Ozone data assimilation with GEOS-Chem: a comparison between 3-D-Var, 4-D-Var, and suboptimal Kalman filter approaches

NASA Astrophysics Data System (ADS)

Singh, K.; Sandu, A.; Bowman, K. W.; Parrington, M.; Jones, D. B. A.; Lee, M.

2011-08-01

Chemistry transport models determine the evolving chemical state of the atmosphere by solving the fundamental equations that govern physical and chemical transformations subject to initial conditions of the atmospheric state and surface boundary conditions, e.g., surface emissions. The development of data assimilation techniques synthesize model predictions with measurements in a rigorous mathematical framework that provides observational constraints on these conditions. Two families of data assimilation methods are currently widely used: variational and Kalman filter (KF). The variational approach is based on control theory and formulates data assimilation as a minimization problem of a cost functional that measures the model-observations mismatch. The Kalman filter approach is rooted in statistical estimation theory and provides the analysis covariance together with the best state estimate. Suboptimal Kalman filters employ different approximations of the covariances in order to make the computations feasible with large models. Each family of methods has both merits and drawbacks. This paper compares several data assimilation methods used for global chemical data assimilation. Specifically, we evaluate data assimilation approaches for improving estimates of the summertime global tropospheric ozone distribution in August 2006 based on ozone observations from the NASA Tropospheric Emission Spectrometer and the GEOS-Chem chemistry transport model. The resulting analyses are compared against independent ozonesonde measurements to assess the effectiveness of each assimilation method. All assimilation methods provide notable improvements over the free model simulations, which differ from the ozonesonde measurements by about 20 % (below 200 hPa). Four dimensional variational data assimilation with window lengths between five days and two weeks is the most accurate method, with mean differences between analysis profiles and ozonesonde measurements of 1-5 %. Two sequential assimilation approaches (three dimensional variational and suboptimal KF), although derived from different theoretical considerations, provide similar ozone estimates, with relative differences of 5-10 % between the analyses and ozonesonde measurements. Adjoint sensitivity analysis techniques are used to explore the role of of uncertainties in ozone precursors and their emissions on the distribution of tropospheric ozone. A novel technique is introduced that projects 3-D-Variational increments back to an equivalent initial condition, which facilitates comparison with 4-D variational techniques.

Variational data assimilation with a semi-Lagrangian semi-implicit global shallow-water equation model and its adjoint

NASA Technical Reports Server (NTRS)

Li, Y.; Navon, I. M.; Courtier, P.; Gauthier, P.

1993-01-01

An adjoint model is developed for variational data assimilation using the 2D semi-Lagrangian semi-implicit (SLSI) shallow-water equation global model of Bates et al. with special attention being paid to the linearization of the interpolation routines. It is demonstrated that with larger time steps the limit of the validity of the tangent linear model will be curtailed due to the interpolations, especially in regions where sharp gradients in the interpolated variables coupled with strong advective wind occur, a synoptic situation common in the high latitudes. This effect is particularly evident near the pole in the Northern Hemisphere during the winter season. Variational data assimilation experiments of 'identical twin' type with observations available only at the end of the assimilation period perform well with this adjoint model. It is confirmed that the computational efficiency of the semi-Lagrangian scheme is preserved during the minimization process, related to the variational data assimilation procedure.

A variational data assimilation system for the range dependent acoustic model using the representer method: Theoretical derivations.

PubMed

Ngodock, Hans; Carrier, Matthew; Fabre, Josette; Zingarelli, Robert; Souopgui, Innocent

2017-07-01

This study presents the theoretical framework for variational data assimilation of acoustic pressure observations into an acoustic propagation model, namely, the range dependent acoustic model (RAM). RAM uses the split-step Padé algorithm to solve the parabolic equation. The assimilation consists of minimizing a weighted least squares cost function that includes discrepancies between the model solution and the observations. The minimization process, which uses the principle of variations, requires the derivation of the tangent linear and adjoint models of the RAM. The mathematical derivations are presented here, and, for the sake of brevity, a companion study presents the numerical implementation and results from the assimilation simulated acoustic pressure observations.

Initialization and simulation of a landfalling typhoon using a variational bogus mapped data assimilation (BMDA)

NASA Astrophysics Data System (ADS)

Zhao, Y.; Wang, B.; Wang, Y.

2007-12-01

Recently, a new data assimilation method called “3-dimensional variational data assimilation of mapped observation (3DVM)” has been developed by the authors. We have shown that the new method is very efficient and inexpensive compared with its counterpart 4-dimensional variational data assimilation (4DVar). The new method has been implemented into the Penn State/NCAR mesoscale model MM5V1 (MM5_3DVM). In this study, we apply the new method to the bogus data assimilation (BDA) available in the original MM5 with the 4DVar. By the new approach, a specified sea-level pressure (SLP) field (bogus data) is incorporated into MM5 through the 3DVM (for convenient, we call it variational bogus mapped data assimilation - BMDA) instead of the original 4DVar data assimilation. To demonstrate the effectiveness of the new 3DVM method, initialization and simulation of a landfalling typhoon - typhoon Dan (1999) over the western North Pacific with the new method are compared with that with its counterpart 4DVar in MM5. Results show that the initial structure and the simulated intensity and track are improved more significantly using 3DVM than 4DVar. Sensitivity experiments also show that the simulated typhoon track and intensity are more sensitive to the size of the assimilation window in the 4DVar than that in the 3DVM. Meanwhile, 3DVM takes much less computing cost than its counterpart 4DVar for a given time window.

Efficient Mean Field Variational Algorithm for Data Assimilation (Invited)

NASA Astrophysics Data System (ADS)

Vrettas, M. D.; Cornford, D.; Opper, M.

2013-12-01

Data assimilation algorithms combine available observations of physical systems with the assumed model dynamics in a systematic manner, to produce better estimates of initial conditions for prediction. Broadly they can be categorized in three main approaches: (a) sequential algorithms, (b) sampling methods and (c) variational algorithms which transform the density estimation problem to an optimization problem. However, given finite computational resources, only a handful of ensemble Kalman filters and 4DVar algorithms have been applied operationally to very high dimensional geophysical applications, such as weather forecasting. In this paper we present a recent extension to our variational Bayesian algorithm which seeks the ';optimal' posterior distribution over the continuous time states, within a family of non-stationary Gaussian processes. Our initial work on variational Bayesian approaches to data assimilation, unlike the well-known 4DVar method which seeks only the most probable solution, computes the best time varying Gaussian process approximation to the posterior smoothing distribution for dynamical systems that can be represented by stochastic differential equations. This approach was based on minimising the Kullback-Leibler divergence, over paths, between the true posterior and our Gaussian process approximation. Whilst the observations were informative enough to keep the posterior smoothing density close to Gaussian the algorithm proved very effective on low dimensional systems (e.g. O(10)D). However for higher dimensional systems, the high computational demands make the algorithm prohibitively expensive. To overcome the difficulties presented in the original framework and make our approach more efficient in higher dimensional systems we have been developing a new mean field version of the algorithm which treats the state variables at any given time as being independent in the posterior approximation, while still accounting for their relationships in the mean solution arising from the original system dynamics. Here we present this new mean field approach, illustrating its performance on a range of benchmark data assimilation problems whose dimensionality varies from O(10) to O(10^3)D. We emphasise that the variational Bayesian approach we adopt, unlike other variational approaches, provides a natural bound on the marginal likelihood of the observations given the model parameters which also allows for inference of (hyper-) parameters such as observational errors, parameters in the dynamical model and model error representation. We also stress that since our approach is intrinsically parallel it can be implemented very efficiently to address very long data assimilation time windows. Moreover, like most traditional variational approaches our Bayesian variational method has the benefit of being posed as an optimisation problem therefore its complexity can be tuned to the available computational resources. We finish with a sketch of possible future directions.

On a comparison of two schemes in sequential data assimilation

NASA Astrophysics Data System (ADS)

Grishina, Anastasiia A.; Penenko, Alexey V.

2017-11-01

This paper is focused on variational data assimilation as an approach to mathematical modeling. Realization of the approach requires a sequence of connected inverse problems with different sets of observational data to be solved. Two variational data assimilation schemes, "implicit" and "explicit", are considered in the article. Their equivalence is shown and the numerical results are given on a basis of non-linear Robertson system. To avoid the "inverse problem crime" different schemes were used to produce synthetic measurement and to solve the data assimilation problem.

Direct variational data assimilation algorithm for atmospheric chemistry data with transport and transformation model

NASA Astrophysics Data System (ADS)

Penenko, Alexey; Penenko, Vladimir; Nuterman, Roman; Baklanov, Alexander; Mahura, Alexander

2015-11-01

Atmospheric chemistry dynamics is studied with convection-diffusion-reaction model. The numerical Data Assimilation algorithm presented is based on the additive-averaged splitting schemes. It carries out ''fine-grained'' variational data assimilation on the separate splitting stages with respect to spatial dimensions and processes i.e. the same measurement data is assimilated to different parts of the split model. This design has efficient implementation due to the direct data assimilation algorithms of the transport process along coordinate lines. Results of numerical experiments with chemical data assimilation algorithm of in situ concentration measurements on real data scenario have been presented. In order to construct the scenario, meteorological data has been taken from EnviroHIRLAM model output, initial conditions from MOZART model output and measurements from Airbase database.

Overview of Chinese GRAPES Data Assimilation System

NASA Astrophysics Data System (ADS)

Liu, Yan

2017-04-01

The development of data assimilation system of Global and Regional Assimilation and Prediction System (GRAPES in short) which is Chinese new generation operational numerical weather prediction system completed in recent years is reviewed in this paper, including the design scheme and main characteristics. GRAPES adopts the variational approach with stresses at application of various remote sensing observational data. Its development path is from three dimensional to four dimensional assimilation. It may be implemented with limited area or global configurations. The three dimensional variational data assimilation systems have been operational in the national and a few of regional meteorological centers. The global four dimensional assimilation system is in pre-operational experiments, and will be upgraded. After a brief introduction to the GRAPES data assimilation system, results of a series of validations of GRAPES analyses against the observation data and analyses derived from other operational NWP center to assess its performance are presented.

Improving Reanalyses Using TRMM and SSM/I-Derived Precipitation and Total Precipitable Water Observations

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.; Zhang, Sara Q.; daSilva, Arlindo M.

1999-01-01

Global reanalyses currently contain significant errors in the primary fields of the hydrological cycle such as precipitation, evaporation, moisture, and the related cloud fields, especially in the tropics. The Data Assimilation Office (DAO) at the NASA Goddard Space Flight Center has been exploring the use of rainfall and total precipitable water (TPW) observations from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and the Special Sensor Microwave/ Imager (SSM/I) instruments to improve these fields in reanalyses. The DAO has developed a "1+1"D procedure to assimilate 6-hr averaged rainfall and TPW into the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS). The algorithm is based on a 6-hr time integration of a column version of the GEOS DAS. The "1+1" designation refers to one spatial dimension plus one temporal dimension. The scheme minimizes the least-square differences between the satellite-retrieved rain rates and those produced by the column model over the 6-hr analysis window. The control variables are analysis increments of moisture within the Incremental Analysis Update (IAU) framework of the GEOS DAS. This 1+1D scheme, in its generalization to four dimensions, is related to the standard 4D variational assimilation but differs in its choice of the control variable. Instead of estimating the initial condition at the beginning of the assimilation cycle, it estimates the constant IAU forcing applied over a 6-hr assimilation cycle. In doing so, it imposes the forecast model as a weak constraint in a manner similar to the variational continuous assimilation techniques. We present results from an experiment in which the observed rain rate and TPW are assumed to be "perfect". They show that assimilating the TMI and SSM/I-derived surface precipitation and TPW observations improves not only the precipitation and moisture fields but also key climate parameters directly linked to convective activities such as clouds, the outgoing longwave radiation, and the large-scale circulation in the tropics. In particular, assimilating these data types reduce the state-dependent systematic errors in the assimilated products. The improved analysis also leads to a better short-range forecast, but the impact is modest compared with improvements in the time-averaged fields. These results suggest that, in the presence of biases and other errors of the forecast model, it is possible to improve the time-averaged "climate content" in the assimilated data without comparable improvements in the short-range forecast skill. Results of this experiment provide a useful benchmark for evaluating error covariance models for optimal use of these data types.

Modification of a variational objective analysis model for new equations for pressure gradient and vertical velocity in the lower troposphere and for spatial resolution and accuracy of satellite data

NASA Technical Reports Server (NTRS)

Achtemeier, G. L.

1986-01-01

Since late 1982 NASA has supported research to develop a numerical variational model for the diagnostic assimilation of conventional and space-based meteorological data. In order to analyze the model components, four variational models are defined dividing the problem naturally according to increasing complexity. The first of these variational models (MODEL I), the subject of this report, contains the two nonlinear horizontal momentum equations, the integrated continuity equation, and the hydrostatic equation. This report summarizes the results of research (1) to improve the way the large nonmeteorological parts of the pressure gradient force are partitioned between the two terms of the pressure gradient force terms of the horizontal momentum equations, (2) to generalize the integrated continuity equation to account for variable pressure thickness over elevated terrain, and (3) to introduce horizontal variation in the precision modulus weights for the observations.

Usefulness of Wave Data Assimilation to the WAVE WATCH III Modeling System

NASA Astrophysics Data System (ADS)

Choi, J. K.; Dykes, J. D.; Yaremchuk, M.; Wittmann, P.

2017-12-01

In-situ and remote-sensed wave data are more abundant currently than in years past, with excellent accuracy at global scales. Forecast skill of the WAVE WATCH III model is improved by assimilation of these measurements and they are also useful for model validation and calibration. It has been known that the impact of assimilation in wind-sea conditions is not large, but spectra that result in large swell with long term propagation are identified and assimilated, the improved accuracy of the initial conditions improve the long-term forecasts. The Navy's assimilation method started with the simple Optimal Interpolation (OI) method. Operationally, Fleet Numerical Meteorology and Oceanography Center uses the sequential 2DVar scheme, but a new approach has been tested based on an adjoint-free method to variational assimilation in WAVE WATCH III. We will present the status of wave data assimilation into the WAVE WATCH III numerical model and upcoming development of this new adjoint-free variational approach.

A reanalysis dataset of the South China Sea.

PubMed

Zeng, Xuezhi; Peng, Shiqiu; Li, Zhijin; Qi, Yiquan; Chen, Rongyu

2014-01-01

Ocean reanalysis provides a temporally continuous and spatially gridded four-dimensional estimate of the ocean state for a better understanding of the ocean dynamics and its spatial/temporal variability. Here we present a 19-year (1992-2010) high-resolution ocean reanalysis dataset of the upper ocean in the South China Sea (SCS) produced from an ocean data assimilation system. A wide variety of observations, including in-situ temperature/salinity profiles, ship-measured and satellite-derived sea surface temperatures, and sea surface height anomalies from satellite altimetry, are assimilated into the outputs of an ocean general circulation model using a multi-scale incremental three-dimensional variational data assimilation scheme, yielding a daily high-resolution reanalysis dataset of the SCS. Comparisons between the reanalysis and independent observations support the reliability of the dataset. The presented dataset provides the research community of the SCS an important data source for studying the thermodynamic processes of the ocean circulation and meso-scale features in the SCS, including their spatial and temporal variability.

A reanalysis dataset of the South China Sea

PubMed Central

Zeng, Xuezhi; Peng, Shiqiu; Li, Zhijin; Qi, Yiquan; Chen, Rongyu

2014-01-01

Ocean reanalysis provides a temporally continuous and spatially gridded four-dimensional estimate of the ocean state for a better understanding of the ocean dynamics and its spatial/temporal variability. Here we present a 19-year (1992–2010) high-resolution ocean reanalysis dataset of the upper ocean in the South China Sea (SCS) produced from an ocean data assimilation system. A wide variety of observations, including in-situ temperature/salinity profiles, ship-measured and satellite-derived sea surface temperatures, and sea surface height anomalies from satellite altimetry, are assimilated into the outputs of an ocean general circulation model using a multi-scale incremental three-dimensional variational data assimilation scheme, yielding a daily high-resolution reanalysis dataset of the SCS. Comparisons between the reanalysis and independent observations support the reliability of the dataset. The presented dataset provides the research community of the SCS an important data source for studying the thermodynamic processes of the ocean circulation and meso-scale features in the SCS, including their spatial and temporal variability. PMID:25977803

A Variational Assimilation Method for Satellite and Conventional Data: Development of Basic Model for Diagnosis of Cyclone Systems

NASA Technical Reports Server (NTRS)

Achtemeier, Gary L.; Scott, Robert W.; Chen, J.

1991-01-01

A summary is presented of the progress toward the completion of a comprehensive diagnostic objective analysis system based upon the calculus of variations. The approach was to first develop the objective analysis subject to the constraints that the final product satisfies the five basic primitive equations for a dry inviscid atmosphere: the two nonlinear horizontal momentum equations, the continuity equation, the hydrostatic equation, and the thermodynamic equation. Then, having derived the basic model, there would be added to it the equations for moist atmospheric processes and the radiative transfer equation.

Efficient Methods to Assimilate Satellite Retrievals Based on Information Content. Part 2; Suboptimal Retrieval Assimilation

NASA Technical Reports Server (NTRS)

Joiner, J.; Dee, D. P.

1998-01-01

One of the outstanding problems in data assimilation has been and continues to be how best to utilize satellite data while balancing the tradeoff between accuracy and computational cost. A number of weather prediction centers have recently achieved remarkable success in improving their forecast skill by changing the method by which satellite data are assimilated into the forecast model from the traditional approach of assimilating retrievals to the direct assimilation of radiances in a variational framework. The operational implementation of such a substantial change in methodology involves a great number of technical details, e.g., pertaining to quality control procedures, systematic error correction techniques, and tuning of the statistical parameters in the analysis algorithm. Although there are clear theoretical advantages to the direct radiance assimilation approach, it is not obvious at all to what extent the improvements that have been obtained so far can be attributed to the change in methodology, or to various technical aspects of the implementation. The issue is of interest because retrieval assimilation retains many practical and logistical advantages which may become even more significant in the near future when increasingly high-volume data sources become available. The central question we address here is: how much improvement can we expect from assimilating radiances rather than retrievals, all other things being equal? We compare the two approaches in a simplified one-dimensional theoretical framework, in which problems related to quality control and systematic error correction are conveniently absent. By assuming a perfect radiative transfer model and perfect knowledge of radiance and background error covariances, we are able to formulate a nonlinear local error analysis for each assimilation method. Direct radiance assimilation is optimal in this idealized context, while the traditional method of assimilating retrievals is suboptimal because it ignores the cross-covariances between background errors and retrieval errors. We show that interactive retrieval assimilation (where the same background used for assimilation is also used in the retrieval step) is equivalent to direct assimilation of radiances with suboptimal analysis weights. We illustrate and extend these theoretical arguments with several one-dimensional assimilation experiments, where we estimate vertical atmospheric profiles using simulated data from both the High-resolution InfraRed Sounder 2 (HIRS2) and the future Atmospheric InfraRed Sounder (AIRS).

A cross-sectional study of learning styles among continuing medical education participants.

PubMed

Collins, C Scott; Nanda, Sanjeev; Palmer, Brian A; Mohabbat, Arya B; Schleck, Cathy D; Mandrekar, Jayawant N; Mahapatra, Saswati; Beckman, Thomas J; Wittich, Christopher M

2018-04-27

Experiential learning has been suggested as a framework for planning continuing medical education (CME). We aimed to (1) determine participants' learning styles at traditional CME courses and (2) explore associations between learning styles and participant characteristics. Cross-sectional study of all participants (n = 393) at two Mayo Clinic CME courses who completed the Kolb Learning Style Inventory and provided demographic data. A total of 393 participants returned 241 surveys (response rate, 61.3%). Among the 143 participants (36.4%) who supplied complete demographic and Kolb data, Kolb learning styles included diverging (45; 31.5%), assimilating (56; 39.2%), converging (8; 5.6%), and accommodating (34; 23.8%). Associations existed between learning style and gender (p = 0.02). For most men, learning styles were diverging (23 of 63; 36.5%) and assimilating (30 of 63; 47.6%); for most women, diverging (22 of 80; 27.5%), assimilating (26 of 80; 32.5%), and accommodating (26 of 80; 32.5%). Internal medicine and psychiatry CME participants had diverse learning styles. Female participants had more variation in their learning styles than men. Teaching techniques must vary to appeal to all learners. The experiential learning theory sequentially moves a learner from Why? to What? to How? to If? to accommodate learning styles.

What is the correct cost functional for variational data assimilation?

NASA Astrophysics Data System (ADS)

Bröcker, Jochen

2018-03-01

Variational approaches to data assimilation, and weakly constrained four dimensional variation (WC-4DVar) in particular, are important in the geosciences but also in other communities (often under different names). The cost functions and the resulting optimal trajectories may have a probabilistic interpretation, for instance by linking data assimilation with maximum aposteriori (MAP) estimation. This is possible in particular if the unknown trajectory is modelled as the solution of a stochastic differential equation (SDE), as is increasingly the case in weather forecasting and climate modelling. In this situation, the MAP estimator (or "most probable path" of the SDE) is obtained by minimising the Onsager-Machlup functional. Although this fact is well known, there seems to be some confusion in the literature, with the energy (or "least squares") functional sometimes been claimed to yield the most probable path. The first aim of this paper is to address this confusion and show that the energy functional does not, in general, provide the most probable path. The second aim is to discuss the implications in practice. Although the mentioned results pertain to stochastic models in continuous time, they do have consequences in practice where SDE's are approximated by discrete time schemes. It turns out that using an approximation to the SDE and calculating its most probable path does not necessarily yield a good approximation to the most probable path of the SDE proper. This suggest that even in discrete time, a version of the Onsager-Machlup functional should be used, rather than the energy functional, at least if the solution is to be interpreted as a MAP estimator.

Technical Report Series on Global Modeling and Data Assimilation, Volume 43. MERRA-2; Initial Evaluation of the Climate

NASA Technical Reports Server (NTRS)

Koster, Randal D. (Editor); Bosilovich, Michael G.; Akella, Santha; Lawrence, Coy; Cullather, Richard; Draper, Clara; Gelaro, Ronald; Kovach, Robin; Liu, Qing; Molod, Andrea;

A VAS-numerical model impact study using the Gal-Chen variational approach

NASA Technical Reports Server (NTRS)

Aune, Robert M.; Tuccillo, James J.; Uccellini, Louis W.; Petersen, Ralph A.

1987-01-01

A numerical study based on the use of a variational assimilation technique of Gal-Chen (1983, 1986) was conducted to assess the impact of incorporating temperature data from the VISSR Atmospheric Sounder (VAS) into a regional-scale numerical model. A comparison with the results of a control forecast using only conventional data indicated that the assimilation technique successfully combines actual VAS temperature observations with the dynamically balanced model fields without destabilizing the model during the assimilation cycle. Moreover, increasing the temporal frequency of VAS temperature insertions during the assimilation cycle was shown to enhance the impact on the model forecast through successively longer forecast periods. The incorporation of a nudging technique, whereby the model temperature field is constrained toward the VAS 'updated' values during the assimilation cycle, further enhances the impact of the VAS temperature data.

A Variational Assimilation Method for Satellite and Conventional Data: Model 2 (version 1)

NASA Technical Reports Server (NTRS)

Achtemeier, Gary L.

1991-01-01

The Model II variational data assimilation model is the second of the four variational models designed to blend diverse meteorological data into a dynamically constrained data set. Model II differs from Model I in that it includes the thermodynamic equation as the fifth dynamical constraint. Thus, Model II includes all five of the primative equations that govern atmospheric flow for a dry atmosphere.

Snow water equivalent monitoring retrieved by assimilating passive microwave observations in a coupled snowpack evolution and microwave emission models over North-Eastern Canada

NASA Astrophysics Data System (ADS)

Royer, A.; Larue, F.; De Sève, D.; Roy, A.; Vionnet, V.; Picard, G.; Cosme, E.

2017-12-01

Over northern snow-dominated basins, the snow water equivalent (SWE) is of primary interest for spring streamflow forecasting. SWE retrievals from satellite data are still not well resolved, in particular from microwave (MW) measurements, the only type of data sensible to snow mass. Also, the use of snowpack models is challenging due to the large uncertainties in meteorological input forcings. This project aims to improve SWE prediction by assimilation of satellite brightness temperature (TB), without any ground-based observations. The proposed approach is the coupling of a detailed multilayer snowpack model (Crocus) with a MW snow emission model (DMRT-ML). The assimilation scheme is a Sequential Importance Resampling Particle filter, through ensembles of perturbed meteorological forcings according to their respective uncertainties. Crocus simulations driven by operational meteorological forecasts from the Canadian Global Environmental Multiscale model at 10 km spatial resolution were compared to continuous daily SWE measurements over Québec, North-Eastern Canada (56° - 45°N). The results show a mean bias of the maximum SWE overestimated by 16% with variations up to +32%. This observed large variability could lead to dramatic consequences on spring flood forecasts. Results of Crocus-DMRT-ML coupling compared to surface-based TB measurements (at 11, 19 and 37 GHz) show that the Crocus snowpack microstructure described by sticky hard spheres within DMRT has to be scaled by a snow stickiness of 0.18, significantly reducing the overall RMSE of simulated TBs. The ability of assimilation of daily TBs to correct the simulated SWE is first presented through twin experiments with synthetic data, and then with AMSR-2 satellite time series of TBs along the winter taking into account atmospheric and forest canopy interferences (absorption and emission). The differences between TBs at 19-37 GHz and at 11-19 GHz, in vertical polarization, were assimilated. This assimilation test with synthetic data gives a SWE RMSE reduced by a factor of 2 after assimilation. Assimilation of AMSR-2 TBs shows improvement in SWE retrievals compared to continuous in-situ SWE measurements. The accuracy is discussed as a function of boreal forest density and LAI (MODIS-based data), having significant effects.

Cell-to-cell variation and specialization in sugar metabolism in clonal bacterial populations

PubMed Central

Schreiber, Frank; Dal Co, Alma; Kiviet, Daniel J.; Littmann, Sten

2017-01-01

While we have good understanding of bacterial metabolism at the population level, we know little about the metabolic behavior of individual cells: do single cells in clonal populations sometimes specialize on different metabolic pathways? Such metabolic specialization could be driven by stochastic gene expression and could provide individual cells with growth benefits of specialization. We measured the degree of phenotypic specialization in two parallel metabolic pathways, the assimilation of glucose and arabinose. We grew Escherichia coli in chemostats, and used isotope-labeled sugars in combination with nanometer-scale secondary ion mass spectrometry and mathematical modeling to quantify sugar assimilation at the single-cell level. We found large variation in metabolic activities between single cells, both in absolute assimilation and in the degree to which individual cells specialize in the assimilation of different sugars. Analysis of transcriptional reporters indicated that this variation was at least partially based on cell-to-cell variation in gene expression. Metabolic differences between cells in clonal populations could potentially reduce metabolic incompatibilities between different pathways, and increase the rate at which parallel reactions can be performed. PMID:29253903

Development of a three-dimensional variational data assimilation system for the Seto Island Sea, Japan

NASA Astrophysics Data System (ADS)

Kurosawa, K.; Uchiyama, Y.

2016-12-01

By optimally combined ocean models with observation data, numerical oceanic reanalysis and forecast systems allow us to predict the ocean more precisely. In general, data assimilation is exploited to prepare the initial condition for the forecast. This technique has widely been employed in atmospheric prediction, whereas oceanic prediction lags behind weather forecast. Accurate oceanic prediction systems have been demanded for operational purposes such as for fisheries, vessel navigation, marine construction, offshore platform management, marine monitoring, etc. In particular, in crowded harbors and estuaries including the Seto Inland Sea (SIS), Japan, data assimilation has seldom been adapted because data from satellites and Argo floats essential to successful oceanic predictions is desperately limited. In addition, although static data assimilation, typically three-dimensional variational data assimilation (3DVAR), is computationally cheap and statistically optimal, but is not physically balanced. For instance, 3DVAR is known to modify velocity and density fields merely mathematically, yet it does not adequately consider quasi-geostrophic balance, which is generally true in most cases. In the present study, we develop a 3DVAR system for Regional Oceanic Modeling Systems (ROMS) and apply to the high-resolution SIS model in a double nested configuration (Kosako et al., 2015). The SIS is the largest estuary in Japan with a number of autonomous in-situ monitoring of vertical profiles of temperature and salinity, tens of tidal gages, along with continuous surface current measurement using HF radars. We first present a theoretical framework of the 3DVAR algorithm by considering geostrophic and thermal-wind balance to find plausible relationships among physical variables to avoid undesirable modifications. Subsequently, the developed 3DVAR is coupled with the SIS ROMS model to compare the model outcomes against some observation data. The 3DVAR ROMS model for the SIS performs much better than the SIS model without assimilation and demonstrates good model skills with reproducing quite complex flows in the SIS because of its complicated topography with more than 3,000 islands in there. Furthermore we will share technical difficulties encountered during the experiment.

Assimilation of total lightning data using the three-dimensional variational method at convection-allowing resolution

NASA Astrophysics Data System (ADS)

Zhang, Rong; Zhang, Yijun; Xu, Liangtao; Zheng, Dong; Yao, Wen

2017-08-01

A large number of observational analyses have shown that lightning data can be used to indicate areas of deep convection. It is important to assimilate observed lightning data into numerical models, so that more small-scale information can be incorporated to improve the quality of the initial condition and the subsequent forecasts. In this study, the empirical relationship between flash rate, water vapor mixing ratio, and graupel mixing ratio was used to adjust the model relative humidity, which was then assimilated by using the three-dimensional variational data assimilation system of the Weather Research and Forecasting model in cycling mode at 10-min intervals. To find the appropriate assimilation time-window length that yielded significant improvement in both the initial conditions and subsequent forecasts, four experiments with different assimilation time-window lengths were conducted for a squall line case that occurred on 10 July 2007 in North China. It was found that 60 min was the appropriate assimilation time-window length for this case, and longer assimilation window length was unnecessary since no further improvement was present. Forecasts of 1-h accumulated precipitation during the assimilation period and the subsequent 3-h accumulated precipitation were significantly improved compared with the control experiment without lightning data assimilation. The simulated reflectivity was optimal after 30 min of the forecast, it remained optimal during the following 42 min, and the positive effect from lightning data assimilation began to diminish after 72 min of the forecast. Overall, the improvement from lightning data assimilation can be maintained for about 3 h.

a Thtee-Dimensional Variational Assimilation Scheme for Satellite Aod

NASA Astrophysics Data System (ADS)

Liang, Y.; Zang, Z.; You, W.

2018-04-01

A three-dimensional variational data assimilation scheme is designed for satellite AOD based on the IMPROVE (Interagency Monitoring of Protected Visual Environments) equation. The observation operator that simulates AOD from the control variables is established by the IMPROVE equation. All of the 16 control variables in the assimilation scheme are the mass concentrations of aerosol species from the Model for Simulation Aerosol Interactions and Chemistry scheme, so as to take advantage of this scheme in providing comprehensive analyses of species concentrations and size distributions as well as be calculating efficiently. The assimilation scheme can save computational resources as the IMPROVE equation is a quadratic equation. A single-point observation experiment shows that the information from the single-point AOD is effectively spread horizontally and vertically.

Endogenous circadian regulation of carbon dioxide exchange in terrestrial ecosystems

Treesearch

Victor Resco de Dios; Michael L. Goulden; Kiona Ogle; Andrew D. Richardson; David Y. Hollinger; Eric A. Davidson; Josu G. Alday; Greg A. Barron-Gafford; Arnaud Carrara; Andrew S. Kowalski; Walt C. Oechel; Borja R. Reverter; Russell L. Scott; Ruth K. Varner; Ruben Diaz-Sierra; Jose M. Moreno

2012-01-01

It is often assumed that daytime patterns of ecosystem carbon assimilation are mostly driven by direct physiological responses to exogenous environmental cues. Under limited environmental variability, little variation in carbon assimilation should thus be expected unless endogenous plant controls on carbon assimilation, which regulate photosynthesis in time, are active...

Quasi-static ensemble variational data assimilation: a theoretical and numerical study with the iterative ensemble Kalman smoother

NASA Astrophysics Data System (ADS)

Fillion, Anthony; Bocquet, Marc; Gratton, Serge

2018-04-01

The analysis in nonlinear variational data assimilation is the solution of a non-quadratic minimization. Thus, the analysis efficiency relies on its ability to locate a global minimum of the cost function. If this minimization uses a Gauss-Newton (GN) method, it is critical for the starting point to be in the attraction basin of a global minimum. Otherwise the method may converge to a local extremum, which degrades the analysis. With chaotic models, the number of local extrema often increases with the temporal extent of the data assimilation window, making the former condition harder to satisfy. This is unfortunate because the assimilation performance also increases with this temporal extent. However, a quasi-static (QS) minimization may overcome these local extrema. It accomplishes this by gradually injecting the observations in the cost function. This method was introduced by Pires et al. (1996) in a 4D-Var context. We generalize this approach to four-dimensional strong-constraint nonlinear ensemble variational (EnVar) methods, which are based on both a nonlinear variational analysis and the propagation of dynamical error statistics via an ensemble. This forces one to consider the cost function minimizations in the broader context of cycled data assimilation algorithms. We adapt this QS approach to the iterative ensemble Kalman smoother (IEnKS), an exemplar of nonlinear deterministic four-dimensional EnVar methods. Using low-order models, we quantify the positive impact of the QS approach on the IEnKS, especially for long data assimilation windows. We also examine the computational cost of QS implementations and suggest cheaper algorithms.

Algal productivity and nitrate assimilation in an effluent dominated concrete lined stream

USGS Publications Warehouse

Kent, Robert; Belitz, Kenneth; Burton, Carmen

2005-01-01

This study examined algal productivity and nitrate assimilation in a 2.85 km reach of Cucamonga Creek, California, a concrete lined channel receiving treated municipal wastewater. Stream nitrate concentrations observed at two stations indicated nearly continuous loss throughout the diel study. Nitrate loss in the reach was approximately 11 mg/L/d or 1.0 g/m2/d as N, most of which occurred during daylight. The peak rate of nitrate loss (1.13 mg/l/hr) occurred just prior to an afternoon total CO2 depletion. Gross primary productivity, as estimated by a model using the observed differences in dissolved oxygen between the two stations, was 228 mg/L/d, or 21 g/m2/d as O2. The observed diel variations in productivity, nitrate loss, pH, dissolved oxygen, and CO2indicate that nitrate loss was primarily due to algal assimilation. The observed levels of productivity and nitrate assimilation were exceptionally high on a mass per volume basis compared to studies on other streams; these rates occurred because of the shallow stream depth. This study suggests that concrete‐lined channels can provide an important environmental service: lowering of nitrate concentrations similar to rates observed in biological treatment systems.

Contextual variation in the acoustic and perceptual similarity of North German and American English vowels

NASA Astrophysics Data System (ADS)

Strange, Winifred; Bohn, Ocke-Schwen; Nishi, Kanae; Trent, Sonja A.

2005-09-01

Strange et al. [J. Acoust. Soc. Am. 115, 1791-1807 (2004)] reported that North German (NG) front-rounded vowels in hVp syllables were acoustically intermediate between front and back American English (AE) vowels. However, AE listeners perceptually assimilated them as poor exemplars of back AE vowels. In this study, speaker- and context-independent cross-language discriminant analyses of NG and AE vowels produced in CVC syllables (C=labial, alveolar, velar stops) in sentences showed that NG front-rounded vowels fell within AE back-vowel distributions, due to the ``fronting'' of AE back vowels in alveolar/velar contexts. NG [smcapi, e, ɛ, openo] were located relatively ``higher'' in acoustic vowel space than their AE counterparts and varied in cross-language similarity across consonantal contexts. In a perceptual assimilation task, naive listeners classified NG vowels in terms of native AE categories and rated their goodness on a 7-point scale (very foreign to very English sounding). Both front- and back-rounded NG vowels were perceptually assimilated overwhelmingly to back AE categories and judged equally good exemplars. Perceptual assimilation patterns did not vary with context, and were not always predictable from acoustic similarity. These findings suggest that listeners adopt a context-independent strategy when judging the cross-language similarity of vowels produced and presented in continuous speech contexts.

Multi-Scale Three-Dimensional Variational Data Assimilation System for Coastal Ocean Prediction

NASA Technical Reports Server (NTRS)

Li, Zhijin; Chao, Yi; Li, P. Peggy

2012-01-01

A multi-scale three-dimensional variational data assimilation system (MS-3DVAR) has been formulated and the associated software system has been developed for improving high-resolution coastal ocean prediction. This system helps improve coastal ocean prediction skill, and has been used in support of operational coastal ocean forecasting systems and field experiments. The system has been developed to improve the capability of data assimilation for assimilating, simultaneously and effectively, sparse vertical profiles and high-resolution remote sensing surface measurements into coastal ocean models, as well as constraining model biases. In this system, the cost function is decomposed into two separate units for the large- and small-scale components, respectively. As such, data assimilation is implemented sequentially from large to small scales, the background error covariance is constructed to be scale-dependent, and a scale-dependent dynamic balance is incorporated. This scheme then allows effective constraining large scales and model bias through assimilating sparse vertical profiles, and small scales through assimilating high-resolution surface measurements. This MS-3DVAR enhances the capability of the traditional 3DVAR for assimilating highly heterogeneously distributed observations, such as along-track satellite altimetry data, and particularly maximizing the extraction of information from limited numbers of vertical profile observations.

Impact of spatio-temporal scale of adjustment on variational assimilation of hydrologic and hydrometeorological data in operational distributed hydrologic models

NASA Astrophysics Data System (ADS)

Lee, H.; Seo, D.; McKee, P.; Corby, R.

2009-12-01

One of the large challenges in data assimilation (DA) into distributed hydrologic models is to reduce the large degrees of freedom involved in the inverse problem to avoid overfitting. To assess the sensitivity of the performance of DA to the dimensionality of the inverse problem, we design and carry out real-world experiments in which the control vector in variational DA (VAR) is solved at different scales in space and time, e.g., lumped, semi-distributed, and fully-distributed in space, and hourly, 6 hourly, etc., in time. The size of the control vector is related to the degrees of freedom in the inverse problem. For the assessment, we use the prototype 4-dimenational variational data assimilator (4DVAR) that assimilates streamflow, precipitation and potential evaporation data into the NWS Hydrology Laboratory’s Research Distributed Hydrologic Model (HL-RDHM). In this talk, we present the initial results for a number of basins in Oklahoma and Texas.

A variational assimilation method for satellite and conventional data: Development of basic model for diagnosis of cyclone systems

NASA Technical Reports Server (NTRS)

Achtemeier, G. L.; Ochs, H. T., III; Kidder, S. Q.; Scott, R. W.; Chen, J.; Isard, D.; Chance, B.

1986-01-01

A three-dimensional diagnostic model for the assimilation of satellite and conventional meteorological data is developed with the variational method of undetermined multipliers. Gridded fields of data from different type, quality, location, and measurement source are weighted according to measurement accuracy and merged using least squares criteria so that the two nonlinear horizontal momentum equations, the hydrostatic equation, and an integrated continuity equation are satisfied. The model is used to compare multivariate variational objective analyses with and without satellite data with initial analyses and the observations through criteria that were determined by the dynamical constraints, the observations, and pattern recognition. It is also shown that the diagnoses of local tendencies of the horizontal velocity components are in good comparison with the observed patterns and tendencies calculated with unadjusted data. In addition, it is found that the day-night difference in TOVS biases are statistically different (95% confidence) at most levels. Also developed is a hybrid nonlinear sigma vertical coordinate that eliminates hydrostatic truncation error in the middle and upper troposphere and reduces truncation error in the lower troposphere. Finally, it is found that the technique used to grid the initial data causes boundary effects to intrude into the interior of the analysis a distance equal to the average separation between observations.

Seasonal and within-canopy variation in shoot-scale resource-use efficiency trade-offs in a Norway spruce stand.

PubMed

Tarvainen, Lasse; Räntfors, Mats; Wallin, Göran

2015-11-01

Previous leaf-scale studies of carbon assimilation describe short-term resource-use efficiency (RUE) trade-offs where high use efficiency of one resource requires low RUE of another. However, varying resource availabilities may cause long-term RUE trade-offs to differ from the short-term patterns. This may have important implications for understanding canopy-scale resource use and allocation. We used continuous gas exchange measurements collected at five levels within a Norway spruce, Picea abies (L.) karst., canopy over 3 years to assess seasonal differences in the interactions between shoot-scale resource availability (light, water and nitrogen), net photosynthesis (An ) and the use efficiencies of light (LUE), water (WUE) and nitrogen (NUE) for carbon assimilation. The continuous data set was used to develop and evaluate multiple regression models for predicting monthly shoot-scale An . These models showed that shoot-scale An was strongly dependent on light availability and was generally well described with simple one- or two-parameter models. WUE peaked in spring, NUE in summer and LUE in autumn. However, the relative importance of LUE for carbon assimilation increased with canopy depth at all times. Our results suggest that accounting for seasonal and within-canopy trade-offs may be important for RUE-based modelling of canopy carbon uptake. © 2015 John Wiley & Sons Ltd.

Evaluation of two momentum control variable schemes and their impact on the variational assimilation of radarwind data: Case study of a squall line

NASA Astrophysics Data System (ADS)

Li, Xin; Zeng, Mingjian; Wang, Yuan; Wang, Wenlan; Wu, Haiying; Mei, Haixia

2016-10-01

Different choices of control variables in variational assimilation can bring about different influences on the analyzed atmospheric state. Based on the WRF model's three-dimensional variational assimilation system, this study compares the behavior of two momentum control variable options—streamfunction velocity potential ( ψ-χ) and horizontal wind components ( U-V)—in radar wind data assimilation for a squall line case that occurred in Jiangsu Province on 24 August 2014. The wind increment from the single observation test shows that the ψ-χ control variable scheme produces negative increments in the neighborhood around the observation point because streamfunction and velocity potential preserve integrals of velocity. On the contrary, the U-V control variable scheme objectively reflects the information of the observation itself. Furthermore, radial velocity data from 17 Doppler radars in eastern China are assimilated. As compared to the impact of conventional observation, the assimilation of radar radial velocity based on the U-V control variable scheme significantly improves the mesoscale dynamic field in the initial condition. The enhanced low-level jet stream, water vapor convergence and low-level wind shear result in better squall line forecasting. However, the ψ-χ control variable scheme generates a discontinuous wind field and unrealistic convergence/divergence in the analyzed field, which lead to a degraded precipitation forecast.

A data assimilation system combining CryoSat-2 data and hydrodynamic river models

NASA Astrophysics Data System (ADS)

Schneider, Raphael; Ridler, Marc-Etienne; Godiksen, Peter Nygaard; Madsen, Henrik; Bauer-Gottwein, Peter

2018-02-01

There are numerous hydrologic studies using satellite altimetry data from repeat-orbit missions such as Envisat or Jason over rivers. This study is one of the first examples for the combination of altimetry from drifting-ground track satellite missions, namely CryoSat-2, with a river model. CryoSat-2 SARIn Level 2 data is used to improve a 1D hydrodynamic model of the Brahmaputra River in South Asia, which is based on the Saint-Venant equations for unsteady flow and set up in the MIKE HYDRO River software. After calibration of discharge and water level the hydrodynamic model can accurately and bias-free represent the spatio-temporal variations of water levels. A data assimilation framework has been developed and linked with the model. It is a flexible framework that can assimilate water level data which are arbitrarily distributed in time and space. The setup has been used to assimilate CryoSat-2 water level observations over the Assam valley for the years 2010-2015, using an Ensemble Transform Kalman Filter (ETKF). Performance improvement in terms of discharge forecasting skill was then evaluated. For experiments with synthetic CryoSat-2 data the continuous ranked probability score (CRPS) was improved by up to 32%, whilst for experiments assimilating real data it could be improved by up to 10%. The developed methods are expected to be transferable to other rivers and altimeter missions. The model setup and calibration is based almost entirely on globally available remote sensing data.

Variational data assimilative modeling of the Gulf of Maine in spring and summer 2010

NASA Astrophysics Data System (ADS)

Li, Yizhen; He, Ruoying; Chen, Ke; McGillicuddy, Dennis J.

2015-05-01

A data assimilative ocean circulation model is used to hindcast the Gulf of Maine [GOM) circulation in spring and summer 2010. Using the recently developed incremental strong constraint 4D Variational data assimilation algorithm, the model assimilates satellite sea surface temperature and in situ temperature and salinity profiles measured by expendable bathythermograph, Argo floats, and shipboard CTD casts. Validation against independent observations shows that the model skill is significantly improved after data assimilation. The data-assimilative model hindcast reproduces the temporal and spatial evolution of the ocean state, showing that a sea level depression southwest of the Scotian Shelf played a critical role in shaping the gulf-wide circulation. Heat budget analysis further demonstrates that both advection and surface heat flux contribute to temperature variability. The estimated time scale for coastal water to travel from the Scotian Shelf to the Jordan Basin is around 60 days, which is consistent with previous estimates based on in situ observations. Our study highlights the importance of resolving upstream and offshore forcing conditions in predicting the coastal circulation in the GOM.

POD/DEIM reduced-order strategies for efficient four dimensional variational data assimilation

NASA Astrophysics Data System (ADS)

Ştefănescu, R.; Sandu, A.; Navon, I. M.

2015-08-01

This work studies reduced order modeling (ROM) approaches to speed up the solution of variational data assimilation problems with large scale nonlinear dynamical models. It is shown that a key requirement for a successful reduced order solution is that reduced order Karush-Kuhn-Tucker conditions accurately represent their full order counterparts. In particular, accurate reduced order approximations are needed for the forward and adjoint dynamical models, as well as for the reduced gradient. New strategies to construct reduced order based are developed for proper orthogonal decomposition (POD) ROM data assimilation using both Galerkin and Petrov-Galerkin projections. For the first time POD, tensorial POD, and discrete empirical interpolation method (DEIM) are employed to develop reduced data assimilation systems for a geophysical flow model, namely, the two dimensional shallow water equations. Numerical experiments confirm the theoretical framework for Galerkin projection. In the case of Petrov-Galerkin projection, stabilization strategies must be considered for the reduced order models. The new reduced order shallow water data assimilation system provides analyses similar to those produced by the full resolution data assimilation system in one tenth of the computational time.

Continuing Themes in Assimilation Through Education.

ERIC Educational Resources Information Center

Strouse, Joan

1987-01-01

Discusses assimilation and adaptation of immigrants in the United States. Summarizes major sociological theories on assimilation. Focuses on schools as instruments of assimilation that attempt to force Anglo-conformity upon students. The refugee student's perceptions of his or her problems and opportunities are discussed. (PS)

Variational data assimilation schemes for transport and transformation models of atmospheric chemistry

NASA Astrophysics Data System (ADS)

Penenko, Alexey; Penenko, Vladimir; Tsvetova, Elena; Antokhin, Pavel

2016-04-01

The work is devoted to data assimilation algorithm for atmospheric chemistry transport and transformation models. In the work a control function is introduced into the model source term (emission rate) to provide flexibility to adjust to data. This function is evaluated as the constrained minimum of the target functional combining a control function norm with a norm of the misfit between measured data and its model-simulated analog. Transport and transformation processes model is acting as a constraint. The constrained minimization problem is solved with Euler-Lagrange variational principle [1] which allows reducing it to a system of direct, adjoint and control function estimate relations. This provides a physically-plausible structure of the resulting analysis without model error covariance matrices that are sought within conventional approaches to data assimilation. High dimensionality of the atmospheric chemistry models and a real-time mode of operation demand for computational efficiency of the data assimilation algorithms. Computational issues with complicated models can be solved by using a splitting technique. Within this approach a complex model is split to a set of relatively independent simpler models equipped with a coupling procedure. In a fine-grained approach data assimilation is carried out quasi-independently on the separate splitting stages with shared measurement data [2]. In integrated schemes data assimilation is carried out with respect to the split model as a whole. We compare the two approaches both theoretically and numerically. Data assimilation on the transport stage is carried out with a direct algorithm without iterations. Different algorithms to assimilate data on nonlinear transformation stage are compared. In the work we compare data assimilation results for both artificial and real measurement data. With these data we study the impact of transformation processes and data assimilation to the performance of the modeling system [3]. The work has been partially supported by RFBR grant 14-01-00125 and RAS Presidium II.4P. References: [1] Penenko V.V., Tsvetova E.A., Penenko A.V. Development of variational approach for direct and inverse problems of atmospheric hydrodynamics and chemistry // IZVESTIYA ATMOSPHERIC AND OCEANIC PHYSICS, 2015, v 51 , p. 311 - 319 [2] A.V. Penenko and V.V. Penenko. Direct data assimilation method for convection-diffusion models based on splitting scheme. Computational technologies, 19(4):69-83, 2014. [3] A. Penenko; V. Penenko; R. Nuterman; A. Baklanov and A. Mahura Direct variational data assimilation algorithm for atmospheric chemistry data with transport and transformation model, Proc. SPIE 9680, 21st International Symposium Atmospheric and Ocean Optics: Atmospheric Physics, 968076 (November 19, 2015); doi:10.1117/12.2206008;http://dx.doi.org/10.1117/12.2206008

Variational data assimilation problem for the Baltic Sea thermodynamics

NASA Astrophysics Data System (ADS)

Zakharova, Natalia; Agoshkov, Valery; Parmuzin, Eugene

2015-04-01

The most versatile and promising technology for solving problems of monitoring and analysis of the natural environment is a four-dimensional variational data assimilation of observation data. In such problems not only the development and justification of algorithms for numerical solution of variational data assimilation problems but the properties of the optimal solution play an important role. In this work the variational data assimilation problems in the Baltic Sea water area were formulated and studied. Numerical experiments on restoring the ocean heat flux and obtaining solution of the system (temperature, salinity, velocity, and sea surface height) in the Baltic Sea primitive equation hydrodynamics model with assimilation procedure were carried out. In the calculations we used daily sea surface temperature observation from Danish meteorological Institute, prepared on the basis of measurements of the radiometer (AVHRR, AATSR and AMSRE) and spectroradiometer (SEVIRI and MODIS). The spatial resolution of the model grid with respect to the horizontal variables amounted to 0.0625x0.03125 degree. The results of the numerical experiments are presented. This study was supported by the Russian Foundation for Basic Research (project 13-01-00753, project 14-01-31195) and project 14-11-00609 by the Russian Science Foundation. References: 1 E.I. Parmuzin, V.I. Agoshkov, Numerical solution of the variational assimilation problem for sea surface temperature in the model of the Black Sea dynamics. Russ. J. Numer. Anal. Math. Modelling (2012) 27, No.1, 69-94 2 Zakharova N.B., Agoshkov V.I., Parmuzin E.I., The new method of ARGO buoys system observation data interpolation. Russian Journal of Numerical Analysis and Mathematical Modelling. Vol. 28, Issue 1, 2013. 3 Zalesny V.B., Gusev A.V., Chernobay S.Yu., Aps R., Tamsalu R., Kujala P., Rytkönen J. The Bal-tic Sea circulation modelling and assessment of marine pollution, Russ. J. Numer. Analysis and Math. Modelling, 2014, V 29, No. 2, pp. 129-138.

Asymmetrical Damage Partitioning in Bacteria: A Model for the Evolution of Stochasticity, Determinism, and Genetic Assimilation

PubMed Central

Chao, Lin; Rang, Camilla Ulla; Proenca, Audrey Menegaz; Chao, Jasper Ubirajara

2016-01-01

Non-genetic phenotypic variation is common in biological organisms. The variation is potentially beneficial if the environment is changing. If the benefit is large, selection can favor the evolution of genetic assimilation, the process by which the expression of a trait is transferred from environmental to genetic control. Genetic assimilation is an important evolutionary transition, but it is poorly understood because the fitness costs and benefits of variation are often unknown. Here we show that the partitioning of damage by a mother bacterium to its two daughters can evolve through genetic assimilation. Bacterial phenotypes are also highly variable. Because gene-regulating elements can have low copy numbers, the variation is attributed to stochastic sampling. Extant Escherichia coli partition asymmetrically and deterministically more damage to the old daughter, the one receiving the mother’s old pole. By modeling in silico damage partitioning in a population, we show that deterministic asymmetry is advantageous because it increases fitness variance and hence the efficiency of natural selection. However, we find that symmetrical but stochastic partitioning can be similarly beneficial. To examine why bacteria evolved deterministic asymmetry, we modeled the effect of damage anchored to the mother’s old pole. While anchored damage strengthens selection for asymmetry by creating additional fitness variance, it has the opposite effect on symmetry. The difference results because anchored damage reinforces the polarization of partitioning in asymmetric bacteria. In symmetric bacteria, it dilutes the polarization. Thus, stochasticity alone may have protected early bacteria from damage, but deterministic asymmetry has evolved to be equally important in extant bacteria. We estimate that 47% of damage partitioning is deterministic in E. coli. We suggest that the evolution of deterministic asymmetry from stochasticity offers an example of Waddington’s genetic assimilation. Our model is able to quantify the evolution of the assimilation because it characterizes the fitness consequences of variation. PMID:26761487

Asymmetrical Damage Partitioning in Bacteria: A Model for the Evolution of Stochasticity, Determinism, and Genetic Assimilation.

PubMed

Chao, Lin; Rang, Camilla Ulla; Proenca, Audrey Menegaz; Chao, Jasper Ubirajara

2016-01-01

Non-genetic phenotypic variation is common in biological organisms. The variation is potentially beneficial if the environment is changing. If the benefit is large, selection can favor the evolution of genetic assimilation, the process by which the expression of a trait is transferred from environmental to genetic control. Genetic assimilation is an important evolutionary transition, but it is poorly understood because the fitness costs and benefits of variation are often unknown. Here we show that the partitioning of damage by a mother bacterium to its two daughters can evolve through genetic assimilation. Bacterial phenotypes are also highly variable. Because gene-regulating elements can have low copy numbers, the variation is attributed to stochastic sampling. Extant Escherichia coli partition asymmetrically and deterministically more damage to the old daughter, the one receiving the mother's old pole. By modeling in silico damage partitioning in a population, we show that deterministic asymmetry is advantageous because it increases fitness variance and hence the efficiency of natural selection. However, we find that symmetrical but stochastic partitioning can be similarly beneficial. To examine why bacteria evolved deterministic asymmetry, we modeled the effect of damage anchored to the mother's old pole. While anchored damage strengthens selection for asymmetry by creating additional fitness variance, it has the opposite effect on symmetry. The difference results because anchored damage reinforces the polarization of partitioning in asymmetric bacteria. In symmetric bacteria, it dilutes the polarization. Thus, stochasticity alone may have protected early bacteria from damage, but deterministic asymmetry has evolved to be equally important in extant bacteria. We estimate that 47% of damage partitioning is deterministic in E. coli. We suggest that the evolution of deterministic asymmetry from stochasticity offers an example of Waddington's genetic assimilation. Our model is able to quantify the evolution of the assimilation because it characterizes the fitness consequences of variation.

Variational assimilation of VAS data into the mass model

NASA Technical Reports Server (NTRS)

Cram, J. M.; Kaplan, M. L.

1984-01-01

Experiments are reported in which VAS data at 1200, 1500, and 1800 GMT 20 July 1981 were assimilated using both the adiabatic and full physics version of the Mesoscale Atmospheric Simulation System (MASS). A nonassimilation forecast is compared with forecasts assimilating temperature gradients only and forecasts assimilating both temperature and humidity gradients. The effects of successive vs single assimilations are also examined. It is noted that the greatest improvements to the forecast resulted when the VAS data resolved the mesoscale structure of the temperature and relative humidity fields. When this structure was assimilated into MASS, the ensuing simulations more clearly defined a mesoscale structure in the developing instabilities.

Most Colorful Example of Genetic Assimilation? Exploring the Evolutionary Destiny of Recurrent Phenotypic Accommodation.

PubMed

Badyaev, Alexander V; Potticary, Ahva L; Morrison, Erin S

2017-08-01

Evolution of adaptation requires both generation of novel phenotypic variation and retention of a locally beneficial subset of this variation. Such retention can be facilitated by genetic assimilation, the accumulation of genetic and molecular mechanisms that stabilize induced phenotypes and assume progressively greater control over their reliable production. A particularly strong inference into genetic assimilation as an evolutionary process requires a system where it is possible to directly evaluate the extent to which an induced phenotype is progressively incorporated into preexisting developmental pathways. Evolution of diet-dependent pigmentation in birds-where external carotenoids are coopted into internal metabolism to a variable degree before being integrated with a feather's developmental processes-provides such an opportunity. Here we combine a metabolic network view of carotenoid evolution with detailed empirical study of feather modifications to show that the effect of physical properties of carotenoids on feather structure depends on their metabolic modification, their environmental recurrence, and biochemical redundancy, as predicted by the genetic assimilation hypothesis. Metabolized carotenoids caused less stochastic variation in feather structure and were more closely integrated with feather growth than were dietary carotenoids of the same molecular weight. These patterns were driven by the recurrence of organism-carotenoid associations: commonly used dietary carotenoids and biochemically redundant derived carotenoids caused less stochastic variation in feather structure than did rarely used or biochemically unique compounds. We discuss implications of genetic assimilation processes for the evolutionary diversification of diet-dependent animal coloration.

Evidence for Cyclical Fractional Crystallization, Recharge, and Assimilation in Basalts of the Kimama Core, Central Snake River Plain, Idaho: A 5.5-million-year Highlight Reel of Petrogenetic processes in a Mid-Crustal Sill Complex

NASA Astrophysics Data System (ADS)

Potter, Katherine E.; Shervais, John W.; Christiansen, Eric H.; Vetter, Scott K.

2018-02-01

Basalts erupted in the Snake River Plain of central Idaho and sampled in the Kimama drill core link eruptive processes to the construction of mafic intrusions over 5.5 Ma. Cyclic variations in basalt composition reveal temporal chemical heterogeneity related to fractional crystallization and the assimilation of previously-intruded mafic sills. A range of compositional types are identified within 1912 m of continuous drill core: Snake River olivine tholeiite (SROT), low K SROT, high Fe-Ti, and evolved and high K-Fe lavas similar to those erupted at Craters of the Moon National Monument. Detailed lithologic and geophysical logs document 432 flow units comprising 183 distinct lava flows and 78 flow groups. Each lava flow represents a single eruptive episode, while flow groups document chemically and temporally related flows that formed over extended periods of time. Temporal chemical variation demonstrates the importance of source heterogeneity and magma processing in basalt petrogenesis. Low-K SROT and high Fe-Ti basalts are genetically related to SROT as, respectively, hydrothermally-altered and fractionated daughters. Cyclic variations in the chemical composition of Kimama flow groups are apparent as 21 upward fractionation cycles, six recharge cycles, eight recharge-fractionation cycles, and five fractionation-recharge cycles. We propose that most Kimama basalt flows represent typical fractionation and recharge patterns, consistent with the repeated influx of primitive SROT parental magmas and extensive fractional crystallization coupled with varying degrees of assimilation of gabbroic to ferrodioritic sills at shallow to intermediate depths over short durations. Trace element models show that parental SROT basalts were generated by 5-10% partial melting of enriched mantle at shallow depths above the garnet-spinel lherzolite transition. The distinctive evolved and high K-Fe lavas are rare. Found at four depths, 319 m, 1045 m, 1078 m, and 1189 m, evolved and high K-Fe flows are compositionally unrelated to SROT magmas and represent highly fractionated basalt, probably accompanied by crustal assimilation. These evolved lavas may be sourced from the Craters of the Moon/Great Rift system to the northeast. The Kimama drill core is the longest record of geochemical variation in the central Snake River Plain and reinforces the concept of magma processing in a layered complex.

Morphodynamic data assimilation used to understand changing coasts

USGS Publications Warehouse

Plant, Nathaniel G.; Long, Joseph W.

2015-01-01

Morphodynamic data assimilation blends observations with model predictions and comes in many forms, including linear regression, Kalman filter, brute-force parameter estimation, variational assimilation, and Bayesian analysis. Importantly, data assimilation can be used to identify sources of prediction errors that lead to improved fundamental understanding. Overall, models incorporating data assimilation yield better information to the people who must make decisions impacting safety and wellbeing in coastal regions that experience hazards due to storms, sea-level rise, and erosion. We present examples of data assimilation associated with morphologic change. We conclude that enough morphodynamic predictive capability is available now to be useful to people, and that we will increase our understanding and the level of detail of our predictions through assimilation of observations and numerical-statistical models.

Assimilating GRACE terrestrial water storage data into a conceptual hydrology model for the River Rhine

NASA Astrophysics Data System (ADS)

Widiastuti, E.; Steele-Dunne, S. C.; Gunter, B.; Weerts, A.; van de Giesen, N.

2009-12-01

Terrestrial water storage (TWS) is a key component of the terrestrial and global hydrological cycles, and plays a major role in the Earth’s climate. The Gravity Recovery and Climate Experiment (GRACE) twin satellite mission provided the first space-based dataset of TWS variations, albeit with coarse resolution and limited accuracy. Here, we examine the value of assimilating GRACE observations into a well-calibrated conceptual hydrology model of the Rhine river basin. In this study, the ensemble Kalman filter (EnKF) and smoother (EnKS) were applied to assimilate the GRACE TWS variation data into the HBV-96 rainfall run-off model, from February 2003 to December 2006. Two GRACE datasets were used, the DMT-1 models produced at TU Delft, and the CSR-RL04 models produced by UT-Austin . Each center uses its own data processing and filtering methods, yielding two different estimates of TWS variations and therefore two sets of assimilated TWS estimates. To validate the results, the model estimated discharge after the data assimilation was compared with measured discharge at several stations. As expected, the updated TWS was generally somewhere between the modeled and observed TWS in both experiments and the variance was also lower than both the prior error covariance and the assumed GRACE observation error. However, the impact on the discharge was found to depend heavily on the assimilation strategy used, in particular on how the TWS increments were applied to the individual storage terms of the hydrology model.

Variation of the North Equatorial Current, Mindanao Current, and Kuroshio Current in a high-resolution data assimilation during 2008-2012

NASA Astrophysics Data System (ADS)

Zhai, Fangguo; Wang, Qingye; Wang, Fujun; Hu, Dunxin

2014-11-01

Outputs from a high-resolution data assimilation system, the global Hybrid Coordinate Ocean Model and Navy Coupled Ocean Data Assimilation (HYCOM+NCODA) 1/12° analysis, were analyzed for the period September 2008 to February 2012. The objectives were to evaluate the performance of the system in simulating ocean circulation in the tropical northwestern Pacific and to examine the seasonal to interannual variations of the western boundary currents. The HYCOM assimilation compares well with altimetry observations and mooring current measurements. The mean structures and standard deviations of velocities of the North Equatorial Current (NEC), Mindanao Current (MC) and Kuroshio Current (KC) also compare well with previous observations. Seasonal to interannual variations of the NEC transport volume are closely correlated with the MC transport volume, instead of that of the KC. The NEC and MC transport volumes mainly show well-defined annual cycles, with their maxima in spring and minima in fall, and are closely related to the circulation changes in the Mindanao Dome (MD) region. In seasons of transport maxima, the MD region experiences negative SSH anomalies and a cyclonic gyre anomaly, and in seasons of transport minima the situation is reversed. The sea surface NEC bifurcation latitude (NBL) in the HYCOM assimilation also agrees well with altimetry observations. In 2009, the NBL shows an annual cycle similar to previous studies, reaching its southernmost position in summer and its northernmost position in winter. In 2010 and 2011, the NBL variations are dominantly influenced by La Niña events. The dynamics responsible for the seasonal to interannual variations of the NEC-MC-KC current system are also discussed.

POD/DEIM reduced-order strategies for efficient four dimensional variational data assimilation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ştefănescu, R., E-mail: rstefane@vt.edu; Sandu, A., E-mail: sandu@cs.vt.edu; Navon, I.M., E-mail: inavon@fsu.edu

2015-08-15

This work studies reduced order modeling (ROM) approaches to speed up the solution of variational data assimilation problems with large scale nonlinear dynamical models. It is shown that a key requirement for a successful reduced order solution is that reduced order Karush–Kuhn–Tucker conditions accurately represent their full order counterparts. In particular, accurate reduced order approximations are needed for the forward and adjoint dynamical models, as well as for the reduced gradient. New strategies to construct reduced order based are developed for proper orthogonal decomposition (POD) ROM data assimilation using both Galerkin and Petrov–Galerkin projections. For the first time POD, tensorialmore » POD, and discrete empirical interpolation method (DEIM) are employed to develop reduced data assimilation systems for a geophysical flow model, namely, the two dimensional shallow water equations. Numerical experiments confirm the theoretical framework for Galerkin projection. In the case of Petrov–Galerkin projection, stabilization strategies must be considered for the reduced order models. The new reduced order shallow water data assimilation system provides analyses similar to those produced by the full resolution data assimilation system in one tenth of the computational time.« less

Contextual variation in the acoustic and perceptual similarity of North German and American English vowels.

PubMed

Strange, Winifred; Bohn, Ocke-Schwen; Nishi, Kanae; Trent, Sonja A

2005-09-01

Strange et al. [J. Acoust. Soc. Am. 115, 1791-1807 (2004)] reported that North German (NG) front-rounded vowels in hVp syllables were acoustically intermediate between front and back American English (AE) vowels. However, AE listeners perceptually assimilated them as poor exemplars of back AE vowels. In this study, speaker- and context-independent cross-language discriminant analyses of NG and AE vowels produced in CVC syllables (C=labial, alveolar, velar stops) in sentences showed that NG front-rounded vowels fell within AE back-vowel distributions, due to the "fronting" of AE back vowels in alveolar/velar contexts. NG [I, e, epsilon, inverted c] were located relatively "higher" in acoustic vowel space than their AE counterparts and varied in cross-language similarity across consonantal contexts. In a perceptual assimilation task, naive listeners classified NG vowels in terms of native AE categories and rated their goodness on a 7-point scale (very foreign to very English sounding). Both front- and back-rounded NG vowels were perceptually assimilated overwhelmingly to back AE categories and judged equally good exemplars. Perceptual assimilation patterns did not vary with context, and were not always predictable from acoustic similarity. These findings suggest that listeners adopt a context-independent strategy when judging the cross-language similarity of vowels produced and presented in continuous speech contexts.

Variational assimilation of streamflow into operational distributed hydrologic models: effect of spatiotemporal adjustment scale

NASA Astrophysics Data System (ADS)

Lee, H.; Seo, D.-J.; Liu, Y.; Koren, V.; McKee, P.; Corby, R.

2012-01-01

State updating of distributed rainfall-runoff models via streamflow assimilation is subject to overfitting because large dimensionality of the state space of the model may render the assimilation problem seriously under-determined. To examine the issue in the context of operational hydrology, we carry out a set of real-world experiments in which streamflow data is assimilated into gridded Sacramento Soil Moisture Accounting (SAC-SMA) and kinematic-wave routing models of the US National Weather Service (NWS) Research Distributed Hydrologic Model (RDHM) with the variational data assimilation technique. Study basins include four basins in Oklahoma and five basins in Texas. To assess the sensitivity of data assimilation performance to dimensionality reduction in the control vector, we used nine different spatiotemporal adjustment scales, where state variables are adjusted in a lumped, semi-distributed, or distributed fashion and biases in precipitation and potential evaporation (PE) are adjusted hourly, 6-hourly, or kept time-invariant. For each adjustment scale, three different streamflow assimilation scenarios are explored, where streamflow observations at basin interior points, at the basin outlet, or at both interior points and the outlet are assimilated. The streamflow assimilation experiments with nine different basins show that the optimum spatiotemporal adjustment scale varies from one basin to another and may be different for streamflow analysis and prediction in all of the three streamflow assimilation scenarios. The most preferred adjustment scale for seven out of nine basins is found to be the distributed, hourly scale, despite the fact that several independent validation results at this adjustment scale indicated the occurrence of overfitting. Basins with highly correlated interior and outlet flows tend to be less sensitive to the adjustment scale and could benefit more from streamflow assimilation. In comparison to outlet flow assimilation, interior flow assimilation at any adjustment scale produces streamflow predictions with a spatial correlation structure more consistent with that of streamflow observations. We also describe diagnosing the complexity of the assimilation problem using the spatial correlation information associated with the streamflow process, and discuss the effect of timing errors in a simulated hydrograph on the performance of the data assimilation procedure.

Multi-parametric variational data assimilation for hydrological forecasting

NASA Astrophysics Data System (ADS)

Alvarado-Montero, R.; Schwanenberg, D.; Krahe, P.; Helmke, P.; Klein, B.

