian hypotheses are not valid and/or for situations in which significant model or observation errors are present. Specific problems arise in situations where coupling is present between different components of the Earth system, which gives rise to the so called coupled data assimilation.
Of interest are also contributions on weakly and strongly coupled data assimilation - methodology and applications, including Numerical Prediction, Environmental forecasts, Earth system monitoring, reanalysis, etc., as well as coupled covariances and the added value of observations at the interfaces of coupled models.
We also welcome contributions dealing with algorithmic aspects and numerical implementation of the solution of inverse problems and quantification of the associated uncertainty, as well as novel methodologies at the crossroad between data assimilation and purely data-driven, machine-learning-type algorithms.

Orals: Wed, 26 Apr | Room -2.31

Chairpersons: Javier Amezcua, Lars Nerger
16:15–16:20
Novel uses of data assimilation and machine learning
16:20–16:30
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EGU23-9529
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solicited
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On-site presentation
Femke Vossepoel, Arundhuti Banerjee, Hamed Diab Montero, Meng Li, Celine Marsman, Rob Govers, and Ylona van Dinther

The highly nonlinear dynamics of earthquake sequences and the limited availability of stress observations near subsurface faults make it very difficult, if not impossible, to forecast earthquakes. Ensemble data-assimilation methods provide a means to estimate state variables and parameters of earthquake sequences that may lead to a better understanding of the associated fault-slip process and contribute to the forecastability of earthquakes. We illustrate the challenges of data assimilation in earthquake simulation with an overview of three studies, each with different objectives and experiments.

In the first study, by reconstructing a laboratory experiment with an advanced numerical simulator we perform synthetic twin experiments to test the performance of an ensemble Kalman Filter (EnKF) and its ability to reconstruct fault slip behaviour in 1D and 3D simulations. The data assimilation estimates and forecasts earthquakes, even when having highly uncertain observations of the stress field. In these experiments, we assume the friction parameters to be perfectly known, which is typically not the case in reality.

A bias in a friction parameter can cause a significant change in earthquake dynamics, which will complicate the application of data assimilation in realistic cases. The second study addresses how well state estimation and state-parameter estimation can account for friction-parameter bias. For this, we use a 0D model for earthquake recurrence with a particle filter with sequential importance resampling. This shows that in case of intermediate to large uncertainty in friction parameters, combined state-and-parameter estimation is critical to correctly estimate earthquake sequences. The study also highlights the advantage of a particle filter over an EnKF for this nonlinear system.

The post- and inter-seismic deformations following an earthquake are rather gradual and do not pose the same challenges for data assimilation as the deformation during an earthquake event. To estimate the model parameters of surface displacements during these phases, a third study illustrates the application of the Ensemble Smoother-Multiple Data Assimilation and the particle filter with actual GPS data of the Tohoku 2011 earthquake.

Based on the comparison of the various experiments, we discuss the choice of data-assimilation method and -approach in earthquake simulation and suggest directions for future research.

How to cite: Vossepoel, F., Banerjee, A., Diab Montero, H., Li, M., Marsman, C., Govers, R., and van Dinther, Y.: Nonlinear Data Assimilation for State and Parameter Estimation in Earthquake Simulation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9529, https://doi.org/10.5194/egusphere-egu23-9529, 2023.

16:30–16:40
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EGU23-14985
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ECS
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On-site presentation
Simon Lentz, Dr. Sebastian Brune, Dr. Christopher Kadow, and Prof. Dr. Johanna Baehr

Slowly varying ocean heat content is one of the most important variables when describing cli-
mate variability on interannual to decadal time scales. Since observation-based estimates of
ocean heat content require extensive observational coverage, incomplete observations are often
combined with numerical models via data assimilation to simulate the evolution of oceanic heat.
However, incomplete observations, particularly in the subsurface ocean, lead to large uncertain-
ties in the resulting model-based estimate. As an alternative approach, Kadow et al (2020) have
proven that artificial intelligence can successfully be utilized to reconstruct missing climate in-
formation for surface temperatures. In the following, we investigate the possibility to train their
three-dimensional convolutional neural network to reconstruct missing subsurface temperatures
to obtain ocean heat content estimates with a focus on the North Atlantic ocean.
The network is trained and tested to reconstruct a 16 member Ensemble Kalman Filter assimi-
lation ensemble constructed with the Max-Planck Institute Earth System Model for the period
from 1958 to 2020. Specifically, we examine whether the partial convolutional U-net represents
a valid alternative to the Ensemble Kalman Filter assimilation to estimate North Atlantic sub-
polar gyre ocean heat content.
The neural network is capable of reproducing the assimilation reduced to datapoints with ob-
servational coverages within its ensemble spread with a correlation coefficient of 0.93 over the
entire time period and of 0.99 over 2004 – 2020 (the Argo-Era). Additionally, the network is
able to reconstruct the observed ocean heat content directly from observations for 12 additional
months with a correlation of 0.97, essentially replacing the assimilation experiment by an extrap-
olation. When reconstructing the pre-Argo-Era, the network is only trained with assimilations
from the Argo-Era. The lower correlation in the resulting reconstruction indicates higher un-
certainties in the assimilation outside of its ensemble spread at times with low observational
density. These uncertainties are highlighted by inconsistencies in the assimilation’s represen-
tations of the North Atlantic Current at times and grid points without observations detected
by the neural network. Our results demonstrate that a neural network is not only capable of
reproducing the observed ocean heat content over the training period, but also before and after
making the neural network a suitable candidate to step-wise extend or replace data assimilation.

