Data Assimilation Results Research Articles

Impact of combining GRACE and GOCE gravity data on ocean circulation estimates

Abstract. With the focus on the Southern Ocean circulation, results of assimilation of multi-mission-altimeter data and the GRACE/GOCE gravity data into the finite element ocean model (FEOM) are investigated. We use the geodetic method to obtain the dynamical ocean topography (DOT). This method combines the multi-mission-altimeter sea surface height and the GRACE/GOCE gravity field. Using the profile approach, the spectral consistency of both fields is achieved by filtering the sea surface height and the geoid. By combining the GRACE and GOCE data, a considerably shorter filter length can be used, which results in more DOT details. We show that this increase in resolution of measured DOT carries onto the results of data assimilation for the surface data. By assimilating only absolute dynamical topography data using the ensemble Kalman filter, we were able to improve modeled fields. Results are closer to observations which were not used for assimilation and lie outside the area covered by altimetry in the Southern Ocean (e.g. temperature of surface drifters or deep temperatures in the Weddell Sea area at 800 m depth derived from Argo composite.)

Assimilating synthetic GOES-R radiances in cloudy conditions using an ensemble-based method

The weather research and forecasting (WRF) model and the maximum likelihood ensemble filter (MLEF) data assimilation approach are used to examine the potential impact of observations from the future Geostationary Operational Environmental Satellite, generation R (GOES-R) on improving our knowledge about clouds. Synthetic radiances are assimilated from the 10.35 μm channel of the GOES-R advanced baseline imager (ABI) employing a ‘non-identical twins’ experimental setup. The experimental results are examined for an extratropical cyclone named Kyrill that produced unusually strong winds, widespread damage and fatalities in Western Europe in January 2007. The data assimilation problem is especially challenging for this case, as there is a large error in the model-simulated radiances resulting from incorrect cloud location. Although this problem is difficult to eliminate, data assimilation results indicate the potential of GOES-R data to significantly reduce these errors.

Estimating turbulent fluxes through assimilation of geostationary operational environmental satellites data using ensemble Kalman filter

[1] In this study, a data assimilation scheme is developed on the basis of the ensemble Kalman filter algorithm and the common land model (CoLM); soil moisture and model parameters are simultaneously optimized to improve the estimates of turbulent fluxes. Land surface temperature (LST) is retrieved from geostationary operational environmental satellites (GOES) data, validated, and then assimilated into the model. The data assimilation results are validated at six observation sites in the United States that include grassland, cropland, and forestland cover types. Data assimilation results indicate that in addition to improvements in the prediction of turbulent fluxes, model uncertainties are also reduced as a result of the assimilation of GOES LST retrieval data. The average reductions in root mean square error (RMSE) values are 47.5 and 31.1 W m−2. The effects of simultaneous optimization of soil moisture and model parameters are compared with those resulting from separate optimization; simultaneous optimization is found to yield smaller RMSE values. Further, in this study, the effects of Moderate Resolution Imaging Spectroradiometer (MODIS) and GOES temporal resolution data on data assimilation results are studied. The assimilation results indicate that the average RMSE values for GOES temporal resolution data are smaller than that for MODIS temporal resolution data. During the assimilation time period, the soil moisture obtained from assimilation closely agrees with the observed values, and the four vegetation parameters show distinct seasonal variations. However, the lack of sufficient information makes it difficult to estimate the true value of these variables and parameters.

Evaluation of an ocean data assimilation system for Chinese marginal seas with a focus on the South China Sea

Data assimilation is a powerful tool to improve ocean forecasting by reducing uncertainties in forecast initial conditions. Recently, an ocean data assimilation system based on the ensemble optimal interpolation (EnOI) scheme and HYbrid Coordinate Ocean Model (HYCOM) for marginal seas around China was developed. This system can assimilate both satellite observations of sea surface temperature (SST) and along-track sea level anomaly (SLA) data. The purpose of this study was to evaluate the performance of the system. Two experiments were performed, which spanned a 3-year period from January 1, 2004 to December 30, 2006, with and without data assimilation. The data assimilation results were promising, with a positive impact on the modeled fields. The SST and SLA were clearly improved in terms of bias and root mean square error over the whole domain. In addition, the assimilations provided improvements in some regions to the surface field where mesoscale processes are not well simulated by the model. Comparisons with surface drifter trajectories showed that assimilated SST and SLA also better represent surface currents, with drifter trajectories fitting better to the contours of SLA field than that without assimilation. The forecasting capacity of this assimilation system was also evaluated through a case study of a birth-and-death process of an anticyclone eddy in the Northern South China Sea (NSCS), in which the anticyclone eddy was successfully hindcasted by the assimilation system. This study suggests the data assimilation system gives reasonable descriptions of the near-surface ocean state and can be applied to forecast mesoscale ocean processes in the marginal seas around China.