2017-12-01

Ensemble forecasting is increasingly applied in flow forecasting systems to provide users with a better understanding of forecast uncertainty and consequently to take better-informed decisions. A common practice in probabilistic streamflow forecasting is to force deterministic hydrological model with an ensemble of numerical weather predictions. This approach aims at the representation of meteorological uncertainty but neglects uncertainty of the hydrological model as well as its initial conditions. Complementary approaches use probabilistic data assimilation techniques to receive a variety of initial states or represent model uncertainty by model pools instead of single deterministic models. This paper introduces a novel approach that extends a variational data assimilation based on Moving Horizon Estimation to enable the assimilation of observations into multi-parametric model pools. It results in a probabilistic estimate of initial model states that takes into account the parametric model uncertainty in the data assimilation. The assimilation technique is applied to the uppermost area of River Main in Germany. We use different parametric pools, each of them with five parameter sets, to assimilate streamflow data, as well as remotely sensed data from the H-SAF project. We assess the impact of the assimilation in the lead time performance of perfect forecasts (i.e. observed data as forcing variables) as well as deterministic and probabilistic forecasts from ECMWF. The multi-parametric assimilation shows an improvement of up to 23% for CRPS performance and approximately 20% in Brier Skill Scores with respect to the deterministic approach. It also improves the skill of the forecast in terms of rank histogram and produces a narrower ensemble spread.

The GMAO Hybrid Ensemble-Variational Atmospheric Data Assimilation System: Version 2.0

NASA Technical Reports Server (NTRS)

Todling, Ricardo; El Akkraoui, Amal

2018-01-01

This document describes the implementation and usage of the Goddard Earth Observing System (GEOS) Hybrid Ensemble-Variational Atmospheric Data Assimilation System (Hybrid EVADAS). Its aim is to provide comprehensive guidance to users of GEOS ADAS interested in experimenting with its hybrid functionalities. The document is also aimed at providing a short summary of the state-of-science in this release of the hybrid system. As explained here, the ensemble data assimilation system (EnADAS) mechanism added to GEOS ADAS to enable hybrid data assimilation applications has been introduced to the pre-existing machinery of GEOS in the most non-intrusive possible way. Only very minor changes have been made to the original scripts controlling GEOS ADAS with the objective of facilitating its usage by both researchers and the GMAO's near-real-time Forward Processing applications. In a hybrid scenario two data assimilation systems run concurrently in a two-way feedback mode such that: the ensemble provides background ensemble perturbations required by the ADAS deterministic (typically high resolution) hybrid analysis; and the deterministic ADAS provides analysis information for recentering of the EnADAS analyses and information necessary to ensure that observation bias correction procedures are consistent between both the deterministic ADAS and the EnADAS. The nonintrusive approach to introducing hybrid capability to GEOS ADAS means, in particular, that previously existing features continue to be available. Thus, not only is this upgraded version of GEOS ADAS capable of supporting new applications such as Hybrid 3D-Var, 3D-EnVar, 4D-EnVar and Hybrid 4D-EnVar, it remains possible to use GEOS ADAS in its traditional 3D-Var mode which has been used in both MERRA and MERRA-2. Furthermore, as described in this document, GEOS ADAS also supports a configuration for exercising a purely ensemble-based assimilation strategy which can be fully decoupled from its variational component. We should point out that Release 1.0 of this document was made available to GMAO in mid-2013, when we introduced Hybrid 3D-Var capability to GEOS ADAS. This initial version of the documentation included a considerably different state-of-science introductory section but many of the same detailed description of the mechanisms of GEOS EnADAS. We are glad to report that a few of the desirable Future Works listed in Release 1.0 have now been added to the present version of GEOS EnADAS. These include the ability to exercise an Ensemble Prediction System that uses the ensemble analyses of GEOS EnADAS and (a very early, but functional version of) a tool to support Ensemble Forecast Sensitivity and Observation Impact applications.

Variational fine-grained data assimilation schemes for atmospheric chemistry transport and transformation models

NASA Astrophysics Data System (ADS)

Penenko, Alexey; Penenko, Vladimir; Tsvetova, Elena

2015-04-01

The paper concerns data assimilation problem for an atmospheric chemistry transport and transformation models. Data assimilation is carried out within variation approach on a single time step of the approximated model. A control function is introduced into the model source term (emission rate) to provide flexibility to adjust to data. This function is evaluated as the minimum of the target functional combining control function norm to a misfit between measured and model-simulated analog of data. This provides a flow-dependent and physically-plausible structure of the resulting analysis and reduces the need to calculate model error covariance matrices that are sought within conventional approach to data assimilation. Extension of the atmospheric transport model with a chemical transformations module influences data assimilation algorithms performance. This influence is investigated with numerical experiments for different meteorological conditions altering convection-diffusion processes characteristics, namely strong, medium and low wind conditions. To study the impact of transformation and data assimilation, we compare results for a convection-diffusion model (without data assimilation), convection-diffusion with assimilation, convection-diffusion-reaction (without data assimilation) and convection-diffusion-reaction-assimilation models. Both high dimensionalities of the atmospheric chemistry models and a real-time mode of operation demand for computational efficiency of the algorithms. Computational issues with complicated models can be solved by using a splitting technique. As the result a model is presented as a set of relatively independent simple models equipped with a kind of coupling procedure. With regard to data assimilation two approaches can be identified. In a fine-grained approach data assimilation is carried out on the separate splitting stages [1,2] independently on shared measurement data. The same situation arises when constructing a hybrid model out of two models each having its own assimilation scheme. In integrated schemes data assimilation is carried out with respect to the split model as a whole. First approach is more efficient from computational point of view, for in some important cases it can be implemented without iterations [2]. Its shortcoming is that control functions in different part of the model are adjusted independently thus having less evident physical sense. With the aid of numerical experiments we compare the two approaches. Work has been partially supported by COST Action ES1004 STSM Grants #16817 and #21654, RFBR 14-01-31482 mol a and 14-01-00125, Programmes # 4 Presidium RAS and # 3 MSD RAS, integration projects SB RAS #8 and #35. References: [1] V. V. Penenko Variational methods of data assimilation and inverse problems for studying the atmosphere, ocean, and environment Num. Anal. and Appl., 2009 V 2 No 4, 341-351. [2] A.V. Penenko and V.V. Penenko. Direct data assimilation method for convection-diffusion models based on splitting scheme. Computational technologies, 19(4):69-83, 2014.

Variational data assimilation for tropospheric chemistry modeling

NASA Astrophysics Data System (ADS)

Elbern, Hendrik; Schmidt, Hauke; Ebel, Adolf

1997-07-01

The method of variational adjoint data assimilation has been applied to assimilate chemistry observations into a comprehensive tropospheric gas phase model. The rationale of this method is to find the correct initial values for a subsequent atmospheric chemistry model run when observations scattered in time are available. The variational adjoint technique is esteemed to be a promising tool for future advanced meteorological forecasting. The stimulating experience gained with the application of four-dimensional variational data assimilation in this research area has motivated the attempt to apply the technique to air quality modeling and analysis of the chemical state of the atmosphere. The present study describes the development and application of the adjoint of the second-generation regional acid deposition model gas phase mechanism, which is used in the European air pollution dispersion model system. Performance results of the assimilation scheme using both model-generated data and real observations are presented for tropospheric conditions. In the former case it is demonstrated that time series of only few or even one measured key species convey sufficient information to improve considerably the analysis of unobserved species which are directly coupled with the observed species. In the latter case a Lagrangian approach is adopted where trajectory calculations between two comprehensively furnished measurement sites are carried out. The method allows us to analyze initial data for air pollution modeling even when only sparse observations are available. Besides remarkable improvements of the model performance by properly analyzed initial concentrations, it is shown that the adjoint algorithm offers the feasibility to estimate the sensitivity of ozone concentrations relative to its precursors.

Hyperspectral Thermal Infrared Remote Sensing of the Land Surface and Target Identification using Airborne Interferometry

DTIC Science & Technology

2009-10-01

variational data assimilation technique are profiles of temperature, water vapour and ozone , surface temperature and spectrally varying emissivity. HOW TO...that are insensitive to the land surface because of the complexity of the land surface emissivity. We have utilised the techniques described here for...state as well as surface properties. Furthermore with by utilising a variational assimilation technique and a state of the art Numerical Weather

Aerosol EnKF at GMAO

NASA Technical Reports Server (NTRS)

Buchard, Virginie; Da Silva, Arlindo; Todling, Ricardo

2017-01-01

In the GEOS near real-time system, as well as in MERRA-2 which is the latest reanalysis produced at NASAs Global Modeling and Assimilation Office(GMAO), the assimilation of aerosol observations is performed by means of a so-called analysis splitting method. In line with the transition of the GEOS meteorological data assimilation system to a hybrid Ensemble-Variational formulation, we are updating the aerosol component of our assimilation system to an ensemble square root filter(EnSRF; Whitaker and Hamill (2002)) type of scheme.We present a summary of our preliminary results of the assimilation of column integrated aerosol observations (Aerosol Optical Depth; AOD) using an Ensemble Square Root Filters (EnSRF) scheme and the ensemble members produced routinely by the meteorological assimilation.

Dynamic Responses of the Earth's Outer Core to Assimilation of Observed Geomagnetic Secular Variation

NASA Technical Reports Server (NTRS)

Kuang, Weijia; Tangborn, Andrew

2014-01-01

Assimilation of surface geomagnetic observations and geodynamo models has advanced very quickly in recent years. However, compared to advanced data assimilation systems in meteorology, geomagnetic data assimilation (GDAS) is still in an early stage. Among many challenges ranging from data to models is the disparity between the short observation records and the long time scales of the core dynamics. To better utilize available observational information, we have made an effort in this study to directly assimilate the Gauss coefficients of both the core field and its secular variation (SV) obtained via global geomagnetic field modeling, aiming at understanding the dynamical responses of the core fluid to these additional observational constraints. Our studies show that the SV assimilation helps significantly to shorten the dynamo model spin-up process. The flow beneath the core-mantle boundary (CMB) responds significantly to the observed field and its SV. The strongest responses occur in the relatively small scale flow (of the degrees L is approx. 30 in spherical harmonic expansions). This part of the flow includes the axisymmetric toroidal flow (of order m = 0) and non-axisymmetric poloidal flow with m (is) greater than 5. These responses can be used to better understand the core flow and, in particular, to improve accuracies of predicting geomagnetic variability in future.

Improved hurricane forecasting from a variational bogus and ozone data assimilation (BODA) scheme: case study

NASA Astrophysics Data System (ADS)

Liu, Yin; Zhang, Wei

2016-12-01

This study develops a proper way to incorporate Atmospheric Infrared Sounder (AIRS) ozone data into the bogus data assimilation (BDA) initialization scheme for improving hurricane prediction. First, the observation operator at some model levels with the highest correlation coefficients is established to assimilate AIRS ozone data based on the correlation between total column ozone and potential vorticity (PV) ranging from 400 to 50 hPa level. Second, AIRS ozone data act as an augmentation to a BDA procedure using a four-dimensional variational (4D-Var) data assimilation system. Case studies of several hurricanes are performed to demonstrate the effectiveness of the bogus and ozone data assimilation (BODA) scheme. The statistical result indicates that assimilating AIRS ozone data at 4, 5, or 6 model levels can produce a significant improvement in hurricane track and intensity prediction, with reasonable computation time for the hurricane initialization. Moreover, a detailed analysis of how BODA scheme affects hurricane prediction is conducted for Hurricane Earl (2010). It is found that the new scheme developed in this study generates significant adjustments in the initial conditions (ICs) from the lower levels to the upper levels, compared with the BDA scheme. With the BODA scheme, hurricane development is found to be much more sensitive to the number of ozone data assimilation levels. In particular, the experiment with the assimilation of AIRS ozone data at proper number of model levels shows great capabilities in reproducing the intensity and intensity changes of Hurricane Earl, as well as improve the track prediction. These results suggest that AIRS ozone data convey valuable meteorological information in the upper troposphere, which can be assimilated into a numerical model to improve hurricane initialization when the low-level bogus data are included.

An Improved GRACE Terrestrial Water Storage Assimilation System For Estimating Large-Scale Soil Moisture and Shallow Groundwater

NASA Astrophysics Data System (ADS)

Girotto, M.; De Lannoy, G. J. M.; Reichle, R. H.; Rodell, M.

2015-12-01

The Gravity Recovery And Climate Experiment (GRACE) mission is unique because it provides highly accurate column integrated estimates of terrestrial water storage (TWS) variations. Major limitations of GRACE-based TWS observations are related to their monthly temporal and coarse spatial resolution (around 330 km at the equator), and to the vertical integration of the water storage components. These challenges can be addressed through data assimilation. To date, it is still not obvious how best to assimilate GRACE-TWS observations into a land surface model, in order to improve hydrological variables, and many details have yet to be worked out. This presentation discusses specific recent features of the assimilation of gridded GRACE-TWS data into the NASA Goddard Earth Observing System (GEOS-5) Catchment land surface model to improve soil moisture and shallow groundwater estimates at the continental scale. The major recent advancements introduced by the presented work with respect to earlier systems include: 1) the assimilation of gridded GRACE-TWS data product with scaling factors that are specifically derived for data assimilation purposes only; 2) the assimilation is performed through a 3D assimilation scheme, in which reasonable spatial and temporal error standard deviations and correlations are exploited; 3) the analysis step uses an optimized calculation and application of the analysis increments; 4) a poor-man's adaptive estimation of a spatially variable measurement error. This work shows that even if they are characterized by a coarse spatial and temporal resolution, the observed column integrated GRACE-TWS data have potential for improving our understanding of soil moisture and shallow groundwater variations.

Choice of Control Variables in Variational Data Assimilation and Its Analysis and Forecast Impact

NASA Astrophysics Data System (ADS)

Xie, Yuanfu; Sun, Jenny; Fang, Wei-ting

2014-05-01

Choice of control variables directly impacts the analysis qualify of a variational data assimilation and its forecasts. A theory on selecting control variables for wind and moisture field is introduced for 3DVAR or 4DVAR. For a good control variable selection, Parseval's theory is applied to 3-4DVAR and the behavior of different control variables is illustrated in physical and Fourier space in terms of minimization condition, meteorological dynamic scales and practical implementation. The computational and meteorological benefits will be discussed. Numerical experiments have been performed using WRF-DA for wind control variables and CRTM for moisture control variables. It is evident of the WRF forecast improvement and faster convergence of CRTM satellite data assimilation.

A four-dimensional variational chemistry data assimilation scheme for Eulerian chemistry transport modeling

NASA Astrophysics Data System (ADS)

Eibern, Hendrik; Schmidt, Hauke

1999-08-01

The inverse problem of data assimilation of tropospheric trace gas observations into an Eulerian chemistry transport model has been solved by the four-dimensional variational technique including chemical reactions, transport, and diffusion. The University of Cologne European Air Pollution Dispersion Chemistry Transport Model 2 with the Regional Acid Deposition Model 2 gas phase mechanism is taken as the basis for developing a full four-dimensional variational data assimilation package, on the basis of the adjoint model version, which includes the adjoint operators of horizontal and vertical advection, implicit vertical diffusion, and the adjoint gas phase mechanism. To assess the potential and limitations of the technique without degrading the impact of nonperfect meteorological analyses and statistically not established error covariance estimates, artificial meteorological data and observations are used. The results are presented on the basis of a suite of experiments, where reduced records of artificial "observations" are provided to the assimilation procedure, while other "data" is retained for performance control of the analysis. The paper demonstrates that the four-dimensional variational technique is applicable for a comprehensive chemistry transport model in terms of computational and storage requirements on advanced parallel platforms. It is further shown that observed species can generally be analyzed, even if the "measurements" have unbiased random errors. More challenging experiments are presented, aiming to tax the skill of the method (1) by restricting available observations mostly to surface ozone observations for a limited assimilation interval of 6 hours and (2) by starting with poorly chosen first guess values. In this first such application to a three-dimensional chemistry transport model, success was also achieved in analyzing not only observed but also chemically closely related unobserved constituents.

Mean Field Variational Bayesian Data Assimilation

NASA Astrophysics Data System (ADS)

Vrettas, M.; Cornford, D.; Opper, M.

2012-04-01

Current data assimilation schemes propose a range of approximate solutions to the classical data assimilation problem, particularly state estimation. Broadly there are three main active research areas: ensemble Kalman filter methods which rely on statistical linearization of the model evolution equations, particle filters which provide a discrete point representation of the posterior filtering or smoothing distribution and 4DVAR methods which seek the most likely posterior smoothing solution. In this paper we present a recent extension to our variational Bayesian algorithm which seeks the most probably posterior distribution over the states, within the family of non-stationary Gaussian processes. Our original work on variational Bayesian approaches to data assimilation sought the best approximating time varying Gaussian process to the posterior smoothing distribution for stochastic dynamical systems. This approach was based on minimising the Kullback-Leibler divergence between the true posterior over paths, and our Gaussian process approximation. So long as the observation density was sufficiently high to bring the posterior smoothing density close to Gaussian the algorithm proved very effective, on lower dimensional systems. However for higher dimensional systems, the algorithm was computationally very demanding. We have been developing a mean field version of the algorithm which treats the state variables at a given time as being independent in the posterior approximation, but still accounts for their relationships between each other in the mean solution arising from the original dynamical system. In this work we present the new mean field variational Bayesian approach, illustrating its performance on a range of classical data assimilation problems. We discuss the potential and limitations of the new approach. We emphasise that the variational Bayesian approach we adopt, in contrast to other variational approaches, provides a bound on the marginal likelihood of the observations given parameters in the model which also allows inference of parameters such as observation errors, and parameters in the model and model error representation, particularly if this is written as a deterministic form with small additive noise. We stress that our approach can address very long time window and weak constraint settings. However like traditional variational approaches our Bayesian variational method has the benefit of being posed as an optimisation problem. We finish with a sketch of the future directions for our approach.

Generalized Background Error covariance matrix model (GEN_BE v2.0)

NASA Astrophysics Data System (ADS)

Descombes, G.; Auligné, T.; Vandenberghe, F.; Barker, D. M.

2014-07-01

The specification of state background error statistics is a key component of data assimilation since it affects the impact observations will have on the analysis. In the variational data assimilation approach, applied in geophysical sciences, the dimensions of the background error covariance matrix (B) are usually too large to be explicitly determined and B needs to be modeled. Recent efforts to include new variables in the analysis such as cloud parameters and chemical species have required the development of the code to GENerate the Background Errors (GEN_BE) version 2.0 for the Weather Research and Forecasting (WRF) community model to allow for a simpler, flexible, robust, and community-oriented framework that gathers methods used by meteorological operational centers and researchers. We present the advantages of this new design for the data assimilation community by performing benchmarks and showing some of the new features on data assimilation test cases. As data assimilation for clouds remains a challenge, we present a multivariate approach that includes hydrometeors in the control variables and new correlated errors. In addition, the GEN_BE v2.0 code is employed to diagnose error parameter statistics for chemical species, which shows that it is a tool flexible enough to involve new control variables. While the generation of the background errors statistics code has been first developed for atmospheric research, the new version (GEN_BE v2.0) can be easily extended to other domains of science and be chosen as a testbed for diagnostic and new modeling of B. Initially developed for variational data assimilation, the model of the B matrix may be useful for variational ensemble hybrid methods as well.

Generalized background error covariance matrix model (GEN_BE v2.0)

NASA Astrophysics Data System (ADS)

Descombes, G.; Auligné, T.; Vandenberghe, F.; Barker, D. M.; Barré, J.

2015-03-01

The specification of state background error statistics is a key component of data assimilation since it affects the impact observations will have on the analysis. In the variational data assimilation approach, applied in geophysical sciences, the dimensions of the background error covariance matrix (B) are usually too large to be explicitly determined and B needs to be modeled. Recent efforts to include new variables in the analysis such as cloud parameters and chemical species have required the development of the code to GENerate the Background Errors (GEN_BE) version 2.0 for the Weather Research and Forecasting (WRF) community model. GEN_BE allows for a simpler, flexible, robust, and community-oriented framework that gathers methods used by some meteorological operational centers and researchers. We present the advantages of this new design for the data assimilation community by performing benchmarks of different modeling of B and showing some of the new features in data assimilation test cases. As data assimilation for clouds remains a challenge, we present a multivariate approach that includes hydrometeors in the control variables and new correlated errors. In addition, the GEN_BE v2.0 code is employed to diagnose error parameter statistics for chemical species, which shows that it is a tool flexible enough to implement new control variables. While the generation of the background errors statistics code was first developed for atmospheric research, the new version (GEN_BE v2.0) can be easily applied to other domains of science and chosen to diagnose and model B. Initially developed for variational data assimilation, the model of the B matrix may be useful for variational ensemble hybrid methods as well.

A study on the characteristics of retrospective optimal interpolation using an Observing System Simulation Experiment

NASA Astrophysics Data System (ADS)

Kim, Shin-Woo; Noh, Nam-Kyu; Lim, Gyu-Ho

2013-04-01

This study presents the introduction of retrospective optimal interpolation (ROI) and its application with Weather Research and Forecasting model (WRF). Song et al. (2009) suggested ROI method which is an optimal interpolation (OI) that gradually assimilates observations over the analysis window for variance-minimum estimate of an atmospheric state at the initial time of the analysis window. The assimilation window of ROI algorithm is gradually increased, similar with that of the quasi-static variational assimilation (QSVA; Pires et al., 1996). Unlike QSVA method, however, ROI method assimilates the data at post analysis time using perturbation method (Verlaan and Heemink, 1997) without adjoint model. Song and Lim (2011) improved this method by incorporating eigen-decomposition and covariance inflation. The computational costs for ROI can be reduced due to the eigen-decomposition of background error covariance which can concentrate ROI analyses on the error variances of governing eigenmodes by transforming the control variables into eigenspace. A total energy norm is used for the normalization of each control variables. In this study, ROI method is applied to WRF model with Observing System Simulation Experiment (OSSE) to validate the algorithm and to investigate the capability. Horizontal wind, pressure, potential temperature, and water vapor mixing ratio are used for control variables and observations. Firstly, 1-profile assimilation experiment is performed. Subsequently, OSSE's are performed using the virtual observing system which consists of synop, ship, and sonde data. The difference between forecast errors with assimilation and without assimilation is obviously increased as time passed, which means the improvement of forecast error with the assimilation by ROI. The characteristics and strength/weakness of ROI method are also investigated by conducting the experiments with 3D-Var (3-dimensional variational) method and 4D-Var (4-dimensional variational) method. In the initial time, ROI produces a larger forecast error than that of 4D-Var. However, the difference between the two experimental results is decreased gradually with time, and the ROI shows apparently better result (i.e., smaller forecast error) than that of 4D-Var after 9-hour forecast.

Variational Assimilation of GOME Total-Column Ozone Satellite Data in a 2D Latitude-Longitude Tracer-Transport Model.

NASA Astrophysics Data System (ADS)

Eskes, H. J.; Piters, A. J. M.; Levelt, P. F.; Allaart, M. A. F.; Kelder, H. M.

1999-10-01

A four-dimensional data-assimilation method is described to derive synoptic ozone fields from total-column ozone satellite measurements. The ozone columns are advected by a 2D tracer-transport model, using ECMWF wind fields at a single pressure level. Special attention is paid to the modeling of the forecast error covariance and quality control. The temporal and spatial dependence of the forecast error is taken into account, resulting in a global error field at any instant in time that provides a local estimate of the accuracy of the assimilated field. The authors discuss the advantages of the 4D-variational (4D-Var) approach over sequential assimilation schemes. One of the attractive features of the 4D-Var technique is its ability to incorporate measurements at later times t > t0 in the analysis at time t0, in a way consistent with the time evolution as described by the model. This significantly improves the offline analyzed ozone fields.

On the resilience of nitrogen assimilation by intact roots under starvation, as revealed by isotopic and metabolomic techniques.

PubMed

Bathellier, Camille; Tcherkez, Guillaume; Mauve, Caroline; Bligny, Richard; Gout, Elizabeth; Ghashghaie, Jaleh

2009-09-01

The response of root metabolism to variations in carbon source availability is critical for whole-plant nitrogen (N) assimilation and growth. However, the effect of changes in the carbohydrate input to intact roots is currently not well understood and, for example, both smaller and larger values of root:shoot ratios or root N uptake have been observed so far under elevated CO(2). In addition, previous studies on sugar starvation mainly focused on senescent or excised organs while an increasing body of data suggests that intact roots may behave differently with, for example, little protein remobilization. Here, we investigated the carbon and nitrogen primary metabolism in intact roots of French bean (Phaseolus vulgaris L.) plants maintained under continuous darkness for 4 days. We combined natural isotopic (15)N/(14)N measurements, metabolomic and (13)C-labeling data and show that intact roots continued nitrate assimilation to glutamate for at least 3 days while the respiration rate decreased. The activity of the tricarboxylic acid cycle diminished so that glutamate synthesis was sustained by the anaplerotic phosphoenolpyruvate carboxylase fixation. Presumably, the pentose phosphate pathway contributed to provide reducing power for nitrate reduction. All the biosynthetic metabolic fluxes were nevertheless down-regulated and, consequently, the concentration of all amino acids decreased. This is the case of asparagine, strongly suggesting that, as opposed to excised root tips, protein remobilization in intact roots remained very low for 3 days in spite of the restriction of respiratory substrates. Copyright (c) 2009 John Wiley & Sons, Ltd.

On the Limitations of Variational Bias Correction

NASA Technical Reports Server (NTRS)

Moradi, Isaac; Mccarty, Will; Gelaro, Ronald

2018-01-01

Satellite radiances are the largest dataset assimilated into Numerical Weather Prediction (NWP) models, however the data are subject to errors and uncertainties that need to be accounted for before assimilating into the NWP models. Variational bias correction uses the time series of observation minus background to estimate the observations bias. This technique does not distinguish between the background error, forward operator error, and observations error so that all these errors are summed up together and counted as observation error. We identify some sources of observations errors (e.g., antenna emissivity, non-linearity in the calibration, and antenna pattern) and show the limitations of variational bias corrections on estimating these errors.

Rapid Weakening of Hurricane Joaquin in Strong Vertical Wind Shear and Cold SSTs: Numerical Simulations with Assimilation of High-Definition Sounding System Dropsondes During Tropical Cyclone Intensity Experiment

NASA Astrophysics Data System (ADS)

Pu, Z.; Zhang, S.

2017-12-01

Observations from High-Definition Sounding System (HDSS) Dropsondes, collected for Hurricane Joaquin (2005) during the Office of Naval Research Tropical Cyclone Intensity (TCI) Experiment in 2015, are assimilated into the Gridpoint Statistical Interpolation (GSI)-based hybrid data assimilation systems embedded in the NCEP Hurricane Weather Research and Forecasting (HWRF) system. A three-dimensional and a four-dimensional ensemble-variational hybrid (3DEnVAR and 4DEnVar) data assimilation configuration are used. It is found that the experiments with assimilation of the HDSS dropsonde observations capture well the intensity changes during the rapid weakening (RW) of Hurricane Joaquin. Compared with 3DEnVAR, 4DEnVar leads to better assimilation results and subsequent forecasts and thus offers a set of simulations to diagnose the processes associated with the RW of Hurricane Joaquin. A drastic increase in the vertical wind shear (VWS, with a magnitude of 12 m s-1) is found before the RW. This high VWS is persistent during the 0-12 h period of RW, inducing changes in the vortex structure of Hurricane Joaquin through dry air intrusion in the mid-level and the dilution of the upper-level warm core. The transport of low air from above into the boundary layer occurs at the same time, resulting in depressed values in the storm inflow layer and reduced eyewall values through the updraft. As a consequence, downdrafts flush the boundary layer with low air, leading to the weakening of inflow in the boundary layers. When Hurricane Joaquin moves over an area where the SSTs are below 28oC within the hurricane inner core during the 18-30 h period of RW, the cold SSTs significantly inhibit latent and sensible heat release within the hurricane inner core and its vicinity, thus resulting in the continuous weakening of Hurricane Joaquin.

Rapid Weakening of Hurricane Joaquin in Strong Vertical Wind Shear and Cold SSTs: Numerical Simulations with Assimilation of High-Definition Sounding System Dropsondes During Tropical Cyclone Intensity Experiment

NASA Astrophysics Data System (ADS)

Pikelnaya, O.; Polidori, A.; Tisopulos, L.; Mellqvist, J.; Samuelsson, J.; Robinson, R. A.; Innocenti, F.; Perry, S.

2016-12-01

Observations from High-Definition Sounding System (HDSS) Dropsondes, collected for Hurricane Joaquin (2005) during the Office of Naval Research Tropical Cyclone Intensity (TCI) Experiment in 2015, are assimilated into the Gridpoint Statistical Interpolation (GSI)-based hybrid data assimilation systems embedded in the NCEP Hurricane Weather Research and Forecasting (HWRF) system. A three-dimensional and a four-dimensional ensemble-variational hybrid (3DEnVAR and 4DEnVar) data assimilation configuration are used. It is found that the experiments with assimilation of the HDSS dropsonde observations capture well the intensity changes during the rapid weakening (RW) of Hurricane Joaquin. Compared with 3DEnVAR, 4DEnVar leads to better assimilation results and subsequent forecasts and thus offers a set of simulations to diagnose the processes associated with the RW of Hurricane Joaquin. A drastic increase in the vertical wind shear (VWS, with a magnitude of 12 m s-1) is found before the RW. This high VWS is persistent during the 0-12 h period of RW, inducing changes in the vortex structure of Hurricane Joaquin through dry air intrusion in the mid-level and the dilution of the upper-level warm core. The transport of low air from above into the boundary layer occurs at the same time, resulting in depressed values in the storm inflow layer and reduced eyewall values through the updraft. As a consequence, downdrafts flush the boundary layer with low air, leading to the weakening of inflow in the boundary layers. When Hurricane Joaquin moves over an area where the SSTs are below 28oC within the hurricane inner core during the 18-30 h period of RW, the cold SSTs significantly inhibit latent and sensible heat release within the hurricane inner core and its vicinity, thus resulting in the continuous weakening of Hurricane Joaquin.

Validation Test Report for the Navy Coastal Ocean Model Four-Dimensional Variational Assimilation (NCOM 4DVAR) System Version 1.0

DTIC Science & Technology

2015-08-14

assimilated directly into a free surface ocean  model using NCOM 4DVAR methods without generating gravity waves.  The  latter is a serious  problem  that... The  bias of buoyancy frequency  in  the   left plot of figure 4‐19  reveals that  the  NCOM 4DVAR is doing fairly well at predicting  the   stratification ...Test Report for the Navy Coastal Ocean Model Four-Dimensional Variational Assimilation (NCOM 4DVAR) System Version 1.0 Scott Smith matthew carrier

Diffusion Filters for Variational Data Assimilation of Sea Surface Temperature in an Intermediate Climate Model

DTIC Science & Technology

2015-01-01

over data-dense regions. After that, a perfect twin data assimilation experiment framework is designed to study the effect of the GDF on the state...is designed to study the effect of the GDF on the state estimation based on an intermediate coupled model. In this framework, the assimilation model...observation. Considering = , (4) is equal to () = 1 2 + 1 2 ( − ) −1 ( − ) . (5) The effect of in (5) can

Improved track forecasting of a typhoon reaching landfall from four-dimensional variational data assimilation of AMSU-A retrieved data

NASA Astrophysics Data System (ADS)

Zhao, Ying; Wang, Bin; Ji, Zhongzhen; Liang, Xudong; Deng, Guo; Zhang, Xin

2005-07-01

In this study, an attempt to improve typhoon forecasts is made by incorporating three-dimensional Advanced Microwave Sounding Unit-A (AMSU-A) retrieved wind and temperature and the central sea level pressure of cyclones from typhoon reports or bogus surface low data into initial conditions, on the basis of the Fifth-Generation National Center for Atmospheric Research/Pennsylvania State University Mesoscale Model (MM5) four-dimensional variational data assimilation (4DVar) system with a full-physics adjoint model. All the above-mentioned data are found to be useful for improvement of typhoon forecasts in this mesoscale data assimilation experiment. The comparison tests showed the following results: (1) The assimilation of the satellite-retrieved data was found to have a positive impact on the typhoon track forecast, but the landing position error is ˜150 km. (2) The assimilation of both the satellite-retrieved data and moving information of the typhoon center dramatically improved the track forecast and captured the recurvature and landfall. The mean track error during the 72-hour forecast is 69 km. The predicted typhoon intensity, however, is much weaker than that from observations. (3) The assimilation of both the satellite-retrieved data and the bogus surface low data improved the intensity and track forecasts more significantly than the assimilation of only bogus surface low data (bogus data assimilation) did. The mean errors during the 72-hour forecast are 2.6 hPa for the minimum sea level pressure and 87 km for track position. However, the forecasted landing time is ˜6 hours earlier than the observed one.

Data Assimilation in a Solar Dynamo Model Using Ensemble Kalman Filters: Sensitivity and Robustness in Reconstruction of Meridional Flow Speed

NASA Astrophysics Data System (ADS)

Dikpati, Mausumi; Anderson, Jeffrey L.; Mitra, Dhrubaditya

2016-09-01

We implement an Ensemble Kalman Filter procedure using the Data Assimilation Research Testbed for assimilating “synthetic” meridional flow-speed data in a Babcock-Leighton-type flux-transport solar dynamo model. By performing several “observing system simulation experiments,” we reconstruct time variation in meridional flow speed and analyze sensitivity and robustness of reconstruction. Using 192 ensemble members including 10 observations, each with 4% error, we find that flow speed is reconstructed best if observations of near-surface poloidal fields from low latitudes and tachocline toroidal fields from midlatitudes are assimilated. If observations include a mixture of poloidal and toroidal fields from different latitude locations, reconstruction is reasonably good for ≤slant 40 % error in low-latitude data, even if observational error in polar region data becomes 200%, but deteriorates when observational error increases in low- and midlatitude data. Solar polar region observations are known to contain larger errors than those in low latitudes; our forward operator (a flux-transport dynamo model here) can sustain larger errors in polar region data, but is more sensitive to errors in low-latitude data. An optimal reconstruction is obtained if an assimilation interval of 15 days is used; 10- and 20-day assimilation intervals also give reasonably good results. Assimilation intervals \\lt 5 days do not produce faithful reconstructions of flow speed, because the system requires a minimum time to develop dynamics to respond to flow variations. Reconstruction also deteriorates if an assimilation interval \\gt 45 days is used, because the system’s inherent memory interferes with its short-term dynamics during a substantially long run without updating.

Multigrid solvers and multigrid preconditioners for the solution of variational data assimilation problems

NASA Astrophysics Data System (ADS)

Debreu, Laurent; Neveu, Emilie; Simon, Ehouarn; Le Dimet, Francois Xavier; Vidard, Arthur

2014-05-01

In order to lower the computational cost of the variational data assimilation process, we investigate the use of multigrid methods to solve the associated optimal control system. On a linear advection equation, we study the impact of the regularization term on the optimal control and the impact of discretization errors on the efficiency of the coarse grid correction step. We show that even if the optimal control problem leads to the solution of an elliptic system, numerical errors introduced by the discretization can alter the success of the multigrid methods. The view of the multigrid iteration as a preconditioner for a Krylov optimization method leads to a more robust algorithm. A scale dependent weighting of the multigrid preconditioner and the usual background error covariance matrix based preconditioner is proposed and brings significant improvements. [1] Laurent Debreu, Emilie Neveu, Ehouarn Simon, François-Xavier Le Dimet and Arthur Vidard, 2014: Multigrid solvers and multigrid preconditioners for the solution of variational data assimilation problems, submitted to QJRMS, http://hal.inria.fr/hal-00874643 [2] Emilie Neveu, Laurent Debreu and François-Xavier Le Dimet, 2011: Multigrid methods and data assimilation - Convergence study and first experiments on non-linear equations, ARIMA, 14, 63-80, http://intranet.inria.fr/international/arima/014/014005.html

A Multigrid NLS-4DVar Data Assimilation Scheme with Advanced Research WRF (ARW)

NASA Astrophysics Data System (ADS)

Zhang, H.; Tian, X.

2017-12-01

The motions of the atmosphere have multiscale properties in space and/or time, and the background error covariance matrix (Β) should thus contain error information at different correlation scales. To obtain an optimal analysis, the multigrid three-dimensional variational data assimilation scheme is used widely when sequentially correcting errors from large to small scales. However, introduction of the multigrid technique into four-dimensional variational data assimilation is not easy, due to its strong dependence on the adjoint model, which has extremely high computational costs in data coding, maintenance, and updating. In this study, the multigrid technique was introduced into the nonlinear least-squares four-dimensional variational assimilation (NLS-4DVar) method, which is an advanced four-dimensional ensemble-variational method that can be applied without invoking the adjoint models. The multigrid NLS-4DVar (MG-NLS-4DVar) scheme uses the number of grid points to control the scale, with doubling of this number when moving from a coarse to a finer grid. Furthermore, the MG-NLS-4DVar scheme not only retains the advantages of NLS-4DVar, but also sufficiently corrects multiscale errors to achieve a highly accurate analysis. The effectiveness and efficiency of the proposed MG-NLS-4DVar scheme were evaluated by several groups of observing system simulation experiments using the Advanced Research Weather Research and Forecasting Model. MG-NLS-4DVar outperformed NLS-4DVar, with a lower computational cost.

Data Assimilation of Photosynthetic Light-use Efficiency using Multi-angular Satellite Data: II Model Implementation and Validation

NASA Technical Reports Server (NTRS)

Hilker, Thomas; Hall, Forest G.; Tucker, J.; Coops, Nicholas C.; Black, T. Andrew; Nichol, Caroline J.; Sellers, Piers J.; Barr, Alan; Hollinger, David Y.; Munger, J. W.

2012-01-01

Spatially explicit and temporally continuous estimates of photosynthesis will be of great importance for increasing our understanding of and ultimately closing the terrestrial carbon cycle. Current capabilities to model photosynthesis, however, are limited by accurate enough representations of the complexity of the underlying biochemical processes and the numerous environmental constraints imposed upon plant primary production. A potentially powerful alternative to model photosynthesis through these indirect observations is the use of multi-angular satellite data to infer light-use efficiency (e) directly from spectral reflectance properties in connection with canopy shadow fractions. Hall et al. (this issue) introduced a new approach for predicting gross ecosystem production that would allow the use of such observations in a data assimilation mode to obtain spatially explicit variations in e from infrequent polar-orbiting satellite observations, while meteorological data are used to account for the more dynamic responses of e to variations in environmental conditions caused by changes in weather and illumination. In this second part of the study we implement and validate the approach of Hall et al. (this issue) across an ecologically diverse array of eight flux-tower sites in North America using data acquired from the Compact High Resolution Imaging Spectroradiometer (CHRIS) and eddy-flux observations. Our results show significantly enhanced estimates of e and therefore cumulative gross ecosystem production (GEP) over the course of one year at all examined sites. We also demonstrate that e is greatly heterogeneous even across small study areas. Data assimilation and direct inference of GEP from space using a new, proposed sensor could therefore be a significant step towards closing the terrestrial carbon cycle.

Geomagnetic inverse problem and data assimilation: a progress report

NASA Astrophysics Data System (ADS)

Aubert, Julien; Fournier, Alexandre

2013-04-01

In this presentation I will present two studies recently undertaken by our group in an effort to bring the benefits of data assimilation to the study of Earth's magnetic field and the dynamics of its liquid iron core, where the geodynamo operates. In a first part I will focus on the geomagnetic inverse problem, which attempts to recover the fluid flow in the core from the temporal variation of the magnetic field (known as the secular variation). Geomagnetic data can be downward continued from the surface of the Earth down to the core-mantle boundary, but not further below, since the core is an electrical conductor. Historically, solutions to the geomagnetic inverse problem in such a sparsely observed system were thus found only for flow immediately below the core mantle boundary. We have recently shown that combining a numerical model of the geodynamo together with magnetic observations, through the use of Kalman filtering, now allows to present solutions for flow throughout the core. In a second part, I will present synthetic tests of sequential geomagnetic data assimilation aiming at evaluating the range at which the future of the geodynamo can be predicted, and our corresponding prospects to refine the current geomagnetic predictions. Fournier, Aubert, Thébault: Inference on core surface flow from observations and 3-D dynamo modelling, Geophys. J. Int. 186, 118-136, 2011, doi: 10.1111/j.1365-246X.2011.05037.x Aubert, Fournier: Inferring internal properties of Earth's core dynamics and their evolution from surface observations and a numerical geodynamo model, Nonlinear Proc. Geoph. 18, 657-674, 2011, doi:10.5194/npg-18-657-2011 Aubert: Flow throughout the Earth's core inverted from geomagnetic observations and numerical dynamo models, Geophys. J. Int., 2012, doi: 10.1093/gji/ggs051

An Initial Assessment of the Impact of CYGNSS Ocean Surface Wind Assimilation on Navy Global and Mesoscale Numerical Weather Prediction

NASA Astrophysics Data System (ADS)

Baker, N. L.; Tsu, J.; Swadley, S. D.

2017-12-01

We assess the impact of assimilation of CYclone Global Navigation Satellite System (CYGNSS) ocean surface winds observations into the NAVGEM[i] global and COAMPS®[ii] mesoscale numerical weather prediction (NWP) systems. Both NAVGEM and COAMPS® used the NRL 4DVar assimilation system NAVDAS-AR[iii]. Long term monitoring of the NAVGEM Forecast Sensitivity Observation Impact (FSOI) indicates that the forecast error reduction for ocean surface wind vectors (ASCAT and WindSat) are significantly larger than for SSMIS wind speed observations. These differences are larger than can be explained by simply two pieces of information (for wind vectors) versus one (wind speed). To help understand these results, we conducted a series of Observing System Experiments (OSEs) to compare the assimilation of ASCAT wind vectors with the equivalent (computed) ASCAT wind speed observations. We found that wind vector assimilation was typically 3 times more effective at reducing the NAVGEM forecast error, with a higher percentage of beneficial observations. These results suggested that 4DVar, in the absence of an additional nonlinear outer loop, has limited ability to modify the analysis wind direction. We examined several strategies for assimilating CYGNSS ocean surface wind speed observations. In the first approach, we assimilated CYGNSS as wind speed observations, following the same methodology used for SSMIS winds. The next two approaches converted CYGNSS wind speed to wind vectors, using NAVGEM sea level pressure fields (following Holton, 1979), and using NAVGEM 10-m wind fields with the AER Variational Analysis Method. Finally, we compared these methods to CYGNSS wind speed assimilation using multiple outer loops with NAVGEM Hybrid 4DVar. Results support the earlier studies suggesting that NAVDAS-AR wind speed assimilation is sub-optimal. We present detailed results from multi-month NAVGEM assimilation runs along with case studies using COAMPS®. Comparisons include the fit of analyses and forecasts with in-situ observations and analyses from other NWP centers (e.g. ECMWF and GFS). [i] NAVy Global Environmental Model [ii] COAMPS® is a registered trademark of the Naval Research Laboratory for the Navy's Coupled Ocean Atmosphere Mesoscale Prediction System. [iii] NRL Atmospheric Variational Data Assimilation System

Assimilation of spatially sparse in situ soil moisture networks into a continuous model domain

USDA-ARS?s Scientific Manuscript database

Growth in the availability of near-real-time soil moisture observations from ground-based networks has spurred interest in the assimilation of these observations into land surface models via a two-dimensional data assimilation system. However, the design of such systems is currently hampered by our ...

4DVAR data Assimilation with the Regional Ocean Modeling System (ROMS): Impact on the Water Mass Distributions in the Yellow Sea

NASA Astrophysics Data System (ADS)

Lee, Joon-Ho; Kim, Taekyun; Pang, Ig-Chan; Moon, Jae-Hong

2018-04-01

In this study, we evaluate the performance of the recently developed incremental strong constraint 4-dimensional variational (4DVAR) data assimilation applied to the Yellow Sea (YS) using the Regional Ocean Modeling System (ROMS). Two assimilation experiments are compared: assimilating remote-sensed sea surface temperature (SST) and both the SST and in-situ profiles measured by shipboard CTD casts into a regional ocean modeling from January to December of 2011. By comparing the two assimilation experiments against a free-run without data assimilation, we investigate how the assimilation affects the hydrographic structures in the YS. Results indicate that the SST assimilation notably improves the model behavior at the surface when compared to the nonassimilative free-run. The SST assimilation also has an impact on the subsurface water structure in the eastern YS; however, the improvement is seasonally dependent, that is, the correction becomes more effective in winter than in summer. This is due to a strong stratification in summer that prevents the assimilation of SST from affecting the subsurface temperature. A significant improvement to the subsurface temperature is made when the in-situ profiles of temperature and salinity are assimilated, forming a tongue-shaped YS bottom cold water from the YS toward the southwestern seas of Jeju Island.

Impact of data assimilation on ocean current forecasts in the Angola Basin

NASA Astrophysics Data System (ADS)

Phillipson, Luke; Toumi, Ralf

2017-06-01

The ocean current predictability in the data limited Angola Basin was investigated using the Regional Ocean Modelling System (ROMS) with four-dimensional variational data assimilation. Six experiments were undertaken comprising a baseline case of the assimilation of salinity/temperature profiles and satellite sea surface temperature, with the subsequent addition of altimetry, OSCAR (satellite-derived sea surface currents), drifters, altimetry and drifters combined, and OSCAR and drifters combined. The addition of drifters significantly improves Lagrangian predictability in comparison to the baseline case as well as the addition of either altimetry or OSCAR. OSCAR assimilation only improves Lagrangian predictability as much as altimetry assimilation. On average the assimilation of either altimetry or OSCAR with drifter velocities does not significantly improve Lagrangian predictability compared to the drifter assimilation alone, even degrading predictability in some cases. When the forecast current speed is large, it is more likely that the combination improves trajectory forecasts. Conversely, when the currents are weaker, it is more likely that the combination degrades the trajectory forecast.

Continuous assimilation of simulated Geosat altimetric sea level into an eddy-resolving numerical ocean model. I - Sea level differences. II - Referenced sea level differences

NASA Technical Reports Server (NTRS)

White, Warren B.; Tai, Chang-Kou; Holland, William R.

1990-01-01

The optimal interpolation method of Lorenc (1981) was used to conduct continuous assimilation of altimetric sea level differences from the simulated Geosat exact repeat mission (ERM) into a three-layer quasi-geostrophic eddy-resolving numerical ocean box model that simulates the statistics of mesoscale eddy activity in the western North Pacific. Assimilation was conducted continuously as the Geosat tracks appeared in simulated real time/space, with each track repeating every 17 days, but occurring at different times and locations within the 17-day period, as would have occurred in a realistic nowcast situation. This interpolation method was also used to conduct the assimilation of referenced altimetric sea level differences into the same model, performing the referencing of altimetric sea sevel differences by using the simulated sea level. The results of this dynamical interpolation procedure are compared with those of a statistical (i.e., optimum) interpolation procedure.

Exploring Climatology and Long-Term Variations of Aerosols from NASA Reanalysis MERRA-2 with Giovanni

NASA Technical Reports Server (NTRS)

Shen, Suhung; Ostrenga, Dana; Vollmer, Bruce; Li, Zhanqing

2016-01-01

Dust plays important roles in energy cycle and climate variations. The dust deposition is the major source of iron in the open ocean, which is an essential micronutrient for phytoplankton growth and therefore may influence the ocean uptake of atmospheric CO2. Mineral dust can also act as fertilizer for forests over long time periods. Over 35 years of simulated global aerosol products from NASA atmospheric reanalysis, second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) are available from NASA Goddard Earth Science Data and Information Services Center (GES DISC). The MERRA-2 covers the period 1980-present, continuing as an ongoing climate analysis. Aerosol assimilation is included throughout the period, using MODIS, MISR, AERONET, and AVHRR (in the pre-EOS period). The aerosols are assimilated by using MERRA-2 aerosol model, which interact directly with the radiation parameterization, and radiatively coupled with atmospheric model dynamics in the Goddard Earth Observing System Model, Version 5 (GEOS-5). Dust deposition data along with other major aerosol compositions (e.g. black carbon, sea salt, and sulfate, etc.) are simulated as dry and wet deposition, respectively. The hourly and monthly data are available at spatial resolution of 0.5ox0.625o (latitude x longitude). Quick data exploration of climatology and interannual variations of MERRA-2 aerosol can be done through the online visualization and analysis tool, Giovanni. This presentation, using dust deposition as an example, demonstrates a number of MERRA-2 data services at GES DISC. Global distributions of dust depositions, and their seasonal and inter-annual variations are investigated from MERRA-2 monthly aerosol products.

Technical Report Series on Global Modeling and Data Assimilation. Volume 13; Interannual Variability and Potential Predictability in Reanalysis Products

NASA Technical Reports Server (NTRS)

Min, Wei; Schubert, Siegfried D.; Suarez, Max J. (Editor)

1997-01-01

The Data Assimilation Office (DAO) at Goddard Space Flight Center and the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) have produced multi-year global assimilations of historical data employing fixed analysis systems. These "reanalysis" products are ideally suited for studying short-term climatic variations. The availability of multiple reanalysis products also provides the opportunity to examine the uncertainty in the reanalysis data. The purpose of this document is to provide an updated estimate of seasonal and interannual variability based on the DAO and NCEP/NCAR reanalyses for the 15-year period 1980-1995. Intercomparisons of the seasonal means and their interannual variations are presented for a variety of prognostic and diagnostic fields. In addition, atmospheric potential predictability is re-examined employing selected DAO reanalysis variables.

Ensemble Analysis of Variational Assimilation of Hydrologic and Hydrometeorological Data into Distributed Hydrologic Model

NASA Astrophysics Data System (ADS)

Lee, H.; Seo, D.; Koren, V.

2008-12-01

A prototype 4DVAR (four-dimensional variational) data assimilator for gridded Sacramento soil-moisture accounting and kinematic-wave routing models in the Hydrology Laboratory's Research Distributed Hydrologic Model (HL-RDHM) has been developed. The prototype assimilates streamflow and in-situ soil moisture data and adjusts gridded precipitation and climatological potential evaporation data to reduce uncertainty in the model initial conditions for improved monitoring and prediction of streamflow and soil moisture at the outlet and interior locations within the catchment. Due to large degrees of freedom involved, data assimilation (DA) into distributed hydrologic models is complex. To understand and assess sensitivity of the performance of DA to uncertainties in the model initial conditions and in the data, two synthetic experiments have been carried out in an ensemble framework. Results from the synthetic experiments shed much light on the potential and limitations with DA into distributed models. For initial real-world assessment, the prototype DA has also been applied to the headwater basin at Eldon near the Oklahoma-Arkansas border. We present these results and describe the next steps.

Ensemble-based data assimilation and optimal sensor placement for scalar source reconstruction

NASA Astrophysics Data System (ADS)

Mons, Vincent; Wang, Qi; Zaki, Tamer

2017-11-01

Reconstructing the characteristics of a scalar source from limited remote measurements in a turbulent flow is a problem of great interest for environmental monitoring, and is challenging due to several aspects. Firstly, the numerical estimation of the scalar dispersion in a turbulent flow requires significant computational resources. Secondly, in actual practice, only a limited number of observations are available, which generally makes the corresponding inverse problem ill-posed. Ensemble-based variational data assimilation techniques are adopted to solve the problem of scalar source localization in a turbulent channel flow at Reτ = 180 . This approach combines the components of variational data assimilation and ensemble Kalman filtering, and inherits the robustness from the former and the ease of implementation from the latter. An ensemble-based methodology for optimal sensor placement is also proposed in order to improve the condition of the inverse problem, which enhances the performances of the data assimilation scheme. This work has been partially funded by the Office of Naval Research (Grant N00014-16-1-2542) and by the National Science Foundation (Grant 1461870).

Drought Indicators Based on Model Assimilated GRACE Terrestrial Water Storage Observations

NASA Technical Reports Server (NTRS)

Houborg, Rasmus; Rodell, Matthew; Li, Bailing; Reichle, Rolf; Zaitchik, Benjamin F.

2012-01-01

The Gravity Recovery and Climate Experiment (GRACE) twin satellites observe time variations in Earth's gravity field which yield valuable information about changes in terrestrial water storage (TWS). GRACE is characterized by low spatial (greater than 150,000 square kilometers) and temporal (greater than 10 day) resolution but has the unique ability to sense water stored at all levels (including groundwater) systematically and continuously. The GRACE Data Assimilation System (GRACE-DAS), based on the Catchment Land Surface Model (CLSM) enhances the value of the GRACE water storage data by enabling spatial and temporal downscaling and vertical decomposition into moisture 39 components (i.e. groundwater, soil moisture, snow), which individually are more useful for scientific applications. In this study, GRACE-DAS was applied to North America and GRACE-based drought indicators were developed as part of a larger effort that investigates the possibility of more comprehensive and objective identification of drought conditions by integrating spatially, temporally and vertically disaggregated GRACE data into the U.S. and North American Drought Monitors. Previously, the Drought Monitors lacked objective information on deep soil moisture and groundwater conditions, which are useful indicators of drought. Extensive datasets of groundwater storage from USGS monitoring wells and soil moisture from the Soil Climate Analysis Network (SCAN) were used to assess improvements in the hydrological modeling skill resulting from the assimilation of GRACE TWS data. The results point toward modest, but statistically significant, improvements in the hydrological modeling skill across major parts of the United States, highlighting the potential value of GRACE assimilated water storage field for improving drought detection.

Comparison of Sequential and Variational Data Assimilation

NASA Astrophysics Data System (ADS)

Alvarado Montero, Rodolfo; Schwanenberg, Dirk; Weerts, Albrecht

2017-04-01

Data assimilation is a valuable tool to improve model state estimates by combining measured observations with model simulations. It has recently gained significant attention due to its potential in using remote sensing products to improve operational hydrological forecasts and for reanalysis purposes. This has been supported by the application of sequential techniques such as the Ensemble Kalman Filter which require no additional features within the modeling process, i.e. it can use arbitrary black-box models. Alternatively, variational techniques rely on optimization algorithms to minimize a pre-defined objective function. This function describes the trade-off between the amount of noise introduced into the system and the mismatch between simulated and observed variables. While sequential techniques have been commonly applied to hydrological processes, variational techniques are seldom used. In our believe, this is mainly attributed to the required computation of first order sensitivities by algorithmic differentiation techniques and related model enhancements, but also to lack of comparison between both techniques. We contribute to filling this gap and present the results from the assimilation of streamflow data in two basins located in Germany and Canada. The assimilation introduces noise to precipitation and temperature to produce better initial estimates of an HBV model. The results are computed for a hindcast period and assessed using lead time performance metrics. The study concludes with a discussion of the main features of each technique and their advantages/disadvantages in hydrological applications.

A nudging-based data assimilation method: the Back and Forth Nudging (BFN) algorithm

NASA Astrophysics Data System (ADS)

Auroux, D.; Blum, J.

2008-03-01

This paper deals with a new data assimilation algorithm, called Back and Forth Nudging. The standard nudging technique consists in adding to the equations of the model a relaxation term that is supposed to force the observations to the model. The BFN algorithm consists in repeatedly performing forward and backward integrations of the model with relaxation (or nudging) terms, using opposite signs in the direct and inverse integrations, so as to make the backward evolution numerically stable. This algorithm has first been tested on the standard Lorenz model with discrete observations (perfect or noisy) and compared with the variational assimilation method. The same type of study has then been performed on the viscous Burgers equation, comparing again with the variational method and focusing on the time evolution of the reconstruction error, i.e. the difference between the reference trajectory and the identified one over a time period composed of an assimilation period followed by a prediction period. The possible use of the BFN algorithm as an initialization for the variational method has also been investigated. Finally the algorithm has been tested on a layered quasi-geostrophic model with sea-surface height observations. The behaviours of the two algorithms have been compared in the presence of perfect or noisy observations, and also for imperfect models. This has allowed us to reach a conclusion concerning the relative performances of the two algorithms.

African refugees in Egypt: trauma, loss, and cultural adjustment.

PubMed

Henry, Hani M

2012-08-01

This study examined the influence of pre-immigration trauma on the acculturation process of refugees, as reflected in the manifestations of their continuing bonds with native cultures. Six African refugees who sought refuge in Egypt because of wars and political persecution were interviewed about the circumstances of their departure from their home countries, as well as their life experiences in Egypt. All participants kept continuing bonds with their native cultures, but these bonds manifested differently depending on their ability to assimilate pre-immigration trauma and cultural losses. Participants who successfully assimilated both pre-immigration trauma and cultural losses developed continuing bonds with their native cultures that helped them (a) integrate the Egyptian culture into their life experiences and (b) tolerate difficult political conditions in Egypt. Participants who could not assimilate their pre-immigration trauma and cultural losses also developed continuing bonds with their native culture, but these bonds only provided them with solace.

A comparison study of the application of data assimilation in the short-term prediction of radiation and precipitation

NASA Astrophysics Data System (ADS)

Ding, Huang; Cui, Fang; Wang, Zhijia; Zhou, Hai; Chen, Weidong

2018-03-01

Based on the meteorological observation of the DG plants in East China, the assimilation effect of the WRF in the summer of 2016 was studied. The results show that, in the case of using data assimilation, the model correctly predicted the occurrence time of precipitation, as well as the variation of the precipitation along with the time were well consistent with the observations, which gives more accurate downward shortwave radiation. The application of data assimilation techniques can provide reliable information to adapt to the high resolution of meso-scale meteorological model. Therefore, it provides the necessary technical support for the development of the distributed power generation.

An Investigation of Multi-Satellite Stratospheric Measurements on Tropospheric Weather Predictions over Continental United States

NASA Astrophysics Data System (ADS)

Shao, Min

The troposphere and stratosphere are the two closest atmospheric layers to the Earth's surface. These two layers are separated by the so-called tropopause. On one hand, these two layers are largely distinguished, on the other hand, lots of evidences proved that connections are also existed between these two layers via various dynamical and chemical feedbacks. Both tropospheric and stratospheric waves can propagate through the tropopause and affect the down streams, despite the fact that this propagation of waves is relatively weaker than the internal interactions in both atmospheric layers. Major improvements have been made in numerical weather predictions (NWP) via data assimilation (DA) in the past 30 years. From optimal interpolation to variational methods and Kalman Filter, great improvements are also made in the development of DA technology. The availability of assimilating satellite radiance observation and the increasing amount of satellite measurements enabled the generation of better atmospheric initials for both global and regional NWP systems. The selection of DA schemes is critical for regional NWP systems. The performance of three major data assimilation (3D-Var, Hybrid, and EnKF) schemes on regional weather forecasts over the continental United States during winter and summer is investigated. Convergence rate in the variational methods can be slightly accelerated especially in summer by the inclusion of ensembles. When the regional model lid is set at 50-mb, larger improvements (10˜20%) in the initials are obtained over the tropopause and lower troposphere. Better forecast skills (˜10%) are obtained in all three DA schemes in summer. Among these three DA schemes, slightly better (˜1%) forecast skills are obtained in Hybrid configuration than 3D-Var. Overall better forecast skills are obtained in summer via EnKF scheme. An extra 22% skill in predicting summer surface pressure but 10% less skills in winter are given by EnKF when compared to 3D-Var. The different forecast skills obtained between variational methods and EnKF are mainly due to the opposite incremental features over ocean and mountainous regions and the inclusion of ensembles. Diurnal variations are observed in predictions. Variations in temperature and humidity are mainly produced by the one-time assimilation in a day and the variations in wind predictions are mainly come from model systematic errors. The assimilation of microwave and infrared satellite measurements alone is compared. Compared to microwave measurements, less than 1% extra performance skill is obtained over the tropopause when infrared measurements are assimilated alone. Large differences are observed in winter analysis when Hybrid scheme is applied. Compared to infrared measurements, an averaged extra 5% performance skill is obtained when microwave measurements are assimilated alone. Predictions made by microwave configuration (MW) shows an extra 3% forecast skill than infrared configuration (IR) at early forecasts. Major differences between MW and IR are located over the tropopause and lower troposphere. Extra 3% and 15% forecast skills for the tropopause wind and temperature are obtained by assimilating microwave measurements alone, respectively. Infrared measurements show slightly better forecast skills at lower troposphere at later forecast lead times. The impacts of the extended stratospheric layers by raising regional model lid from 50-mb to 10-mb and then to 1-mb and the assimilated stratospheric satellite measurements on tropospheric weather predictions are explored in the last section. An extra 10% performance skill over the initial tropopause is obtained by extending the model top to 1-mb. Significant improvements (15˜50%) in initials are obtained over tropopause and lower troposphere by assimilating stratospheric measurements. In the predictions, the stratospheric information can propagate through the tropopause layers and affect the lower troposphere after 2-3 days' propagation. The major improvements made by the extended stratospheric layers and measurements are located in the tropopause. An averaged extra 5% forecast skill is obtained by raising the model lid from 10-mb to 1-mb. An extra 7% forecast skill is obtained in the tropospheric humidity by assimilating stratospheric measurements. Significant improvements in the tropopause and tropospheric predictions are observed when multi-satellite stratospheric measurements extended to 1-mb are assimilated in regional NWP system. Major positive impacts on the tropospheric weather predictions are observed in the first 72-h forecast lead times due to the downward propagation of the microwave stratospheric measurements. A two-season comparison study shows that the assimilation of microwave stratospheric measurements extended to 1-mb will lead to an adjusted stratospheric temperature distribution which may related to an adjusted BDC. Small impacts on the tropospheric general circulations are also found. The tropospheric forecast skills are slightly improved in response to the stratospheric initial conditions and adjusted tropospheric general circulations. For the prediction of heavy precipitation events, an extra 14% forecast skill is obtained when the microwave stratospheric measurements extend to 1-mb are assimilated. The results obtained in this thesis indicate that the assimilation of satellite microwave measurements has the advantages for short-term regional weather forecast using ensemble related data assimilation scheme. Also, this thesis proposed that the assimilation of microwave stratospheric measurements extended to 1-mb can slightly improve the tropospheric weather forecast skills as a result of the tropospheric general circulations responded to the adjusted stratospheric initials.

Assimilation of remote sensing observations into a continuous distributed hydrological model: impacts on the hydrologic cycle

NASA Astrophysics Data System (ADS)

Laiolo, Paola; Gabellani, Simone; Campo, Lorenzo; Cenci, Luca; Silvestro, Francesco; Delogu, Fabio; Boni, Giorgio; Rudari, Roberto

2015-04-01

The reliable estimation of hydrological variables (e.g. soil moisture, evapotranspiration, surface temperature) in space and time is of fundamental importance in operational hydrology to improve the forecast of the rainfall-runoff response of catchments and, consequently, flood predictions. Nowadays remote sensing can offer a chance to provide good space-time estimates of several hydrological variables and then improve hydrological model performances especially in environments with scarce in-situ data. This work investigates the impact of the assimilation of different remote sensing products on the hydrological cycle by using a continuous physically based distributed hydrological model. Three soil moisture products derived by ASCAT (Advanced SCATterometer) are used to update the model state variables. The satellite-derived products are assimilated into the hydrological model using different assimilation techniques: a simple nudging and the Ensemble Kalman Filter. Moreover two assimilation strategies are evaluated to assess the impact of assimilating the satellite products at model spatial resolution or at the satellite scale. The experiments are carried out for three Italian catchments on multi year period. The benefits on the model predictions of discharge, LST, evapotranspiration and soil moisture dynamics are tested and discussed.

Assimilation of Spatially Sparse In Situ Soil Moisture Networks into a Continuous Model Domain

NASA Astrophysics Data System (ADS)

Gruber, A.; Crow, W. T.; Dorigo, W. A.

2018-02-01

Growth in the availability of near-real-time soil moisture observations from ground-based networks has spurred interest in the assimilation of these observations into land surface models via a two-dimensional data assimilation system. However, the design of such systems is currently hampered by our ignorance concerning the spatial structure of error afflicting ground and model-based soil moisture estimates. Here we apply newly developed triple collocation techniques to provide the spatial error information required to fully parameterize a two-dimensional (2-D) data assimilation system designed to assimilate spatially sparse observations acquired from existing ground-based soil moisture networks into a spatially continuous Antecedent Precipitation Index (API) model for operational agricultural drought monitoring. Over the contiguous United States (CONUS), the posterior uncertainty of surface soil moisture estimates associated with this 2-D system is compared to that obtained from the 1-D assimilation of remote sensing retrievals to assess the value of ground-based observations to constrain a surface soil moisture analysis. Results demonstrate that a fourfold increase in existing CONUS ground station density is needed for ground network observations to provide a level of skill comparable to that provided by existing satellite-based surface soil moisture retrievals.

Extraction of wind and temperature information from hybrid 4D-Var assimilation of stratospheric ozone using NAVGEM

NASA Astrophysics Data System (ADS)

Allen, Douglas R.; Hoppel, Karl W.; Kuhl, David D.

2018-03-01

Extraction of wind and temperature information from stratospheric ozone assimilation is examined within the context of the Navy Global Environmental Model (NAVGEM) hybrid 4-D variational assimilation (4D-Var) data assimilation (DA) system. Ozone can improve the wind and temperature through two different DA mechanisms: (1) through the flow-of-the-day ensemble background error covariance that is blended together with the static background error covariance and (2) via the ozone continuity equation in the tangent linear model and adjoint used for minimizing the cost function. All experiments assimilate actual conventional data in order to maintain a similar realistic troposphere. In the stratosphere, the experiments assimilate simulated ozone and/or radiance observations in various combinations. The simulated observations are constructed for a case study based on a 16-day cycling truth experiment (TE), which is an analysis with no stratospheric observations. The impact of ozone on the analysis is evaluated by comparing the experiments to the TE for the last 6 days, allowing for a 10-day spin-up. Ozone assimilation benefits the wind and temperature when data are of sufficient quality and frequency. For example, assimilation of perfect (no applied error) global hourly ozone data constrains the stratospheric wind and temperature to within ˜ 2 m s-1 and ˜ 1 K. This demonstrates that there is dynamical information in the ozone distribution that can potentially be used to improve the stratosphere. This is particularly important for the tropics, where radiance observations have difficulty constraining wind due to breakdown of geostrophic balance. Global ozone assimilation provides the largest benefit when the hybrid blending coefficient is an intermediate value (0.5 was used in this study), rather than 0.0 (no ensemble background error covariance) or 1.0 (no static background error covariance), which is consistent with other hybrid DA studies. When perfect global ozone is assimilated in addition to radiance observations, wind and temperature error decreases of up to ˜ 3 m s-1 and ˜ 1 K occur in the tropical upper stratosphere. Assimilation of noisy global ozone (2 % errors applied) results in error reductions of ˜ 1 m s-1 and ˜ 0.5 K in the tropics and slightly increased temperature errors in the Northern Hemisphere polar region. Reduction of the ozone sampling frequency also reduces the benefit of ozone throughout the stratosphere, with noisy polar-orbiting data having only minor impacts on wind and temperature when assimilated with radiances. An examination of ensemble cross-correlations between ozone and other variables shows that a single ozone observation behaves like a potential vorticity (PV) charge, or a monopole of PV, with rotation about a vertical axis and vertically oriented temperature dipole. Further understanding of this relationship may help in designing observation systems that would optimize the impact of ozone on the dynamics.

A Method of Successive Corrections of the Control Subspace in the Reduced-Order Variational Data Assimilation

DTIC Science & Technology

2009-02-01

Evensen, G., 2003: The ensemble Kalman filter : Theoretical formulation and practical implementation. Ocean Dyn., 53, 343–357, doi:10.1007/s10236-003...0036-9. ——, 2006: Data Assimilation: The Ensemble Kalman Filter . Springer, 288 pp. Fang, F., C. C. Pain, I. M. Navon, G. J. Gorman, M. D. Piggott, P. A...E. J. Kostelich, M. Corazza, E. Kalnay, and D. J. Patil, 2004: A local ensemble Kalman filter for atmospheric data assimilation. Tellus, 56A, 415–428

The Impact of Strong Assimilation on the Perception of Connected Speech

ERIC Educational Resources Information Center

Gaskell, M. Gareth; Snoeren, Natalie D.

2008-01-01

Models of compensation for phonological variation in spoken word recognition differ in their ability to accommodate complete assimilatory alternations (such as run assimilating fully to rum in the context of a quick run picks you up). Two experiments addressed whether such complete changes can be observed in casual speech, and if so, whether they…

An inverse method to estimate emission rates based on nonlinear least-squares-based ensemble four-dimensional variational data assimilation with local air concentration measurements.

PubMed

Geng, Xiaobing; Xie, Zhenghui; Zhang, Lijun; Xu, Mei; Jia, Binghao

2018-03-01

An inverse source estimation method is proposed to reconstruct emission rates using local air concentration sampling data. It involves the nonlinear least squares-based ensemble four-dimensional variational data assimilation (NLS-4DVar) algorithm and a transfer coefficient matrix (TCM) created using FLEXPART, a Lagrangian atmospheric dispersion model. The method was tested by twin experiments and experiments with actual Cs-137 concentrations measured around the Fukushima Daiichi Nuclear Power Plant (FDNPP). Emission rates can be reconstructed sequentially with the progression of a nuclear accident, which is important in the response to a nuclear emergency. With pseudo observations generated continuously, most of the emission rates were estimated accurately, except under conditions when the wind blew off land toward the sea and at extremely slow wind speeds near the FDNPP. Because of the long duration of accidents and variability in meteorological fields, monitoring networks composed of land stations only in a local area are unable to provide enough information to support an emergency response. The errors in the estimation compared to the real observations from the FDNPP nuclear accident stemmed from a shortage of observations, lack of data control, and an inadequate atmospheric dispersion model without improvement and appropriate meteorological data. The proposed method should be developed further to meet the requirements of a nuclear emergency response. Copyright © 2017 Elsevier Ltd. All rights reserved.