How to cite: Lentz, S., Brune, Dr. S., Kadow, Dr. C., and Baehr, P. Dr. J.: Reconstructing North Atlantic Ocean Heat Content Using Convolutional Neural Networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14985, https://doi.org/10.5194/egusphere-egu23-14985, 2023.

Mathematics and methods
16:40–16:50
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EGU23-8640
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On-site presentation
Michael Ghil, Eviatar Bach, and Dan Crisan

There is a history of simple error growth models designed to capture the key properties of error growth in operational numerical weather prediction models. We propose here such a scalar model that relies on the previous ones, but captures the effect of small scales on the error growth via additive noise in a nonlinear stochastic differential equation (SDE). We nondimensionalize the equation and study its behavior with respect to the error saturation value, the growth rate of small errors, and the magnitude of noise. We show that the addition of noise can change the curvature of the error growth curve. The SDE model seems to improve substantially the fit to operational error growth curves, compared to the deterministic counterparts.

How to cite: Ghil, M., Bach, E., and Crisan, D.: Forecast error growth: A stochastic differential equation model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8640, https://doi.org/10.5194/egusphere-egu23-8640, 2023.

16:50–17:00
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EGU23-7480
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ECS
|
Highlight
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On-site presentation
Francine Schevenhoven, Mao-Lin Shen, Noel Keenlyside, Jeffrey B. Weiss, and Gregory S. Duane

Instead of combining data from an ensemble of different models after the simulations are already performed, as in a standard multi-model ensemble, we let the models interact with each other during their simulation. This ensemble of interacting models is called a supermodel. By exchanging information, models can compensate for each other's errors before the errors grow and spread to other regions or variables. Effectively, we create a new dynamical system. The exchange between the models is frequent enough such that the models synchronize, in order to prevent loss of variance when the models are combined. In previous work, we experimented successfully with combining atmospheric models of intermediate complexity in the context of parametric error. Here we will show results of combining two different AGCMs, NorESM1-ATM and CESM1-ATM. The models have different horizontal and vertical resolutions. To combine states from the different grids, we convert the individual model states to a ‘common state space’ with interpolation techniques. The weighted superposition of different model states is called a ‘pseudo-observation’. The pseudo-observations are assimilated back into the individual models, after which the models continue their run. We apply recently developed methods to train the weights determining the superposition of the model states, in order to obtain a supermodel that will outperform the individual models and any weighted average of their outputs.

How to cite: Schevenhoven, F., Shen, M.-L., Keenlyside, N., Weiss, J. B., and Duane, G. S.: Supermodelling: synchronising models to further improve predictions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7480, https://doi.org/10.5194/egusphere-egu23-7480, 2023.

The role of observations
17:00–17:10
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EGU23-7719
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ECS
|
On-site presentation
Devon Francis, Alison Fowler, Amos Lawless, Stefano Migliorini, and John Eyre

Data assimilation theory relies on the assumption that the background, model, and observations are unbiased. However, this is often not the case and, if biases are left uncorrected, this can cause significant systematic errors in the analysis. When bias is only present in the observations, Variational Bias Correction (VarBC) can correct for observation bias, and when bias is only present in the model, Weak-Constraint 4D Variational Assimilation (WC4DVar) can correct for model bias. However, when both observation and model biases are present, it can be very difficult to understand how the different bias correction methods interact, and the role of anchor (unbiased) observations becomes crucial for providing a frame of reference from which the biases may be estimated. This work presents a systematic study of the properties of the network of anchor observations needed to disentangle between model and observation biases when correcting for one or both types of bias in 4DVar.

We extend the theory of VarBC and WC4DVar to include both biased and anchor observations, to find that the precision and timing of the anchor observations are important in reducing the contamination of model/observation bias in the correction of observation/model bias. We show that anchor observations have the biggest impact in reducing the contamination of bias when they are later in the assimilation window than the biased observations, as such, operational systems that rely on anchor observations that are earlier in the window will be more susceptible to the contamination of model and/or observation biases. We also compare the role of anchor observations when VarBC/WC4DVar/both are used in the presence of both observation and model biases. We find that the ability of VarBC to effectively correct for observation bias when model bias is present, is very dependent on precise anchor observations, whereas correcting model bias with WC4DVar or correcting for both biases performs reasonably well regardless of the precision of anchor observations (although more precise anchor observations reduces the bias in the state analysis compared with less precise anchor observations for all three cases). This demonstrates that, when it is not possible to use anchor observations, it may be better to correct for both observation and model biases, rather than relying on only one bias correction technique.

We demonstrate these results in a series of idealised numerical experiments that use the Lorenz 96 model as a simplified model of the atmosphere.

How to cite: Francis, D., Fowler, A., Lawless, A., Migliorini, S., and Eyre, J.: The role of anchor observations in disentangling observation and model bias corrections in 4DVar, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7719, https://doi.org/10.5194/egusphere-egu23-7719, 2023.