The Effects of Snow Cover and Soil Moisture on Asian Dust: II. Emission Estimation by Lidar Data Assimilation

This paper is the second of a series that describes the effects of snow cover and soil moisture on Asian dust during spring. Whereas the first paper in this series discussed the importance of snow cover and soil moisture estimation, here, we focus on the correctness of the dust emission intensity results based on data assimilation under the assumption that simulation models yield errors in snow cover and soil moisture. We utilized global satellite lidar measurements and a four-dimensional ensemble Kalman filter to optimize the dust emission simulation. The data assimilation results were evaluated by a comparison with independent ground-based lidar measurements. The data assimilation procedure resulted in an increase in the dust emission in the Gobi region during the dust event from March 25 to April 3, 2007, and it improved the analysis of dust concentrations in the leeward region. Without data assimilation, the dust concentrations were underestimated owing to the wet surface conditions of the dust source region. This paper confirms that the improvement of snow cover and soil moisture estimation is important in the analysis of Asian dust levels, and it demonstrates that data assimilation is a powerful tool that can contribute to such improvement.

Estimation of Arctic O3 loss during winter 2006/2007 using data assimilation and comparison with a chemical transport model

The evolution of Arctic stratospheric O3 throughout the winter and spring of 2006/2007 is estimated by a state-of-the-art chemical transport model (CTM) and by a 3D-Var assimilation system using O3 data from the Earth Observing System (EOS) Microwave Limb Sounder (MLS) and Solar Backscatter Ultraviolet Radiometer (SBUV/2) satellites. Modelled and assimilated O3 compare well with MLS measurements. The aim of this article is to compare O3 loss estimates derived from the CTM and data-assimilation results, and as a result point to further developments in the method of inferring O3 loss using data assimilation. The methods for inferring O3 loss are discussed and compared with other published methods. The assimilation-system vertical transport is found to be too fast, in agreement with an earlier study, although this affects only the O3 reference field used for the loss estimation. Improving the O3 reference in the assimilation method used here provides a maximum vortex average O3 loss range of 0.8–1.2 ppmv at 68 hPa and 1.0–1.5 ppmv at 46 hPa, peaking at the beginning of March. The corresponding CTM values are 1.4 and 1.6 ppmv, respectively, with the peak lagging the assimilation by a few days at 46 hPa. We show that using a passive tracer as reference for O3 loss does not provide the best estimate for polar stratospheric cloud (PSC)-related loss for this winter; up to 40% of total O3 loss is shown not to be related to PSCs or heterogenous chemistry. Hence, the use of a passive O3 reference for estimating PSC-related O3 loss should be made with care. In addition to the vortex average losses, we estimate an innermost vortex O3 loss of 1.4 ppmv due to PSCs only. Transport effects and differences between the CTM and the assimilation system are discussed, and possible improvements for both models are suggested. Copyright © Royal Meteorological Society and Crown Copyright, 2011

Validation of Atmospheric Infrared Sounder temperature and moisture profiles over tropical oceans and their impact on numerical simulations of tropical cyclones

