Direct Data Assimilation Research Articles

Assimilation of transformed water surface elevation to improve river discharge estimation in a continental-scale river

Abstract. Quantifying continental-scale river discharge is essential for understanding the terrestrial water cycle, but it is susceptible to errors caused by a lack of observations and the limitations of hydrodynamic modeling. Data assimilation (DA) methods are increasingly used to estimate river discharge in combination with emerging river-related remote sensing products (e.g., water surface elevation (WSE), water surface slope, river width, and flood extent). However, directly comparing simulated WSE to satellite altimetry data remains challenging (e.g., because of large biases between simulations and observations or uncertainties in parameters), and large errors can be introduced when satellite observations are assimilated into hydrodynamic models. In this study we performed direct, anomaly, and normalized value assimilation experiments to investigate the capacity of DA to improve river discharge within the current limitations of hydrodynamic modeling. We performed hydrological DA using a physically based empirical localization method applied to the Amazon basin. We used satellite altimetry data from ENVISAT, Jason 1, and Jason 2. Direct DA was the baseline assimilation method and was subject to errors due to biases in the simulated WSE. To overcome these errors, we used anomaly DA as an alternative to direct DA. We found that the modeled and observed WSE distributions differed considerably (e.g., differences in amplitude, seasonal flow variation, and a skewed distribution due to limitations of the hydrodynamic models). Therefore, normalized value DA was performed to improve discharge estimation. River discharge estimates were improved at 24 %, 38 %, and 62 % of stream gauges in the direct, anomaly, and normalized value assimilations relative to simulations without DA. Normalized value assimilation performed best for estimating river discharge given the current limitations of hydrodynamic models. Most gauges within the river reaches covered by satellite observations accurately estimated river discharge, with the Nash–Sutcliffe efficiency (NSE) > 0.6. The amplitudes of WSE variation were improved in the normalized DA experiment. Furthermore, in the Amazon basin, normalized assimilation (median NSE =0.50) improved river discharge estimation compared to open-loop simulation with the global hydrodynamic model (median NSE =0.42). River discharge estimation using direct DA methods was improved by 7 % with calibration of river bathymetry based on NSE. The direct DA approach outperformed the other DA approaches when runoff was considerably biased, but anomaly DA performed best when the river bathymetry was erroneous. The uncertainties in hydrodynamic modeling (e.g., river bottom elevation, river width, simplified floodplain dynamics, and the rectangular cross-section assumption) should be improved to fully realize the advantages of river discharge DA through the assimilation of satellite altimetry. This study contributes to the development of a global river discharge reanalysis product that is consistent spatially and temporally.

Adjoint-based direct data assimilation of GNSS time series for optimizing frictional parameters and predicting postseismic deformation following the 2003 Tokachi-oki earthquake

Postseismic Global Navigation Satellite System (GNSS) time series followed by megathrust earthquakes can be interpreted as a result of afterslip on the plate interface, especially in its early phase. Afterslip is a stress release process accumulated by adjacent coseismic slip and can be considered a recovery process for future events during earthquake cycles. Spatio-temporal evolution of afterslip often triggers subsequent earthquakes through stress perturbation. Therefore, it is important to quantitatively capture the spatio-temporal evolution of afterslip and related postseismic crustal deformation and to predict their future evolution with a physics-based simulation. We developed an adjoint data assimilation method, which directly assimilates GNSS time series into a physics-based model to optimize the frictional parameters that control the slip behavior on the fault. The developed method was validated with synthetic data. Through the optimization of frictional parameters, the spatial distributions of afterslip could roughly (but not in detail) be reproduced if the observation noise was included. The optimization of frictional parameters reproduced not only the postseismic displacements used for the assimilation, but also improved the prediction skill of the following time series. Then, we applied the developed method to the observed GNSS time series for the first 15 days following the 2003 Tokachi-oki earthquake. The frictional parameters in the afterslip regions were optimized to A–B ~ O(10 kPa), A ~ O(100 kPa), and L ~ O(10 mm). A large afterslip is inferred on the shallower side of the coseismic slip area. The optimized frictional parameters quantitatively predicted the postseismic GNSS time series for the following 15 days. These characteristics can also be detected if the simulation variables can be simultaneously optimized. The developed data assimilation method, which can be directly applied to GNSS time series following megathrust earthquakes, is an effective quantitative evaluation method for assessing risks of subsequent earthquakes and for monitoring the recovery process of megathrust earthquakes.