An Investigation of the Characterization of Cloud Contamination in Hyperspectral Radiances

NASA Technical Reports Server (NTRS)

McCarty, William; Jedlovec, Gary J.; LeMarshall, John

2007-01-01

In regions lacking direct observations, the assimilation of radiances from infrared and microwave sounders is the primary method for characterizing the atmosphere in the analysis process. In recent years, technological advances have led to the launching of more advanced sounders, particularly in the thermal infrared spectrum. With the advent of these hyperspectral sounders, the amount of data available for the analysis process has and will continue to be dramatically increased. However, the utilization of infrared radiances in variational assimilation can be problematic in the presence of clouds; specifically the assessment of the presence of clouds in an instantaneous field of view (IFOV) and the contamination in the individual channels within the IFOV. Various techniques have been developed to determine if a channel is contaminated by clouds. The work presented in this paper and subsequent presentation will investigate traditional techniques and compare them to a new technique, the C02 sorting technique, which utilizes the high spectral resolution of the Atmospheric Infrared Sounder (AIRS) within the framework of the Gridpoint Statistical Interpolation (GSI) 3DVAR system. Ultimately, this work is done in preparation for the assessment of short-term forecast impacts with the regional assimilation of AIRS radiances within the analysis fields of the Weather Research and Forecast Nonhydrostatic Mesoscale Model (WRF-NMM) at the NASA Short-term Prediction Research and Transition (SPORT) Center.

Satellite radiance data assimilation for binary tropical cyclone cases over the western North Pacific

NASA Astrophysics Data System (ADS)

Choi, Yonghan; Cha, Dong-Hyun; Lee, Myong-In; Kim, Joowan; Jin, Chun-Sil; Park, Sang-Hun; Joh, Min-Su

2017-06-01

A total of three binary tropical cyclone (TC) cases over the Western North Pacific are selected to investigate the effects of satellite radiance data assimilation on analyses and forecasts of binary TCs. Two parallel cycling experiments with a 6 h interval are performed for each binary TC case, and the difference between the two experiments is whether satellite radiance observations are assimilated. Satellite radiance observations are assimilated using the Weather Research and Forecasting Data Assimilation (WRFDA)'s three-dimensional variational (3D-Var) system, which includes the observation operator, quality control procedures, and bias correction algorithm for radiance observations. On average, radiance assimilation results in slight improvements of environmental fields and track forecasts of binary TC cases, but the detailed effects vary with the case. When there is no direct interaction between binary TCs, radiance assimilation leads to better depictions of environmental fields, and finally it results in improved track forecasts. However, positive effects of radiance assimilation on track forecasts can be reduced when there exists a direct interaction between binary TCs and intensities/structures of binary TCs are not represented well. An initialization method (e.g., dynamic initialization) combined with radiance assimilation and/or more advanced DA techniques (e.g., hybrid method) can be considered to overcome these limitations.

Mesoscale Assimilation of TMI Rainfall Data with 4DVAR: Sensitivity Studies

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Pu, Zhaoxia

2003-01-01

Sensitivity studies are performed on the assimilation of TRMM (Tropical Rainfall Measurement Mission) Microwave Imager (TMI) derived rainfall data into a mesoscale model using a four-dimensional variational data assimilation (4DVAR) technique. A series of numerical experiments is conducted to evaluate the impact of TMI rainfall data on the numerical simulation of Hurricane Bonnie (1998). The results indicate that rainfall data assimilation is sensitive to the error characteristics of the data and the inclusion of physics in the adjoint and forward models. In addition, assimilating the rainfall data alone is helpful for producing a more realistic eye and rain bands in the hurricane but does not ensure improvements in hurricane intensity forecasts. Further study indicated that it is necessary to incorporate TMI rainfall data together with other types of data such as wind data into the model, in which case the inclusion of the rainfall data further improves the intensity forecast of the hurricane. This implies that proper constraints may be needed for rainfall assimilation.

Variational data assimilation problem for the thermodynamics model with displaced pole

NASA Astrophysics Data System (ADS)

Parmuzin, Eugene; Agosgkov, Valery; Zakharova, Natalia

2017-04-01

The most versatile and promising technology for solving problems of monitoring and analysis of the natural environment is a four-dimensional variational data assimilation of observation data. The development of computational algorithms for the solution of data assimilation problems in geophysical hydrodynamics is important in the contemporary computation and informational science to improve the quality of long-term prediction by using the hydrodynamics sea model. These problems are applied to close and solve in practice the appropriate inverse problems of the geophysical hydrodynamics. In this work the variational data assimilation problems in the Baltic Sea water area with displaced pole were formulated and studied [1]. We assume, that the unique function which is obtained by observation data processing is the function and we permit that the function is known only on a part of considering area (for example, on a part of the Baltic Sea). Numerical experiments on restoring the ocean heat flux and obtaining solution of the system (temperature, salinity, velocity, and sea surface height) in the Baltic Sea primitive equation hydrodynamics model [2] with assimilation procedure were carried out. In the calculations we used daily sea surface temperature observation from Danish meteorological Institute, prepared on the basis of measurements of the radiometer (AVHRR, AATSR and AMSRE) and spectroradiometer (SEVIRI and MODIS). The spatial resolution of the model grid with respect to the horizontal variables is uniform on latitude (0.2 degree) and varies on longitude from 0.04 to 0.0004 degree . The results of the numerical experiments are presented. This study was supported by the Russian Foundation for Basic Research (project №16-01-00548) and project №14-11-00609 by the Russian Science Foundation. References: [1] Agoshkov V.I., Parmuzin E.I., Zakharova N.B., Zalesny V.B., Shutyaev V.P., Gusev A.V. Variational assimilation of observation data in the mathematical model of the Baltic Sea dynamics // Russ. J. Numer. Anal. Math. Modelling, 2015, V. 30, No. 4, PP. 203-212. [2] Zalesny V.B., Gusev A.V., Chernobay S.Yu., Aps R., Tamsalu R., Kujala P., Rytkönen J. The Baltic Sea circulation modelling and assessment of marine pollution, Russ. J. Numer. Analysis and Math. Modelling, 2014, V 29, No. 2, pp. 129-138.

Three-dimensional variational assimilation of MODIS aerosol optical depth: Implementation and application to a dust storm over East Asia

NASA Astrophysics Data System (ADS)

Liu, Zhiquan; Liu, Quanhua; Lin, Hui-Chuan; Schwartz, Craig S.; Lee, Yen-Huei; Wang, Tijian

2011-12-01

Assimilation of the Moderate Resolution Imaging Spectroradiometer (MODIS) total aerosol optical depth (AOD) retrieval products (at 550 nm wavelength) from both Terra and Aqua satellites have been developed within the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) three-dimensional variational (3DVAR) data assimilation system. This newly developed algorithm allows, in a one-step procedure, the analysis of 3-D mass concentration of 14 aerosol variables from the Goddard Chemistry Aerosol Radiation and Transport (GOCART) module. The Community Radiative Transfer Model (CRTM) was extended to calculate AOD using GOCART aerosol variables as input. Both the AOD forward model and corresponding Jacobian model were developed within the CRTM and used in the 3DVAR minimization algorithm to compute the AOD cost function and its gradient with respect to 3-D aerosol mass concentration. The impact of MODIS AOD data assimilation was demonstrated by application to a dust storm from 17 to 24 March 2010 over East Asia. The aerosol analyses initialized Weather Research and Forecasting/Chemistry (WRF/Chem) model forecasts. Results indicate that assimilating MODIS AOD substantially improves aerosol analyses and subsequent forecasts when compared to MODIS AOD, independent AOD observations from the Aerosol Robotic Network (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument, and surface PM10 (particulate matter with diameters less than 10 μm) observations. The newly developed AOD data assimilation system can serve as a tool to improve simulations of dust storms and general air quality analyses and forecasts.

Assimilation by lunar mare basalts: Melting of crustal material and dissolution of anorthite

NASA Astrophysics Data System (ADS)

Finnila, A. B.; Hess, P. C.; Rutherford, M. J.

1994-07-01

We discuss techniques for calculating the amount of crustal assimilation possible in lunar magma chambers and dikes based on thermal energy balances, kinetic rates, and simple fluid mechanical constraints. Assuming parent magmas of picritic compositions, we demonstrate the limits on the capacity of such magmas to melt and dissolve wall rock of anorthitic, troctolitic, noritic, and KREEP (quartz monzodiorite) compositions. Significant melting of the plagioclase-rich crustal lithologies requires turbulent convection in the assimilating magma and an efficient method of mixing in the relatively buoyant and viscous new melt. Even when this occurs, the major element chemistry of the picritic magmas will change by less than 1-2 wt %. Diffusion coefficients measured for Al2O3 from an iron-free basalt and an orange glass composition are 10-12 sq m/s at 1340 C and 10-11 sq m/s at 1390 C. These rates are too slow to allow dissolution of plagioclase to significantly affect magma compositions. Picritic magmas can melt significant quantities of KREEP, which suggests that their trace element chemistry may still be affected by assimilation processes; however, mixing viscous melts of KREEP composition with the fluid picritic magmas could be prohibitively difficult. We conclude that only a small part of the total major element chemical variation in the mare basalt and volcanic glass collection is due to assimilation/fractional crystallization processes near the lunar surface. Instead, most of the chemical variation in the lunar basalts and volcanic glasses must result from assimilation at deeper levels or from having distinct source regions in a heterogeneous lunar mantle.

Assimilation by lunar mare basalts: Melting of crustal material and dissolution of anorthite

NASA Technical Reports Server (NTRS)

Finnila, A. B.; Hess, P. C.; Rutherford, M. J.

1994-01-01

We discuss techniques for calculating the amount of crustal assimilation possible in lunar magma chambers and dikes based on thermal energy balances, kinetic rates, and simple fluid mechanical constraints. Assuming parent magmas of picritic compositions, we demonstrate the limits on the capacity of such magmas to melt and dissolve wall rock of anorthitic, troctolitic, noritic, and KREEP (quartz monzodiorite) compositions. Significant melting of the plagioclase-rich crustal lithologies requires turbulent convection in the assimilating magma and an efficient method of mixing in the relatively buoyant and viscous new melt. Even when this occurs, the major element chemistry of the picritic magmas will change by less than 1-2 wt %. Diffusion coefficients measured for Al2O3 from an iron-free basalt and an orange glass composition are 10(exp -12) sq m/s at 1340 C and 10(exp -11) sq m/s at 1390 C. These rates are too slow to allow dissolution of plagioclase to significantly affect magma compositions. Picritic magmas can melt significant quantities of KREEP, which suggests that their trace element chemistry may still be affected by assimilation processes; however, mixing viscous melts of KREEP composition with the fluid picritic magmas could be prohibitively difficult. We conclude that only a small part of the total major element chemical variation in the mare basalt and volcanic glass collection is due to assimilation/fractional crystallization processes near the lunar surface. Instead, most of the chemical variation in the lunar basalts and volcanic glasses must result from assimilation at deeper levels or from having distinct source regions in a heterogeneous lunar mantle.

Evaluation of Bogus Vortex Techniques with Four-Dimensional Variational Data Assimilation

NASA Technical Reports Server (NTRS)

Pu, Zhao-Xia; Braun, Scott A.

2000-01-01

The effectiveness of techniques for creating "bogus" vortices in numerical simulations of hurricanes is examined by using the Penn State/NCAR nonhydrostatic mesoscale model (MM5) and its adjoint system. A series of four-dimensional variational data assimilation (4-D VAR) experiments is conducted to generate an initial vortex for Hurricane Georges (1998) in the Atlantic Ocean by assimilating bogus sea-level pressure and surface wind information into the mesoscale numerical model. Several different strategies are tested for improving the vortex representation. The initial vortices produced by the 4-D VAR technique are able to reproduce many of the structural features of mature hurricanes. The vortices also result in significant improvements to the hurricane forecasts in terms of both intensity and track. In particular, with assimilation of only bogus sea-level pressure information, the response in the wind field is contained largely within the divergent component, with strong convergence leading to strong upward motion near the center. Although the intensity of the initial vortex seems to be well represented, a dramatic spin down of the storm occurs within the first 6 h of the forecast. With assimilation of bogus surface wind data only, an expected dominance of the rotational component of the wind field is generated, but the minimum pressure is adjusted inadequately compared to the actual hurricane minimum pressure. Only when both the bogus surface pressure and wind information are assimilated together does the model produce a vortex that represents the actual intensity of the hurricane and results in significant improvements to forecasts of both hurricane intensity and track.

Variation in shape of the lingula in the adult human mandible

PubMed Central

TULI, A.; CHOUDHRY, R.; CHOUDHRY, S.; RAHEJA, S.; AGARWAL, S.

2000-01-01

The lingulae of both sides of 165 dry adult human mandibles, 131 males and 34 females of Indian origin, were classified by their shape into 4 types: 1, triangular; 2, truncated; 3, nodular; and 4, assimilated. Triangular lingulae were found in 226 (68.5%) sides, truncated in 52 (15.8%), nodular in 36 (10.9%) and assimilated in 16 (4.8%) sides. Triangular lingulae were found bilaterally in 110, truncated in 23, nodular in 17 and assimilated in 7 mandibles. Of the remaining 8 mandibles with different appearances on the 2 sides, 6 had a combination of triangular and truncated and 2 had nodular and assimilated. The incidence of triangular and assimilated types in the male and female mandibles are almost equal. In the truncated type it was double in the male mandibles while the nodular type was a little less than double in the female mandibles. PMID:11005723

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

NASA Astrophysics Data System (ADS)

Ham, Yoo-Geun; Song, Hyo-Jong; Jung, Jaehee; Lim, Gyu-Ho

2017-04-01

This study introduces a altered version of the incremental analysis updates (IAU), called the nonstationary IAU (NIAU) method, to enhance the assimilation accuracy of the IAU while retaining the continuity of the analysis. Analogous 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. Still, unlike the IAU, the NIAU method applies time-evolved forcing employing the forward operator as rectifications to the model. The solution of the NIAU is better than that of the IAU, of which analysis is performed at the start 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.

Determining water storage depletion within Iran by assimilating GRACE data into the W3RA hydrological model

NASA Astrophysics Data System (ADS)

Khaki, M.; Forootan, E.; Kuhn, M.; Awange, J.; van Dijk, A. I. J. M.; Schumacher, M.; Sharifi, M. A.

2018-04-01

Groundwater depletion, due to both unsustainable water use and a decrease in precipitation, has been reported in many parts of Iran. In order to analyze these changes during the recent decade, in this study, we assimilate Terrestrial Water Storage (TWS) data from the Gravity Recovery And Climate Experiment (GRACE) into the World-Wide Water Resources Assessment (W3RA) model. This assimilation improves model derived water storage simulations by introducing missing trends and correcting the amplitude and phase of seasonal water storage variations. The Ensemble Square-Root Filter (EnSRF) technique is applied, which showed stable performance in propagating errors during the assimilation period (2002-2012). Our focus is on sub-surface water storage changes including groundwater and soil moisture variations within six major drainage divisions covering the whole Iran including its eastern part (East), Caspian Sea, Centre, Sarakhs, Persian Gulf and Oman Sea, and Lake Urmia. Results indicate an average of -8.9 mm/year groundwater reduction within Iran during the period 2002 to 2012. A similar decrease is also observed in soil moisture storage especially after 2005. We further apply the canonical correlation analysis (CCA) technique to relate sub-surface water storage changes to climate (e.g., precipitation) and anthropogenic (e.g., farming) impacts. Results indicate an average correlation of 0.81 between rainfall and groundwater variations and also a large impact of anthropogenic activities (mainly for irrigations) on Iran's water storage depletions.

Toward variational assimilation of SARAL/Altika altimeter data in a North Atlantic circulation model at eddy-permitting resolution: assessment of a NEMO-based 4D-VAR system

NASA Astrophysics Data System (ADS)

Bouttier, Pierre-Antoine; Brankart, Jean-Michel; Candille, Guillem; Vidard, Arthur; Blayo, Eric; Verron, Jacques; Brasseur, Pierre

2015-04-01

In this project, the response of a variational data assimilation system based on NEMO and its linear tangent and adjoint model is investigated using a 4DVAR algorithm into a North-Atlantic model at eddy-permitting resolution. The assimilated data consist of Jason-2 and SARAL/AltiKA dataset collected during the 2013-2014 period. The main objective is to explore the robustness of the 4DVAR algorithm in the context of a realistic turbulent oceanic circulation at mid-latitude constrained by multi-satellite altimetry missions. This work relies on two previous studies. First, a study with similar objectives was performed based on academic double-gyre turbulent model and synthetic SARAL/AltiKA data, using the same DA experimental framework. Its main goal was to investigate the impact of turbulence on variational DA methods performance. The comparison with this previous work will bring to light the methodological and physical issues encountered by variational DA algorithms in a realistic context at similar, eddy-permitting spatial resolution. We also have demonstrated how a dataset mimicking future SWOT observations improves 4DVAR incremental performances at eddy-permitting resolution. Then, in the context of the OSTST and FP7 SANGOMA projects, an ensemble DA experiment based on the same model and observational datasets has been realized (see poster by Brasseur et al.). This work offers the opportunity to compare efficiency, pros and cons of both DA methods in the context of KA-band altimetric data, at spatial resolution commonly used today for research and operational applications. In this poster we will present the validation plan proposed to evaluate the skill of variational experiment vs. ensemble assimilation experiments covering the same period using independent observations (e.g. from Cryosat-2 mission).

Developing hydrological monitoring system based on HF radar for islands and reefs in the South China Sea

NASA Astrophysics Data System (ADS)

Li, J.; Shi, P.; Chen, J.; Zhu, Y.; Li, B.

2016-12-01

There are many islands (or reefs) in the South China Sea. The hydrological properties (currents and waves) around the islands are highly spatially variable compared to those of coastal region of mainland, because the shorelines are more complex with much smaller scale, and the topographies are step-shape with a much sharper slope. The currents and waves with high spatial variations may destroy the buildings or engineering on shorelines, or even influence the structural stability of reefs. Therefore, it is necessary to establish monitoring systems to obtain the high-resolution hydrological information. This study propose a plan for developing a hydrological monitoring system based on HF radar on the shoreline of a typical island in the southern South China Sea: firstly, the HF radar are integrated with auxiliary equipment (such as dynamo, fuel tank, air conditioner, communication facilities) in a container to build a whole monitoring platform; synchronously, several buoys are set within the radar visibility for data calibration and validation; and finally, the current and wave observations collected by the HF radar are assimilated with numerical models to obtain long-term and high-precision reanalysis products. To test the feasibility of this plan, our research group has built two HF radar sites at the western coastal region of Guangdong Province. The collected data were used to extract surface current information and assimilated with an ocean model. The results show that the data assimilation can highly improve the surface current simulation, especially for typhoon periods. Continuous data with intervals between 6 and 12 hour are the most suitable for ideal assimilations. On the other hand, the test also reveal that developing similar monitoring system on island environments need advanced radars that have higher resolutions and a better performance for persistent work.

Impact of assimilating GOES imager clear-sky radiance with a rapid refresh assimilation system for convection-permitting forecast over Mexico

NASA Astrophysics Data System (ADS)

Yang, Chun; Liu, Zhiquan; Gao, Feng; Childs, Peter P.; Min, Jinzhong

2017-05-01

The Geostationary Operational Environmental Satellite (GOES) imager data could provide a continuous image of the evolutionary pattern of severe weather phenomena with its high spatial and temporal resolution. The capability to assimilate the GOES imager radiances has been developed within the Weather Research and Forecasting model's data assimilation system. Compared to the benchmark experiment with no GOES imager data, the impact of assimilating GOES imager radiances on the analysis and forecast of convective process over Mexico in 7-10 March 2016 was assessed through analysis/forecast cycling experiments using rapid refresh assimilation system with hybrid-3DEnVar scheme. With GOES imager radiance assimilation, better analyses were obtained in terms of the humidity, temperature, and simulated water vapor channel brightness temperature distribution. Positive forecast impacts from assimilating GOES imager radiance were seen when verified against the Tropospheric Airborne Meteorological Data Reporting observation, GOES imager observation, and Mexico station precipitation data.

Regional Ocean Data Assimilation

NASA Astrophysics Data System (ADS)

Edwards, Christopher A.; Moore, Andrew M.; Hoteit, Ibrahim; Cornuelle, Bruce D.

2015-01-01

This article reviews the past 15 years of developments in regional ocean data assimilation. A variety of scientific, management, and safety-related objectives motivate marine scientists to characterize many ocean environments, including coastal regions. As in weather prediction, the accurate representation of physical, chemical, and/or biological properties in the ocean is challenging. Models and observations alone provide imperfect representations of the ocean state, but together they can offer improved estimates. Variational and sequential methods are among the most widely used in regional ocean systems, and there have been exciting recent advances in ensemble and four-dimensional variational approaches. These techniques are increasingly being tested and adapted for biogeochemical applications.

Regional ocean data assimilation.

PubMed

Edwards, Christopher A; Moore, Andrew M; Hoteit, Ibrahim; Cornuelle, Bruce D

2015-01-01

This article reviews the past 15 years of developments in regional ocean data assimilation. A variety of scientific, management, and safety-related objectives motivate marine scientists to characterize many ocean environments, including coastal regions. As in weather prediction, the accurate representation of physical, chemical, and/or biological properties in the ocean is challenging. Models and observations alone provide imperfect representations of the ocean state, but together they can offer improved estimates. Variational and sequential methods are among the most widely used in regional ocean systems, and there have been exciting recent advances in ensemble and four-dimensional variational approaches. These techniques are increasingly being tested and adapted for biogeochemical applications.

Is Oklahoma getting drier?

NASA Astrophysics Data System (ADS)

Lin, Bing; Stackhouse, Paul, Jr.; Sun, Wenbo; Hu, Yongxiang; Liu, Zhaoyan; Fan, Tai-Fang (Alice)

2013-06-01

Land surface hydrology is important to regional climate, ecosystem, agriculture, and even human activities. Changes in soil moisture can produce considerable impacts on socioeconomics. Analysis of assimilation model results, especially those from the Community Land Model, shows that soil moisture over Oklahoma region is continuously reduced from 1980 to 2009. The potential drying trend in the Oklahoma region is evaluated by observations taken during last three decades in this study. Satellite data from Global Precipitation Climatology Project exhibit a clear precipitation decrease in the Oklahoma region during the last decade or so compared with those of two or three decades ago. Accompanying with the precipitation variation, land surface net radiation and temperature over the region are found increases by satellite and/or in-situ measurements. These changes in regional climate conditions also likely result in reduction of regional evaporation and enhancement of sensible heat transport from land surface into the atmosphere as indicated in assimilated data. These observed and modeled evidences of the changes in regional water and energy cycles lead us to conclude that the soil moisture over the Oklahoma region was reduced during the last decade. This soil moisture drop could increase a risk in water shortage for agriculture in the Oklahoma state if the dry period continues. Further investigations on the drying in the Oklahoma State or even entire Southern Great Plains are needed to mitigate potential droughts, reductions in vegetation products, and other socioeconomic impacts.

Development and evaluation of a high-resolution reanalysis of the East Australian Current region using the Regional Ocean Modelling System (ROMS 3.4) and Incremental Strong-Constraint 4-Dimensional Variational (IS4D-Var) data assimilation

NASA Astrophysics Data System (ADS)

Kerry, Colette; Powell, Brian; Roughan, Moninya; Oke, Peter

2016-10-01

As with other Western Boundary Currents globally, the East Australian Current (EAC) is highly variable making it a challenge to model and predict. For the EAC region, we combine a high-resolution state-of-the-art numerical ocean model with a variety of traditional and newly available observations using an advanced variational data assimilation scheme. The numerical model is configured using the Regional Ocean Modelling System (ROMS 3.4) and takes boundary forcing from the BlueLink ReANalysis (BRAN3). For the data assimilation, we use an Incremental Strong-Constraint 4-Dimensional Variational (IS4D-Var) scheme, which uses the model dynamics to perturb the initial conditions, atmospheric forcing, and boundary conditions, such that the modelled ocean state better fits and is in balance with the observations. This paper describes the data assimilative model configuration that achieves a significant reduction of the difference between the modelled solution and the observations to give a dynamically consistent "best estimate" of the ocean state over a 2-year period. The reanalysis is shown to represent both assimilated and non-assimilated observations well. It achieves mean spatially averaged root mean squared (rms) residuals with the observations of 7.6 cm for sea surface height (SSH) and 0.4 °C for sea surface temperature (SST) over the assimilation period. The time-mean rms residual for subsurface temperature measured by Argo floats is a maximum of 0.9 °C between water depths of 100 and 300 m and smaller throughout the rest of the water column. Velocities at several offshore and continental shelf moorings are well represented in the reanalysis with complex correlations between 0.8 and 1 for all observations in the upper 500 m. Surface radial velocities from a high-frequency radar array are assimilated and the reanalysis provides surface velocity estimates with complex correlations with observed velocities of 0.8-1 across the radar footprint. A comparison with independent (non-assimilated) shipboard conductivity temperature depth (CTD) cast observations shows a marked improvement in the representation of the subsurface ocean in the reanalysis, with the rms residual in potential density reduced to about half of the residual with the free-running model in the upper eddy-influenced part of the water column. This shows that information is successfully propagated from observed variables to unobserved regions as the assimilation system uses the model dynamics to adjust the model state estimate. This is the first study to generate a reanalysis of the region at such a high resolution, making use of an unprecedented observational data set and using an assimilation method that uses the time-evolving model physics to adjust the model in a dynamically consistent way. As such, the reanalysis potentially represents a marked improvement in our ability to capture important circulation dynamics in the EAC. The reanalysis is being used to study EAC dynamics, observation impact in state-estimation, and as forcing for a variety of downscaling studies.

Processing of phonological variation in children with hearing loss: compensation for English place assimilation in connected speech.

PubMed

Skoruppa, Katrin; Rosen, Stuart

2014-06-01

In this study, the authors explored phonological processing in connected speech in children with hearing loss. Specifically, the authors investigated these children's sensitivity to English place assimilation, by which alveolar consonants like t and n can adapt to following sounds (e.g., the word ten can be realized as tem in the phrase ten pounds). Twenty-seven 4- to 8-year-old children with moderate to profound hearing impairments, using hearing aids (n = 10) or cochlear implants (n = 17), and 19 children with normal hearing participated. They were asked to choose between pictures of familiar (e.g., pen) and unfamiliar objects (e.g., astrolabe) after hearing t- and n-final words in sentences. Standard pronunciations (Can you find the pen dear?) and assimilated forms in correct (… pem please?) and incorrect contexts (… pem dear?) were presented. As expected, the children with normal hearing chose the familiar object more often for standard forms and correct assimilations than for incorrect assimilations. Thus, they are sensitive to word-final place changes and compensate for assimilation. However, the children with hearing impairment demonstrated reduced sensitivity to word-final place changes, and no compensation for assimilation. Restricted analyses revealed that children with hearing aids who showed good perceptual skills compensated for assimilation in plosives only.

Joint Assimilation of SMOS Brightness Temperature and GRACE Terrestrial Water Storage Observations for Improved Soil Moisture Estimation

NASA Technical Reports Server (NTRS)

Girotto, Manuela; Reichle, Rolf H.; De Lannoy, Gabrielle J. M.; Rodell, Matthew

2017-01-01

Observations from recent soil moisture missions (e.g. SMOS) have been used in innovative data assimilation studies to provide global high spatial (i.e. 40 km) and temporal resolution (i.e. 3-days) soil moisture profile estimates from microwave brightness temperature observations. In contrast with microwave-based satellite missions that are only sensitive to near-surface soil moisture (0 - 5 cm), the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage column but, it is characterized by low spatial (i.e. 150,000 km2) and temporal (i.e. monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). This work hypothesizes that unprecedented soil water profile accuracy can be obtained through the joint assimilation of GRACE terrestrial water storage and SMOS brightness temperature observations. A particular challenge of the joint assimilation is the use of the two different types of measurements that are relevant for hydrologic processes representing different temporal and spatial scales. The performance of the joint assimilation strongly depends on the chosen assimilation methods, measurement and model error spatial structures. The optimization of the assimilation technique constitutes a fundamental step toward a multi-variate multi-resolution integrative assimilation system aiming to improve our understanding of the global terrestrial water cycle.

Application of an Ensemble Smoother to Precipitation Assimilation

NASA Technical Reports Server (NTRS)

Zhang, Sara; Zupanski, Dusanka; Hou, Arthur; Zupanski, Milija

2008-01-01

Assimilation of precipitation in a global modeling system poses a special challenge in that the observation operators for precipitation processes are highly nonlinear. In the variational approach, substantial development work and model simplifications are required to include precipitation-related physical processes in the tangent linear model and its adjoint. An ensemble based data assimilation algorithm "Maximum Likelihood Ensemble Smoother (MLES)" has been developed to explore the ensemble representation of the precipitation observation operator with nonlinear convection and large-scale moist physics. An ensemble assimilation system based on the NASA GEOS-5 GCM has been constructed to assimilate satellite precipitation data within the MLES framework. The configuration of the smoother takes the time dimension into account for the relationship between state variables and observable rainfall. The full nonlinear forward model ensembles are used to represent components involving the observation operator and its transpose. Several assimilation experiments using satellite precipitation observations have been carried out to investigate the effectiveness of the ensemble representation of the nonlinear observation operator and the data impact of assimilating rain retrievals from the TMI and SSM/I sensors. Preliminary results show that this ensemble assimilation approach is capable of extracting information from nonlinear observations to improve the analysis and forecast if ensemble size is adequate, and a suitable localization scheme is applied. In addition to a dynamically consistent precipitation analysis, the assimilation system produces a statistical estimate of the analysis uncertainty.

Joint assimilation of SMOS brightness temperature and GRACE terrestrial water storage observations for improved soil moisture estimation

NASA Astrophysics Data System (ADS)

Girotto, M.; Reichle, R. H.; De Lannoy, G.; Rodell, M.

2017-12-01

Observations from recent soil moisture missions (e.g. SMOS) have been used in innovative data assimilation studies to provide global high spatial (i.e. 40 km) and temporal resolution (i.e. 3-days) soil moisture profile estimates from microwave brightness temperature observations. In contrast with microwave-based satellite missions that are only sensitive to near-surface soil moisture (0-5 cm), the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage column but, it is characterized by low spatial (i.e. 150,000 km2) and temporal (i.e. monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). This work hypothesizes that unprecedented soil water profile accuracy can be obtained through the joint assimilation of GRACE terrestrial water storage and SMOS brightness temperature observations. A particular challenge of the joint assimilation is the use of the two different types of measurements that are relevant for hydrologic processes representing different temporal and spatial scales. The performance of the joint assimilation strongly depends on the chosen assimilation methods, measurement and model error spatial structures. The optimization of the assimilation technique constitutes a fundamental step toward a multi-variate multi-resolution integrative assimilation system aiming to improve our understanding of the global terrestrial water cycle.

Towards magnetic sounding of the Earth's core by an adjoint method

NASA Astrophysics Data System (ADS)

Li, K.; Jackson, A.; Livermore, P. W.

2013-12-01

Earth's magnetic field is generated and sustained by the so called geodynamo system in the core. Measurements of the geomagnetic field taken at the surface, downwards continued through the electrically insulating mantle to the core-mantle boundary (CMB), provide important constraints on the time evolution of the velocity, magnetic field and temperature anomaly in the fluid outer core. The aim of any study in data assimilation applied to the Earth's core is to produce a time-dependent model consistent with these observations [1]. Snapshots of these ``tuned" models provide a window through which the inner workings of the Earth's core, usually hidden from view, can be probed. We apply a variational data assimilation framework to an inertia-free magnetohydrodynamic system (MHD) [2]. Such a model is close to magnetostrophic balance [3], to which we have added viscosity to the dominant forces of Coriolis, pressure, Lorentz and buoyancy, believed to be a good approximation of the Earth's dynamo in the convective time scale. We chose to study the MHD system driven by a static temperature anomaly to mimic the actual inner working of Earth's dynamo system, avoiding at this stage the further complication of solving for the time dependent temperature field. At the heart of the models is a time-dependent magnetic field to which the core-flow is enslaved. In previous work we laid the foundation of the adjoint methodology, applied to a subset of the full equations [4]. As an intermediate step towards our ultimate vision of applying the techniques to a fully dynamic mode of the Earth's core tuned to geomagnetic observations, we present the intermediate step of applying the adjoint technique to the inertia-free Navier-Stokes equation in continuous form. We use synthetic observations derived from evolving a geophysically-reasonable magnetic field profile as the initial condition of our MHD system. Based on our study, we also propose several different strategies for accurately determining the entire trajectory of Earth's geodynamo system. [1] A. Fournier, G. Hulot, D. Jault, W. Kuang, A. Tangborn, N. Gillet, E. Canet, J. Aubert, and F. Lhuillier. An introduction to data assimilation and predictability in geomagnetism. Space. Sci. Rev., 155:247-291, 2010. [2] G. A. Glatzmaier and P. H. Roberts. A three-dimensional convective dynamo solution with rotating and finitely conducting inner core and mantle. Phys. Earth Planet. Inter., 91:63-75, 1995. [3] J. B. Taylor. The magneto-hydrodynamics of a rotating fluid and the earth's dynamo problem. Proc. R. Soc. Lond. A, 274(1357):274-283, 1963. [4] K. Li, A. Jackson, and P. W. Livermore. Variational data assimilation for the initial value dynamo problem. Phys. Rev. E, 84:056321, 2011.

Processing of Phonological Variation in Children with Hearing Loss: Compensation for English Place Assimilation in Connected Speech

ERIC Educational Resources Information Center

Skoruppa, Katrin; Rosen, Stuart

2014-01-01

Purpose: In this study, the authors explored phonological processing in connected speech in children with hearing loss. Specifically, the authors investigated these children's sensitivity to English place assimilation, by which alveolar consonants like t and n can adapt to following sounds (e.g., the word ten can be realized as tem in the…

Assessing the value of variational assimilation of streamflow data into distributed hydrologic models for improved streamflow monitoring and prediction at ungauged and gauged locations in the catchment

NASA Astrophysics Data System (ADS)

Lee, Hak Su; Seo, Dong-Jun; Liu, Yuqiong; McKee, Paul; Corby, Robert

2010-05-01

State updating of distributed hydrologic models via assimilation of streamflow data is subject to "overfitting" because large dimensionality of the state space of the model may render the assimilation problem seriously underdetermined. To examine the issue in the context of operational hydrology, we carried out a set of real-world experiments in which we assimilate streamflow data at interior and/or outlet locations into gridded SAC and kinematic-wave routing models of the U.S. National Weather Service (NWS) Research Distributed Hydrologic Model (RDHM). We used for the experiments nine basins in the southern plains of the U.S. The experiments consist of selectively assimilating streamflow at different gauge locations, outlet and/or interior, and carrying out both dependent and independent validation. To assess the sensitivity of the quality of assimilation-aided streamflow simulation to the reduced dimensionality of the state space, we carried out data assimilation at spatially semi-distributed or lumped scale and by adjusting biases in precipitation and potential evaporation at a 6-hourly or larger scale. In this talk, we present the results and findings.

Preconditioning of the background error covariance matrix in data assimilation for the Caspian Sea

NASA Astrophysics Data System (ADS)

Arcucci, Rossella; D'Amore, Luisa; Toumi, Ralf

2017-06-01

Data Assimilation (DA) is an uncertainty quantification technique used for improving numerical forecasted results by incorporating observed data into prediction models. As a crucial point into DA models is the ill conditioning of the covariance matrices involved, it is mandatory to introduce, in a DA software, preconditioning methods. Here we present first studies concerning the introduction of two different preconditioning methods in a DA software we are developing (we named S3DVAR) which implements a Scalable Three Dimensional Variational Data Assimilation model for assimilating sea surface temperature (SST) values collected into the Caspian Sea by using the Regional Ocean Modeling System (ROMS) with observations provided by the Group of High resolution sea surface temperature (GHRSST). We also present the algorithmic strategies we employ.

Toward the S3DVAR data assimilation software for the Caspian Sea

NASA Astrophysics Data System (ADS)

Arcucci, Rossella; Celestino, Simone; Toumi, Ralf; Laccetti, Giuliano

2017-07-01

Data Assimilation (DA) is an uncertainty quantification technique used to incorporate observed data into a prediction model in order to improve numerical forecasted results. The forecasting model used for producing oceanographic prediction into the Caspian Sea is the Regional Ocean Modeling System (ROMS). Here we propose the computational issues we are facing in a DA software we are developing (we named S3DVAR) which implements a Scalable Three Dimensional Variational Data Assimilation model for assimilating sea surface temperature (SST) values collected into the Caspian Sea with observations provided by the Group of High resolution sea surface temperature (GHRSST). We present the algorithmic strategies we employ and the numerical issues on data collected in two of the months which present the most significant variability in water temperature: August and March.

Investigating the role of background and observation error correlations in improving a model forecast of forest carbon balance using four dimensional variational data assimilation.

NASA Astrophysics Data System (ADS)

Pinnington, Ewan; Casella, Eric; Dance, Sarah; Lawless, Amos; Morison, James; Nichols, Nancy; Wilkinson, Matthew; Quaife, Tristan

2016-04-01

Forest ecosystems play an important role in sequestering human emitted carbon-dioxide from the atmosphere and therefore greatly reduce the effect of anthropogenic induced climate change. For that reason understanding their response to climate change is of great importance. Efforts to implement variational data assimilation routines with functional ecology models and land surface models have been limited, with sequential and Markov chain Monte Carlo data assimilation methods being prevalent. When data assimilation has been used with models of carbon balance, background "prior" errors and observation errors have largely been treated as independent and uncorrelated. Correlations between background errors have long been known to be a key aspect of data assimilation in numerical weather prediction. More recently, it has been shown that accounting for correlated observation errors in the assimilation algorithm can considerably improve data assimilation results and forecasts. In this paper we implement a 4D-Var scheme with a simple model of forest carbon balance, for joint parameter and state estimation and assimilate daily observations of Net Ecosystem CO2 Exchange (NEE) taken at the Alice Holt forest CO2 flux site in Hampshire, UK. We then investigate the effect of specifying correlations between parameter and state variables in background error statistics and the effect of specifying correlations in time between observation error statistics. The idea of including these correlations in time is new and has not been previously explored in carbon balance model data assimilation. In data assimilation, background and observation error statistics are often described by the background error covariance matrix and the observation error covariance matrix. We outline novel methods for creating correlated versions of these matrices, using a set of previously postulated dynamical constraints to include correlations in the background error statistics and a Gaussian correlation function to include time correlations in the observation error statistics. The methods used in this paper will allow the inclusion of time correlations between many different observation types in the assimilation algorithm, meaning that previously neglected information can be accounted for. In our experiments we compared the results using our new correlated background and observation error covariance matrices and those using diagonal covariance matrices. We found that using the new correlated matrices reduced the root mean square error in the 14 year forecast of daily NEE by 44 % decreasing from 4.22 g C m-2 day-1 to 2.38 g C m-2 day-1.

Assimilation by Lunar Mare Basalts: Melting of Crustal Material and Dissolution of Anorthite

NASA Technical Reports Server (NTRS)

Finnila, A. B.; Hess, P. C.; Rutherford, M. J.

1994-01-01

We discuss techniques for calculating the amount of crustal assimilation possible in lunar magma chambers and dikes based on thermal energy balances, kinetic rates, and simple fluid mechanical constraints. Assuming parent magmas of picritic compositions, we demonstrate the limits on the capacity of such magmas to melt and dissolve wall rock of anorthitic, troctolitic, noritic, and KREEP (quartz monzodiorite) compositions. Significant melting of the plagioclase-rich crustal lithologies requires turbulent convection in the assimilating magma and an efficient method of mixing in the relatively buoyant and viscous new melt. Even when this occurs, the major element chemistry of the picritic magmas will change by less than 1-2 wt %. Diffusion coefficients measured for Al2O3 from an iron-free basalt and an orange glass composition are 10(exp -12) m(exp 2) s(exp -1) at 1340 C and 10(exp -11) m(exp 2) s(exp -1) at 1390 C. These rates are too slow to allow dissolution of plagioclase to significantly affect magma compositions. Picritic magmas can melt significant quantities of KREEP, which suggests that their trace element chemistry may still be affected by assimilation processes; however, mixing viscous melts of KREEP composition with the fluid picritic magmas could be prohibitively difficult. We conclude that only a small part of the total major element chemical variation in the mare basalt and volcanic glass collection is due to assimilation/fractional crystallization processes near the lunar surface. Instead, most of the chemical variation in the lunar basalts and volcanic glasses must result from assimilation at deeper levels or from having distinct source regions in a heterogeneous lunar mantle.

Prospect of Using Numerical Dynamo Model for Prediction of Geomagnetic Secular Variation

NASA Technical Reports Server (NTRS)

Kuang, Weijia; Tangborn, Andrew

2003-01-01

Modeling of the Earth's core has reached a level of maturity to where the incorporation of observations into the simulations through data assimilation has become feasible. Data assimilation is a method by which observations of a system are combined with a model output (or forecast) to obtain a best guess of the state of the system, called the analysis. The analysis is then used as an initial condition for the next forecast. By doing assimilation, not only we shall be able to predict partially secular variation of the core field, we could also use observations to further our understanding of dynamical states in the Earth's core. One of the first steps in the development of an assimilation system is a comparison between the observations and the model solution. The highly turbulent nature of core dynamics, along with the absence of any regular external forcing and constraint (which occurs in atmospheric dynamics, for example) means that short time comparisons (approx. 1000 years) cannot be made between model and observations. In order to make sensible comparisons, a direct insertion assimilation method has been implemented. In this approach, magnetic field observations at the Earth's surface have been substituted into the numerical model, such that the ratio of the multiple components and the dipole component from observation is adjusted at the core-mantle boundary and extended to the interior of the core, while the total magnetic energy remains unchanged. This adjusted magnetic field is then used as the initial field for a new simulation. In this way, a time tugged simulation is created which can then be compared directly with observations. We present numerical solutions with and without data insertion and discuss their implications for the development of a more rigorous assimilation system.

River discharge estimation from synthetic SWOT-type observations using variational data assimilation and the full Saint-Venant hydraulic model

NASA Astrophysics Data System (ADS)

Oubanas, Hind; Gejadze, Igor; Malaterre, Pierre-Olivier; Mercier, Franck

2018-04-01

The upcoming Surface Water and Ocean Topography satellite mission, to be launched in 2021, will measure river water surface elevation, slope and width, with an unprecedented level of accuracy for a remote sensing tool. This work investigates the river discharge estimation from synthetic SWOT observations, in the presence of strong uncertainties in the model inputs, i.e. the river bathymetry and bed roughness. The estimation problem is solved by a novel variant of the standard variational data assimilation, the '4D-Var' method, involving the full Saint-Venant 1.5D-network hydraulic model SIC2. The assimilation scheme simultaneously estimates the discharge, bed elevation and bed roughness coefficient and is designed to assimilate both satellite and in situ measurements. The method is tested on a 50 km-long reach of the Garonne River during a five-month period of the year 2010, characterized by multiple flooding events. First, the impact of the sampling frequency on discharge estimation is investigated. Secondly, discharge as well as the spatially distributed bed elevation and bed roughness coefficient are determined simultaneously. Results demonstrate feasibility and efficiency of the chosen combination of the estimation method and of the hydraulic model. Assimilation of the SWOT data results into an accurate estimation of the discharge at observation times, and a local improvement in the bed level and bed roughness coefficient. However, the latter estimates are not generally usable for different independent experiments.

A Three-Dimensional Variational Data Assimilation Scheme for the Regional Ocean Modeling System: Implementation and Basic Experiments

NASA Technical Reports Server (NTRS)

Li, Zhijin; Chao, Yi; McWilliams, James C.; Ide, Kayo

2008-01-01

A three-dimensional variational data assimilation scheme for the Regional Ocean Modeling System (ROMS), named ROMS3DVAR, has been described in the work of Li et al. (2008). In this paper, ROMS3DVAR is applied to the central California coastal region, an area characterized by inhomogeneity and anisotropy, as well as by dynamically unbalanced flows. A method for estimating the model error variances from limited observations is presented, and the construction of the inhomogeneous and anisotropic error correlations based on the Kronecker product is demonstrated. A set of single observation experiments illustrates the inhomogeneous and anisotropic error correlations and weak dynamic constraints used. Results are presented from the assimilation of data gathered during the Autonomous Ocean Sampling Network (AOSN) experiment during August 2003. The results show that ROMS3DVAR is capable of reproducing complex flows associated with upwelling and relaxation, as well as the rapid transitions between them. Some difficulties encountered during the experiment are also discussed.

Continuous data assimilation experiments with the NMC eta model: A GALE IOP 1 case study. [NMC (National Meteorological Center); GALE IOP (Genesis of Atlantic Lows Experiment intensive observing period)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramamurthy, M.K.; Xu, T.Y.

1993-11-01

The current major expansion in observational capability of the National Weather Service is principally in the volume of asynchronous data rather than synchronous observations at the standard synoptic times. Hence, the National Meteorological Center is considering a continuous data assimilation system to replace at some time the intermittent system now used by its regional and global operational models. We describe this system, based on the Newtonian relaxation technique, as developed for the eta model. Experiments are performed for the first intensive observing period of the Genesis of Atlantic Lows Experiment (GALE) in January 1986, when strong upper-level cyclogenesis occurred, withmore » a pronounced tropopause fold but only modest surface development. The GALE level IIIb dataset was used for initializing and updating the model. Issues addressed in the experiments include choice of update variable, number, and length of update segments; need for updating moisture and surface pressure information; nudging along boundaries; and noise control. Assimilation of data from a single level was also studied. Use of a preforecast assimilation cycle was found to eliminate the spinup problem almost entirely. Multiple, shorter assimilation segments produced better forecasts than a single, longer cycle. Updating the mass field was less effective than nudging the wind field but assimilating both was best. Assimilation of moisture data, surprisingly, affected the spinup adversely, but nudging the surface pressure information reduced the spurious pillow effect. Assimilation of single-level information was ineffective unless accompanied by increased vertical coupling, obtained from a control integration. 52 refs., 19 figs., 1 tab.« less

Impact of variational assimilation using multivariate background error covariances on the simulation of monsoon depressions over India

NASA Astrophysics Data System (ADS)

Dhanya, M.; Chandrasekar, A.

2016-02-01

The background error covariance structure influences a variational data assimilation system immensely. The simulation of a weather phenomenon like monsoon depression can hence be influenced by the background correlation information used in the analysis formulation. The Weather Research and Forecasting Model Data assimilation (WRFDA) system includes an option for formulating multivariate background correlations for its three-dimensional variational (3DVar) system (cv6 option). The impact of using such a formulation in the simulation of three monsoon depressions over India is investigated in this study. Analysis and forecast fields generated using this option are compared with those obtained using the default formulation for regional background error correlations (cv5) in WRFDA and with a base run without any assimilation. The model rainfall forecasts are compared with rainfall observations from the Tropical Rainfall Measurement Mission (TRMM) and the other model forecast fields are compared with a high-resolution analysis as well as with European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis. The results of the study indicate that inclusion of additional correlation information in background error statistics has a moderate impact on the vertical profiles of relative humidity, moisture convergence, horizontal divergence and the temperature structure at the depression centre at the analysis time of the cv5/cv6 sensitivity experiments. Moderate improvements are seen in two of the three depressions investigated in this study. An improved thermodynamic and moisture structure at the initial time is expected to provide for improved rainfall simulation. The results of the study indicate that the skill scores of accumulated rainfall are somewhat better for the cv6 option as compared to the cv5 option for at least two of the three depression cases studied, especially at the higher threshold levels. Considering the importance of utilising improved flow-dependent correlation structures for efficient data assimilation, the need for more studies on the impact of background error covariances is obvious.

Strategies to reduce the complexity of hydrologic data assimilation for high-dimensional models

NASA Astrophysics Data System (ADS)

Hernandez, F.; Liang, X.

2017-12-01

Probabilistic forecasts in the geosciences offer invaluable information by allowing to estimate the uncertainty of predicted conditions (including threats like floods and droughts). However, while forecast systems based on modern data assimilation algorithms are capable of producing multi-variate probability distributions of future conditions, the computational resources required to fully characterize the dependencies between the model's state variables render their applicability impractical for high-resolution cases. This occurs because of the quadratic space complexity of storing the covariance matrices that encode these dependencies and the cubic time complexity of performing inference operations with them. In this work we introduce two complementary strategies to reduce the size of the covariance matrices that are at the heart of Bayesian assimilation methods—like some variants of (ensemble) Kalman filters and of particle filters—and variational methods. The first strategy involves the optimized grouping of state variables by clustering individual cells of the model into "super-cells." A dynamic fuzzy clustering approach is used to take into account the states (e.g., soil moisture) and forcings (e.g., precipitation) of each cell at each time step. The second strategy consists in finding a compressed representation of the covariance matrix that still encodes the most relevant information but that can be more efficiently stored and processed. A learning and a belief-propagation inference algorithm are developed to take advantage of this modified low-rank representation. The two proposed strategies are incorporated into OPTIMISTS, a state-of-the-art hybrid Bayesian/variational data assimilation algorithm, and comparative streamflow forecasting tests are performed using two watersheds modeled with the Distributed Hydrology Soil Vegetation Model (DHSVM). Contrasts are made between the efficiency gains and forecast accuracy losses of each strategy used in isolation, and of those achieved through their coupling. We expect these developments to help catalyze improvements in the predictive accuracy of large-scale forecasting operations by lowering the costs of deploying advanced data assimilation techniques.

Continued Development and Validation of the USU GAIM Models

DTIC Science & Technology

2010-08-01

Markov data assimilation model (GAIM-GM) uses a physics-based model of the ionosphere ( IFM ) and a Kalman filter as a basis for assimilating a diverse... a data assimilation model of the ionosphere that is based on the Ionosphere Forecast Model ( IFM ) (Schunk, 1988; Sojka, 1989; Schunk et al., 1997...Monograph 181, (ed. R M . Kitner. A . J. Coster, T. Fuller-Rowell, A J. Mannucci, M . Mendill, and R. Heelis), pp. 35-49, AGU. Washington, DC, 2008

Combining Ensemble and Variational Data Assimilation

DTIC Science & Technology

2013-09-30

the river plume, simulating the effect of more turbid waters within the plume). Analysis of adjoint sensitivity fields and representer functions...that the are many assimilated temperature and salinity profiles to the north of the Hawaiian Islands, but very few to the south and west .] 5...Great Barrier Reef, the Great Australian Bight, parts of the north– west shelf, and the Gulf of Carpentaria. The assessment of IMOS performed by Oke

Continuous data assimilation for downscaling large-footprint soil moisture retrievals

NASA Astrophysics Data System (ADS)

Altaf, Muhammad U.; Jana, Raghavendra B.; Hoteit, Ibrahim; McCabe, Matthew F.

2016-10-01

Soil moisture is a key component of the hydrologic cycle, influencing processes leading to runoff generation, infiltration and groundwater recharge, evaporation and transpiration. Generally, the measurement scale for soil moisture is found to be different from the modeling scales for these processes. Reducing this mismatch between observation and model scales in necessary for improved hydrological modeling. An innovative approach to downscaling coarse resolution soil moisture data by combining continuous data assimilation and physically based modeling is presented. In this approach, we exploit the features of Continuous Data Assimilation (CDA) which was initially designed for general dissipative dynamical systems and later tested numerically on the incompressible Navier-Stokes equation, and the Benard equation. A nudging term, estimated as the misfit between interpolants of the assimilated coarse grid measurements and the fine grid model solution, is added to the model equations to constrain the model's large scale variability by available measurements. Soil moisture fields generated at a fine resolution by a physically-based vadose zone model (HYDRUS) are subjected to data assimilation conditioned upon coarse resolution observations. This enables nudging of the model outputs towards values that honor the coarse resolution dynamics while still being generated at the fine scale. Results show that the approach is feasible to generate fine scale soil moisture fields across large extents, based on coarse scale observations. Application of this approach is likely in generating fine and intermediate resolution soil moisture fields conditioned on the radiometerbased, coarse resolution products from remote sensing satellites.

Variational estimation of process parameters in a simplified atmospheric general circulation model

NASA Astrophysics Data System (ADS)

Lv, Guokun; Koehl, Armin; Stammer, Detlef

2016-04-01

Parameterizations are used to simulate effects of unresolved sub-grid-scale processes in current state-of-the-art climate model. The values of the process parameters, which determine the model's climatology, are usually manually adjusted to reduce the difference of model mean state to the observed climatology. This process requires detailed knowledge of the model and its parameterizations. In this work, a variational method was used to estimate process parameters in the Planet Simulator (PlaSim). The adjoint code was generated using automatic differentiation of the source code. Some hydrological processes were switched off to remove the influence of zero-order discontinuities. In addition, the nonlinearity of the model limits the feasible assimilation window to about 1day, which is too short to tune the model's climatology. To extend the feasible assimilation window, nudging terms for all state variables were added to the model's equations, which essentially suppress all unstable directions. In identical twin experiments, we found that the feasible assimilation window could be extended to over 1-year and accurate parameters could be retrieved. Although the nudging terms transform to a damping of the adjoint variables and therefore tend to erases the information of the data over time, assimilating climatological information is shown to provide sufficient information on the parameters. Moreover, the mechanism of this regularization is discussed.

Challenges in Ocean Data Assimilation for the US West Coast

NASA Astrophysics Data System (ADS)

Li, Z.; Chao, Y.; Farrara, J.; Wang, X.

2006-12-01

A three-dimensional variational data assimilation (3DVAR) system has been developed for the Regional Ocean Modeling System (ROMS), and it is called ROMS-DAS. This system provides a capability of predicting meso- to small-scale variations with temporal scales from hours to days in the coastal oceans. To cope with the particular difficulties that result from complex coastlines and bottom topography, unbalanced flows and sparse observations, ROMS-DAS utilizes several novel strategies. These strategies include the implementation of three-dimensional anisotropic and inhomogeneous error correlations, application of particular weak dynamic constraints, and implementation of efficient and reliable algorithms for minimizing the cost function. The ROMS-DAS system was applied in field experiments for Monterey Bay during both 2003 (Autonomous Ocean Sampling Network - AOSN) and 2006 (MB06). These two experiments included intensive data collection from a variety of observational platforms, including satellites, airplanes, High Frequency radars, Acoustic Doppler Current Profilers, ships, drifters, buoys, autonomous underwater vehicles (AUV), and particularly a fleet of undersea gliders. Using these data sets, various data assimilation experiments were performed to address several major data assimilation challenges that arise from multi-scales structures, inhomogeneous properties, dynamical imbalance of the flow, and tides. Basing on these experiments, a set of strategies were formulated to deal with those challenges.

The Soil-Plant-Atmosphere Continuum of Mangroves: A Simple Ecohydrological model

NASA Astrophysics Data System (ADS)

Perri, Saverio; Viola, Francesco; Valerio Noto, Leonardo; Molini, Annalisa

2016-04-01

Mangroves represent the only forest able to grow at the interface between a terrestrial and a marine habitat. Although globally they have been estimated to account only for 1% of carbon sequestration from forests, as coastal ecosystems they account for about 14% of carbon sequestration by the global ocean. Despite the continuously increasing number of hydrological and ecological field observations, the ecohydrology of mangroves remains largely understudied. Modeling mangrove response to variations in environmental conditions needs to take into account the effect of waterlogging and salinity on transpiration and CO2 assimilation. However, similar ecohydrological models for halophytes are not yet documented in the literature. In this contribution we adapt a Soil-Plant-Atmosphere Continuum (SPAC) model to the mangrove ecosystems. Such SPAC model is based on a macroscopic approach and the transpiration rate is hence obtained by solving the plant and leaf water balance and the leaf energy balance, taking explicitly into account the role of osmotic water potential and salinity in governing plant resistance to water fluxes. Exploiting the well-known coupling of transpiration and CO2 exchange through the stomatal conductance, we also estimate the CO2 assimilation rate. The SPAC is hence tested against experimental data obtained from the literature, showing the reliability and effectiveness of this minimalist approach in reproducing observed processes. Results show that the developed SPAC model is able to realistically simulate the main ecohydrological traits of mangroves, indicating the salinity as a crucial limiting factor for mangrove trees transpiration and CO2 assimilation.

Effects of sounding temperature assimilation on weather forecasting - Model dependence studies

NASA Technical Reports Server (NTRS)

Ghil, M.; Halem, M.; Atlas, R.

1979-01-01

In comparing various methods for the assimilation of remote sounding information into numerical weather prediction (NWP) models, the problem of model dependence for the different results obtained becomes important. The paper investigates two aspects of the model dependence question: (1) the effect of increasing horizontal resolution within a given model on the assimilation of sounding data, and (2) the effect of using two entirely different models with the same assimilation method and sounding data. Tentative conclusions reached are: first, that model improvement as exemplified by increased resolution, can act in the same direction as judicious 4-D assimilation of remote sounding information, to improve 2-3 day numerical weather forecasts. Second, that the time continuous 4-D methods developed at GLAS have similar beneficial effects when used in the assimilation of remote sounding information into NWP models with very different numerical and physical characteristics.

Diurnal variation in the functioning of cowpea nodules.

PubMed

Rainbird, R M; Atkins, C A; Pate, J S

1983-06-01

Nitrogenase (EC 1.7.99.2) activity of nodules of cowpea (Vigna unguiculata [L.] Walp), maintained under conditions of a 12-hour day at 30 degrees C and 800 to 1,000 microeinsteins per square meter per second (photosynthetically active radiation) and a 12-hour night at 20 degrees C, showed a marked diurnal variation with the total electron flux through the enzyme at night being 60% of that in the photoperiod. This diurnal pattern was, however, due to changes in hydrogen evolution. The rate of nitrogen fixation, measured by short-term (15)N(2) assimilation or estimated from the difference in hydrogen evolution in air or Ar:O(2) (80:20; v/v), showed no diurnal variation. Carbon dioxide released from nodules showed a diurnal variation synchronized with that of nitrogenase functioning and, as a consequence, the apparent ;respiratory cost' of nitrogen fixation in the photoperiod was almost double that at night (9.74 +/- 0.38 versus 5.70 +/- 0.90 moles CO(2) evolved per mole N(2) fixed). Separate carbon and nitrogen balances constructed for nodules during the photoperiod and dark period showed that, at night, nodule functioning required up to 40% less carbohydrate to achieve the same level of nitrogen fixation as during the photoperiod (2.4 versus 1.4 moles hexose per mole N(2) fixed).Stored reserves of nonstructural carbohydrate of the nodule only partly satisfied the requirement for carbon at night, and fixation was dependent on continued import of translocated assimilates at all times. Measurements of the soluble nitrogen pools of the nodule together with (15)N studies indicated that, both during the day and night, nitrogenous products of fixation were effectively translocated to all organs of the host plant despite low rates of transpiration at night. Reduced fluxes of water through the plant at night were apparently counteracted by increased concentration of nitrogen, especially as ureides, in the xylem stream.

The compatible balancing approach to initialization, and four-dimensional data assimilation. [in meteorology

NASA Technical Reports Server (NTRS)

Ghil, M.

1980-01-01

A unified theoretical approach to both the four-dimensional assimilation of asynoptic data and the initialization problem is attempted. This approach relies on the derivation of certain relationships between geopotential tendencies and tendencies of the horizontal velocity field in primitive-equation models of atmospheric flow. The approach is worked out and analyzed in detail for some simple barotropic models. Certain independent results of numerical experiments for the time-continuous assimilation of real asynoptic meteorological data into a complex, baroclinic weather prediction model are discussed in the context of the present approach. Tentative inferences are drawn for practical assimilation procedures.

Evaluation of radar and automatic weather station data assimilation for a heavy rainfall event in southern China

NASA Astrophysics Data System (ADS)

Hou, Tuanjie; Kong, Fanyou; Chen, Xunlai; Lei, Hengchi; Hu, Zhaoxia

2015-07-01

To improve the accuracy of short-term (0-12 h) forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System (HAPS), has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting (WRF-ARW) model and the Advanced Regional Prediction System (ARPS) three-dimensional variational data assimilation (3DVAR) package. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station (AWS) data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting (QPF) by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to 9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6-9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score (FSS) and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.

Assimilative model for ionospheric dynamics employing delay, Doppler, and direction of arrival measurements from multiple HF channels

NASA Astrophysics Data System (ADS)

Fridman, Sergey V.; Nickisch, L. J.; Hausman, Mark; Zunich, George

2016-03-01

We describe the development of new HF data assimilation capabilities for our ionospheric inversion algorithm called GPSII (GPS Ionospheric Inversion). Previously existing capabilities of this algorithm included assimilation of GPS total electron content data as well as assimilation of backscatter ionograms. In the present effort we concentrated on developing assimilation tools for data related to HF propagation channels. Measurements of propagation delay, angle of arrival, and the ionosphere-induced Doppler from any number of known propagation links can now be utilized by GPSII. The resulting ionospheric model is consistent with all assimilated measurements. This means that ray tracing simulations of the assimilated propagation links are guaranteed to be in agreement with measured data within the errors of measurement. The key theoretical element for assimilating HF data is the raypath response operator (RPRO) which describes response of raypath parameters to infinitesimal variations of electron density in the ionosphere. We construct the RPRO out of the fundamental solution of linearized ray tracing equations for a dynamic magnetoactive plasma. We demonstrate performance and internal consistency of the algorithm using propagation delay data from multiple oblique ionograms (courtesy of Defence Science and Technology Organisation, Australia) as well as with time series of near-vertical incidence sky wave data (courtesy of the Intelligence Advanced Research Projects Activity HFGeo Program Government team). In all cases GPSII produces electron density distributions which are smooth in space and in time. We simulate the assimilated propagation links by performing ray tracing through GPSII-produced ionosphere and observe that simulated data are indeed in agreement with assimilated measurements.

Implicit assimilation for marine ecological models

NASA Astrophysics Data System (ADS)

Weir, B.; Miller, R.; Spitz, Y. H.

2012-12-01

We use a new data assimilation method to estimate the parameters of a marine ecological model. At a given point in the ocean, the estimated values of the parameters determine the behaviors of the modeled planktonic groups, and thus indicate which species are dominant. To begin, we assimilate in situ observations, e.g., the Bermuda Atlantic Time-series Study, the Hawaii Ocean Time-series, and Ocean Weather Station Papa. From there, we estimate the parameters at surrounding points in space based on satellite observations of ocean color. Given the variation of the estimated parameters, we divide the ocean into regions meant to represent distinct ecosystems. An important feature of the data assimilation approach is that it refines the confidence limits of the optimal Gaussian approximation to the distribution of the parameters. This enables us to determine the ecological divisions with greater accuracy.

Language experience and consonantal context effects on perceptual assimilation of French vowels by American-English learners of French1

PubMed Central

Levy, Erika S.

2009-01-01

Recent research has called for an examination of perceptual assimilation patterns in second-language speech learning. This study examined the effects of language learning and consonantal context on perceptual assimilation of Parisian French (PF) front rounded vowels ∕y∕ and ∕œ∕ by American English (AE) learners of French. AE listeners differing in their French language experience (no experience, formal instruction, formal-plus-immersion experience) performed an assimilation task involving PF ∕y, œ, u, o, i, ε, a∕ in bilabial ∕rabVp∕ and alveolar ∕radVt∕ contexts, presented in phrases. PF front rounded vowels were assimilated overwhelmingly to back AE vowels. For PF ∕œ∕, assimilation patterns differed as a function of language experience and consonantal context. However, PF ∕y∕ revealed no experience effect in alveolar context. In bilabial context, listeners with extensive experience assimilated PF ∕y∕ to ∕ju∕ less often than listeners with no or only formal experience, a pattern predicting the poorest ∕u-y∕ discrimination for the most experienced group. An “internal consistency” analysis indicated that responses were most consistent with extensive language experience and in bilabial context. Acoustical analysis revealed that acoustical similarities among PF vowels alone cannot explain context-specific assimilation patterns. Instead it is suggested that native-language allophonic variation influences context-specific perceptual patterns in second-language learning. PMID:19206888

An algorithm for variational data assimilation of contact concentration measurements for atmospheric chemistry models

NASA Astrophysics Data System (ADS)

Penenko, Alexey; Penenko, Vladimir

2014-05-01

Contact concentration measurement data assimilation problem is considered for convection-diffusion-reaction models originating from the atmospheric chemistry study. High dimensionality of models imposes strict requirements on the computational efficiency of the algorithms. Data assimilation is carried out within the variation approach on a single time step of the approximated model. A control function is introduced into the source term of the model to provide flexibility for data assimilation. This function is evaluated as the minimum of the target functional that connects its norm to a misfit between measured and model-simulated data. In the case mathematical model acts as a natural Tikhonov regularizer for the ill-posed measurement data inversion problem. This provides flow-dependent and physically-plausible structure of the resulting analysis and reduces a need to calculate model error covariance matrices that are sought within conventional approach to data assimilation. The advantage comes at the cost of the adjoint problem solution. This issue is solved within the frameworks of splitting-based realization of the basic convection-diffusion-reaction model. The model is split with respect to physical processes and spatial variables. A contact measurement data is assimilated on each one-dimensional convection-diffusion splitting stage. In this case a computationally-efficient direct scheme for both direct and adjoint problem solution can be constructed based on the matrix sweep method. Data assimilation (or regularization) parameter that regulates ratio between model and data in the resulting analysis is obtained with Morozov discrepancy principle. For the proper performance the algorithm takes measurement noise estimation. In the case of Gaussian errors the probability that the used Chi-squared-based estimate is the upper one acts as the assimilation parameter. A solution obtained can be used as the initial guess for data assimilation algorithms that assimilate outside the splitting stages and involve iterations. Splitting method stage that is responsible for chemical transformation processes is realized with the explicit discrete-analytical scheme with respect to time. The scheme is based on analytical extraction of the exponential terms from the solution. This provides unconditional positive sign for the evaluated concentrations. Splitting-based structure of the algorithm provides means for efficient parallel realization. The work is partially supported by the Programs No 4 of Presidium RAS and No 3 of Mathematical Department of RAS, by RFBR project 11-01-00187 and Integrating projects of SD RAS No 8 and 35. Our studies are in the line with the goals of COST Action ES1004.

Assimilation, Ethnic Competition, and Ethnic Identities of U.S.-Born Persons of Mexican Origin.

ERIC Educational Resources Information Center

Ono, Hiromi

2002-01-01

Explores processes governing the ethnic identification of second and later generations of Mexican immigrant descendants using the Latino National Political Survey. Ethnic identification arises directly from cultural continuity and lower levels of assimilation, experiences with ethnic competition, and a combination of both processes. Experiences…

Variational Assimilation of Sparse and Uncertain Satellite Data For 1D Saint-Venant River Models

NASA Astrophysics Data System (ADS)

Garambois, P. A.; Brisset, P.; Monnier, J.; Roux, H.

2016-12-01

Profusion of satellites are providing increasingly accurate measurements of continental water cyle, and water bodies variations while in situ observability is declining. The future Surface Water and Ocean Topography (SWOT) mission will provide maps of river surface elevations widths and slopes with an almost global coverage and temporal revisits. This will offer the possibility to address a larger variety of inverse problems in surface hydrology. Data assimilation techniques, that are broadly used in several scientific fields, aim to optimally combine models, system observations and prior information. Variational assimilation consists in iterative minimization of a discrepency measure between model outputs and observations, here for retrieving boundary conditions and parameters of a 1D Saint Venant model. Nevertheless, inferring river discharge and hydraulic parameters thanks to the observation of river surface is not straightforward. This is particularly true in the case of sparse and uncertain observations of flow state variables since they are governed by nonlinear physical processes. This paper investigates the identifiability of hydraulic controls given sparse and uncertain satellite observations of a river. The identifiability of river discharge alone and with roughness is tested for several spatio temporal patterns of river observations, including SWOT like observations. A new 1D Shallow water model with variational data assimilation, within the DassFlow chain is presented as well as postprocessing and observation operator dedicated to the future SWOT and SWOT simulator data. In view to decrease inverse problem dimensionality discharge is represented in a reduced basis. Moreover we introduce an original and reduced parametrization of the flow resistance that can account for various flow regimes along with a cross section design dedicated to remote sensing. We show which discharge temporal frequencies can be identified w.r.t observation ones and at which accuracy. Eventually the important question of the discharge identifiability potential between observation times and depending on the spatio-temporal sampling is adressed with respect to the wave lengths of the hydrological signals.