The quality of the retrieved temperature and moisture profiles acquired from the Atmospheric Infrared Sounder (AIRS) aboard the NASA Aqua spacecraft was evaluated by comparing the data with dropsonde observations obtained from NASA African Monsoon Multidisciplinary Analyses (NAMMA 2006) and international THORPEX Pacific Asian Regional Campaign (T‐PARC 2008) field programs. Results indicate that the AIRS retrieved temperature profiles are in good agreement with dropsonde observations. However, the AIRS retrieved moisture profiles show a larger bias compared with the dropsondes over the tropical oceans where tropical cyclones developed. A series of data assimilation experiments is then performed with an advanced research version of the Weather Research and Forecasting model and its three‐dimensional variational data assimilation system. Results show that the assimilation of the AIRS retrieved temperature and moisture profiles has a significant impact on the numerical simulations of tropical cyclones. However, the overall impacts of the data assimilation on numerical simulations of tropical cyclones are very sensitive to the bias corrections of the data. Specifically, the dry biases of moisture profiles cause the decay of Tropical Storm Debby (2006) in the numerical simulations. Only with bias correction can data assimilation result in a reasonable portrayal of storm development. Compared with the moisture profiles, temperature profiles show a larger impact on the track forecasting. Assimilation of the temperature profiles resulted in significant improvements in the track forecasts for both Debby (2006) and Typhoon Jangmi (2008).

ANALYSIS AND QUANTIFICATION OF DATA ASSIMILATION BASED ON SEQUENTIAL MONTE CARLO METHODS FOR WILDFIRE SPREAD SIMULATION

Data assimilation is an important technique to improve simulation results by assimilating real time sensor data into a simulation model. A data assimilation framework based on Sequential Monte Carlo (SMC) methods for wildfire spread simulation has been developed in previous work. This paper provides systematic analysis and measurement to quantify the effectiveness and robustness of the developed data assimilation method. Measurement metrics are used to evaluate the robustness of SMC methods in data assimilation for wildfire spread simulation. Sensitivity analysis is carried out to examine the influences of important parameters to the data assimilation results. This work of analysis and quantification provides information to assess the effectiveness of the data assimilation method and suggests guidelines to further improve the data assimilation method for wildfire spread simulation.

An ensemble Kalman filter for atmospheric data assimilation: Application to wind tunnel data

In the previous work ( Zheng et al., 2007, 2009), a data assimilation method, based on ensemble Kalman filter, has been applied to a Monte Carlo Dispersion Model (MCDM). The results were encouraging when the method was tested by the twin experiment and a short-range field experiment. In this technical note, the measured data collected in a wind tunnel experiment have been assimilated into the Monte Carlo dispersion model. The uncertain parameters in the dispersion model, including source term, release height, turbulence intensity and wind direction have been considered. The 3D parameters, i.e. the turbulence intensity and wind direction, have been perturbed by 3D random fields. In order to find the factors which may influence the assimilation results, eight tests with different specifications were carried out. Two strategies of constructing the 3D perturbation field of wind direction were proposed, and the result shows that the two level strategy performs better than the one level strategy. It is also found that proper standard deviation and the correlation radius of the perturbation field play an important role for the data assimilation results.

A New Technique for Estimation of Lower-Tropospheric Temperature and Water Vapor Profiles from Radio Occultation Refractivity

Abstract An empirical technique is proposed to obtain temperature and humidity profiles over the tropics using radio occultation refractivity profiles and surface/available lower-altitude temperature and pressure measurements over humid tropical regions. The technique is tested on a large number of diverse radiosonde-derived refractivity profiles over the tropics (30°S–30°N) and selected Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation refractivity profiles that have collocated radiosonde observations over the region 10°S–30°N during the boreal summer of 2006. In a number of cases, the results were in good agreement with the collocated radiosonde data. The error statistics of temperature and humidity profiles obtained from the proposed technique are discussed and compared with the previously published results from another technique and also with the results of a one-dimensional variational data assimilation (1DVAR) technique given with COSMIC data. It is found that the previously published results and proposed technique are marginally better (worse) in reproducing observed relative humidity (specific humidity) when compared to the 1DVAR technique. The proposed new technique is applied on COSMIC refractivity profiles over the Bay of Bengal during summer 2007 to derive changes in vertical thermal and moisture changes in the troposphere between active and break phases of the monsoon pattern and many of the observed features are captured reasonably well.

