Assimilation Algorithm Research Articles

Variational assimilation of surface wave data for bathymetry reconstruction. Part I: algorithm and test cases

Accurate mapping of ocean bathymetry is needed for effective modelling of ocean dynamics, such as tsunami prediction. Available bathymetry data does not always provide the resolution to model such nonlinear waves accurately, and collection of accurate data is logistically challenging. As an alternative, in this study we develop and evaluate a variational data assimilation scheme for the one-dimensional nonlinear shallow water equations that estimates bathymetry using a finite set of observations of surface wave height. We demonstrate that convergence to exact bathymetry is improved by including more observation locations and by implementing a low-pass filter in the data assimilation algorithm to remove small-scale noise. A necessary condition for convergence of the bathymetry reconstruction is that the amplitude of the initial conditions is less than 1% of the bathymetry height. We use density-based global sensitivity analysis (GSA) to assess the sensitivity of the surface wave and reconstruction error to model parameters. By demonstrating low sensitivity of the surface wave to the reconstruction error, we show that reconstructing the bathymetry with a relative error of about 10% is sufficiently accurate for surface wave modelling in most cases. These results can be used to guide the development of similar assimilation schemes in higher dimensions and more realistic geometries.

EVALUATING PREDICTIONS OF THE SOIL MOISTURE MODEL WITH DATA ASSIMILATION BY THE TRIPLE COLLOCATION METHOD

For a long time, ground measurements are considered the most accurate method of regular monitoring of soil moisture. However, ground stations are expensive, require local calibration and thus often not practical to use. Other, more affordable means of soil moisture monitoring can be developed with the recent advancement of Earth remote sensing technologies. In this paper, we describe a nonlinear problem of soil moisture transfer problem with addition of satellite soil moisture measurements. The mathematical model is based on the Richards equation for soil moisture transport, and solved with the finite difference method on implicit iterative scheme. Satellite moisture retrievals are acquired by combining active and passive sensors data with the decomposition algorithm. The satellite data are incorporated into the model with the data assimilation algorithm called Newtonian nudging. This method adds a special ‘nudging’ term into the model governing equation. This assures that the model is corrected by satellite measurements without affecting the process physics. Moreover, we look into the nudging factor problem, and propose a simple empirical relation based on the soil properties for more universally stable work of the method. For validation purpose, we conduct a massive numerical experiments over all registered ground stations in the USA. Evaluation is done by the use of triple collocation method, which allows assessing the errors of three independent data sources. The data sources used for evaluation are the model results, ground station measurements and ERA5 satellite observations. The results demonstrate that the presented model is capable of producing results with close accuracy to the ground station measurements.

China Ocean ReAnalysis (CORA) version 1.0 products and validation for 2009–18: 2009–18年西北太平洋区域海洋再分析CORA1.0产品研制及检验

China Ocean ReAnalysis (CORA) version 1.0 products for the period 2009–18 have been developed and validated. The model configuration and assimilation algorithm have both been updated compared to those of the 51-year (1958–2008) products. The assimilated observations include temperature and salinity field data, satellite remote sensing sea surface temperature, and merged sea surface height (SSH) anomaly data. The validation includes the following three aspects: (1) Temperature, salinity, and SSH anomaly root-mean-square errors (RMSEs) are computed as a primary evaluation of the reanalysis quality. The 0–2000 m domain-averaged RMSEs of temperature and salinity are 0.61°C and 0.08 psu, respectively. The SSH anomaly RMSE is less than 0.2 m in most regions. (2) The 35°N temperature section is used to evaluate the ability to reproduce the thermocline, mixing layer, and Yellow Sea cold water mass. In summer, the thermocline is reinforced, with the gradient changing from 3°C in May to 10°C in August. The mixing-layer depth reproduced by CORA is consistent with that computed from the observed climatology. The Yellow Sea cold water mass forms at a depth of 50 m. (3) The reanalysis current is examined against the tracks of some drifting buoys. The results show that the reanalysis current can capture the mesoscale eddies near the Kuroshio, which are similar to those described by the drifting buoys. Overall, the 2009–18 CORA reanalysis products are capable of reproducing major oceanic phenomena and processes in the coastal waters of China and adjacent seas.摘要在51年 (1958–2008) 西北太平洋区域海洋再分析CORA1.0产品的基础上, 改进了模式配置和同化方法, 研制了2009-18年的CORA产品并对其进行以下检验: (1) 温盐和海表高度异常均方根误差分布检验; (2) 35°N处温度断面分布检验; (3) 再分析流场和表漂浮标轨迹对比检验.结果显示, 2009–18年的CORA产品可以再现海洋要素长时间序列,时空多尺度的变化特征, 为研究特征海洋现象和过程提供背景信息.