Data Analysis, Modeling, and Ensemble Forecasting to Support NOWCAST and Forecast Activities at the Fallon Naval Station

DTIC Science & Technology

2011-09-30

forecasting and use of satellite data assimilation for model evaluation (Jiang et al, 2011a). He is a task leader on another NSF EPSCoR project...K. Horvath, R. Belu, 2011a: Application of variational data assimilation to dynamical downscaling of regional wind energy resources in the western...1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Data Analysis, Modeling, and Ensemble Forecasting to

Estimation of Turbulent Heat Fluxes by Assimilation of Land Surface Temperature Observations From GOES Satellites Into an Ensemble Kalman Smoother Framework

NASA Astrophysics Data System (ADS)

Xu, Tongren; Bateni, S. M.; Neale, C. M. U.; Auligne, T.; Liu, Shaomin

2018-03-01

In different studies, land surface temperature (LST) observations have been assimilated into the variational data assimilation (VDA) approaches to estimate turbulent heat fluxes. The VDA methods yield accurate turbulent heat fluxes, but they need an adjoint model, which is difficult to derive and code. They also cannot directly calculate the uncertainty of their estimates. To overcome the abovementioned drawbacks, this study assimilates LST data from Geostationary Operational Environmental Satellite into the ensemble Kalman smoother (EnKS) data assimilation system to estimate turbulent heat fluxes. EnKS does not need to derive the adjoint term and directly generates statistical information on the accuracy of its predictions. It uses the heat diffusion equation to simulate LST. EnKS with the state augmentation approach finds the optimal values for the unknown parameters (i.e., evaporative fraction and neutral bulk heat transfer coefficient, CHN) by minimizing the misfit between LST observations from Geostationary Operational Environmental Satellite and LST estimations from the heat diffusion equation. The augmented EnKS scheme is tested over six Ameriflux sites with a wide range of hydrological and vegetative conditions. The results show that EnKS can predict not only the model parameters and turbulent heat fluxes but also their uncertainties over a variety of land surface conditions. Compared to the variational method, EnKS yields suboptimal turbulent heat fluxes. However, suboptimality of EnKS is small, and its results are comparable to those of the VDA method. Overall, EnKS is a feasible and reliable method for estimation of turbulent heat fluxes.

Determination of ocean surface heat fluxes by a variational method

NASA Astrophysics Data System (ADS)

Roquet, H.; Planton, S.; Gaspar, P.

1993-06-01

A new technique of determination of the "nonsolar" heat flux (sum of the latent, sensible, and net infrared fluxes) at the ocean surface is proposed. It applies when oceanic advection remains weak and thus relies on a one-dimensional modeling approach. It is based on a variational data assimilation scheme using the adjoint equation formalism. This allows to take advantage of all observed data with their error estimates. Results from experiments performed with station Papa (Gulf of Alaska) and Long-Term Upper Ocean Study (LOTUS, Sargasso Sea) data sets are discussed. The temperature profiles assimilation allows the one-dimensional model to reproduce correctly the temperature evolution at the surface and under the oceanic mixed layer at the two sites. The retrieved fluxes are compared to the fluxes calculated through classical empirical formulae. The diurnal dependence of the fluxes at the LOTUS site is particularly investigated. The results are also compared with those obtained using a simpler technique based on an iterative shooting method and allowing the assimilation of the only sea surface temperature. This second comparison reveals that the variability of the retrieved fluxes is damped when temperature in the inner ocean are assimilated. This is the case for the diurnal cycle at the LOTUS mooring. When the available current data at this site are assimilated, the diurnal variability of the retrieved fluxes is further decreased. This points out a model discrepancy in the representation of mixing processes associated to internal wave activity. The remaining part of the diurnal cycle is significant and could be due to a direct effect of air-sea temperature difference.

Delineating Facies Spatial Distribution by Integrating Ensemble Data Assimilation and Indicator Geostatistics with Level Set Transformation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hammond, Glenn Edward; Song, Xuehang; Ye, Ming

A new approach is developed to delineate the spatial distribution of discrete facies (geological units that have unique distributions of hydraulic, physical, and/or chemical properties) conditioned not only on direct data (measurements directly related to facies properties, e.g., grain size distribution obtained from borehole samples) but also on indirect data (observations indirectly related to facies distribution, e.g., hydraulic head and tracer concentration). Our method integrates for the first time ensemble data assimilation with traditional transition probability-based geostatistics. The concept of level set is introduced to build shape parameterization that allows transformation between discrete facies indicators and continuous random variables. Themore » spatial structure of different facies is simulated by indicator models using conditioning points selected adaptively during the iterative process of data assimilation. To evaluate the new method, a two-dimensional semi-synthetic example is designed to estimate the spatial distribution and permeability of two distinct facies from transient head data induced by pumping tests. The example demonstrates that our new method adequately captures the spatial pattern of facies distribution by imposing spatial continuity through conditioning points. The new method also reproduces the overall response in hydraulic head field with better accuracy compared to data assimilation with no constraints on spatial continuity on facies.« less

Data Assimilation of Lightning using 1D+3D/4D WRF Var Assimilation Schemes with Non-Linear Observation Operators

NASA Astrophysics Data System (ADS)

Navon, M. I.; Stefanescu, R.; Fuelberg, H. E.; Marchand, M.

2012-12-01

NASA's launch of the GOES-R Lightning Mapper (GLM) in 2015 will provide continuous, full disc, high resolution total lightning (IC + CG) data. The data will be available at a horizontal resolution of approximately 9 km. Compared to other types of data, the assimilation of lightning data into operational numerical models has received relatively little attention. Previous efforts of lightning assimilation mostly have employed nudging. This paper will describe the implementation of 1D+3D/4D Var assimilation schemes of existing ground-based WTLN (Worldwide Total Lightning Network) lightning observations using non-linear observation operators in the incremental WRFDA system. To mimic the expected output of GLM, the WTLN data were used to generate lightning super-observations characterized by flash rates/81 km2/20 min. A major difficulty associated with variational approaches is the complexity of the observation operator that defines the model equivalent of lightning. We use Convective Available Potential Energy (CAPE) as a proxy between lightning data and model variables. This operator is highly nonlinear. Marecal and Mahfouf (2003) have shown that nonlinearities can prevent direct assimilation of rainfall rates in the ECMWF 4D-VAR (using the incremental formulation proposed by Courtier et al. (1994)) from being successful. Using data from the 2011 Tuscaloosa, AL tornado outbreak, we have proved that the direct assimilation of lightning data into the WRF 3D/4D - Var systems is limited due to this incremental approach. Severe threshold limits must be imposed on the innovation vectors to obtain an improved analysis. We have implemented 1D+3D/4D Var schemes to assimilate lightning observations into the WRF model. Their use avoids innovation vector constrains from preventing the inclusion of a greater number of lightning observations Their use also minimizes the problem that nonlinearities in the moist convective scheme can introduce discontinuities in the cost function between inner and outer loops of the incremental 3-D/4-D VAR minimization. The first part of this paper will describe the methodology and performance analysis of the 1D-Var retrieval scheme that adjusts the WRF temperature profiles closer to an observed value as in Mahfouf et al. (2005). The second part will show the positive impact of these 1D-Var pseudo - temperature observations on both model 3D/4D-Var WRF analyses and short-range forecasts for three cases - the Tuscaloosa tornado outbreak (April 27, 2011) with intense but localized lightning, a second severe storm outbreak with more widespread but less intense lightning (June 27, 2011), and a northeaster containing much less lightning.

Sulfate and Pb-210 Simulated in a Global Model Using Assimilated Meteorological Fields

NASA Technical Reports Server (NTRS)

Chin, Mian; Rood, Richard; Lin, S.-J.; Jacob, Daniel; Muller, Jean-Francois

1999-01-01

This report presents the results of distributions of tropospheric sulfate, Pb-210 and their precursors from a global 3-D model. This model is driven by assimilated meteorological fields generated by the Goddard Data Assimilation Office. Model results are compared with observations from surface sites and from multiplatform field campaigns of Pacific Exploratory Missions (PEM) and Advanced Composition Explorer (ACE). The model generally captures the seasonal variation of sulfate at the surface sites, and reproduces well the short-term in-situ observations. We will discuss the roles of various processes contributing to the sulfate levels in the troposphere, and the roles of sulfate aerosol in regional and global radiative forcing.

From Low Altitude to High Altitude: Assimilating SAMPEX Data in Global Radiation Belt Models by Quantifying Precipitation and Loss

NASA Astrophysics Data System (ADS)

Tu, W.; Reeves, G. D.; Cunningham, G.; Selesnick, R. S.; Li, X.; Looper, M. D.

2012-12-01

Since its launch in 1992, SAMPEX has been continuously providing measurements of radiation belt electrons at low altitude, which are not only ideal for the direct quantification of the electron precipitation loss in the radiation belt, but also provide data coverage in a critical region for global radiation belt data assimilation models. However, quantitatively combining high-altitude and low-earth-orbit (LEO) measurements on the same L-shell is challenging because LEO measurements typically contain a dynamic mixture of trapped and precipitating populations. Specifically, the electrons measured by SAMPEX can be distinguished as trapped, quasi-trapped (in the drift loss cone), and precipitating (in the bounce loss cone). To simulate the low-altitude electron distribution observed by SAMPEX/PET, a drift-diffusion model has been developed that includes the effects of azimuthal drift and pitch angle diffusion. The simulation provides direct quantification of the rates and variations of electron loss to the atmosphere, a direct input to our Dynamic Radiation Environment Assimilation Model (DREAM) as the electron loss lifetimes. The current DREAM uses data assimilation to combine a 1D radial diffusion model with observational data of radiation belt electrons. In order to implement the mixed electron measurements from SAMPEX into DREAM, we need to map the SAMPEX data from low altitude to high altitudes. To perform the mapping, we will first examine the well-known 'global coherence' of radiation belt electrons by comparing SAMPEX electron fluxes with the energetic electron data from LANL GEO and GPS spacecraft. If the correlation is good, we can directly map the SAMPEX fluxes to high altitudes based on the global coherence; if not, we will use the derived pitch angle distribution from the drift-diffusion model to map up the field and test the mapping by comparing to the high-altitude flux measurements. Then the globally mapped electron fluxes can be assimilated into DREAM. This new implementation of SAMPEX data will greatly augment the data coverage of DREAM and contribute to the global specification of the radiation belt environment.

The Mediterranean Forecasting System: recent developments

NASA Astrophysics Data System (ADS)

Tonani, Marina; Oddo, Paolo; Korres, Gerasimos; Clementi, Emanuela; Dobricic, Srdjan; Drudi, Massimiliano; Pistoia, Jenny; Guarnieri, Antonio; Romaniello, Vito; Girardi, Giacomo; Grandi, Alessandro; Bonaduce, Antonio; Pinardi, Nadia

2014-05-01

Recent developments of the Mediterranean Monitoring and Forecasting Centre of the EU-Copernicus marine service, the Mediterranean Forecasting System (MFS), are presented. MFS provides forecast, analysis and reanalysis for the physical and biogeochemical parameters of the Mediterranean Sea. The different components of the system are continuously updated in order to provide to the users the best available product. This work is focus on the physical component of the system. The physical core of MFS is composed by an ocean general circulation model (NEMO) coupled with a spectral wave model (Wave Watch-III). The NEMO model provides to WW-III surface currents and SST fields, while WW-III returns back to NEMO the neutral component of the surface drag coefficient. Satellite Sea Level Anomaly observations and in-situ T & S vertical profiles are assimilated into this system using a variational assimilation scheme based on 3DVAR (Dobricic, 2008) . Sensitive experiments have been performed in order to assess the impact of the assimilation of the latest available SLA missions, Altika and Cryosat together with the long term available mission of Jason2. The results show a significant improvement of the MFS skill due to the multi-mission along track assimilation. The primitive equations module has been recently upgraded with the introduction of the atmospheric pressure term and a new, explicit, numerical scheme has been adopted to solve the barotropic component of the equations of motion. The SLA satellite observations for data assimilation have been consequently modified in order to account for the new atmospheric pressure term introduced in the equations. This new system has been evaluated using tide gauge coastal buoys and the satellite along track data. The quality of the SSH has improved significantly while a minor impact has been observed on the other state variables (temperature, salinity and currents). Experiments with a higher resolution NWP (numerical weather prediction) forcing provided by the COSMO-MED system (provided by the Italian Meteorological Office), have been performed and a pre-operational 3-day forecast production system has been developed. The comparison between this system and the official one forced by the ECMWF NWP data will be discussed.

A variational ensemble scheme for noisy image data assimilation

NASA Astrophysics Data System (ADS)

Yang, Yin; Robinson, Cordelia; Heitz, Dominique; Mémin, Etienne

2014-05-01

Data assimilation techniques aim at recovering a system state variables trajectory denoted as X, along time from partially observed noisy measurements of the system denoted as Y. These procedures, which couple dynamics and noisy measurements of the system, fulfill indeed a twofold objective. On one hand, they provide a denoising - or reconstruction - procedure of the data through a given model framework and on the other hand, they provide estimation procedures for unknown parameters of the dynamics. A standard variational data assimilation problem can be formulated as the minimization of the following objective function with respect to the initial discrepancy, η, from the background initial guess: δ« J(η(x)) = 1∥Xb (x) - X (t ,x)∥2 + 1 tf∥H(X (t,x ))- Y (t,x)∥2dt. 2 0 0 B 2 t0 R (1) where the observation operator H links the state variable and the measurements. The cost function can be interpreted as the log likelihood function associated to the a posteriori distribution of the state given the past history of measurements and the background. In this work, we aim at studying ensemble based optimal control strategies for data assimilation. Such formulation nicely combines the ingredients of ensemble Kalman filters and variational data assimilation (4DVar). It is also formulated as the minimization of the objective function (1), but similarly to ensemble filter, it introduces in its objective function an empirical ensemble-based background-error covariance defined as: B ≡ <(Xb - )(Xb - )T>. (2) Thus, it works in an off-line smoothing mode rather than on the fly like sequential filters. Such resulting ensemble variational data assimilation technique corresponds to a relatively new family of methods [1,2,3]. It presents two main advantages: first, it does not require anymore to construct the adjoint of the dynamics tangent linear operator, which is a considerable advantage with respect to the method's implementation, and second, it enables the handling of a flow-dependent background error covariance matrix that can be consistently adjusted to the background error. These nice advantages come however at the cost of a reduced rank modeling of the solution space. The B matrix is at most of rank N - 1 (N is the size of the ensemble) which is considerably lower than the dimension of state space. This rank deficiency may introduce spurious correlation errors, which particularly impact the quality of results associated with a high resolution computing grid. The common strategy to suppress these distant correlations for ensemble Kalman techniques is through localization procedures. In this paper we present key theoretical properties associated to different choices of methods involved in this setup and compare with an incremental 4DVar method experimentally the performances of several variations of an ensemble technique of interest. The comparisons have been led on the basis of a Shallow Water model and have been carried out both with synthetic data and real observations. We particularly addressed the potential pitfalls and advantages of the different methods. The results indicate an advantage in favor of the ensemble technique both in quality and computational cost when dealing with incomplete observations. We highlight as the premise of using ensemble variational assimilation, that the initial perturbation used to build the initial ensemble has to fit the physics of the observed phenomenon . We also apply the method to a stochastic shallow-water model which incorporate an uncertainty expression if the subgrid stress tensor related to the ensemble spread. References [1] A. C. Lorenc, The potential of the ensemble kalman filter for nwp - a comparison with 4d-var, Quart. J. Roy. Meteor. Soc., Vol. 129, pp. 3183-3203, 2003. [2] C. Liu, Q. Xiao, and B. Wang, An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test, Mon. Wea. Rev., Vol. 136(9), pp. 3363-3373, 2008. [3] M. Buehner, Ensemble-derived stationary and flow-dependent background-error covariances: Evaluation in a quasi- operational NWP setting, Quart. J. Roy. Meteor. Soc., Vol. 131(607), pp. 1013-1043, April 2005.

Numerical sensitivity analysis of a variational data assimilation procedure for cardiac conductivities

NASA Astrophysics Data System (ADS)

Barone, Alessandro; Fenton, Flavio; Veneziani, Alessandro

2017-09-01

An accurate estimation of cardiac conductivities is critical in computational electro-cardiology, yet experimental results in the literature significantly disagree on the values and ratios between longitudinal and tangential coefficients. These are known to have a strong impact on the propagation of potential particularly during defibrillation shocks. Data assimilation is a procedure for merging experimental data and numerical simulations in a rigorous way. In particular, variational data assimilation relies on the least-square minimization of the misfit between simulations and experiments, constrained by the underlying mathematical model, which in this study is represented by the classical Bidomain system, or its common simplification given by the Monodomain problem. Operating on the conductivity tensors as control variables of the minimization, we obtain a parameter estimation procedure. As the theory of this approach currently provides only an existence proof and it is not informative for practical experiments, we present here an extensive numerical simulation campaign to assess practical critical issues such as the size and the location of the measurement sites needed for in silico test cases of potential experimental and realistic settings. This will be finalized with a real validation of the variational data assimilation procedure. Results indicate the presence of lower and upper bounds for the number of sites which guarantee an accurate and minimally redundant parameter estimation, the location of sites being generally non critical for properly designed experiments. An effective combination of parameter estimation based on the Monodomain and Bidomain models is tested for the sake of computational efficiency. Parameter estimation based on the Monodomain equation potentially leads to the accurate computation of the transmembrane potential in real settings.

Evaluation of the tropical variability from the Beijing Climate Center's real-time operational global Ocean Data Assimilation System

NASA Astrophysics Data System (ADS)

Zhou, Wei; Chen, Mengyan; Zhuang, Wei; Xu, Fanghua; Zheng, Fei; Wu, Tongwen; Wang, Xin

2016-02-01

The second-generation Global Ocean Data Assimilation System of the Beijing Climate Center (BCC GODAS2.0) has been run daily in a pre-operational mode. It spans the period 1990 to the present day. The goal of this paper is to introduce the main components and to evaluate BCC GODAS2.0 for the user community. BCC GODAS2.0 consists of an observational data preprocess, ocean data quality control system, a three-dimensional variational (3DVAR) data assimilation, and global ocean circulation model [Modular Ocean Model 4 (MOM4)]. MOM4 is driven by six-hourly fluxes from the National Centers for Environmental Prediction. Satellite altimetry data, SST, and in-situ temperature and salinity data are assimilated in real time. The monthly results from the BCC GODAS2.0 reanalysis are compared and assessed with observations for 1990-2011. The climatology of the mixed layer depth of BCC GODAS2.0 is generally in agreement with that ofWorld Ocean Atlas 2001. The modeled sea level variations in the tropical Pacific are consistent with observations from satellite altimetry on interannual to decadal time scales. Performances in predicting variations in the SST using BCC GODAS2.0 are evaluated. The standard deviation of the SST in BCC GODAS2.0 agrees well with observations in the tropical Pacific. BCC GODAS2.0 is able to capture the main features of El Ni˜no Modoki I and Modoki II, which have different impacts on rainfall in southern China. In addition, the relationships between the Indian Ocean and the two types of El Ni˜no Modoki are also reproduced.

Assimilation of SBUV Version 8 Radiances into the GEOS Ozone DAS

NASA Technical Reports Server (NTRS)

Mueller, Martin D.; Stajner, Ivanka; Bhartia, Pawan K.

2004-01-01

In operational weather forecasting, the assimilation of brightness temperatures from satellite sounders, instead of assimilation of 1D-retrievals has become increasingly common practice over the last two decades. Compared to these systems, assimilation of trace gases is still at a relatively early stage of development, and efforts to directly assimilate radiances instead of retrieved products have just begun a few years ago, partially because it requires much more computation power due to the employment of a radiative transport forward model (FM). This paper will focus on a method to assimilate SBUV/2 radiances (albedos) into the Global Earth Observation System Ozone Data Assimilation Scheme (GEOS-03DAS). While SBUV-type instruments cannot compete with newer sensors in terms of spectral and horizontal resolution, they feature a continuous data record back to 1978, which makes them very valuable for trend studies. Assimilation can help spreading their ground coverage over the whole globe, as has been previously demonstrated with the GEOS-03DAS using SBUV Version 6 ozone profiles. Now, the DAS has been updated to use the newly released SBUV Version 8 data. We will compare pre]lmlnarv results of SBUV radiance assimilation with the assimilation of retrieved ozone profiles, discuss methods to deal with the increased computational load, and try to assess the error characteristics and future potential of the new approach.

Multi-Scale 4DVAR Assimilation of Glider Teams on the North Carolina Shelf

NASA Astrophysics Data System (ADS)

Osborne, J. J. V.; Carrier, M.; Book, J. W.; Barron, C. N.; Rice, A. E.; Rowley, C. D.; Smedstad, L.; Souopgui, I.; Teague, W. J.

2017-12-01

We demonstrate a method to assimilate glider profile data from multiple gliders in close proximity ( 10 km or less). Gliders were deployed in a field experiment from 17 May until 4 June 2017, north of Cape Hatteras and inshore of the Gulf Stream. Gliders were divided into two teams, generally two or three gliders per team. One team was tasked with station keeping and the other with moving and sampling regions of high variability in temperature and salinity. Glider data are assimilated into the Relocatable Navy Coastal Ocean Model (RELO NCOM) with four dimensional variational assimilation (NCOM-4DVAR). RELO NCOM is used by the US Navy to predict the ocean. RELO NCOM is a baroclinic, Boussinesq, free-surface, and hydrostatic ocean model with a flexible sigma-z vertical coordinate. Two domains are used, one focused north and one focused south of Cape Hatteras. The domains overlap near the gliders, thus providing two forecasts near the gliders. Both domains have 1 km horizontal resolution. Data are assimilated in a newly-developed multi-scale data-processing and assimilating approach using NCOM-4DVAR. This enables NCOM-4DVAR to use many more observations than standard NCOM-4DVAR, improving the analysis and forecast. Assimilation experiments use station-keeping glider data, moving glider data, or all glider data. Sea surface temperature (SST) data and satellite altimeter (SSH) data are also assimilated. An additional experiment omits glider data but still assimilates SST and SSH data. Conductivity, temperature, and depth (CTD) profiles from the R/V Savannah are used for validation, including data from an underway CTD (UCTD). Data from glider teams have the potential to significantly improve model forecasts. Missions using teams of gliders can be planned to maximize data assimilation for optimal impact on model predictions.

Evaluation of Oceanic Surface Observation for Reproducing the Upper Ocean Structure in ECHAM5/MPI-OM

NASA Astrophysics Data System (ADS)

Luo, Hao; Zheng, Fei; Zhu, Jiang

2017-12-01

Better constraints of initial conditions from data assimilation are necessary for climate simulations and predictions, and they are particularly important for the ocean due to its long climate memory; as such, ocean data assimilation (ODA) is regarded as an effective tool for seasonal to decadal predictions. In this work, an ODA system is established for a coupled climate model (ECHAM5/MPI-OM), which can assimilate all available oceanic observations using an ensemble optimal interpolation approach. To validate and isolate the performance of different surface observations in reproducing air-sea climate variations in the model, a set of observing system simulation experiments (OSSEs) was performed over 150 model years. Generally, assimilating sea surface temperature, sea surface salinity, and sea surface height (SSH) can reasonably reproduce the climate variability and vertical structure of the upper ocean, and assimilating SSH achieves the best results compared to the true states. For the El Niño-Southern Oscillation (ENSO), assimilating different surface observations captures true aspects of ENSO well, but assimilating SSH can further enhance the accuracy of ENSO-related feedback processes in the coupled model, leading to a more reasonable ENSO evolution and air-sea interaction over the tropical Pacific. For ocean heat content, there are still limitations in reproducing the long time-scale variability in the North Atlantic, even if SSH has been taken into consideration. These results demonstrate the effectiveness of assimilating surface observations in capturing the interannual signal and, to some extent, the decadal signal but still highlight the necessity of assimilating profile data to reproduce specific decadal variability.

Using Enhanced Grace Water Storage Data to Improve Drought Detection by the U.S. and North American Drought Monitors

NASA Technical Reports Server (NTRS)

Houborg, Rasmus; Rodell, Matthew; Lawrimore, Jay; Li, Bailing; Reichle, Rolf; Heim, Richard; Rosencrans, Matthew; Tinker, Rich; Famiglietti, James S.; Svoboda, Mark;

Opinion Dynamics with Heterogeneous Interactions and Information Assimilation

ERIC Educational Resources Information Center

Mir Tabatabaei, Seydeh Anahita

2013-01-01

In any modern society, individuals interact to form opinions on various topics, including economic, political, and social aspects. Opinions evolve as the result of the continuous exchange of information among individuals and of the assimilation of information distributed by media. The impact of individuals' opinions on each other forms a network,…

Longitudinal Variations of Low-Latitude Ionospheric Irregularities during the 2015 St. Patrick's Day Storm

NASA Astrophysics Data System (ADS)

Pi, X.; Vergados, P.

2017-12-01

GPS data from more than 2000 globally distributed ground-based stations are processed to generate Global Map of Ionospheric Irregularities and Scintillation (GMIIS) at 5-minite cadence for the 2015 St. Patrick's Day Storm. The time sequence of GMIIS provides global snapshots of evolving ionospheric irregularities that are helpful in investigations of small-scale ionospheric perturbations globally. Such data from selected stations at longitudes distributed around the globe are also analyzed to investigate longitudinal variations of low-latitude ionospheric irregularities (LLII) during the storm. Prior to the storm day, The GPS data show typical seasonal (March equinox) activities of LLII during evening hours in different longitude regions, i.e., active in American through Asian longitudes but relatively inactive in the Pacific sector. The data also reveal dramatic changes in LLII during the storm main phase (17 March 2015) and recovery phase (18-19 March 2015). While remaining inactive in the Pacific region, LLII have gone through complicated variations in the longitude regions of high scintillation season. The variations include active, weakened or suppressed, or post-midnight triggering during the storm main phase and recovery phase depending on specific longitude. To understand possible responsible causes of these variations in different longitudes, the Global Assimilative Ionospheric Model (GAIM) is used to reproduce ambient ionospheric state and its disturbances. For this storm study, GAIM assimilates GPS data from about 650 globally distributed stations and from spaceborne receivers onboard the COSMIC satellites. The global assimilative modeling enables us to investigate the changes of the equatorial ionospheric anomaly (EIA) and corresponding ionospheric dynamical processes in the concerned longitudes. This presentation will combine pictures of small- and large-scale ionospheric perturbations and attempt to obtain insight into mechanisms that drive LLII changes during the major storm.

Parameter estimation for a cohesive sediment transport model by assimilating satellite observations in the Hangzhou Bay: Temporal variations and spatial distributions

NASA Astrophysics Data System (ADS)

Wang, Daosheng; Zhang, Jicai; He, Xianqiang; Chu, Dongdong; Lv, Xianqing; Wang, Ya Ping; Yang, Yang; Fan, Daidu; Gao, Shu

2018-01-01

Model parameters in the suspended cohesive sediment transport models are critical for the accurate simulation of suspended sediment concentrations (SSCs). Difficulties in estimating the model parameters still prevent numerical modeling of the sediment transport from achieving a high level of predictability. Based on a three-dimensional cohesive sediment transport model and its adjoint model, the satellite remote sensing data of SSCs during both spring tide and neap tide, retrieved from Geostationary Ocean Color Imager (GOCI), are assimilated to synchronously estimate four spatially and temporally varying parameters in the Hangzhou Bay in China, including settling velocity, resuspension rate, inflow open boundary conditions and initial conditions. After data assimilation, the model performance is significantly improved. Through several sensitivity experiments, the spatial and temporal variation tendencies of the estimated model parameters are verified to be robust and not affected by model settings. The pattern for the variations of the estimated parameters is analyzed and summarized. The temporal variations and spatial distributions of the estimated settling velocity are negatively correlated with current speed, which can be explained using the combination of flocculation process and Stokes' law. The temporal variations and spatial distributions of the estimated resuspension rate are also negatively correlated with current speed, which are related to the grain size of the seabed sediments under different current velocities. Besides, the estimated inflow open boundary conditions reach the local maximum values near the low water slack conditions and the estimated initial conditions are negatively correlated with water depth, which is consistent with the general understanding. The relationships between the estimated parameters and the hydrodynamic fields can be suggestive for improving the parameterization in cohesive sediment transport models.

Bayesian Hierarchical Model Characterization of Model Error in Ocean Data Assimilation and Forecasts

DTIC Science & Technology

2013-09-30

wind ensemble with the increments in the surface momentum flux control vector in a four-dimensional variational (4dvar) assimilation system. The...stability  effects?   surface  stress   Surface   Momentum  Flux  Ensembles  from  Summaries  of  BHM  Winds  (Mediterranean...surface wind speed given ensemble winds from a Bayesian Hierarchical Model to provide surface momentum flux ensembles. 3 Figure 2: Domain of

Exploring and Analyzing Climate Variations Online by Using MERRA-2 data at GES DISC

NASA Astrophysics Data System (ADS)

Shen, S.; Ostrenga, D.; Vollmer, B.; Kempler, S.

2016-12-01

NASA Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) (http://giovanni.sci.gsfc.nasa.gov/giovanni/) is a web-based data visualization and analysis system developed by the Goddard Earth Sciences Data and Information Services Center (GES DISC). Current data analysis functions include Lat-Lon map, time series, scatter plot, correlation map, difference, cross-section, vertical profile, and animation etc. The system enables basic statistical analysis and comparisons of multiple variables. This web-based tool facilitates data discovery, exploration and analysis of large amount of global and regional remote sensing and model data sets from a number of NASA data centers. Recently, long term global assimilated atmospheric, land, and ocean data have been integrated into the system that enables quick exploration and analysis of climate data without downloading, and preprocessing the data. Example data include climate reanalysis from NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) which provides data beginning 1980 to present; land data from NASA Global Land Data Assimilation System (GLDAS) which assimilates data from 1948 to 2012; as well as ocean biological data from NASA Ocean Biogeochemical Model (NOBM) which assimilates data from 1998 to 2012. This presentation, using surface air temperature, precipitation, ozone, and aerosol, etc. from MERRA-2, demonstrates climate variation analysis with Giovanni at selected regions.

Assimilation of MLS and OMI Ozone Data

NASA Technical Reports Server (NTRS)

Stajner, I.; Wargan, K.; Chang, L.-P.; Hayashi, H.; Pawson, S.; Froidevaux, L.; Livesey, N.

2005-01-01

Ozone data from Aura Microwave Limb Sounder (MLS) and Ozone Monitoring Instrument (OMI) were assimilated into the ozone model at NASA's Global Modeling and Assimilation Office (GMAO). This assimilation produces ozone fields that are superior to those from the operational GMAO assimilation of Solar Backscatter Ultraviolet (SBUV/2) instrument data. Assimilation of Aura data improves the representation of the "ozone hole" and the agreement with independent Stratospheric Aerosol and Gas Experiment (SAGE) III and ozone sonde data. Ozone in the lower stratosphere is captured better: mean state, vertical gradients, spatial and temporal variability are all improved. Inclusion of OMI and MLS data together, or separately, in the assimilation system provides a way of checking how consistent OMI and MLS data are with each other, and with the ozone model. We found that differences between OMI total ozone column data and model forecasts decrease after MLS data are assimilated. This indicates that MLS stratospheric ozone profiles are consistent with OMI total ozone columns. The evaluation of error characteristics of OMI and MLS ozone will continue as data from newer versions of retrievals becomes available. We report on the initial step in obtaining global assimilated ozone fields that combine measurements from different Aura instruments, the ozone model at the GMAO, and their respective error characteristics. We plan to use assimilated ozone fields in estimation of tropospheric ozone. We also plan to investigate impacts of assimilated ozone fields on numerical weather prediction through their use in radiative models and in the assimilation of infrared nadir radiance data from NASA's Advanced Infrared Sounder (AIRS).

Reconstructing meaning through occupation after the death of a family member: accommodation, assimilation, and continuing bonds.

PubMed

Hoppes, Steve; Segal, Ruth

2010-01-01

Reactions to death have been studied extensively from psychological, behavioral, and physiological perspectives. Occupational adaptation to loss has received scant attention. Qualitative research was undertaken to identify and describe occupational responses in bereavement. The constant comparative approach was used to analyze and interpret the occupational responses. Adaptive strategies of occupational accommodation and assimilation were used after the death of a family member. Desire to sustain bonds with the deceased motivated specific occupational engagements. These occupational responses served to reconstruct meaning after the death of a family member. These findings contribute to understanding adaptation after death by adding an occupational perspective to previous theories. Occupational therapists' abilities to support clients after loss can be enhanced through appreciation of occupational accommodation and assimilation and the role of continuing occupational bonds after the death of a loved one.

Synthesis and Assimilation Systems - Essential Adjuncts to the Global Ocean Observing System

NASA Technical Reports Server (NTRS)

Rienecker, Michele M.; Balmaseda, Magdalena; Awaji, Toshiyuki; Barnier, Bernard; Behringer, David; Bell, Mike; Bourassa, Mark; Brasseur, Pierre; Breivik, Lars-Anders; Carton, James;

Effects of sucrose amendment on ammonia assimilation during sewage sludge composting.

PubMed

Meng, Liqiang; Li, Weiguang; Zhang, Shumei; Wu, Chuandong; Wang, Ke

2016-06-01

The aim of this study was to evaluate the laboratory-scale composting of sewage sludge and pumice mixtures that were amended with sucrose. The variation in temperature, pH, NH4(+)-N, ammonia emission, bacterial community, ammonia assimilating bacteria (AAB) populations and enzymatic activity related to ammonia assimilation were detected. The addition of sucrose increased the AAB population by 2.5-3.5 times, reduced ammonia emission by 24.7-31.1% compared with the control treatment, and promoted the growth of Bacillus and Wautersiella. The activities of glutamate dehydrogenase (GDH), glutamate synthase (GS) and glutamine synthetase (GOGAT), were enhanced by the addition of sucrose. GDH made a substantial contribution to ammonia assimilation when the ammonia concentration was high (⩾1.5g/kg) in the thermophilic phase. The GS/GOGAT cycle played an important role at low ammonia concentrations (⩽1.1g/kg) in the cooling phase. These results suggested that adding sucrose to sludge compost could promote ammonia assimilation and reduce ammonia emission. Copyright © 2016 Elsevier Ltd. All rights reserved.

Tropospheric ozone reduces carbon assimilation in trees: estimates from analysis of continuous flux measurements.

PubMed

Fares, Silvano; Vargas, Rodrigo; Detto, Matteo; Goldstein, Allen H; Karlik, John; Paoletti, Elena; Vitale, Marcello

2013-08-01

High ground-level ozone concentrations are typical of Mediterranean climates. Plant exposure to this oxidant is known to reduce carbon assimilation. Ozone damage has been traditionally measured through manipulative experiments that do not consider long-term exposure and propagate large uncertainty by up-scaling leaf-level observations to ecosystem-level interpretations. We analyzed long-term continuous measurements (>9 site-years at 30 min resolution) of environmental and eco-physiological parameters at three Mediterranean ecosystems: (i) forest site dominated by Pinus ponderosa in the Sierra Mountains in California, USA; (ii) forest site composed of a mixture of Quercus spp. and P. pinea in the Tyrrhenian sea coast near Rome, Italy; and (iii) orchard site of Citrus sinensis cultivated in the California Central Valley, USA. We hypothesized that higher levels of ozone concentration in the atmosphere result in a decrease in carbon assimilation by trees under field conditions. This hypothesis was tested using time series analysis such as wavelet coherence and spectral Granger causality, and complemented with multivariate linear and nonlinear statistical analyses. We found that reduction in carbon assimilation was more related to stomatal ozone deposition than to ozone concentration. The negative effects of ozone occurred within a day of exposure/uptake. Decoupling between carbon assimilation and stomatal aperture increased with the amount of ozone pollution. Up to 12-19% of the carbon assimilation reduction in P. ponderosa and in the Citrus plantation was explained by higher stomatal ozone deposition. In contrast, the Italian site did not show reductions in gross primary productivity either by ozone concentration or stomatal ozone deposition, mainly due to the lower ozone concentrations in the periurban site over the shorter period of investigation. These results highlight the importance of plant adaptation/sensitivity under field conditions, and the importance of continuous long-term measurements to explain ozone damage to real-world forests and calculate metrics for ozone-risk assessment. © 2013 John Wiley & Sons Ltd.

CATS Near Real Time Data Products: Applications for Assimilation Into the NASA GEOS-5 AGCM

NASA Technical Reports Server (NTRS)

Hlavka, D. L.; Nowottnick, E. P.; Yorks, J. E.; Da Silva, A.; McGill, M. J.; Palm, S. P.; Selmer, P. A.; Pauly, R. M.; Ozog, S.

2017-01-01

From February 2015 through October 2017, the NASA Cloud-Aerosol Transport System (CATS) backscatter lidar operated on the International Space Station (ISS) as a technology demonstration for future Earth Science Missions, providing vertical measurements of cloud and aerosols properties. Owing to its location on the ISS, a cornerstone technology demonstration of CATS was the capability to acquire, process, and disseminate near-real time (NRT) data within 6 hours of observation time. CATS NRT data has several applications, including providing notification of hazardous events for air traffic control and air quality advisories, field campaign flight planning, as well as for constraining cloud and aerosol distributions in via data assimilation in aerosol transport models.   Recent developments in aerosol data assimilation techniques have permitted the assimilation of aerosol optical thickness (AOT), a 2-dimensional column integrated quantity that is reflective of the simulated aerosol loading in aerosol transport models. While this capability has greatly improved simulated AOT forecasts, the vertical position, a key control on aerosol transport, is often not impacted when 2-D AOT is assimilated. Here, we present preliminary efforts to assimilate CATS aerosol observations into the NASA Goddard Earth Observing System version 5 (GEOS-5) atmospheric general circulation model and assimilation system using a 1-D Variational (1-D VAR) ensemble approach, demonstrating the utility of CATS for future Earth Science Missions.

Data assimilation of GNSS zenith total delays from a Nordic processing centre

NASA Astrophysics Data System (ADS)

Lindskog, Magnus; Ridal, Martin; Thorsteinsson, Sigurdur; Ning, Tong

2017-11-01

Atmospheric moisture-related information estimated from Global Navigation Satellite System (GNSS) ground-based receiver stations by the Nordic GNSS Analysis Centre (NGAA) have been used within a state-of-the-art kilometre-scale numerical weather prediction system. Different processing techniques have been implemented to derive the moisture-related GNSS information in the form of zenith total delays (ZTDs) and these are described and compared. In addition full-scale data assimilation and modelling experiments have been carried out to investigate the impact of utilizing moisture-related GNSS data from the NGAA processing centre on a numerical weather prediction (NWP) model initial state and on the ensuing forecast quality. The sensitivity of results to aspects of the data processing, station density, bias-correction and data assimilation have been investigated. Results show benefits to forecast quality when using GNSS ZTD as an additional observation type. The results also show a sensitivity to thinning distance applied for GNSS ZTD observations but not to modifications to the number of predictors used in the variational bias correction applied. In addition, it is demonstrated that the assimilation of GNSS ZTD can benefit from more general data assimilation enhancements and that there is an interaction of GNSS ZTD with other types of observations used in the data assimilation. Future plans include further investigation of optimal thinning distances and application of more advanced data assimilation techniques.

The Impact of the Assimilation of AIRS Radiance Measurements on Short-term Weather Forecasts

NASA Technical Reports Server (NTRS)

McCarty, Will; Jedlovec, Gary; Miller, Timothy L.

2009-01-01

Advanced spaceborne instruments have the ability to improve the horizontal and vertical characterization of temperature and water vapor in the atmosphere through the explicit use of hyperspectral thermal infrared radiance measurements. The incorporation of these measurements into a data assimilation system provides a means to continuously characterize a three-dimensional, instantaneous atmospheric state necessary for the time integration of numerical weather forecasts. Measurements from the National Aeronautics and Space Administration (NASA) Atmospheric Infrared Sounder (AIRS) are incorporated into the gridpoint statistical interpolation (GSI) three-dimensional variational (3D-Var) assimilation system to provide improved initial conditions for use in a mesoscale modeling framework mimicking that of the operational North American Mesoscale (NAM) model. The methodologies for the incorporation of the measurements into the system are presented. Though the measurements have been shown to have a positive impact in global modeling systems, the measurements are further constrained in this system as the model top is physically lower than the global systems and there is no ozone characterization in the background state. For a study period, the measurements are shown to have positive impact on both the analysis state as well as subsequently spawned short-term (0-48 hr) forecasts, particularly in forecasted geopotential height and precipitation fields. At 48 hr, height anomaly correlations showed an improvement in forecast skill of 2.3 hours relative to a system without the AIRS measurements. Similarly, the equitable threat and bias scores of precipitation forecasts of 25 mm (6 hr)-1 were shown to be improved by 8% and 7%, respectively.

Evaluating the extreme precipitation events using a mesoscale atmopshere model

NASA Astrophysics Data System (ADS)

Yucel, I.; Onen, A.

2012-04-01

Evidence is showing that global warming or climate change has a direct influence on changes in precipitation and the hydrological cycle. Extreme weather events such as heavy rainfall and flooding are projected to become much more frequent as climate warms. Mesoscale atmospheric models coupled with land surface models provide efficient forecasts for meteorological events in high lead time and therefore they should be used for flood forecasting and warning issues as they provide more continuous monitoring of precipitation over large areas. This study examines the performance of the Weather Research and Forecasting (WRF) model in producing the temporal and spatial characteristics of the number of extreme precipitation events observed in West Black Sea Region of Turkey. Extreme precipitation events usually resulted in flood conditions as an associated hydrologic response of the basin. The performance of the WRF system is further investigated by using the three dimensional variational (3D-VAR) data assimilation scheme within WRF. WRF performance with and without data assimilation at high spatial resolution (4 km) is evaluated by making comparison with gauge precipitation and satellite-estimated rainfall data from Multi Precipitation Estimates (MPE). WRF-derived precipitation showed capabilities in capturing the timing of the precipitation extremes and in some extent spatial distribution and magnitude of the heavy rainfall events. These precipitation characteristics are enhanced with the use of 3D-VAR scheme in WRF system. Data assimilation improved area-averaged precipitation forecasts by 9 percent and at some points there exists quantitative match in precipitation events, which are critical for hydrologic forecast application.

Carbon Monoxide Data Assimilation for Atmospheric Composition and Climate Science: Evaluating Performance with Current and Future Observations

NASA Astrophysics Data System (ADS)

Barre, J.; Edwards, D. P.; Gaubert, B.; Worden, H. M.; Arellano, A. F.; Anderson, J. L.

2015-12-01

Current satellite observations of tropospheric composition made from low Earth orbit provide at best one or two measurements each day at any given location. Comparisons of Terra/MOPITT carbon monoxide (CO) and IASI/Metop CO observation assimilations will be presented. We use the DART Ensemble Adjustment Kalman Filter to assimilate observations in the CAM-Chem global chemistry-climate model. Data assimilation impacts due to both different instrument capabilities (i.e. vertical sensitivity and global coverage) will be discussed. Coverage is global but sparse, often with large uncertainties in individual measurements that limit examination of local and regional atmospheric composition over short time periods. This has hindered the operational uptake of these data for monitoring air quality and population exposure, and for initializing and evaluating chemical weather forecasts. By the end of the current decade there are planned geostationary Earth orbit (GEO) satellite missions for atmospheric composition over North America, East Asia and Europe with additional missions proposed. Together, these present the possibility of a constellation of geostationary platforms to achieve continuous time-resolved high-density observations of continental domains for mapping pollutant sources and variability on diurnal and local scales. We describe Observing System Simulation Experiments (OSSEs) to evaluate the contributions of these GEO missions to improve knowledge of near-surface air pollution due to intercontinental long-range transport and quantify chemical precursor emissions. Our approach uses an efficient computational method to sample a high-resolution global GEOS-5 chemistry Nature Run over each geographical region of the GEO constellation. The demonstration carbon monoxide (CO) observation simulator, which will be expanded to other chemical pollutants, currently produces multispectral retrievals (MOPITT-like) and captures realistic scene-dependent variation in measurement vertical sensitivity and cloud cover. The impact of observing over each region is evaluated independently. Winter and summer cases studies are investigated i.e. where emissions, cloud cover and CO lifetime significantly change.

Discharge estimation in ungauged basins through variational data assimilation : The potential of the SWOT mission.

NASA Astrophysics Data System (ADS)

Oubanas, H.; Gejadze, I.; Malaterre, P. O.; Durand, M. T.; Wei, R.; Frasson, R. P. M.; Domeneghetti, A.

2017-12-01

This work investigates the estimation of river discharge from simulated observations of the forthcoming Surface Water and Ocean Topography (SWOT) mission, to be launched in 2021, using a variant of the standard variational data assimilation method `4D-Var'. The hydrology SWOT simulator, developed at the Jet Propulsion Laboratory (JPL) has been used to simulate the expected performance of the KaRIn instrument onboard the satellite, producing synthetic SWOT observations of height and width, at each satellite overpass. SWOT data products were synthesized at the spatial scale of 200 m along the river centerline. Using a 1.5D full Saint-Venant hydraulic model, variational data assimilation simultaneously estimates the inflow discharge, river bathymetry and bed roughness. The proposed method has been designed for an application to fully ungauged basins; therefore, the prior information is derived from the SWOT observations only and the globally available ancillary information. Two reaches of the Po and Sacramento Rivers of about 130 km and 150 km, respectively, have been considered in this study. Discharge was successfully recovered at the overpass time with a relative-root-mean-square error of 16% and 12.3% for the Po and Sacramento Rivers, respectively. The estimates of the bed level and the roughness coefficient demonstrate a local improvement; however they may not provide reliable global information of the river bathymetry and roughness.

Scalar and Vector Spherical Harmonics for Assimilation of Global Datasets in the Ionosphere and Thermosphere

NASA Astrophysics Data System (ADS)

Miladinovich, D.; Datta-Barua, S.; Bust, G. S.; Ramirez, U.

2017-12-01

Understanding physical processes during storm time in the ionosphere-thermosphere (IT) system is limited, in part, due to the inability to obtain accurate estimates of IT states on a global scale. One reason for this inability is the sparsity of spatially distributed high quality data sets. Data assimilation is showing promise toward enabling global estimates by blending high quality observational data sets with established climate models. We are continuing development of an algorithm called Estimating Model Parameters for Ionospheric Reverse Engineering (EMPIRE) to enable assimilation of global datasets for storm time estimates of IT drivers. EMPIRE is a data assimilation algorithm that uses a Kalman filtering routine to ingest model and observational data. The EMPIRE algorithm is based on spherical harmonics which provide a spherically symmetric, smooth, continuous, and orthonormal set of basis functions suitable for a spherical domain such as Earth's IT region (200-600 km altitude). Once the basis function coefficients are determined, the newly fitted function represents the disagreement between observational measurements and models. We apply spherical harmonics to study the March 17, 2015 storm. Data sources include Fabry-Perot interferometer neutral wind measurements and global Ionospheric Data Assimilation 4 Dimensional (IDA4D) assimilated total electron content (TEC). Models include Weimer 2000 electric potential, International Geomagnetic Reference Field (IGRF) magnetic field, and Horizontal Wind Model 2014 (HWM14) neutral winds. We present the EMPIRE assimilation results of Earth's electric potential and thermospheric winds. We also compare EMPIRE storm time E cross B ion drift estimates to measured drifts produced from the Super Dual Auroral Radar Network (SuperDARN) and Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE) measurement datasets. The analysis from these results will enable the generation of globally assimilated storm time IT state estimates for future studies. In particular, the ability to provide data assimilated estimation of the drivers of the IT system from high to low latitudes is a critical step toward forecasting the influence of geomagnetic storms on the near Earth space environment.

Assimilation of granite by basaltic magma at Burnt Lava flow, Medicine Lake volcano, northern California: Decoupling of heat and mass transfer

USGS Publications Warehouse

Grove, T.L.; Kinzler, R.J.; Baker, M.B.; Donnelly-Nolan, J. M.; Lesher, C.E.

1988-01-01

At Medicine Lake volcano, California, andesite of the Holocene Burnt Lava flow has been produced by fractional crystallization of parental high alumina basalt (HAB) accompanied by assimilation of granitic crustal material. Burnt Lava contains inclusions of quenched HAB liquid, a potential parent magma of the andesite, highly melted granitic crustal xenoliths, and xenocryst assemblages which provide a record of the fractional crystallization and crustal assimilation process. Samples of granitic crustal material occur as xenoliths in other Holocene and Pleistocene lavas, and these xenoliths are used to constrain geochemical models of the assimilation process. A large amount of assimilation accompanied fractional crystallization to produce the contaminated Burnt lava andesites. Models which assume that assimilation and fractionation occurred simultaneously estimate the ratio of assimilation to fractional crystallization (R) to be >1 and best fits to all geochemical data are at an R value of 1.35 at F=0.68. Petrologic evidence, however, indicates that the assimilation process did not involve continuous addition of granitic crust as fractionation occurred. Instead, heat and mass transfer were separated in space and time. During the assimilation process, HAB magma underwent large amounts of fractional crystallization which was not accompanied by significant amounts of assimilation. This fractionation process supplied heat to melt granitic crust. The models proposed to explain the contamination process involve fractionation, replenishment by parental HAB, and mixing of evolved and parental magmas with melted granitic crust. ?? 1988 Springer-Verlag.

North Adriatic Tides: Observations, Variational Data Assimilation Modeling, and Linear Tide Dynamics

DTIC Science & Technology

2009-12-01

of the North Adriatic ( Lee et al., 2005). In addition to the ADCP measurements of currents through- out the water column, bottom pressure (by ADCP or...of the year with low levels of stratification (Figure 2, Jeffries and Lee , 2007). Actual generation of internal tides in the North Adriatic would...Thompson, K.R., Teague, W. J., Jacobs, G.A., Suk, M.-S., Chang, K.-I., Lee , J.-C. and Choi, B.H. (2004): Data assimilation modeling of the barotropic

Improving Global Reanalyses and Short Range Forecast Using TRMM and SSM/I-Derived Precipitation and Moisture Observations

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.; Zhang, Sara Q.; deSilva, Arlindo M.

2000-01-01

Global reanalyses currently contain significant errors in the primary fields of the hydrological cycle such as precipitation, evaporation, moisture, and the related cloud fields, especially in the tropics. The Data Assimilation Office (DAO) at the NASA Goddard Space Flight Center has been exploring the use of tropical rainfall and total precipitable water (TPW) observations from the TRMM Microwave Imager (TMI) and the Special Sensor Microwave/ Imager (SSM/I) instruments to improve short-range forecast and reanalyses. We describe a "1+1"D procedure for assimilating 6-hr averaged rainfall and TPW in the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS). The algorithm is based on a 6-hr time integration of a column version of the GEOS DAS, hence the "1+1"D designation. The scheme minimizes the least-square differences between the observed TPW and rain rates and those produced by the column model over the 6-hr analysis window. This 1+lD scheme, in its generalization to four dimensions, is related to the standard 4D variational assimilation but uses analysis increments instead of the initial condition as the control variable. Results show that assimilating the TMI and SSM/I rainfall and TPW observations improves not only the precipitation and moisture fields but also key climate parameters such as clouds, the radiation, the upper-tropospheric moisture, and the large-scale circulation in the tropics. In particular, assimilating these data reduce the state-dependent systematic errors in the assimilated products. The improved analysis also provides better initial conditions for short-range forecasts, but the improvements in forecast are less than improvements in the time-averaged assimilation fields, indicating that using these data types is effective in correcting biases and other errors of the forecast model in data assimilation.

Improving Global Reanalyses and Short-Range Forecast Using TRMM and SSM/I-Derived Precipitation and Moisture Observations

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.; Zhang, Sara Q.; daSilva, Arlindo M.

1999-01-01

Global reanalyses currently contain significant errors in the primary fields of the hydrological cycle such as precipitation, evaporation, moisture, and the related cloud fields, especially in the tropics. The Data Assimilation Office (DAO) at the NASA Goddard Space Flight Center has been exploring the use of tropical rainfall and total precipitable water (TPW) observations from the TRMM Microwave Imager (TMI) and the Special Sensor Microwave/ Imager (SSM/I) instruments to improve short-range forecast and reanalyses. We describe a 1+1D procedure for assimilating 6-hr averaged rainfall and TPW in the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS). The algorithm is based on a 6-hr time integration of a column version of the GEOS DAS, hence the 1+1D designation. The scheme minimizes the least-square differences between the observed TPW and rain rates and those produced by the column model over the 6-hr analysis window. This 1+1D scheme, in its generalization to four dimensions, is related to the standard 4D variational assimilation but uses analysis increments instead of the initial condition as the control variable. Results show that assimilating the TMI and SSW rainfall and TPW observations improves not only the precipitation and moisture fields but also key climate parameters such as clouds, the radiation, the upper-tropospheric moisture, and the large-scale circulation in the tropics. In particular, assimilating these data reduce the state-dependent systematic errors in the assimilated products. The improved analysis also provides better initial conditions for short-range forecasts, but the improvements in forecast are less than improvements in the time-averaged assimilation fields, indicating that using these data types is effective in correcting biases and other errors of the forecast model in data assimilation.

Evaluation of the observation operator Jacobian for leaf area index data assimilation with an extended Kalman filter

NASA Astrophysics Data System (ADS)

Rüdiger, Christoph; Albergel, CléMent; Mahfouf, Jean-FrançOis; Calvet, Jean-Christophe; Walker, Jeffrey P.

2010-05-01

To quantify carbon and water fluxes between the vegetation and the atmosphere in a consistent manner, land surface models now include interactive vegetation components. These models treat the vegetation biomass as a prognostic model state, allowing the model to dynamically adapt the vegetation states to environmental conditions. However, it is expected that the prediction skill of such models can be greatly increased by assimilating biophysical observations such as leaf area index (LAI). The Jacobian of the observation operator, a central aspect of data assimilation methods such as the extended Kalman filter (EKF) and the variational assimilation methods, provides the required linear relationship between the observation and the model states. In this paper, the Jacobian required for assimilating LAI into the Interaction between the Soil, Biosphere and Atmosphere-A-gs land surface model using the EKF is studied. In particular, sensitivity experiments were undertaken on the size of the initial perturbation for estimating the Jacobian and on the length of the time window between initial state and available observation. It was found that small perturbations (0.1% of the state) typically lead to accurate estimates of the Jacobian. While other studies have shown that the assimilation of LAI with 10 day assimilation windows is possible, 1 day assimilation intervals can be chosen to comply with numerical weather prediction requirements. Moreover, the seasonal dependence of the Jacobian revealed contrasted groups of Jacobian values due to environmental factors. Further analyses showed the Jacobian values to vary as a function of the LAI itself, which has important implications for its assimilation in different seasons, as the size of the LAI increments will subsequently vary due to the variability of the Jacobian.

Comparison of different assimilation schemes in an operational assimilation system with Ensemble Kalman Filter

NASA Astrophysics Data System (ADS)

Yan, Yajing; Barth, Alexander; Beckers, Jean-Marie; Candille, Guillem; Brankart, Jean-Michel; Brasseur, Pierre

2016-04-01

In this paper, four assimilation schemes, including an intermittent assimilation scheme (INT) and three incremental assimilation schemes (IAU 0, IAU 50 and IAU 100), are compared in the same assimilation experiments with a realistic eddy permitting primitive equation model of the North Atlantic Ocean using the Ensemble Kalman Filter. The three IAU schemes differ from each other in the position of the increment update window that has the same size as the assimilation window. 0, 50 and 100 correspond to the degree of superposition of the increment update window on the current assimilation window. Sea surface height, sea surface temperature, and temperature profiles at depth collected between January and December 2005 are assimilated. Sixty ensemble members are generated by adding realistic noise to the forcing parameters related to the temperature. The ensemble is diagnosed and validated by comparison between the ensemble spread and the model/observation difference, as well as by rank histogram before the assimilation experiments The relevance of each assimilation scheme is evaluated through analyses on thermohaline variables and the current velocities. The results of the assimilation are assessed according to both deterministic and probabilistic metrics with independent/semi-independent observations. For deterministic validation, the ensemble means, together with the ensemble spreads are compared to the observations, in order to diagnose the ensemble distribution properties in a deterministic way. For probabilistic validation, the continuous ranked probability score (CRPS) is used to evaluate the ensemble forecast system according to reliability and resolution. The reliability is further decomposed into bias and dispersion by the reduced centered random variable (RCRV) score in order to investigate the reliability properties of the ensemble forecast system.

Application of data assimilation methods for analysis and integration of observed and modeled Arctic Sea ice motions

NASA Astrophysics Data System (ADS)

Meier, Walter Neil

This thesis demonstrates the applicability of data assimilation methods to improve observed and modeled ice motion fields and to demonstrate the effects of assimilated motion on Arctic processes important to the global climate and of practical concern to human activities. Ice motions derived from 85 GHz and 37 GHz SSM/I imagery and estimated from two-dimensional dynamic-thermodynamic sea ice models are compared to buoy observations. Mean error, error standard deviation, and correlation with buoys are computed for the model domain. SSM/I motions generally have a lower bias, but higher error standard deviations and lower correlation with buoys than model motions. There are notable variations in the statistics depending on the region of the Arctic, season, and ice characteristics. Assimilation methods are investigated and blending and optimal interpolation strategies are implemented. Blending assimilation improves error statistics slightly, but the effect of the assimilation is reduced due to noise in the SSM/I motions and is thus not an effective method to improve ice motion estimates. However, optimal interpolation assimilation reduces motion errors by 25--30% over modeled motions and 40--45% over SSM/I motions. Optimal interpolation assimilation is beneficial in all regions, seasons and ice conditions, and is particularly effective in regimes where modeled and SSM/I errors are high. Assimilation alters annual average motion fields. Modeled ice products of ice thickness, ice divergence, Fram Strait ice volume export, transport across the Arctic and interannual basin averages are also influenced by assimilated motions. Assimilation improves estimates of pollutant transport and corrects synoptic-scale errors in the motion fields caused by incorrect forcings or errors in model physics. The portability of the optimal interpolation assimilation method is demonstrated by implementing the strategy in an ice thickness distribution (ITD) model. This research presents an innovative method of combining a new data set of SSM/I-derived ice motions with three different sea ice models via two data assimilation methods. The work described here is the first example of assimilating remotely-sensed data within high-resolution and detailed dynamic-thermodynamic sea ice models. The results demonstrate that assimilation is a valuable resource for determining accurate ice motion in the Arctic.

Finding the Key Periods for Assimilating HJ-1A/B CCD Data and the WOFOST Model to Evaluate Heavy Metal Stress in Rice.

PubMed

Zhao, Shuang; Qian, Xu; Liu, Xiangnan; Xu, Zhao

2018-04-17

Accurately monitoring heavy metal stress in crops is vital for food security and agricultural production. The assimilation of remote sensing images into the World Food Studies (WOFOST) model provides an efficient way to solve this problem. In this study, we aimed at investigating the key periods of the assimilation framework for continuous monitoring of heavy metal stress in rice. The Harris algorithm was used for the leaf area index (LAI) curves to select the key period for an optimized assimilation. To obtain accurate LAI values, the measured dry weight of rice roots (WRT), which have been proven to be the most stress-sensitive indicator of heavy metal stress, were incorporated into the improved WOFOST model. Finally, the key periods, which contain four dominant time points, were used to select remote sensing images for the RS-WOFOST model for continuous monitoring of heavy metal stress. Compared with the key period which contains all the available remote sensing images, the results showed that the optimal key period can significantly improve the time efficiency of the assimilation framework by shortening the model operation time by more than 50%, while maintaining its accuracy. This result is highly significant when monitoring heavy metals in rice on a large-scale. Furthermore, it can also offer a reference for the timing of field measurements in monitoring heavy metal stress in rice.

Impact of GPM Rainrate Data Assimilation on Simulation of Hurricane Harvey (2017)

NASA Technical Reports Server (NTRS)

Li, Xuanli; Srikishen, Jayanthi; Zavodsky, Bradley; Mecikalski, John

2018-01-01

Built upon Tropical Rainfall Measuring Mission (TRMM) legacy for next-generation global observation of rain and snow. The GPM was launched in February 2014 with Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI) onboard. The GPM has a broad global coverage approximately 70deg S -70deg N with a swath of 245/125-km for the Ka (35.5 GHz)/Ku (13.6 GHz) band radar, and 850-km for the 13-channel GMI. GPM also features better retrievals for heavy, moderate, and light rain and snowfall To develop methodology to assimilate GPM surface precipitation data with Grid-point Statistical Interpolation (GSI) data assimilation system and WRF ARW model To investigate the potential and the value of utilizing GPM observation into NWP for operational environment The GPM rain rate data has been successfully assimilated using the GSI rain data assimilation package. Impacts of rain rate data have been found in temperature and moisture fields of initial conditions. 2.Assimilation of either GPM IMERG or GPROF rain product produces significant improvement in precipitation amount and structure for Hurricane Harvey (2017) forecast. Since IMERG data is available half-hourly, further forecast improvement is expected with continuous assimilation of IMERG data

Effects of data assimilation on the global aerosol key optical properties simulations

NASA Astrophysics Data System (ADS)

Yin, Xiaomei; Dai, Tie; Schutgens, Nick A. J.; Goto, Daisuke; Nakajima, Teruyuki; Shi, Guangyu

2016-09-01

We present the one month results of global aerosol optical properties for April 2006, using the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) coupled with the Non-hydrostatic ICosahedral Atmospheric Model (NICAM), by assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) with Local Ensemble Transform Kalman Filter (LETKF). The simulated AOD, Ångström Exponent (AE) and single scattering albedo (SSA) are validated by independent Aerosol Robotic Network (AERONET) observations over the global sites. The data assimilation has the strongest positive effect on the AOD simulation and slight positive influences on the AE and SSA simulations. For the time-averaged globally spatial distribution, the data assimilation increases the model skill score (S) of AOD, AE, and SSA from 0.55, 0.92, and 0.75 to 0.79, 0.94, and 0.80, respectively. Over the North Africa (NAF) and Middle East region where the aerosol composition is simple (mainly dust), the simulated AODs are best improved by the data assimilation, indicating the assimilation correctly modifies the wrong dust burdens caused by the uncertainties of the dust emission parameterization. Assimilation also improves the simulation of the temporal variations of the aerosol optical properties over the AERONET sites, with improved S at 60 (62%), 45 (55%) and 11 (50%) of 97, 82 and 22 sites for AOD, AE and SSA. By analyzing AOD and AE at five selected sites with best S improvement, this study further indicates that the assimilation can reproduce short duration events and ratios between fine and coarse aerosols more accurately.

Constraining DALECv2 using multiple data streams and ecological constraints: analysis and application

DOE PAGES

Delahaies, Sylvain; Roulstone, Ian; Nichols, Nancy

2017-07-10

We use a variational method to assimilate multiple data streams into the terrestrial ecosystem carbon cycle model DALECv2 (Data Assimilation Linked Ecosystem Carbon). Ecological and dynamical constraints have recently been introduced to constrain unresolved components of this otherwise ill-posed problem. We recast these constraints as a multivariate Gaussian distribution to incorporate them into the variational framework and we demonstrate their advantage through a linear analysis. By using an adjoint method we study a linear approximation of the inverse problem: firstly we perform a sensitivity analysis of the different outputs under consideration, and secondly we use the concept of resolution matricesmore » to diagnose the nature of the ill-posedness and evaluate regularisation strategies. We then study the non-linear problem with an application to real data. Finally, we propose a modification to the model: introducing a spin-up period provides us with a built-in formulation of some ecological constraints which facilitates the variational approach.« less

Constraining DALECv2 using multiple data streams and ecological constraints: analysis and application

DOE Office of Scientific and Technical Information (OSTI.GOV)

Delahaies, Sylvain; Roulstone, Ian; Nichols, Nancy

We use a variational method to assimilate multiple data streams into the terrestrial ecosystem carbon cycle model DALECv2 (Data Assimilation Linked Ecosystem Carbon). Ecological and dynamical constraints have recently been introduced to constrain unresolved components of this otherwise ill-posed problem. We recast these constraints as a multivariate Gaussian distribution to incorporate them into the variational framework and we demonstrate their advantage through a linear analysis. By using an adjoint method we study a linear approximation of the inverse problem: firstly we perform a sensitivity analysis of the different outputs under consideration, and secondly we use the concept of resolution matricesmore » to diagnose the nature of the ill-posedness and evaluate regularisation strategies. We then study the non-linear problem with an application to real data. Finally, we propose a modification to the model: introducing a spin-up period provides us with a built-in formulation of some ecological constraints which facilitates the variational approach.« less

Improved Monitoring of Vegetation Productivity using Continuous Assimilation of Radiometric Data

NASA Astrophysics Data System (ADS)

Baret, F.; Lauvernet, C.; Weiss, M.; Prevot, L.; Rochdi, N.

Canopy functioning models describe crop production from meteorological and soil inputs. However, because of the large number of variables and parameters used, and the poor knowledge of the actual values of some of them, the time course of the canopy and thus final production simulated by these models is often not very accurate. Satellite observations sensors allow controlling the simulations through assimilation of the radiometric data within radiative transfer models coupled to canopy functioning models. An assimilation scheme is presented with application to wheat crops. The coupling between radiative transfer models and canopy functioning models is described. The assimilation scheme is then applied to an experiment achieved within the ReSeDA project. Several issues relative to the assimilation process are discussed. They concern the type of canopy functioning model used, the possibility to assimilate biophysical products rather than radiances, and the use of ancillary information. Further, considerations associated to the problems linked to high spatial and temporal resolution data are listed and illustrated by preliminary results acquired within the ADAM project. Further discussion is made on the required temporal sampling for space observations.

Lognormal Kalman filter for assimilating phase space density data in the radiation belts

NASA Astrophysics Data System (ADS)

Kondrashov, D.; Ghil, M.; Shprits, Y.

2011-11-01

Data assimilation combines a physical model with sparse observations and has become an increasingly important tool for scientists and engineers in the design, operation, and use of satellites and other high-technology systems in the near-Earth space environment. Of particular importance is predicting fluxes of high-energy particles in the Van Allen radiation belts, since these fluxes can damage spaceborne platforms and instruments during strong geomagnetic storms. In transiting from a research setting to operational prediction of these fluxes, improved data assimilation is of the essence. The present study is motivated by the fact that phase space densities (PSDs) of high-energy electrons in the outer radiation belt—both simulated and observed—are subject to spatiotemporal variations that span several orders of magnitude. Standard data assimilation methods that are based on least squares minimization of normally distributed errors may not be adequate for handling the range of these variations. We propose herein a modification of Kalman filtering that uses a log-transformed, one-dimensional radial diffusion model for the PSDs and includes parameterized losses. The proposed methodology is first verified on model-simulated, synthetic data and then applied to actual satellite measurements. When the model errors are sufficiently smaller then observational errors, our methodology can significantly improve analysis and prediction skill for the PSDs compared to those of the standard Kalman filter formulation. This improvement is documented by monitoring the variance of the innovation sequence.

Covariance Function for Nearshore Wave Assimilation Systems

DTIC Science & Technology

2018-01-30

covariance can be modeled by a parameterized Gaussian function, for nearshore wave assimilation applications, the covariance function depends primarily on...case of missing values at the compiled time series, the gaps were filled by weighted interpolation. The weights depend on the number of the...averaging, in order to create the continuous time series, filters out the dependency on the instantaneous meteorological and oceanographic conditions

Global Modeling and Assimilation Office Annual Report and Research Highlights 2011-2012

NASA Technical Reports Server (NTRS)

Rienecker, Michele M.

2012-01-01

Over the last year, the Global Modeling and Assimilation Office (GMAO) has continued to advance our GEOS-5-based systems, updating products for both weather and climate applications. We contributed hindcasts and forecasts to the National Multi-Model Ensemble (NMME) of seasonal forecasts and the suite of decadal predictions to the Coupled Model Intercomparison Project (CMIP5).

Leaf-level gas-exchange uniformity and photosynthetic capacity among loblolly pine (Pinus taeda L.) genotypes of contrasting inherent genetic variation

Treesearch

Michael J. Aspinwall; John S. King; Steven E. McKeand; Jean-Christophe Domec

2011-01-01

Variation in leaf-level gas exchange among widely planted genetically improved loblolly pine (Pinus taeda L.) genotypes could impact stand-level water use, carbon assimilation, biomass production, C allocation, ecosystem sustainability and biogeochemical cycling under changing environmental conditions. We examined uniformity in leaf-level light-saturated photosynthesis...

NASA GEOS-3/TRMM Re-analysis: Capturing Observed Tropical Rainfall Variability in Global Analysis for Climate Research

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.

