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

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.

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Strongly coupled data assimilation with the coupled ocean-atmosphere model AWI-CM: comparison with the weakly coupled data assimilation

10.5194/egusphere-egu21-14181 ◽

2021 ◽

Author(s):

Qi Tang ◽

Longjiang Mu ◽

Helge Goessling ◽

Tido Semmler ◽

Lars Nerger

Keyword(s):

Data Assimilation ◽

Wind Velocity ◽

Climate Model ◽

Specific Humidity ◽

The Arctic ◽

Tropical Region ◽

Strongly Coupled ◽

Weakly Coupled ◽

Coupled Data Assimilation ◽

Atmospheric Variables

We compare the results of strongly coupled data assimilation and weakly coupled data assimilation by analyzing the assimilation effect on the prediction of the ocean as well as the atmosphere variables. The AWI climate model (AWI-CM), which couples the ocean model FESOM and the atmospheric model ECHAM, is coupled with the parallel data assimilation framework (PDAF, http://pdaf.awi.de). The satellite sea surface temperature is assimilated. For the weakly coupled data assimilation, only the ocean variables are directly updated by the assimilation while the atmospheric variables are influenced through the model. For the strongly coupled data assimilation, both the ocean and the atmospheric variables are directly updated by the assimilation algorithm. The results are evaluated by comparing the estimated ocean variables with the dependent/independent observational data, and the estimated atmospheric variables with the ERA-interim data. In the ocean, both the WCDA and the SCDA improve the prediction of the temperature and SCDA and WCDA give the same RMS error of SST. In the atmosphere, WCDA gives slightly better results for the 2m temperature and 10m wind velocity than the SCDA. In the free atmosphere, SCDA yields smaller errors for the temperature, wind velocity and specific humidity than the WCDA in the Arctic region, while in the tropical region, the error are larger in general.

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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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Assessment of ocean analysis and forecast from an atmosphere–ocean coupled data assimilation operational system

Ocean Science ◽

10.5194/os-15-1307-2019 ◽

2019 ◽

Vol 15 (5) ◽

pp. 1307-1326 ◽

Cited By ~ 6

Author(s):

Catherine Guiavarc'h ◽

Jonah Roberts-Jones ◽

Chris Harris ◽

Daniel J. Lea ◽

Andrew Ryan ◽

...

Keyword(s):

Data Assimilation ◽

Weather Forecasting ◽

Current System ◽

Weather Prediction ◽

Surface Ocean ◽

Weakly Coupled ◽

Ocean Prediction ◽

Coupled Data Assimilation ◽

Ocean Forecasting ◽

Prediction Systems

Abstract. The development of coupled atmosphere–ocean prediction systems with utility on short-range numerical weather prediction (NWP) and ocean forecasting timescales has accelerated over the last decade. This builds on a body of evidence showing the benefit, particularly for weather forecasting, of more correctly representing the feedbacks between the surface ocean and atmosphere. It prepares the way for more unified prediction systems with the capability of providing consistent surface meteorology, wave and surface ocean products to users for whom this is important. Here we describe a coupled ocean–atmosphere system, with weakly coupled data assimilation, which was operationalised at the Met Office as part of the Copernicus Marine Environment Service (CMEMS). We compare the ocean performance to that of an equivalent ocean-only system run at the Met Office and other CMEMS products. Sea surface temperatures in particular are shown to verify better than in the ocean-only systems, although other aspects including temperature profiles and surface currents are slightly degraded. We then discuss the plans to improve the current system in future as part of the development of a “coupled NWP” system at the Met Office.

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Facilitating Strongly Coupled Ocean–Atmosphere Data Assimilation with an Interface Solver

Monthly Weather Review ◽

10.1175/mwr-d-15-0041.1 ◽

2015 ◽

Vol 144 (1) ◽

pp. 3-20 ◽

Cited By ~ 24

Author(s):

Sergey Frolov ◽

Craig H. Bishop ◽

Teddy Holt ◽

James Cummings ◽

David Kuhl

Keyword(s):

Data Assimilation ◽

Impact Analysis ◽

Coupled Model ◽

Deep Ocean ◽

Accurate Solution ◽

Fluid Interface ◽

Strongly Coupled ◽

Grid Points ◽

Coupled Data Assimilation ◽

Observation Vector

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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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

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.