Four‐dimensional data assimilation of atmospheric CO2 using AIRS observations

The European Global and regional Earth‐system (Atmosphere) Monitoring using Satellite and in situ data (GEMS) project has built a system that is capable of assimilating various sources of satellite and in situ observations to monitor the atmospheric concentrations of CO2 and its surface fluxes. This consists of an atmospheric four‐dimensional variational data assimilation system that provides atmospheric fields to a separate variational flux inversion scheme. In this paper, we describe the atmospheric data assimilation system that currently uses radiance observations from the Atmospheric Infrared Sounder (AIRS) to constrain the CO2 mixing ratios of the data assimilation model. We present the CO2 transport model, the bias correction of the observation‐model mismatch, and the estimation of the background error covariance matrix. Data assimilation results are compared to independent CO2 observations from NOAA/ESRL aircraft showing a reduction of the mean difference of up to 50% depending on the altitude of the aircraft observations relative to an unconstrained transport model simulation. In the coming years, observations from dedicated CO2 satellite missions will be added to the system. Together with improved error characterization and bias correction, we hope to show that satellite observations can indeed complement the in situ observation system to get a better estimate of global carbon fluxes.

Impact Assessment of Simulated Doppler Wind Lidars with a Multivariate Variational Assimilation in the Tropics

Abstract This paper deals with the dynamical aspect of variational data assimilation in the tropics and the role of the background-error covariances in the observing system simulation experiments for the tropics. The study uses a model that describes the horizontal structure of the potential temperature and wind fields in regions of deep tropical convection. The assimilation method is three- and four-dimensional variational data assimilation. The background-error covariance model for the assimilation is a multivariate model that includes the mass–wind couplings representative of equatorial inertio-gravity modes and equatorial Kelvin and mixed Rossby–gravity modes in addition to those representative of balanced equatorial Rossby waves. Spectra of the background errors based on these waves are derived from the tropical forecast errors of the European Centre for Medium-Range Weather Forecasts (ECMWF) model. Tropical mass–wind (im)balances are illustrated by studying the potential impact of the spaceborne Doppler wind lidar (DWL) Atmospheric Dynamic Mission (ADM)-Aeolus, which measures horizontal line-of-sight (LOS) wind components. Several scenarios with two DWLs of ADM-Aeolus type are compared under different flow conditions and using different assumptions about the quality of the background-error covariances. Results of three-dimensional variational data assimilation (3DVAR) illustrate the inefficiency of multivariate assimilation in the tropics. The consequence for the assimilation of LOS winds is that the missing part of the wind vector can hardly be reconstructed from the mass-field observations and applied balances as in the case of the midlatitudes. Results of four-dimensional variational data assimilation (4DVAR) show that for large-scale tropical conditions and using reliable background-error statistics, differences among various DWL scenarios are not large. As the background-error covariances becomes less reliable, horizontal scales become smaller and the flow becomes less zonal, the importance of obtaining information about the wind vector increases. The added value of another DWL satellite increases as the quality of the background-error covariances deteriorates and it can be more than twice as large as in the case of reliable covariances.

가우시안 프로세스 기반 함수근사와 서포트 벡터 학습을 이용한 레이더 및 강우계 관측 데이터의 융합

최근들어, 커널 기법(kernel method)은 패턴 분류, 함수 근사 및 비정상 상태 탐지 등의 분야에서 상당한 관심을 끌고 있다. 특히, 서포트 벡터 머신(support vector machine)이나 커널 주성분 분석(kernel principal component analysis) 등의 방법론에서 커널의 역할은 매우 중요한데, 이는 고전적인 선형 머신이 비선형성을 효과적으로 다룰 수 있도록 일반화 해줄 수 있기 때문이다. 본 논문에서는 커널 기반 가우시안 프로세스(gaussian process) 함수근사 기법과 서포트 벡터 학습을 이용하여 레이더와 강우계의 관측 데이터를 융합하는 문제를 고려한다. 그리고, 국내의 강원, 경북 및 충북에 걸쳐있는 지역에 대한 레이더 자료 및 강우계 자료를 대상으로 하여 본 논문에서 고려하는 방법론들에 의해 데이터 융합을 수행한 결과를 제시하고, 성능비교를 수행한다. Recently, kernel methods have attracted great interests in the areas of pattern classification, function approximation, and anomaly detection. The role of the kernel is particularly important in the methods such as SVM(support vector machine) and KPCA(kernel principal component analysis), for it can generalize the conventional linear machines to be capable of efficiently handling nonlinearities. This paper considers the problem of combining radar and rain gauge observations utilizing the regression approach based on the kernel-based gaussian process and support vector learning. The data-assimilation results of the considered methods are reported for the radar and rain gauge observations collected over the region covering parts of Gangwon, Kyungbuk, and Chungbuk provinces of Korea, along with performance comparison.