17:10–17:20
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EGU23-3011
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Highlight
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Virtual presentation
Lili Lei

All-sky radiance assimilation often has non-Gaussian observation error distributions, which can be exacerbated by high model spatial resolutions due to better resolved nonlinear physical processes. For ensemble Kalman filters, observation ensemble perturbations can be approximated by linearized observation operator (LinHx) that uses the observation operator Jacobian of ensemble mean rather than full observation operator (FullHx). The impact of observation operator on infrared radiance data assimilation is examined here by assimilating synthetic radiance observations from channel 1025 of GIIRS with increased model spatial resolutions from 7.5 km to 300 m. A tropical cyclone is used, while the findings are expected to be generally applied. Compared to FullHx, LinHx provides larger magnitudes of correlations and stronger corrections around observation locations, especially when all-sky radiances are assimilated at fine model resolutions. For assimilating clear-sky radiances with increasing model resolutions, LinHx has smaller errors and improved vortex intensity and structure than FullHx. But when all-sky radiances are assimilated, FullHx has advantages over LinHx. Thus for regimes with more linearity, LinHx provides stronger correlations and imposes more corrections than FullHx; but for regimes with more nonlinearity, LinHx provides detrimental non-Gaussian prior error distributions in observation space, unrealistic correlations and overestimated corrections, compared to FullHx.

How to cite: Lei, L.: Impacts of Observation Forward Operator on Infrared Radiance Data Assimilation with Fine Model Resolutions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3011, https://doi.org/10.5194/egusphere-egu23-3011, 2023.

Coupled data assimilation
17:20–17:30
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EGU23-16806
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solicited
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On-site presentation
Patricia de Rosnay, Phil browne, Eric de Boisséson, David Fairbairn, Sébastien Garrigues, Christoph Herbert, Kenta Ochi, Dinand Schepers, Pete Weston, and Hao Zuo

In this presentation we introduce coupled assimilation activities conducted in support of seamless Earth system approach developments for Numerical Weather Prediction and climate reanalysis at the European Centre for Medium-Range Weather Forecasts (ECMWF). For operational applications coupled assimilation requires to have reliable and timely access to observations in all the Earth system components and it relies on consistent acquisition and monitoring approaches across the components. We show recent and future infrastructure developments and implementations to support consistent observations acquisition and monitoring for land and ocean at ECMWF. We discuss challenges of surface sensitive observations assimilation and we show ongoing forward operator and coupling developments to enhance the exploitation of interface observations over land and ocean surfaces. We present plans to use new and future observation types from future observing systems such as the Copernicus Expansion missions.

How to cite: de Rosnay, P., browne, P., de Boisséson, E., Fairbairn, D., Garrigues, S., Herbert, C., Ochi, K., Schepers, D., Weston, P., and Zuo, H.: Coupled data assimilation for numerical weather prediction at ECMWF, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16806, https://doi.org/10.5194/egusphere-egu23-16806, 2023.

17:30–17:40
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EGU23-15189
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ECS
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On-site presentation
Qi Tang, Hugo Delottier, Oliver S. Schilling, Wolfgang Kurtz, and Philip Brunner

We developed an ensemble based data assimilation (DA) system for an integrated hydrological model to facilitate real-time operational simulations of water quantity and quality. The integrated surface and subsurface hydrologic model HydroGeoSphere (HGS) (Brunner & Simmons, 2012) which simulates surface water and variably saturated groundwater flow as well as solute transport, was coupled with the Parallel Data Assimilation Framework (PDAF) (Nerger et al., 2005). The developed DA system allows joint assimilation of multiple types of observations such as piezometric heads, streamflow, and tracer concentrations. By explicitly considering tracer and streamflow data we substantially expand the hydrologic information which can be used to constrain the simulations.    Both the model states and the parameters can be separately or jointly updated by the assimilation algorithm.  

A synthetic alluvial plain model set up by Delottier et al., (2022) was used as an example to test the performance of our DA system.  For flow simulations, piezometric head observations were assimilated, while for transport simulations, noble gas concentrations (222Rn, 37Ar, and 4He) were assimilated. Both model states (e.g., hydraulic head or noble gas concentrations) and parameters (e.g. hydraulic conductivities and porosity) are jointly updated by the DA. Results were evaluated by comparing the estimated model variables with independent observation data between the assimilation runs and the free run where no data assimilation was conducted. In a further evaluation step, a real-world, field scale model featuring realistic forcing functions and material properties was set up for a site in Switzerland and carried out for numerical simulations with the developed DA system. The synthetic and real-world examples demonstrate the significant potential in combing state of the art numerical models, data assimilation and novel tracer observations such as noble gases or Radon.

How to cite: Tang, Q., Delottier, H., Schilling, O. S., Kurtz, W., and Brunner, P.: A coupled data assimilation framework with an integrated surface and subsurface hydrological model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15189, https://doi.org/10.5194/egusphere-egu23-15189, 2023.