Evaluation of Winter Wheat Yield Simulation Based on Assimilating LAI Retrieved From Networked Optical and SAR Remotely Sensed Images Into the WOFOST Model

To obtain sufficient observation data and simulate higher-precision crop yields, a crop yield simulation scheme was built based on the WOrld FOod STudies (WOFOST) crop growth model and a 4-D ensemble square root filter (4-DEnSRF) assimilation algorithm, and the time series of the leaf area index (LAI) retrieved by optical and synthetic aperture radar (SAR) networking data was introduced into the crop yield estimation scheme. Taking Shenzhou County, Hebei Province, as the study area, using the field-measured data as verification data, the regional application of winter wheat yield estimation was effectively carried out with a grid size of 500 m. Comparisons were made between the simulated yields based on different networked data of three key phenologies of winter wheat. The regional yield estimation results revealed an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> and normalized root mean squared error (NRMSE) between the simulated yield based on optical LAIs and the field-measured yield of 0.517 and 17.60%, respectively, while the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> and NRMSE between the simulated yield based on networked optical-SAR LAIs filtered by the Gaussian filtering algorithm (GFA) and the field-measured yield were 0.573 and 12.98%, respectively. From the comparisons between the simulated yields based on networked data of different combinations of key phenologies, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> and NRMSE between the simulated yield based on the introduced SAR LAI at the jointing stage and the field-measured yield were 0.437 and 21.49%, respectively, and were higher correlation among the three modes of networked data of different combinations of key phenologies. The winter wheat yield simulation results showed that the introduction of SAR LAIs at key crop growth stages (especially the jointing and booting stage) as outer observation data had a mild impact on the value of simulated winter wheat yield. Moreover, Gaussian filtering could reduce errors caused by multisource networked data to a certain extent. Thus, it can be concluded that using some radar images instead of optical images to retrieve LAI and assimilating multisource remotely sensed LAI into the crop model to simulate crop yield could enhance the reliability and robustness of the crop yield simulation system to some extent.

The Global Analysis of the Ionospheric Correlation Time and Its Implications for Ionospheric Data Assimilation

AbstractIonospheric correlation time is an important parameter that contains information about the temporal variability, structures, and dynamics of the ionosphere. This parameter is also important in forecasting of the ionospheric state. Ionospheric data assimilation algorithms employing empirical background models, such as Ionospheric Data Assimilation Four‐Dimensional (IDA4D), apply Gauss‐Markov approximation for the propagation of temporal updates from one time stamp to the next. In this process, the relaxation parameter, or the correlation time, determines to what degree the projected state depends on the background model and analysis density. An ad hoc approach is usually applied to choose this user‐defined parameter. This paper focuses on the estimation of the ionospheric correlation time using high temporal resolution global ionospheric maps (GIMs). It is found that the correlation time changes significantly with latitude. The longest correlation time is observed in the equatorial region, whereas the shortest correlation time is observed at high latitudes and in the polar cap regions. The global distribution of the correlation time exhibits seasonal variation and depends on the solar flux conditions. The correlation time at the equatorial and mid‐latitude regions increases with increasing solar and geomagnetic activity, whereas the correlation time at high‐latitude regions decreases. The results of this study can be directly applied to improve ionospheric data assimilation models.

