Strongly Coupled Data Assimilation in Multiscale Media: Experiments Using a Quasi‐Geostrophic Coupled Model

Abstract In a strongly coupled data assimilation (DA), a cross-fluid covariance is specified that allows measurements from a coupled fluid (e.g., atmosphere) to directly impact analysis increments in a target fluid (e.g., ocean). The exhaustive solution to this coupled DA problem calls for a covariance where all available measurements can influence all grid points in all fluids. Solution of such a large algebraic problem is computationally expensive, often calls for a substantial rewrite of existing fluid-specific DA systems, and, as shown in this paper, can be avoided. The proposed interface solver assumes that covariances between coupled measurements and target fluid are often close to null (e.g., between stratospheric observations and the deep ocean within a 6-h forecast cycle). In the interface solver, two separate DA solvers are run in parallel: one that produces an analysis solution in the atmosphere, and one in the ocean. Each system uses a coupled observation vector where in addition to resident measurements in the target fluid it also includes nonresident measurements in the coupled fluid that are likely to have significant influence on the analysis in the target fluid (interface measurements). An ensemble-based method is employed and a localization function for coupled ensembles is proposed. Using a coupled model for the Mediterranean Sea (in a twin setting), it is demonstrated that (i) the solution of the interface solver converges to the exhaustive solution and (ii) that in presence of poorly known error covariances, the interface solver can be configured to produce a more accurate solution than an exhaustive solver.

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Correlation-Cutoff Method for Covariance Localization in Strongly Coupled Data Assimilation

Monthly Weather Review ◽

10.1175/mwr-d-17-0365.1 ◽

2018 ◽

Vol 146 (9) ◽

pp. 2881-2889 ◽

Cited By ~ 5

Author(s):

Takuma Yoshida ◽

Eugenia Kalnay

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Coupled Model ◽

Error Variance ◽

State Variables ◽

Background Error ◽

Strongly Coupled ◽

Error Correlation ◽

Analysis Error ◽

Coupled Data Assimilation

Abstract Strongly coupled data assimilation (SCDA), where observations of one component of a coupled model are allowed to directly impact the analysis of other components, sometimes fails to improve the analysis accuracy with an ensemble Kalman filter (EnKF) as compared with weakly coupled data assimilation (WCDA). It is well known that an observation’s area of influence should be localized in EnKFs since the assimilation of distant observations often degrades the analysis because of spurious correlations. This study derives a method to estimate the reduction of the analysis error variance by using estimates of the cross covariances between the background errors of the state variables in an idealized situation. It is shown that the reduction of analysis error variance is proportional to the squared background error correlation between the analyzed and observed variables. From this, the authors propose an offline method to systematically select which observations should be assimilated into which model state variable by cutting off the assimilation of observations when the squared background error correlation between the observed and analyzed variables is small. The proposed method is tested with the local ensemble transform Kalman filter (LETKF) and a nine-variable coupled model, in which three Lorenz models with different time scales are coupled with each other. The covariance localization with the correlation-cutoff method achieves an analysis more accurate than either the full SCDA or the WCDA methods, especially with smaller ensemble sizes.

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Weakly and strongly coupled data assimilation with the coupled ocean-atmosphere model AWI-CM

10.5194/egusphere-egu2020-5023 ◽

2020 ◽

Author(s):

Qi Tang ◽

Longjiang Mu ◽

Dmitry Sidorenko ◽

Lars Nerger

Keyword(s):

Data Assimilation ◽

Climate Model ◽

Current System ◽

Coupled Model ◽

Atmospheric Model ◽

Ocean Model ◽

Strongly Coupled ◽

Weakly Coupled ◽

Coupled Data Assimilation ◽

Ocean Observations

<p>In this study we compare the results of strongly coupled data assimilation (SCDA) and weakly coupled data assimilation (WCDA), and among the different WCDAs by analyzing the assimilation effect on the prediction of the ocean as well as the atmosphere variables. We have implemented the parallel data assimilation framework (PDAF, http://pdaf.awi.de) with the AWI climate model (AWI-CM), which couples the ocean model FESOM and the atmospheric model ECHAM. In the WCDA, the assimilation acts separately on each component in the coupled model and observations of one component only directly influence its own component. The other components can benefit from the DA through the model dynamics. The alternative to WCDA is SCDA, in which the atmosphere as well as the ocean variables are updated jointly using cross-covariances between the two components. Our current system allows both the SCDA and the WCDA. For the SCDA configuration, either the ocean observations (e.g., satellite sea surface temperature, profiles of temperature and salinity) or the atmosphere observations (e.g., air temperature, surface pressure) or both of them can be assimilated to update the ocean as well as the atmosphere variables. For the WCDA, it allows 1) assimilating only the ocean observations into the ocean state; 2) assimilating only the atmosphere observations into the atmosphere state; 3) assimilating both types of observations into the corresponding component models. The results are evaluated by comparing the estimated ocean and atmosphere variables with the observational data.</p>