A numerical comparative study on data assimilation using Kalman filters

Kalman filtering has become a powerful framework for solving data assimilation problems. Of interest here are the low-rank filters which are computationally efficient for solving large-scale data assimilation problems. Together with theoretical aspects on the basis of which some common low-rank filters are designed, the paper also presents numerically comparative results of data assimilation using an air pollution model. The performance of such filters, as depending on the distance between the measurement locations and emission points, is investigated.

Quasi-reversibility method for data assimilation in models of mantle dynamics

SUMMARY Rapid progress in imaging deep Earth structures using seismic tomography and in studies of physical and chemical properties of mantle rocks facilitates research in assimilation of data related to mantle dynamics. In this paper, we present a new numerical approach for data assimilation, which allows for incorporating observations (at present) and unknown initial conditions (in the past) for mantle temperature and flow into a 3-D dynamic model in order to determine the initial conditions. The dynamic model is described by the backward heat, motion and continuity equations. The use of the quasi-reversibility (QRV) method implies the introduction into the backward heat equation of the additional term involving the product of a small regularization parameter and a higher order temperature derivative. The data assimilation in this case is based on a search of the best fit between the forecast model state and the observations by minimizing the regularization parameter. We apply the QRV data assimilation method to restore the evolution of (i) mantle plumes (a synthetic case study) and (ii) the lithospheric slab imaged by teleseismic body-wave tomography in the southeastern Carpathians. For both models the present temperature and mantle flow are assimilated to the geological past, and the prominent features of mantle structures are recovered. We then model the evolution of the mantle structures forward in time starting from the restored state to the present state and estimate the accuracy of the model predictions. The results of the QRV data assimilation are compared to that obtained by the variational (VAR) and backward advection data assimilation. Although the accuracy of the QRV data assimilation is lower than that of the VAR data assimilation, the QRV method does not require any additional smoothing of the input data or filtering of temperature noise as the VAR method does. Based on the results and the comparison of the methods, we consider the QRV method to be a highly promising approach to assimilation of data related to mantle dynamics.

A Reduced-Order Kalman Filter for Data Assimilation in Physical Oceanography

A central task of physical oceanography is the prediction of ocean circulation at various time scales. Mathematical techniques are used in this domain not only for the modeling of ocean circulation but also for the enhancement of simulation through data assimilation. The ocean circulation model of concern here, namely, HYCOM, is briefly presented through its variables, equations, and specific vertical coordinate system. The main part of this paper focuses on the Kalman filter as a data assimilation method, and especially on how this mathematical technique, usually associated with a prohibitively high computing cost for operational sciences, is simplified in order to make it applicable to the simulation of realistic ocean circulation models. Some practical issues are presented, such as a brief explanation about ocean observation systems, together with examples of data assimilation results.

Reconstruction of summer Barents Sea circulation from climatological data

An estimate of the summer Barents Sea circulation is constructed as a four‐dimensional variational inverse of the monthly hydrographic and atmospheric climatologies. The reconstructed evolution of temperature, salinity, and velocity fields provides the best fit to climatological data and satisfies dynamical and kinematic constraints of a primitive equation ocean circulation model. The data‐optimized Barents Sea state is in general agreement ‐with the existing schemes of circulation in the region. The circulation is characterized by the 3.2 Sv inflow from the Norwegian Sea with the Norwegian Atlantic Current. Approximately 1.5 Sv of this inflow recirculates along the northern flank of Bear Island Trough, while the major branch of the current crosses the Barents Sea and outflows through Franz Josef Land—Novaya Zemliya and the Kara Gate straits with transports of 1.1 Sv and 0.6 Sv, respectively. The data assimilation reveals an eastward current between the Great and Central banks and cyclonic circulation in the region between Central Bank and Novaya Zemliya. These two circulation feature s have recently been confirmed in a number of publications. The reconstructed surface heat and salt fluxes are in qualitative and quantitative agreement with the known observational estimates. The posterior error analysis and sensitivity experiments provide additional arguments in support of the reliability of the data assimilation results.