Ionospheric Horizontal Correlation Distances: Estimation, Analysis, and Implications for Ionospheric Data Assimilation

AbstractThe background covariance matrix establishes the transition from the data‐ to model‐driven regions in the ionospheric data assimilation algorithms. To construct the background covariance matrix, the information about the spatial ionospheric correlations of model errors is required. This paper focuses on the horizontal component of the covariance matrix. It is the first study that presents global maps of zonal and meridional ionospheric correlation lengths derived for IRI‐2016 model errors. The model errors were calculated using 20 years of GPS total electron content (TEC) values from the Jet Propulsion Laboratory Global Ionospheric Maps (GIMs) for different seasons, geomagnetic conditions, and universal times. The correlation lengths derived from IRI model errors were analyzed and compared to correlation lengths derived from day‐to‐day ionospheric variability calculated from GIM. It was found that the global distributions of the zonal and meridional correlation lengths between the two approaches are very different and that the correlation lengths derived from day‐to‐day TEC variability cannot be used as a proxy for the construction of covariance matrix for ionospheric data assimilation. A new method is proposed for the modeling of azimuthal distribution of the correlation distances that considers the nonisotropic nature of the distribution of correlations around the reference point.

Spatiotemporal dynamics in assimilated-LAI phenology and its impact on subtropical bamboo forest productivity

Phenology has a significant effect on forest growth and directly affects the forest ecosystem carbon cycle. Bamboo forests possess strong carbon sequestration capacities. However, it is not clear whether variations in phenology increase or decrease carbon uptake and storage in subtropical bamboo forests. We first extracted the length of the growing season (LOS) by coupling a data assimilation algorithm and MODIS leaf area index (LAI) data, and then the LOS was used to drive the integrated terrestrial ecosystem carbon-budget (InTEC) model to simulate gross primary productivity (GPP) and net ecosystem productivity (NEP) in Zhejiang Province from 2001 to 2017. Our results showed that the LOS estimation using the assimilated LAI time series was more reliable than that of the MODIS LAI and enhanced vegetation index (EVI). The annual average LOS increased on average by 0.76 day yr−1 from 2001 to 2017. The GPP and NEP simulations based on the LAI assimilation-based phenology indicated that bamboo forest ecosystems possess strong carbon sequestration capacities and act as carbon sinks, with mean annual GPP and NEP values of 434.74 ± 257.93 g C m−2 yr−1 and 141.42 ± 82.54 g C m−2 yr−1, respectively, during 2001–2017. An increase of one day in the regional annual LOS increases the annual average GPP and NEP by 1.34 g C m−2 yr−1 and 0.75 g C m−2 yr−1, respectively. Moreover, the interannual variation of NEP was significantly correlated with precipitation and temperature, whereas GPP was not. Our results demonstrated that phenology extraction based on LAI data assimilation should play an important role in the simulation of bamboo forest productivity with ecological process models. The variation in phenology induced by climate change can strengthen the bamboo forest carbon sink, which is of great significance for subtropical forests coping with climate change in the future.

Reconstruction of turbulent flow fields from lidar measurements using large-eddy simulation

Abstract

A Near‐Term Iterative Forecasting System Successfully Predicts Reservoir Hydrodynamics and Partitions Uncertainty in Real Time

AbstractFreshwater ecosystems are experiencing greater variability due to human activities, necessitating new tools to anticipate future water quality. In response, we developed and deployed a real‐time iterative water temperature forecasting system (FLARE—Forecasting Lake And Reservoir Ecosystems). FLARE is composed of water temperature and meteorology sensors that wirelessly stream data, a data assimilation algorithm that uses sensor observations to update predictions from a hydrodynamic model and calibrate model parameters, and an ensemble‐based forecasting algorithm to generate forecasts that include uncertainty. Importantly, FLARE quantifies the contribution of different sources of uncertainty (driver data, initial conditions, model process, and parameters) to each daily forecast of water temperature at multiple depths. We applied FLARE to Falling Creek Reservoir (Vinton, Virginia, USA), a drinking water supply, during a 475‐day period encompassing stratified and mixed thermal conditions. Aggregated across this period, root mean square error (RMSE) of daily forecasted water temperatures was 1.13°C at the reservoir's near‐surface (1.0 m) for 7‐day ahead forecasts and 1.62°C for 16‐day ahead forecasts. The RMSE of forecasted water temperatures at the near‐sediments (8.0 m) was 0.87°C for 7‐day forecasts and 1.20°C for 16‐day forecasts. FLARE successfully predicted the onset of fall turnover 4–14 days in advance in two sequential years. Uncertainty partitioning identified meteorology driver data as the dominant source of uncertainty in forecasts for most depths and thermal conditions, except for the near‐sediments in summer, when model process uncertainty dominated. Overall, FLARE provides an open‐source system for lake and reservoir water quality forecasting to improve real‐time management.