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Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part I: Simple Model Study*

Monthly Weather Review ◽

10.1175/mwr-d-14-00322.1 ◽

2015 ◽

Vol 143 (9) ◽

pp. 3823-3837 ◽

Cited By ~ 19

Author(s):

Feiyu Lu ◽

Zhengyu Liu ◽

Shaoqing Zhang ◽

Yun Liu

Keyword(s):

Data Assimilation ◽

Signal To Noise Ratio ◽

Coupled Model ◽

Coupled System ◽

Asymmetric Structure ◽

Multiple Model ◽

Strongly Coupled ◽

Model Study ◽

Coupled Data Assimilation ◽

Model Components

Abstract This paper studies a new leading averaged coupled covariance (LACC) method for the strongly coupled data assimilation (SCDA). The SCDA not only uses the coupled model to generate the forecast and assimilate observations into multiple model components like the weakly coupled version (WCDA), but also applies a cross update using the coupled covariance between variables from different model components. The cross update could potentially improve the balance and quality of the analysis, but its implementation has remained a great challenge in practice because of different time scales between model components. In a typical extratropical coupled system, the ocean–atmosphere correlation shows a strong asymmetry with the maximum correlation occurring when the atmosphere leads the ocean by about the decorrelation time of the atmosphere. The LACC method utilizes such asymmetric structure by using the leading forecasts and observations of the fast atmospheric variable for cross update, therefore, increasing the coupled correlation and enhancing the signal-to-noise ratio in calculating the coupled covariance. Here it is applied to a simple coupled model with the ensemble Kalman filter (EnKF). With the LACC method, the SCDA reduces the analysis error of the oceanic variable by over 20% compared to the WCDA and 10% compared to the SCDA using simultaneous coupled covariance. The advantage of the LACC method is more notable when the system contains larger errors, such as in the cases with smaller ensemble size, bigger time-scale difference, or model biases.

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Impact of an observational time window on coupled data assimilation: simulation with a simple climate model

Nonlinear Processes in Geophysics ◽

10.5194/npg-24-681-2017 ◽

2017 ◽

Vol 24 (4) ◽

pp. 681-694 ◽

Cited By ~ 5

Author(s):

Yuxin Zhao ◽

Xiong Deng ◽

Shaoqing Zhang ◽

Zhengyu Liu ◽

Chang Liu ◽

...

Keyword(s):

Data Assimilation ◽

General Circulation ◽

Climate Model ◽

Time Window ◽

Coupled Model ◽

Circulation Model ◽

Scale Interactions ◽

Climate Signals ◽

Coupled Data Assimilation ◽

Climate Analysis

Abstract. Climate signals are the results of interactions of multiple timescale media such as the atmosphere and ocean in the coupled earth system. Coupled data assimilation (CDA) pursues balanced and coherent climate analysis and prediction initialization by incorporating observations from multiple media into a coupled model. In practice, an observational time window (OTW) is usually used to collect measured data for an assimilation cycle to increase observational samples that are sequentially assimilated with their original error scales. Given different timescales of characteristic variability in different media, what are the optimal OTWs for the coupled media so that climate signals can be most accurately recovered by CDA? With a simple coupled model that simulates typical scale interactions in the climate system and twin CDA experiments, we address this issue here. Results show that in each coupled medium, an optimal OTW can provide maximal observational information that best fits the characteristic variability of the medium during the data blending process. Maintaining correct scale interactions, the resulting CDA improves the analysis of climate signals greatly. These simple model results provide a guideline for when the real observations are assimilated into a coupled general circulation model for improving climate analysis and prediction initialization by accurately recovering important characteristic variability such as sub-diurnal in the atmosphere and diurnal in the ocean.

