Sequential Data Assimilation System Research Articles

A Multiscale Sequential Data Assimilation System and Its Application to Short-term Traffic Flow Prediction

A Multiscale Sequential Data Assimilation System and Its Application to Short-term Traffic Flow Prediction

An Approach for Filter Divergence Suppression in a Sequential Data Assimilation System and Its Application in Short-Term Traffic Flow Forecasting

Mathematically describing the physical process of a sequential data assimilation system perfectly is difficult and inevitably results in errors in the assimilation model. Filter divergence is a common phenomenon because of model inaccuracies and affects the quality of the assimilation results in sequential data assimilation systems. In this study, an approach based on an L1-norm constraint for filter-divergence suppression in sequential data assimilation systems was proposed. The method adjusts the weights of the state-simulated values and measurements based on new measurements using an L1-norm constraint when filter divergence is about to occur. Results for simulation data and real-world traffic flow measurements collected from a sub-area of the highway between Leeds and Sheffield, England, showed that the proposed method produced a higher assimilation accuracy than the other filter-divergence suppression methods. This indicates the effectiveness of the proposed approach based on the L1-norm constraint for filter-divergence suppression.

Development of the Nonstationary Incremental Analysis Update Algorithm for Sequential Data Assimilation System

This study introduces a modified version of the incremental analysis updates (IAU), called the nonstationary IAU (NIAU) method, to improve the assimilation accuracy of the IAU while keeping the continuity of the analysis. Similar to the IAU, the NIAU is designed to add analysis increments at every model time step to improve the continuity in the intermittent data assimilation. However, unlike the IAU, the NIAU procedure uses time-evolved forcing using the forward operator as corrections to the model. The solution of the NIAU is superior to that of the forward IAU, of which analysis is performed at the beginning of the time window for adding the IAU forcing, in terms of the accuracy of the analysis field. It is because, in the linear systems, the NIAU solution equals that in an intermittent data assimilation method at the end of the assimilation interval. To have the filtering property in the NIAU, a forward operator to propagate the increment is reconstructed with only dominant singular vectors. An illustration of those advantages of the NIAU is given using the simple 40-variable Lorenz model.

Estimation of the Degree of Saturation of Shallow Soils from Satellite Observations to Model Soil Slips Occurred in Emilia Romagna Region of Northern Italy

For the development of alert systems for soil slip occurrence, it is important to evaluate the degree of saturation of shallow soils (Sr) over wide areas. Taking into account the possibility to estimate spatial and temporal variation of soil moisture using remote sensing techniques, a possible correlation between Sr and the daily output of a sequential data assimilation system called ACHAB (Assimilation Code for HeAt and moisture Balance) has been studied. ACHAB is based on integrated use of remotely sensed land surface temperature (LST) and common data on meteorological forcing such as air temperature, wind-speed and incident solar radiation. The aim of this study is to understand if it is possible to use ACHAB output (a daily value of evaporative fraction for the whole Italian territory) to define the parameter Sr that could be introduced in a simplified model for the description of soil slip triggering mechanisms on territorial scale.

A new data assimilation approach for improving runoff prediction using remotely-sensed soil moisture retrievals

Abstract. A number of recent studies have focused on enhancing runoff prediction via the assimilation of remotely-sensed surface soil moisture retrievals into a hydrologic model. The majority of these approaches have viewed the problem from purely a state or parameter estimation perspective in which remotely-sensed soil moisture estimates are assimilated to improve the characterization of pre-storm soil moisture conditions in a hydrologic model, and consequently, its simulation of runoff response to subsequent rainfall. However, recent work has demonstrated that soil moisture retrievals can also be used to filter errors present in satellite-based rainfall accumulation products. This result implies that soil moisture retrievals have potential benefit for characterizing both antecedent moisture conditions (required to estimate sub-surface flow intensities and subsequent surface runoff efficiencies) and storm-scale rainfall totals (required to estimate the total surface runoff volume). In response, this work presents a new sequential data assimilation system that exploits remotely-sensed surface soil moisture retrievals to simultaneously improve estimates of both pre-storm soil moisture conditions and storm-scale rainfall accumulations. Preliminary testing of the system, via a synthetic twin data assimilation experiment based on the Sacramento hydrologic model and data collected from the Model Parameterization Experiment, suggests that the new approach is more efficient at improving stream flow predictions than data assimilation techniques focusing solely on the constraint of antecedent soil moisture conditions.