17:40–17:50
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EGU23-1095
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On-site presentation
Jean-Christophe Calvet, Bertrand Bonan, and Yiwen Xu

Land data assimilation aims to monitor the evolution of soil and vegetation variables. These variables are driven by climatic conditions and by anthropogenic factors such as agricultural practices. Monitoring terrestrial surfaces involves a number of variables of the soil-plant system such as land cover, snow, surface albedo, soil water content and leaf area index. These variables can be monitored by integrating satellite observations into models. This process is called data assimilation. Integrating observations into land surface models is particularly important in changing climate conditions because environmental conditions and trends never experienced before are emerging. Because data assimilation is able to weight the information coming from contrasting sources of information and to account for uncertainties, it can produce an analysis of terrestrial variables that is the best possible estimation. In this work, data assimilation is implemented at a global scale by regularly updating the model state variables of the ISBA land surface model within the SURFEX modelling platform: the LDAS-Monde sequential assimilation approach. Model-state variable analysis is done for initializing weather forecast atmospheric models. Weather forecast relies on observations to a large extent because of the chaotic nature of the atmosphere. Land variables are not chaotic per se but rapid and complex processes impacting the land carbon budget such as forest management (thinning, deforestation, ...), forest fires and agricultural practices are not easily predictable with a good temporal precision. They cannot be monitored without integrating observations as soon as they are available. We focus on the assimilation of leaf area index (LAI), using land surface temperature (LST) for verification. We show that (1) analyzing LAI together with root-zone soil moisture is needed to monitor the impact of irrigation and heat waves on the vegetation, (2) LAI can be forecasted after properly initializing ISBA. This paves the way to more interactive assimilation of land variables into numerical weather forecast and seasonal forecast models, as well as in atmospheric chemistry models.

 

How to cite: Calvet, J.-C., Bonan, B., and Xu, Y.: Recent offline land data assimilation results and future steps towards coupled DA at Meteo-France, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1095, https://doi.org/10.5194/egusphere-egu23-1095, 2023.

17:50–18:00
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EGU23-14227
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On-site presentation
Svetlana N. Losa, Longjiang Mu, Marylou Athanase, Jan Streffing, Miguel Andrés-Martínez, Lars Nerger, Tido Semmler, Dmitry Sidorenko, and Helge F. Goessling

Assimilation of sea ice and ocean observational data into coupled sea-ice, ocean and atmosphere models is known as an efficient approach for providing a reliable sea-ice prediction (Mu et al. 2022). However, implementations of the data assimilation in the coupled systems still remain a challenge. This challenge is partly originated from the chaoticity possessed in the atmospheric module, which leads to biases and, therefore, to divergence of predictive characteristics. An additional constrain of the atmosphere is proposed as a tool to tackle the aforementioned problem. To test this approach, we use the recently developed AWI Coupled Prediction System (AWI-CPS). The system is built upon the AWI climate model AWI-CM-3 (Streffing et al. 2022) that includes FESOM2.0 as a sea-ice ocean component and the Integrated Forecasting System (OpenIFS) as an atmospheric component. An Ensemble-type Kalman filter within the Parallel Data Assimilation Framework (PDAF; Nerger and Hiller, 2013) is used to assimilate sea ice concentration, sea ice thickness, sea ice drift, sea surface height, sea surface temperature and salinity, as well as temperature and salinity vertical profiles. The additional constrain of the atmosphere is introduced by relaxing, or “nudging”, the AWI-CPS large-scale atmospheric dynamics to the ERA5 reanalysis data. This nudging of the large scale atmospheric circulation towards reanalysis has allowed to reduce biases in the atmospheric state, and, therefore, to reduce the analysis increments. The most prominent improvement has been achieved for the predicted sea ice drift. Comprehensive analyses will be presented based upon the new system’s performance over the time period 2003 – 2022.

Mu, L., Nerger, L., Streffing, J., Tang, Q., Niraula, B., Zampieri, L., Loza, S. N. and H. F. Goessling, Sea-ice forecasts with an upgraded AWI Coupled Prediction System (Journal of Advances in Modeling Earth Systems, 14, e2022MS003176. doi: 10.1029/2022MS003176.

Nerger, L. and Hiller, W., 2013. Software for ensemble-based data assimilation systems—Implementation strategies and scalability. Computers & Geosciences, 55, pp.110-118.

Streffing, J., Sidorenko, D., Semmler, T., Zampieri, L., Scholz, P., Andrés-Martínez, M., Koldunov, N., Rackow, T., Kjellsson, J., Goessling, H., Athanase, M., Wang, Q., Sein, D., Mu, L., Fladrich, U., Barbi, D., Gierz, P., Danilov, S.,  Juricke, S., Lohmann, G. and Jung, T. (2022) AWI-CM3 coupled climate model: Description and evaluation experiments for a prototype post-CMIP6 model, EGUsphere, 2022, 1—37, doi: 10.5194/egusphere-2022-32

How to cite: Losa, S. N., Mu, L., Athanase, M., Streffing, J., Andrés-Martínez, M., Nerger, L., Semmler, T., Sidorenko, D., and Goessling, H. F.: Combining sea-ice and ocean data assimilation with nudging atmospheric circulation in the AWI Coupled Prediction System, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14227, https://doi.org/10.5194/egusphere-egu23-14227, 2023.