A comparison of error subspace Kalman filters

Three advanced filter algorithms based on the Kalman filter are reviewed and presented in a unified notation. They are the well-known ensemble Kalman filter (EnKF), the singular evolutive extended Kalman (SEEK) filter, and the less common singular evolutive interpolated Kalman (SEIK) filter. For comparison, the mathematical formulations of the filters are reviewed in relation to the extended Kalman filter as error subspace Kalman filters. The algorithms are presented in their original form and possible variations are discussed. A comparison of the algorithms shows their theoretical capabilities for efficient data assimilation with large-scale non-linear systems. In particular, problems of the analysis equations are apparent in the original EnKF algorithm due to the Monte Carlo sampling of ensembles. Theoretically, the SEIK filter appears to be a numerically very efficient algorithm with high potential for use with non-linear models. The superiority of the SEIK filter is demonstrated on the basis of identical twin experiments using a shallow-water model with non-linear evolution. Identical initial conditions for all three filters allow for a consistent comparison of the data assimilation results. These show how choices of particular state ensembles and assimilation schemes lead to significant variations of the filter performance. This is related to different qualities of the predicted error subspaces, as is demonstrated in an examination of the predicted state covariance matrices.

A comparison of error subspace Kalman filters

Three advanced filter algorithms based on the Kalman filter are reviewed and presented in a unified notation. They are the well-known ensemble Kalman filter (EnKF), the singular evolutive extended Kalman (SEEK) filter, and the less common singular evolutive interpolated Kalman (SEIK) filter. For comparison, the mathematical formulations of the filters are reviewed in relation to the extended Kalman filter as error subspace Kalman filters. The algorithms are presented in their original form and possible variations are discussed. A comparison of the algorithms shows their theoretical capabilities for efficient data assimilation with large-scale non-linear systems. In particular, problems of the analysis equations are apparent in the original EnKF algorithm due to the Monte Carlo sampling of ensembles. Theoretically, the SEIK filter appears to be a numerically very efficient algorithm with high potential for use with non-linear models. The superiority of the SEIK filter is demonstrated on the basis of identical twin experiments using a shallow-water model with non-linear evolution. Identical initial conditions for all three filters allow for a consistent comparison of the data assimilation results. These show how choices of particular state ensembles and assimilation schemes lead to significant variations of the filter performance. This is related to different qualities of the predicted error subspaces, as is demonstrated in an examination of the predicted state covariance matrices.

Investigation of the tidal variations in a 3-D dynamics-chemistry-transport model of the middle atmosphere

The Monitoring of Stratospheric Depletion of Ozone Layer (MSDOL) system is a software tool that is used to assimilate continuously satellite ozone data into a 3-D dynamics-chemistry-transport mechanistic model of the middle atmosphere. Because atmospheric tides lead to variations in temperature, which then affect ozone amounts, we suspect that the ability of the model to converge toward the true 3-D ozone field should therefore depend on how well atmospheric tides are simulated in the model. In this paper, the MSDOL model run without ozone data assimilation was used to explore the representation of the diurnal and semidiurnal tidal components between 10 and 80 km . Comparison with the predictions from the Global-Scale Wave Model (GSWM) showed the model to be in good qualitative agreement with GSWM results, despite quantitative differences. These differences might be explained by the differences in the representation of the background atmosphere, tidal forcing and tidal dissipation in MSDOL and GSWM. A series of sensitivity simulations were then conducted by changing some input parameters. The sensitivity of the tidal amplitude to changes of the tidal forcing and the effect of variations in the structure of the background wind and temperature fields were described; in particular, it was shown that significant fluctuations of the diurnal amplitude (∼20%) could be obtained due to variable tidal forcing. The results here provide some hints on the way the model should be used in the future. Indeed, they suggest the need for updating the model with revised tidal forcing, and for assimilating some part of the real background atmosphere in order to provide real-time estimates of the tides and improve the accuracy of the ozone data assimilation result.