2004-01-01

Understanding climate variability over a wide range of space-time scales requires a comprehensive description of the earth system. Global analyses produced by a fixed assimilation system (i.e., re-analyses) - as their quality continues to improve - have the potential of providing a vital tool for meeting this challenge. But at the present time, the usefulness of re-analyses is limited by uncertainties in such basic fields as clouds, precipitation, and evaporation - especially in the tropics, where observations are relatively sparse. Analyses of the tropics have long been shown to be sensitive to. the treatment of cloud precipitation processes, which remains a major source of uncertainty in current models. Yet, for many climate studies it is crucial that analyses can accurately reproduce the observed rainfall intensity and variability since a small error of 1 mm/d in surface rain translates into an error of approx. 30 W/sq m in energy (latent heat) flux. Currently, discrepancies between the observed and analyzed monthly-mean rain rates averaged to 100 km x 100 km resolution can exceed 4 mm/d (or 120 W/sq m ), compared to uncertainties in surface radiative fluxes of approx. 10-20 W/sq m . Improving precipitation in analyses would reduce a major source of uncertainty in the global energy budget. Uncertainties in tropical precipitation have also been a major impediment in understanding how the tropics interact with other regions, including the remote response to El Nino/Southern Oscillation (ENSO) variability on interannual time scales, the influence of Madden-Julian Oscillation (MJO) and monsoons on intraseasonal time scales. A global analysis that can replicate the observed precipitation variability together with physically consistent estimates of other atmospheric variables provides the key to breaking this roadblock. NASA Goddard Space Flight Center has been exploring the use of satellite-based microwave rainfall measurements in improving global analyses and has recently produced a multi-year, 1 x 1 TRMM re-analysis , which assimilates 6-hourly TMI and SSM/I surface rain rates over tropical oceans using a ID variational continuous assimilation (VCA) procedure in the GEOS-3 global data assimilation system. The analysis period extends from 1 November 1997 through 3 1 December 2002. The goal is to produce a multi-year global analysis that is dynamically consistent with available tropical precipitation observations for the community to assess its utility in climate applications and identify areas for further improvements. A distinct feature of the GEOS-3RRMh4 re-analysis is that its precipitation analysis is not derived from a short-term forecast (as done in most operational systems) but is given by a time- continuous model integration constrained by precipitation observations within a 6-h analysis window, while the wind, temperature, and pressure fields are allowed to directly respond to the improved precipitation and associated latent heating structures within the same analysis window. In this talk, I will assess the impact VCA precipitation assimilation on analyses of climate signals ranging from a few weeks to interannual time scales and compare results against other operational and reanalysis products.

High-Frequency Planetary Waves in the Polar Middle Atmosphere as seen in a data Assimilation System

NASA Technical Reports Server (NTRS)

Coy, L.; Stajner, I.; DaSilva, A. M.; Joiner, J.; Rood, R. B.; Pawson, S.; Lin, S. J.

2003-01-01

This study examines the winter southern hemisphere vortex of 1998 using four times daily output from a data assimilation system to focus on the polar 2-day, wave number 2 component of the 4-day wave. The data assimilation system products are from a test version of the finite volume data assimilation system (fvDAS) being developed at Goddard Space Flight Center (GSFC) and include an ozone assimilation system. Results show that the polar 2-day wave dominates during July 1998 at 70 degrees. The period of the quasi 2-day wave is somewhat shorter than 2 days (about 1.7 days) during July 1998 with an average perturbation temperature amplitude for the month of over 2.5 K. The 2-day wave propagates more slowly than the zonal mean zonal wind, consistent with Rossby wave theory, and has EP flux divergence regions associated with regions of negative horizontal potential vorticity gradients, as expected from linear instability theory. Results for the assimilation-produced ozone mixing ratio show that the 2-day wave represents a major source of ozone variation in this region. The ozone wave in the assimilation system is in good agreement with the wave seen in the POAM (Polar Ozone and Aerosol Measurement) ozone observations for the same time period. Some differences with linear instability theory are noted as well as spectral peaks in the ozone field, not seen in the temperature field, that may be a consequence of advection.

CATS Near Real Time Data Products: Applications for Assimilation into the NASA GEOS-5 AGCM

NASA Astrophysics Data System (ADS)

Nowottnick, E. P.; Hlavka, D. L.; Yorks, J. E.; da Silva, A. M., Jr.; McGill, M. J.; Palm, S. P.; Selmer, P. A.; Pauly, R.; Ozog, S.

2017-12-01

Since February 2015, the NASA Cloud-Aerosol Transport System (CATS) backscatter lidar has been operating on the International Space Station (ISS) as a technology demonstration for future Earth Science Missions, providing vertical measurements of cloud and aerosols properties. Owing to its location on the ISS, a cornerstone technology demonstration of CATS is the capability to acquire, process, and disseminate near-real time (NRT) data within 6 hours of observation time. Here, we present CATS NRT data products and outline improved CATS algorithms used to discriminate clouds from aerosols, and subsequently identify cloud and aerosol type. CATS NRT data has several applications, including providing notification of hazardous events for air traffic control and air quality advisories, field campaign flight planning, as well as for constraining cloud and aerosol distributions in via data assimilation in aerosol transport models. Recent developments in aerosol data assimilation techniques have permitted the assimilation of aerosol optical thickness (AOT), a 2-dimensional column integrated quantity that is reflective of the simulated aerosol loading in aerosol transport models. While this capability has greatly improved simulated AOT forecasts, the vertical position, a key control on aerosol transport, is often not impacted when 2-D AOT is assimilated. Here, we also present preliminary efforts to assimilate CATS observations into the NASA Goddard Earth Observing System version 5 (GEOS-5) atmospheric general circulation model and assimilation system using a 1-D Variational (1-D VAR) approach, demonstrating the utility of CATS for future Earth Science Missions.

Towards Year-round Estimation of Terrestrial Water Storage over Snow-Covered Terrain via Multi-sensor Assimilation of GRACE/GRACE-FO and AMSR-E/AMSR-2.

NASA Astrophysics Data System (ADS)

Wang, J.; Xue, Y.; Forman, B. A.; Girotto, M.; Reichle, R. H.

2017-12-01

The Gravity and Recovery Climate Experiment (GRACE) has revolutionized large-scale remote sensing of the Earth's terrestrial hydrologic cycle and has provided an unprecedented observational constraint for global land surface models. However, the coarse-scale (in space and time), vertically-integrated measure of terrestrial water storage (TWS) limits GRACE's applicability to smaller scale hydrologic applications. In order to enhance model-based estimates of TWS while effectively adding resolution (in space and time) to the coarse-scale TWS retrievals, a multi-variate, multi-sensor data assimilation framework is presented here that simultaneously assimilates gravimetric retrievals of TWS in conjunction with passive microwave (PMW) brightness temperature (Tb) observations over snow-covered terrain. The framework uses the NASA Catchment Land Surface Model (Catchment) and an ensemble Kalman filter (EnKF). A synthetic assimilation experiment is presented for the Volga river basin in Russia. The skill of the output from the assimilation of synthetic observations is compared with that of model estimates generated without the benefit of assimilating the synthetic observations. It is shown that the EnKF framework improves modeled estimates of TWS, snow depth, and snow mass (a.k.a. snow water equivalent). The data assimilation routine produces a conditioned (updated) estimate that is more accurate and contains less uncertainty during both the snow accumulation phase of the snow season as well as during the snow ablation season.

Enhancement of the southward return flow of the Atlantic Meridional Overturning Circulation by data assimilation and its influence in an assimilative ocean simulation forced by CORE-II atmospheric forcing

NASA Astrophysics Data System (ADS)

Fujii, Yosuke; Tsujino, Hiroyuki; Toyoda, Takahiro; Nakano, Hideyuki

2017-08-01

This paper examines the difference in the Atlantic Meridional Overturning Circulation (AMOC) mean state between free and assimilative simulations of a common ocean model using a common interannual atmospheric forcing. In the assimilative simulation, the reproduction of cold cores in the Nordic Seas, which is absent in the free simulation, enhances the overflow to the North Atlantic and improves AMOC with enhanced transport of the deeper part of the southward return flow. This improvement also induces an enhanced supply of North Atlantic Deep Water (NADW) and causes better representation of the Atlantic deep layer despite the fact that correction by the data assimilation is applied only to temperature and salinity above a depth of 1750 m. It also affects Circumpolar Deep Water in the Southern Ocean. Although the earliest influence of the improvement propagated by coastal waves reaches the Southern Ocean in 10-15 years, substantial influence associated with the arrival of the renewed NADW propagates across the Atlantic Basin in several decades. Although the result demonstrates that data assimilation is able to improve the deep ocean state even if there is no data there, it also indicates that long-term integration is required to reproduce variability in the deep ocean originating from variations in the upper ocean. This study thus provides insights on the reliability of AMOC and the ocean state in the Atlantic deep layer reproduced by data assimilation systems.

The hourly updated US High-Resolution Rapid Refresh (HRRR) storm-scale forecast model

NASA Astrophysics Data System (ADS)

Alexander, Curtis; Dowell, David; Benjamin, Stan; Weygandt, Stephen; Olson, Joseph; Kenyon, Jaymes; Grell, Georg; Smirnova, Tanya; Ladwig, Terra; Brown, John; James, Eric; Hu, Ming

2016-04-01

The 3-km convective-allowing High-Resolution Rapid Refresh (HRRR) is a US NOAA hourly updating weather forecast model that use a specially configured version of the Advanced Research WRF (ARW) model and assimilate many novel and most conventional observation types on an hourly basis using Gridpoint Statistical Interpolation (GSI). Included in this assimilation is a procedure for initializing ongoing precipitation systems from observed radar reflectivity data (and proxy reflectivity from lightning and satellite data), a cloud analysis to initialize stable layer clouds from METAR and satellite observations, and special techniques to enhance retention of surface observation information. The HRRR is run hourly out to 15 forecast hours over a domain covering the entire conterminous United States using initial and boundary conditions from the hourly-cycled 13km Rapid Refresh (RAP, using similar physics and data assimilation) covering North America and a significant part of the Northern Hemisphere. The HRRR is continually developed and refined at NOAA's Earth System Research Laboratory, and an initial version was implemented into the operational NOAA/NCEP production suite in September 2014. Ongoing experimental RAP and HRRR model development throughout 2014 and 2015 has culminated in a set of data assimilation and model enhancements that will be incorporated into the first simultaneous upgrade of both the operational RAP and HRRR that is scheduled for spring 2016 at NCEP. This presentation will discuss the operational RAP and HRRR changes contained in this upgrade. The RAP domain is being expanded to encompass the NAM domain and the forecast lengths of both the RAP and HRRR are being extended. RAP and HRRR assimilation enhancements have focused on (1) extending surface data assimilation to include mesonet observations and improved use of all surface observations through better background estimates of 2-m temperature and dewpoint including projection of 2-m temperature observations through the model boundary layer and (2) extending the use of radar observations to include both radial velocity and 3-D retrieval of rain hydrometeors from observed radar reflectivities in the warm-season. The RAP hybrid EnKF 3D-variational data assimilation will increase weighting of GFS ensemble-based background error covariance estimation and introduce this hybrid data assimilation configuration in the HRRR. Enhancement of RAP and HRRR model physics include improved land surface and boundary layer prediction using the updated Mellor-Yamada-Nakanishi-Niino (MYNN) parameterization scheme, Grell-Freitas-Olson (GFO) shallow and deep convective parameterization, aerosol-aware Thompson microphysics and upgraded Rapid Update Cycle (RUC) land-surface model. The presentation will highlight improvements in the RAP and HRRR model physics to reduce certain systematic forecast biases including a warm and dry daytime bias over the central and eastern CONUS during the warm season along with improved convective forecasts in more weakly-forced diurnally-driven events. Examples of RAP and HRRR forecast improvements will be demonstrated through both retrospective and real-time verification statistics and case-study examples.

An ocean data assimilation system and reanalysis of the World Ocean hydrophysical fields

NASA Astrophysics Data System (ADS)

Zelenko, A. A.; Vil'fand, R. M.; Resnyanskii, Yu. D.; Strukov, B. S.; Tsyrulnikov, M. D.; Svirenko, P. I.

2016-07-01

A new version of the ocean data assimilation system (ODAS) developed at the Hydrometcentre of Russia is presented. The assimilation is performed following the sequential scheme analysis-forecast-analysis. The main components of the ODAS are procedures for operational observation data processing, a variational analysis scheme, and an ocean general circulation model used to estimate the first guess fields involved in the analysis. In situ observations of temperature and salinity in the upper 1400-m ocean layer obtained from various observational platforms are used as input data. In the new ODAS version, the horizontal resolution of the assimilating model and of the output products is increased, the previous 2D-Var analysis scheme is replaced by a more general 3D-Var scheme, and a more flexible incremental analysis updating procedure is introduced to correct the model calculations. A reanalysis of the main World Ocean hydrophysical fields over the 2005-2015 period has been performed using the updated ODAS. The reanalysis results are compared with data from independent sources.

Can plant phloem properties affect the link between ecosystem assimilation and respiration?

NASA Astrophysics Data System (ADS)

Mencuccini, M.; Hölttä, T.; Sevanto, S.; Nikinmaa, E.

2012-04-01

Phloem transport of carbohydrates in plants under field conditions is currently not well understood. This is largely the result of the lack of techniques suitable for measuring phloem physiological properties continuously under field conditions. This lack of knowledge is currently hampering our efforts to link ecosystem-level processes of carbon fixation, allocation and use, especially belowground. On theoretical grounds, the properties of the transport pathway from canopy to roots must be important in affecting the link between carbon assimilation and respiration, but it is unclear whether their effect is partially or entirely masked by processes occurring in other parts of the ecosystem. One can also predict the characteristic time scales over which these effects should occur and, as consequence, predict whether the transfer of turgor and osmotic signals from the site of carbon assimilation to the sites of carbon use are likely to control respiration. We will present two sources of evidence suggesting that the properties of the phloem transport system may affect processes that are dependent on the supply of carbon substrate, such as root or soil respiration. Firstly, we will summarize the results of a literature survey on soil and ecosystem respiration where the speed of transfer of photosynthetic sugars from the plant canopy to the soil surface was determined. Estimates of the transfer speed could be grouped according to whether the study employed isotopic or canopy soil flux-based techniques. These two groups provided very different estimates of transfer times likely because transport of sucrose molecules, and pressure-concentration waves, in phloem differed. Secondly, we will argue that simultaneous measurements of bark and xylem diameters provide a novel tool to determine the continuous variations of phloem turgor in vivo in the field. We will present a model that interprets these changes in xylem and live bark diameters and present data testing the model predictions for mature trees in the field. At the diurnal scale, the calculated phloem turgor signal related to patterns of photosynthetic activity and inferred phloem loading. At the seasonal scale, phloem turgor showed rapid changes during two droughts and after two rainfall events consistent with physiological predictions of phloem transport. Daily cumulative totals of calculated phloem osmotic concentrations were strongly related to daily cumulative totals of canopy photosynthesis. We propose that this method has potential for continuous field monitoring of tree phloem function.

Variational data assimilation for the initial-value dynamo problem.

PubMed

Li, Kuan; Jackson, Andrew; Livermore, Philip W

2011-11-01

The secular variation of the geomagnetic field as observed at the Earth's surface results from the complex magnetohydrodynamics taking place in the fluid core of the Earth. One way to analyze this system is to use the data in concert with an underlying dynamical model of the system through the technique of variational data assimilation, in much the same way as is employed in meteorology and oceanography. The aim is to discover an optimal initial condition that leads to a trajectory of the system in agreement with observations. Taking the Earth's core to be an electrically conducting fluid sphere in which convection takes place, we develop the continuous adjoint forms of the magnetohydrodynamic equations that govern the dynamical system together with the corresponding numerical algorithms appropriate for a fully spectral method. These adjoint equations enable a computationally fast iterative improvement of the initial condition that determines the system evolution. The initial condition depends on the three dimensional form of quantities such as the magnetic field in the entire sphere. For the magnetic field, conservation of the divergence-free condition for the adjoint magnetic field requires the introduction of an adjoint pressure term satisfying a zero boundary condition. We thus find that solving the forward and adjoint dynamo system requires different numerical algorithms. In this paper, an efficient algorithm for numerically solving this problem is developed and tested for two illustrative problems in a whole sphere: one is a kinematic problem with prescribed velocity field, and the second is associated with the Hall-effect dynamo, exhibiting considerable nonlinearity. The algorithm exhibits reliable numerical accuracy and stability. Using both the analytical and the numerical techniques of this paper, the adjoint dynamo system can be solved directly with the same order of computational complexity as that required to solve the forward problem. These numerical techniques form a foundation for ultimate application to observations of the geomagnetic field over the time scale of centuries.

Added Value of Assimilating Himawari-8 AHI Water Vapor Radiances on Analyses and Forecasts for "7.19" Severe Storm Over North China

NASA Astrophysics Data System (ADS)

Wang, Yuanbing; Liu, Zhiquan; Yang, Sen; Min, Jinzhong; Chen, Liqiang; Chen, Yaodeng; Zhang, Tao

2018-04-01

Himawari-8 is the first launched and operational new-generation geostationary meteorological satellite. The Advanced Himawari Imager (AHI) on board Himawari-8 provides continuous high-resolution observations of severe weather phenomena in space and time. In this study, the capability to assimilate AHI radiances has been developed within the Weather Research and Forecasting (WRF) model's data assimilation system. As the first attempt to assimilate AHI using WRF data assimilation at convective scales, the added value of hourly AHI clear-sky radiances from three water vapor channels on convection-permitting (3 km) analyses and forecasts of the "7.19" severe rainstorm that occurred over north China during 18-21 July 2016 was investigated. Analyses were produced hourly, and 24 h forecasts were produced every 6 h. The results showed that improved wind and humidity fields were obtained in analyses and forecasts verified against conventional observations after assimilating AHI water vapor radiances when compared to the control experiment which assimilated only conventional observations. It was also found that the assimilation of AHI water vapor radiances had a clearly positive impact on the rainfall forecast for the first 6 h lead time, especially for heavy rainfall exceeding 100 mm when verified against the observed rainfall. Furthermore, the horizontal and vertical distribution of features in the moisture fields were improved after assimilating AHI water vapor radiances, eventually contributing to a better forecast of the severe rainstorm.

A Hybrid Approach to Data Assimilation for Reconstructing the Evolution of Mantle Dynamics

NASA Astrophysics Data System (ADS)

Zhou, Quan; Liu, Lijun

2017-11-01

Quantifying past mantle dynamic processes represents a major challenge in understanding the temporal evolution of the solid earth. Mantle convection modeling with data assimilation is one of the most powerful tools to investigate the dynamics of plate subduction and mantle convection. Although various data assimilation methods, both forward and inverse, have been created, these methods all have limitations in their capabilities to represent the real earth. Pure forward models tend to miss important mantle structures due to the incorrect initial condition and thus may lead to incorrect mantle evolution. In contrast, pure tomography-based models cannot effectively resolve the fine slab structure and would fail to predict important subduction-zone dynamic processes. Here we propose a hybrid data assimilation approach that combines the unique power of the sequential and adjoint algorithms, which can properly capture the detailed evolution of the downgoing slab and the tomographically constrained mantle structures, respectively. We apply this new method to reconstructing mantle dynamics below the western U.S. while considering large lateral viscosity variations. By comparing this result with those from several existing data assimilation methods, we demonstrate that the hybrid modeling approach recovers the realistic 4-D mantle dynamics the best.

Hydrologic and geochemical data assimilation at the Hanford 300 Area

NASA Astrophysics Data System (ADS)

Chen, X.; Hammond, G. E.; Murray, C. J.; Zachara, J. M.

2012-12-01

In modeling the uranium migration within the Integrated Field Research Challenge (IFRC) site at the Hanford 300 Area, uncertainties arise from both hydrologic and geochemical sources. The hydrologic uncertainty includes the transient flow boundary conditions induced by dynamic variations in Columbia River stage and the underlying heterogeneous hydraulic conductivity field, while the geochemical uncertainty is a result of limited knowledge of the geochemical reaction processes and parameters, as well as heterogeneity in uranium source terms. In this work, multiple types of data, including the results from constant-injection tests, borehole flowmeter profiling, and conservative tracer tests, are sequentially assimilated across scales within a Bayesian framework to reduce the hydrologic uncertainty. The hydrologic data assimilation is then followed by geochemical data assimilation, where the goal is to infer the heterogeneous distribution of uranium sources using uranium breakthrough curves from a desorption test that took place at high spring water table. We demonstrate in our study that Ensemble-based data assimilation techniques (Ensemble Kalman filter and smoother) are efficient in integrating multiple types of data sequentially for uncertainty reduction. The computational demand is managed by using the multi-realization capability within the parallel PFLOTRAN simulator.

Technical Report Series on Global Modeling and Data Assimilation. Volume 14; A Comparison of GEOS Assimilated Data with FIFE Observations

NASA Technical Reports Server (NTRS)

Bosilovich, Michael G.; Suarez, Max J. (Editor); Schubert, Siegfried D.

1998-01-01

First ISLSCP Field Experiment (FIFE) observations have been used to validate the near-surface proper- ties of various versions of the Goddard Earth Observing System (GEOS) Data Assimilation System. The site- averaged FIFE data set extends from May 1987 through November 1989, allowing the investigation of several time scales, including the annual cycle, daily means and diurnal cycles. Furthermore, the development of the daytime convective planetary boundary layer is presented for several days. Monthly variations of the surface energy budget during the summer of 1988 demonstrate the affect of the prescribed surface soil wetness boundary conditions. GEOS data comes from the first frozen version of the assimilation system (GEOS-1 DAS) and two experimental versions of GEOS (v. 2.0 and 2.1) with substantially greater vertical resolution and other changes that influence the boundary layer. This report provides a baseline for future versions of the GEOS data assimilation system that will incorporate a state-of-the-art land surface parameterization. Several suggestions are proposed to improve the generality of future comparisons. These include the use of more diverse field experiment observations and an estimate of gridpoint heterogeneity from the new land surface parameterization.

A Hybrid Forward-Adjoint Data Assimilation Method for Reconstructing the Temporal Evolution of Mantle Dynamics

NASA Astrophysics Data System (ADS)

Zhou, Q.; Liu, L.

2017-12-01

Quantifying past mantle dynamic processes represents a major challenge in understanding the temporal evolution of the solid earth. Mantle convection modeling with data assimilation is one of the most powerful tools to investigate the dynamics of plate subduction and mantle convection. Although various data assimilation methods, both forward and inverse, have been created, these methods all have limitations in their capabilities to represent the real earth. Pure forward models tend to miss important mantle structures due to the incorrect initial condition and thus may lead to incorrect mantle evolution. In contrast, pure tomography-based models cannot effectively resolve the fine slab structure and would fail to predict important subduction-zone dynamic processes. Here we propose a hybrid data assimilation method that combines the unique power of the sequential and adjoint algorithms, which can properly capture the detailed evolution of the downgoing slab and the tomographically constrained mantle structures, respectively. We apply this new method to reconstructing mantle dynamics below the western U.S. while considering large lateral viscosity variations. By comparing this result with those from several existing data assimilation methods, we demonstrate that the hybrid modeling approach recovers the realistic 4-D mantle dynamics to the best.

Assimilation of MODIS Ice Surface Temperature and Albedo into the Snow and Ice Model CROCUS Over the Greenland Ice Sheet Along the K-transect Stations

NASA Astrophysics Data System (ADS)

Navari, M.; Margulis, S. A.; Bateni, S. M.; Alexander, P. M.; Tedesco, M.

2016-12-01

Estimating the Greenland Ice Sheet (GrIS) surface mass balance (SMB) is an important component of current and future projections of sea level rise. In situ measurement provides direct estimates of the SMB, but are inherently limited by their spatial extent and representativeness. Given this limitation, physically based regional climate models (RCMs) are critical for understanding GrIS physical processes and estimating of the GrIS SMB. However, the uncertainty in estimates of SMB from RCMs is still high. Surface remote sensing (RS) has been used as a complimentary tool to characterize various aspects related to the SMB. The difficulty of using these data streams is that the links between them and the SMB terms are most often indirect and implicit. Given the lack of in situ information, imperfect models, and under-utilized RS data it is critical to merge the available data in a systematic way to better characterize the spatial and temporal variation of the GrIS SMB. This work proposes a data assimilation (DA) framework that yields temporally-continuous and physically consistent SMB estimates that benefit from state-of-the-art models and relevant remote sensing data streams. Ice surface temperature (IST) is the most important factor that regulates partitioning of the net radiation into the subsurface snow/ice, sensible and latent heat fluxes and plays a key role in runoff generation. Therefore it can be expected that a better estimate of surface temperature from a data assimilation system would contribute to a better estimate of surface mass fluxes. Albedo plays an important role in the surface energy balance of the GrIS. However, even advanced albedo modules are not adequate to simulate albedo over the GrIS. Therefore, merging remotely sensed albedo product into a physically based model has a potential to improve the estimates of the GrIS SMB. In this work a MODIS-derived IST and a 16-day albedo product are independently assimilated into the snow and ice model CROCUS. Comparison of our results against the in situ SMB measurements over the K-transect stations shows that assimilation of IST does not considerably improve the GrIS SMB terms. The main reason is hypothesized to be due to a cold bias in the IST product. On the other hand, assimilation of 16-day albedo product reduces the RMSE of the posterior estimates of the SMB by 63%.

A data assimilation experiment of RASTA airborne cloud radar data during HyMeX IOP16

NASA Astrophysics Data System (ADS)

Saussereau, Gaël; Caumont, Olivier; Delanoë, Julien

2015-04-01

The main goal of HyMeX first special observing period (SOP1), which took place from 5 September to 5 November 2012, was to document the heavy precipitation events and flash floods that regularly affect the north-western Mediterranean coastal areas. In the two-month campaign, around twenty rainfall events were documented in France, Italy, and Spain. Among the instrumental platforms that were deployed during SOP1, the Falcon 20 of the Safire unit (http://www.safire.fr/) made numerous flights in storm systems so as to document their thermodynamic, microphysical, and dynamical properties. In particular, the RASTA cloud radar (http://rali.projet.latmos.ipsl.fr/) was aboard this aircraft. This radar measures vertical profiles of reflectivity and Doppler velocity above and below the aircraft. This unique instrument thus allows us to document the microphysical properties and the speed of wind and hydrometeors in the clouds, quasi-continuously in time and at a 60-m vertical resolution. For this field campaign, a special version of the numerical weather prediction (NWP) Arome system was developed to cover the whole north-western Mediterranean basin. This version, called Arome-WMed, ran in real time during the SOP in order to, notably, schedule the airborne operations, especially in storm systems. Like the operational version, Arome-WMed delivers forecasts at a horizontal resolution of 2.5 km with a one-moment microphysical scheme that predicts the evolution of six water species: water vapour, cloud liquid water, rainwater, pristine ice, snow, and graupel. Its three-dimensional variational (3DVar) data assimilation (DA) system ingests every three hours (at 00 UTC, 03 UTC, etc.) numerous observations (radiosoundings, ground automatic weather stations, radar, satellite, GPS, etc.). In order to provide improved initial conditions to Arome-WMed, especially for heavy precipitation events, RASTA data were assimilated in Arome-WMed 3DVar DA system for IOP16 (26 October 2012), to begin with. There were two flights on 26 October and thus RASTA data were assimilated at 2+2 consecutive analysis times (06, 09, 12, and 15 UTC). This task involved a preliminary step to convert the original data into vertical profiles that are suitable for assimilation: the data were averaged to remove noise and match the model's resolution, they were converted to appropriate physical quantities and in a format that is readable by the DA system, etc.). The presentation will show the impact of RASTA data on Arome-WMed analyses and forecasts, both with respect to RASTA data and to independent data (either also assimilated or not).

Considerations for Using Hybrid Ensemble-Variational Data Assimilation in NASA-GMAO's Next Reanalysis System

NASA Technical Reports Server (NTRS)

El Akkraoui, Amal; Todling, Ricardo

2017-01-01

The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) is the latest reanalysis produced by GMAO, and provides global data spanning the period 1980-present. The atmospheric data assimilation component of MERRA-2 used a 3D-Var scheme, which was operational at the time of its design. Since then, a Hybrid 3D-Var, then a Hybrid 4D-EnVar were implemented, adding an ensemble component to the data assimilation scheme. In this work, we will be examining the benefits of using hybrid ensemble flow-dependent covariances to represent errors and uncertainties in historic periods. Specifically, periods of pre- and post-satellites, as well as periods of active tropical cyclone seasons. Finally, we will also be exploring the use of adaptive localization scales.

Retrospective evaluation of continental-scale streamflow nudging with WRF-Hydro National Water Model V1

NASA Astrophysics Data System (ADS)

McCreight, J. L.; Wu, Y.; Gochis, D.; Rafieeinasab, A.; Dugger, A. L.; Yu, W.; Cosgrove, B.; Cui, Z.; Oubeidillah, A.; Briar, D.

2016-12-01

The streamflow (discharge) data assimilation capability in version 1 of the National Water Model (NWM; a WRF-Hydro configuration) is applied and evaluated in a 5-year (2011-2015) retrospective study using NLDAS2 forcing data over CONUS. This talk will describe the NWM V1 operational nudging (continuous-time) streamflow data assimilation approach, its motivation, and its relationship to this retrospective evaluation. Results from this study will provide a an analysis-based (not forecast-based) benchmark for streamflow DA in the NWM. The goal of the assimilation is to reduce discharge bias and improve channel initial conditions for discharge forecasting (though forecasts are not considered here). The nudging method assimilates discharge observations at nearly 7,000 USGS gages (at frequency up to 1/15 minutes) to produce a (univariate) discharge reanalysis (i.e. this is the only variable affected by the assimilation). By withholding 14% nested gages throughout CONUS in a separate validation run, we evaluate the downstream impact of assimilation at upstream gages. Based on this sample, we estimate the skill of the streamflow reanalysis at ungaged locations and examine factors governing the skill of the assimilation. Comparison of assimilation and open-loop runs is presented. Performance of DA under both high and low flow regimes and selected flooding events is examined. Preliminary evaluation of nudging parameter sensitivity and its relationship to flow regime will be presented.

Data assimilation of citizen collected information for real-time flood hazard mapping

NASA Astrophysics Data System (ADS)

Sayama, T.; Takara, K. T.

2017-12-01

Many studies in data assimilation in hydrology have focused on the integration of satellite remote sensing and in-situ monitoring data into hydrologic or land surface models. For flood predictions also, recent studies have demonstrated to assimilate remotely sensed inundation information with flood inundation models. In actual flood disaster situations, citizen collected information including local reports by residents and rescue teams and more recently tweets via social media also contain valuable information. The main interest of this study is how to effectively use such citizen collected information for real-time flood hazard mapping. Here we propose a new data assimilation technique based on pre-conducted ensemble inundation simulations and update inundation depth distributions sequentially when local data becomes available. The propose method is composed by the following two-steps. The first step is based on weighting average of preliminary ensemble simulations, whose weights are updated by Bayesian approach. The second step is based on an optimal interpolation, where the covariance matrix is calculated from the ensemble simulations. The proposed method was applied to case studies including an actual flood event occurred. It considers two situations with more idealized one by assuming continuous flood inundation depth information is available at multiple locations. The other one, which is more realistic case during such a severe flood disaster, assumes uncertain and non-continuous information is available to be assimilated. The results show that, in the first idealized situation, the large scale inundation during the flooding was estimated reasonably with RMSE < 0.4 m in average. For the second more realistic situation, the error becomes larger (RMSE 0.5 m) and the impact of the optimal interpolation becomes comparatively less effective. Nevertheless, the applications of the proposed data assimilation method demonstrated a high potential of this method for assimilating citizen collected information for real-time flood hazard mapping in the future.

Impact of different satellite soil moisture products on the predictions of a continuous distributed hydrological model

NASA Astrophysics Data System (ADS)

Laiolo, P.; Gabellani, S.; Campo, L.; Silvestro, F.; Delogu, F.; Rudari, R.; Pulvirenti, L.; Boni, G.; Fascetti, F.; Pierdicca, N.; Crapolicchio, R.; Hasenauer, S.; Puca, S.

2016-06-01

The reliable estimation of hydrological variables in space and time is of fundamental importance in operational hydrology to improve the flood predictions and hydrological cycle description. Nowadays remotely sensed data can offer a chance to improve hydrological models especially in environments with scarce ground based data. The aim of this work is to update the state variables of a physically based, distributed and continuous hydrological model using four different satellite-derived data (three soil moisture products and a land surface temperature measurement) and one soil moisture analysis to evaluate, even with a non optimal technique, the impact on the hydrological cycle. The experiments were carried out for a small catchment, in the northern part of Italy, for the period July 2012-June 2013. The products were pre-processed according to their own characteristics and then they were assimilated into the model using a simple nudging technique. The benefits on the model predictions of discharge were tested against observations. The analysis showed a general improvement of the model discharge predictions, even with a simple assimilation technique, for all the assimilation experiments; the Nash-Sutcliffe model efficiency coefficient was increased from 0.6 (relative to the model without assimilation) to 0.7, moreover, errors on discharge were reduced up to the 10%. An added value to the model was found in the rainfall season (autumn): all the assimilation experiments reduced the errors up to the 20%. This demonstrated that discharge prediction of a distributed hydrological model, which works at fine scale resolution in a small basin, can be improved with the assimilation of coarse-scale satellite-derived data.

Photosynthetic limitation as a factor influencing yield in highbush blueberries (Vaccinium corymbosum) grown in a northern European environment.

PubMed

Petridis, Antonios; van der Kaay, Jeroen; Chrysanthou, Elina; McCallum, Susan; Graham, Julie; Hancock, Robert D

2018-05-25

Published evidence indicates that nearly 60% of blueberry-producing countries experience yield instability. Yield is a complex trait determined by genetic and environmental factors. Here, using physiological and biochemical approaches, we tested the hypothesis that yield instability results from year-to-year environmental variation that limits carbon assimilation, storage and partitioning. The data indicate that fruit development depends primarily on the daily production of non-structural carbohydrates by leaves, and there is no accumulation of a starch buffer to allow continuous ripening under conditions limiting for photosynthesis. Photosynthesis was saturated at moderate light irradiance and this was mainly due to stomatal and biochemical limitations. In a dynamic light environment, photosynthesis was further limited by slow stomatal response to increasing light. Finally, labelling with 13CO2 at specific stages of fruit development revealed a relatively even distribution of newly assimilated carbon between stems, roots and fruits, suggesting that the fruit is not a strong sink. We conclude that a significant component of yield variability results from limitations in photosynthetic efficiency that are compounded by an inability to accumulate starch reserves in blueberry storage tissues in a typical northern European environment. This work informs techniques for improving agronomic management and indicates key traits required for yield stability in such environments.

Primative components, crustal assimilation, and magmatic degassing of the 2008 Kilauea summit eruption

USGS Publications Warehouse

Rowe, Michael C.; Thornber, Carl R.; Orr, Tim R.

2015-01-01

Simultaneous summit and rift zone eruptions at Kīlauea starting in 2008 reflect a shallow eruptive plumbing system inundated by a bourgeoning supply of new magma from depth. Olivine-hosted melt inclusions, host glass, and bulk lava compositions of magma erupted at both the summit and east rift zone demonstrate chemical continuity at both ends of a well-worn summit-to-rift pipeline. Analysis of glass within dense-cored lapilli erupted from the summit in March – August 2008 show these are not samplings of compositionally distinct magmas stored in the shallow summit magma reservoir, but instead result from remelting and assimilation of fragments from conduit wall and vent blocks. Summit pyroclasts show the predominant and most primitive component erupted to be a homogenous, relatively trace-element-depleted melt that is a compositionally indistinguishable from east rift lava. Based on a “top-down” model for the geochemical variation in east rift zone lava over the past 30 years, we suggest that the apparent absence of a 1982 enriched component in melt inclusions, as well as the proposed summit-rift zone connectivity based on sulfur and mineral chemistry, indicate that the last of the pre-1983 magma has been flushed out of the summit reservoir during the surge of mantle-derived magma from 2003-2007.

Photosynthetic limitation as a factor influencing yield in highbush blueberries (Vaccinium corymbosum) grown in a northern European environment

PubMed Central

van der Kaay, Jeroen; Chrysanthou, Elina; McCallum, Susan

2018-01-01

Abstract Published evidence indicates that nearly 60% of blueberry-producing countries experience yield instability. Yield is a complex trait determined by genetic and environmental factors. Here, using physiological and biochemical approaches, we tested the hypothesis that yield instability results from year-to-year environmental variation that limits carbon assimilation, storage and partitioning. The data indicate that fruit development depends primarily on the daily production of non-structural carbohydrates by leaves, and there is no accumulation of a starch buffer to allow continuous ripening under conditions limiting for photosynthesis. Photosynthesis was saturated at moderate light irradiance and this was mainly due to stomatal and biochemical limitations. In a dynamic light environment, photosynthesis was further limited by slow stomatal response to increasing light. Finally, labelling with 13CO2 at specific stages of fruit development revealed a relatively even distribution of newly assimilated carbon between stems, roots and fruits, suggesting that the fruit is not a strong sink. We conclude that a significant component of yield variability results from limitations in photosynthetic efficiency that are compounded by an inability to accumulate starch reserves in blueberry storage tissues in a typical northern European environment. This work informs techniques for improving agronomic management and indicates key traits required for yield stability in such environments. PMID:29590429

Sensitivity of the model error parameter specification in weak-constraint four-dimensional variational data assimilation

NASA Astrophysics Data System (ADS)

Shaw, Jeremy A.; Daescu, Dacian N.

2017-08-01

This article presents the mathematical framework to evaluate the sensitivity of a forecast error aspect to the input parameters of a weak-constraint four-dimensional variational data assimilation system (w4D-Var DAS), extending the established theory from strong-constraint 4D-Var. Emphasis is placed on the derivation of the equations for evaluating the forecast sensitivity to parameters in the DAS representation of the model error statistics, including bias, standard deviation, and correlation structure. A novel adjoint-based procedure for adaptive tuning of the specified model error covariance matrix is introduced. Results from numerical convergence tests establish the validity of the model error sensitivity equations. Preliminary experiments providing a proof-of-concept are performed using the Lorenz multi-scale model to illustrate the theoretical concepts and potential benefits for practical applications.

Idealized Experiments for Optimizing Model Parameters Using a 4D-Variational Method in an Intermediate Coupled Model of ENSO

NASA Astrophysics Data System (ADS)

Gao, Chuan; Zhang, Rong-Hua; Wu, Xinrong; Sun, Jichang

2018-04-01

Large biases exist in real-time ENSO prediction, which can be attributed to uncertainties in initial conditions and model parameters. Previously, a 4D variational (4D-Var) data assimilation system was developed for an intermediate coupled model (ICM) and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer ( T e), which is empirically and explicitly related to sea level (SL) variation. The strength of the thermocline effect on SST (referred to simply as "the thermocline effect") is represented by an introduced parameter, α Te. A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having their initial condition optimized only, and having their initial condition plus this additional model parameter optimized, are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameters and initial conditions together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Var data assimilation system implemented in the ICM are also discussed.

Using a 4D-Variational Method to Optimize Model Parameters in an Intermediate Coupled Model of ENSO

NASA Astrophysics Data System (ADS)

Gao, C.; Zhang, R. H.

2017-12-01

Large biases exist in real-time ENSO prediction, which is attributed to uncertainties in initial conditions and model parameters. Previously, a four dimentional variational (4D-Var) data assimilation system was developed for an intermediate coupled model (ICM) and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer (Te), which is empirically and explicitly related to sea level (SL) variation, written as Te=αTe×FTe (SL). The introduced parameter, αTe, represents the strength of the thermocline effect on sea surface temperature (SST; referred as the thermocline effect). A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having initial condition optimized only and having initial condition plus this additional model parameter optimized both are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameter and initial condition together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Var data assimilation system implemented in the ICM are also discussed.

Impact of archeomagnetic field model data on modern era geomagnetic forecasts

NASA Astrophysics Data System (ADS)

Tangborn, Andrew; Kuang, Weijia

2018-03-01

A series of geomagnetic data assimilation experiments have been carried out to demonstrate the impact of assimilating archeomagnetic data via the CALS3k.4 geomagnetic field model from the period between 10 and 1590 CE. The assimilation continues with the gufm1 model from 1590 to 1990 and CM4 model from 1990 to 2000 as observations, and comparisons between these models and the geomagnetic forecasts are used to determine an optimal maximum degree for the archeomagnetic observations, and to independently estimate errors for these observations. These are compared with an assimilation experiment that uses the uncertainties provided with CALS3k.4. Optimal 20 year forecasts in 1990 are found when the Gauss coefficients up to degree 3 are assimilated. In addition we demonstrate how a forecast and observation bias correction scheme could be used to reduce bias in modern era forecasts. Initial experiments show that this approach can reduce modern era forecast biases by as much as 50%.

Bioavailability of particle-associated silver, cadmium, and zinc to the estuarine amphipod Leptocheirus plumulosus through dietary ingestion

USGS Publications Warehouse

Schlekat, C.E.; Decho, Alan W.; Chandler, G.T.

2000-01-01

We conducted experiments to determine effects of particle type on assimilatory metal bioavailability to Leptocheirus plumulosus, an infaunal, estuarine amphipod that is commonly used in sediment toxicity tests. The following particles were used to represent natural food items encountered by this surface-deposit and suspension-feeding amphipod: bacterial exopolymeric sediment coatings, polymeric coatings made from Spartina alterniflora extract, amorphous iron oxide coatings, the diatom Phaeodactylum tricornutum, the chlorophyte Dunaliella tertiolecta, processed estuarine sediment, and fresh estuarine sediment. Bioavailability of the gamma-emitting radioisotopes 110mAg, 109Cd, and 65Zn was measured as the efficiency with which L. plumulosus assimilated metals from particles using pulse-chase methods. Ag and Cd assimilation efficiencies were highest from bacterial exopolymeric coatings. Zn assimilation efficiency exhibited considerable interexperimental variation; the highest Zn assimilation efficiencies were measured from phytoplankton and processed sediment. In general, Ag and Cd assimilation efficiencies from phytoplankton were low and not related to the proportion of metal associated with cell cytosol or cytoplasm, a phenomenon reported for other particle-ingesting invertebrates. Amphipod digestive processes explain differences in Ag and Cd assimilation efficiencies between exopolymeric coatings and phytoplankton. Results highlight the importance of labile polymeric organic carbon sediment coatings in dietary metals uptake by this benthic invertebrate, rather than recalcitrant organic carbon, mineralogical features such as iron oxides, or phytoplankton.

Spectral characteristics of background error covariance and multiscale data assimilation

DOE PAGES

Li, Zhijin; Cheng, Xiaoping; Gustafson, Jr., William I.; ...

2016-05-17

The steady increase of the spatial resolutions of numerical atmospheric and oceanic circulation models has occurred over the past decades. Horizontal grid spacing down to the order of 1 km is now often used to resolve cloud systems in the atmosphere and sub-mesoscale circulation systems in the ocean. These fine resolution models encompass a wide range of temporal and spatial scales, across which dynamical and statistical properties vary. In particular, dynamic flow systems at small scales can be spatially localized and temporarily intermittent. Difficulties of current data assimilation algorithms for such fine resolution models are numerically and theoretically examined. Ourmore » analysis shows that the background error correlation length scale is larger than 75 km for streamfunctions and is larger than 25 km for water vapor mixing ratios, even for a 2-km resolution model. A theoretical analysis suggests that such correlation length scales prevent the currently used data assimilation schemes from constraining spatial scales smaller than 150 km for streamfunctions and 50 km for water vapor mixing ratios. Moreover, our results highlight the need to fundamentally modify currently used data assimilation algorithms for assimilating high-resolution observations into the aforementioned fine resolution models. Lastly, within the framework of four-dimensional variational data assimilation, a multiscale methodology based on scale decomposition is suggested and challenges are discussed.« less

Nitrogen assimilation in denitrifier Bacillus azotoformans LMG 9581T.

PubMed

Sun, Yihua; De Vos, Paul; Willems, Anne

2017-12-01

Until recently, it has not been generally known that some bacteria can contain the gene inventory for both denitrification and dissimilatory nitrate (NO 3 - )/nitrite (NO 2 - ) reduction to ammonium (NH 4 + ) (DNRA). Detailed studies of these microorganisms could shed light on the differentiating environmental drivers of both processes without interference of organism-specific variation. Genome analysis of Bacillus azotoformans LMG 9581 T shows a remarkable redundancy of dissimilatory nitrogen reduction, with multiple copies of each denitrification gene as well as DNRA genes nrfAH, but a reduced capacity for nitrogen assimilation, with no nas operon nor amtB gene. Here, we explored nitrogen assimilation in detail using growth experiments in media with different organic and inorganic nitrogen sources at different concentrations. Monitoring of growth, NO 3 - NO 2 - , NH 4 + concentration and N 2 O production revealed that B. azotoformans LMG 9581 T could not grow with NH 4 + as sole nitrogen source and confirmed the hypothesis of reduced nitrogen assimilation pathways. However, NH 4 + could be assimilated and contributed up to 50% of biomass if yeast extract was also provided. NH 4 + also had a significant but concentration-dependent influence on growth rate. The mechanisms behind these observations remain to be resolved but hypotheses for this deficiency in nitrogen assimilation are discussed. In addition, in all growth conditions tested a denitrification phenotype was observed, with all supplied NO 3 - converted to nitrous oxide (N 2 O).

Supporting Operational Data Assimilation Capabilities to the Research Community

NASA Astrophysics Data System (ADS)

Shao, H.; Hu, M.; Stark, D. R.; Zhou, C.; Beck, J.; Ge, G.

2017-12-01

The Developmental Testbed Center (DTC), in partnership with the National Centers for Environmental Prediction (NCEP) and other operational and research institutions, provides operational data assimilation capabilities to the research community and helps transition research advances to operations. The primary data assimilation system supported currently by the DTC is the Gridpoint Statistical Interpolation (GSI) system and the National Oceanic and Atmospheric Administration (NOAA) Ensemble Kalman Filter (EnKF) system. GSI is a variational based system being used for daily operations at NOAA, NCEP, the National Aeronautics and Space Administration, and other operational agencies. Recently, GSI has evolved into a four-dimensional EnVar system. Since 2009, the DTC has been releasing the GSI code to the research community annually and providing user support. In addition to GSI, the DTC, in 2015, began supporting the ensemble based EnKF data assimilation system. EnKF shares the observation operator with GSI and therefore, just as GSI, can assimilate both conventional and non-conventional data (e.g., satellite radiance). Currently, EnKF is being implemented as part of the GSI based hybrid EnVar system for NCEP Global Forecast System operations. This paper will summarize the current code management and support framework for these two systems. Following that is a description of available community services and facilities. Also presented is the pathway for researchers to contribute their development to the daily operations of these data assimilation systems.

Ensemble kalman filtering to perform data assimilation with soil water content probes and pedotransfer functions in modeling water flow in variably saturated soils

USDA-ARS?s Scientific Manuscript database

Data from modern soil water contents probes can be used for data assimilation in soil water flow modeling, i.e. continual correction of the flow model performance based on observations. The ensemble Kalman filter appears to be an appropriate method for that. The method requires estimates of the unce...

Robust Hydrological Forecasting for High-resolution Distributed Models Using a Unified Data Assimilation Approach

NASA Astrophysics Data System (ADS)

Hernandez, F.; Liang, X.

2017-12-01

Reliable real-time hydrological forecasting, to predict important phenomena such as floods, is invaluable to the society. However, modern high-resolution distributed models have faced challenges when dealing with uncertainties that are caused by the large number of parameters and initial state estimations involved. Therefore, to rely on these high-resolution models for critical real-time forecast applications, considerable improvements on the parameter and initial state estimation techniques must be made. In this work we present a unified data assimilation algorithm called Optimized PareTo Inverse Modeling through Inverse STochastic Search (OPTIMISTS) to deal with the challenge of having robust flood forecasting for high-resolution distributed models. This new algorithm combines the advantages of particle filters and variational methods in a unique way to overcome their individual weaknesses. The analysis of candidate particles compares model results with observations in a flexible time frame, and a multi-objective approach is proposed which attempts to simultaneously minimize differences with the observations and departures from the background states by using both Bayesian sampling and non-convex evolutionary optimization. Moreover, the resulting Pareto front is given a probabilistic interpretation through kernel density estimation to create a non-Gaussian distribution of the states. OPTIMISTS was tested on a low-resolution distributed land surface model using VIC (Variable Infiltration Capacity) and on a high-resolution distributed hydrological model using the DHSVM (Distributed Hydrology Soil Vegetation Model). In the tests streamflow observations are assimilated. OPTIMISTS was also compared with a traditional particle filter and a variational method. Results show that our method can reliably produce adequate forecasts and that it is able to outperform those resulting from assimilating the observations using a particle filter or an evolutionary 4D variational method alone. In addition, our method is shown to be efficient in tackling high-resolution applications with robust results.

Assimilation of HF Radar Observations in the Chesapeake-Delaware Bay Region Using the Navy Coastal Ocean Model (NCOM) and the Four-Dimensional Variational (4DVAR) Method

DTIC Science & Technology

2015-01-01

6. Zhang WG, Wilkin JL, Arango HG. Towards an integrated observation and modeling system in the New York Bight using variational methods. Part 1...1992;7:262- 72. ---- -- - ---------------------------- References 391 17. Rosmond TE, Teixeria J, Pcng M, Hogan TF, Pauley R. Navy operational global

SEVIRI 4D-var assimilation analysing the April 2010 Eyjafjallajökull ash dispersion

NASA Astrophysics Data System (ADS)

Lange, Anne Caroline; Elbern, Hendrik

2016-04-01

We present first results of four dimensional variational (4D-var) data assimilation analysis applying SEVIRI observations to the Eulerian regional chemistry and aerosol transport model EURAD-IM (European Air Pollution Dispersion - Inverse Model). Optimising atmospheric dispersion models in terms of volcanic ash transport predictions by observations is especially essential for the aviation industry and associated interests. Remote sensing satellite observations are instrumental for ash detection and monitoring. We choose volcanic ash column retrievals of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) because as infrared instrument on the geostationary satellite Meteosat Second Generation it delivers measurements with high temporal resolution during day and night. The retrieval method relies on the reverse absorption effect. In the framework of the national initiative ESKP (Earth System Knowledge Platform) and the European ACTRIS-2 (Aerosol, Clouds, and Trace gases Research InfraStructure) project, we developed new modules (forward and adjoint) within the EURAD-IM, which are able to process SEVIRI ash column data as observational input to the 4D-var system. The focus of the 4D-var analysis is on initial value optimisation of the volcanic ash clouds that were emitted during the explosive Eyjafjallajökull eruption in April 2010. This eruption caused high public interest because of air traffic closures and it was particularly well observed from many different observation systems all over Europe. Considering multiple observation periods simultaneously in one assimilation window generates a continuous trajectory in the phase space and ensures that past observations are considered within their uncertainties. Results are validated mainly by lidar (LIght Detection And Ranging) observations, both ground and satellite based.

Geotechnical parameter spatial distribution stochastic analysis based on multi-precision information assimilation

NASA Astrophysics Data System (ADS)

Wang, C.; Rubin, Y.

2014-12-01

Spatial distribution of important geotechnical parameter named compression modulus Es contributes considerably to the understanding of the underlying geological processes and the adequate assessment of the Es mechanics effects for differential settlement of large continuous structure foundation. These analyses should be derived using an assimilating approach that combines in-situ static cone penetration test (CPT) with borehole experiments. To achieve such a task, the Es distribution of stratum of silty clay in region A of China Expo Center (Shanghai) is studied using the Bayesian-maximum entropy method. This method integrates rigorously and efficiently multi-precision of different geotechnical investigations and sources of uncertainty. Single CPT samplings were modeled as a rational probability density curve by maximum entropy theory. Spatial prior multivariate probability density function (PDF) and likelihood PDF of the CPT positions were built by borehole experiments and the potential value of the prediction point, then, preceding numerical integration on the CPT probability density curves, the posterior probability density curve of the prediction point would be calculated by the Bayesian reverse interpolation framework. The results were compared between Gaussian Sequential Stochastic Simulation and Bayesian methods. The differences were also discussed between single CPT samplings of normal distribution and simulated probability density curve based on maximum entropy theory. It is shown that the study of Es spatial distributions can be improved by properly incorporating CPT sampling variation into interpolation process, whereas more informative estimations are generated by considering CPT Uncertainty for the estimation points. Calculation illustrates the significance of stochastic Es characterization in a stratum, and identifies limitations associated with inadequate geostatistical interpolation techniques. This characterization results will provide a multi-precision information assimilation method of other geotechnical parameters.

Degraded Land Restoration in Reinstating CH4 Sink

PubMed Central

Singh, Jay Shankar; Gupta, Vijai K.

2016-01-01

Methane (CH4), a potent greenhouse gas, contributes about one third to the global green house gas emissions. CH4-assimilating microbes (mostly methanotrophs) in upland soils play very crucial role in mitigating the CH4 release into the atmosphere. Agricultural, environmental, and climatic shifts can alter CH4 sink profiles of soils, likely through shifts in CH4-assimilating microbial community structure and function. Landuse change, as forest and grassland ecosystems altered to agro-ecosystems, has already attenuated the soil CH4 sink potential, and are expected to be continued in the future. We hypothesized that variations in CH4 uptake rates in soils under different landuse practices could be an indicative of alterations in the abundance and/or type of methanotrophic communities in such soils. However, only a few studies have addressed to number and methanotrophs diversity and their correlation with the CH4 sink potential in soils of rehabilitated/restored lands. We focus on landuse practices that can potentially mitigate CH4 gas emissions, the most prominent of which are improved cropland, grazing land management, use of bio-fertilizers, and restoration of degraded lands. In this perspective paper, it is proposed that restoration of degraded lands can contribute considerably to improved soil CH4 sink strength by retrieving/conserving abundance and assortment of efficient methanotrophic communities. We believe that this report can assist in identifying future experimental directions to the relationships between landuse changes, methane-assimilating microbial communities and soil CH4 sinks. The exploitation of microbial communities other than methanotrophs can contribute significantly to the global CH4 sink potential and can add value in mitigating the CH4 problems. PMID:27379053

Degraded Land Restoration in Reinstating CH4 Sink.

PubMed

Singh, Jay Shankar; Gupta, Vijai K

2016-01-01

Methane (CH4), a potent greenhouse gas, contributes about one third to the global green house gas emissions. CH4-assimilating microbes (mostly methanotrophs) in upland soils play very crucial role in mitigating the CH4 release into the atmosphere. Agricultural, environmental, and climatic shifts can alter CH4 sink profiles of soils, likely through shifts in CH4-assimilating microbial community structure and function. Landuse change, as forest and grassland ecosystems altered to agro-ecosystems, has already attenuated the soil CH4 sink potential, and are expected to be continued in the future. We hypothesized that variations in CH4 uptake rates in soils under different landuse practices could be an indicative of alterations in the abundance and/or type of methanotrophic communities in such soils. However, only a few studies have addressed to number and methanotrophs diversity and their correlation with the CH4 sink potential in soils of rehabilitated/restored lands. We focus on landuse practices that can potentially mitigate CH4 gas emissions, the most prominent of which are improved cropland, grazing land management, use of bio-fertilizers, and restoration of degraded lands. In this perspective paper, it is proposed that restoration of degraded lands can contribute considerably to improved soil CH4 sink strength by retrieving/conserving abundance and assortment of efficient methanotrophic communities. We believe that this report can assist in identifying future experimental directions to the relationships between landuse changes, methane-assimilating microbial communities and soil CH4 sinks. The exploitation of microbial communities other than methanotrophs can contribute significantly to the global CH4 sink potential and can add value in mitigating the CH4 problems.

Drought effect on growth, gas exchange and yield, in two strains of local barley Ardhaoui, under water deficit conditions in southern Tunisia.

PubMed

Thameur, Afwa; Lachiheb, Belgacem; Ferchichi, Ali

2012-12-30

Two local barley strains cv. Ardhaoui originated from Tlalit and Switir, sourthern Tunisia were grown in pots in a glasshouse assay, under well-watered conditions for a month. Plants were then either subjected to water deficit (treatment) or continually well-watered (control). Control pots were irrigated several times each week to maintain soil moisture near field capacity (FC), while stress pots experienced soil drying by withholding irrigation until they reached 50% of FC. Variation in relative water content, leaf area, leaf appearance rate and leaf gas exchange (i.e. net CO(2) assimilation rate (A), transpiration (E), and stomatal conductance (gs)) in response to water deficit was investigated. High leaf relative water content (RWC) was maintained in Tlalit by stomatal closure and a reduction of leaf area. Reduction in leaf area was due to decline in leaf gas exchange during water deficit. Tlalit was found to be drought tolerant and able to maintain higher leaf RWC under drought conditions. Water deficit treatment reduced stomatal conductance by 43% at anthesis. High net CO(2) assimilation rate under water deficit was associated with high RWC (r = 0.998; P < 0.01). Decline in net CO(2) assimilation rate was due mainly to stomatal closure. Significant differences between studied strains in leaf gas exchange parameters were found, which can give some indications on the degree of drought tolerance. Thus, the ability of the low leaf area plants to maintain higher RWC could explain the differences in drought tolerance in studied barley strains. Results showed that Tlalit showed to be more efficient and more productive than Switir. Copyright © 2012 Elsevier Ltd. All rights reserved.

Hybrid Data Assimilation without Ensemble Filtering

NASA Technical Reports Server (NTRS)

Todling, Ricardo; Akkraoui, Amal El

2014-01-01

The Global Modeling and Assimilation Office is preparing to upgrade its three-dimensional variational system to a hybrid approach in which the ensemble is generated using a square-root ensemble Kalman filter (EnKF) and the variational problem is solved using the Grid-point Statistical Interpolation system. As in most EnKF applications, we found it necessary to employ a combination of multiplicative and additive inflations, to compensate for sampling and modeling errors, respectively and, to maintain the small-member ensemble solution close to the variational solution; we also found it necessary to re-center the members of the ensemble about the variational analysis. During tuning of the filter we have found re-centering and additive inflation to play a considerably larger role than expected, particularly in a dual-resolution context when the variational analysis is ran at larger resolution than the ensemble. This led us to consider a hybrid strategy in which the members of the ensemble are generated by simply converting the variational analysis to the resolution of the ensemble and applying additive inflation, thus bypassing the EnKF. Comparisons of this, so-called, filter-free hybrid procedure with an EnKF-based hybrid procedure and a control non-hybrid, traditional, scheme show both hybrid strategies to provide equally significant improvement over the control; more interestingly, the filter-free procedure was found to give qualitatively similar results to the EnKF-based procedure.

The Roles of the Yellowstone Hotspot and Crustal Assimilation in Generating Pleistocene-Holocene Basalts on the Eastern Snake River Plain

NASA Astrophysics Data System (ADS)

Mintz, H.; Chadwick, J.

2017-12-01

The southwest motion of the North American plate across the Yellowstone hotspot created a chain of age-progressive rhyolitic calderas over the past 16 myr. in southern Idaho, U.S. The focus of Yellowstone activity now resides in northwest Wyoming, but basaltic volcanism has continued in its wake in southern Idaho on the eastern Snake River Plain (ESRP). These younger basaltic lavas are not age progressive and have buried the Yellowstone rhyolites on the ESRP. The ultimate source of the basalts is commonly ascribed to the passage or presence of the hotspot. However, the mechanisms involved, and the relative roles of the hotspot, other mantle sources, and the North American crust in generating the ESRP basalts remain unclear and have been the subject of recent geochemical and isotopic studies. In this study, the role of crustal assimilation is addressed by analyzing the chemical and isotopic characteristics of some of the youngest Pleistocene-Holocene tholeiitic volcanic fields on the ESRP, which were erupted through varying thicknesses of continental crust. Samples were analyzed from the Hell's Half Acre flow (5,200 years old; all dates Kuntz et al., 1986, 1994), Cerro Grande flow (13,380 years), and Black Butte Crater (a.k.a. Shoshone) flow (10,130 years), which were erupted at distances from between about 200 to 300 km from the current location of the hotspot. The crust of the ESRP thins from northeast to southwest, from about 47 km at the Hells Half Acre flow to 40 km at the Black Butte Crater flow, a thickness difference of about 15%. The apparently similar tectonic and magmatic environments of the three sampled flows suggest the crustal thickness variation may be a primary influence on the magnitude of assimilation and therefore the isotopic characteristics of the lavas. The goal of this work is to constrain the relative role of assimilation and to understand the source(s) of the magmas and the Yellowstone hotspot contribution. Major elements, trace elements, and Pb-Sr-Nd isotopic data for the flows and crustal xenoliths, in conjunction with mixing modeling with MELTS software, constrain potential differences in fractional crystallization, residence times in the crust, and crustal assimilation for the flows.

Source Attribution of Near-surface Ozone in the Western US: Improved Estimates by TF HTAP2 Multi-model Experiment and Multi-scale Chemical Data Assimilation

NASA Astrophysics Data System (ADS)

Huang, M.; Bowman, K. W.; Carmichael, G. R.; Lee, M.; Park, R.; Henze, D. K.; Chai, T.; Flemming, J.; Lin, M.; Weinheimer, A. J.; Wisthaler, A.; Jaffe, D. A.

2014-12-01

Near-surface ozone in the western US can be sensitive to transported background pollutants from the free troposphere over the eastern Pacific, as well as various local emissions sources. Accurately estimating ozone source contributions in this region has strong policy-relevant significance as the air quality standards tend to go down. Here we improve modeled contributions from local and non-local sources to western US ozone base on the HTAP2 (Task Force on Hemispheric Transport of Air Pollution) multi-model experiment, along with multi-scale chemical data assimilation. We simulate western US air quality using the STEM regional model on a 12 km horizontal resolution grid, during the NASA ARCTAS field campaign period in June 2008. STEM simulations use time-varying boundary conditions downscaled from global GEOS-Chem model simulations. Standard GEOS-Chem simulation overall underpredicted ozone at 1-5 km in the eastern Pacific, resulting in underestimated contributions from the transported background pollutants to surface ozone inland. These negative biases can be reduced by using the output from several global models that support the HTAP2 experiment, which all ran with the HTAP2 harmonized emission inventory and also calculated the contributions from east Asian anthropogenic emissions. We demonstrate that the biases in GEOS-Chem boundary conditions can be more efficiently reduced via assimilating satellite ozone profiles from the Tropospheric Emission Spectrometer (TES) instrument using the three dimensional variational (3D-Var) approach. Base upon these TES-constrained GEOS-Chem boundary conditions, we then update regional nitrogen dioxide and isoprene emissions in STEM through the four dimensional variational (4D-Var) assimilation of the Ozone Monitoring Instrument (OMI) nitrogen dioxide columns and the NASA DC-8 aircraft isoprene measurements. The 4D-Var assimilation spatially redistributed the emissions of nitrogen oxides and isoprene from various US sources, and in the meantime updated the modeled ozone and its US source contributions. Compared with available independent measurements (e.g., ozone observed on the DC-8 aircraft, and at EPA and Mt. Bachelor monitoring stations) during this period, modeled ozone fields after the multi-scale assimilation show overall improvement.

Studying Diurnal Variations of Aerosols with NASA MERRA-2 Reanalysis Data

NASA Technical Reports Server (NTRS)

Shen, Suhung; Ostrenga, Dana M.; Zeng, Jian; Vollmer, Bruce E.

2018-01-01

Aerosols play an important role in atmospheric dynamics, climate variations, and Earth's energy cycle by altering the radiation balance in the atmosphere through interaction with clouds, providing fertilizer for forests and canopy, and as a supply of iron to the ocean over long time periods. Studies suggest that much of the feedback between dust aerosols and dynamics is associated with diurnal and synoptic scale variability. However, the lack of sub-daily resolution of aerosols from satellite observations makes it difficult to study the diurnal characteristics, especially over tropical and subtropical regions. Investigation of this topic utilizes over 37 years of simulated global aerosol products from NASA atmospheric reanalysis, in the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) data set, available from NASA Goddard Earth Science Data and Information Services Center (GES DISC). MERRA-2 covers the period 1980-present, and is continuing as an ongoing climate analysis. Aerosol assimilation is included throughout the period, using data from MODIS, MISR, AERONET, and AVHRR (in the pre-EOS period). The aerosols are assimilated using the MERRA-2 aerosol model, which interacts directly with radiation parameterization, and is radiatively coupled with atmospheric model dynamics in the Goddard Earth Observing System Model, Version 5 (GEOS-5). Hourly, monthly, and monthly diurnal data are available at spatial resolution of 0.5o x 0.625o (latitude x longitude). By using MERRA-2 hourly and monthly diurnal products, different aerosol diurnal variabilities are observed over North America, Africa, Asia, and Australia, that may be due to different meteorological conditions and aerosol sources. The presentation will also provide an overview of MERRA-2 data services at GES DISC, such as how to find and download data, and how to quickly visualize and analyze data online with Giovanni.

Holocene population history of the Sabana de Bogotá region, Northern South America: An assessment of the craniofacial shape variation.

PubMed

Delgado, Miguel

2017-02-01

Several authors using multiple and independent lines of evidence investigating the biocultural continuity versus discontinuity in the Sabana de Bogotá region, in the eastern highlands of Colombia, have arrived at contradictory conclusions supporting either scenarios. This study analyzes the craniofacial size and shape variation of diachronic samples from the study region to test distinct population history scenarios that support continuity or, alternatively, divergence. A total of 92 adult skulls belonging to five chronological groups, ranging from c. 10,100 to 350 14 C YBP, were analyzed through Procrustean geometric morphometric techniques. Matrix correlation analysis, multivariate exploratory (PCA, FDA), and evolutionary quantitative genetic methods (R-matrix analysis and β-test) were used to study the diachronic craniofacial shape variation. A model that supports strong evolutionary diversification over the Holocene better explains the patterns of morphological variation observed. At least two periods of significant craniofacial size and shape change were detected: one during the middle to initial late Holocene transition (c. 4,000-3,200 14 C YBP) and other toward the final late Holocene (post-2,000 14 C YBP), which exhibit differences in the pattern and magnitude of cranial divergence. In addition, the differentiation viewed between early and mid-Holocene foragers could mark the initial entry of non-local populations into the region toward the beginnings of the middle Holocene. Distinct to previous investigations the present study supports a more complex regional population history where multiple population contractions/extinctions, dispersals and assimilations along with dietary adaptations took place during the last 10,000 years. These results are in agreement with the archaeological and paleoecological record which suggests marked periods of change rather than temporal stability. © 2016 Wiley Periodicals, Inc.

Diel variation of the cellular carbon to nitrogen ratio of Chlorella autotrophica (Chlorophyta) growing in phosphorus- and nitrogen-limited continuous cultures.

PubMed

Ng, Wai Ho Albert; Liu, Hongbin

2015-02-01

We investigated the relationship between daily growth rates and diel variation of carbon (C) metabolism and C to nitrogen (N) ratio under P- and N-limitation in the green algae Chlorella autotrophica. To do this, continuous cultures of C. autotrophica were maintained in a cyclostat culture system under 14:10 light:dark cycle over a series of P- and N-limited growth rates. Cell abundance, together with cell size, as reflected by side scatter signal from flow cytometric analysis demonstrated a synchronized diel pattern with cell division occurring at night. Under either type of nutrient limitation, the cellular C:N ratio increased through the light period and decreased through the dark period over all growth rates, indicating a higher diel variation of C metabolism than that of N. Daily average cellular C:N ratios were higher at lower dilution rates under both types of nutrient limitation but cell enlargement was only observed at lower dilution rates under P-limitation. Carbon specific growth rates during the dark period positively correlated with cellular daily growth rates (dilution rates), with net loss of C during night at the lowest growth rates under N-limitation. Under P-limitation, dark C specific growth rates were close to zero at low dilution rates but also exhibited an increasing trend at high dilution rates. In general, diel variations of cellular C:N were low when dark C specific growth rates were high. This result indicated that the fast growing cells performed dark C assimilation at high rates, hence diminished the uncoupling of C and N metabolism at night. © 2014 Phycological Society of America.

On the assimilation of absolute geodetic dynamic topography in a global ocean model: impact on the deep ocean state

NASA Astrophysics Data System (ADS)

Androsov, Alexey; Nerger, Lars; Schnur, Reiner; Schröter, Jens; Albertella, Alberta; Rummel, Reiner; Savcenko, Roman; Bosch, Wolfgang; Skachko, Sergey; Danilov, Sergey

2018-05-01

General ocean circulation models are not perfect. Forced with observed atmospheric fluxes they gradually drift away from measured distributions of temperature and salinity. We suggest data assimilation of absolute dynamical ocean topography (DOT) observed from space geodetic missions as an option to reduce these differences. Sea surface information of DOT is transferred into the deep ocean by defining the analysed ocean state as a weighted average of an ensemble of fully consistent model solutions using an error-subspace ensemble Kalman filter technique. Success of the technique is demonstrated by assimilation into a global configuration of the ocean circulation model FESOM over 1 year. The dynamic ocean topography data are obtained from a combination of multi-satellite altimetry and geoid measurements. The assimilation result is assessed using independent temperature and salinity analysis derived from profiling buoys of the AGRO float data set. The largest impact of the assimilation occurs at the first few analysis steps where both the model ocean topography and the steric height (i.e. temperature and salinity) are improved. The continued data assimilation over 1 year further improves the model state gradually. Deep ocean fields quickly adjust in a sustained manner: A model forecast initialized from the model state estimated by the data assimilation after only 1 month shows that improvements induced by the data assimilation remain in the model state for a long time. Even after 11 months, the modelled ocean topography and temperature fields show smaller errors than the model forecast without any data assimilation.