Data assimilation

Posters on site: Tue, 25 Apr, 14:00–15:45 | Hall X4

X4.104
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EGU23-3761
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ECS
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Saori Nakashita and Takeshi Enomoto

The maximum likelihood ensemble filter (MLEF) can handle nonlinearity of observation operators more appropriately than conventional ensemble Kalman filters. Here we consider the observation space localization method for MLEF to enable application to large-scale problems in the atmosphere. Optimization of the cost function in MLEF, however, impedes local analysis, suitable for massive parallel computers, in the same manner as the local ensemble transform Kalman filter (LETKF). In this study two approaches to observation space localization for MLEF (LMLEF) are compared. The first method introduces local gradients to minimize the global cost function (Yokota et al. 2016). An alternative approach, proposed here, defines a local cost function for each grid assuming a constant ensemble weight in the local domain to enable embarrassingly parallel analysis. The two approaches are compared to LETKF in cycled data assimilation experiments using the Lorenz-96 and the SPEEDY models. LMLEFs are found to be more accurate and stable than LETKF when nonlinear observations are assimilated into each model. Our proposed method is comparable to Yokota's global optimization method when dense observations are assimilated into the Lorenz-96 model. This result is consistent with the fact that ensemble weights have high spatial correlations with those at neighboring grids. Although our method also yields similar analysis in the SPEEDY experiments with a more realistic observation network, Yokota’s global optimization method shows faster error convergence in the earlier cycles. The error convergence rate seems to be related to the difference between global and local optimization and the validity of the assumption of constant weights, which depends strongly on the observation density.

How to cite: Nakashita, S. and Enomoto, T.: Observation space localizations for the maximum likelihood ensemble filter, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3761, https://doi.org/10.5194/egusphere-egu23-3761, 2023.

X4.105
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EGU23-6050
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ECS
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Highlight
Lei Kong, Xiao Tang, Jiang Zhu, Zifa Wang, Yele Sun, Pingqing Fu, Meng Gao, Huangjian Wu, Jie Li, Xiaole Pan, Lin Wu, Hajime Akimoto, and Gregory R. Carmichael

The unprecedented lockdown of human activities during the COVID-19 pandemic have significantly influenced the social life in China. However, understanding of the impact of this unique event on the emissions of different species is still insufficient, prohibiting the proper assessment of the environmental impacts of COVID-19 restrictions. Here we developed a multi-air pollutant inversion system to simultaneously estimate the emissions of NOx, SO2, CO, PM2.5 and PM10 in China during COVID-19 restrictions with high temporal (daily) and horizontal (15km) resolutions. Subsequently, contributions of emission changes versus meteorology variations during COVID-19 lockdown were separated and quantified. The results demonstrated that the inversion system effectively reproduced the actual emission variations of multi-air pollutants in China during different periods of COVID-19 lockdown, which indicate that the lockdown is largely a nationwide road traffic control measurement with NOx emissions decreased substantially by ~40%. However, emissions of other air pollutants were found only decreased by ~10%, both because power generation and heavy industrial processes were not halted during lockdown, and residential activities may actually have increased due to the stay-at-home orders. Consequently, although obvious reductions of PM2.5 concentrations occurred over North China Plain (NCP) during lockdown period, the emission change only accounted for 8.6% of PM2.5 reductions, and even led to substantial increases of O3. The meteorological variation instead dominated the changes in PM2.5 concentrations over NCP, which contributed 90% of the PM2.5 reductions over most parts of NCP region. Meanwhile, our results also suggest that the local stagnant meteorological conditions together with inefficient reductions in PM2.5 emissions were the main drivers of the unexpected COVID-19 haze in Beijing. These results highlighted that traffic control as a separate pollution control measure has limited effects on the coordinated control of O3 and PM2.5 concentrations under current complex air pollution conditions in China. More comprehensive and balanced regulations for multiple precursors from different sectors are required to address O3 and PM2.5 pollution in China.

How to cite: Kong, L., Tang, X., Zhu, J., Wang, Z., Sun, Y., Fu, P., Gao, M., Wu, H., Li, J., Pan, X., Wu, L., Akimoto, H., and Carmichael, G. R.: Unbalanced emission reductions of different species and sectors in China during COVID-19 lockdown derived by multi-species surface observation assimilation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6050, https://doi.org/10.5194/egusphere-egu23-6050, 2023.

X4.106
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EGU23-11889
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ECS
Bastian Waldowski, Insa Neuweiler, and Natascha Brandhorst

Reliable estimates of soil water content and groundwater levels are essential in evaluating water availability for plants and as drinking water and thus both subsurface components (vadose zone and groundwater) are commonly monitored. Such measurements can be used for data assimilation in order to improve predictions of numerical subsurface flow models. Within this work, we investigate to what extent measurements from one subsurface component are able to improve predictions in the other one.
For this purpose, we utilize idealized test cases at a subcatchment scale using a Localized Ensemble Kalman Filter to update the water table height and soil moisture at certain depths with measurements taken from a numerical reference model. We do joint, as well as single component updates. We test strongly coupled data assimilation, which implies utilizing correlations between the subsurface components for updating the ensemble and compare it to weakly coupled data assimilation. We also update soil hydraulic parameters and examine the role of their heterogeneity with respect to data assimilation. We run simulations with both a complex 3D model (using TSMP-PDAF) as well as a more simplified and computationally efficient 2.5D model, which consists of multiple 1D vadose-zone columns coupled iteratively with a 2D groundwater-flow model. In idealized settings, such as homogeneous subsurface structures, we find that predictions in one component consistently benefit from updating the other component.