Skill assessment of global, regional, and coastal circulation forecast models: evaluating the benefits of dynamical downscaling in IBI (Iberia–Biscay–Ireland) surface waters

Abstract. In this work, a multi-parameter inter-comparison of diverse ocean forecast models was conducted at the sea surface ranging from global to local scales in a two-phase stepwise strategy. Firstly, a comparison of CMEMS GLOBAL and the nested CMEMS IBI regional system was performed against satellite-derived and in situ observations. Results highlighted the overall benefits of both the GLOBAL direct data assimilation in open water and the increased horizontal resolution of IBI in coastal areas. Besides, IBI (Iberia–Biscay–Ireland) proved to capture shelf dynamics by better representing the horizontal extent and strength of a river freshwater plume, according to the results derived from the validation against in situ observations from a buoy moored in NW Spain. Secondly, a multi-model inter-comparison exercise for 2017 was performed in the Strait of Gibraltar among GLOBAL, IBI, and SAMPA (Sánchez-Garrido et al., 2013) high-resolution coastal forecast systems (partially nested to IBI) in order to elucidate the accuracy of each system to characterize the Atlantic Jet (AJ) inflow dynamics. A quantitative validation against hourly currents from high-frequency radar (HFR) highlighted both the steady improvement in AJ representation in terms of speed and direction when zooming from global to coastal scales through a multi-nesting model approach and also the relevance of a variety of factors at local scale such as a refined horizontal resolution, a tailored bathymetry, and a higher spatio-temporal resolution of the atmospheric forcing. The ability of each model to reproduce a 2 d quasi-permanent full reversal of the AJ surface inflow was examined in terms of wind-induced circulation patterns. SAMPA appeared to better reproduce the reversal events detected with HFR estimations, demonstrating the added value of imposing accurate meteorologically driven barotropic velocities in the open boundaries (imported from the NIVMAR (Álvarez-Fanjul et al., 2001) storm surge model) to take into account the remote effect of the atmospheric forcing over the entire Mediterranean basin, which was only partially included in IBI and GLOBAL systems. Finally, SAMPA coastal model outputs were also qualitatively analysed in the western Alboran Sea to put in a broader perspective the context of the onset, development, and end of such flow reversal episodes.

Variational Deconvolution of Conically Scanned Passive Microwave Observations With Error Quantification

The deconvolution of potentially cloud-affected passive microwave brightness temperatures is an important step for utilization in direct data assimilation in cloud-resolving numerical weather prediction (NWP) models for the purpose of improving model initial conditions. Geophysical retrieval algorithms, such as precipitation rate retrievals, also benefit from consistent resolution across channels. In this paper, we explore how to derive the posterior error estimates that are required for ingestion into data assimilation models or end-to-end error-quantified retrieval algorithms. To this end, we present a minimum variance, best linear-unbiased estimator approach that seeks an optimal estimate of the apparent (i.e., without the effects of antenna pattern convolution) brightness temperatures by iteratively minimizing a cost function measuring the lack of fit between observations and departures from a first guess. Both the observation and first-guess departure terms are weighed by a corresponding covariance term that estimates their relative uncertainty. The first-guess uncertainty, a Bayesian prior “belief” in the spread of the first-guess error, is estimated using geophysical fields from an NWP model in a radiative transfer model plus an antenna pattern forward operator, then iteratively improved using the posterior deconvolved brightness temperatures of actual special sensor microwave imager/sounder observations. The error for the posterior distribution, subject to the initial belief, is derived. The error-quantified results are shown to increase the spatial resolution of microwave observations.