Analysis of aerosol optical depth over Bahrain and Eastern Province of Saudi Arabia based on MERRA-2 model

Spatial and temporal variability of aerosol optical depth (AOD) together with aerosol-type specification were analyzed over Eastern Province of Kingdom of Saudi Arabia (KSA) and Bahrain from 2008 to 2019. To investigate study region with more details, the Eastern Province of Saudi Arabia and Bahrain was divided into three and four zones, respectively. This work utilized comprehensive satellite-based air quality data using NASA’s Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) model. The retrieved total AOD data exhibited relatively lower average values in the Bahraini zones in comparison with the Eastern KSA during the study period. The monthly total AOD values in Bahraini zones were in the range of 0.20 and 0.87, while they were in the range of 0.23 and 0.91 in KSA zones. The AOD values were positively correlated with ambient air temperature (r = 0.43), while they were negatively correlated with relative humidity (r = 0.59) in Bahrain. Seasonal wind rose diagrams for Bahrain and monthly synoptic maps of (20–40°N, 40–60°E) West Asia region demonstrates that dominant wind direction is from NNW to Bahrain. The data assimilation algorithms in MERRA-2 showed that the major aerosol species that contributed to total AOD were as follows: mineral dust (68–74%), sulfate (17–20%), organic carbon (4–5%), sea salt (3–5%) and black carbon (2%). Monthly, seasonal and annual variations of speciated AOD values assisted in understanding aerosols loading over study area.

COV-OBS.x2: 180 years of geomagnetic field evolution from ground-based and satellite observations

We present the geomagnetic field model COV-OBS.x2 that covers the period 1840–2020. It is primarily constrained by observatory series, satellite data, plus older surveys. Over the past two decades, we consider annual differences of 4-monthly means at ground-based stations (since 1996), and virtual observatory series derived from magnetic data of the satellite missions CHAMP (over 2001–2010) and Swarm (since 2013). A priori information is needed to complement the constraints carried by geomagnetic records and solve the ill-posed geomagnetic inverse problem. We use for this purpose temporal cross-covariances associated with auto-regressive stochastic processes of order 2, whose parameters are chosen so as to mimic the temporal power spectral density observed in paleomagnetic and observatory series. We aim this way to obtain as far as possible realistic posterior model uncertainties. These can be used to infer for instance the core dynamics through data assimilation algorithms, or an envelope for short-term magnetic field forecasts. We show that because of the projection onto splines, one needs to inflate the formal model error variances at the most recent epochs, in order to account for unmodeled high frequency core field changes. As a by-product of the core field model, we co-estimate the external magnetospheric dipole evolution on periods longer than 2 years. It is efficiently summarized as the sum of a damped oscillator (of period 10.5 years and decay rate 55 years), plus a short-memory (6 years) damped random walk.

Analysis and computation of continuous data assimilation algorithms for Lorenz 63 system based on nonlinear nudging techniques

In this work, continuous data assimilation algorithms based on three nonlinear nudging techniques (simple power nonlinearity, hybrid linear–nonlinear method and concave–convex nonlinearity) are studied for Lorenz 63 system to speed up the convergence rate of the approximate solution to the reference solution. The well-posedness of these three nonlinear continuous data assimilation algorithms are proven. For two cases that include synchronizing the first or second variable only are considered, the approximate solution is proven to converge to the reference solution as times go to infinity provided that the relaxed parameter related to the corresponding variable is sufficiently large. The byproduct of the proof is to observe that for these three nonlinear nudging techniques, synchronizing the second variable could be more efficient than synchronizing the first variable because the convergence rate for the first variable is shown to be slower than the second variable. Although the convergence rate may not be optimal, this still gives some possible explanation why synchronizing the second variable is more efficient than synchronizing the first which are numerically found in our numerical simulations. Moreover, numerical simulations are presented to illustrate the advantages of continuous data assimilation algorithms based on nonlinear nudging techniques. Some open problems arising from numerical experiments are reported.