How to cite: Waldowski, B., Neuweiler, I., and Brandhorst, N.: Data Assimilation and Subsurface Flow Modeling: Interactions between Groundwater and the Vadose Zone, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11889, https://doi.org/10.5194/egusphere-egu23-11889, 2023.

X4.107
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EGU23-12304
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ECS
Nils Risse, Mario Mech, Catherine Prigent, Gunnar Spreen, and Susanne Crewell

Passive microwave radiometers onboard polar-orbiting satellites provide global information on the atmospheric state. The underlying retrievals require accurate knowledge of the surface radiative properties to distinguish atmospheric from surface contributions to the measured radiance. Polar surfaces such as sea ice contribute up to 400 GHz to the measured radiance due to the high atmospheric transmissivity under cold and dry conditions. Currently, we lack an understanding of sea ice parameters driving the variability in its radiative properties, i.e., its emissivity, at frequencies above 200 GHz due to limited field data and the heterogeneous sea ice structure. This will limit the use of future satellite missions such as the Ice Cloud Imager (ICI) onboard Metop-SG and the Arctic Weather Satellite (AWS) in polar regions.

To better understand sea ice emission, we analyze unique airborne measurements from 89 to 340 GHz obtained during the ACLOUD (summer 2017) and AFLUX (spring 2019) airborne campaigns and co-located satellite observations in the Fram Strait. The Polar 5 aircraft carried the Microwave Radar/radiometer for Arctic Clouds (MiRAC) cloud radar MiRAC-A with an 89 GHz passive channel and MiRAC-P with six double-sideband channels at 183.31 GHz and two window channels at 243 and 340 GHz. We calculate the emissivity with the non-scattering radiative transfer equation from observed upwelling radiation at 25° (MiRAC-A) and 0° (MiRAC-P) and Passive and Active Microwave radiative TRAnsfer (PAMTRA) simulations. The PAMTRA simulations are based on atmospheric profiles from dropsondes and surface temperatures from an infrared radiometer.

The airborne-derived sea ice emissivity (O(0.1km)) varies on small spatial scales (O(1km)), which align with sea ice properties identified by visual imagery. High-resolution airborne-derived emissivities vary more than emissivities from co-located overflights of the GPM constellation due to the smaller footprint size, which resolve sea ice variations. The emissivity of frozen and snow-free leads separates clearly from more compact and snow-covered ice flows at all frequencies. The comparison of summer and spring emissivities reveals an emissivity reduction due to melting. We will also conduct evaluations of emissivity parameterizations (e.g. TELSEM²) and provide insights into observations at ICI and AWS frequencies over Arctic sea ice. Findings based on the field data may be useful for the assimilation of radiances from existing and future microwave radiometers into weather prediction models in polar regions.

How to cite: Risse, N., Mech, M., Prigent, C., Spreen, G., and Crewell, S.: Analysis of airborne-derived sea ice emissivities up to 340 GHz in preparation for future satellite missions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12304, https://doi.org/10.5194/egusphere-egu23-12304, 2023.

X4.108
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EGU23-3086
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Takeshi Enomoto and Saori Nakashita

The Newton method, which requires the Hessian matrix, is prohibitively expensive in adjoint-based variational data assimilation (VAR). It may be rather attractive for ensemble-based VAR because the ensemble size is usually several orders of magnitude smaller than that of the state size. In the present paper the Newton method is compared against the conjugate-gradient (CG) method, which is one of the most popular choices in adjoint-based VAR. To make comparisons, the maximum likelihood ensemble filter (MLEF) is used as a framework for data assimilation experiments. The Hessian preconditioning is used with CG as formulated in the original MLEF. Alternatively we propose to use the Hessian in the Newton method. In the exact Newton (EN) method, the Newton equation is solved exactly, i.e. the step size is fixed to unity avoiding a line search. In the 1000-member wind-speed assimilation test, CG is stagnated early in iteration and terminated due to a line search error while EN converges quadratically. This behaviour is consistent with the workings of the EN and CG in the minimization of the Rosenbrock function. In the repetitive cycled experiments using the Korteweg-de Vries-Burgers (KdVB) model with a quadratic observation operator, EN performs competitively in accuracy to CG with significantly enhanced stability. These idealized experiments indicate the benefit of adopting EN for the optimization in MLEF.

How to cite: Enomoto, T. and Nakashita, S.: Comparison of optimization methods for the maximum likelihood ensemble filter, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3086, https://doi.org/10.5194/egusphere-egu23-3086, 2023.

