Relevant bibliographies by topics / Data assimilation

Academic literature on the topic 'Data assimilation'

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Published: 4 June 2021
Last updated: 31 August 2024

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Journal articles on the topic "Data assimilation"

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Nie, Suping, Xiaolong Jia, Weitao Deng, Yixiong Lu, Dongyan He, Liang Zhao, Weihua Cao, and Xueliang Deng. "The Influence of FY-4A High-Frequency LST Data on Data Assimilation in a Climate Model." Remote Sensing 15, no. 1 (December 22, 2022): 59. http://dx.doi.org/10.3390/rs15010059.

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Based on the Beijing Climate Center’s land surface model BCC_AVIM2.0, an ensemble Kalman filter (EnKF) algorithm is developed to assimilate the land surface temperature (LST) product of the first satellite of Fengyun-4 series meteorological satellites of China to study the influence of LST data with different time frequencies on the surface temperature data assimilations. The MODIS daytime and nighttime LST products derived from Terra and Aqua satellites are used as independent validation data to test the assimilation results. The results show that diurnal variation information in the FY-4A LST data has significant effect on the assimilation results. When the time frequencies of the assimilated FY-4A LST data are sufficient, the assimilation scheme can effectively reduce the errors and the assimilation results reflect more reasonable spatial and temporal distributions. The assimilation experiments with a 3 h time frequency show less bias as well as RMSEs and higher temporal correlations than that of the model simulations at both daytime and nighttime periods. As the temporal frequency of assimilated LST observations decreases, the assimilation effects gradually deteriorate. When diurnal variation information is not considered at all in the assimilation, the assimilation with 24 h time frequency showed the largest errors and smallest time correlations in all experiments. The results demonstrate the potential of assimilating high-frequency FY-4A LST data to improve the performance of the BCC_AVIM2.0 land surface model. Furthermore, this study indicates that the diurnal variation information is a necessary factor needed to be considered when assimilating the FY-4A LST.
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Liu, Huaran, Feiyu Lu, Zhengyu Liu, Yun Liu, and Shaoqing Zhang. "Assimilating atmosphere reanalysis in coupled data assimilation." Journal of Meteorological Research 30, no. 4 (June 2016): 572–83. http://dx.doi.org/10.1007/s13351-016-6014-1.

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Wang, Zhaoyi, Andrea Storto, Nadia Pinardi, Guimei Liu, and Hui Wang. "Data assimilation of Argo profiles in a northwestern Pacific model." Natural Hazards and Earth System Sciences 17, no. 1 (January 5, 2017): 17–30. http://dx.doi.org/10.5194/nhess-17-17-2017.

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Abstract. Based on a novel estimation of background-error covariances for assimilating Argo profiles, an oceanographic three-dimensional variational (3DVAR) data assimilation scheme was developed for the northwestern Pacific Ocean model (NwPM) for potential use in operational predictions and maritime safety applications. Temperature and salinity data extracted from Argo profiles from January to December 2010 were assimilated into the NwPM. The results show that the average daily temperature (salinity) root mean square error (RMSE) decreased from 0.99 °C (0.10 psu) to 0.62 °C (0.07 psu) in assimilation experiments throughout the northwestern Pacific, which represents a 37.2 % (27.6 %) reduction in the error. The temperature (salinity) RMSE decreased by ∼ 0.60 °C ( ∼ 0.05 psu) for the upper 900 m (1000 m). Sea level, temperature and salinity were in better agreement with in situ and satellite datasets after data assimilation than before. In addition, a 1-month experiment with daily analysis cycles and 5-day forecasts explored the performance of the system in an operational configuration. The results highlighted the positive impact of the 3DVAR initialization at all forecast ranges compared to the non-assimilative experiment. Therefore, the 3DVAR scheme proposed here, coupled to ROMS, shows a good predictive performance and can be used as an assimilation scheme for operational forecasting.
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Sun, Juanzhen, Ying Zhang, Junmei Ban, Jing-Shan Hong, and Chung-Yi Lin. "Impact of Combined Assimilation of Radar and Rainfall Data on Short-Term Heavy Rainfall Prediction: A Case Study." Monthly Weather Review 148, no. 5 (May 1, 2020): 2211–32. http://dx.doi.org/10.1175/mwr-d-19-0337.1.