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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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Assessing a New Coupled Data Assimilation System Based on the Met Office Coupled Atmosphere–Land–Ocean–Sea Ice Model

Monthly Weather Review ◽

10.1175/mwr-d-15-0174.1 ◽

2015 ◽

Vol 143 (11) ◽

pp. 4678-4694 ◽

Cited By ~ 45

Author(s):

D. J. Lea ◽

I. Mirouze ◽

M. J. Martin ◽

R. R. King ◽

A. Hines ◽

...

Keyword(s):

Data Assimilation ◽

Sea Ice ◽

Coupled Model ◽

Background Information ◽

Ocean Data Assimilation ◽

Weakly Coupled ◽

Coupled Data Assimilation ◽

The Impact ◽

Data Assimilation System ◽

Assimilation System

Abstract A new coupled data assimilation (DA) system developed with the aim of improving the initialization of coupled forecasts for various time ranges from short range out to seasonal is introduced. The implementation here is based on a “weakly” coupled data assimilation approach whereby the coupled model is used to provide background information for separate ocean–sea ice and atmosphere–land analyses. The increments generated from these separate analyses are then added back into the coupled model. This is different from the existing Met Office system for initializing coupled forecasts, which uses ocean and atmosphere analyses that have been generated independently using the FOAM ocean data assimilation system and NWP atmosphere assimilation systems, respectively. A set of trials has been run to investigate the impact of the weakly coupled data assimilation on the analysis, and on the coupled forecast skill out to 5–10 days. The analyses and forecasts have been assessed by comparing them to observations and by examining differences in the model fields. Encouragingly for this new system, both ocean and atmospheric assessments show the analyses and coupled forecasts produced using coupled DA to be very similar to those produced using separate ocean–atmosphere data assimilation. This work has the benefit of highlighting some aspects on which to focus to improve the coupled DA results. In particular, improving the modeling and data assimilation of the diurnal SST variation and the river runoff should be examined.

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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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Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: example of AWI-CM (AWI-CM-PDAF 1.0)

Geoscientific Model Development ◽

10.5194/gmd-13-4305-2020 ◽

2020 ◽

Vol 13 (9) ◽

pp. 4305-4321

Author(s):

Lars Nerger ◽

Qi Tang ◽

Longjiang Mu

Keyword(s):

Data Assimilation ◽

Climate Model ◽

Numerical Models ◽

Computing Time ◽

Coupled Model ◽

Ensemble Methods ◽

Ocean Model ◽

Coupled Models ◽

Parallel Data ◽

Efficient Data

Abstract. Data assimilation integrates information from observational measurements with numerical models. When used with coupled models of Earth system compartments, e.g., the atmosphere and the ocean, consistent joint states can be estimated. A common approach for data assimilation is ensemble-based methods which utilize an ensemble of state realizations to estimate the state and its uncertainty. These methods are far more costly to compute than a single coupled model because of the required integration of the ensemble. However, with uncoupled models, the ensemble methods also have been shown to exhibit a particularly good scaling behavior. This study discusses an approach to augment a coupled model with data assimilation functionality provided by the Parallel Data Assimilation Framework (PDAF). Using only minimal changes in the codes of the different compartment models, a particularly efficient data assimilation system is generated that utilizes parallelization and in-memory data transfers between the models and the data assimilation functions and hence avoids most of the file reading and writing, as well as model restarts during the data assimilation process. This study explains the required modifications to the programs with the example of the coupled atmosphere–sea-ice–ocean model AWI-CM (AWI Climate Model). Using the case of the assimilation of oceanic observations shows that the data assimilation leads only to small overheads in computing time of about 15 % compared to the model without data assimilation and a very good parallel scalability. The model-agnostic structure of the assimilation software ensures a separation of concerns in which the development of data assimilation methods can be separated from the model application.

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