X4.109
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EGU23-1846
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Highlight
Sébastien Barthélémy, Julien Brajard, Laurent Bertino, and François Counillon

This work extends the concept of "Super-resolution data assimilation" (SRDA, Barthélémy et al. 2022)) to the case of mixed-resolution ensembles pursuing two goals: (1) emulate the Ensemble Kalman Filter while (2) benefit from high-resolution observations. The forecast step is performed by two ensembles at two different resolutions, high and low-resolution. Before the assimilation step the low-resolution ensemble is downscaled to the high-resolution space, then both ensembles are updated with high-resolution observations. After the assimilation step, the low-resolution ensemble is upscaled back to its low-resolution grid for the next forecast. The downscaling step before the data assimilation step is performed either with a neural network, or with a simple cubic spline interpolation operator. The background error covariance matrix used for the update of both ensembles is a hybrid matrix between the high and low resolution background error covariance matrices. This flavor of the SRDA is called "Hybrid covariance super-resolution data assimilation" (Hybrid SRDA). We test the method with a quasi-geostrophic model in the context of twin-experiments with the low-resolution model being twice and four times coarser than the high-resolution one. The Hybrid SRDA with neural network performs equally or better than its counterpart with cubic spline interpolation, and drastically reduces the errors of the low-resolution ensemble. At equivalent computational cost, the Hybrid SRDA outperforms both the SRDA (8.4%) and the standard EnKF (14%). Conversely, for a given value of the error, the Hybrid SRDA requires as little as  50% of the computational resources of  the EnKF. Finally, the Hybrid SRDA can be formulated as a low-resolution scheme, in the sense that the assimilation is performed in the low-resolution space, encouraging the application of the scheme with realistic ocean models.

How to cite: Barthélémy, S., Brajard, J., Bertino, L., and Counillon, F.: Hybrid covariance super-resolution data assimilation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1846, https://doi.org/10.5194/egusphere-egu23-1846, 2023.

X4.110
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EGU23-5421
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ECS
Nabir Mamnun, Christoph Völker, Mihalis Vrekoussis, and Lars Nerger

Ocean biogeochemical (BGC) models are, in addition to measurements, the primary tools for investigating ocean biogeochemistry, marine ecosystem functioning, and the global carbon cycle. These models contain a large number of not precisely known parameters and are highly uncertain regarding those parametrizations.  The values of these parameters depend on the physical and biogeochemical context, but in practice values derived from limited field measurements or laboratory experiments are used in the model keeping them constant in space and time. This study aims to estimate spatially and temporally varying parameters in a global ocean BGC model and to assess the effect of those estimated parameters on model fields and dynamics. Utilizing the BGC model Regulated Ecosystem Model 2 (REcoM2), we estimate ten selected BGC parameters with heterogeneity in parameter values both across space and over time using an ensemble data assimilation technique. We assimilate satellite ocean color and BGC-ARGO data using an ensemble Kalman filter provided by the Parallel Data Assimilation Framework (PDAF) to simultaneously estimate the BGC model states and parameters. We assess the improvement in the model predictions with space and time-dependent parameters in reference to the simulation with globally constant parameters against both assimilative and independent data. We quantify the spatiotemporal uncertainties regarding the parameter estimation and the prediction uncertainties induced by those parameters. We study the effect of estimated parameters on the biogeochemical fields and dynamics to get deeper insights into modeling processes and discuss insights from spatially and temporally varying parameters beyond parameter values.

How to cite: Mamnun, N., Völker, C., Vrekoussis, M., and Nerger, L.: Estimation of Spatially and Temporally Varying Biogeochemical Parameters in a Global Ocean Model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5421, https://doi.org/10.5194/egusphere-egu23-5421, 2023.

X4.111
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EGU23-5506
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ECS
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Tobias Necker, Philipp Griewank, Takemasa Miyoshi, and Martin Weissmann

Ensemble-based estimates of error covariances suffer from limited ensemble size due to computational restrictions in data assimilation systems for numerical weather prediction. Localization of error covariances can mitigate sampling errors and is crucial for ensemble-based data assimilation. However, finding optimal localization methods, functions, or scales is challenging. We present a new approach to derive an empirical optimal localization (EOL) from a large ensemble dataset. The EOL allows for a better understanding of localization requirements and can guide toward improved localization.

Our study presents EOL estimates using 40-member subsamples assuming a 1000-member ensemble covariance as truth. The EOL is derived from a 5-day training period. In the presentation, we cover both model and observation space vertical localization and discuss:

  • vertical error correlations and EOL estimates for different variables and settings;

  • the effect of the EOL compared to common localization approaches, such as distance-dependent localization with a Gaspari-Cohn function;

  • and vertical localization of infrared and visible satellite observations in the context of observation space localization.

Proper observation space localization of error covariances between non-local satellite observations and state space is non-trivial and still an open research question. First, we evaluate requirements for optimal localization for different variables and spectral channels. And secondly, we investigate the situation dependence of vertical localization in convection-permitting NWP simulations, which suggests an advantage of using adaptive situation-dependent localization approaches.

How to cite: Necker, T., Griewank, P., Miyoshi, T., and Weissmann, M.: Empirical optimal vertical localization derived from large ensembles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5506, https://doi.org/10.5194/egusphere-egu23-5506, 2023.