Use of the 1991 ASCOT field study data in a mesoscale model employing a four-dimensional data assimilation technique

NASA Astrophysics Data System (ADS)

Fast, Jerome D.; Osteen, B. Lance

In this study, a four-dimensional data assimilation technique based on Newtonian relaxation is incorporated into the Colorado State University (CSU) Regional Atmospheric Modeling System (RAMS) and evaluated using data taken from one experiment of the US Department of Energy's (DOE) 1991 Atmospheric Studies in COmplex Terrain (ASCOT) field study along the front range of the Rockies in Colorado. The main objective of this study is to determine the ability of the model to predict small-scale circulations influenced by terrain, such as drainage flows, and assess the impact of data assimilation on the numerical results. In contrast to previous studies in which the smallest horizontal grid spacing was 10 km and 8 km, data assimilation is applied in this study to domains with a horizontal grid spacing as small as 1 km. The prognostic forecasts made by RAMS are evaluated by comparing simulations that employ static initial conditions, with simulations that incorporate continuous data assimilation, and data assimilation for a fixed period of time (dynamic initialization). This paper will also elaborate on the application and limitation of the Newtonian relaxation technique in limited-area mesoscale models with a relatively small grid spacing.

Continuous data assimilation for the three-dimensional Brinkman-Forchheimer-extended Darcy model

NASA Astrophysics Data System (ADS)

Markowich, Peter A.; Titi, Edriss S.; Trabelsi, Saber

2016-04-01

In this paper we introduce and analyze an algorithm for continuous data assimilation for a three-dimensional Brinkman-Forchheimer-extended Darcy (3D BFeD) model of porous media. This model is believed to be accurate when the flow velocity is too large for Darcy’s law to be valid, and additionally the porosity is not too small. The algorithm is inspired by ideas developed for designing finite-parameters feedback control for dissipative systems. It aims to obtain improved estimates of the state of the physical system by incorporating deterministic or noisy measurements and observations. Specifically, the algorithm involves a feedback control that nudges the large scales of the approximate solution toward those of the reference solution associated with the spatial measurements. In the first part of the paper, we present a few results of existence and uniqueness of weak and strong solutions of the 3D BFeD system. The second part is devoted to the convergence analysis of the data assimilation algorithm.

Inclusion of Linearized Moist Physics in Nasa's Goddard Earth Observing System Data Assimilation Tools

NASA Technical Reports Server (NTRS)

Holdaway, Daniel; Errico, Ronald; Gelaro, Ronaldo; Kim, Jong G.

2013-01-01

Inclusion of moist physics in the linearized version of a weather forecast model is beneficial in terms of variational data assimilation. Further, it improves the capability of important tools, such as adjoint-based observation impacts and sensitivity studies. A linearized version of the relaxed Arakawa-Schubert (RAS) convection scheme has been developed and tested in NASA's Goddard Earth Observing System data assimilation tools. A previous study of the RAS scheme showed it to exhibit reasonable linearity and stability. This motivates the development of a linearization of a near-exact version of the RAS scheme. Linearized large-scale condensation is included through simple conversion of supersaturation into precipitation. The linearization of moist physics is validated against the full nonlinear model for 6- and 24-h intervals, relevant to variational data assimilation and observation impacts, respectively. For a small number of profiles, sudden large growth in the perturbation trajectory is encountered. Efficient filtering of these profiles is achieved by diagnosis of steep gradients in a reduced version of the operator of the tangent linear model. With filtering turned on, the inclusion of linearized moist physics increases the correlation between the nonlinear perturbation trajectory and the linear approximation of the perturbation trajectory. A month-long observation impact experiment is performed and the effect of including moist physics on the impacts is discussed. Impacts from moist-sensitive instruments and channels are increased. The effect of including moist physics is examined for adjoint sensitivity studies. A case study examining an intensifying Northern Hemisphere Atlantic storm is presented. The results show a significant sensitivity with respect to moisture.

Development of the nowcasting system for the XVII Asiad at Korea Meteorological Administration

NASA Astrophysics Data System (ADS)

Park, Kyungjeen; Kim, Juwon; Jang, Taekyu; Hwang, Seung On; Park, Yunho; Kim, Yoonjae; Park, Seonjoo; Joo, Sangwon; Noh, Hae Mi

2014-05-01

The XVII Asiad, known as the 2014 Asian game, is the largest sporting event in Asia. It will be held in Incheon, South Korea from September 19 to October 4, with 437 events in 36 sports. To support this game, Korea Meteorological Administration developed Incheon Data Assimilation and Prediction System (IDAPS) for nowcasting and very short range forecasts. The domain is centered at Incheon city and covers the central region of the Korean peninsula and adjacent seas. It repeats analysis and forecast processes with 1 hour cycling interval. IDAPS has approximately 1 km horizontal resolution with 324 x 360 grids and 70 vertical layers. Three dimensional variational data assimilation is applied to assimilate AWS, windprofiler, buoy, sonde, aircraft, scatwinds, rain rate, and radar products. The details of IDAPS and the experiment results will be given during the conference.

Impact of TRMM and SSM/I Rainfall Assimilation on Global Analysis and QPF

NASA Technical Reports Server (NTRS)

Hou, Arthur; Zhang, Sara; Reale, Oreste

2002-01-01

Evaluation of QPF skills requires quantitatively accurate precipitation analyses. We show that assimilation of surface rain rates derived from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager and Special Sensor Microwave/Imager (SSM/I) improves quantitative precipitation estimates (QPE) and many aspects of global analyses. Short-range forecasts initialized with analyses with satellite rainfall data generally yield significantly higher QPF threat scores and better storm track predictions. These results were obtained using a variational procedure that minimizes the difference between the observed and model rain rates by correcting the moist physics tendency of the forecast model over a 6h assimilation window. In two case studies of Hurricanes Bonnie and Floyd, synoptic analysis shows that this procedure produces initial conditions with better-defined tropical storm features and stronger precipitation intensity associated with the storm.

A new version of variational integrated technology for environmental modeling with assimilation of available data

NASA Astrophysics Data System (ADS)

Penenko, Vladimir; Tsvetova, Elena; Penenko, Aleksey

2014-05-01

A modeling technology based on coupled models of atmospheric dynamics and chemistry are presented [1-3]. It is the result of application of variational methods in combination with the methods of decomposition and splitting. The idea of Euler's integrating factors combined with technique of adjoint problems is also used. In online technologies, a significant part of algorithmic and computational work consist in solving the problems like convection-diffusion-reaction and in organizing data assimilation techniques based on them. For equations of convection-diffusion, the methodology gives us the unconditionally stable and monotone discrete-analytical schemes in the frames of methods of decomposition and splitting. These schemes are exact for locally one-dimensional problems respect to the spatial variables. For stiff systems of equations describing transformation of gas and aerosol substances, the monotone and stable schemes are also obtained. They are implemented by non- iterative algorithms. By construction, all schemes for different components of state functions are structurally uniform. They are coordinated among themselves in the sense of forward and inverse modeling. Variational principles are constructed taking into account the fact that the behavior of the different dynamic and chemical components of the state function is characterized by high variability and uncertainty. Information on the parameters of models, sources and emission impacts is also not determined precisely. Therefore, to obtain the consistent solutions, we construct methods of the sensitivity theory taking into account the influence of uncertainty. For this purpose, new methods of data assimilation of hydrodynamic fields and gas-aerosol substances measured by different observing systems are proposed. Optimization criteria for data assimilation problems are defined so that they include a set of functionals evaluating the total measure of uncertainties. The latter are explicitly introduced into the equations of the model of processes as desired deterministic control functions. This method of data assimilation with control functions is implemented by direct algorithms. The modeling technology presented here focuses on various scientific and applied problems of environmental prediction and design, including risk assessment in relation to existing and potential sources of natural and anthropogenic influences. The work is partially supported by the Programs No 4 of Presidium RAS and No 3 of Mathematical Department of RAS; by RFBR projects NN 11-01-00187 and 14-01-31482; by Integrating projects of SD RAS No 8 and 35. Our studies are in the line with the goals of COST Action ES1004. References 1. V. Penenko, A.Baklanov, E. Tsvetova and A. Mahura. Direct and Inverse Problems in a Variational Concept of Environmental Modeling, Pure and Applied Geoph. 2012.V.169:447-465. 2. A.V. Penenko, Discrete-analytic schemes for solving an inverse coefficient heat conduction problem in a layered medium with gradient methods, Numerical Analysis and Applications, 2012. V. 5:326-341. 3. V. Penenko, E. Tsvetova. Variational methods for constructing the monotone approximations for atmospheric chemistry models, Numerical analysis and applications, 2013. V. 6: 210-220.

White Ethnic Residential Segregation in Historical Perspective: U.S. Cities in 1880

PubMed Central

Logan, John R.; Zhang, Weiwei

2013-01-01

Investigating immigrant residential patterns in 1880 offers a baseline for understanding residential assimilation trajectories in subsequent eras. This study uses 100% count information from the 1880 Census to estimate a multilevel model of ethnic isolation and exposure to native whites in 67 cities for individual Irish, German and British residents. At the individual level, the key predictors are drawn from assimilation theory: nativity, occupation, and marital status. The multilevel model makes it possible to control for these predictors and to study independent sources of variation in segregation across cities. There is considerable variation at the city level, especially due to differences in the relative sizes of groups. Other significant city-level predictors of people’s neighborhood composition include the share of group members who are foreign-born, the disparity in occupational standing between group members and native whites, and the degree of occupational segregation between them. PMID:23017933

On the Likely Utility of Hybrid Weights Optimized for Variances in Hybrid Error Covariance Models

NASA Astrophysics Data System (ADS)

Satterfield, E.; Hodyss, D.; Kuhl, D.; Bishop, C. H.

2017-12-01

Because of imperfections in ensemble data assimilation schemes, one cannot assume that the ensemble covariance is equal to the true error covariance of a forecast. Previous work demonstrated how information about the distribution of true error variances given an ensemble sample variance can be revealed from an archive of (observation-minus-forecast, ensemble-variance) data pairs. Here, we derive a simple and intuitively compelling formula to obtain the mean of this distribution of true error variances given an ensemble sample variance from (observation-minus-forecast, ensemble-variance) data pairs produced by a single run of a data assimilation system. This formula takes the form of a Hybrid weighted average of the climatological forecast error variance and the ensemble sample variance. Here, we test the extent to which these readily obtainable weights can be used to rapidly optimize the covariance weights used in Hybrid data assimilation systems that employ weighted averages of static covariance models and flow-dependent ensemble based covariance models. Univariate data assimilation and multi-variate cycling ensemble data assimilation are considered. In both cases, it is found that our computationally efficient formula gives Hybrid weights that closely approximate the optimal weights found through the simple but computationally expensive process of testing every plausible combination of weights.

Assimilation of surface NO2 and O3 observations into the SILAM chemistry transport model

NASA Astrophysics Data System (ADS)

Vira, J.; Sofiev, M.

2014-08-01

This paper describes assimilation of trace gas observations into the chemistry transport model SILAM using the 3D-Var method. Assimilation results for year 2012 are presented for the prominent photochemical pollutants ozone (O3) and nitrogen dioxide (NO2). Both species are covered by the Airbase observation database, which provides the observational dataset used in this study. Attention is paid to the background and observation error covariance matrices, which are obtained primarily by iterative application of a posteriori diagnostics. The diagnostics are computed separately for two months representing summer and winter conditions, and further disaggregated by time of day. This allows deriving background and observation error covariance definitions which include both seasonal and diurnal variation. The consistency of the obtained covariance matrices is verified using χ2 diagnostics. The analysis scores are computed for a control set of observation stations withheld from assimilation. Compared to a free-running model simulation, the correlation coefficient for daily maximum values is improved from 0.8 to 0.9 for O3 and from 0.53 to 0.63 for NO2.

Nonlinear data assimilation: towards a prediction of the solar cycle

NASA Astrophysics Data System (ADS)

Svedin, Andreas

The solar cycle is the cyclic variation of solar activity, with a span of 9-14 years. The prediction of the solar cycle is an important and unsolved problem with implications for communications, aviation and other aspects of our high-tech society. Our interest is model-based prediction, and we present a self-consistent procedure for parameter estimation and model state estimation, even when only one of several model variables can be observed. Data assimilation is the art of comparing, combining and transferring observed data into a mathematical model or computer simulation. We use the 3DVAR methodology, based on the notion of least squares, to present an implementation of a traditional data assimilation. Using the Shadowing Filter — a recently developed method for nonlinear data assimilation — we outline a path towards model based prediction of the solar cycle. To achieve this end we solve a number of methodological challenges related to unobserved variables. We also provide a new framework for interpretation that can guide future predictions of the Sun and other astrophysical objects.

Technical report series on global modeling and data assimilation. Volume 4: Documentation of the Goddard Earth Observing System (GEOS) data assimilation system, version 1

NASA Technical Reports Server (NTRS)

Suarez, Max J. (Editor); Pfaendtner, James; Bloom, Stephen; Lamich, David; Seablom, Michael; Sienkiewicz, Meta; Stobie, James; Dasilva, Arlindo

1995-01-01

This report describes the analysis component of the Goddard Earth Observing System, Data Assimilation System, Version 1 (GEOS-1 DAS). The general features of the data assimilation system are outlined, followed by a thorough description of the statistical interpolation algorithm, including specification of error covariances and quality control of observations. We conclude with a discussion of the current status of development of the GEOS data assimilation system. The main components of GEOS-1 DAS are an atmospheric general circulation model and an Optimal Interpolation algorithm. The system is cycled using the Incremental Analysis Update (IAU) technique in which analysis increments are introduced as time independent forcing terms in a forecast model integration. The system is capable of producing dynamically balanced states without the explicit use of initialization, as well as a time-continuous representation of non- observables such as precipitation and radiational fluxes. This version of the data assimilation system was used in the five-year reanalysis project completed in April 1994 by Goddard's Data Assimilation Office (DAO) Data from this reanalysis are available from the Goddard Distributed Active Center (DAAC), which is part of NASA's Earth Observing System Data and Information System (EOSDIS). For information on how to obtain these data sets, contact the Goddard DAAC at (301) 286-3209, EMAIL daac@gsfc.nasa.gov.

Assimilation of gridded terrestrial water storage observations from GRACE into a land surface model

NASA Astrophysics Data System (ADS)

Girotto, Manuela; De Lannoy, Gabriëlle J. M.; Reichle, Rolf H.; Rodell, Matthew

2016-05-01

Observations of terrestrial water storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE) satellite mission have a coarse resolution in time (monthly) and space (roughly 150,000 km2 at midlatitudes) and vertically integrate all water storage components over land, including soil moisture and groundwater. Data assimilation can be used to horizontally downscale and vertically partition GRACE-TWS observations. This work proposes a variant of existing ensemble-based GRACE-TWS data assimilation schemes. The new algorithm differs in how the analysis increments are computed and applied. Existing schemes correlate the uncertainty in the modeled monthly TWS estimates with errors in the soil moisture profile state variables at a single instant in the month and then apply the increment either at the end of the month or gradually throughout the month. The proposed new scheme first computes increments for each day of the month and then applies the average of those increments at the beginning of the month. The new scheme therefore better reflects submonthly variations in TWS errors. The new and existing schemes are investigated here using gridded GRACE-TWS observations. The assimilation results are validated at the monthly time scale, using in situ measurements of groundwater depth and soil moisture across the U.S. The new assimilation scheme yields improved (although not in a statistically significant sense) skill metrics for groundwater compared to the open-loop (no assimilation) simulations and compared to the existing assimilation schemes. A smaller impact is seen for surface and root-zone soil moisture, which have a shorter memory and receive smaller increments from TWS assimilation than groundwater. These results motivate future efforts to combine GRACE-TWS observations with observations that are more sensitive to surface soil moisture, such as L-band brightness temperature observations from Soil Moisture Ocean Salinity (SMOS) or Soil Moisture Active Passive (SMAP). Finally, we demonstrate that the scaling parameters that are applied to the GRACE observations prior to assimilation should be consistent with the land surface model that is used within the assimilation system.

Application of multi-constituent satellite data assimilation for KORUS-AQ

NASA Astrophysics Data System (ADS)

Miyazaki, K.; Sekiya, T.; Fu, D.; Bowman, K. W.; Kulawik, S. S.; Walker, T.; Takigawa, M.; Ogochi, K.; Gaubert, B.; Barré, J.; Emmons, L. K.

2017-12-01

Comprehensive tropospheric maps of multi-constituent fields at 1.1 degree resolution, provided by an assimilation of multiple satellite measurements of O3, CO, NO2, and HNO3 from multiple satellite (OMI, GOME-2, MOPITT, MLS, and AIRS) using an ensemble Kalman filter, are used to study variations in tropospheric composition over east Asia during KORUS-AQ. Assimilated model results for both direct ozone assimilations and assimilations of ozone precursors (NOx and CO) were compared to DC-8 aircraft observations, with significant improvements in model/aircraft comparisons for ozone (the negative model bias was reduced by up to 80 %), CO (by up to 90 %), OH (by up to 40 %), and NOx seen in both approaches. Corrections made to the precursor emissions (i.e., surface NOx and CO emissions), especially over eastern and central China and over South Korea, were important in reducing the negative bias of O3 and CO over South Korea. We obtained additional bias reductions from assimilation of multispectral retrievals of tropospheric ozone profiles from AIRS and OMI, especially for the middle troposphere ozone. Improved agreements with the ground-based measurements at remote sites over South Korea and western Japan suggest that the representation of long-range transport of polluted air is improved by data assimilation, as a result of the optimization of precursor emissions, mainly over China. The higher estimated NOx, by 60-90 % over South Korea and by 20-40 % over eastern China compared to bottom-up inventories, suggests an important underestimation of anthropogenic sources in the emission inventories in these areas. Additional bias reductions were obtained by assimilating the multispectral retrievals, especially for the middle troposphere O3. In the future, assimilating datasets from a new constellation of low Earth orbiting sounders (e.g., IASI, AIRS, CrIS, Sentinel-5p (TROPOMI), and Sentinel-5) and geostationary satellites (Sentinel-4, GEMS, and TEMPO) will provide more detailed knowledge of ozone and its precursors for east Asia and the entire globe. The data assimilation framework will also be used for chemical OSSE studies to evaluate and optimize the current and future observing systems.

Assimilation of Gridded Terrestrial Water Storage Observations from GRACE into a Land Surface Model

NASA Technical Reports Server (NTRS)

Girotto, Manuela; De Lannoy, Gabrielle J. M.; Reichle, Rolf H.; Rodell, Matthew

2016-01-01

Observations of terrestrial water storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE) satellite mission have a coarse resolution in time (monthly) and space (roughly 150,000 km(sup 2) at midlatitudes) and vertically integrate all water storage components over land, including soil moisture and groundwater. Data assimilation can be used to horizontally downscale and vertically partition GRACE-TWS observations. This work proposes a variant of existing ensemble-based GRACE-TWS data assimilation schemes. The new algorithm differs in how the analysis increments are computed and applied. Existing schemes correlate the uncertainty in the modeled monthly TWS estimates with errors in the soil moisture profile state variables at a single instant in the month and then apply the increment either at the end of the month or gradually throughout the month. The proposed new scheme first computes increments for each day of the month and then applies the average of those increments at the beginning of the month. The new scheme therefore better reflects submonthly variations in TWS errors. The new and existing schemes are investigated here using gridded GRACE-TWS observations. The assimilation results are validated at the monthly time scale, using in situ measurements of groundwater depth and soil moisture across the U.S. The new assimilation scheme yields improved (although not in a statistically significant sense) skill metrics for groundwater compared to the open-loop (no assimilation) simulations and compared to the existing assimilation schemes. A smaller impact is seen for surface and root-zone soil moisture, which have a shorter memory and receive smaller increments from TWS assimilation than groundwater. These results motivate future efforts to combine GRACE-TWS observations with observations that are more sensitive to surface soil moisture, such as L-band brightness temperature observations from Soil Moisture Ocean Salinity (SMOS) or Soil Moisture Active Passive (SMAP). Finally, we demonstrate that the scaling parameters that are applied to the GRACE observations prior to assimilation should be consistent with the land surface model that is used within the assimilation system.

Does Ocean Color Data Assimilation Improve Estimates of Global Ocean Inorganic Carbon?

NASA Technical Reports Server (NTRS)

Gregg, Watson

2012-01-01

Ocean color data assimilation has been shown to dramatically improve chlorophyll abundances and distributions globally and regionally in the oceans. Chlorophyll is a proxy for phytoplankton biomass (which is explicitly defined in a model), and is related to the inorganic carbon cycle through the interactions of the organic carbon (particulate and dissolved) and through primary production where inorganic carbon is directly taken out of the system. Does ocean color data assimilation, whose effects on estimates of chlorophyll are demonstrable, trickle through the simulated ocean carbon system to produce improved estimates of inorganic carbon? Our emphasis here is dissolved inorganic carbon, pC02, and the air-sea flux. We use a sequential data assimilation method that assimilates chlorophyll directly and indirectly changes nutrient concentrations in a multi-variate approach. The results are decidedly mixed. Dissolved organic carbon estimates from the assimilation model are not meaningfully different from free-run, or unassimilated results, and comparisons with in situ data are similar. pC02 estimates are generally worse after data assimilation, with global estimates diverging 6.4% from in situ data, while free-run estimates are only 4.7% higher. Basin correlations are, however, slightly improved: r increase from 0.78 to 0.79, and slope closer to unity at 0.94 compared to 0.86. In contrast, air-sea flux of C02 is noticeably improved after data assimilation. Global differences decline from -0.635 mol/m2/y (stronger model sink from the atmosphere) to -0.202 mol/m2/y. Basin correlations are slightly improved from r=O.77 to r=0.78, with slope closer to unity (from 0.93 to 0.99). The Equatorial Atlantic appears as a slight sink in the free-run, but is correctly represented as a moderate source in the assimilation model. However, the assimilation model shows the Antarctic to be a source, rather than a modest sink and the North Indian basin is represented incorrectly as a sink rather than the source indicated by the free-run model and data estimates.

A study of Bangladesh's sub-surface water storages using satellite products and data assimilation scheme.

PubMed

Khaki, M; Forootan, E; Kuhn, M; Awange, J; Papa, F; Shum, C K

2018-06-01

Climate change can significantly influence terrestrial water changes around the world particularly in places that have been proven to be more vulnerable such as Bangladesh. In the past few decades, climate impacts, together with those of excessive human water use have changed the country's water availability structure. In this study, we use multi-mission remotely sensed measurements along with a hydrological model to separately analyze groundwater and soil moisture variations for the period 2003-2013, and their interactions with rainfall in Bangladesh. To improve the model's estimates of water storages, terrestrial water storage (TWS) data obtained from the Gravity Recovery And Climate Experiment (GRACE) satellite mission are assimilated into the World-Wide Water Resources Assessment (W3RA) model using the ensemble-based sequential technique of the Square Root Analysis (SQRA) filter. We investigate the capability of the data assimilation approach to use a non-regional hydrological model for a regional case study. Based on these estimates, we investigate relationships between the model derived sub-surface water storage changes and remotely sensed precipitations, as well as altimetry-derived river level variations in Bangladesh by applying the empirical mode decomposition (EMD) method. A larger correlation is found between river level heights and rainfalls (78% on average) in comparison to groundwater storage variations and rainfalls (57% on average). The results indicate a significant decline in groundwater storage (∼32% reduction) for Bangladesh between 2003 and 2013, which is equivalent to an average rate of 8.73 ± 2.45mm/year. Copyright © 2018 Elsevier B.V. All rights reserved.

Discharge Estimation in Ungauged Basins Through Variational Data Assimilation: The Potential of the SWOT Mission

NASA Astrophysics Data System (ADS)

Oubanas, H.; Gejadze, I.; Malaterre, P.-O.; Durand, M.; Wei, R.; Frasson, R. P. M.; Domeneghetti, A.

2018-03-01

Space-borne instruments can measure river water surface elevation, slope, and width. Remote sensing of river discharge in ungauged basins is far more challenging, however. This work investigates the estimation of river discharge from simulated observations of the forthcoming Surface Water and Ocean Topography (SWOT) satellite mission using a variant of the classical variational data assimilation method "4D-Var." The variational assimilation scheme simultaneously estimates discharge, river bathymetry, and bed roughness in the context of a 1.5 D full Saint-Venant hydraulic model. Algorithms and procedures are developed to apply the method to fully ungauged basins. The method was tested on the Po and Sacramento Rivers. The SWOT hydrology simulator was used to produce synthetic SWOT observations at each overpass time by simulating the interaction of SWOT radar measurements with the river water surface and nearby land surface topography at a scale of approximately 1 m, thus accounting for layover, thermal noise, and other effects. SWOT data products were synthesized by vectorizing the simulated radar returns, leading to height and width estimates at 200 m increments along the river centerlines. The ingestion of simulated SWOT data generally led to local improvements on prior bathymetry and roughness estimates which allowed the prediction of river discharge at the overpass times with relative root mean squared errors of 12.1% and 11.2% for the Po and Sacramento Rivers, respectively. Nevertheless, equifinality issues that arise from the simultaneous estimation of bed elevation and roughness may prevent their use for different applications, other than discharge estimation through the presented framework.

The Tangent Linear and Adjoint of the FV3 Dynamical Core: Development and Applications

NASA Technical Reports Server (NTRS)

Holdaway, Daniel

2018-01-01

GMAO (NASA's Global Modeling and Assimilation Office) has developed a highly sophisticated adjoint modeling system based on the most recent version of the finite volume cubed sphere (FV3) dynamical core. This provides a mechanism for investigating sensitivity to initial conditions and examining observation impacts. It also allows for the computation of singular vectors and for the implementation of hybrid 4DVAR (4-Dimensional Variational Assimilation). In this work we will present the scientific assessment of the new adjoint system and show results from a number of research application of the adjoint system.

Parameter Estimation and Model Validation of Nonlinear Dynamical Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abarbanel, Henry; Gill, Philip

In the performance period of this work under a DOE contract, the co-PIs, Philip Gill and Henry Abarbanel, developed new methods for statistical data assimilation for problems of DOE interest, including geophysical and biological problems. This included numerical optimization algorithms for variational principles, new parallel processing Monte Carlo routines for performing the path integrals of statistical data assimilation. These results have been summarized in the monograph: “Predicting the Future: Completing Models of Observed Complex Systems” by Henry Abarbanel, published by Spring-Verlag in June 2013. Additional results and details have appeared in the peer reviewed literature.

Assimilation efficiencies and turnover rates of trace elements in marine bivalves: A comparison of oysters, clams and mussels

USGS Publications Warehouse

Reinfelder, J.R.; Wang, W.-X.; Luoma, S. N.; Fisher, N.S.

1997-01-01

Assimilation efficiencies (AEs) and physiological turnover-rate constants (k) of six trace elements (Ag, Am, Cd, Co, Se, Zn) in four marine bivalves (Crassostrea virginica Gmelin,Macoma balthica Linnaeus, Mercenaria mercenaria Linnaeus, and Mytilus edulis Linnaeus) were measured in radiotracer-depuration experiments. Egestion rates of unassimilated elements were highest during the first 24 h of depuration and declined thereafter. Significant egestion of unassimilated Co, however, continued for up to 5 d in Macoma balthica,Mercenaria mercenaria and Mytilus edulis. With the exception of the extremely low values for110 mAg, 109Cd, and 65Zn in C. virginica, physiological turnover-rate constants (k) showed no general pattern of variation among elements, bivalve species or food types, and were relatively invariant. Values from  ≤0.001 to 0.1 d−1 were observed, but excluding those for Co, most values were  ≤0.04 d−1. In all four species, the AEs of Ag, Am, and Co were generally lower than those of Cd, Se, and Zn. The AEs of Ag, Cd, Se, and Zn in these bivalves are directly related to the proportion of each element in the cytoplasmic fraction of ingested phytoplankton, indicating that >80% of elements in a prey alga's cytoplasm was assimilated. C. virginica, Macoma balthica, and Mercenaria mercenaria assimilated ∼36% of the Ag and Cd associated with the non-cytoplasmic (membrane/organelle) fraction of ingested cells in addition to the cytoplasmic fraction. The ratio of AE:k, which is proportional to the consumer–prey trace-element bioaccumulation factor (concentration in consumer:concentration in prey) was generally greater for Cd, Se, and Zn than for Ag, Am, and Co. This ratio was lowest in Mytilus edulis, suggesting that this bivalve, the most widely employed organism in global biomonitoring, is relatively inefficient at accumulating important elements such as Ag, Cd, and Zn from ingested phytoplankton.

Biomass burning influences on atmospheric composition: A case study to assess the impact of aerosol data assimilation

NASA Astrophysics Data System (ADS)

Keslake, Tim; Chipperfield, Martyn; Mann, Graham; Flemming, Johannes; Remy, Sam; Dhomse, Sandip; Morgan, Will

2016-04-01

The C-IFS (Composition Integrated Forecast System) developed under the MACC series of projects and to be continued under the Copernicus Atmospheric Monitoring System, provides global operational forecasts and re-analyses of atmospheric composition at high spatial resolution (T255, ~80km). Currently there are 2 aerosol schemes implemented within C-IFS, a mass-based scheme with externally mixed particle types and an aerosol microphysics scheme (GLOMAP-mode). The simpler mass-based scheme is the current operational system, also used in the existing system to assimilate satellite measurements of aerosol optical depth (AOD) for improved forecast capability. The microphysical GLOMAP scheme has now been implemented and evaluated in the latest C-IFS cycle alongside the mass-based scheme. The upgrade to the microphysical scheme provides for higher fidelity aerosol-radiation and aerosol-cloud interactions, accounting for global variations in size distribution and mixing state, and additional aerosol properties such as cloud condensation nuclei concentrations. The new scheme will also provide increased aerosol information when used as lateral boundary conditions for regional air quality models. Here we present a series of experiments highlighting the influence and accuracy of the two different aerosol schemes and the impact of MODIS AOD assimilation. In particular, we focus on the influence of biomass burning emissions on aerosol properties in the Amazon, comparing to ground-based and aircraft observations from the 2012 SAMBBA campaign. Biomass burning can affect regional air quality, human health, regional weather and the local energy budget. Tropical biomass burning generates particles primarily composed of particulate organic matter (POM) and black carbon (BC), the local ratio of these two different constituents often determining the properties and subsequent impacts of the aerosol particles. Therefore, the model's ability to capture the concentrations of these two carbonaceous aerosol types, during the tropical dry season, is essential for quantifying these wide ranging impacts. Comparisons to SAMBBA aircraft observations show that while both schemes underestimate POM and BC mass concentrations, the GLOMAP scheme provides a more accurate simulation. When satellite AOD is assimilated into the GEMS-AER scheme, the model is successfully adjusted, capturing observed mass concentrations to a good degree of accuracy.

Towards the Operational Ensemble-based Data Assimilation System for the Wave Field at the National Weather Service

NASA Astrophysics Data System (ADS)

Flampouris, Stylianos; Penny, Steve; Alves, Henrique

2017-04-01

The National Centers for Environmental Prediction (NCEP) of the National Oceanic and Atmospheric Administration (NOAA) provides the operational wave forecast for the US National Weather Service (NWS). Given the continuous efforts to improve forecast, NCEP is developing an ensemble-based data assimilation system, based on the local ensemble transform Kalman filter (LETKF), the existing operational global wave ensemble system (GWES) and on satellite and in-situ observations. While the LETKF was designed for atmospheric applications (Hunt et al 2007), and has been adapted for several ocean models (e.g. Penny 2016), this is the first time applied for oceanic waves assimilation. This new wave assimilation system provides a global estimation of the surface sea state and its approximate uncertainty. It achieves this by analyzing the 21-member ensemble of the significant wave height provided by GWES every 6h. Observations from four altimeters and all the available in-situ measurements are used in this analysis. The analysis of the significant wave height is used for initializing the next forecasting cycle; the data assimilation system is currently being tested for operational use.

Carbon Assimilation Pathways, Water Relationships and Plant Ecology.

ERIC Educational Resources Information Center

Etherington, John R.

1988-01-01

Discusses between-species variation in adaptation of the photosynthetic mechanism to cope with wide fluctuations of environmental water regime. Describes models for water conservation in plants and the role of photorespiration in the evolution of the different pathways. (CW)

Joint Center for Satellite Data Assimilation Overview and Research Activities

NASA Astrophysics Data System (ADS)

Auligne, T.

2017-12-01

In 2001 NOAA/NESDIS, NOAA/NWS, NOAA/OAR, and NASA, subsequently joined by the US Navy and Air Force, came together to form the Joint Center for Satellite Data Assimilation (JCSDA) for the common purpose of accelerating the use of satellite data in environmental numerical prediction modeling by developing, using, and anticipating advances in numerical modeling, satellite-based remote sensing, and data assimilation methods. The primary focus was to bring these advances together to improve operational numerical model-based forecasting, under the premise that these partners have common technical and logistical challenges assimilating satellite observations into their modeling enterprises that could be better addressed through cooperative action and/or common solutions. Over the last 15 years, the JCSDA has made and continues to make major contributions to operational assimilation of satellite data. The JCSDA is a multi-agency U.S. government-owned-and-operated organization that was conceived as a venue for the several agencies NOAA, NASA, USAF and USN to collaborate on advancing the development and operational use of satellite observations into numerical model-based environmental analysis and forecasting. The primary mission of the JCSDA is to "accelerate and improve the quantitative use of research and operational satellite data in weather, ocean, climate and environmental analysis and prediction systems." This mission is fulfilled through directed research targeting the following key science objectives: Improved radiative transfer modeling; new instrument assimilation; assimilation of humidity, clouds, and precipitation observations; assimilation of land surface observations; assimilation of ocean surface observations; atmospheric composition; and chemistry and aerosols. The goal of this presentation is to briefly introduce the JCSDA's mission and vision, and to describe recent research activities across various JCSDA partners.

On the assimilation of SWOT type data into 2D shallow-water models

NASA Astrophysics Data System (ADS)

Frédéric, Couderc; Denis, Dartus; Pierre-André, Garambois; Ronan, Madec; Jérôme, Monnier; Jean-Paul, Villa

2013-04-01

In river hydraulics, assimilation of water level measurements at gauging stations is well controlled, while assimilation of images is still delicate. In the present talk, we address the richness of satellite mapped information to constrain a 2D shallow-water model, but also related difficulties. 2D shallow models may be necessary for small scale modelling in particular for low-water and flood plain flows. Since in both cases, the dynamics of the wet-dry front is essential, one has to elaborate robust and accurate solvers. In this contribution we introduce robust second order, stable finite volume scheme [CoMaMoViDaLa]. Comparisons of real like tests cases with more classical solvers highlight the importance of an accurate flood plain modelling. A preliminary inverse study is presented in a flood plain flow case, [LaMo] [HoLaMoPu]. As a first step, a 0th order data processing model improves observation operator and produces more reliable water level derived from rough measurements [PuRa]. Then, both model and flow behaviours can be better understood thanks to variational sensitivities based on a gradient computation and adjoint equations. It can reveal several difficulties that a model designer has to tackle. Next, a 4D-Var data assimilation algorithm used with spatialized data leads to improved model calibration and potentially leads to identify river discharges. All the algorithms are implemented into DassFlow software (Fortran, MPI, adjoint) [Da]. All these results and experiments (accurate wet-dry front dynamics, sensitivities analysis, identification of discharges and calibration of model) are currently performed in view to use data from the future SWOT mission. [CoMaMoViDaLa] F. Couderc, R. Madec, J. Monnier, J.-P. Vila, D. Dartus, K. Larnier. "Sensitivity analysis and variational data assimilation for geophysical shallow water flows". Submitted. [Da] DassFlow - Data Assimilation for Free Surface Flows. Computational software http://www-gmm.insa-toulouse.fr/~monnier/DassFlow/ [HoLaMoPu] R. Hostache, X. Lai, J. Monnier, C. Puech. "Assimilation of spatial distributed water levels into a shallow-water flood model. Part II: using a remote sensing image of Mosel river". J. Hydrology (2010). [LaMo] X. Lai, J. Monnier. "Assimilation of spatial distributed water levels into a shallow-water flood model. Part I: mathematical method and test case". J. Hydrology (2009). [PuRa] C. Puech, D. Raclot. "Using geographic information systems and aerial photographs to determine water levels during floods". Hydrol. Process., 16, 1593 - 1602, (2002). [RoDa] H. Roux, D. Dartus. "Use of Parameter Optimization to Estimate a Flood Wave: Potential Applications to Remote Sensing of Rivers". J. Hydrology (2006).

The development of global GRAPES 4DVAR

NASA Astrophysics Data System (ADS)

Liu, Yongzhu

2017-04-01

Four-dimensional variation data assimilation (4DVAR) has given a great contribution to the improvement of NWP system over the past twenty years. Therefore, our strategy is to develop an operational global 4D-Var system from the outset. The aim at the paper is to introduce the development of the global GRAPES four-dimensional variation data assimilation (4DVAR) using incremental analysis schemes and to presents results of a comparison between 4DVAR using 6-hour assimilation window and simplified physics during the minimization with three-dimensional variation data assimilation (3DVAR). The dynamical cores of the tangent-linear and adjoint models are developed directly based on the non-hydrostatic forecast model. In addition, the standard correctness checks have been performed. As well as the development adjoint codes, most of our work is focused on improving the computational efficiency since the bulk of the computational cost of 4D-Var is in the integration of the tangent-linear and adjoint models. In terms of tangent-linear model, the wall-clock time is reduced to about 1.2 times as much as one of nonlinear model through the optimizing of the software framework. The significant computational cost savings on adjoint model result from the removing the redundant recompilations of model trajectories. It is encouraging that the wall-clock time of adjoint model is less than 1.5 times as much as one of nonlinear model. The current difficulty is that the numerical scheme used within the linear model is based on strategically on the numeric of the corresponding nonlinear model. Further computational acceleration should be expected from the improvement on nonlinear numerical algorithm. A series of linearized physical parameterization schemes has been developed to improve the representation of perturbed fields in the linear model. It consists of horizontal and vertical diffusion, sub-grid scale orographic gravity wave drag, large-scale condensation and cumulus convection schemes. We also found the straightforward linearization based on the nonlinear physical scheme might lead to significant growing of spurious unstable perturbations. It is essential to simplify the linear physics with respect to the non-linear schemes. The improvement on the perturbed fields in the tangent-linear model is visible with the linear physics included, especially at the low level. GRAPES variation data assimilation system adopts the incremental approach. The work is ongoing to develop a pre-operational 4DVAR suite with 0.25° outer loop resolution and multiple outer-loops configurations. One 4DVAR analysis using 6-hour assimilation windows can be finished within 40-minutes when using the available conventional and satellite data. In summary, it was found that the analysis over the northern, southern hemispheres, tropical region and East Asian area of GRAPES 4DVAR performed better than GRAPES 3DVAR for one month experiments. Moreover, the forecast results show that northern and southern extra-tropical scores for GRAPES 4DVAR are already better than GRAPES 3DVAR, but the tropical performance needs further investigations. Therefore, the subsequent main improvements will aim to enhance its computational efficiency and accuracy in 2017. The global GRAPES 4DVAR is planned for operation in 2018.

Role of the Polar Oceans in Global Climate

NASA Technical Reports Server (NTRS)

Rothrock, D. A.

2003-01-01

The project focused on ice-ocean model development and in particular on the assimilation of ice motion data and ice concentration data into both regional and global models. Many of the resulting publications below deal with improvements made in the physics treated by the model and the procedures for assimilating data. Several papers examine how the ability of the model to simulate the past behavior of the ice cover, especially to represent the ice thickness and ice deformation, is improved by data assimilation. A second aspect of the work involved interpretation of modeled behavior. Resulting papers treat the decline of arctic ice thickness over the last thirty years, and how that decline was caused by a slight warming of the near-surface atmosphere, and also how large variation in ice thickness are due to changes in wind patterns associated with a well- known oscillation of the atmospheric circulation. The research resulted in over 20 published papers on these topics.

Assimilative modeling of low latitude ionosphere

NASA Technical Reports Server (NTRS)

Pi, Xiaoqing; Wang, Chunining; Hajj, George A.; Rosen, I. Gary; Wilson, Brian D.; Mannucci, Anthony J.

2004-01-01

In this paper we present an observation system simulation experiment for modeling low-latitude ionosphere using a 3-dimensional (3-D) global assimilative ionospheric model (GAIM). The experiment is conducted to test the effectiveness of GAIM with a 4-D variational approach (4DVAR) in estimation of the ExB drift and thermospheric wind in the magnetic meridional planes simultaneously for all longitude or local time sectors. The operational Global Positioning System (GPS) satellites and the ground-based global GPS receiver network of the International GPS Service are used in the experiment as the data assimilation source. 'The optimization of the ionospheric state (electron density) modeling is performed through a nonlinear least-squares minimization process that adjusts the dynamical forces to reduce the difference between the modeled and observed slant total electron content in the entire modeled region. The present experiment for multiple force estimations reinforces our previous assessment made through single driver estimations conducted for the ExB drift only.

Assimilation of Remotely Sensed Leaf Area Index into the Community Land Model with Explicit Carbon and Nitrogen Components using Data Assimilation Research Testbed

NASA Astrophysics Data System (ADS)

Ling, X.; Fu, C.; Yang, Z. L.; Guo, W.

2017-12-01

Information of the spatial and temporal patterns of leaf area index (LAI) is crucial to understand the exchanges of momentum, carbon, energy, and water between the terrestrial ecosystem and the atmosphere, while both in-situ observation and model simulation usually show distinct deficiency in terms of LAI coverage and value. Land data assimilation, combined with observation and simulation together, is a promising way to provide variable estimation. The Data Assimilation Research Testbed (DART) developed and maintained by the National Centre for Atmospheric Research (NCAR) provides a powerful tool to facilitate the combination of assimilation algorithms, models, and real (as well as synthetic) observations to better understanding of all three. Here we systematically investigated the effects of data assimilation on improving LAI simulation based on NCAR Community Land Model with the prognostic carbon-nitrogen option (CLM4CN) linked with DART using the deterministic Ensemble Adjustment Kalman Filter (EAKF). Random 40-member atmospheric forcing was used to drive the CLM4CN with or without LAI assimilation. The Global Land Surface Satellite LAI data (GLASS LAI) LAI is assimilated into the CLM4CN at a frequency of 8 days, and LAI (and leaf carbon / nitrogen) are adjusted at each time step. The results show that assimilating remotely sensed LAI into the CLM4CN is an effective method for improving model performance. In detail, the CLM4-CN simulated LAI systematically overestimates global LAI, especially in low latitude with the largest bias of 5 m2/m2. While if updating both LAI and leaf carbon and leaf nitrogen simultaneously during assimilation, the analyzed LAI can be corrected, especially in low latitude regions with the bias controlled around ±1 m2/m2. Analyzed LAI could also represent the seasonal variation except for the Southern Temperate (23°S-90°S). The obviously improved regions located in the center of Africa, Amazon, the South of Eurasia, the northeast of China, and the west of Europe, where were mainly covered by evergreen/deciduous forests and mixed forests. In addition, the best method for LAI assimilation should include the EAKF method, the accepted percentage of all observation, as well as the carbon-nitrogen control.

A joint data assimilation system (Tan-Tracker) to simultaneously estimate surface CO2 fluxes and 3-D atmospheric CO2 concentrations from observations

NASA Astrophysics Data System (ADS)

Tian, X.; Xie, Z.; Liu, Y.; Cai, Z.; Fu, Y.; Zhang, H.; Feng, L.

2014-12-01

We have developed a novel framework ("Tan-Tracker") for assimilating observations of atmospheric CO2 concentrations, based on the POD-based (proper orthogonal decomposition) ensemble four-dimensional variational data assimilation method (PODEn4DVar). The high flexibility and the high computational efficiency of the PODEn4DVar approach allow us to include both the atmospheric CO2 concentrations and the surface CO2 fluxes as part of the large state vector to be simultaneously estimated from assimilation of atmospheric CO2 observations. Compared to most modern top-down flux inversion approaches, where only surface fluxes are considered as control variables, one major advantage of our joint data assimilation system is that, in principle, no assumption on perfect transport models is needed. In addition, the possibility for Tan-Tracker to use a complete dynamic model to consistently describe the time evolution of CO2 surface fluxes (CFs) and the atmospheric CO2 concentrations represents a better use of observation information for recycling the analyses at each assimilation step in order to improve the forecasts for the following assimilations. An experimental Tan-Tracker system has been built based on a complete augmented dynamical model, where (1) the surface atmosphere CO2 exchanges are prescribed by using a persistent forecasting model for the scaling factors of the first-guess net CO2 surface fluxes and (2) the atmospheric CO2 transport is simulated by using the GEOS-Chem three-dimensional global chemistry transport model. Observing system simulation experiments (OSSEs) for assimilating synthetic in situ observations of surface CO2 concentrations are carefully designed to evaluate the effectiveness of the Tan-Tracker system. In particular, detailed comparisons are made with its simplified version (referred to as TT-S) with only CFs taken as the prognostic variables. It is found that our Tan-Tracker system is capable of outperforming TT-S with higher assimilation precision for both CO2 concentrations and CO2 fluxes, mainly due to the simultaneous estimation of CO2 concentrations and CFs in our Tan-Tracker data assimilation system. A experiment for assimilating the real dry-air column CO2 retrievals (XCO2) from the Japanese Greenhouse Gases Observation Satellite (GOSAT) further demonstrates its potential wide applications.

Some new mathematical methods for variational objective analysis

NASA Technical Reports Server (NTRS)

Wahba, Grace; Johnson, Donald R.

1994-01-01

Numerous results were obtained relevant to remote sensing, variational objective analysis, and data assimilation. A list of publications relevant in whole or in part is attached. The principal investigator gave many invited lectures, disseminating the results to the meteorological community as well as the statistical community. A list of invited lectures at meetings is attached, as well as a list of departmental colloquia at various universities and institutes.

Decadal variations of Pacific North Equatorial Current bifurcation from multiple ocean products

NASA Astrophysics Data System (ADS)

Zhai, Fangguo; Wang, Qingye; Wang, Fujun; Hu, Dunxin

2014-02-01

In this study, we examine the decadal variations of the Pacific North Equatorial Current (NEC) bifurcation latitude (NBL) averaged over upper 100 m and underlying dynamics over the past six decades using 11 ocean products, including seven kinds of ocean reanalyzes based on ocean data assimilation systems, two kinds of numerical simulations without assimilating observations and two kinds of objective analyzes based on in situ observations only. During the period of 1954-2007, the multiproduct mean of decadal NBL anomalies shows maxima around 1965/1966, 1980/1981, 1995/1996, and 2003/2004, and minima around 1958, 1971/1972, 1986/1987, and 2000/2001, respectively. The NBL decadal variations are related to the first Empirical Orthogonal Function mode of decadal anomalies of sea surface height (SSH) in the northwestern tropical Pacific Ocean, which shows spatially coherent variation over the whole region and explains most of the total variance. Further regression and composite analyzes indicate that northerly/southerly NBL corresponds to negative/positive SSH anomalies and cyclonic/anticyclonic gyre anomalies in the northwestern tropical Pacific Ocean. These decadal circulation variations and thus the decadal NBL variations are governed mostly by the first two vertical modes and attribute the most to the first baroclinic mode. The NBL decadal variation is highly positively correlated with the tropical Pacific decadal variability (TPDV) around the zero time lag. With a lead of about half the decadal cycle the NBL displays closer but negative relationship to TPDV in four ocean products, possibly manifesting the dynamical role of the circulation in the northwestern tropical Pacific in the phase-shifting of TPDV.

Data Assimilation on a Quantum Annealing Computer: Feasibility and Scalability

NASA Astrophysics Data System (ADS)

Nearing, G. S.; Halem, M.; Chapman, D. R.; Pelissier, C. S.

2014-12-01

Data assimilation is one of the ubiquitous and computationally hard problems in the Earth Sciences. In particular, ensemble-based methods require a large number of model evaluations to estimate the prior probability density over system states, and variational methods require adjoint calculations and iteration to locate the maximum a posteriori solution in the presence of nonlinear models and observation operators. Quantum annealing computers (QAC) like the new D-Wave housed at the NASA Ames Research Center can be used for optimization and sampling, and therefore offers a new possibility for efficiently solving hard data assimilation problems. Coding on the QAC is not straightforward: a problem must be posed as a Quadratic Unconstrained Binary Optimization (QUBO) and mapped to a spherical Chimera graph. We have developed a method for compiling nonlinear 4D-Var problems on the D-Wave that consists of five steps: Emulating the nonlinear model and/or observation function using radial basis functions (RBF) or Chebyshev polynomials. Truncating a Taylor series around each RBF kernel. Reducing the Taylor polynomial to a quadratic using ancilla gadgets. Mapping the real-valued quadratic to a fixed-precision binary quadratic. Mapping the fully coupled binary quadratic to a partially coupled spherical Chimera graph using ancilla gadgets. At present the D-Wave contains 512 qbits (with 1024 and 2048 qbit machines due in the next two years); this machine size allows us to estimate only 3 state variables at each satellite overpass. However, QAC's solve optimization problems using a physical (quantum) system, and therefore do not require iterations or calculation of model adjoints. This has the potential to revolutionize our ability to efficiently perform variational data assimilation, as the size of these computers grows in the coming years.

Development of the WRF-CO2 4D-Var assimilation system v1.0

NASA Astrophysics Data System (ADS)

Zheng, Tao; French, Nancy H. F.; Baxter, Martin

2018-05-01

Regional atmospheric CO2 inversions commonly use Lagrangian particle trajectory model simulations to calculate the required influence function, which quantifies the sensitivity of a receptor to flux sources. In this paper, an adjoint-based four-dimensional variational (4D-Var) assimilation system, WRF-CO2 4D-Var, is developed to provide an alternative approach. This system is developed based on the Weather Research and Forecasting (WRF) modeling system, including the system coupled to chemistry (WRF-Chem), with tangent linear and adjoint codes (WRFPLUS), and with data assimilation (WRFDA), all in version 3.6. In WRF-CO2 4D-Var, CO2 is modeled as a tracer and its feedback to meteorology is ignored. This configuration allows most WRF physical parameterizations to be used in the assimilation system without incurring a large amount of code development. WRF-CO2 4D-Var solves for the optimized CO2 flux scaling factors in a Bayesian framework. Two variational optimization schemes are implemented for the system: the first uses the limited memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) minimization algorithm (L-BFGS-B) and the second uses the Lanczos conjugate gradient (CG) in an incremental approach. WRFPLUS forward, tangent linear, and adjoint models are modified to include the physical and dynamical processes involved in the atmospheric transport of CO2. The system is tested by simulations over a domain covering the continental United States at 48 km × 48 km grid spacing. The accuracy of the tangent linear and adjoint models is assessed by comparing against finite difference sensitivity. The system's effectiveness for CO2 inverse modeling is tested using pseudo-observation data. The results of the sensitivity and inverse modeling tests demonstrate the potential usefulness of WRF-CO2 4D-Var for regional CO2 inversions.

Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing.

PubMed

McHenry, John N; Vukovich, Jeffery M; Hsu, N Christina

2015-12-01

This two-part paper reports on the development, implementation, and improvement of a version of the Community Multi-Scale Air Quality (CMAQ) model that assimilates real-time remotely-sensed aerosol optical depth (AOD) information and ground-based PM2.5 monitor data in routine prognostic application. The model is being used by operational air quality forecasters to help guide their daily issuance of state or local-agency-based air quality alerts (e.g. action days, health advisories). Part 1 describes the development and testing of the initial assimilation capability, which was implemented offline in partnership with NASA and the Visibility Improvement State and Tribal Association of the Southeast (VISTAS) Regional Planning Organization (RPO). In the initial effort, MODIS-derived aerosol optical depth (AOD) data are input into a variational data-assimilation scheme using both the traditional Dark Target and relatively new "Deep Blue" retrieval methods. Evaluation of the developmental offline version, reported in Part 1 here, showed sufficient promise to implement the capability within the online, prognostic operational model described in Part 2. In Part 2, the addition of real-time surface PM2.5 monitoring data to improve the assimilation and an initial evaluation of the prognostic modeling system across the continental United States (CONUS) is presented. Air quality forecasts are now routinely used to understand when air pollution may reach unhealthy levels. For the first time, an operational air quality forecast model that includes the assimilation of remotely-sensed aerosol optical depth and ground based PM2.5 observations is being used. The assimilation enables quantifiable improvements in model forecast skill, which improves confidence in the accuracy of the officially-issued forecasts. This helps air quality stakeholders be more effective in taking mitigating actions (reducing power consumption, ride-sharing, etc.) and avoiding exposures that could otherwise result in more serious air quality episodes or more deleterious health effects.

Discharge data assimilation in a distributed hydrologic model for flood forecasting purposes

NASA Astrophysics Data System (ADS)

Ercolani, G.; Castelli, F.

2017-12-01

Flood early warning systems benefit from accurate river flow forecasts, and data assimilation may improve their reliability. However, the actual enhancement that can be obtained in the operational practice should be investigated in detail and quantified. In this work we assess the benefits that the simultaneous assimilation of discharge observations at multiple locations can bring to flow forecasting through a distributed hydrologic model. The distributed model, MOBIDIC, is part of the operational flood forecasting chain of Tuscany Region in Central Italy. The assimilation system adopts a mixed variational-Monte Carlo approach to update efficiently initial river flow, soil moisture, and a parameter related to runoff production. The evaluation of the system is based on numerous hindcast experiments of real events. The events are characterized by significant rainfall that resulted in both high and relatively low flow in the river network. The area of study is the main basin of Tuscany Region, i.e. Arno river basin, which extends over about 8300 km2 and whose mean annual precipitation is around 800 mm. Arno's mainstream, with its nearly 240 km length, passes through major Tuscan cities, as Florence and Pisa, that are vulnerable to floods (e.g. flood of November 1966). The assimilation tests follow the usage of the model in the forecasting chain, employing the operational resolution in both space and time (500 m and 15 minutes respectively) and releasing new flow forecasts every 6 hours. The assimilation strategy is evaluated in respect to open loop simulations, i.e. runs that do not exploit discharge observations through data assimilation. We compare hydrographs in their entirety, as well as classical performance indexes, as error on peak flow and Nash-Sutcliffe efficiency. The dependence of performances on lead time and location is assessed. Results indicate that the operational forecasting chain can benefit from the developed assimilation system, although with a significant variability due to the specific characteristics of any single event, and with downstream locations more sensitive to observations than upstream sites.

Regional 3-D ionospheric electron density specification on the basis of data assimilation of ground-based GNSS and radio occultation data

NASA Astrophysics Data System (ADS)

Aa, Ercha; Liu, Siqing; Huang, Wengeng; Shi, Liqin; Gong, Jiancun; Chen, Yanhong; Shen, Hua; Li, Jianyong

2016-06-01

In this paper, a regional 3-D ionospheric electron density specification over China and adjacent areas (70°E-140°E in longitude, 15°N-55°N in latitude, and 100-900 km in altitude) is developed on the basis of data assimilation technique. The International Reference Ionosphere (IRI) is used as a background model, and a three-dimensional variational technique is used to assimilate both the ground-based Global Navigation Satellite System (GNSS) observations from the Crustal Movement Observation Network of China (CMONOC) and International GNSS Service (IGS) and the ionospheric radio occultation (RO) data from FORMOSAT-3/COSMIC (F3/C) satellites. The regional 3-D gridded ionospheric electron densities can be generated with temporal resolution of 5 min in universal time, horizontal resolution of 2° × 2° in latitude and longitude, and vertical resolution of 20 km between 100 and 500 km and 50 km between 500 and 900 km. The data assimilation results are validated through extensive comparison with several sources of electron density information, including (1) ionospheric total electron content (TEC); (2) Abel-retrieved F3/C electron density profiles (EDPs); (3) ionosonde foF2 and bottomside EDPs; and (4) the Utah State University Global Assimilation of Ionospheric Measurements (USU-GAIM) under both geomagnetic quiet and disturbed conditions. The validation results show that the data assimilation procedure pushes the climatological IRI model toward the observation, and a general accuracy improvement of 15-30% can be expected. Thecomparisons also indicate that the data assimilation results are more close to the Center for Orbit Determination of Europe (CODE) TEC and Madrigal TEC products than USU-GAIM. These initial results might demonstrate the effectiveness of the data assimilation technique in improving specification of local ionospheric morphology.

An Experimental Real-Time Ocean Nowcast/Forecast System for Intra America Seas

NASA Astrophysics Data System (ADS)

Ko, D. S.; Preller, R. H.; Martin, P. J.

2003-04-01

An experimental real-time Ocean Nowcast/Forecast System has been developed for the Intra America Seas (IASNFS). The area of coverage includes the Caribbean Sea, the Gulf of Mexico and the Straits of Florida. The system produces nowcast and up to 72 hours forecast the sea level variation, 3D ocean current, temperature and salinity fields. IASNFS consists an 1/24 degree (~5 km), 41-level sigma-z data-assimilating ocean model based on NCOM. For daily nowcast/forecast the model is restarted from previous nowcast. Once model is restarted it continuously assimilates the synthetic temperature/salinity profiles generated by a data analysis model called MODAS to produce nowcast. Real-time data come from satellite altimeter (GFO, TOPEX/Poseidon, ERS-2) sea surface height anomaly and AVHRR sea surface temperature. Three hourly surface heat fluxes, including solar radiation, wind stresses and sea level air pressure from NOGAPS/FNMOC are applied for surface forcing. Forecasts are produced with available NOGAPS forecasts. Once the nowcast/forecast are produced they are distributed through the Internet via the updated web pages. The open boundary conditions including sea surface elevation, transport, temperature, salinity and currents are provided by the NRL 1/8 degree Global NCOM which is operated daily. An one way coupling scheme is used to ingest those boundary conditions into the IAS model. There are 41 rivers with monthly discharges included in the IASNFS.

Ensemble Kalman Filter versus Ensemble Smoother for Data Assimilation in Groundwater Modeling

NASA Astrophysics Data System (ADS)

Li, L.; Cao, Z.; Zhou, H.

2017-12-01

Groundwater modeling calls for an effective and robust integrating method to fill the gap between the model and data. The Ensemble Kalman Filter (EnKF), a real-time data assimilation method, has been increasingly applied in multiple disciplines such as petroleum engineering and hydrogeology. In this approach, the groundwater models are sequentially updated using measured data such as hydraulic head and concentration data. As an alternative to the EnKF, the Ensemble Smoother (ES) was proposed with updating models using all the data together, and therefore needs a much less computational cost. To further improve the performance of the ES, an iterative ES was proposed for continuously updating the models by assimilating measurements together. In this work, we compare the performance of the EnKF, the ES and the iterative ES using a synthetic example in groundwater modeling. The hydraulic head data modeled on the basis of the reference conductivity field are utilized to inversely estimate conductivities at un-sampled locations. Results are evaluated in terms of the characterization of conductivity and groundwater flow and solute transport predictions. It is concluded that: (1) the iterative ES could achieve a comparable result with the EnKF, but needs a less computational cost; (2) the iterative ES has the better performance than the ES through continuously updating. These findings suggest that the iterative ES should be paid much more attention for data assimilation in groundwater modeling.

Assimilation of Consanguineous Mafic Intrutions: Layered Crustal Sill Complexes as Reactive Filters for Continental Basalts

NASA Astrophysics Data System (ADS)

Shervais, J. W.; Hanan, B. B.; Vetter, S. K.

2007-12-01

Continental basalts commonly display variations in their chemical compositions that are inferred to reflect fractionational crystallization (FC), recharge-FC (RFC), assimilation-FC (AFC), or recharge-AFC (RAFC). The dominance of AFC-related processes reflects the intrinsic linkage between crystallization (which releases latent heat) and assimilation (which consumes latent heat). One of the central questions in any assimilation process, however, is what exactly is being assimilated. It is commonly assumed in most AFC models for the intrusion of basalt into continental crust that the contaminant is pre-existing continental crust - that is, felsic gneiss of roughly granodioritic to tonalitic composition, which is enriched in K2O and other large ion lithophiles relative to mantle-derived basalts. These continental gneisses are commonly Precambrian in age and are enriched in the lithophilic isotope ratios 87Sr/86Sr, 207Pb/204Pb, and 208Pb/204Pb, and depleted in 143Nd/144Nd. As a result, AFC-related processes involving this ancient continental crust component typically result in basaltic lavas that are enriched in LILE (e.g., K) relative to high-field strength elements (e.g., Ti, P) and enriched in the heavy isotopes of Sr, Pb, and Nd compared to the primary or parental magma. Contrary to these expectations, basalts of the Snake River volcanic province that display chemical variations diagnostic of AFC (e.g., increasing La/Lu with decreasing mg#) are commonly characterized by essentially constant isotopic ratios of Sr, Pb and Nd, and by LILE/HFSE ratios (e.g., K/P) that decrease with decreasing mg#. We propose that these basalts assimilated a ferrogabbro derived from a parent magma that was the same or similar to the magmas being intruded to recharge the system. Melts derived from this ferrogabbro would be low in K and enriched in Fe, Ti, P, and La/Lu relative to the primitive recharge magma; the isotopic composition would be the same as the primitive recharge magma. We infer that this exchange took place within a 10 km thick mafic sill complex that has been imaged seismically at depths of 12-22 km the middle crust. We propose that this process may apply to a wide range of continental basalts.

Application of satellite data in variational analysis for global cyclonic systems

NASA Technical Reports Server (NTRS)

Achtemeier, G. L.

1987-01-01

The research goal was a variational data assimilation method that incorporates as dynamical constraints, the primitive equations for a moist, convectively unstable atmosphere and the radiative transfer equation. Variables to be adjusted include the three-dimensional vector wind, height, temperature, and moisture from rawinsonde data, and cloud-wind vectors, moisture, and radiance from satellite data. This presents a formidable mathematical problem. In order to facilitate thorough analysis of each of the model components, four variational models that divide the problem naturally according to increasing complexity are defined. Each model is summarized.

The phenology of leaf quality and its within-canopy variation is essential for accurate modeling of photosynthesis in tropical evergreen forests

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Jin; Serbin, Shawn P.; Xu, Xiangtao

Leaf quantity (i.e., canopy leaf area index, LAI), quality (i.e., per-area photosynthetic capacity), and longevity all influence the photosynthetic seasonality of tropical evergreen forests. However, these components of tropical leaf phenology are poorly represented in most terrestrial biosphere models (TBMs). Here in this paper, we explored alternative options for the representation of leaf phenology effects in TBMs that employ the Farquahar, von Caemmerer & Berry (FvCB) representation of CO 2 assimilation. We developed a two-fraction leaf (sun and shade), two-layer canopy (upper and lower) photosynthesis model to evaluate different modeling approaches and assessed three components of phenological variations (i.e., leafmore » quantity, quality, and within-canopy variation in leaf longevity). Our model was driven by the prescribed seasonality of leaf quantity and quality derived from ground-based measurements within an Amazonian evergreen forest. Modeled photosynthetic seasonality was not sensitive to leaf quantity, but was highly sensitive to leaf quality and its vertical distribution within the canopy, with markedly more sensitivity to upper canopy leaf quality. This is because light absorption in tropical canopies is near maximal for the entire year, implying that seasonal changes in LAI have little impact on total canopy light absorption; and because leaf quality has a greater effect on photosynthesis of sunlit leaves than light limited, shade leaves and sunlit foliage are more abundant in the upper canopy. Our two-fraction leaf, two-layer canopy model, which accounted for all three phenological components, was able to simulate photosynthetic seasonality, explaining ~90% of the average seasonal variation in eddy covariance-derived CO 2 assimilation. This work identifies a parsimonious approach for representing tropical evergreen forest photosynthetic seasonality in TBMs that utilize the FvCB model of CO 2 assimilation and highlights the importance of incorporating more realistic phenological mechanisms in models that seek to improve the projection of future carbon dynamics in tropical evergreen forests.« less

The phenology of leaf quality and its within-canopy variation is essential for accurate modeling of photosynthesis in tropical evergreen forests

DOE PAGES

Wu, Jin; Serbin, Shawn P.; Xu, Xiangtao; ...

2017-04-18

Leaf quantity (i.e., canopy leaf area index, LAI), quality (i.e., per-area photosynthetic capacity), and longevity all influence the photosynthetic seasonality of tropical evergreen forests. However, these components of tropical leaf phenology are poorly represented in most terrestrial biosphere models (TBMs). Here in this paper, we explored alternative options for the representation of leaf phenology effects in TBMs that employ the Farquahar, von Caemmerer & Berry (FvCB) representation of CO 2 assimilation. We developed a two-fraction leaf (sun and shade), two-layer canopy (upper and lower) photosynthesis model to evaluate different modeling approaches and assessed three components of phenological variations (i.e., leafmore » quantity, quality, and within-canopy variation in leaf longevity). Our model was driven by the prescribed seasonality of leaf quantity and quality derived from ground-based measurements within an Amazonian evergreen forest. Modeled photosynthetic seasonality was not sensitive to leaf quantity, but was highly sensitive to leaf quality and its vertical distribution within the canopy, with markedly more sensitivity to upper canopy leaf quality. This is because light absorption in tropical canopies is near maximal for the entire year, implying that seasonal changes in LAI have little impact on total canopy light absorption; and because leaf quality has a greater effect on photosynthesis of sunlit leaves than light limited, shade leaves and sunlit foliage are more abundant in the upper canopy. Our two-fraction leaf, two-layer canopy model, which accounted for all three phenological components, was able to simulate photosynthetic seasonality, explaining ~90% of the average seasonal variation in eddy covariance-derived CO 2 assimilation. This work identifies a parsimonious approach for representing tropical evergreen forest photosynthetic seasonality in TBMs that utilize the FvCB model of CO 2 assimilation and highlights the importance of incorporating more realistic phenological mechanisms in models that seek to improve the projection of future carbon dynamics in tropical evergreen forests.« less

Coordination between leaf and stem traits related to leaf carbon gain and hydraulics across 32 drought-tolerant angiosperms.

PubMed

Ishida, Atsushi; Nakano, Takashi; Yazaki, Kenichi; Matsuki, Sawako; Koike, Nobuya; Lauenstein, Diego L; Shimizu, Michiru; Yamashita, Naoko

2008-05-01

We examined 15 traits in leaves and stems related to leaf C economy and water use for 32 co-existing angiosperms at ridge sites with shallow soil in the Bonin Islands. Across species, stem density was positively correlated to leaf mass per area (LMA), leaf lifespan (LLS), and total phenolics and condensed tannins per unit leaf N (N-based), and negatively correlated to leaf osmotic potential and saturated water content in leaves. LMA and LLS were negatively correlated to photosynthetic parameters, such as area-, mass-, and N-based assimilation rates. Although stem density and leaf osmotic potential were not associated with photosynthetic parameters, they were associated with some parameters of the leaf C economy, such as LMA and LLS. In the principal component (PCA) analysis, the first three axes accounted for 74.4% of total variation. Axis 1, which explained 41.8% of the total variation, was well associated with parameters for leaf C and N economy. Similarly, axis 2, which explained 22.3% of the total variation, was associated with parameters for water use. Axis 3, which explained 10.3% of the total variation, was associated with chemical defense within leaves. Axes 1 and 2 separated functional types relatively well, i.e., creeping trees, ruderal trees, other woody plants, C(3) shrubs and forbs, palms, and CAM plants, indicating that plant functional types were characterized by similar attributes of traits related to leaf C and N economy and water use. In addition, when the plot was extended by two unrelated traits, leaf mass-based assimilation rates and stem density, it also separated these functional types. These data indicate that differences in the functional types with contrasting plant strategies can be attributed to functional integration among leaf C economy, hydraulics, and leaf longevity, and that both leaf mass-based assimilation rates and stem density are key factors reflecting the different functions of plant species.

Aerosol data assimilation in the chemical transport model MOCAGE during the TRAQA/ChArMEx campaign: aerosol optical depth

NASA Astrophysics Data System (ADS)

Sič, Bojan; El Amraoui, Laaziz; Piacentini, Andrea; Marécal, Virginie; Emili, Emanuele; Cariolle, Daniel; Prather, Michael; Attié, Jean-Luc

2016-11-01

In this study, we describe the development of the aerosol optical depth (AOD) assimilation module in the chemistry transport model (CTM) MOCAGE (Modèle de Chimie Atmosphérique à Grande Echelle). Our goal is to assimilate the spatially averaged 2-D column AOD data from the National Aeronautics and Space Administration (NASA) Moderate-resolution Imaging Spectroradiometer (MODIS) instrument, and to estimate improvements in a 3-D CTM assimilation run compared to a direct model run. Our assimilation system uses 3-D-FGAT (first guess at appropriate time) as an assimilation method and the total 3-D aerosol concentration as a control variable. In order to have an extensive validation dataset, we carried out our experiment in the northern summer of 2012 when the pre-ChArMEx (CHemistry and AeRosol MEditerranean EXperiment) field campaign TRAQA (TRAnsport à longue distance et Qualité de l'Air dans le bassin méditerranéen) took place in the western Mediterranean basin. The assimilated model run is evaluated independently against a range of aerosol properties (2-D and 3-D) measured by in situ instruments (the TRAQA size-resolved balloon and aircraft measurements), the satellite Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument and ground-based instruments from the Aerosol Robotic Network (AERONET) network. The evaluation demonstrates that the AOD assimilation greatly improves aerosol representation in the model. For example, the comparison of the direct and the assimilated model run with AERONET data shows that the assimilation increased the correlation (from 0.74 to 0.88), and reduced the bias (from 0.050 to 0.006) and the root mean square error in the AOD (from 0.12 to 0.07). When compared to the 3-D concentration data obtained by the in situ aircraft and balloon measurements, the assimilation consistently improves the model output. The best results as expected occur when the shape of the vertical profile is correctly simulated by the direct model. We also examine how the assimilation can influence the modelled aerosol vertical distribution. The results show that a 2-D continuous AOD assimilation can improve the 3-D vertical profile, as a result of differential horizontal transport of aerosols in the model.