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Abstract Radar and surface rainfall observations are two sources of operational data crucial for heavy rainfall prediction. Their individual values on improving convective forecasting through data assimilation have been examined in the past using convection-permitting numerical models. However, the benefit of their simultaneous assimilations has not yet been evaluated. The objective of this study is to demonstrate that, using a 4D-Var data assimilation system with a microphysical scheme, these two data sources can be assimilated simultaneously and the combined assimilation of radar data and estimated rainfall data from radar reflectivity and surface network can lead to improved short-term heavy rainfall prediction. In our study, a combined data assimilation experiment is compared with a rainfall-only and a radar-only (with or without reflectivity) experiments for a heavy rainfall event occurring in Taiwan during the passage of a mei-yu system. These experiments are conducted by applying the Weather Research and Forecasting (WRF) 4D-Var data assimilation system with a 20-min time window aiming to improve 6-h convective heavy rainfall prediction. Our results indicate that the rainfall data assimilation contributes significantly to the analyses of humidity and temperature whereas the radar data assimilation plays a crucial role in wind analysis, and further, combining the two data sources results in reasonable analyses of all three fields by eliminating large, unphysical analysis increments from the experiments of assimilating individual data only. The results also show that the combined assimilation improves forecasts of heavy rainfall location and intensity of 6-h accumulated rainfall for the case studied.
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Tang, Wenfu, Benjamin Gaubert, Louisa Emmons, Daniel Ziskin, Debbie Mao, David Edwards, Avelino Arellano, Kevin Raeder, Jeffrey Anderson, and Helen Worden. "Advantages of assimilating multispectral satellite retrievals of atmospheric composition: a demonstration using MOPITT carbon monoxide products." Atmospheric Measurement Techniques 17, no. 7 (April 5, 2024): 1941–63. http://dx.doi.org/10.5194/amt-17-1941-2024.

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Abstract. The Measurements Of Pollution In The Troposphere (MOPITT) is an ideal instrument to understand the impact of (1) assimilating multispectral and joint retrievals versus single spectral products, (2) assimilating satellite profile products versus column products, and (3) assimilating multispectral and joint retrievals versus assimilating individual products separately. We use the Community Atmosphere Model with chemistry with the Data Assimilation Research Testbed (CAM-chem+DART) to assimilate different MOPITT carbon monoxide (CO) products to address these three questions. Both anthropogenic and fire CO emissions are optimized in the data assimilation experiments. The results are compared with independent CO observations from TROPOspheric Monitoring Instrument (TROPOMI), the Total Carbon Column Observing Network (TCCON), NOAA Carbon Cycle Greenhouse Gases (CCGG) sites, In-service Aircraft for a Global Observing System (IAGOS), and Western wildfire Experiment for Cloud chemistry, Aerosol absorption and Nitrogen (WE-CAN). We find that (1) assimilating the MOPITT joint (multispectral; near-IR and thermal IR) column product leads to better model–observation agreement at and near the surface than assimilating the MOPITT thermal-IR-only column retrieval. (2) Assimilating column products has a larger impact and improvement for background and large-scale CO compared to assimilating profile products due to vertical localization in profile assimilation. However, profile assimilation can outperform column assimilations in fire-impacted regions and near the surface. (3) Assimilating multispectral and joint products results in similar or slightly better agreement with observations compared to assimilating the single spectral products separately.
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Li, Jiajing, Yu Zhang, Siqi Chen, Duanzhou Shao, Jiazheng Hu, Junjie Feng, Qichang Tan, Deping Wu, and Jiaqi Kang. "Comparing Quality Control Procedures Based on Minimum Covariance Determinant and One-Class Support Vector Machine Methods of Aircraft Meteorological Data Relay Data Assimilation in a Binary Typhoon Forecasting Case." Atmosphere 14, no. 9 (August 25, 2023): 1341. http://dx.doi.org/10.3390/atmos14091341.

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This study investigates the impact of assimilating Aircraft Meteorological Data Relay (AMDAR) observations on the prediction of two typhoons, Nesat and Haitang (2017), using the Gridpoint Statistical Interpolation (GSI) assimilation system and the Weather Research and Forecasting (WRF) model. Two quality control (QC) methods, Minimum Covariance Determinant (MCD) and one-class Support Vector Machine (OCSVM), were employed to perform QC on the AMDAR observations before data assimilation. The QC results indicated that both methods significantly reduced kurtosis, skewness, and discrepancies between the AMDAR data and the reanalysis data. The data distribution after applying the MCD-QC method exhibited a closer resemblance to a Gaussian distribution. Four numerical experiments were conducted to assess the impact of different AMDAR data qualities on typhoon forecasting, including a control experiment without data assimilation (EXP-CNTL), assimilating all AMDAR observations (EXP-RAW), assimilating observations after applying MCD-QC (EXP-MCD), and assimilating observations after applying OCSVM-QC (EXP-SVM). The results demonstrated that using AMDAR data in assimilation improved the track and intensity prediction of the typhoons. Furthermore, utilizing QC before assimilation enhanced the performance of track forecasting prediction, with EXP-MCD showing the best performance. As for intensity prediction, the three assimilation experiments exhibited varying strengths and weaknesses at different times, with EXP-MCD showing smaller intensity forecast errors on average.
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Santana, Rafael, Helen Macdonald, Joanne O'Callaghan, Brian Powell, Sarah Wakes, and Sutara H. Suanda. "Data assimilation sensitivity experiments in the East Auckland Current system using 4D-Var." Geoscientific Model Development 16, no. 13 (July 6, 2023): 3675–98. http://dx.doi.org/10.5194/gmd-16-3675-2023.