X4.112
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EGU23-8030
Amos Lawless, Maria Valdivieso, Nancy Nichols, Daniel Lea, and Matthew Martin

As part of the design of future coupled forecasting systems, operational centres such as the Met Office are starting to include interactions between the atmosphere and the ocean within the data assimilation system. This requires an improved understanding and representation of the correlations between short-range forecast errors in different variables. To understand the potential benefit of further coupling in the data assimilation scheme it is important to understand the significance of any cross-correlations between atmosphere and ocean short-range forecast errors as well as their temporal and spatial variability. In this work we examine atmosphere-ocean cross-covariances from an ensemble of the Met Office coupled NWP system for December 2019, with particular focus on short-range forecast errors that evolve at lead times up to 6 hours.

We find that significant correlations exist between atmosphere and ocean forecast errors on these timescales, and that these vary diurnally, from day to day, spatially and synoptically. Negative correlations between errors in sea-surface temperature (SST) and 10m wind correlations strengthen as the solar radiation varies from zero at night (local time) to a maximum insolation around midday (local time). In addition, there are significant variations in correlation intensities and structures in response to synoptic-timescale forcing. Significant positive correlations between SST and 10m wind errors appear in the western North Atlantic in early December and are associated with variations in low surface pressures and their associated high wind speeds, that advect cold, dry continental air eastward over the warmer Atlantic ocean. Negative correlations across the Indo-Pacific Warm Pool are instead associated with light wind conditions on these short timescales.

When we consider the spatial extent of cross-correlations, we find that in the Gulf Stream region positive correlations between wind speed and sub-surface ocean temperatures are generally vertically coherent down to a depth of about 100m, consistent with the mixing depth; however, in the tropical Indian and West Pacific oceans, negative correlations break down just below the surface layer. This is likely due to the presence of surface freshwater layers that form from heavy precipitation on the tropical oceans, manifested by the presence of salinity-stratified barrier layers within deeper isothermal layers that can effectively limit turbulent mixing of heat between the ocean surface and the deeper thermocline.

How to cite: Lawless, A., Valdivieso, M., Nichols, N., Lea, D., and Martin, M.: Assessment of short-range forecast atmosphere-ocean cross-covariances from the Met Office coupled NWP system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8030, https://doi.org/10.5194/egusphere-egu23-8030, 2023.

Posters virtual: Tue, 25 Apr, 14:00–15:45 | vHall ESSI/GI/NP

vEGN.7
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EGU23-4668
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ECS
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Highlight
Meiyi Hou and Youmin Tang

The optimal observational array for improving the El Niño-Southern Oscillation (ENSO) prediction is investigated by exploring sensitive areas for target observations of two types of El Niño events in the Pacific. A target observation method based on the particle filter and pre-industrial control runs from six coupled model outputs in Coupled Model Intercomparison Project Phase 5 (CMIP5) experiments are used to quantify the relative importance of the initial accuracy of sea surface temperature (SST) in different Pacific areas. The initial accuracy of the tropical Pacific, subtropical Pacific, and extratropical Pacific can influence both types of El Niño predictions. The relative importance of different areas changes along with different lead times of predictions. Tropical Pacific observations are crucial for decreasing the root mean square error of predictions of all lead times. Subtropical and extratropical observations play an important role in reducing the prediction uncertainty, especially when the prediction is made before and throughout the boreal spring. To consider different El Niño types and different start months for predictions, a quantitative frequency method based on frequency distribution is applied to determine the optimal observations of ENSO predictions. The final optimal observational array contains 31 grid points, including 21 grid points in the equatorial Pacific and 10 grid points in the North Pacific, suggesting the importance of the initial SST conditions for ENSO predictions in the tropical Pacific and also in the area outside the tropics. Furthermore, the predictions made by assimilating SST in sensitive areas have better prediction skills in the verification experiment, which can indicate the validity of the optimal observational array designed in this study. This result provided guidance on how to initialize models in predictions of El Niño types. 

How to cite: Hou, M. and Tang, Y.: A particle filter based target observation method and its application to two types of El Niño events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4668, https://doi.org/10.5194/egusphere-egu23-4668, 2023.

vEGN.8
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EGU23-14826
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Highlight
Ross Bannister
One of the most appealing uses of data assimilation is to infer useful information about a dynamical system that is not observed directly. This is the case for the estimation of surface fluxes of trace gases (like methane). Such fluxes are not easy to measure directly on a global scale, but it is possible to measure the trace gas itself as it is transported around the globe. This is the purpose of INVICAT (the inverse modelling system of the chemical transport model TOMCAT), which has been developed here. INVICAT interprets observations of (e.g.) methane over a time window to estimate the initial conditions (ICs) and surface fluxes (SFs) of the TOMCAT model.
This talk will show how INVICAT has been expanded from a diagonal background error covariance matrix (B-matrix, DB) to allow an efficient representation of a non-diagonal B-matrix (NDB). The results of this process are mixed. A NDB-matrix for the SF field improves the analysis against independent data, but a NDB-matrix for the IC field appears to degrade the analysis. This paper presents these results and suggests that a possible reason for the degraded analyses is the presence of a possible bias in the system.

How to cite: Bannister, R.: Inverse modelling for trace gas surface flux estimation, impact of a non-diagonal B-matrix, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14826, https://doi.org/10.5194/egusphere-egu23-14826, 2023.