A high resolution data assimilation system for the Aegean Sea hydrodynamics

We present the development and validation of an eddy-resolving sequential data assimilation system for the Aegean Sea hydrodynamics that has been developed as part of the Poseidon operational system. The assimilation scheme is based on the Singular Evolutive Extended Kalman (SEEK) filter which is an error subspace Extended Kalman filter that operates with low-rank error covariance matrices as a way to reduce the computational burden. The filter was used to correct the forecast state of a 1/20° Princeton Ocean Model (POM) of the Aegean Sea every time new observations were available. The model is forced with hourly fluxes from a 1/10° ETA regional atmospheric model and is one-way nested to a 1/10° POM model of the Eastern Mediterranean Sea. The assimilated data set is multivariate including weekly AVISO sea level anomalies, weekly AVHRR sea surface temperature, and daily Ferrybox sea surface salinity (SSS) along the ferry boat route from Piraeus to Heraklion. Data assimilation experiments are performed to validate the system over a 6-month period (January–June, 2004). In one of the assimilation experiments, where the Ferrybox SSS data were not assimilated and used as independent observations, the multivariate assimilation system was able to reduce the error on the SSS prediction. Additional assimilation of the Ferrybox data on a daily basis shows their positive impact on other predicted variables of the system and their significant local effect on the SSS prediction within the southern Aegean Sea.

Stratospheric transport using 6‐h‐averaged winds from a data assimilation system

Stratospheric transport calculated using assimilated winds has been shown to be too fast in many cases, because of excessive mixing and an overstrong residual circulation. It is shown that the use of 6‐h‐averaged wind fields instead of instantaneous analyses can substantially reduce this problem for NASA's Goddard Earth Observing System version 4 (GEOS‐4) sequential data assimilation system. Two examples are used to illustrate impacts in an off‐line chemistry transport model. An age‐of‐air computation shows that the stratosphere becomes substantially older when time‐averaged winds are used, yet still not as old as that determined from observations. An ozone assimilation experiment reveals improvements in the spatial structure of assimilated ozone, better agreement with independent observations, and a 40–60% reduction in observation‐minus‐forecast residuals. The averaged meteorological fields have also been incorporated in the on‐line transport computations: This is equivalent to using a 6‐h square‐wave filter at the analysis times. Sub‐6‐h noise in the system is substantially reduced with this approach. Two additional examples of transport are shown. A simulation of 2004–2005 shows that the descent of N2O in the Arctic polar vortex is represented more accurately than in previous studies. A tape recorder signal is evident in the tropical moisture; it ascends about 30% faster than that in the real atmosphere. In summary, the use of 6‐h‐averaged winds substantially improves the transport characteristics of the assimilated data, although the circulation remains too fast.

Assimilation of ocean colour data into a Biogeochemical Flux Model of the Eastern Mediterranean Sea

Abstract. An advanced multivariate sequential data assimilation system has been implemented within the framework of the European MFSTEP project to fit a three-dimensional biogeochemical model of the Eastern Mediterranean to satellite chlorophyll data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). The physics are described by the Princeton Ocean Model (POM) while the biochemistry of the ecosystem is tackled with the Biogeochemical Flux Model (BFM). The assimilation scheme is based on the Singular Evolutive Extended Kalman (SEEK) filter, in which the error statistics were parameterized by means of a suitable set of Empirical Orthogonal Functions (EOFs). To avoid spurious long-range correlations associated with the limited number of EOFs, the filter covariance matrix was given compact support through a radius of influence around every data point location. Hindcast experiments were performed for one year over 1999 and forced with ECMWF 6 h atmospheric fields. The solution of the assimilation system was evaluated against the assimilated data and the MedAtlas climatology, and by assessing the impact of the assimilation on non-observed biogeochemical processes. It is found that the assimilation of SeaWiFS data improves the overall behavior of the BFM model and efficiently removes long term biases from the model despite some difficulties during the spring bloom period. Results, however, suggest the need of subsurface data to enhance the estimation of the ecosystem variables in the deep layers.

Stratospheric transport by planetary wave mixing as observed during CRISTA‐2

Planetary waves drive the mean meridional circulation of the stratosphere and at the same time facilitate quasi‐horizontal mixing of trace gases. This paper presents significant day‐to‐day variability of stratospheric trace gas fields associated with large planetary wave activity observed during the second mission of the Cryogenic Infrared Spectrometers and Telescopes for the Atmosphere (CRISTA) experiment. Geopotential height data of the UK Met Office show that the CRISTA‐2 observations in the Southern Hemisphere winter were made during a period of extremely large amplitudes of both wave‐1 and wave‐2. The planetary wave‐1, usually a quasi‐stationary feature, moved eastward with the traveling planetary wave‐2. The large amplitudes of both wave‐1 and wave‐2 led to a significant displacement of the edge of the polar vortex toward the tropics (down to 30°S). As a result of the large wave amplitudes and favorable phase alignment, the anticyclone drawing up tropical air was unusually strong, and thus considerable wave‐induced trace gas flux from the tropics toward midlatitudes was observed, mainly in the form of a pronounced planetary‐scale tongue advected out of the tropics around the vortex and into the anticyclone. Quantitative transport calculations based on a sequential data assimilation system highlight the importance of such transport events for trace gas eddy‐flux in the Southern Hemisphere winter stratosphere.