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Abstract. This study analyses data assimilative numerical simulations in an eddy-dominated western boundary current: the East Auckland Current (EAuC). The goal is to assess the impact of assimilating surface and subsurface data into a model of the EAuC via running observing system experiments (OSEs). We used the Regional Ocean Modeling System (ROMS) in conjunction with the 4-dimensional variational (4D-Var) data assimilation scheme to incorporate sea surface height (SSH) and temperature (SST), as well as subsurface temperature, salinity and velocity from three moorings located at the upper, mid and lower continental slope using a 7 d assimilation window. Assimilation of surface fields (SSH and SST) reduced SSH root mean square deviation (RMSD) by 25 % in relation to the non-assimilative (NoDA) run. The inclusion of velocity subsurface data further reduced SSH RMSD up- and downstream the moorings by 18 %–25 %. By improving the representation of the mesoscale eddy field, data assimilation increased complex correlation between modelled and observed velocity in all experiments by at least three times. However, the inclusion of temperature and salinity slightly decreased the velocity complex correlation. The assimilative experiments reduced the SST RMSD by 36 % in comparison to the NoDA run. The lack of subsurface temperature for assimilation led to larger RMSD (>1 ∘C) around 100 m in relation to the NoDA run. Comparisons to independent Argo data also showed larger errors at 100 m in experiments that did not assimilate subsurface temperature data. Withholding subsurface temperature forces near-surface average negative temperature increments to the initial conditions that are corrected by increased net heat flux at the surface, but this had limited or no effect on water temperature at 100 m depth. Assimilation of mooring temperature generates mean positive increments to the initial conditions that reduces 100 m water temperature RMSD. In addition, negative heat flux and positive wind stress curl were generated near the moorings in experiments that assimilated subsurface temperature data. Positive wind stress curl generates convergence and downwelling that can correct interior temperature but might also be responsible for decreased velocity correlations. The few moored CTDs (eight) had little impact in correcting salinity in comparison to independent Argo data. However, using doubled decorrelation length scales of tracers and a 2 d assimilation window improved model salinity and temperature in comparison to Argo profiles throughout the domain. This assimilation configuration, however, led to large errors when subsurface temperature data were not assimilated due to incorrect increments to the subsurface. As all reanalyses show improved model-observation skill relative to HYCOM–NCODA (the model boundary conditions), these results highlight the benefit of numerical downscaling to a regional model of the EAuC.
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Gwirtz, K., M. Morzfeld, W. Kuang, and A. Tangborn. "A testbed for geomagnetic data assimilation." Geophysical Journal International 227, no. 3 (August 14, 2021): 2180–203. http://dx.doi.org/10.1093/gji/ggab327.

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SUMMARY Geomagnetic data assimilation merges past and present-day observations of the Earth’s magnetic field with numerical geodynamo models and the results are used to initialize forecasts. We present a new ‘proxy model’ that can be used to test, or rapidly prototype, numerical techniques for geomagnetic data assimilation. The basic idea for constructing a proxy is to capture the conceptual difficulties one encounters when assimilating observations into high-resolution, 3-D geodynamo simulations, but at a much lower computational cost. The framework of using proxy models as ‘gate-keepers’ for numerical methods that could/should be considered for more extensive testing on operational models has proven useful in numerical weather prediction, where advances in data assimilation and, hence, improved forecast skill, are at least in part enabled by the common use of a wide range of proxy models. We also present a large set of systematic data assimilation experiments with the proxy to reveal the importance of localization and inflation in geomagnetic data assimilation.
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Valler, Veronika, Yuri Brugnara, Jörg Franke, and Stefan Brönnimann. "Assimilating monthly precipitation data in a paleoclimate data assimilation framework." Climate of the Past 16, no. 4 (July 24, 2020): 1309–23. http://dx.doi.org/10.5194/cp-16-1309-2020.