Continuous data assimilation applied to a velocity-vorticity formulation of the 2D Navier-Stokes equations

&lt;p style='text-indent:20px;'&gt;We study a continuous data assimilation (CDA) algorithm for a velocity-vorticity formulation of the 2D Navier-Stokes equations in two cases: nudging applied to the velocity and vorticity, and nudging applied to the velocity only. We prove that under a typical finite element spatial discretization and backward Euler temporal discretization, application of CDA preserves the unconditional long-time stability property of the velocity-vorticity method and provides optimal long-time accuracy. These properties hold if nudging is applied only to the velocity, and if nudging is also applied to the vorticity then the optimal long-time accuracy is achieved more rapidly in time. Numerical tests illustrate the theory, and show its effectiveness on an application problem of channel flow past a flat plate.&lt;/p&gt;

Identification of non-Gaussian parameters in heterogeneous aquifers by a modified probability conditioning method through hydraulic-head assimilation

Parameter estimation with uncertainty quantification is essential in groundwater modeling to ensure model quality; however, parameter estimation, especially for non-Gaussian distributed parameters in highly heterogeneous aquifers, is still a great challenge. The ensemble smoother with multiple data assimilation (ES-MDA) is one of the most popular and effective ensemble-based data assimilation algorithms. However, it only works for multi-Gaussian fields, since two-point statistics are used to estimate the co-relation between parameters and state variables. The probability conditioning method (PCM) has the capability to integrate nonlinear flow data into facies simulation, but it has an assumption of homogeneity within each facies. Full characterization of facies and estimates of hydraulic conductivity within each facies are equally important. This work firstly modifies the original PCM, introducing a new probability assignment method, to consider within-facies heterogeneities, and then it is further combined with the ES-MDA to estimate non-Gaussian distributed hydraulic parameters in a groundwater model. The proposed method is evaluated using a two-facies case and a three-facies case in groundwater modeling. Both cases demonstrate that the modified PCM is effective for facies delineation, especially to identify high heterogeneities in each facies, as well as non-Gaussian characteristics with good connectivity within certain facies. The results also show that the performances of data reproduction and model prediction are of high accuracy and low uncertainty, which is attributed to the accurate characterization of the non-Gaussian parameters in the heterogeneous aquifers used.

Observing System Impact on Ionospheric Specification Over China Using EnKF Assimilation

AbstractAccurate ionospheric specification for the current and future is one of the key tasks in operational space weather. In this work, we have assessed the effect of a dense ground network consisting of different radio instruments either developed or under developing on ionosphere nowcasting and forecasting over China and adjacent region (0–60°N and 70–140°E) through observing system simulation experiments. The data assimilation system is an Ensemble Kalman filter (EnKF) ionosphere and thermosphere data assimilation algorithm. The National Center for Atmospheric Research Thermosphere Ionosphere Electrodynamics General Circulation Model is used as a background model. Effects of different observation types, including the slant total electron content from Beidou System geostationary satellite and Global Position System and electron density observations from ionosondes and incoherent scattering radar on the ionospheric nowcasting and forecasting, are examined by the accurate specification of ionosphere key parameters (total electron content and 3‐D electron density). We found that simultaneously assimilating different observation types can greatly improve the quality of ionosphere specification. Furthermore, updating the thermospheric state variables in the coupled thermosphere‐ionosphere forecast model in the assimilation step plays an important role in improving the ionosphere forecasting. The ionosphere forecasting capability can last longer (&gt;24 hr) in each observing system simulation experiment due to the adjusted thermosphere states. This study can provide a reference for observing system design over the China region and future ionosphere forecasting operation.

Data Assimilation and Online Optimization With Performance Guarantees

This article considers a class of real-time stochastic optimization problems dependent on an unknown probability distribution. In the considered scenario, data are streaming frequently while trying to reach a decision. Thus, we aim to devise a procedure that incorporates samples (data) of the distribution sequentially and adjusts decisions accordingly. We approach this problem in a distributionally robust optimization framework and propose a novel Online Data Assimilation Algorithm (OnDA Algorithm) for this purpose. This algorithm guarantees out-of-sample performance of decisions with high probability, and gradually improves the quality of the decisions by incorporating the streaming data. We show that the OnDA Algorithm converges under a sufficiently slow data streaming rate, and provide a criteria for its termination after certain number of data have been collected. Simulations illustrate the results.