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Application of a local attractor dimension to reduced space strongly coupled data assimilation for chaotic multiscale systems

Nonlinear Processes in Geophysics ◽

10.5194/npg-27-51-2020 ◽

2020 ◽

Vol 27 (1) ◽

pp. 51-74 ◽

Cited By ~ 1

Author(s):

Courtney Quinn ◽

Terence J. O'Kane ◽

Vassili Kitsios

Keyword(s):

Data Assimilation ◽

Accurate Estimate ◽

Variable Number ◽

Frobenius Norm ◽

Accurate Analysis ◽

Attractor Dimension ◽

Strongly Coupled ◽

Cross Domain ◽

Local Attractor ◽

Coupled Data Assimilation

Abstract. The basis and challenge of strongly coupled data assimilation (CDA) is the accurate representation of cross-domain covariances between various coupled subsystems with disparate spatio-temporal scales, where often one or more subsystems are unobserved. In this study, we explore strong CDA using ensemble Kalman filtering methods applied to a conceptual multiscale chaotic model consisting of three coupled Lorenz attractors. We introduce the use of the local attractor dimension (i.e. the Kaplan–Yorke dimension, dimKY) to prescribe the rank of the background covariance matrix which we construct using a variable number of weighted covariant Lyapunov vectors (CLVs). Specifically, we consider the ability to track the nonlinear trajectory of each of the subsystems with different variants of sparse observations, relying only on the cross-domain covariance to determine an accurate analysis for tracking the trajectory of the unobserved subdomain. We find that spanning the global unstable and neutral subspaces is not sufficient at times where the nonlinear dynamics and intermittent linear error growth along a stable direction combine. At such times a subset of the local stable subspace is also needed to be represented in the ensemble. In this regard the local dimKY provides an accurate estimate of the required rank. Additionally, we show that spanning the full space does not improve performance significantly relative to spanning only the subspace determined by the local dimension. Where weak coupling between subsystems leads to covariance collapse in one or more of the unobserved subsystems, we apply a novel modified Kalman gain where the background covariances are scaled by their Frobenius norm. This modified gain increases the magnitude of the innovations and the effective dimension of the unobserved domains relative to the strength of the coupling and timescale separation. We conclude with a discussion on the implications for higher-dimensional systems.

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Impact of Atmospheric and Aerosol Optical Depth Observations on Aerosol Initial Conditions in a strongly-coupled data assimilation system

10.5194/acp-2019-2 ◽

2019 ◽

Cited By ~ 2

Author(s):

Milija Zupanski ◽

Anton Kliewer ◽

Ting-Chi Wu ◽

Karina Apodaca ◽

Qijing Bian ◽

...

Keyword(s):

Data Assimilation ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Initial Conditions ◽

Positive Impact ◽

Storm Event ◽

Strongly Coupled ◽

Atmospheric Observations ◽

Coupled Data Assimilation ◽

The Impact

Abstract. Strongly coupled data assimilation frameworks provide a mechanism for including additional information about aerosols through the coupling between aerosol and atmospheric variables, effectively utilizing atmospheric observations to change the aerosol analysis. Here, we investigate the impact of these observations on aerosol using the Maximum Likelihood Ensemble Filter (MLEF) algorithm with Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) which includes the Godard Chemistry Aerosol Radiation and Transport (GOCART) module. We apply this methodology to a dust storm event over the Arabian Peninsula and examine in detail the error covariance and in particular the impact of atmospheric observations on improving the aerosol initial conditions. The assimilated observations include conventional atmospheric observations and Aerosol Optical Depth (AOD) retrievals. Results indicate a positive impact of using strongly coupled data assimilation and atmospheric observations on the aerosol initial conditions, quantified using Degrees of Freedom for Signal.

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Examining the Impact of SMAP Soil Moisture Retrievals on Short-Range Weather Prediction under Weakly and Strongly Coupled Data Assimilation with WRF-Noah

Monthly Weather Review ◽

10.1175/mwr-d-19-0017.1 ◽

2019 ◽

Vol 147 (12) ◽

pp. 4345-4366 ◽

Cited By ~ 2

Author(s):