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Abstract. Data assimilation approaches such as the ensemble Kalman filter method have become an important technique for paleoclimatological reconstructions and reanalysis. Different sources of information, from proxy records and documentary data to instrumental measurements, were assimilated in previous studies to reconstruct past climate fields. However, precipitation reconstructions are often based on indirect sources (e.g., proxy records). Assimilating precipitation measurements is a challenging task because they have high uncertainties, often represent only a small region, and generally do not follow a Gaussian distribution. In this paper, experiments are conducted to test the possibility of using information about precipitation in climate reconstruction with monthly resolution by assimilating monthly instrumental precipitation amounts or the number of wet days per month, solely or in addition to other climate variables such as temperature and sea-level pressure, into an ensemble of climate model simulations. The skill of all variables (temperature, precipitation, sea-level pressure) improved over the pure model simulations when only monthly precipitation amounts were assimilated. Assimilating the number of wet days resulted in similar or better skill compared to assimilating the precipitation amount. The experiments with different types of instrumental observations being assimilated indicate that precipitation data can be useful, particularly if no other variable is available from a given region. Overall the experiments show promising results because with the assimilation of precipitation information a new data source can be exploited for climate reconstructions. The wet day records can become an especially important data source in future climate reconstructions because many existing records date several centuries back in time and are not limited by the availability of meteorological instruments.
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Fabry, Frédéric, and Véronique Meunier. "Why Are Radar Data so Difficult to Assimilate Skillfully?" Monthly Weather Review 148, no. 7 (June 24, 2020): 2819–36. http://dx.doi.org/10.1175/mwr-d-19-0374.1.

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Abstract Although radar is our most useful tool for monitoring severe weather, the benefits of assimilating its data are often short lived. To understand why, we documented the assimilation requirements, the data characteristics, and the common practices that could hinder optimum data assimilation by traditional approaches. Within storms, radars provide dense measurements of a few highly variable storm outcomes (precipitation and wind) in atmospherically unstable conditions. However, statistical relationships between errors of observed and unobserved quantities often become nonlinear because the errors in these areas tend to become large rapidly. Beyond precipitating areas lie large regions for which radars provide limited new information, yet whose properties will soon shape the outcome of future storms. For those areas, any innovation must consequently be projected from sometimes distant precipitating areas. Thus, radar data assimilation must contend with a double need at odds with many traditional assimilation implementations: correcting in-storm properties with complex errors while projecting information at unusually far distances outside precipitating areas. To further complicate the issue, other data properties and practices, such as assimilating reflectivity in logarithmic units, are not optimal to correct all state variables. Therefore, many characteristics of radar measurements and common practices of their assimilation are incompatible with necessary conditions for successful data assimilation. Facing these dataset-specific challenges may force us to consider new approaches that use the available information differently.
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Dissertations / Theses on the topic "Data assimilation"

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Peubey, Carole. "Assimilation of ENVISAT data in an advanced data assimilation system." Thesis, University of Reading, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.485367.

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i~to a stratosphere-troposphere version of the Met Office assimilation system, producing one of the first analyses to reproduce the September 2002 split of the Antarctic polar vortex. The aim of the project was to investigate the benefit of assimilating MIPAS retrievals and to assess the Met Office 3D-Var assimilation system by examining its different components. The ozone analysis was found to agree with independent ozone observations through most of the middle and upper stratosphere, biases above 60 hPa being within the range -20% to +10% and typically smaller. More significant positive biases were found in the lower stratosphere and inside the polar vortex. Although ozone amounts are shown to be slightly overestimated by MIPAS retrievals in these same regions, these biases are demonstrated to be caused by shortcomings in the model chemistry and transport. MIPAS data have been shown to have a limited impact on the Met Office temperature analysis, although a ' positive effect was identified at the mesopause. It is shown that MIPAScould bring larger benefits if more realistic background error statistics were used for ozone, especially in the lower stratosphere. Based on an evaluation of these statistics using independent datasets, it is suggested that background error variances should be decreased near the ozone maximum and increased below 70 hPa It is also recommended to introduce latitudedependence in vertical error correlations and height-dependence in horizontal error correlations. Improvements are also proposed to improve the ozone assimilation in the polar vortex region. Finally, analysed winds have been found to induce errOneous transport of ozone by increasing vertical diffusion of ozone and enhancing the mean zonal circulations. This especially affects the tropics, where ozone analyses reveal excessive exchanges of air parcels between the stratosphere and the troposphere.
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Barillec, Remi Louis. "Bayesian data assimilation." Thesis, Aston University, 2008. http://publications.aston.ac.uk/15276/.

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This thesis addresses data assimilation, which typically refers to the estimation of the state of a physical system given a model and observations, and its application to short-term precipitation forecasting. A general introduction to data assimilation is given, both from a deterministic and stochastic point of view. Data assimilation algorithms are reviewed, in the static case (when no dynamics are involved), then in the dynamic case. A double experiment on two non-linear models, the Lorenz 63 and the Lorenz 96 models, is run and the comparative performance of the methods is discussed in terms of quality of the assimilation, robustness in the non-linear regime and computational time.
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Gregory, Alastair. "Multilevel ensemble data assimilation." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/60645.