Simulation of tidal hydrodynamics in Sabang Bay, Indonesia

Tidal hydrodynamics of currents and sea level in Sabang Bay are simulated by a one-dimensional numerical model. We generate the dynamics by using the amplitude and tidal components of Hamburg direct data Assimilation Methods for TIDEs (HAMTIDE) in the open boundary. HAMTIDE is a model data obtained from the tidal inverse method. The tidal simulation is based on several harmonic constants of M2, K1, K2, S2, O1, and N2. Simulations of the combined harmonic constants are run over a 30 day period and verified by the measurement data of the Hydrography and Oceanography Center, Indonesian Navy. The verification results indicate that the numerical sea level model is quite good, with the correlation coefficient r = 0.81.

Ensemble-Based Atmospheric Reanalysis Using a Global Coupled Atmosphere–Ocean GCM

ABSTRACT Ensemble-based atmospheric data assimilation (DA) systems are sometimes afflicted with an underestimation of the ensemble spread near the surface caused by the use of identical boundary conditions for all ensemble members and the lack of atmosphere–ocean interaction. To overcome these problems, a new DA system has been developed by replacing an atmospheric GCM with a coupled atmosphere–ocean GCM, in which atmospheric observational data are assimilated every 6 h to update the atmospheric variables, whereas the oceanic variables are subject to no direct DA. Although SST suffers from the common biases among many coupled GCMs, two months of a retrospective analysis–forecast cycle reveals that the ensemble spreads of air temperature and specific humidity in the surface boundary layer are slightly increased and the forecast skill in the midtroposphere is rather improved by using the coupled DA system in comparison with the atmospheric DA system. In addition, surface atmospheric variables over the tropical Pacific have the basinwide horizontal correlation in ensemble space in the coupled DA system but not in the atmospheric DA system. This suggests the potential benefit of using a coupled GCM rather than an atmospheric GCM even for atmospheric reanalysis with an ensemble-based DA system.

Assimilation of High-Resolution Satellite-Derived Atmospheric Motion Vectors: Impact on HWRF Forecasts of Tropical Cyclone Track and Intensity

It is well known that global numerical model analyses and forecasts benefit from the routine assimilation of atmospheric motion vectors (AMVs) derived from meteorological satellites. Recent studies have also shown that the assimilation of enhanced (spatial and temporal) AMVs can benefit research-mode regional model forecasts of tropical cyclone track and intensity. In this study, the impact of direct assimilation of enhanced (higher resolution) AMV datasets in the NCEP operational Hurricane Weather Research and Forecasting Model (HWRF) system is investigated. Forecasts of Atlantic tropical cyclone track and intensity are examined for impact by inclusion of enhanced AMVs via direct data assimilation. Experiments are conducted for AMVs derived using two methodologies (“HERITAGE” and “GOES-R”), and also for varying levels of quality control in order to assess and inform the optimization of the AMV assimilation process. Results are presented for three selected Atlantic tropical cyclone events and compared to Control forecasts without the enhanced AMVs as well as the corresponding operational HWRF forecasts. The findings indicate that the direct assimilation of high-resolution AMVs has an overall modest positive impact on HWRF forecasts, but the impact magnitudes are dependent on the 1) availability of rapid scan imagery used to produce the AMVs, 2) AMV derivation approach, 3) level of quality control employed in the assimilation, and 4) vortex initialization procedure (including the degree to which unbalanced states are allowed to enter the model analyses).