A prototype data assimilation framework for generating spatiotemporally continuous SWOT data products

NASA Astrophysics Data System (ADS)

Andreadis, K.; Margulis, S. A.; Li, D.; Lettenmaier, D. P.

2017-12-01

The Surface Water and Ocean Topography (SWOT) satellite will provide critical surface water observations for the hydrologic community. However, production of key SWOT variables, such as river discharge and surface inundation, as well as lake, reservoir, and wetland storage change will be complicated by the discontinuity of the observations in space and time. A methodology that generates products with spatially and temporally continuous fields based on SWOT observables would be highly desirable. Data assimilation provides a mechanism for merging observations from SWOT with model predictions in order to produce estimates of quantities such as river discharge, storage change, and water heights for locations and times when there is no satellite overpass or other constraints (such as layover) render the measurement unusable. We describe here a prototype assimilation system with application to the Upper Mississippi basin, implemented using synthetic SWOT observations. We use a hydrologic model (VIC) coupled with a hydrodynamic model (LISFLOOD-FP) which generates "true" fields of surface water variables. The true fields are then used to generate synthetic SWOT observations using the SWOT Instrument Simulator. We also perform a "first-guess" (or open-loop) simulation with the coupled model using a configuration that contains errors representative of the imperfect knowledge of parameters and input data, including channel topography, bankfull widths and depths, and inflows, to create an ensemble of 20 model trajectories. Subsequently we assimilate the synthetic SWOT observations into the open-loop model results to estimate water surface elevation, discharge, and storage change. Our preliminary results using three data assimilation strategies show that all improve the water surface elevation estimate accuracy by 25% - 35% for a river reach of the upper Mississippi River. Ongoing work is examining whether the improved water surface elevation estimates propagate to improvements in river discharge.

Assimilation of drifters' trajectories in velocity fields from coastal radar and model via the Lagrangian assimilation algorithm LAVA.

NASA Astrophysics Data System (ADS)

Berta, Maristella; Bellomo, Lucio; Griffa, Annalisa; Gatimu Magaldi, Marcello; Marmain, Julien; Molcard, Anne; Taillandier, Vincent

2013-04-01

The Lagrangian assimilation algorithm LAVA (LAgrangian Variational Analysis) is customized for coastal areas in the framework of the TOSCA (Tracking Oil Spills & Coastal Awareness network) Project, to improve the response to maritime accidents in the Mediterranean Sea. LAVA assimilates drifters' trajectories in the velocity fields which may come from either coastal radars or numerical models. In the present study, LAVA is applied to the coastal area in front of Toulon (France). Surface currents are available from a WERA radar network (2km spatial resolution, every 20 minutes) and from the GLAZUR model (1/64° spatial resolution, every hour). The cluster of drifters considered is constituted by 7 buoys, transmitting every 15 minutes for a period of 5 days. Three assimilation cases are considered: i) correction of the radar velocity field, ii) correction of the model velocity field and iii) reconstruction of the velocity field from drifters only. It is found that drifters' trajectories compare well with the ones obtained by the radar and the correction to radar velocity field is therefore minimal. Contrarily, observed and numerical trajectories separate rapidly and the correction to the model velocity field is substantial. For the reconstruction from drifters only, the velocity fields obtained are similar to the radar ones, but limited to the neighbor of the drifter paths.

Volcanic Ash Data Assimilation System for Atmospheric Transport Model

NASA Astrophysics Data System (ADS)

Ishii, K.; Shimbori, T.; Sato, E.; Tokumoto, T.; Hayashi, Y.; Hashimoto, A.

2017-12-01

The Japan Meteorological Agency (JMA) has two operations for volcanic ash forecasts, which are Volcanic Ash Fall Forecast (VAFF) and Volcanic Ash Advisory (VAA). In these operations, the forecasts are calculated by atmospheric transport models including the advection process, the turbulent diffusion process, the gravitational fall process and the deposition process (wet/dry). The initial distribution of volcanic ash in the models is the most important but uncertain factor. In operations, the model of Suzuki (1983) with many empirical assumptions is adopted to the initial distribution. This adversely affects the reconstruction of actual eruption plumes.We are developing a volcanic ash data assimilation system using weather radars and meteorological satellite observation, in order to improve the initial distribution of the atmospheric transport models. Our data assimilation system is based on the three-dimensional variational data assimilation method (3D-Var). Analysis variables are ash concentration and size distribution parameters which are mutually independent. The radar observation is expected to provide three-dimensional parameters such as ash concentration and parameters of ash particle size distribution. On the other hand, the satellite observation is anticipated to provide two-dimensional parameters of ash clouds such as mass loading, top height and particle effective radius. In this study, we estimate the thickness of ash clouds using vertical wind shear of JMA numerical weather prediction, and apply for the volcanic ash data assimilation system.

A statistical data assimilation method for seasonal streamflow forecasting to optimize hydropower reservoir management in data-scarce regions

NASA Astrophysics Data System (ADS)

Arsenault, R.; Mai, J.; Latraverse, M.; Tolson, B.

2017-12-01

Probabilistic ensemble forecasts generated by the ensemble streamflow prediction (ESP) methodology are subject to biases due to errors in the hydrological model's initial states. In day-to-day operations, hydrologists must compensate for discrepancies between observed and simulated states such as streamflow. However, in data-scarce regions, little to no information is available to guide the streamflow assimilation process. The manual assimilation process can then lead to more uncertainty due to the numerous options available to the forecaster. Furthermore, the model's mass balance may be compromised and could affect future forecasts. In this study we propose a data-driven approach in which specific variables that may be adjusted during assimilation are defined. The underlying principle was to identify key variables that would be the most appropriate to modify during streamflow assimilation depending on the initial conditions such as the time period of the assimilation, the snow water equivalent of the snowpack and meteorological conditions. The variables to adjust were determined by performing an automatic variational data assimilation on individual (or combinations of) model state variables and meteorological forcing. The assimilation aimed to simultaneously optimize: (1) the error between the observed and simulated streamflow at the timepoint where the forecasts starts and (2) the bias between medium to long-term observed and simulated flows, which were simulated by running the model with the observed meteorological data on a hindcast period. The optimal variables were then classified according to the initial conditions at the time period where the forecast is initiated. The proposed method was evaluated by measuring the average electricity generation of a hydropower complex in Québec, Canada driven by this method. A test-bed which simulates the real-world assimilation, forecasting, water release optimization and decision-making of a hydropower cascade was developed to assess the performance of each individual process in the reservoir management chain. Here the proposed method was compared to the PF algorithm while keeping all other elements intact. Preliminary results are encouraging in terms of power generation and robustness for the proposed approach.

Regional Precipitation Forecast with Atmospheric InfraRed Sounder (AIRS) Profile Assimilation

NASA Technical Reports Server (NTRS)

Chou, S.-H.; Zavodsky, B. T.; Jedloved, G. J.

2010-01-01

Advanced technology in hyperspectral sensors such as the Atmospheric InfraRed Sounder (AIRS; Aumann et al. 2003) on NASA's polar orbiting Aqua satellite retrieve higher vertical resolution thermodynamic profiles than their predecessors due to increased spectral resolution. Although these capabilities do not replace the robust vertical resolution provided by radiosondes, they can serve as a complement to radiosondes in both space and time. These retrieved soundings can have a significant impact on weather forecasts if properly assimilated into prediction models. Several recent studies have evaluated the performance of specific operational weather forecast models when AIRS data are included in the assimilation process. LeMarshall et al. (2006) concluded that AIRS radiances significantly improved 500 hPa anomaly correlations in medium-range forecasts of the Global Forecast System (GFS) model. McCarty et al. (2009) demonstrated similar forecast improvement in 0-48 hour forecasts in an offline version of the operational North American Mesoscale (NAM) model when AIRS radiances were assimilated at the regional scale. Reale et al. (2008) showed improvements to Northern Hemisphere 500 hPa height anomaly correlations in NASA's Goddard Earth Observing System Model, Version 5 (GEOS-5) global system with the inclusion of partly cloudy AIRS temperature profiles. Singh et al. (2008) assimilated AIRS temperature and moisture profiles into a regional modeling system for a study of a heavy rainfall event during the summer monsoon season in Mumbai, India. This paper describes an approach to assimilate AIRS temperature and moisture profiles into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimensional variational (3DVAR) assimilation system (WRF-Var; Barker et al. 2004). Section 2 describes the AIRS instrument and how the quality indicators are used to intelligently select the highest-quality data for assimilation. Section 3 presents an overall precipitation improvement with AIRS assimilation during a 37-day case study period, and Section 4 focuses on a single case study to further investigate the meteorological impact of AIRS profiles on synoptic scale models. Finally, Section 5 provides a summary of the paper.

Description and verification of a U.S. Naval Research Lab's loosely coupled data assimilation system for the Navy's Earth System Model

NASA Astrophysics Data System (ADS)

Barton, N. P.; Metzger, E. J.; Smedstad, O. M.; Ruston, B. C.; Wallcraft, A. J.; Whitcomb, T.; Ridout, J. A.; Zamudio, L.; Posey, P.; Reynolds, C. A.; Richman, J. G.; Phelps, M.

2017-12-01

The Naval Research Laboratory is developing an Earth System Model (NESM) to provide global environmental information to meet Navy and Department of Defense (DoD) operations and planning needs from the upper atmosphere to under the sea. This system consists of a global atmosphere, ocean, ice, wave, and land prediction models and the individual models include: atmosphere - NAVy Global Environmental Model (NAVGEM); ocean - HYbrid Coordinate Ocean Model (HYCOM); sea ice - Community Ice CodE (CICE); WAVEWATCH III™; and land - NAVGEM Land Surface Model (LSM). Data assimilation is currently loosely coupled between the atmosphere component using a 6-hour update cycle in the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System - Accelerated Representer (NAVDAS-AR) and the ocean/ice components using a 24-hour update cycle in the Navy Coupled Ocean Data Assimilation (NCODA) with 3 hours of incremental updating. This presentation will describe the US Navy's coupled forecast model, the loosely coupled data assimilation, and compare results against stand-alone atmosphere and ocean/ice models. In particular, we will focus on the unique aspects of this modeling system, which includes an eddy resolving ocean model and challenges associated with different update-windows and solvers for the data assimilation in the atmosphere and ocean. Results will focus on typical operational diagnostics for atmosphere, ocean, and ice analyses including 500 hPa atmospheric height anomalies, low-level winds, temperature/salinity ocean depth profiles, ocean acoustical proxies, sea ice edge, and sea ice drift. Overall, the global coupled system is performing with comparable skill to the stand-alone systems.

Effect of Cross-Correlation on Geomagnetic Forecast Accuracies

NASA Technical Reports Server (NTRS)

Kuang, Weijia; Wei, Zigang; Tangborn, Andrew

2011-01-01

Surface geomagnetic observation can determine up to degree L = 14 time-varying spherical harmonic coefficients of the poloidal magnetic field. Assimilation of these coefficients to numerical dynamo simulation could help us understand better the dynamical processes in the Earth's outer core, and to provide more accurate forecast of geomagnetic secular variations (SV). In our previous assimilation studies, only the poloidal magnetic field in the core is corrected by the observations in the analysis. Unobservable core state variables (the toroidal magnetic field and the core velocity field) are corrected via the dynamical equations of the geodynamo. Our assimilation experiments show that the assimilated core state converges near the CMB, implying that the dynamo state is strongly constrained by surface geomagnetic observations, and is pulled closer to the truth by the data. We are now carrying out an ensemble of assimilation runs with 1000 years of geomagnetic and archeo/paleo magnetic record. In these runs the cross correlation between the toroidal and the poloidal magnetic fields is incorporated into the analysis. This correlation is derived from the physical boundary conditions of the toroidal field at the core-mantle boundary (CMB). The assimilation results are then compared with those of the ensemble runs without the cross-correlation, aiming at understanding two fundamental issues: the effect of the crosscorrelation on (1) the convergence of the core state, and (2) the SV prediction accuracies. The constrained dynamo solutions will provide valuable insights on interpreting the observed SV, e.g. the near-equator magnetic flux patches, the core-mantle interactions, and possibly other geodynamic observables.

The role of ocean-atmosphere interaction in Typhoon Sinlaku (2008) using a regional coupled data assimilation system

NASA Astrophysics Data System (ADS)

Wada, Akiyoshi; Kunii, Masaru

2017-05-01

For improving analyses of tropical cyclone (TC) and sea surface temperature (SST) and thereby TC simulations, a regional mesoscale strongly coupled atmosphere-ocean data assimilation system was developed with the local ensemble transform Kalman filter (LETKF) implemented with the Japan Meteorological Agency's nonhydrostatic model (NHM) coupled with a multilayer ocean model and the third-generation ocean wave model. The NHM-LETKF coupled data assimilation system was applied to Typhoon Sinlaku (2008) along with the original NHM-LETKF system to investigate the sensitivity of Sinlaku to SST assimilation with the Level 2 Pre-processed (L2P) standard product of satellite SST. SST calculated in the coupled-assimilation experiment with the coupled data assimilation system and the satellite SST (CPL) showed a better correlation with Optimally Interpolated SST than SST used in the control experiment with the original NHM-LETKF (CNTL) and SST calculated in the succession experiment with the coupled system without satellite SST (SUCC). The time series in the CPL experiment well captured the variation in the SST observed at the Kuroshio Extension Observation buoy site. In addition, TC-induced sea surface cooling was analyzed more realistically in the CPL experiment than that in the CNTL and SUCC experiments. However, the central pressure analyzed in each three experiments was overestimated compared with the Regional Specialized Meteorological Center Tokyo best-track central pressure, mainly due to the coarse horizontal resolution of 15 km. The 96 h TC simulations indicated that the CPL experiment provided more favorable initial and boundary conditions than the CNTL experiment to simulate TC tracks more accurately.

Impact of Assimilation of Conventional and Satellite Radiance GTS Observations on Simulation of Mesoscale Convective System Over Southeast India Using WRF-3DVar

NASA Astrophysics Data System (ADS)

Madhulatha, A.; Rajeevan, M.; Bhowmik, S. K. Roy; Das, A. K.

2018-01-01

The primary goal of present study is to investigate the impact of assimilation of conventional and satellite radiance observations in simulating the mesoscale convective system (MCS) formed over south east India. An assimilation methodology based on Weather Research and Forecasting model three dimensional variational data assimilation is considered. Few numerical experiments are carried out to examine the individual and combined impact of conventional and non-conventional (satellite radiance) observations. After the successful inclusion of additional observations, strong analysis increments of temperature and moisture fields are noticed and contributed to significant improvement in model's initial fields. The resulting model simulations are able to successfully reproduce the prominent synoptic features responsible for the initiation of MCS. Among all the experiments, the final experiment in which both conventional and satellite radiance observations assimilated has showed considerable impact on the prediction of MCS. The location, genesis, intensity, propagation and development of rain bands associated with the MCS are simulated reasonably well. The biases of simulated temperature, moisture and wind fields at surface and different pressure levels are reduced. Thermodynamic, dynamic and vertical structure of convective cells associated with the passage of MCS are well captured. Spatial distribution of rainfall is fairly reproduced and comparable to TRMM observations. It is demonstrated that incorporation of conventional and satellite radiance observations improved the local and synoptic representation of temperature, moisture fields from surface to different levels of atmosphere. This study highlights the importance of assimilation of conventional and satellite radiances in improving the models initial conditions and simulation of MCS.

The Impact of Assimilation of GPM Clear Sky Radiance on HWRF Hurricane Track and Intensity Forecasts

NASA Astrophysics Data System (ADS)

Yu, C. L.; Pu, Z.

2016-12-01

The impact of GPM microwave imager (GMI) clear sky radiances on hurricane forecasting is examined by ingesting GMI level 1C recalibrated brightness temperature into the NCEP Gridpoint Statistical Interpolation (GSI)- based ensemble-variational hybrid data assimilation system for the operational Hurricane Weather Research and Forecast (HWRF) system. The GMI clear sky radiances are compared with the Community Radiative Transfer Model (CRTM) simulated radiances to closely study the quality of the radiance observations. The quality check result indicates the presence of bias in various channels. A static bias correction scheme, in which the appropriate bias correction coefficients for GMI data is evaluated by applying regression method on a sufficiently large sample of data representative to the observational bias in the regions of concern, is used to correct the observational bias in GMI clear sky radiances. Forecast results with and without assimilation of GMI radiance are compared using hurricane cases from recent hurricane seasons (e.g., Hurricane Joaquin in 2015). Diagnoses of data assimilation results show that the bias correction coefficients obtained from the regression method can correct the inherent biases in GMI radiance data, significantly reducing observational residuals. The removal of biases also allows more data to pass GSI quality control and hence to be assimilated into the model. Forecast results for hurricane Joaquin demonstrates that the quality of analysis from the data assimilation is sensitive to the bias correction, with positive impacts on the hurricane track forecast when systematic biases are removed from the radiance data. Details will be presented at the symposium.

Modeling Circulation along the Vietnamese Coast Influenced by Monsoon Variability in Meteorology, River Discharge and Interactions with the Vietnamese East Sea

DTIC Science & Technology

2013-09-30

productivity. Advanced variational methods for the assimilation of satellite and in situ observations to achieve improved state estimation and subsequent...time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection...South China Sea (SCS) using the Regional Ocean Modeling System (ROMS) with Incremental Strong Constraint 4-Dimensional Variational (IS4DVAR) data

Grain growth prediction based on data assimilation by implementing 4DVar on multi-phase-field model

NASA Astrophysics Data System (ADS)

Ito, Shin-ichi; Nagao, Hiromichi; Kasuya, Tadashi; Inoue, Junya

2017-12-01

We propose a method to predict grain growth based on data assimilation by using a four-dimensional variational method (4DVar). When implemented on a multi-phase-field model, the proposed method allows us to calculate the predicted grain structures and uncertainties in them that depend on the quality and quantity of the observational data. We confirm through numerical tests involving synthetic data that the proposed method correctly reproduces the true phase-field assumed in advance. Furthermore, it successfully quantifies uncertainties in the predicted grain structures, where such uncertainty quantifications provide valuable information to optimize the experimental design.

Optimization of Terrestrial Ecosystem Model Parameters Using Atmospheric CO2 Concentration Data With the Global Carbon Assimilation System (GCAS)

NASA Astrophysics Data System (ADS)

Chen, Zhuoqi; Chen, Jing M.; Zhang, Shupeng; Zheng, Xiaogu; Ju, Weiming; Mo, Gang; Lu, Xiaoliang

2017-12-01

The Global Carbon Assimilation System that assimilates ground-based atmospheric CO2 data is used to estimate several key parameters in a terrestrial ecosystem model for the purpose of improving carbon cycle simulation. The optimized parameters are the leaf maximum carboxylation rate at 25°C (Vmax25), the temperature sensitivity of ecosystem respiration (Q10), and the soil carbon pool size. The optimization is performed at the global scale at 1° resolution for the period from 2002 to 2008. The results indicate that vegetation from tropical zones has lower Vmax25 values than vegetation in temperate regions. Relatively high values of Q10 are derived over high/midlatitude regions. Both Vmax25 and Q10 exhibit pronounced seasonal variations at middle-high latitudes. The maxima in Vmax25 occur during growing seasons, while the minima appear during nongrowing seasons. Q10 values decrease with increasing temperature. The seasonal variabilities of Vmax25 and Q10 are larger at higher latitudes. Optimized Vmax25 and Q10 show little seasonal variabilities at tropical regions. The seasonal variabilities of Vmax25 are consistent with the variabilities of LAI for evergreen conifers and broadleaf evergreen forests. Variations in leaf nitrogen and leaf chlorophyll contents may partly explain the variations in Vmax25. The spatial distribution of the total soil carbon pool size after optimization is compared favorably with the gridded Global Soil Data Set for Earth System. The results also suggest that atmospheric CO2 data are a source of information that can be tapped to gain spatially and temporally meaningful information for key ecosystem parameters that are representative at the regional and global scales.

Impact assessment of GPS radio occultation data on Antarctic analysis and forecast using WRF 3DVAR

NASA Astrophysics Data System (ADS)

Zhang, H.; Wee, T. K.; Liu, Z.; Lin, H. C.; Kuo, Y. H.

2016-12-01

This study assesses the impact of Global Positioning System (GPS) Radio Occultation (RO) refractivity data on the analysis and forecast in the Antarctic region. The RO data are continuously assimilated into the Weather Research and Forecasting (WRF) Model using the WRF 3DVAR along with other observations that were operationally available to the National Center for Environmental Prediction (NCEP) during a month period, October 2010, including the Advance Microwave Sounding Unit (AMSU) radiance data. For the month-long data assimilation experiments, three RO datasets are used: 1) The actual operational dataset, which was produced by the near real-time RO processing at that time and provided to weather forecasting centers; 2) a post-processed dataset with posterior clock and orbit estimates, and with improved RO processing algorithms; and, 3) another post-processed dataset, produced with a variational RO processing. The data impact is evaluated with comparing the forecasts and analyses to independent driftsonde observations that are made available through the Concordiasi field campaign, in addition to utilizing other traditional means of verification. A denial of RO data (while keeping all other observations) resulted in a remarkable quality degradation of analysis and forecast, indicating the high value of RO data over the Antarctic area. The post-processed RO data showed a significantly larger positive impact compared to the near real-time data, due to extra RO data from the TerraSAR-X satellite (unavailable at the time of the near real-time processing) as well as the supposedly improved data quality as a result of the post-processing. This strongly suggests that the future polar constellation of COSMIC-2 is vital. The variational RO processing further reduced the systematic and random errors in both analysis and forecasts, for instance, leading to a smaller background departure of AMSU radiance. This indicates that the variational RO processing provides an improved reference for the bias correction of satellite radiance, making the bias correction more effective. This study finds that advanced RO data processing algorithms may further enhance the high quality of RO data in high Southern latitudes.

Exploring and Analyzing Climate Variations Online by Using NASA MERRA-2 Data at GES DISC

NASA Technical Reports Server (NTRS)

Shen, Suhung; Ostrenga, Dana M.; Vollmer, Bruce E.; Kempler, Steven J.

2016-01-01

NASA Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) (http:giovanni.sci.gsfc.nasa.govgiovanni) is a web-based data visualization and analysis system developed by the Goddard Earth Sciences Data and Information Services Center (GES DISC). Current data analysis functions include Lat-Lon map, time series, scatter plot, correlation map, difference, cross-section, vertical profile, and animation etc. The system enables basic statistical analysis and comparisons of multiple variables. This web-based tool facilitates data discovery, exploration and analysis of large amount of global and regional remote sensing and model data sets from a number of NASA data centers. Long term global assimilated atmospheric, land, and ocean data have been integrated into the system that enables quick exploration and analysis of climate data without downloading, preprocessing, and learning data. Example data include climate reanalysis data from NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) which provides data beginning in 1980 to present; land data from NASA Global Land Data Assimilation System (GLDAS), which assimilates data from 1948 to 2012; as well as ocean biological data from NASA Ocean Biogeochemical Model (NOBM), which provides data from 1998 to 2012. This presentation, using surface air temperature, precipitation, ozone, and aerosol, etc. from MERRA-2, demonstrates climate variation analysis with Giovanni at selected regions.

Four-Dimensional Data Assimilation Using the Adjoint Method

NASA Astrophysics Data System (ADS)

Bao, Jian-Wen

The calculus of variations is used to confirm that variational four-dimensional data assimilation (FDDA) using the adjoint method can be implemented when the numerical model equations have a finite number of first-order discontinuous points. These points represent the on/off switches associated with physical processes, for which the Jacobian matrix of the model equation does not exist. Numerical evidence suggests that, in some situations when the adjoint method is used for FDDA, the temperature field retrieved using horizontal wind data is numerically not unique. A physical interpretation of this type of non-uniqueness of the retrieval is proposed in terms of energetics. The adjoint equations of a numerical model can also be used for model-parameter estimation. A general computational procedure is developed to determine the size and distribution of any internal model parameter. The procedure is then applied to a one-dimensional shallow -fluid model in the context of analysis-nudging FDDA: the weighting coefficients used by the Newtonian nudging technique are determined. The sensitivity of these nudging coefficients to the optimal objectives and constraints is investigated. Experiments of FDDA using the adjoint method are conducted using the dry version of the hydrostatic Penn State/NCAR mesoscale model (MM4) and its adjoint. The minimization procedure converges and the initialization experiment is successful. Temperature-retrieval experiments involving an assimilation of the horizontal wind are also carried out using the adjoint of MM4.

Estimating Water and Heat Fluxes with a Four-dimensional Weak-constraint Variational Data Assimilation Approach

NASA Astrophysics Data System (ADS)

Bateni, S. M.; Xu, T.

2015-12-01

Accurate estimation of water and heat fluxes is required for irrigation scheduling, weather prediction, and water resources planning and management. A weak-constraint variational data assimilation (WC-VDA) scheme is developed to estimate water and heat fluxes by assimilating sequences of land surface temperature (LST) observations. The commonly used strong-constraint VDA systems adversely affect the accuracy of water and heat flux estimates as they assume the model is perfect. The WC-VDA approach accounts for structural and model errors and generates more accurate results via adding a model error term into the surface energy balance equation. The two key unknown parameters of the WC-VDA system (i.e., CHN, the bulk heat transfer coefficient and EF, evaporative fraction) and the model error term are optimized by minimizing the cost function. The WC-VDA model was tested at two sites with contrasting hydrological and vegetative conditions: the Daman site (a wet site located in an oasis area and covered by seeded corn) and the Huazhaizi site (a dry site located in a desert area and covered by sparse grass) in middle stream of Heihe river basin, northwest China. Compared to the strong-constraint VDA system, the WC-VDA method generates more accurate estimates of water and energy fluxes over the desert and oasis sites with dry and wet conditions.

Assimilation of surface NO2 and O3 observations into the SILAM chemistry transport model

NASA Astrophysics Data System (ADS)

Vira, J.; Sofiev, M.

2015-02-01

This paper describes the assimilation of trace gas observations into the chemistry transport model SILAM (System for Integrated modeLling of Atmospheric coMposition) using the 3D-Var method. Assimilation results for the year 2012 are presented for the prominent photochemical pollutants ozone (O3) and nitrogen dioxide (NO2). Both species are covered by the AirBase observation database, which provides the observational data set used in this study. Attention was paid to the background and observation error covariance matrices, which were obtained primarily by the iterative application of a posteriori diagnostics. The diagnostics were computed separately for 2 months representing summer and winter conditions, and further disaggregated by time of day. This enabled the derivation of background and observation error covariance definitions, which included both seasonal and diurnal variation. The consistency of the obtained covariance matrices was verified using χ2 diagnostics. The analysis scores were computed for a control set of observation stations withheld from assimilation. Compared to a free-running model simulation, the correlation coefficient for daily maximum values was improved from 0.8 to 0.9 for O3 and from 0.53 to 0.63 for NO2.

Quantifying emissions of CO and NOx using observations from MOPITT, OMI, TES, and OSIRIS

NASA Astrophysics Data System (ADS)

Zhang, X.; Jones, D. B. A.; Keller, M.; Walker, T. W.; Jiang, Z.; Henze, D. K.; Bourassa, A. E.; Degenstein, D. A.; Rochon, Y. J.

2016-12-01

We use the GEOS-Chem four-dimensional variational (4D-var) data assimilation with satellite observations of multiple chemical species to estimate emissions of CO and NOx, as well as the tropospheric concentrations of O3. In doing so, we utilize CO retrievals from The Measurements of Pollution In The Troposphere (MOPITT), O3 retrievals from the Tropospheric Emission Spectrometer (TES), O3 retrievals from the Optical Spectrograph and InfraRed Imager System (OSIRIS), and NO2 columns from the Ozone Monitoring Instrument (OMI). By integrating these data in the 4D-Var scheme, we obtain a chemical state in the model that is consistent with all of the data over the assimilation period. In this context, for example, we find that combining TES and OSIRIS improves O3, particularly in the tropical upper troposphere (by 10-20%), which leads to a reduction in the uncertainty of the NOx emission estimates. However, although assimilating multiple chemical species provides a stronger constraint on the chemical, state, there are still large uncertainties on the CO and NOx emission estimates, due to the dependence of the results on the selection of the assimilation window and how the datasets are weighted in the cost function.

Assimilation of seasonal chlorophyll and nutrient data into an adjoint three-dimensional ocean carbon cycle model: Sensitivity analysis and ecosystem parameter optimization

NASA Astrophysics Data System (ADS)

Tjiputra, Jerry F.; Polzin, Dierk; Winguth, Arne M. E.

2007-03-01

An adjoint method is applied to a three-dimensional global ocean biogeochemical cycle model to optimize the ecosystem parameters on the basis of SeaWiFS surface chlorophyll observation. We showed with identical twin experiments that the model simulated chlorophyll concentration is sensitive to perturbation of phytoplankton and zooplankton exudation, herbivore egestion as fecal pellets, zooplankton grazing, and the assimilation efficiency parameters. The assimilation of SeaWiFS chlorophyll data significantly improved the prediction of chlorophyll concentration, especially in the high-latitude regions. Experiments that considered regional variations of parameters yielded a high seasonal variance of ecosystem parameters in the high latitudes, but a low variance in the tropical regions. These experiments indicate that the adjoint model is, despite the many uncertainties, generally capable to optimize sensitive parameters and carbon fluxes in the euphotic zone. The best fit regional parameters predict a global net primary production of 36 Pg C yr-1, which lies within the range suggested by Antoine et al. (1996). Additional constraints of nutrient data from the World Ocean Atlas showed further reduction in the model-data misfit and that assimilation with extensive data sets is necessary.

The Bi-Modal Pattern of the Summer Circulation Over South America

NASA Technical Reports Server (NTRS)

Herdies, Dirceu Luis; daSilva, Arlindo; SilvaDias, Maria A. F.; Atlas, Robert (Technical Monitor)

2001-01-01

Submonthly variations in warm-season (January-February) precipitation over South America, in special over the Amazon basin, central southwest Brazil, north Argentina, and Paraguay are shown to be strongly linked to variations in the moisture entering the continent from the Atlantic ocean. Two distinct regimes of lower tropospheric winds (westerlies and easterlies) were observed in Rondonia during the Wet Season Atmospheric Mesoscale Campaign (WETAMC) component of the Large Scale Atmosphere-Biosphere Experiment in Amazonia (LBA) and the Tropical Rainfall Measuring Mission (TRMM) field campaign. The westerly (easterly) winds were associated with the strong (weak) convective activity over the South Atlantic Convergence Zone (SACZ). The whole period of this study (January-February) was divided into SACZ and NSACZ (No SACZ) events. The vertically integrated moisture fluxes over the Amazon and Prata basin from the National Aeronautics and Space Administration/Goddard Data Assimilation Office (NASA/DAO) assimilation show that during SACZ (NSACZ) event strong (weak) convergence occurred over the Amazon basin with divergence (convergence) over the Prata basin. Submonthly variations in the SACZ also can be linked to extreme climate anomalies such as droughts or flooding conditions over the Amazon and Prata basin.

Status and Plans for Reanalysis at NASA/GMAO

NASA Technical Reports Server (NTRS)

Gelaro, Ron

2017-01-01

Reanalysis plays a critical role in GMAOs goal to enhance NASA's program of Earth observations, providing vital data sets for climate research and the development of future missions. As the breadth of NASAs observations expands to include multiple components of the Earth system, so does the need to assimilate observations from currently uncoupled components of the system in a more physically consistent manner. GMAOs most recent reanalysis of the satellite era, MERRA-2, has completed the period 1980-present, and is now running as a continuing global climate analysis with two- to three-week latency. MERRA-2 assimilates meteorological and aerosol observations as a weakly coupled assimilation system as a first step toward GMAOs longer term goal of developing an integrated Earth system analysis (IESA) capability that will couple assimilation systems for the atmosphere, ocean, land and chemistry. The GMAO strategy is to progress incrementally toward an IESA through an evolving combination of coupled systems and offline component reanalyses driven by, for example, MERRA-2 atmospheric forcing. Most recently, the GMAO has implemented a weakly coupled assimilation scheme for analyzing ocean skin temperature within the existing atmospheric analysis. The scheme uses background fields from a near-surface ocean diurnal layer model to assimilate surface-sensitive radiances plus in-situ observations along with all other observations in the atmospheric assimilation system. In addition, MERRA-2-driven simulations of the ocean (plus sea ice) and atmospheric chemistry (for the EOS period) are currently underway, as is the development of a coupled atmosphere-ocean assimilation system. This talk will describe the status of these ongoing efforts and the planned steps toward an IESA capability for climate research.

Sequential estimation and satellite data assimilation in meteorology and oceanography

NASA Technical Reports Server (NTRS)

Ghil, M.

1986-01-01

The role of dynamics in estimating the state of the atmosphere and ocean from incomplete and noisy data is discussed and the classical applications of four-dimensional data assimilation to large-scale atmospheric dynamics are presented. It is concluded that sequential updating of a forecast model with continuously incoming conventional and remote-sensing data is the most natural way of extracting the maximum amount of information from the imperfectly known dynamics, on the one hand, and the inaccurate and incomplete observations, on the other.

Current and future data assimilation development in the Copernicus Atmosphere Monitoring Service

NASA Astrophysics Data System (ADS)

Engelen, R. J.; Ades, M.; Agusti-panareda, A.; Flemming, J.; Inness, A.; Kipling, Z.; Parrington, M.; Peuch, V. H.

2017-12-01

The European Copernicus Atmosphere Monitoring Service (CAMS) operationally provides daily forecasts of global atmospheric composition and regional air quality. The global forecasting system is using ECMWF's Integrated Forecasting System (IFS), which is used for numerical weather prediction and which has been extended with modules for atmospheric chemistry, aerosols and greenhouse gases. The system assimilates observations from more than 60 satellite sensors to constrain both the meteorology and the atmospheric composition species. While an operational forecasting system needs to be robust and reliable, it also needs to stay state-of-the-art to provide the best possible forecasts. Continuous development is therefore an important component of the CAMS systems. We will present on-going efforts on improving the 4D-Var data assimilation system, such as using ensemble data assimilation to improve the background error covariances and more accurate use of satellite observations. We will also outline plans for including emissions in the daily CAMS analyses, which is an area where research activities have a large potential to feed into operational applications.

Long-term observations minus background monitoring of ground-based brightness temperatures from a microwave radiometer network

NASA Astrophysics Data System (ADS)

De Angelis, Francesco; Cimini, Domenico; Löhnert, Ulrich; Caumont, Olivier; Haefele, Alexander; Pospichal, Bernhard; Martinet, Pauline; Navas-Guzmán, Francisco; Klein-Baltink, Henk; Dupont, Jean-Charles; Hocking, James

2017-10-01

Ground-based microwave radiometers (MWRs) offer the capability to provide continuous, high-temporal-resolution observations of the atmospheric thermodynamic state in the planetary boundary layer (PBL) with low maintenance. This makes MWR an ideal instrument to supplement radiosonde and satellite observations when initializing numerical weather prediction (NWP) models through data assimilation. State-of-the-art data assimilation systems (e.g. variational schemes) require an accurate representation of the differences between model (background) and observations, which are then weighted by their respective errors to provide the best analysis of the true atmospheric state. In this perspective, one source of information is contained in the statistics of the differences between observations and their background counterparts (O-B). Monitoring of O-B statistics is crucial to detect and remove systematic errors coming from the measurements, the observation operator, and/or the NWP model. This work illustrates a 1-year O-B analysis for MWR observations in clear-sky conditions for an European-wide network of six MWRs. Observations include MWR brightness temperatures (TB) measured by the two most common types of MWR instruments. Background profiles are extracted from the French convective-scale model AROME-France before being converted into TB. The observation operator used to map atmospheric profiles into TB is the fast radiative transfer model RTTOV-gb. It is shown that O-B monitoring can effectively detect instrument malfunctions. O-B statistics (bias, standard deviation, and root mean square) for water vapour channels (22.24-30.0 GHz) are quite consistent for all the instrumental sites, decreasing from the 22.24 GHz line centre ( ˜ 2-2.5 K) towards the high-frequency wing ( ˜ 0.8-1.3 K). Statistics for zenith and lower-elevation observations show a similar trend, though values increase with increasing air mass. O-B statistics for temperature channels show different behaviour for relatively transparent (51-53 GHz) and opaque channels (54-58 GHz). Opaque channels show lower uncertainties (< 0.8-0.9 K) and little variation with elevation angle. Transparent channels show larger biases ( ˜ 2-3 K) with relatively low standard deviations ( ˜ 1-1.5 K). The observations minus analysis TB statistics are similar to the O-B statistics, suggesting a possible improvement to be expected by assimilating MWR TB into NWP models. Lastly, the O-B TB differences have been evaluated to verify the normal-distribution hypothesis underlying variational and ensemble Kalman filter-based DA systems. Absolute values of excess kurtosis and skewness are generally within 1 and 0.5, respectively, for all instrumental sites, demonstrating O-B normal distribution for most of the channels and elevations angles.

Coastal Aerosol Distribution by Data Assimilation

DTIC Science & Technology

2005-09-30

Emissions ( FLAMBE ; NASA and ONR funded) make these simulations possible. Similarly, Honrath et al. (2004) used NAAPS to attribute interannual variations...NAAPS/ FLAMBE smoke aerosol optical thickness (AOT) each summer. CO is plotted with red squares, ozone is plotted with blue circles, and smoke AOT is

Gender and migration on the labour market: Additive or interacting disadvantages in Germany?

PubMed

Fleischmann, Fenella; Höhne, Jutta

2013-09-01

Despite substantial differences in labour market attainment according to gender and migration status, gender and ethnic differences in labour market behaviour are most often studied separately. In contrast, this study describes and analyses interactions between gender, ethnic background and immigrant generation with regard to labour market participation, part-time work, and occupational status. The double comparison aims to reveal whether gender gaps in these labour market outcomes among the majority population generalise to ethnic minorities. Moreover, we ask whether variation in gender gaps in labour market behaviour follows the patterns in migrants' origin countries, and whether gender gaps show signs of intergenerational assimilation. Our heterogeneous choice and OLS regressions of 2009 German Microcensus data reveal considerable variation in gender gaps in labour market behaviour between East and West Germany, across ethnic groups and across generations. Intergenerational comparisons show that most ethnic minorities assimilate towards German patterns of gendered labour market attainment. Copyright © 2013 Elsevier Inc. All rights reserved.

Special data base of Informational - Computational System 'INM RAS - Black Sea' for solving inverse and data assimilation problems

NASA Astrophysics Data System (ADS)

Zakharova, Natalia; Piskovatsky, Nicolay; Gusev, Anatoly

2014-05-01

Development of Informational-Computational Systems (ICS) for data assimilation procedures is one of multidisciplinary problems. To study and solve these problems one needs to apply modern results from different disciplines and recent developments in: mathematical modeling; theory of adjoint equations and optimal control; inverse problems; numerical methods theory; numerical algebra and scientific computing. The above problems are studied in the Institute of Numerical Mathematics of the Russian Academy of Science (INM RAS) in ICS for personal computers. In this work the results on the Special data base development for ICS "INM RAS - Black Sea" are presented. In the presentation the input information for ICS is discussed, some special data processing procedures are described. In this work the results of forecast using ICS "INM RAS - Black Sea" with operational observation data assimilation are presented. This study was supported by the Russian Foundation for Basic Research (project No 13-01-00753) and by Presidium Program of Russian Academy of Sciences (project P-23 "Black sea as an imitational ocean model"). References 1. V.I. Agoshkov, M.V. Assovskii, S.A. Lebedev, Numerical simulation of Black Sea hydrothermodynamics taking into account tide-forming forces. Russ. J. Numer. Anal. Math. Modelling (2012) 27, No.1, pp. 5-31. 2. E.I. Parmuzin, V.I. Agoshkov, Numerical solution of the variational assimilation problem for sea surface temperature in the model of the Black Sea dynamics. Russ. J. Numer. Anal. Math. Modelling (2012) 27, No.1, pp. 69-94. 3. V.B. Zalesny, N.A. Diansky, V.V. Fomin, S.N. Moshonkin, S.G. Demyshev, Numerical model of the circulation of Black Sea and Sea of Azov. Russ. J. Numer. Anal. Math. Modelling (2012) 27, No.1, pp. 95-111. 4. Agoshkov V.I.,Assovsky M.B., Giniatulin S. V., Zakharova N.B., Kuimov G.V., Parmuzin E.I., Fomin V.V. Informational Computational system of variational assimilation of observation data "INM RAS - Black sea"// Ecological safety of coastal and shelf zones and complex use of shelf resources: Collection of scientific works. Issue 26, Volume 2. - National Academy of Sciences of Ukraine, Marine Hydrophysical Institute, Sebastopol, 2012. Pages 352-360. (In russian)

Experimenting with the GMAO 4D Data Assimilation

NASA Technical Reports Server (NTRS)

Todling, R.; El Akkraoui, A.; Errico, R. M.; Guo, J.; Kim, J.; Kliest, D.; Parrish, D. F.; Suarez, M.; Trayanov, A.; Tremolet, Yannick;

Japanese 25-year reanalysis (JRA-25)

NASA Astrophysics Data System (ADS)

Ohkawara, Nozomu

2006-12-01

A long term global atmospheric reanalysis Japanese 25-year Reanalysis (JRA-25) which covers from 1979 to 2004 was completed using the Japan Meteorological Agency (JMA) numerical assimilation and forecast system. This is the first long term reanalysis undertaken in Asia. JMA's latest numerical assimilation system, and observational data collected as much as possible, were used in JRA-25 to generate a consistent and high quality reanalysis dataset to contribute to climate research and operational work. One purpose of JRA-25 is to enhance to a high quality the analysis in the Asian region. 6-hourly data assimilation cycles were performed and produced 6-hourly atmospheric analysis and forecast fields with various kinds of physical variables. The global model used in JRA-25 has a spectral resolution of T106 (equivalent to a horizontal grid size of around 120km) and 40 vertical layers with the top level at 0.4hPa. For observational data, a great deal of satellite data was used in addition to conventional surface and upper air data. Atmospheric Motion Vector (AMV) data retrieved from geostationary satellites, brightness temperature (TBB) data from TIROS Operational Vertical Sounder (TOVS), precipitable water retrieved from radiance of microwave radiometer from orbital satellites and some other satellite data were assimilated with 3-dimensional variational method (3DVAR). Many advantages have been found in the JRA-25 reanalysis. Firstly, forecast 6-hour global total precipitation in JRA-25 performs well, distribution and amount are properly represented both in space and time. JRA-25 has the best performance compared to other reanalysis with respect to time series of global precipitation over many years, with few unrealistic variations caused by degraded quality of satellite data due to volcanic eruptions. Secondly, JRA-25 is the first reanalysis which assimilated wind profiles surrounding tropical cyclones retrieved from historical best track information; tropical cyclones were analyzed correctly in all the global regions. Additionally, low-level cloud along the subtropical western coast of continents is forecast very accurately, and snow depth analysis is also good.

Examining the Suitability of a Sparse In Situ Soil Moisture Monitoring Network for Assimilation into a Spatially Distributed Hydrologic Model

NASA Astrophysics Data System (ADS)

De Vleeschouwer, N.; Verhoest, N.; Pauwels, V. R. N.

2015-12-01

The continuous monitoring of soil moisture in a permanent network can yield an interesting data product for use in hydrological data assimilation. Major advantages of in situ observations compared to remote sensing products are the potential vertical extent of the measurements, the finer temporal resolution of the observation time series, the smaller impact of land cover variability on the observation bias, etc. However, two major disadvantages are the typical small integration volume of in situ measurements and the often large spacing between monitoring locations. This causes only a small part of the modelling domain to be directly observed. Furthermore, the spatial configuration of the monitoring network is typically temporally non-dynamic. Therefore two questions can be raised. Do spatially sparse in situ soil moisture observations contain a sufficient data representativeness to successfully assimilate them into the largely unobserved spatial extent of a distributed hydrological model? And if so, how is this assimilation best performed? Consequently two important factors that can influence the success of assimilating in situ monitored soil moisture are the spatial configuration of the monitoring network and the applied assimilation algorithm. In this research the influence of those factors is examined by means of synthetic data-assimilation experiments. The study area is the ± 100 km² catchment of the Bellebeek in Flanders, Belgium. The influence of the spatial configuration is examined by varying the amount of locations and their position in the landscape. The latter is performed using several techniques including temporal stability analysis and clustering. Furthermore the observation depth is considered by comparing assimilation of surface layer (5 cm) and deeper layer (50 cm) observations. The impact of the assimilation algorithm is assessed by comparing the performance obtained with two well-known algorithms: Newtonian nudging and the Ensemble Kalman Filter.

Sexual variation in assimilation efficiency: its link to phenotype and potential role in sexual dimorphism.

PubMed

Stahlschmidt, Zachary R; Davis, Jon R; Denardo, Dale F

2011-04-01

Sex-specific variation in morphology (sexual dimorphism) is a prevalent phenomenon among animals, and both dietary intake and resource allocation strategies influence sexually dimorphic traits (e.g., body size or composition). However, we investigated whether assimilation efficiency (AE), an intermediate step between dietary intake and allocation, can also vary between the sexes. Specifically, we tested whether sex-based differences in AE can explain variation in phenotypic traits. We measured morphometric characteristics (i.e., body length, mass, condition, and musculature) and AE of total energy, crude protein, and crude fat in post-reproductive adult Children's pythons (which exhibit a limited female-biased sexual size dimorphism) fed both low and high dietary intakes. Meal size was negatively related to AE of energy. Notably, male snakes absorbed crude protein more efficiently and increased epaxial (dorsal) musculature faster than females, which demonstrates a link between AE and phenotype. However, females grew in body length faster but did not absorb any nutrient more efficiently than males. Although our results do not provide a direct link between AE and sexual size dimorphism, they demonstrate that sexual variation in nutrient absorption exists and can contribute to other types of sex-based differences in phenotype (i.e., sexual dimorphism in growth of musculature). Hence, testing the broader applicability of AE's role in sexually dimorphic traits among other species is warranted.

On the ability of a global atmospheric inversion to constrain variations of CO2 fluxes over Amazonia

NASA Astrophysics Data System (ADS)

Molina, L.; Broquet, G.; Imbach, P.; Chevallier, F.; Poulter, B.; Bonal, D.; Burban, B.; Ramonet, M.; Gatti, L. V.; Wofsy, S. C.; Munger, J. W.; Dlugokencky, E.; Ciais, P.

2015-01-01

The exchanges of carbon, water, and energy between the atmosphere and the Amazon Basin have global implications for current and future climate. Here, the global atmospheric inversion system of the Monitoring of Atmospheric Composition and Climate service (MACC) was used to further study the seasonal and interannual variations of biogenic CO2 fluxes in Amazonia. The system assimilated surface measurements of atmospheric CO2 mole fractions made over more than 100 sites over the globe into an atmospheric transport model. This study added four surface stations located in tropical South America, a region poorly covered by CO2 observations. The estimates of net ecosystem exchange (NEE) optimized by the inversion were compared to independent estimates of NEE upscaled from eddy-covariance flux measurements in Amazonia, and against reports on the seasonal and interannual variations of the land sink in South America from the scientific literature. We focused on the impact of the interannual variation of the strong droughts in 2005 and 2010 (due to severe and longer-than-usual dry seasons), and of the extreme rainfall conditions registered in 2009. The spatial variations of the seasonal and interannual variability of optimized NEE were also investigated. While the inversion supported the assumption of strong spatial heterogeneity of these variations, the results revealed critical limitations that prevent global inversion frameworks from capturing the data-driven seasonal patterns of fluxes across Amazonia. In particular, it highlighted issues due to the configuration of the observation network in South America and the lack of continuity of the measurements. However, some robust patterns from the inversion seemed consistent with the abnormal moisture conditions in 2009.

Mediterranea Forecasting System: a focus on wave-current coupling

NASA Astrophysics Data System (ADS)

Clementi, Emanuela; Delrosso, Damiano; Pistoia, Jenny; Drudi, Massimiliano; Fratianni, Claudia; Grandi, Alessandro; Pinardi, Nadia; Oddo, Paolo; Tonani, Marina

2016-04-01

The Mediterranean Forecasting System (MFS) is a numerical ocean prediction system that produces analyses, reanalyses and short term forecasts for the entire Mediterranean Sea and its Atlantic Ocean adjacent areas. MFS became operational in the late 90's and has been developed and continuously improved in the framework of a series of EU and National funded programs and is now part of the Copernicus Marine Service. The MFS is composed by the hydrodynamic model NEMO (Nucleus for European Modelling of the Ocean) 2-way coupled with the third generation wave model WW3 (WaveWatchIII) implemented in the Mediterranean Sea with 1/16 horizontal resolution and forced by ECMWF atmospheric fields. The model solutions are corrected by the data assimilation system (3D variational scheme adapted to the oceanic assimilation problem) with a daily assimilation cycle, using a background error correlation matrix varying seasonally and in different sub-regions of the Mediterranean Sea. The focus of this work is to present the latest modelling system upgrades and the related achieved improvements. In order to evaluate the performance of the coupled system a set of experiments has been built by coupling the wave and circulation models that hourly exchange the following fields: the sea surface currents and air-sea temperature difference are transferred from NEMO model to WW3 model modifying respectively the mean momentum transfer of waves and the wind speed stability parameter; while the neutral drag coefficient computed by WW3 model is passed to NEMO that computes the turbulent component. In order to validate the modelling system, numerical results have been compared with in-situ and remote sensing data. This work suggests that a coupled model might be capable of a better description of wave-current interactions, in particular feedback from the ocean to the waves might assess an improvement on the prediction capability of wave characteristics, while suggests to proceed toward a fully coupled modelling system in order to achieve stronger enhancements of the hydrodynamic fields.

Application Of Multi-grid Method On China Seas' Temperature Forecast

NASA Astrophysics Data System (ADS)

Li, W.; Xie, Y.; He, Z.; Liu, K.; Han, G.; Ma, J.; Li, D.

2006-12-01

Correlation scales have been used in traditional scheme of 3-dimensional variational (3D-Var) data assimilation to estimate the background error covariance for the numerical forecast and reanalysis of atmosphere and ocean for decades. However there are still some drawbacks of this scheme. First, the correlation scales are difficult to be determined accurately. Second, the positive definition of the first-guess error covariance matrix cannot be guaranteed unless the correlation scales are sufficiently small. Xie et al. (2005) indicated that a traditional 3D-Var only corrects some certain wavelength errors and its accuracy depends on the accuracy of the first-guess covariance. And in general, short wavelength error can not be well corrected until long one is corrected and then inaccurate first-guess covariance may mistakenly take long wave error as short wave ones and result in erroneous analysis. For the purpose of quickly minimizing the errors of long and short waves successively, a new 3D-Var data assimilation scheme, called multi-grid data assimilation scheme, is proposed in this paper. By assimilating the shipboard SST and temperature profiles data into a numerical model of China Seas, we applied this scheme in two-month data assimilation and forecast experiment which ended in a favorable result. Comparing with the traditional scheme of 3D-Var, the new scheme has higher forecast accuracy and a lower forecast Root-Mean-Square (RMS) error. Furthermore, this scheme was applied to assimilate the SST of shipboard, AVHRR Pathfinder Version 5.0 SST and temperature profiles at the same time, and a ten-month forecast experiment on sea temperature of China Seas was carried out, in which a successful forecast result was obtained. Particularly, the new scheme is demonstrated a great numerical efficiency in these analyses.

Development of KIAPS Observation Processing Package for Data Assimilation System

NASA Astrophysics Data System (ADS)

Kang, Jeon-Ho; Chun, Hyoung-Wook; Lee, Sihye; Han, Hyun-Jun; Ha, Su-Jin

2015-04-01

The Korea Institute of Atmospheric Prediction Systems (KIAPS) was founded in 2011 by the Korea Meteorological Administration (KMA) to develop Korea's own global Numerical Weather Prediction (NWP) system as nine year (2011-2019) project. Data assimilation team at KIAPS has been developing the observation processing system (KIAPS Package for Observation Processing: KPOP) to provide optimal observations to the data assimilation system for the KIAPS Global Model (KIAPS Integrated Model - Spectral Element method based on HOMME: KIM-SH). Currently, the KPOP is capable of processing the satellite radiance data (AMSU-A, IASI), GPS Radio Occultation (GPS-RO), AIRCRAFT (AMDAR, AIREP, and etc…), and synoptic observation (SONDE and SURFACE). KPOP adopted Radiative Transfer for TOVS version 10 (RTTOV_v10) to get brightness temperature (TB) for each channel at top of the atmosphere (TOA), and Radio Occultation Processing Package (ROPP) 1-dimensional forward module to get bending angle (BA) at each tangent point. The observation data are obtained from the KMA which has been composited with BUFR format to be converted with ODB that are used for operational data assimilation and monitoring at the KMA. The Unified Model (UM), Community Atmosphere - Spectral Element (CAM-SE) and KIM-SH model outputs are used for the bias correction (BC) and quality control (QC) of the observations, respectively. KPOP provides radiance and RO data for Local Ensemble Transform Kalman Filter (LETKF) and also provides SONDE, SURFACE and AIRCRAFT data for Three-Dimensional Variational Assimilation (3DVAR). We are expecting all of the observation type which processed in KPOP could be combined with both of the data assimilation method as soon as possible. The preliminary results from each observation type will be introduced with the current development status of the KPOP.

Quantifying the Stress Responses of Brassica Rapa Genotypes, With Experimental Drought in Two Nitrogen Treatments

NASA Astrophysics Data System (ADS)

Hickerson, J. L.; Pleban, J. R.; Mackay, D. S.; Aston, T.; Ewers, B. E.; Weinig, C.

2014-12-01

In a greenhouse study designed to quantify and compare stress responses of four genotypes of Brassica rapa, broccolette (bro), cabbage (cab), turnip (tur), and oil, leaf water potential and net CO2 assimilations were measured. Individuals from each genotype, grown either with high or low nitrogen, were exposed to experimental drought of the same duration. One hypothesis was that the genotypes would differ significantly in their responses to periodic drought. The other hypothesis was that the nitrogen treatment versus no nitrogen treatment would play a significant role in the stress responses during drought. It would be expected that the nitrogen treated would have greater dry leaf mass. A LI-6400 XT portable photosynthesis system was used to obtain A/Ci curves (net CO2 assimilation rate versus substomatal CO2) for each treatment group. Predawn and midday water potentials were obtained throughout the hydrated and drought periods using a Model 670 pressure chamber. The dry leaf mass was significantly greater among the high nitrogen group versus the low nitrogen group for each genotype. Nitrogen and genotype were both determinants in variation of water potentials and net CO2 assimilation. Bro and cab genotypes with high nitrogen showed the highest net CO2 assimilation rates during hydration, but the assimilation rates dropped to the lowest during droughts. The water potentials for bro and cab were lower than values for tur and oil. Nitrogen treated genotypes had lower water potentials, but higher net CO2 assimilation rates. Bayesian ecophysiological modeling with the TREES model showed significant differences in trait expression, quantified in terms of differences in model parameter posteriors, among the four genotypes.

Data Assimilation for Applied Meteorology

NASA Astrophysics Data System (ADS)

Haupt, S. E.

2012-12-01

Although atmospheric models provide a best estimate of the future state of the atmosphere, due to sensitivity to initial condition, it is difficult to predict the precise future state. For applied problems, however, users often depend on having accurate knowledge of that future state. To improve prediction of a particular realization of an evolving flow field requires knowledge of the current state of that field and assimilation of local observations into the model. This talk will consider how dynamic assimilation can help address the concerns of users of atmospheric forecasts. First, we will look at the value of assimilation for the renewable energy industry. If the industry decision makers can have confidence in the wind and solar power forecasts, they can build their power allocations around the expected renewable resource, saving money for the ratepayers as well as reducing carbon emissions. We will assess the value to that industry of assimilating local real-time observations into the model forecasts and the value that is provided. The value of the forecasts with assimilation is important on both short (several hour) to medium range (within two days). A second application will be atmospheric transport and dispersion problems. In particular, we will look at assimilation of concentration data into a prediction model. An interesting aspect of this problem is that the dynamics are a one-way coupled system, with the fluid dynamic equations affecting the concentration equation, but not vice versa. So when the observations are of the concentration, one must infer the fluid dynamics. This one-way coupled system presents a challenge: one must first infer the changes in the flow field from observations of the contaminant, then assimilate that to recover both the advecting flow and information on the subgrid processes that provide the mixing. To accomplish such assimilation requires a robust method to match the observed contaminant field to that modeled. One approach is to separate the problem into a transport portion and a dispersion portion, representing the resolved flow and the unresolved portion. One then treats the resolved portion in a Lagrangian framework and the unresolved in an Eulerian framework to pose an optimization problem for both the transport and dispersion variables. We demonstrate how this problem can be solved by assimilating the data dynamically using a genetic algorithm variation approach (GA-Var). This technique is demonstrated on both a basic Gaussian puff problem and a Large Eddy Simulation. Finally we will show how assimilation can help bridge the gap between modeling flows at the mesoscale and flows at the fine scale that is often important for resolving flow around local features. By assimilating mesoscale model data into a computational fluid dynamics model, we can force the fine scale model to with the features at the mesoscale, providing a coupling mechanism.

The Evolution of the Stratopause During the 2006 Major Warming: Satellite Data and Assimilated Meteorological Analyses

NASA Technical Reports Server (NTRS)

Manney, Gloria L.; Krueger, Kirstin; Pawson, Steven; Schwartz, Michael J.; Daffer, William H.; Livesey, Nathaniel J.; Remsberg, Ellis E.; Mlynczak, Martin G.; Russell, James M., III; Waters, Joe W.

2007-01-01

Microwave Limb Sounder and Sounding of the Atmosphere with Broadband Emission Radiometry data show the polar stratopause, usually higher than and separated from that at midlatitudes, dropping from <55-60 to near 30 km, and cooling dramatically in January 2006 during a major stratospheric sudden warming (SSW). After a nearly isothermal period, a cool stratopause reforms near 75 km in early February, then drops to <55 km and warms. The stratopause is separated in longitude as well as latitude, with lowest temperatures in the transition regions between higher and lower stratopauses. Operational assimilated meteorological analyses, which are not constrained by data at stratopause altitude, do not capture a secondary temperature maximum that overlies the stratopause or the very high stratopause that reforms after the SSW; they underestimate the stratopause altitude variation during the SSW. High-quality daily satellite temperature measurements are invaluable in improving our understanding of stratopause evolution and its representation in models and assimilation systems.

Preliminary studies on the evolution of carbon assimilation abilities within Mucorales.

PubMed

Pawłowska, Julia; Aleksandrzak-Piekarczyk, Tamara; Banach, Agnieszka; Kiersztyn, Bartosz; Muszewska, Anna; Serewa, Lidia; Szatraj, Katarzyna; Wrzosek, Marta

2016-05-01

Representatives of Mucorales belong to one of the oldest lineages of terrestrial fungi. Although carbon is of fundamental importance for fungal growth and functioning, relatively little is known about enzymatic capacities of Mucorales. The evolutionary history and the variability of the capacity to metabolize different carbon sources among representatives of the order Mucorales was studied using Phenotypic Microarray Plates. The ability of 26 strains belonging to 23 nonpathogenic species of Mucorales to use 95 different carbon sources was tested. Intraspecies variability of carbon assimilation profiles was lower than interspecies variation for some selected strains. Although similarities between the phylogenetic tree and the dendrogram created from carbon source utilization data were observed, the ability of the various strains to use the analyzed substrates did not show a clear correlation with the evolutionary history of the group. Instead, carbon assimilation profiles are probably shaped by environmental conditions. Copyright © 2016 The British Mycological Society. Published by Elsevier Ltd. All rights reserved.

Water Quality Planning in Rivers: Assimilative Capacity and Dilution Flow.

PubMed

Hashemi Monfared, Seyed Arman; Dehghani Darmian, Mohsen; Snyder, Shane A; Azizyan, Gholamreza; Pirzadeh, Bahareh; Azhdary Moghaddam, Mehdi

2017-11-01

Population growth, urbanization and industrial expansion are consequentially linked to increasing pollution around the world. The sources of pollution are so vast and also include point and nonpoint sources, with intrinsic challenge for control and abatement. This paper focuses on pollutant concentrations and also the distance that the pollution is in contact with the river water as objective functions to determine two main necessary characteristics for water quality management in the river. These two necessary characteristics are named assimilative capacity and dilution flow. The mean area of unacceptable concentration [Formula: see text] and affected distance (X) are considered as two objective functions to determine the dilution flow by a non-dominated sorting genetic algorithm II (NSGA-II) optimization algorithm. The results demonstrate that the variation of river flow discharge in different seasons can modify the assimilation capacity up to 97%. Moreover, when using dilution flow as a water quality management tool, results reveal that the content of [Formula: see text] and X change up to 97% and 93%, respectively.

Impact of single-point GPS integrated water vapor estimates on short-range WRF model forecasts over southern India

NASA Astrophysics Data System (ADS)

Kumar, Prashant; Gopalan, Kaushik; Shukla, Bipasha Paul; Shyam, Abhineet

2017-11-01

Specifying physically consistent and accurate initial conditions is one of the major challenges of numerical weather prediction (NWP) models. In this study, ground-based global positioning system (GPS) integrated water vapor (IWV) measurements available from the International Global Navigation Satellite Systems (GNSS) Service (IGS) station in Bangalore, India, are used to assess the impact of GPS data on NWP model forecasts over southern India. Two experiments are performed with and without assimilation of GPS-retrieved IWV observations during the Indian winter monsoon period (November-December, 2012) using a four-dimensional variational (4D-Var) data assimilation method. Assimilation of GPS data improved the model IWV analysis as well as the subsequent forecasts. There is a positive impact of ˜10 % over Bangalore and nearby regions. The Weather Research and Forecasting (WRF) model-predicted 24-h surface temperature forecasts have also improved when compared with observations. Small but significant improvements were found in the rainfall forecasts compared to control experiments.

Moving horizon estimation for assimilating H-SAF remote sensing data into the HBV hydrological model

NASA Astrophysics Data System (ADS)

Montero, Rodolfo Alvarado; Schwanenberg, Dirk; Krahe, Peter; Lisniak, Dmytro; Sensoy, Aynur; Sorman, A. Arda; Akkol, Bulut

2016-06-01

Remote sensing information has been extensively developed over the past few years including spatially distributed data for hydrological applications at high resolution. The implementation of these products in operational flow forecasting systems is still an active field of research, wherein data assimilation plays a vital role on the improvement of initial conditions of streamflow forecasts. We present a novel implementation of a variational method based on Moving Horizon Estimation (MHE), in application to the conceptual rainfall-runoff model HBV, to simultaneously assimilate remotely sensed snow covered area (SCA), snow water equivalent (SWE), soil moisture (SM) and in situ measurements of streamflow data using large assimilation windows of up to one year. This innovative application of the MHE approach allows to simultaneously update precipitation, temperature, soil moisture as well as upper and lower zones water storages of the conceptual model, within the assimilation window, without an explicit formulation of error covariance matrixes and it enables a highly flexible formulation of distance metrics for the agreement of simulated and observed variables. The framework is tested in two data-dense sites in Germany and one data-sparse environment in Turkey. Results show a potential improvement of the lead time performance of streamflow forecasts by using perfect time series of state variables generated by the simulation of the conceptual rainfall-runoff model itself. The framework is also tested using new operational data products from the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) of EUMETSAT. This study is the first application of H-SAF products to hydrological forecasting systems and it verifies their added value. Results from assimilating H-SAF observations lead to a slight reduction of the streamflow forecast skill in all three cases compared to the assimilation of streamflow data only. On the other hand, the forecast skill of soil moisture shows a significant improvement.

VARIATION IN THE ABUNDANCE OF SYNECHOCOCCUS SP. CC9311 NARB MRNA RELATIVE TO CHANGES IN LIGHT, NITROGEN GROWTH CONDITIONS AND NITRATE ASSIMILATION(1).

PubMed

Paerl, Ryan W; Tozzi, Sasha; Kolber, Zbigniew S; Zehr, Jonathan P

2012-08-01

Synechococcus- and Prochlorococcus-specific narB genes that encode for an assimilatory nitrate reductase are found in coastal to open-ocean waters. However, it remains uncertain if these picocyanobacteria assimilate nitrate in situ. This unknown can potentially be addressed by examining narB mRNA from the environment, but this requires a better understanding of the influence of environmental factors on narB gene transcription. In laboratory experiments with Synechococcus sp. CC9311 cultures exposed to diel light fluctuations and grown on nitrate or ammonium, there was periodic change in narB transcript abundance. This periodicity was broken in cultures subjected to a doubling of irradiance (40-80 μmol photons · m(-2)  · s(-1) ) during the mid-light period. Therefore, the irradiance level, not circadian rhythm, was the dominant factor controlling narB transcription. In nitrate-grown cultures, diel change in narB transcript abundance and nitrate assimilation rate did not correlate; suggesting narB mRNA levels better indicate nitrate assimilation activity than assimilation rate. Growth history also affected narB transcription, as changes in narB mRNA levels in nitrogen-deprived CC9311 cultures following nitrate amendment were distinct from cultures grown solely on nitrate. Environmental sampling for narB transcripts should consider time, irradiance, and the growth status of cells to ecologically interpret narB transcript abundances. © 2012 Phycological Society of America.

A mesoscale hybrid data assimilation system based on the JMA nonhydrostatic model

NASA Astrophysics Data System (ADS)

Ito, K.; Kunii, M.; Kawabata, T. T.; Saito, K. K.; Duc, L. L.

2015-12-01

This work evaluates the potential of a hybrid ensemble Kalman filter and four-dimensional variational (4D-Var) data assimilation system for predicting severe weather events from a deterministic point of view. This hybrid system is an adjoint-based 4D-Var system using a background error covariance matrix constructed from the mixture of a so-called NMC method and perturbations in a local ensemble transform Kalman filter data assimilation system, both of which are based on the Japan Meteorological Agency nonhydrostatic model. To construct the background error covariance matrix, we investigated two types of schemes. One is a spatial localization scheme and the other is neighboring ensemble approach, which regards the result at a horizontally spatially shifted point in each ensemble member as that obtained from a different realization of ensemble simulation. An assimilation of a pseudo single-observation located to the north of a tropical cyclone (TC) yielded an analysis increment of wind and temperature physically consistent with what is expected for a mature TC in both hybrid systems, whereas an analysis increment in a 4D-Var system using a static background error covariance distorted a structure of the mature TC. Real data assimilation experiments applied to 4 TCs and 3 local heavy rainfall events showed that hybrid systems and EnKF provided better initial conditions than the NMC-based 4D-Var, both for predicting the intensity and track forecast of TCs and for the location and amount of local heavy rainfall events.

Impact of advanced technology microwave sounder data in the NCMRWF 4D-VAR data assimilation system

NASA Astrophysics Data System (ADS)

Rani, S. Indira; Srinivas, D.; Mallick, Swapan; George, John P.

2016-05-01

This study demonstrates the added benefits of assimilating the Advanced Technology Microwave Sounder (ATMS) radiances from the Suomi-NPP satellite in the NCMRWF Unified Model (NCUM). ATMS is a cross-track scanning microwave radiometer inherited the legacy of two very successful instrument namely, Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS). ATMS has 22 channels: 11 temperature sounding channels around 50-60 GHz oxygen band and 6 moisture sounding channels around the 183GHz water vapour band in addition to 5 channels sensitive to the surface in clear conditions, or to water vapour, rain, and cloud when conditions are not clear (at 23, 31, 50, 51 and 89 GHz). Before operational assimilation of any new observation by NWP centres it is standard practice to assess data quality with respect to NWP model background (short-forecast) fields. Quality of all channels is estimated against the model background and the biases are computed and compared against that from the similar observations. The impact of the ATMS data on global analyses and forecasts is tested by adding the ATMS data in the NCUM Observation Processing system (OPS) and 4D-Var variational assimilation (VAR) system. This paper also discusses the pre-operational numerical experiments conducted to assess the impact of ATMS radiances in the NCUM assimilation system. It is noted that the performance of ATMS is stable and it contributes to the performance of the model, complimenting observations from other instruments.