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This thesis aims to investigate and improve the efficiency of ensemble transform methods for data assimilation, using an application of multilevel Monte Carlo. Multilevel Monte Carlo is an interesting framework to estimate statistics of discretized random variables, since it uses a hierarchy of discretizations with a refinement in resolution. This is in contrast to standard Monte Carlo estimators that only use a discretization at a fine resolution. A linear combination of sub-estimators, on different levels of this hierarchy, can provide new statistical estimators to random variables at the finest level of resolution with significantly greater efficiency than a standard Monte Carlo equivalent. Therefore, the extension to computing filtering estimators for data assimilation is a natural, but challenging area of study. These challenges arise due to the fact that correlation must be imparted between ensembles on adjacent levels of resolution and maintained during the assimilation of data. The methodology proposed in this thesis, considers coupling algorithms to establish this correlation. This generates multilevel estimators that significantly reduce the computational expense of propagating ensembles of discretizations through time and space, in between stages of data assimilation. An effective benchmark of this methodology is realised by filtering data into high-dimensional spatio-temporal systems, where a high computational complexity is required to solve the underlying partial differential equations. A novel extension of an ensemble transform localisation framework to finite element approximations within random spatio-temporal systems is proposed, in addition to a multilevel equivalent.
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Woodgate, Rebecca A. "Data assimilation in ocean models." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359566.

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Moore, A. M. "Data assimilation in ocean models." Thesis, University of Oxford, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.375276.

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Da, Dalt Federico. "Ionospheric modelling and data assimilation." Thesis, University of Bath, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.665450.

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A New Ionospheric Model (ANIMo) based upon the physics of production, loss, and vertical transport has been developed. The model is driven by estimates of neutral composition, temperature and solar flux and is applicable to the mid-latitude regions of the Earth under quiet and moderate geomagnetic conditions. This model was designed to exhibit specific features that were not easy to find all together in other existing ionospheric models. ANIMo needed to be simple to use and interact with, relatively accurate, reliable, robust and computationally efficient. The definition of these characteristics was mostly driven by the intention to use ANIMo in a Data Assimilation (DA) scheme. DA or data ingestion can be described as a technique where observations and model realizations, called background information, are combined together to achieve a level of accuracy that is higher than the accuracy of the two elements taken separately. In this project ANIMo was developed to provide a robust and reliable background contribution. The observations are given by the Global Positioning System (GPS) ionospheric measurements, collected from several networks of GPS ground-station receivers and are available on on-line repositories. The research benefits from the Multi-Instrument Data Analysis System (MIDAS) [Mitchell and Spencer, 2003; Spencer and Mitchell, 2007], which is an established ionospheric tomography software package that produces three dimensional reconstructions of the ionosphere starting from GPS measurements. Utilizing ANIMo in support of MIDAS has therefore the potential to generate a very stable set-up for monitoring and study the ionosphere. In particular, the model is expected to compensate some of the typical limitations of ionospheric tomography techniques described by Yeh and Raymund [1991] and Raymund et al. [1994]. These are associated with the lack of data due to the uneven distribution of ground-based receivers and limitations to viewing angles. Even in regions of good receiver coverage there is a need to compensate for information on the vertical profile of ionisation. MIDAS and other tomography techniques introduce regularization factors that can assure the achievement of a unique solution in the inversion operation. These issues could be solved by aiding the operation with external information provided by a physical model, like ANIMo, through a data ingestion scheme; this ensures that the contribution is completely independent and there is an effective accuracy improvement. Previously, the limitation in vertical resolution has been solved by applying vertical orthonormal functions based upon empirical models in different ways [Fougere, 1995; Fremouw et al., 1992; Sutton and Na, 1994]. The potential for the application of a physical model, such ANIMo is that it can provide this information according to the current ionospheric conditions. During the project period ANIMo has been developed and incorporated with MIDAS. The result is A New Ionospheric Data Assimilation System (ANIDAS); its name suggests that the system is the implementation of ANIMo in MIDAS. Because ANIDAS is a data ingestion scheme, it has the potential to be used to perform not only more accurate now-casting but also forecasting. The outcomes of ANIDAS at the current time can be used to initialise ANIMo for the next time step and therefore trigger another assimilation turn. In future, it is intended that ANIMo will form the basis to a new system to predict the electron density of the ionosphere – ionospheric forecasting.
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Shukla, Abhishek. "Analysis of data assimilation schemes." Thesis, University of Warwick, 2016. http://wrap.warwick.ac.uk/90880/.

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Data assimilation schemes are methods to estimate true underlying state of the physical systems of interest by combining the theoretical knowledge about the underlying system with available observations of the state. However, in most of the physical systems the observations often are noisy and only partially available. In the first part of this thesis we study the case of sequential data assimilation scheme, when the underlying system is nonlinear chaotic and the observations are partial and noisy. We produce a rigorous and quantitative analysis of data assimilation process for fixed observation modes. We also introduce a novel method of dynamically rearranging observation modes, leading to the requirement of fewer observation modes while maintaining the accuracy of the data assimilation process. In the second part of the thesis we focus on 4DVAR data assimilation scheme which is a variational method. 4DVAR data assimilation is a method that solves a variational problem; given a set of observations and a numerical model for the underlying physical system together with a priori information on the initial condition to estimate the initial condition for the underlying model. We propose a hybrid data assimilation scheme where, we consider the 3DVAR scheme for the model as the constraint on the variational form, rather than constraining the variational form with the original model. We observe that this method reduces the computational cost of the minimization of the 4DVAR variational form, however, it introduces a bias in the estimate of the initial condition. We then explore how the results can be extended to weak constraint 4DVAR.
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Lindskog, Magnus. "On errors in meteorological data assimilation." Doctoral thesis, Stockholm : Department of Meteorology, Stockholm university, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-7258.