Liao-Fan Lin ◽

Zhaoxia Pu

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Weather Prediction ◽

Surface Model ◽

Strongly Coupled ◽

Soil Moisture Data ◽

Analysis Scheme ◽

Coupled Data Assimilation ◽

The Impact

Abstract Remotely sensed soil moisture data are typically incorporated into numerical weather models under a framework of weakly coupled data assimilation (WCDA), with a land surface analysis scheme independent from the atmospheric analysis component. In contrast, strongly coupled data assimilation (SCDA) allows simultaneous correction of atmospheric and land surface states but has not been sufficiently explored with land surface soil moisture data assimilation. This study implemented a variational approach to assimilate the Soil Moisture Active Passive (SMAP) 9-km enhanced retrievals into the Noah land surface model coupled with the Weather Research and Forecasting (WRF) Model under a framework of both WCDA and SCDA. The goal of the study is to quantify the relative impact of assimilating SMAP data under different coupling frameworks on the atmospheric forecasts in the summer. The results of the numerical experiments during July 2016 show that SCDA can provide additional benefits on the forecasts of air temperature and humidity compared to WCDA. Over the U.S. Great Plains, assimilation of SMAP data under WCDA reduces a warm bias in temperature and a dry bias in humidity by 7.3% and 19.3%, respectively, while the SCDA case contributes an additional bias reduction of 2.2% (temperature) and 3.3% (humidity). While WCDA leads to a reduction of RMSE in temperature forecasts by 4.1%, SCDA results in additional reduction of RMSE by 0.8%. For the humidity, the reduction of RMSE is around 1% for both WCDA and SCDA.

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Air-sea strongly coupled data assimilation for tropical cyclone prediction

10.5194/egusphere-egu21-3316 ◽

2021 ◽

Author(s):

Xingchao Chen

Keyword(s):

Tropical Cyclone ◽

Data Assimilation ◽

Forecast Error ◽

Regional Scale ◽

Forecast Errors ◽

State Variables ◽

Strongly Coupled ◽

Weakly Coupled ◽

Coupled Data Assimilation ◽

Tc Prediction

<p>Air-sea interactions are critical to tropical cyclone (TC) energetics. However, oceanic state variables are still poorly initialized, and are inconsistent with atmospheric initial fields in most operational coupled TC forecast models. In this study, we first investigate the forecast error covariance across the oceanic and atmospheric domains during the rapid intensification of Hurricane Florence (2018) using a 200-member ensemble of convection-permitting forecasts from a coupled atmosphere-ocean regional model. Meaningful and dynamically consistent cross domain ensemble error correlations suggest that it is possible to use atmospheric and oceanic observations to simultaneously update model state variables associated with the coupled ocean-atmosphere prediction of TCs using strongly coupled data assimilation (DA). A regional-scale strongly coupled DA system based on the ensemble Kalman filter (EnKF) is then developed for TC prediction. The potential impacts of different atmospheric and oceanic observations on TC analysis and prediction are examined through observing system simulation experiments (OSSEs) of Hurricane Florence (2018). Results show that strongly coupled DA resulted in better analysis and forecast of both the oceanic and atmospheric variables than weakly coupled DA. Compared to weakly coupled DA in which the analysis update is performed separately for the atmospheric and oceanic domains, strongly coupled DA reduces the forecast errors of TC track and intensity. Results show promise in potential further improvement in TC prediction through assimilation of both atmospheric and oceanic observations using the ensemble-based strongly coupled DA system.</p>

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Coupled data assimilation in climate research: A brief review of applications in ocean and land

Satellite Oceanography and Meteorology ◽

10.18063/som.v3i2.599 ◽

2018 ◽

Vol 3 (2) ◽

Cited By ~ 1

Author(s):

Kazuyoshi Suzuki 1 ◽

Milija ZUPANSKI 2

Keyword(s):

Data Assimilation ◽

Observational Data ◽

Initial Conditions ◽

Monitoring Networks ◽

Coupled Models ◽

Climate Change Research ◽

Empirical Parameter ◽

Strongly Coupled ◽

Weakly Coupled ◽

Coupled Data Assimilation

Regions of the cryosphere, including the poles, that are currently unmonitored are expanding, therefore increasing the importance of satellite observations for such regions. With the increasing availability of satellite data in recent years, data assimilation research that combines forecasting models with observational data has begun to flourish. Coupled land/ice-atmosphere/ocean models generally improve the forecasting ability of models. Data assimilation plays an important role in such coupled models, by providing initial conditions and/or empirical parameter estimation. Coupled data assimilation can generally be divided into three types: uncoupled, weakly coupled, or strongly coupled. This review provides an overview of coupled data assimilation, introduces examples of its use in research on sea ice-ocean interactions and the land, and discusses its future outlook. Assimilation of coupled data constitutes an effective method for monitoring cold regions for which observational data are scarce and should prove useful for climate change research and the design of efficient monitoring networks in the future.

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