Dual states estimation of a subsurface flow-transport coupled model using ensemble Kalman filtering

Modeling the spread of subsurface contaminants requires coupling a groundwater flow model with a contaminant transport model. Such coupling may provide accurate estimates of future subsurface hydrologic states if essential flow and contaminant data are assimilated in the model. Assuming perfect flow, an ensemble Kalman filter (EnKF) can be used for direct data assimilation into the transport model. This is, however, a crude assumption as flow models can be subject to many sources of uncertainty. If the flow is not accurately simulated, contaminant predictions will likely be inaccurate even after successive Kalman updates of the contaminant model with the data. The problem is better handled when both flow and contaminant states are concurrently estimated using the traditional joint state augmentation approach. In this paper, we introduce a dual estimation strategy for data assimilation into a one-way coupled system by treating the flow and the contaminant models separately while intertwining a pair of distinct EnKFs, one for each model. The presented strategy only deals with the estimation of state variables but it can also be used for state and parameter estimation problems. This EnKF-based dual state-state estimation procedure presents a number of novel features: (i) it allows for simultaneous estimation of both flow and contaminant states in parallel; (ii) it provides a time consistent sequential updating scheme between the two models (first flow, then transport); (iii) it simplifies the implementation of the filtering system; and (iv) it yields more stable and accurate solutions than does the standard joint approach. We conducted synthetic numerical experiments based on various time stepping and observation strategies to evaluate the dual EnKF approach and compare its performance with the joint state augmentation approach. Experimental results show that on average, the dual strategy could reduce the estimation error of the coupled states by 15% compared with the joint approach. Furthermore, the dual estimation is proven to be very effective computationally, recovering accurate estimates at a reasonable cost.

4次元変分データ同化法を用いた密度分布と水質の再現性向上

Assimilation of 3D observed salinity and temperature data obtained by steady observation stations using 4D-Var (four dimensional variational method) are applied and tested in Osaka Bay. Data assimilation becomes fully effective after around 8 hour calculation. Appropriate horizontal and vertical correlation scales are found to be 10km and more than 3m by calibration with the consideration of density distribution under the influence of river plume and location of the observation stations. Water quality model is modified to represent hypoxia better and run with 4dvar flow model. Without direct data assimilation of dissolved oxygen, the improvement of density profiles has an effect on that of DO profiles.

Development of the Flux-Adjusting Surface Data Assimilation System for Mesoscale Models

Abstract The flux-adjusting surface data assimilation system (FASDAS) is developed to provide continuous adjustments for initial soil moisture and temperature and for surface air temperature and water vapor mixing ratio for mesoscale models. In the FASDAS approach, surface air temperature and water vapor mixing ratio are directly assimilated by using the analyzed surface observations. Then, the difference between the analyzed surface observations and model predictions of surface layer temperature and water vapor mixing ratio are converted into respective heat fluxes, referred to as adjustment heat fluxes of sensible and latent heat. These adjustment heat fluxes are then used in the prognostic equations for soil temperature and moisture via indirect assimilation in the form of several new adjustment evaporative fluxes. Thus, simulated surface fluxes for the subsequent model time step are affected such that the predicted surface air temperature and water vapor mixing ratio conform more closely to observations. The simultaneous application of indirect and direct data assimilation maintains greater consistency between the soil temperature–moisture and the surface layer mass-field variables. The FASDAS is coupled to a land surface submodel in a three-dimensional mesoscale model and tests are performed for a 10-day period with three one-way nested domains. The FASDAS is applied in the analysis nudging mode for two coarse-resolution nested domains and in the observational nudging mode for a fine-resolution nested domain. Further, the effects of FASDAS on two different initial specifications of a three-dimensional soil moisture field are also studied. Results indicate that the FASDAS consistently improved the accuracy of the model simulations.

Direct Data Assimilation Over Solar Cycle Time-Scales to Improve Proton Radiation Belt Models

Data assimilation technique has already been developed and proved its worth to provide dasiathe bestpsila estimate of the current state of the electron radiation belts. Data assimilation proceeds by analysis cycles. In each analysis cycle, observations of the current (and possibly, past) state of a system are combined with the results from a mathematical model (the forecast) to produce an analysis, which is considered as dasiathe bestpsila estimate of the current state of the system. Here we present its adaptation to the case of proton radiation belts over solar cycle time-scales. Many deficiencies of AP8 models can be overcome thanks to direct data assimilation. An anticorrelation between the 10 MeV proton radiation belts. Point-like but intense events can have long term effects which depend on whether a SPE (solar proton event) is associated or not with this storm period.