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Milewski, Thomas. "Stratospheric chemical-dynamical ensemble data assimilation." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=110352.

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Ensemble data assimilation uses Monte-Carlo methods to estimate flow-dependent error covariances which allow the transfer of information from observed variables to correlated ones. As the winds are largely unobserved in the stratosphere and models have biases there, the possibility to constrain the dynamical analysis from temperature or ozone observations is attempted using ensemble data assimilation.The applicability of coupled chemical/dynamical ensemble data assimilation in the stratosphere is tested in idealized perfect model observation system simulation experiments with the IGCM-FASTOC chemistry-climate model. Covariance localization is found to be necessary for stability of the Ensemble Kalman Filter (EnKF) data assimilation system and optimal localization parameters yield a strong constraint on the global dynamical state of the model when assimilating synthetic limb-sounding stratospheric temperature or ozone observations only. The multivariate coupling between ozone, temperature and winds is investigated in the optimized EnKF system. Stratospheric temperature and ozone observations induce valuable dynamical analysis increments during the analysis step. There is additional feedback during the forecast steps in the ensemble data assimilation system, further constraining the global dynamical and ozone states. The potential impact of assimilating observations posterior to analysis time in multivariate mode was estimated with an Ensemble Kalman Smoother (EnKS). Assimilation of additional asynchronous observations up to 48 hours posterior toanalysis time provided improvements on the EnKF analysis nearly similar to the ones obtained from the assimilation of a same amount of additional synchronous observations. The EnKS assimilation showed beneficial impacts on the unobserved variables analysis state but mixed impacts on the observed variable analysis state.The capacity to constrain the unobserved stratospheric winds by assimilating ozone observations is demonstrated in the ensemble data assimilation system with the EnKF and EnKS. The chemical-dynamical error covariances are critical to reduce the wind error in the model analysis state particularly through the ozone-wind covariances effective in the upper-troposphere lower-stratosphere region. Additional tests with strongly-biased initial forecasts, within a stratospheric sudden warming experiment, confirm the ability of the EnKF to efficiently propagate information from ozone observations to the dynamical model state.
L'assimilation d'ensemble utilise une méthode de Monte-Carlo pour estimer les covariances d'erreur du moment qui permettent le transfert d'information des variables observées aux variables corrélées à celles-ci. Puisque les vents sont très peu observés dans la stratosphère et que les modèles y présentent des biais, la possibilité de contraindre l'état dynamique du modèle par l'assimilation d'observations de température et d'ozone par la technique d'ensemble est tentée. L'applicabilité de l'assimilation d'ensemble dans un système chimique/dynamique couplé est testé lors d'une expérience idéalisé (modèle parfait) de simulation de système d'observation avec le modèle de chimie-climat IGCM-FASTOC. La localisation des covariances est indispensable à la stabilité du système d'assimilation avec filtre de Kalman d'ensemble (EnKF) et les paramètres optimaux offrent une forte contrainte sur l'état dynamique global du modèle lorsque l'on assimile des observations satellites synthétiques de température et d'ozone stratosphériques uniquement. Le couplage entre l'ozone, la température et les vents est étudié dans le système EnKF optimisé. Les observations de température et d'ozone stratosphériques créent des incréments dynamiques bénéfiques lors des phases d'analyses. Il y a également une rétroaction lors de la phase de prédiction du système d'assimilation de données, qui aide à contraindre davantage les états chimiques et dynamiques globaux. L'impact potentiel de l'assimilation de données postérieures au temps d'analyse en mode multivarié est estimé avec un lisseur d'ensemble de Kalman (EnKS). L'assimilation d'observations additionnelles asynchrones, ayant jusqu'à 48 heures d'écart avec le temps d'analyse, offre des améliorations aux analyses de l'EnKF presque équivalentes à celles obtenues par assimilation d'une quantité égale d'observations additionnelles synchrones. L'EnKS présente des impacts bénéfiques sur l'état d'analyse des variables non observées mais des impacts mitigés sur l'état analysé des variables observées. La capacité de contraindre les vents stratosphériques non-observés grâce à l'assimilation d'observations d'ozone est démontrée dans le système d'assimilation d'ensemble avec l'EnKF et l'EnKS. Les covariances d'erreurs chimiques- dynamiques sont essentielles à la réduction de l'erreur de vents dans l'état analysé du modèle, en particulier les covariances ozone-vent qui font effet dans la haute troposphère et basse stratosphère. Des expériences additionelles avec un état initial fortement biaisé, en l'occurence un réchauffement stratosphérique soudain, confirment l'abilité de l'EnKF à transférer de façon efficace l'information depuis les observations d'ozone vers l'état dynamique du modèle.
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Stewart, Laura M. "Correlated observation errors in data assimilation." Thesis, University of Reading, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.553080.