A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation

AbstractData assimilation combines forecasts from a numerical model with observations. Most of the current data assimilation algorithms consider the model and observation error terms as additive Gaussian noise, specified by their covariance matrices and , respectively. These error covariances, and specifically their respective amplitudes, determine the weights given to the background (i.e., the model forecasts) and to the observations in the solution of data assimilation algorithms (i.e., the analysis). Consequently, and matrices significantly impact the accuracy of the analysis. This review aims to present and to discuss, with a unified framework, different methods to jointly estimate the and matrices using ensemble-based data assimilation techniques. Most of the methods developed to date use the innovations, defined as differences between the observations and the projection of the forecasts onto the observation space. These methods are based on two main statistical criteria: 1) the method of moments, in which the theoretical and empirical moments of the innovations are assumed to be equal, and 2) methods that use the likelihood of the observations, themselves contained in the innovations. The reviewed methods assume that innovations are Gaussian random variables, although extension to other distributions is possible for likelihood-based methods. The methods also show some differences in terms of levels of complexity and applicability to high-dimensional systems. The conclusion of the review discusses the key challenges to further develop estimation methods for and . These challenges include taking into account time-varying error covariances, using limited observational coverage, estimating additional deterministic error terms, or accounting for correlated noise.

PyAEM: A Python toolkit for aquatic ecosystem modelling

Aquatic ecosystem modelling has been a challenging task for scientists partly due to limited tools/software to support. This study attempted to develop a Python toolkit (PyAEM) that was able to support multiple procedures in aquatic ecosystem modelling. PyAEM included improved algorithms for sensitivity analysis, parameter optimization, data assimilation and model visualization. In PyAEM, one-at-a-time and variance-based methods were implemented to identify sensitive parameters in target models. Genetic algorithm was improved for global optimization of model parameters. Ensemble Kalman Filter was implemented for assimilating multi-source measured data to improve model performance. An aquatic ecosystem model viewer was specifically developed for model visualization with the advantages of multi-scenario comparison, model fit evaluation, multi-mesh support and time series data extraction. An application overview of PyAEM in watershed and lake modelling is given, and revealed that PyAEM can be easily adapted to support the implementation of a case for aquatic ecosystem modelling.

Multi‐resolution incremental 4D‐Var for WRF: Implementation and application at convective scale

AbstractThe current four‐dimensional variational (4D‐Var) data assimilation (DA) algorithm, available in the community Weather Research and Forecasting (WRF) model's DA (WRFDA) system, can run only at the same resolution as that of the WRF model forecast, which makes it computationally prohibitive for operational applications at convective scale. The Multi‐Resolution Incremental 4D‐Var (MRI‐4DVar) has been developed in this study in order to speed up WRF 4D‐Var through a three‐stage procedure in each outer loop. One key aspect of WRF MRI‐4DVar is the introduction of the inverse control variable transform within WRFDA that allows the proper resolution change between different outer loops for the control variables projected in the vertical empirical orthogonal function (EOF) space. MRI‐4DVar's computational efficiency and forecast performance are demonstrated by applying it to an afternoon thunderstorm event over northern Taiwan with a 2 km model resolution setting. Comparing to the full‐resolution 4D‐Var experiment, two MRI‐4DVar configurations with a speed‐up of 4.5 and 7.5 times performed similarly well in terms of Fractions Skill Score (FSS) of 6‐hr accumulated rainfall forecasts and hourly variation of total rainfall amount, indicating MRI‐4DVar's potential for operational applications at convective scale.

Water Surface Elevation Constraints in a Data Assimilation Scheme to Infer Floodplain Topography: A Case Study in the Logone Floodplain

AbstractAccuracy of inundation extents from flood models are directly related to the quality of topographic information. Globally available digital elevation models (DEMs) are not accurate enough to support flood modeling. Satellite derived flood maps provide indirect information about ground elevations. Previous work proposed a data assimilation algorithm to leverage flood maps for improving topography but was tested only in context of a numerical experiment with synthetic data. Here we show for the first time that a data assimilation algorithm leveraging real remote sensing measurements to modify floodplain DEMs improves floodplain simulations. Over our study area, we observe a significant increase in inundation prediction capability using the updated DEM, with 9–19% average improvement in skill assessment characteristics. This algorithm is promising and fairly straightforward and can be used in data‐poor floodplains where high‐resolution DEMs do not exist.