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Data assimilation techniques combine observations and prior model forecasts to create initial conditions for numerical weather prediction (NWP). The relative weighting as- signed to each observation in the analysis is determined by the error associated with its measurement. Remote sensing data often have correlated errors, but the correlations are typically ignored in NWP. As operational centres move towards high-resolution fore- casting, the assumption of uncorrelated errors becomes impractical. This thesis provides new evidence that including observation error correlations in data assimilation schemes is both feasible and beneficial. We study the dual problem of quantifying and modelling observation error correlation structure. Firstly, in original work using statistics from the Met Office 4D- Var assimilation system, we diagnose strong cross-channel error eo- variances for the IASI satellite instrument. We then see how in a 3D- Var framework, information content is degraded under the assumption of uncorrelated errors, while re- tention of an approximate correlation gives clear benefits. These novel results motivate further study. We conclude by modelling observation error correlation structure in the framework of a one-dimensional shallow water model. Using an incremental 4D- Var assimilation system we observe that analysis errors are smallest when correlated error covariance matrix approximations are used over diagonal approximations. The new re- sults reinforce earlier conclusions on the benefits of including some error correlation structure.
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Books on the topic "Data assimilation"

1

Lahoz, William, Boris Khattatov, and Richard Menard, eds. Data Assimilation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-74703-1.

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Evensen, Geir. Data Assimilation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03711-5.

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Law, Kody, Andrew Stuart, and Konstantinos Zygalakis. Data Assimilation. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20325-6.

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Brasseur, Pierre P., and Jacques C. J. Nihoul, eds. Data Assimilation. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-78939-7.

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Evensen, Geir, Femke C. Vossepoel, and Peter Jan van Leeuwen. Data Assimilation Fundamentals. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96709-3.

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Van Leeuwen, Peter Jan, Yuan Cheng, and Sebastian Reich. Nonlinear Data Assimilation. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18347-3.

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Swinbank, Richard, Victor Shutyaev, and William Albert Lahoz, eds. Data Assimilation for the Earth System. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-010-0029-1.

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Wang, Yuchen. Tsunami Data Assimilation for Early Warning. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-7339-0.

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Richard, Swinbank, Shuti͡aev V. P, and Lahoz William Albert, eds. Data assimilation for the earth system. Dordrecht: Kluwer Academic Publishers, 2003.

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Evensen, Geir. Data Assimilation: The Ensemble Kalman Filter. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.

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Book chapters on the topic "Data assimilation"

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Lahoz, William, and Quentin Errera. "Constituent Assimilation." In Data Assimilation, 449–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-74703-1_18.

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Talagrand, Olivier. "Variational Assimilation." In Data Assimilation, 41–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-74703-1_3.

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Law, Kody, Andrew Stuart, and Konstantinos Zygalakis. "Mathematical Background." In Data Assimilation, 1–23. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20325-6_1.

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Law, Kody, Andrew Stuart, and Konstantinos Zygalakis. "Discrete Time: Formulation." In Data Assimilation, 25–52. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20325-6_2.

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Law, Kody, Andrew Stuart, and Konstantinos Zygalakis. "Discrete Time: Smoothing Algorithms." In Data Assimilation, 53–77. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20325-6_3.

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Law, Kody, Andrew Stuart, and Konstantinos Zygalakis. "Discrete Time: Filtering Algorithms." In Data Assimilation, 79–114. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20325-6_4.

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Law, Kody, Andrew Stuart, and Konstantinos Zygalakis. "Discrete Time: MATLAB Programs." In Data Assimilation, 115–49. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20325-6_5.

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Law, Kody, Andrew Stuart, and Konstantinos Zygalakis. "Continuous Time: Formulation." In Data Assimilation, 151–74. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20325-6_6.

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Law, Kody, Andrew Stuart, and Konstantinos Zygalakis. "Continuous Time: Smoothing Algorithms." In Data Assimilation, 175–85. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20325-6_7.

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Law, Kody, Andrew Stuart, and Konstantinos Zygalakis. "Continuous Time: Filtering Algorithms." In Data Assimilation, 187–206. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20325-6_8.

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Conference papers on the topic "Data assimilation"

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Nævdal, Geir, Ove Sævareid, and Rolf J. Lorentzen. "DATA ASSIMILATION USING MRI DATA." In VII European Congress on Computational Methods in Applied Sciences and Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2016. http://dx.doi.org/10.7712/100016.2101.9975.

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Suzuki, Shoko, and Takayuki Osogami. "Real-time data assimilation." In 2011 Winter Simulation Conference - (WSC 2011). IEEE, 2011. http://dx.doi.org/10.1109/wsc.2011.6147791.

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Penenko, A. V. "Sequential variational data assimilation." In SPIE Proceedings, edited by Gelii A. Zherebtsov and Gennadii G. Matvienko. SPIE, 2006. http://dx.doi.org/10.1117/12.675876.

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Nava, B. "Data assimilation into NeQuick." In 2015 1st URSI Atlantic Radio Science Conference (URSI AT-RASC). IEEE, 2015. http://dx.doi.org/10.1109/ursi-at-rasc.2015.7303113.

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"INCONSISTENCY-TOLERANT KNOWLEDGE ASSIMILATION." In 2nd International Conference on Software and Data Technologies. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0001331701980205.

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Erichsen, A. C., J. V. T. Sorensen, I. S. Hansen, and F. Moehlenberg. "Water forecasts and data assimilation." In 2008 IEEE/OES US/EU-Baltic International Symposium (BALTIC). IEEE, 2008. http://dx.doi.org/10.1109/baltic.2008.4625490.

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Reddy, K. V., Tarunraj Singh, Yang Cheng, and Peter Scott. "Data Assimilation for Dispersion Models." In 2006 9th International Conference on Information Fusion. IEEE, 2006. http://dx.doi.org/10.1109/icif.2006.301615.

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Kato, Hiroshi, and Shigeru Obayashi. "Data Assimilation for Turbulent Flows." In 16th AIAA Non-Deterministic Approaches Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2014. http://dx.doi.org/10.2514/6.2014-1177.

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Djurcilov, Suzana, and Alex Pang. "Visualization tools for data assimilation." In Electronic Imaging '97, edited by Georges G. Grinstein and Robert F. Erbacher. SPIE, 1997. http://dx.doi.org/10.1117/12.270332.

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Lu, Qifeng, Xuebao Wu, Peng Zhang, Songyan Gu, Chaohua Dong, Jiandong Gong, Xueshun Shen, Chenli Qi, and Gang Ma. "Assimilating FY-3A VASS data into Chinese 3Dvar assimilation system (Grapes 3Dvar)." In 2009 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2009. http://dx.doi.org/10.1109/igarss.2009.5418215.

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Reports on the topic "Data assimilation"

1

Bennett, Andrew F. Open Ocean Data Assimilation. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada627701.

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Bennett, Andrew F. Open Ocean Data Assimilation. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada629134.

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Bennett, Andrew F. Open Ocean Data Assimilation. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada629176.

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Godinez Vazquez, Humberto C. Data Assimilation with GITM. Office of Scientific and Technical Information (OSTI), February 2013. http://dx.doi.org/10.2172/1062701.

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Cossarini, Gianpiero. Results of the BGC data assimilation. EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d4.10.

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This document presents the results of simulations that include glider profiles assimilation. Simulations are performed with the Marine Copernicus operational biogeochemical model system of the Mediterranean Sea. The deliverable shows that the assimilation of BGC-glider is feasible in the contest of biogeochemical operational systems and that it is built upon the experience of BGC-Argo float data assimilation. Different configuration of the assimilation of glider data have been tested to assess the impact of the physical and biogeochemical glider observations. The deliverable also describes the pre-processing activities of the BGC-glider data to provide qualified observations for the data assimilation and the cross validation of chlorophyll glider data with other sensors (ocean colour and BGC-Argo floats). Results of the simulations show that BGC-glider data assimilation, as already shown for BGC-Argo floats, provides complementary information with respect to Ocean Colour data (which is the only or the most commonly assimilated data in biogeochemical operational systems). Beside their relatively limited horizontal spatial impact, the assimilation of BGC profiles can constrain model simulations for relevant biogeochemical processes in specific periods (summer and transition periods) and layers (surface and subsurface). Results also highlight the importance of the assimilation modelling systems that can efficiently resolve the inconsistencies between chlorophyll observations of different sensors. (EuroSea Deliverable ; D4.10)
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Williams, Brian J. Data Assimilation - Advances and Applications. Office of Scientific and Technical Information (OSTI), July 2014. http://dx.doi.org/10.2172/1148964.

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Mariano, Arthur J., and Toshio M. Chin. Coastal and Ocean Data Assimilation. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada612624.

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Mariano, Arthur J., and Toshio M. Chin. Coastal And Ocean Data Assimilation. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada533825.

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Szunyogh, Istvan. Tropical Cyclone Ensemble Data Assimilation. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada542060.

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Moura, Jose M. Data Assimilation in Ocean Prediction. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada630869.

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