Reduced Uncertainties in Health Impacts and Radiative Forcing Estimates in Winter Haze in eastern China through constraints of surface PM2.5 predictions

NASA Astrophysics Data System (ADS)

Gao, M.; Saide, P. E.; Xin, J.; Wang, Y.; Liu, Z.; Wang, Z.; Pagowski, M.; Guttikunda, S. K.; Carmichael, G. R.

2016-12-01

The Gridpoint Statistical Interpolation (GSI) Three-Dimensional Variational (3DVAR) data assimilation system is extended to treat the MOSAIC aerosol model in WRF-Chem, and to be capable of assimilating surface PM2.5 concentrations. The coupled GSI-WRF-Chem system is applied to reproduce aerosol levels over China during an extremely polluted winter month, January 2013. After assimilating surface PM2.5 concentrations, the correlation coefficients between observations and model results averaged over the assimilated sites are improved from 0.67 to 0.94. At non-assimilated sites, improvements are also found in PM2.5, PM10 and AOD predictions. Using the constrained aerosol fields, we estimate that the PM2.5 concentrations in January 2013 might cause 7550 premature deaths in Jing-Jin-Ji areas, and 113.9 million (92.1% of Jing-Jin-Ji population) people in Jing-Jin-Ji are exposed to unhealthy air (monthly averaged PM2.5 concentration over 75µg/m3). We also estimate that the daytime monthly mean anthropogenic aerosol radiative forcing (ARF) to be -29.9W/m2 at the surface, 27.0W/m2 inside the atmosphere, and -2.9W/m2 at the top of the atmosphere. Our estimates reduce the previously reported overestimations along Yangtze River region and underestimations in North China. This system will also be beneficial for more reliable air quality forecasts in China.

Regional ionospheric TEC data assimilation and now-casting service

NASA Astrophysics Data System (ADS)

Aa, E.; Liu, S.; Wengeng, H.

2017-12-01

Ionospheric data assimilation is a now-casting technique to incorporate irregular ionospheric measurements into certain background model, which is an effective and efficient way to overcome the limitation of the unbalanced data distribution and to improve the accuracy of the model, so that the model and the data can be optimally combined with each other to produce a more reliable and reasonable system specification. In this study, a regional total electron content (TEC) now-casting system over China and adjacent areas (70E-140E and 15N-55N) is developed on the basis of data assimilation technique. The International Reference Ionosphere (IRI) is used here as background model, and the GNSS data are derived from both the Space Environment Monitoring Network of Chinese Academy of Sciences (SEMnet) and International GNSS Service (IGS) data. A Three-dimensional variation algorithm (3DVAR) combined with Gauss-Markov Kalman filter technique is used to implement the data assimilation. The regional gridded TEC maps and the position errors of single-frequency GPS receivers can be generated and publicized online (http://sepc.ac.cn/TEC_chn.php) in quasi-real time, which is updated for every 15 min. It is one of the ionospheric now-casting systems in China based on data assimilation algorithm, which can be used not only for real-time monitoring of ionosphere environment over China and adjacent areas, but also in providing accurate and effective specification of regional ionospheric TEC and error correction for satellite navigation, radar imaging, shortwave communication, and other relevant applications.

Towards improved hydrologic predictions using data assimilation techniques for water resource management at the continental scale

NASA Astrophysics Data System (ADS)

Naz, Bibi; Kurtz, Wolfgang; Kollet, Stefan; Hendricks Franssen, Harrie-Jan; Sharples, Wendy; Görgen, Klaus; Keune, Jessica; Kulkarni, Ketan

2017-04-01

More accurate and reliable hydrologic simulations are important for many applications such as water resource management, future water availability projections and predictions of extreme events. However, simulation of spatial and temporal variations in the critical water budget components such as precipitation, snow, evaporation and runoff is highly uncertain, due to errors in e.g. model structure and inputs (hydrologic parameters and forcings). In this study, we use data assimilation techniques to improve the predictability of continental-scale water fluxes using in-situ measurements along with remotely sensed information to improve hydrologic predications for water resource systems. The Community Land Model, version 3.5 (CLM) integrated with the Parallel Data Assimilation Framework (PDAF) was implemented at spatial resolution of 1/36 degree (3 km) over the European CORDEX domain. The modeling system was forced with a high-resolution reanalysis system COSMO-REA6 from Hans-Ertel Centre for Weather Research (HErZ) and ERA-Interim datasets for time period of 1994-2014. A series of data assimilation experiments were conducted to assess the efficiency of assimilation of various observations, such as river discharge data, remotely sensed soil moisture, terrestrial water storage and snow measurements into the CLM-PDAF at regional to continental scales. This setup not only allows to quantify uncertainties, but also improves streamflow predictions by updating simultaneously model states and parameters utilizing observational information. The results from different regions, watershed sizes, spatial resolutions and timescales are compared and discussed in this study.

Oregon Washington Coastal Ocean Forecast System: Real-time Modeling and Data Assimilation

NASA Astrophysics Data System (ADS)

Erofeeva, S.; Kurapov, A. L.; Pasmans, I.

2016-02-01

Three-day forecasts of ocean currents, temperature and salinity along the Oregon and Washington coasts are produced daily by a numerical ROMS-based ocean circulation model. NAM is used to derive atmospheric forcing for the model. Fresh water discharge from Columbia River, Fraser River, and small rivers in Puget Sound are included. The forecast is constrained by open boundary conditions derived from the global Navy HYCOM model and once in 3 days assimilation of recent data, including HF radar surface currents, sea surface temperature from the GOES satellite, and SSH from several satellite altimetry missions. 4-dimensional variational data assimilation is implemented in 3-day time windows using the tangent linear and adjoint codes developed at OSU. The system is semi-autonomous - all the data, including NAM and HYCOM fields are automatically updated, and daily operational forecast is automatically initiated. The pre-assimilation data quality control and post-assimilation forecast quality control require the operator's involvement. The daily forecast and 60 days of hindcast fields are available for public on opendap. As part of the system model validation plots to various satellites and SEAGLIDER are also automatically updated and available on the web (http://ingria.coas.oregonstate.edu/rtdavow/). Lessons learned in this pilot real-time coastal ocean forecasting project help develop and test metrics for forecast skill assessment for the West Coast Operational Forecast System (WCOFS), currently at testing and development phase at the National Oceanic and Atmospheric Administration (NOAA).

Validation of a Wave Data Assimilation System Based on SWAN

NASA Astrophysics Data System (ADS)

Flampourisi, Stylianos; Veeramony, Jayaram; Orzech, Mark D.; Ngodock, Hans E.

2013-04-01

SWAN is one of the most broadly used models for wave predictions in the nearshore, with known and extensively studied limitations due to the physics and/or to the numerical implementation. In order to improve the performance of the model, a 4DVAR data assimilation system based on a tangent linear code and the corresponding adjoint from the numerical SWAN model has been developed at NRL(Orzech et. al., 2013), by implementing the methodology of Bennett 2002. The assimilation system takes into account the nonlinear triad and quadruplet interactions, depth-limited breaking, wind forcing, bottom friction and white-capping. Using conjugate gradient method, the assimilation system minimizes a quadratic penalty functional (which represents the overall error of the simulation) and generates the correction of the forward simulation in spatial, temporal and spectral domain. The weights are given to the output of the adjoint by calculating the covariance to an ensemble of forward simulations according to Evensen 2009. This presentation will focus on the extension of the system to a weak-constrainted data assimilation system and on the extensive validation of the system by using wave spectra for forcing, assimilation and validation, from FRF Duck, North Carolina, during August 2011. During this period, at the 17 m waverider buoy location, the wind speed was up to 35 m/s (due to Hurricane Irene) and the significant wave height varied from 0.5 m to 6 m and the peak period between 5 s and 18 s. In general, this study shows significant improvement of the integrated spectral properties, but the main benefit of assimilating the wave spectra (and not only their integrated properties) is that the accurate simulation of separated, in frequency and in direction, wave systems is possible even nearshore, where non-linear phenomena are dominant. The system is ready to be used for more precise reanalysis of the wave climate and climate variability, and determination of coastal hazards in regional or local scales, in case of available wave data. References: Orzech, M.D., J. Veeramony, and H.E. Ngodock, 2013: A variational assimilation system for nearshore wave modeling. J. Atm. & Oc. Tech., in press.

Paramedic Learning Style Preferences and Continuing Medical Education Activities: A Cross-Sectional Survey Study.

PubMed

Staple, Louis; Carter, Alix; Jensen, Jan L; Walker, Mark

2018-01-01

Paramedics participate in continuing medical education (CME) to maintain their skills and knowledge. An understanding of learning styles is important for education to be effective. This study examined the preferred learning styles of ground ambulance paramedics and describes how their preferred learning styles relate to the elective CME activities these paramedics attend. All paramedics (n=1,036) employed in a provincial ground ambulance service were invited to participate in a survey containing three parts: demographics, learning style assessed by the Kolb Learning Style Inventory (LSI), and elective CME activity. 260 paramedics (25%) participated in the survey. Preferred learning styles were: assimilator, 28%; diverger, 25%; converger, 24%; and accommodator, 23%. Advanced life support (ALS) providers had a higher proportion of assimilators (36%), and basic life support (BLS) providers had a higher proportion of divergers (30%). The learning style categories of CME activities attended by paramedics were: assimilators, 25%; divergers, 26%; convergers, 25%; and accommodators, 24%. These results suggest that paramedics are a diverse group of learners, and learning style differs within their demographics. Paramedics attend CME activities that complement all learning styles. Organizations providing education opportunities to paramedics should consider paramedics a diverse learning group when designing their CME programs.

Physiological responses in potato plants under continuous irradiation

NASA Technical Reports Server (NTRS)

Cao, W.; Tibbitts, T. W.

1991-01-01

The physiological responses of four potato (Solanum tuberosum L.) cultivars to continuous irradiation were determined in a controlled environment. Under a constant 18C and a constant photoperiod of 470 micromoles s-1 m-2 of photosynthetic photon flux, 'Denali' and 'Haig' grew well and produced large plant and tuber dry weights when harvested 56 days after transplanting. 'Kennebec' and 'Superior' were severely stunted, producing only 10% of the plant dry matter produced by 'Denali' and 'Haig'. The differences in leaf chlorophyll concentration and stomatal conductance were not consistent between these two groups of cultivars. The leaf net CO2 assimilation rates in 'Kennebec' and 'Superior' were lower, and intercellular CO2 partial pressures were higher than in 'Denali' and 'Haig'. These results indicate that inhibition of net CO2 assimilation in 'Kennebec' and 'Superior' was not due to a limiting amount of chlorophyll or to CO2 in the leaf tissues. Concentrations of starch in leaflets of 'Kennebec' and 'Superior' plants were only 10% of those in 'Denali' and 'Haig' plants, although soluble sugar concentrations were similar in the four cultivars. Therefore, the lower net CO2 assimilation rates in stunted 'Kennebec' and 'Superior' plants were not associated with an excess carbohydrate accumulation in the leaves.

Effects of salinity and flooding on seedlings of cabbage palm (Sabal palmetto).

PubMed

Perry, L; Williams, K

1996-03-01

Sabal palmetto (Walt.) Lodd. ex Schultes (cabbage palm) dominates the coastal limit of many forests in North Florida and Georgia, United States. Changes in saltwater flooding due to sea level rise have been credicted with pushing the coastal limit of cabbage palms inland, eliminating regeneration before causing death of mature trees. Localized freshwater discharge along the coast causes different forest stands to experience tidal flooding with waters that differ in salinity. To elucidate the effect of such variation on regeneration failure under tidal flooding, we examined relative effects of flooding and salinity on the performance of cabbage palm seedlings. We examined the relationship between seedling establishment and degree of tidal inundation in the field, compared the ability of seedlings to withstand tidal flooding at two coastal sites that differed in tidal water salinity, and investigated the physiological responses of cabbage palm seedlings to salinity and flooding in a factorial greenhouse experiment. Seedling survival was inversely correlated with depth and frequency of tidal flooding. Survival of seedlings at a coastal site flooded by waters low in salinity [c. 3 parts per thousand (ppt)] was greater than that at a site flooded by waters higher in salinity (up to 23 ppt). Greenhouse experiments revealed that leaves of seedlings in pots flushed twice daily with salt solutions of 0 ppt and 8 ppt exhibited little difference in midmorning net CO 2 assimilation rates; those flushed with solutions of 15 ppt and 22 ppt, in contrast, had such low rates that they could not be detected. Net CO 2 assimilation rates also declined with increasing salinity for seedlings in pots that were continuously inundated. Continuous root zone inundation appeared to ameliorate effects of salinity on photosynthesis, presumably due to increased salt concentrations and possibly water deficits in periodically flushed pots. Such problems associated with periodic flushing by salt water may play a role in the mortality of cabbage palm seedlings in the field. The salinity range in which plant performance plummeted in the greenhouse was consistent with the salinity difference found between our two coastal study sites, suggesting that variation in tidal water salinity along the coast plays an important role in the ability of cabbage palm seedlings to withstand tidal flooding.

Assessment of Variation in Microbial Community Amplicon Sequencing by the Microbiome Quality Control (MBQC) Project Consortium

EPA Science Inventory

Precision medicine has been made possible by the translation of ‘omics to the clinic, and human microbiome studies must likewise transition to applications in public health. This will require especially robust measurements and assimilation of data from multiple population-scale c...

Quantifying Chemical Ozone Loss in the Arctic Stratosphere with GEOS-STRATCHEM Data Assimilation System

NASA Technical Reports Server (NTRS)

Wargan, K.; Nielsen, J. E.

2017-01-01

A faithful representation of polar stratospheric chemistry in models and its connection with dynamical variability is essential for our understanding of the evolution of the ozone layer in a changing climate and during the projected continuing decline of ozone depleting substances in the atmosphere. We use a new configuration of the Goddard Earth Observing System Data Assimilation System with a stratospheric chemistry model to study ozone depletion in the Arctic polar stratosphere during the exceptionally cold (in the stratosphere) winters 2015/2016 and 2010/2011.

Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites

NASA Astrophysics Data System (ADS)

Aalstad, Kristoffer; Westermann, Sebastian; Vikhamar Schuler, Thomas; Boike, Julia; Bertino, Laurent

2018-01-01

With its high albedo, low thermal conductivity and large water storing capacity, snow strongly modulates the surface energy and water balance, which makes it a critical factor in mid- to high-latitude and mountain environments. However, estimating the snow water equivalent (SWE) is challenging in remote-sensing applications already at medium spatial resolutions of 1 km. We present an ensemble-based data assimilation framework that estimates the peak subgrid SWE distribution (SSD) at the 1 km scale by assimilating fractional snow-covered area (fSCA) satellite retrievals in a simple snow model forced by downscaled reanalysis data. The basic idea is to relate the timing of the snow cover depletion (accessible from satellite products) to the peak SSD. Peak subgrid SWE is assumed to be lognormally distributed, which can be translated to a modeled time series of fSCA through the snow model. Assimilation of satellite-derived fSCA facilitates the estimation of the peak SSD, while taking into account uncertainties in both the model and the assimilated data sets. As an extension to previous studies, our method makes use of the novel (to snow data assimilation) ensemble smoother with multiple data assimilation (ES-MDA) scheme combined with analytical Gaussian anamorphosis to assimilate time series of Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 fSCA retrievals. The scheme is applied to Arctic sites near Ny-Ålesund (79° N, Svalbard, Norway) where field measurements of fSCA and SWE distributions are available. The method is able to successfully recover accurate estimates of peak SSD on most of the occasions considered. Through the ES-MDA assimilation, the root-mean-square error (RMSE) for the fSCA, peak mean SWE and peak subgrid coefficient of variation is improved by around 75, 60 and 20 %, respectively, when compared to the prior, yielding RMSEs of 0.01, 0.09 m water equivalent (w.e.) and 0.13, respectively. The ES-MDA either outperforms or at least nearly matches the performance of other ensemble-based batch smoother schemes with regards to various evaluation metrics. Given the modularity of the method, it could prove valuable for a range of satellite-era hydrometeorological reanalyses.

Dynamic Tsunami Data Assimilation (DTDA) Based on Green's Function: Theory and Application

NASA Astrophysics Data System (ADS)

Wang, Y.; Satake, K.; Gusman, A. R.; Maeda, T.

2017-12-01

Tsunami data assimilation estimates the tsunami arrival time and height at Points of Interest (PoIs) by assimilating tsunami data observed offshore into a numerical simulation, without the need of calculating initial sea surface height at the source (Maeda et al., 2015). The previous tsunami data assimilation has two main problems: one is that it requires quite large calculating time because the tsunami wavefield of the whole interested region is computed continuously; another is that it relies on dense observation network such as Dense Oceanfloor Network system for Earthquakes and Tsunamis (DONET) in Japan or Cascadia Initiative (CI) in North America (Gusman et al., 2016), which is not practical for some area. Here we propose a new approach based on Green's function to speed up the tsunami data assimilation process and to solve the problem of sparse observation: Dynamic Tsunami Data Assimilation (DTDA). If the residual between the observed and calculated tsunami height is not zero, there will be an assimilation response around the station, usually a Gaussian-distributed sea surface displacement. The Green's function Gi,j is defined as the tsunami waveform at j-th grid caused by the propagation of assimilation response at i-th station. Hence, the forecasted waveforms at PoIs are calculated as the superposition of the Green's functions. In case of sparse observation, we could use the aircraft and satellite observations. The previous assimilation approach is not practical because it costs much time to assimilate moving observation, and to compute the tsunami wavefield of the interested region. In contrast, DTDA synthesizes the waveforms quickly as long as the Green's functions are calculated in advance. We apply our method to a hypothetic earthquake off the west coast of Sumatra Island similar to the 2004 Indian Ocean earthquake. Currently there is no dense observation network in that area, making it difficult for the previous assimilation approach. We used DTDA with aircraft and satellite observation above the Indian Ocean, to forecast the tsunami in Sri Lanka, India and Thailand. It shows that DTDA provides reliable tsunami forecasting for these countries, and the tsunami early warning can be issued half an hour before the tsunami arrives to reduce the damage along the coast.

Assimilation of DMSP/SSUSI UV data into IDA4D

NASA Astrophysics Data System (ADS)

Gelinas, L. J.; Bust, G. S.; Brinkman, D. G.; Straus, P. R.; Swartz, R. L.

2014-12-01

Ionospheric Data Assimilation Four-Dimensional (IDA4D) is a continuous-time, three-dimensional imaging algorithm that can produce 4D electron density specifications for various science investigations [e.g., Bust et al., 2007]. IDA4D is based on three-dimensional variational (3DVAR) data assimilation [Daley and Barker, 2001]. The algorithm combines various data sources and their associated error covariances with a background model (in this case the IRI) and its covariances to produce an ionospheric specification with formal uncertainties. IDA4D employs a Gauss- Markov Kalman filter technique similar to that used by operational assimilation models. The model can ingest a broad spectrum of data types that are either linearly or non-linearly related to electron density, including ground-based TEC, space-based TEC as measured by GPS occultation sensors and UV emissions associated with nightside recombination of O+. IDA4D has been undergoing testing at The Aerospace Corporation to determine its performance with respect to combinations of input data sets under different conditions (solar minimum, solar maximum, geomagnetic activity). The results presented here summarize the performance of IDA4D when UV data is ingested, both with and without additional TEC measurements. The UV data used in the study summarized here are 135.6 nm emissions measured the SSUSI instruments on F16 and F18 DMSP. We discuss the process by which UV data is ingested into IDA4D, including data binning, error estimation and correction of 135.6 nm contamination from mutual neutralization of O+ and O-. Model performance is then assessed using comparisons to various ground truth data, including ISR data, Jason VTEC, CNOF/S in-situ plasma density and ionosonde-derived NmF2 values. The results of this study show that UV data improves model performance, particularly when TEC data coverage is sparse. Bust, G. S., G. Crowley, T. W. Garner, T. L. Gaussiran II, R. W. Meggs, C. N. Mitchell, P. S. J. Spencer, P. Yin, and B. Zapfe (2007) ,Four Dimensional GPS Imaging of Space-Weather Storms, Space Weather, 5, S02003, doi:10.1029/2006SW000237. Daley, R. & Barker, E., NAVDAS: Formulation and Diagnostics. Monthly Weather Review 129, 869 (2001).

Data assimilation problems in glaciology

NASA Astrophysics Data System (ADS)

Shapero, Daniel

Rising sea levels due to mass loss from Greenland and Antarctica threaten to inundate coastal areas the world over. For the purposes of urban planning and hazard mitigation, policy makers would like to know how much sea-level rise can be anticipated in the next century. To make these predictions, glaciologists use mathematical models of ice sheet flow, together with remotely-sensed observations of the current state of the ice sheets. The quantities that are observable over large spatial scales are the ice surface elevation and speed, and the elevation of the underlying bedrock. There are other quantities, such as the viscosity within the ice and the friction coefficient for sliding over the bed, that are just as important in dictating how fast the glacier flows, but that are not observable at large scales using current methods. These quantities can be inferred from observations by using data assimilation methods, applied to a model of glacier flow. In this dissertation, I will describe my work on data assimilation problems in glaciology. My main contributions so far have been: computing the bed stress underneath the three biggest Greenland outlet glaciers; developing additional tools for glacier modeling and data assimilation in the form of the open-source library icepack ; and improving the statistical methodology through the user of total variation priors.

Assimilation of satellite altimeter data into an open ocean model

NASA Astrophysics Data System (ADS)

Vogeler, Armin; SchröTer, Jens

1995-08-01

Geosat sea surface height data are assimilated into an eddy-resolving quasi-geostrophic open ocean model using the adjoint technique. The method adjusts the initial conditions for all layers and is successful on the timescale of a few weeks. Time-varying values for the open boundaries are prescribed by a much larger quasi-geostrophic model of the Antarctic Circumpolar Current (ACC). Both models have the same resolution of approximately 20×20 km (1/3°×1/6°), have three layers, and include realistic bottom topography and coastlines. The open model box is embedded in the African sector of the ACC. For continuous assimilation of satellite data into the larger model the nudging technique is applied. These results are used for the adjoint optimization procedure as boundary conditions and as a first guess for the initial condition. For the open model box the difference between model and satellite sea surface height that remains after the nudging experiment amounts to a 19-cm root-mean-square error (rmse). By assimilation into the regional model this value can be reduced to a 6-cm rmse for an assimilation period of 20 days. Several experiments which attempt to improve the convergence of the iterative optimization method are reported. Scaling and regularization by smoothing have to be applied carefully. Especially during the first 10 iterations, the convergence can be improved considerably by low-pass filtering of the cost function gradient. The result of a perturbation experiment shows that for longer assimilation periods the influence of the boundary values becomes dominant and they should be determined inversely by data assimilation into the open ocean model.

Assimilation and subcellular partitioning of elements by grass shrimp collected along an impact gradient.

PubMed

Seebaugh, David R; Wallace, William G

2009-06-28

Chronic exposure to polluted field conditions can impact metal bioavailability in prey and may influence metal transfer to predators. The present study investigated the assimilation of Cd, Hg and organic carbon by grass shrimp Palaemonetes pugio, collected along an impact gradient within the New York/New Jersey Harbor Estuary. Adult shrimp were collected from five Staten Island, New York study sites, fed (109)Cd- or (203)Hg-labeled amphipods or (14)C-labeled meals and analyzed for assimilation efficiencies (AE). Subsamples of amphipods and shrimp were subjected to subcellular fractionation to isolate metal associated with a compartment presumed to contain trophically available metal (TAM) (metal associated with heat-stable proteins [HSP - e.g., metallothionein-like proteins], heat-denatured proteins [HDP - e.g., enzymes] and organelles [ORG]). TAM-(109)Cd% and TAM-(203)Hg% in radiolabeled amphipods were approximately 64% and approximately 73%, respectively. Gradients in AE-(109)Cd% ( approximately 54% to approximately 75%) and AE-(203)Hg% ( approximately 61% to approximately 78%) were observed for grass shrimp, with the highest values exhibited by shrimp collected from sites within the heavily polluted Arthur Kill complex. Population differences in AE-(14)C% were not observed. Assimilated (109)Cd% partitioned to the TAM compartment in grass shrimp varied between approximately 67% and approximately 75%. (109)Cd bound to HSP in shrimp varied between approximately 15% and approximately 47%, while (109)Cd associated with metal-sensitive HDP was approximately 17% to approximately 44%. Percentages of assimilated (109)Cd bound to ORG were constant at approximately 10%. Assimilated (203)Hg% associated with TAM in grass shrimp did not exhibit significant variation. Percentages of assimilated (203)Hg bound to HDP ( approximately 47%) and ORG ( approximately 11%) did not vary among populations and partitioning of (203)Hg to HSP was not observed. Using a simplified biokinetic model of metal accumulation from the diet, it is estimated that site-specific variability in Cd AE by shrimp and tissue Cd burdens in field-collected prey (polychaetes Nereis spp.) could potentially result in up to approximately 3.2-fold differences in the dose of Cd assimilated by shrimp from a meal in the field. The results of this study also suggest that chronic field exposure can impact mechanisms of metal transport across the gut epithelium that do not influence carbon assimilation. Differences in the assimilation and subcellular partitioning of metal may have important implications for metal toxicity in impacted shrimp populations.

Hsp90 prevents phenotypic variation by suppressing the mutagenic activity of transposons.

PubMed

Specchia, Valeria; Piacentini, Lucia; Tritto, Patrizia; Fanti, Laura; D'Alessandro, Rosalba; Palumbo, Gioacchino; Pimpinelli, Sergio; Bozzetti, Maria P

2010-02-04

The canalization concept describes the resistance of a developmental process to phenotypic variation, regardless of genetic and environmental perturbations, owing to the existence of buffering mechanisms. Severe perturbations, which overcome such buffering mechanisms, produce altered phenotypes that can be heritable and can themselves be canalized by a genetic assimilation process. An important implication of this concept is that the buffering mechanism could be genetically controlled. Recent studies on Hsp90, a protein involved in several cellular processes and development pathways, indicate that it is a possible molecular mechanism for canalization and genetic assimilation. In both flies and plants, mutations in the Hsp90-encoding gene induce a wide range of phenotypic abnormalities, which have been interpreted as an increased sensitivity of different developmental pathways to hidden genetic variability. Thus, Hsp90 chaperone machinery may be an evolutionarily conserved buffering mechanism of phenotypic variance, which provides the genetic material for natural selection. Here we offer an additional, perhaps alternative, explanation for proposals of a concrete mechanism underlying canalization. We show that, in Drosophila, functional alterations of Hsp90 affect the Piwi-interacting RNA (piRNA; a class of germ-line-specific small RNAs) silencing mechanism leading to transposon activation and the induction of morphological mutants. This indicates that Hsp90 mutations can generate new variation by transposon-mediated 'canonical' mutagenesis.

Conditions for successful data assimilation

NASA Astrophysics Data System (ADS)

Morzfeld, M.; Chorin, A. J.

2013-12-01

Many applications in science and engineering require that the predictions of uncertain models be updated by information from a stream of noisy data. The model and the data jointly define a conditional probability density function (pdf), which contains all the information one has about the process of interest and various numerical methods can be used to study and approximate this pdf, e.g. the Kalman filter, variational methods or particle filters. Given a model and data, each of these algorithms will produce a result. We are interested in the conditions under which this result is reasonable, i.e. consistent with the real-life situation one is modeling. In particular, we show, using idealized models, that numerical data assimilation is feasible in principle only if a suitably defined effective dimension of the problem is not excessive. This effective dimension depends on the noise in the model and the data, and in physically reasonable problems it can be moderate even when the number of variables is huge. In particular, we find that the effective dimension being moderate induces a balance condition between the noises in the model and the data; this balance condition is often satisfied in realistic applications or else the noise levels are excessive and drown the underlying signal. We also study the effects of the effective dimension on particle filters in two instances, one in which the importance function is based on the model alone, and one in which it is based on both the model and the data. We have three main conclusions: (1) the stability (i.e., non-collapse of weights) in particle filtering depends on the effective dimension of the problem. Particle filters can work well if the effective dimension is moderate even if the true dimension is large (which we expect to happen often in practice). (2) A suitable choice of importance function is essential, or else particle filtering fails even when data assimilation is feasible in principle with a sequential algorithm. (3) There is a parameter range in which the model noise and the observation noise are roughly comparable, and in which even the optimal particle filter collapses, even under ideal circumstances. We further study the role of the effective dimension in variational data assimilation and particle smoothing, for both the weak and strong constraint problem. It was found that these methods too require a moderate effective dimension or else no accurate predictions can be expected. Moreover, variational data assimilation or particle smoothing may be applicable in the parameter range where particle filtering fails, because the use of more than one consecutive data set helps reduce the variance which is responsible for the collapse of the filters.

A longitudinal qualitative study examining the factors impacting on the ability of persons with T1DM to assimilate the Dose Adjustment for Normal Eating (DAFNE) principles into daily living and how these factors change over time

PubMed Central

2011-01-01

Background The literature reveals that structured education programmes, such as DAFNE, result in many positive outcomes for people with Type 1 diabetes including a decrease in HbA1c levels and reductions in hypoglycaemia. While there is evidence that some of these outcomes are maintained we do not know at present what factors are most important over time. The study aim was to identify the key factors impacting on persons with Type 1 diabetes ability to assimilate the Dose Adjustment For Normal Eating (DAFNE) DAFNE principles into their daily lives and how these factors change over time. Methods This is a longitudinal descriptive qualitative study. Interviews were undertaken with 40 participants who had attended DAFNE in one of 5 study sites across the Island of Ireland, at 6 weeks, 6 and 12 months after completion of the programme. The interviews lasted from 30 to 60 minutes and were transcribed verbatim. Data were analysed in three ways, a within time analysis, a cross sectional analysis for each participant and a thematic analysis which focused on examining changes over time Results Four themes that influenced participants' ability to assimilate DAFNE into their daily lives over time were identified. These were: embedded knowledge, continued responsive support, enduring motivation and being empowered. Support at the 6 month period was found to be crucial to continued motivation. Conclusions Understanding the factors that influence people's ability to assimilate DAFNE principles over time into their daily lives can help health professionals give focused responsive support that helps people with diabetes become more empowered. Understanding that continued support matters, particularly around 6 months, is important as health professionals can influence good management by providing appropriate support and enhancing motivation. Trial registration ISRCTN79759174 PMID:21878104

Role of Satellite Rainfall Information in Improving Understanding of the Dynamical Link Between the Tropics and Extratropics Prospects of Improved Forecasts of Weather and Short-Term Climate Variability on Sub-Seasonal Time Scales

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.

2002-01-01

The tropics and extratropics are two dynamically distinct regimes. The coupling between these two regimes often defies simple analytical treatment. Progress in understanding of the dynamical interaction between the tropics and extratropics relies on better observational descriptions to guide theoretical development. However, global analyses currently contain significant errors in primary hydrological variables such as precipitation, evaporation, moisture, and clouds, especially in the tropics. Tropical analyses have been shown to be sensitive to parameterized precipitation processes, which are less than perfect, leading to order-one discrepancies between estimates produced by different data assimilation systems. One strategy for improvement is to assimilate rainfall observations to constrain the analysis and reduce uncertainties in variables physically linked to precipitation. At the Data Assimilation Office at the NASA Goddard Space Flight Center, we have been exploring the use of tropical rain rates derived from the TRMM Microwave Imager (TMI) and the Special Sensor Microwave/ Imager (SSM/I) instruments in global data assimilation. Results show that assimilating these data improves not only rainfall and moisture fields but also related climate parameters such as clouds and radiation, as well as the large-scale circulation and short-range forecasts. These studies suggest that assimilation of microwave rainfall observations from space has the potential to significantly improve the quality of 4-D assimilated datasets for climate investigations (Hou et al. 2001). In the next few years, there will be a gradual increase in microwave rain products available from operational and research satellites, culminating to a target constellation of 9 satellites to provide global rain measurements every 3 hours with the proposed Global Precipitation Measurement (GPM) mission in 2007. Continued improvements in assimilation methodology, rainfall error estimates, and model parameterizations are needed to ensure that we derive maximum benefits from these observations.

Land Surface Data Assimilation

NASA Astrophysics Data System (ADS)

Houser, P. R.

2012-12-01

Information about land surface water, energy and carbon conditions is of critical importance to real-world applications such as agricultural production, water resource management, flood prediction, water supply, weather and climate forecasting, and environmental preservation. While ground-based observational networks are improving, the only practical way to observe these land surface states on continental to global scales is via satellites. Remote sensing can make spatially comprehensive measurements of various components of the terrestrial system, but it cannot provide information on the entire system (e.g. evaporation), and the observations represent only an instant in time. Land surface process models may be used to predict temporal and spatial terrestrial dynamics, but these predictions are often poor, due to model initialization, parameter and forcing, and physics errors. Therefore, an attractive prospect is to combine the strengths of land surface models and observations (and minimize the weaknesses) to provide a superior terrestrial state estimate. This is the goal of land surface data assimilation. Data Assimilation combines observations into a dynamical model, using the model's equations to provide time continuity and coupling between the estimated fields. Land surface data assimilation aims to utilize both our land surface process knowledge, as embodied in a land surface model, and information that can be gained from observations. Both model predictions and observations are imperfect and we wish to use both synergistically to obtain a more accurate result. Moreover, both contain different kinds of information, that when used together, provide an accuracy level that cannot be obtained individually. Model biases can be mitigated using a complementary calibration and parameterization process. Limited point measurements are often used to calibrate the model(s) and validate the assimilation results. This presentation will provide a brief background on land surface observation, modeling and data assimilation, followed by a discussion of various hydrologic data assimilation challenges, and finally conclude with several land surface data assimilation case studies.

Mechanisms and Control of Phloem Transport in Trees: Fast and Slow - Sink and Source

NASA Astrophysics Data System (ADS)

Gessler, Arthur; Hagedorn, Frank; Galiano, Lucia; Schaub, Marcus; Joseph, Jobin; Arend, Matthias; Hommel, Robert; Kayler, Zachary

2017-04-01

Trees are large global stores of carbon that will be affected by increased carbon dioxide levels and climate change in the future. However, at present we cannot properly predict the carbon balance of forests as we lack knowledge on how plant physiological processes and especially the transport of carbon within the plant interact with environmental drivers and ecosystem-scale processes. The central conveyor belt for C allocation and distribution within the tree is the phloem and its functionality under environmental stress (esp. drought) is important for the avoidance of C starvation. This paper addresses the distribution of new assimilates within the plant, and sheds light on phloem transport mechanisms and transport control using 13C pulse labelling techniques. We provide experimental evidence that at least two mechanisms are employed to couple C sink processes to assimilation. We observed a fast increase of belowground respiration with the onset of photosynthesis, which we assume is induced by pressure concentration waves travelling through the phloem. A second, much later occurring peak in respiration is fueled by new 13C labeled assimilates. Moreover, we relate phloem transport velocity and intensity of labelled 13C assimilates to drought stress intensity and give indication how sink rather than source control might affect phloem transport in trees. During drought, net photosynthesis, soil respiration and the allocation of recent assimilates below ground were reduced. Carbohydrates accumulated in metabolically resting roots but not in leaves, indicating sink control of the tree carbon balance. After drought release, soil respiration recovered faster than assimilation and CO2 fluxes exceeded those in continuously watered trees for months. This stimulation was related to greater assimilate allocation to and metabolization in the rhizosphere. These findings show that trees prioritize the investment of assimilates below ground, probably to regain root functions after drought and indicate that sink activity governs carbon allocation not only during drought stress but also after stress release.

Introduction to focus issue: Synchronization in large networks and continuous media—data, models, and supermodels

NASA Astrophysics Data System (ADS)

Duane, Gregory S.; Grabow, Carsten; Selten, Frank; Ghil, Michael

2017-12-01

The synchronization of loosely coupled chaotic systems has increasingly found applications to large networks of differential equations and to models of continuous media. These applications are at the core of the present Focus Issue. Synchronization between a system and its model, based on limited observations, gives a new perspective on data assimilation. Synchronization among different models of the same system defines a supermodel that can achieve partial consensus among models that otherwise disagree in several respects. Finally, novel methods of time series analysis permit a better description of synchronization in a system that is only observed partially and for a relatively short time. This Focus Issue discusses synchronization in extended systems or in components thereof, with particular attention to data assimilation, supermodeling, and their applications to various areas, from climate modeling to macroeconomics.

Introduction to focus issue: Synchronization in large networks and continuous media-data, models, and supermodels.

PubMed

Duane, Gregory S; Grabow, Carsten; Selten, Frank; Ghil, Michael

2017-12-01

The synchronization of loosely coupled chaotic systems has increasingly found applications to large networks of differential equations and to models of continuous media. These applications are at the core of the present Focus Issue. Synchronization between a system and its model, based on limited observations, gives a new perspective on data assimilation. Synchronization among different models of the same system defines a supermodel that can achieve partial consensus among models that otherwise disagree in several respects. Finally, novel methods of time series analysis permit a better description of synchronization in a system that is only observed partially and for a relatively short time. This Focus Issue discusses synchronization in extended systems or in components thereof, with particular attention to data assimilation, supermodeling, and their applications to various areas, from climate modeling to macroeconomics.

Resisting Assimilation: Deliberate Acculturation by the American English Language Learner

ERIC Educational Resources Information Center

Schultz, Lyndsie

2016-01-01

Education policy has historically been viewed as having an influential part in crafting the roles of immigrants in American society. However, while policy makers continue to push their own agendas on English language learners (ELLs), ELLs continue to push back to create their own sense of what it means be an American. This article analyzes how…

Geomagnetic Secular Variation Prediction with Thermal Heterogeneous Boundary Conditions

NASA Astrophysics Data System (ADS)

Kuang, W.; Tangborn, A.; Jiang, W.

2011-12-01

It has long been conjectured that thermal heterogeneity at the core-mantle boundary (CMB) affects the geodynamo substantially. The observed two pairs of steady and strong magnetic flux lobes near the Polar Regions and the low secular variation in the Pacific over the past 400 years (and perhaps longer) are likely the consequences of this CMB thermal heterogeneity. There are several studies on the impact of the thermal heterogeneity with numerical geodynamo simulations. However, direct correlation between the numerical results and the observations is found very difficult, except qualitative comparisons of certain features in the radial component of the magnetic field at the CMB. This makes it difficult to assess accurately the impact of thermal heterogeneity on the geodynamo and the geomagnetic secular variation. We revisit this problem with our MoSST_DAS system in which geomagnetic data are assimilated with our geodynamo model to predict geomagnetic secular variations. In this study, we implement a heterogeneous heat flux across the CMB that is chosen based on the seismic tomography of the lowermost mantle. The amplitude of the heat flux (relative to the mean heat flux across the CMB) varies in the simulation. With these assimilation studies, we will examine the influences of the heterogeneity on the forecast accuracies, e.g. the accuracies as functions of the heterogeneity amplitude. With these, we could be able to assess the model errors to the true core state, and thus the thermal heterogeneity in geodynamo modeling.

Assimilation of GOES Land Surface Data Within a Rapid Update Cycle Format: Impact on MM5 Warm Season QPF

NASA Technical Reports Server (NTRS)

Lapenta, William M.; Suggs, Ron; Jedlovec, Gary; McNider, Richard T.; Dembek, Scott; Arnold, James E. (Technical Monitor)

2001-01-01

A technique has been developed for assimilating GOES-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite-observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. The focus of this paper is to examine how the satellite assimilation technique impacts simulations of near-surface meteorology on the 0-to 12-hour time scale when implemented within a local rapid update cycle (LRUC) format. The PSU/NCAR MM5 V34 is used and configured with a 36-km CONUS domain and a 12-km nest centered over the southeastern US. The LRUC format consists of a sequence of 12-hour forecasts initialized every hour between 12 and 18 UTC seven days a week. GOES skin temperature tendencies and solar insolation are assimilated in a 1-hour period prior to the start of each twelve-hour forecast. A unique aspect of the LRUC is the satellite assimilation and the continuous recycling of the adjusted moisture availability field from one forecast cycle to the next. Preliminary results for a seven-day trial period indicate that hourly LST tendencies assimilated in a 1 hour LRUC showed improved simulated air and dewpoint temperatures for all cycles on each day. The LRUC will be used during the 2001 summer months to identify the impact of the assimilation on warm season QPF Results will be presented at the meeting.

Advances in Land Data Assimilation at the NASA Goddard Space Flight Center

NASA Technical Reports Server (NTRS)

Reichle, Rolf

2009-01-01

Research in land surface data assimilation has grown rapidly over the last decade. In this presentation we provide a brief overview of key research contributions by the NASA Goddard Space Flight Center (GSFC). The GSFC contributions to land assimilation primarily include the continued development and application of the Land Information System (US) and the ensemble Kalman filter (EnKF). In particular, we have developed a method to generate perturbation fields that are correlated in space, time, and across variables and that permit the flexible modeling of errors in land surface models and observations, along with an adaptive filtering approach that estimates observation and model error input parameters. A percentile-based scaling method that addresses soil moisture biases in model and observational estimates opened the path to the successful application of land data assimilation to satellite retrievals of surface soil moisture. Assimilation of AMSR-E surface soil moisture retrievals into the NASA Catchment model provided superior surface and root zone assimilation products (when validated against in situ measurements and compared to the model estimates or satellite observations alone). The multi-model capabilities of US were used to investigate the role of subsurface physics in the assimilation of surface soil moisture observations. Results indicate that the potential of surface soil moisture assimilation to improve root zone information is higher when the surface to root zone coupling is stronger. Building on this experience, GSFC leads the development of the Level 4 Surface and Root-Zone Soil Moisture (L4_SM) product for the planned NASA Soil-Moisture-Active-Passive (SMAP) mission. A key milestone was the design and execution of an Observing System Simulation Experiment that quantified the contribution of soil moisture retrievals to land data assimilation products as a function of retrieval and land model skill and yielded an estimate of the error budget for the SMAP L4_SM product. Terrestrial water storage observations from GRACE satellite system were also successfully assimilated into the NASA Catchment model and provided improved estimates of groundwater variability when compared to the model estimates alone. Moreover, satellite-based land surface temperature (LST) observations from the ISCCP archive were assimilated using a bias estimation module that was specifically designed for LST assimilation. As with soil moisture, LST assimilation provides modest yet statistically significant improvements when compared to the model or satellite observations alone. To achieve the improvement, however, the LST assimilation algorithm must be adapted to the specific formulation of LST in the land model. An improved method for the assimilation of snow cover observations was also developed. Finally, the coupling of LIS to the mesoscale Weather Research and Forecasting (WRF) model enabled investigations into how the sensitivity of land-atmosphere interactions to the specific choice of planetary boundary layer scheme and land surface model varies across surface moisture regimes, and how it can be quantified and evaluated against observations. The on-going development and integration of land assimilation modules into the Land Information System will enable the use of GSFC software with a variety of land models and make it accessible to the research community.

Application of Observed Precipitation in NCEP Global and Regional Data Assimilation Systems, Including Reanalysis and Land Data Assimilation

NASA Astrophysics Data System (ADS)

Mitchell, K. E.

2006-12-01

The Environmental Modeling Center (EMC) of the National Centers for Environmental Prediction (NCEP) applies several different analyses of observed precipitation in both the data assimilation and validation components of NCEP's global and regional numerical weather and climate prediction/analysis systems (including in NCEP global and regional reanalysis). This invited talk will survey these data assimilation and validation applications and methodologies, as well as the temporal frequency, spatial domains, spatial resolution, data sources, data density and data quality control in the precipitation analyses that are applied. Some of the precipitation analyses applied by EMC are produced by NCEP's Climate Prediction Center (CPC), while others are produced by the River Forecast Centers (RFCs) of the National Weather Service (NWS), or by automated algorithms of the NWS WSR-88D Radar Product Generator (RPG). Depending on the specific type of application in data assimilation or model forecast validation, the temporal resolution of the precipitation analyses may be hourly, daily, or pentad (5-day) and the domain may be global, continental U.S. (CONUS), or Mexico. The data sources for precipitation include ground-based gauge observations, radar-based estimates, and satellite-based estimates. The precipitation analyses over the CONUS are analyses of either hourly, daily or monthly totals of precipitation, and they are of two distinct types: gauge-only or primarily radar-estimated. The gauge-only CONUS analysis of daily precipitation utilizes an orographic-adjustment technique (based on the well-known PRISM precipitation climatology of Oregon State University) developed by the NWS Office of Hydrologic Development (OHD). The primary NCEP global precipitation analysis is the pentad CPC Merged Analysis of Precipitation (CMAP), which blends both gauge observations and satellite estimates. The presentation will include a brief comparison between the CMAP analysis and other global precipitation analyses by other institutions. Other global precipitation analyses produced by other methodologies are also used by EMC in certain applications, such as CPC's well-known satellite-IR based technique known as "GPI", and satellite-microwave based estimates from NESDIS or NASA. Finally, the presentation will cover the three assimilation methods used by EMC to assimilate precipitation data, including 1) 3D-VAR variational assimilation in NCEP's Global Data Assimilation System (GDAS), 2) direct insertion of precipitation-inferred vertical latent heating profiles in NCEP's N. American Data Assimilation System (NDAS) and its N. American Regional Reanalysis (NARR) counterpart, and 3) direct use of observed precipitation to drive the Noah land model component of NCEP's Global and N. American Land Data Assimilation Systems (GLDAS and NLDAS). In the applications of precipitation analyses in data assimilation at NCEP, the analyses are temporally disaggregated to hourly or less using time-weights calculated from A) either radar-based estimates or an analysis of hourly gauge-observations for the CONUS-domain daily precipitation analyses, or B) global model forecasts of 6-hourly precipitation (followed by linear interpolation to hourly or less) for the global CMAP precipitation analysis.

Southern Polar Ozone in MERRA-2

NASA Technical Reports Server (NTRS)

Wargan, Krzysztof

2016-01-01

MERRA-2 provides a good representation of the year-to-year variations and the long-term changes in total ozone column over Antarctica for the entire data record, beginning in 1980. When MLS data are introduced into MERRA-2 in 2004, agreement with independent data improves compared to earlier years when the SBUV observations were assimilated.

Reassessing the Status of Black English (Review Article).

ERIC Educational Resources Information Center

Spears, Arthur K.

1992-01-01

Summarizes the main points presented in the 1989 book, "The Death of Black English" by R.R. Butlers (1989). Butler's book presents most important research of last 20 years and subjects the results to variation analysis. It is concluded that the history of linguistic assimilation points to the eventual disappearance of Black English in…

Spatial and temporal variation in invertebrate consumer diets in forested and herbaceous wetlands

Treesearch

Alani N. Taylor; Darold P. Batzer

2010-01-01

Macroinvertebrates have important functional roles in wetland ecosystems, but these roles are not always well understood. This study assessed which foods invertebrate consumers assimilate within a set of wetland habitats. During 2006 and 2007, non-Tanypodinae chironomid larvae and select crustaceans (Crangonyx amphipods, Caecidotea isopods, Simocephalus cladocerans)...

A Complementary Note to 'A Lag-1 Smoother Approach to System-Error Estimation': The Intrinsic Limitations of Residual Diagnostics

NASA Technical Reports Server (NTRS)

Todling, Ricardo

2015-01-01

Recently, this author studied an approach to the estimation of system error based on combining observation residuals derived from a sequential filter and fixed lag-1 smoother. While extending the methodology to a variational formulation, experimenting with simple models and making sure consistency was found between the sequential and variational formulations, the limitations of the residual-based approach came clearly to the surface. This note uses the sequential assimilation application to simple nonlinear dynamics to highlight the issue. Only when some of the underlying error statistics are assumed known is it possible to estimate the unknown component. In general, when considerable uncertainties exist in the underlying statistics as a whole, attempts to obtain separate estimates of the various error covariances are bound to lead to misrepresentation of errors. The conclusions are particularly relevant to present-day attempts to estimate observation-error correlations from observation residual statistics. A brief illustration of the issue is also provided by comparing estimates of error correlations derived from a quasi-operational assimilation system and a corresponding Observing System Simulation Experiments framework.

Reduction of initial shock in decadal predictions using a new initialization strategy

NASA Astrophysics Data System (ADS)

He, Yujun; Wang, Bin; Liu, Mimi; Liu, Li; Yu, Yongqiang; Liu, Juanjuan; Li, Ruizhe; Zhang, Cheng; Xu, Shiming; Huang, Wenyu; Liu, Qun; Wang, Yong; Li, Feifei

2017-08-01

A novel full-field initialization strategy based on the dimension-reduced projection four-dimensional variational data assimilation (DRP-4DVar) is proposed to alleviate the well-known initial shock occurring in the early years of decadal predictions. It generates consistent initial conditions, which best fit the monthly mean oceanic analysis data along the coupled model trajectory in 1 month windows. Three indices to measure the initial shock intensity are also proposed. Results indicate that this method does reduce the initial shock in decadal predictions by Flexible Global Ocean-Atmosphere-Land System model, Grid-point version 2 (FGOALS-g2) compared with the three-dimensional variational data assimilation-based nudging full-field initialization for the same model and is comparable to or even better than the different initialization strategies for other fifth phase of the Coupled Model Intercomparison Project (CMIP5) models. Better hindcasts of global mean surface air temperature anomalies can be obtained than in other FGOALS-g2 experiments. Due to the good model response to external forcing and the reduction of initial shock, higher decadal prediction skill is achieved than in other CMIP5 models.

Model Improvement by Assimilating Observations of Storm-Induced Coastal Change

NASA Astrophysics Data System (ADS)

Long, J. W.; Plant, N. G.; Sopkin, K.

2010-12-01

Discrete, large scale, meteorological events such as hurricanes can cause wide-spread destruction of coastal islands, habitats, and infrastructure. The effects can vary significantly along the coast depending on the configuration of the coastline, variable dune elevations, changes in geomorphology (sandy beach vs. marshland), and alongshore variations in storm hydrodynamic forcing. There are two primary methods of determining the changing state of a coastal system. Process-based numerical models provide highly resolved (in space and time) representations of the dominant dynamics in a physical system but must employ certain parameterizations due to computational limitations. The predictive capability may also suffer from the lack of reliable initial or boundary conditions. On the other hand, observations of coastal topography before and after the storm allow the direct quantification of cumulative storm impacts. Unfortunately these measurements suffer from instrument noise and a lack of necessary temporal resolution. This research focuses on the combination of these two pieces of information to make more reliable forecasts of storm-induced coastal change. Of primary importance is the development of a data assimilation strategy that is efficient, applicable for use with highly nonlinear models, and able to quantify the remaining forecast uncertainty based on the reliability of each individual piece of information used in the assimilation process. We concentrate on an event time-scale and estimate/update unobserved model information (boundary conditions, free parameters, etc.) by assimilating direct observations of coastal change with those simulated by the model. The data assimilation can help estimate spatially varying quantities (e.g. friction coefficients) that are often modeled as homogeneous and identify processes inadequately characterized in the model.

Ontogenetic shifts in thermal tolerance, selected body temperature and thermal dependence of food assimilation and locomotor performance in a lacertid lizard, Eremias brenchleyi.

PubMed

Xu, Xue-Feng; Ji, Xiang

2006-01-01

We used Eremias brenchleyi as a model animal to examine differences in thermal tolerance, selected body temperature, and the thermal dependence of food assimilation and locomotor performance between juvenile and adult lizards. Adults selected higher body temperatures (33.5 vs. 31.7 degrees C) and were able to tolerate a wider range of body temperatures (3.4-43.6 vs. 5.1-40.8 degrees C) than juveniles. Within the body temperature range of 26-38 degrees C, adults overall ate more than juveniles, and food passage rate was faster in adults than juveniles. Apparent digestive coefficient (ADC) and assimilation efficiency (AE) varied among temperature treatments but no clear temperature associated patterns could be discerned for these two variables. At each test temperature ADC and AE were both higher in adults than in juveniles. Sprint speed increased with increase in body temperature at lower body temperatures, but decreased at higher body temperatures. At each test temperature adults ran faster than did juveniles, and the range of body temperatures where lizards maintained 90% of maximum speed differed between adults (27-34 degrees C) and juveniles (29-37 degrees C). Optimal temperatures and thermal sensitivities differed between food assimilation and sprint speed. Our results not only show strong patterns of ontogenetic variation in thermal tolerance, selected body temperature and thermal dependence of food assimilation and locomotor performance in E. brenchleyi, but also add support for the multiple optima hypothesis for the thermal dependence of behavioral and physiological variables in reptiles.

New Developments in the Data Assimilation Research Testbed

NASA Astrophysics Data System (ADS)

Hoar, T. J.; Anderson, J. L.; Raeder, K.; Karspeck, A. R.; Romine, G.; Liu, H.; Collins, N.

2011-12-01

NCAR's Data Assimilation Research Testbed (DART) is a community facility that provides ensemble data assimilation tools for geophysical applications. DART works with an expanding set of models and a wide range of conventional and novel observations, and provides a variety of assimilation algorithms and diagnostic tools. The Kodiak release of DART became available in July 2011 and includes more than 20 major feature enhancements, support for 24 models, support for (at least) 14 observation formats, expanded documentation and diagnostic tools, and 12 new utilities. A few examples of research projects that demonstrate the effectiveness and flexibility of the DART are described. The Community Atmosphere Model (CAM) and DART assimilated all the observations that were used in the NCEP/NCAR Reanalysis to produce a global, 6-hourly, 80-member ensemble reanalysis for 1998 through the present. The dataset is ideal for research applications that would benefit from an ensemble of equally-likely atmospheric states that are consistent with observations. Individual ensemble members may be used as a "data atmosphere" in any Community Earth System Model (CESM) experiment. The CESM interfaces for the Parallel Ocean Program (POP) and the Community Land Model (CLM) also support multiple instances, allowing data assimilation experiments exploiting unique atmospheric forcing for each POP or CLM model instance. A multi-year DART ocean assimilation has been completed and provides valuable insight into the successes and challenges of oceanic data assimilation. The DART/CLM research focuses on snow cover fraction and snow depth. The Weather Research and Forecasting (WRF) model was used with DART to perform a real-time CONUS domain mesoscale ensemble analysis with continuous cycling for 47 days. A member was selected once daily for high-resolution convective forecasts supporting a test phase of the Deep Convective Clouds and Chemistry experiment and the Storm Prediction Center spring experiment. The impacts of Moderate Resolution Imaging Spectroradiometer (MODIS) infrared and Advanced Microwave Scanning Radiometer (AMSR) microwave total precipitable water (TPW) observations on analyses and forecasts of tropical cyclone Sinlaku (2008) are investigated by performing assimilations with a 45km resolution WRF model over the Western Pacific domain for 8-14 Septmber, 2008. Particular emphasis is on the performance of the assimilation algorithms in the hurricane core and the impact of novel observations in the hurricane core.

4D Hybrid Ensemble-Variational Data Assimilation for the NCEP GFS: Outer Loops and Variable Transforms

NASA Astrophysics Data System (ADS)

Kleist, D. T.; Ide, K.; Mahajan, R.; Thomas, C.

2014-12-01

The use of hybrid error covariance models has become quite popular for numerical weather prediction (NWP). One such method for incorporating localized covariances from an ensemble within the variational framework utilizes an augmented control variable (EnVar), and has been implemented in the operational NCEP data assimilation system (GSI). By taking the existing 3D EnVar algorithm in GSI and allowing for four-dimensional ensemble perturbations, coupled with the 4DVAR infrastructure already in place, a 4D EnVar capability has been developed. The 4D EnVar algorithm has a few attractive qualities relative to 4DVAR, including the lack of need for tangent-linear and adjoint model as well as reduced computational cost. Preliminary results using real observations have been encouraging, showing forecast improvements nearly as large as were found in moving from 3DVAR to hybrid 3D EnVar. 4D EnVar is the method of choice for the next generation assimilation system for use with the operational NCEP global model, the global forecast system (GFS). The use of an outer-loop has long been the method of choice for 4DVar data assimilation to help address nonlinearity. An outer loop involves the re-running of the (deterministic) background forecast from the updated initial condition at the beginning of the assimilation window, and proceeding with another inner loop minimization. Within 4D EnVar, a similar procedure can be adopted since the solver evaluates a 4D analysis increment throughout the window, consistent with the valid times of the 4D ensemble perturbations. In this procedure, the ensemble perturbations are kept fixed and centered about the updated background state. This is analogous to the quasi-outer loop idea developed for the EnKF. Here, we present results for both toy model and real NWP systems demonstrating the impact from incorporating outer loops to address nonlinearity within the 4D EnVar context. The appropriate amplitudes for observation and background error covariances in subsequent outer loops will be explored. Lastly, variable transformations on the ensemble perturbations will be utilized to help address issues of non-Gaussianity. This may be particularly important for variables that clearly have non-Gaussian error characteristics such as water vapor and cloud condensate.

Assimilation of global versus local data sets into a regional model of the Gulf Stream system. 1. Data effectiveness

NASA Astrophysics Data System (ADS)

Malanotte-Rizzoli, Paola; Young, Roberta E.

1995-12-01

The primary objective of this paper is to assess the relative effectiveness of data sets with different space coverage and time resolution when they are assimilated into an ocean circulation model. We focus on obtaining realistic numerical simulations of the Gulf Stream system typically of the order of 3-month duration by constructing a "synthetic" ocean simultaneously consistent with the model dynamics and the observations. The model used is the Semispectral Primitive Equation Model. The data sets are the "global" Optimal Thermal Interpolation Scheme (OTIS) 3 of the Fleet Numerical Oceanography Center providing temperature and salinity fields with global coverage and with bi-weekly frequency, and the localized measurements, mostly of current velocities, from the central and eastern array moorings of the Synoptic Ocean Prediction (SYNOP) program, with daily frequency but with a very small spatial coverage. We use a suboptimal assimilation technique ("nudging"). Even though this technique has already been used in idealized data assimilation studies, to our knowledge this is the first study in which the effectiveness of nudging is tested by assimilating real observations of the interior temperature and salinity fields. This is also the first work in which a systematic assimilation is carried out of the localized, high-quality SYNOP data sets in numerical experiments longer than 1-2 weeks, that is, not aimed to forecasting. We assimilate (1) the global OTIS 3 alone, (2) the local SYNOP observations alone, and (3) both OTIS 3 and SYNOP observations. We assess the success of the assimilations with quantitative measures of performance, both on the global and local scale. The results can be summarized as follows. The intermittent assimilation of the global OTIS 3 is necessary to keep the model "on track" over 3-month simulations on the global scale. As OTIS 3 is assimilated at every model grid point, a "gentle" weight must be prescribed to it so as not to overconstrain the model. However, in these assimilations the predicted velocity fields over the SYNOP arrays are greatly in error. The continuous assimilation of the localized SYNOP data sets with a strong weight is necessary to obtain local realistic evolutions. Then assimilation of velocity measurements alone recovers the density structure over the array area. However, the spatial coverage of the SYNOP measurements is too small to constrain the model on the global scale. Thus the blending of both types of datasets is necessary in the assimilation as they constrain different time and space scales. Our choice of "gentle" nudging weight for the global OTIS 3 and "strong" weight for the local SYNOP data provides for realistic simulations of the Gulf Stream system, both globally and locally, on the 3- to 4-month-long timescale, the one governed by the Gulf Stream jet internal dynamics.

Short-Term Flooding Effects on Gas Exchange and Quantum Yield of Rabbiteye Blueberry (Vaccinium ashei Reade) 1

PubMed Central

Davies, Frederick S.; Flore, James A.

1986-01-01

Roots of 1.5-year-old `Woodard' rabbiteye blueberry plants (Vaccinium ashei Reade) were flooded in containers or maintained at container capacity over a 5-day period. Carbon assimilation, and stomatal and residual conductances were monitored on one fully expanded shoot/plant using an open flow gas analysis system. Quantum yield was calculated from light response curves. Carbon assimilation and quantum yield of flooded plants decreased to 64 and 41% of control values, respectively, after 1 day of flooding and continued decreasing to 38 and 27% after 4 days. Stomatal and residual conductances to CO2 also decreased after 1 day of flooding compared with those of unflooded plants with residual conductance severely limiting carbon assimilation after 4 days of flooding. Stomatal opening occurred in 75 to 90 minutes and rate of opening was unaffected by flooding. PMID:16664791

Tropical cyclones in the North American Regional Reanalysis: The impact of satellite-derived precipitation over ocean

NASA Astrophysics Data System (ADS)

Zick, Stephanie E.; Matyas, Corene J.

2015-09-01

Continued advancement in the realm of tropical cyclone (TC) forecasting requires a more accurate depiction of these storms at model initialization. This study examines the impact of precipitation assimilation on the representation of TCs in the North American Regional Reanalysis before and after the 2004 introduction of precipitation assimilation over ocean in the vicinity of TCs. The probability distribution function of rainfall rates indicates that light (heavy) precipitation was overforecast (underforecast) in the early time period. Since the precipitation assimilation is applied through an adjustment to the latent heating distribution, the data assimilation system in the later time period initializes a low-level moisture and heating profile that is more conducive to the initiation of deep convection and the generation of precipitation. Consequently, the deep convection and enhanced latent heat release lead to a more robust warm-core temperature perturbation and a better developed secondary circulation, which supplies the TC with larger quantities of moisture from the large-scale environment. Furthermore, the evolution of TC size, which was objectively estimated though the radius of outermost closed isobar, is significantly more skillful (p < 0.05) in post-2003 storms. Based on this study, precipitation assimilation leads to a better analysis of temperature, winds, and moisture in the vicinity of TCs, resulting in improved representations of the water budget and storm life cycle. Therefore, we conclude that efforts toward the development of precipitation assimilation techniques from radar and satellite data sets will be valuable toward the construction of improved TC forecasting tools with more authentic TC representation.

Modeling Global Ocean Biogeochemistry With Physical Data Assimilation: A Pragmatic Solution to the Equatorial Instability

NASA Astrophysics Data System (ADS)

Park, Jong-Yeon; Stock, Charles A.; Yang, Xiaosong; Dunne, John P.; Rosati, Anthony; John, Jasmin; Zhang, Shaoqing

2018-03-01

Reliable estimates of historical and current biogeochemistry are essential for understanding past ecosystem variability and predicting future changes. Efforts to translate improved physical ocean state estimates into improved biogeochemical estimates, however, are hindered by high biogeochemical sensitivity to transient momentum imbalances that arise during physical data assimilation. Most notably, the breakdown of geostrophic constraints on data assimilation in equatorial regions can lead to spurious upwelling, resulting in excessive equatorial productivity and biogeochemical fluxes. This hampers efforts to understand and predict the biogeochemical consequences of El Niño and La Niña. We develop a strategy to robustly integrate an ocean biogeochemical model with an ensemble coupled-climate data assimilation system used for seasonal to decadal global climate prediction. Addressing spurious vertical velocities requires two steps. First, we find that tightening constraints on atmospheric data assimilation maintains a better equatorial wind stress and pressure gradient balance. This reduces spurious vertical velocities, but those remaining still produce substantial biogeochemical biases. The remainder is addressed by imposing stricter fidelity to model dynamics over data constraints near the equator. We determine an optimal choice of model-data weights that removed spurious biogeochemical signals while benefitting from off-equatorial constraints that still substantially improve equatorial physical ocean simulations. Compared to the unconstrained control run, the optimally constrained model reduces equatorial biogeochemical biases and markedly improves the equatorial subsurface nitrate concentrations and hypoxic area. The pragmatic approach described herein offers a means of advancing earth system prediction in parallel with continued data assimilation advances aimed at fully considering equatorial data constraints.

Development and validation of a minimal growth medium for recycling Chlorella vulgaris culture.

PubMed

Hadj-Romdhane, F; Jaouen, P; Pruvost, J; Grizeau, D; Van Vooren, G; Bourseau, P

2012-11-01

When microalgae culture medium is recycled, ions (e.g. Na(+), K(+), Ca(2+)) that were not assimilated by the microalgae accumulate in the medium. Therefore, a growth medium (HAMGM) was developed that included ions that were more easily assimilated by Chlorella vulgaris, such as ammonium one (NH(4)(+)). Recycling performance was studied by carrying out 8-week continuous cultivation of C. vulgaris with recycled HAMGM medium. No loss of biomass productivity was observed compared to culture in a conventional medium, and accumulation of ions over time was negligible. Copyright © 2012 Elsevier Ltd. All rights reserved.

CO2 bubble generation and migration during magma-carbonate interaction

NASA Astrophysics Data System (ADS)

Blythe, L. S.; Deegan, F. M.; Freda, C.; Jolis, E. M.; Masotta, M.; Misiti, V.; Taddeucci, J.; Troll, V. R.

2015-04-01

We conducted quantitative textural analysis of vesicles in high temperature and pressure carbonate assimilation experiments (1200 °C, 0.5 GPa) to investigate CO2 generation and subsequent bubble migration from carbonate into magma. We employed Mt. Merapi (Indonesia) and Mt. Vesuvius (Italy) compositions as magmatic starting materials and present three experimental series using (1) a dry basaltic-andesite, (2) a hydrous basaltic-andesite (2 wt% H2O), and (3) a hydrous shoshonite (2 wt% H2O). The duration of the experiments was varied from 0 to 300 s, and carbonate assimilation produced a CO2-rich fluid and CaO-enriched melts in all cases. The rate of carbonate assimilation, however, changed as a function of melt viscosity, which affected the 2D vesicle number, vesicle volume, and vesicle size distribution within each experiment. Relatively low-viscosity melts (i.e. Vesuvius experiments) facilitated efficient removal of bubbles from the reaction site. This allowed carbonate assimilation to continue unhindered and large volumes of CO2 to be liberated, a scenario thought to fuel sustained CO2-driven eruptions at the surface. Conversely, at higher viscosity (i.e. Merapi experiments), bubble migration became progressively inhibited and bubble concentration at the reaction site caused localised volatile over-pressure that can eventually trigger short-lived explosive outbursts. Melt viscosity therefore exerts a fundamental control on carbonate assimilation rates and, by consequence, the style of CO2-fuelled eruptions.

Development of an Aura Chemical Reanalysis in support Air Quality Applications

NASA Astrophysics Data System (ADS)

Pierce, R. B.; Lenzen, A.; Schaack, T.

2015-12-01

We present results of chemical data assimilation experiments utilizing the NOAA National Environmental Satellite, Data, and Information Service (NESDIS), University of Wisconsin Space Science and Engineering (SSEC) Real-time Air Quality Modeling System (RAQMS) in conjunction with the NOAA National Centers for Environmental Prediction (NCEP) Operational Gridpoint Statistical Interpolation (GSI) 3-dimensional variational data assimilation system. The impact of assimilating NASA Ozone Monitoring Instrument (OMI) total column ozone, OMI tropospheric nitrogen dioxide columns, and Microwave Limb Sounder (MLS) stratospheric ozone profiles on background ozone is assessed using measurements from the 2010 NSF High-performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole-to-Pole Observation (HIPPO) and NOAA California Nexus (CalNex) campaigns. Results show that the RAQMS/GSI Chemical Reanalysis is able to provide very good estimates of background ozone and large-scale ozone variability and is suitable for use in constraining regional air quality modeling activities. These experiments are being used to guide the development of a multi-year global chemical and aerosol reanalysis using NASA Aura and A-Train measurements to support air quality applications.

Impact of lateral boundary conditions on regional analyses

NASA Astrophysics Data System (ADS)

Chikhar, Kamel; Gauthier, Pierre

2017-04-01

Regional and global climate models are usually validated by comparison to derived observations or reanalyses. Using a model in data assimilation results in a direct comparison to observations to produce its own analyses that may reveal systematic errors. In this study, regional analyses over North America are produced based on the fifth-generation Canadian Regional Climate Model (CRCM5) combined with the variational data assimilation system of the Meteorological Service of Canada (MSC). CRCM5 is driven at its boundaries by global analyses from ERA-interim or produced with the global configuration of the CRCM5. Assimilation cycles for the months of January and July 2011 revealed systematic errors in winter through large values in the mean analysis increments. This bias is attributed to the coupling of the lateral boundary conditions of the regional model with the driving data particularly over the northern boundary where a rapidly changing large scale circulation created significant cross-boundary flows. Increasing the time frequency of the lateral driving and applying a large-scale spectral nudging improved significantly the circulation through the lateral boundaries which translated in a much better agreement with observations.

A storm-time plasmasphere evolution study using data assimilation

NASA Astrophysics Data System (ADS)

Nikoukar, R.; Bust, G. S.; Bishop, R. L.; Coster, A. J.; Lemon, C.; Turner, D. L.; Roeder, J. L.

2017-12-01

In this work, we study the evolution of the Earth's plasmasphere during geomagnetic active periods using the Plasmasphere Data Assimilation (PDA) model. The total electron content (TEC) measurements from an extensive network of global ground-based GPS receivers as well as GPS receivers on-board Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) satellites and Communications/Navigation Outage Forecasting System (C/NOFS) satellite are ingested into the model. Global Core Plasma model, which is an empirical plasmasphere model, is utilized as the background model. Based on the 3D-VAR optimization, the PDA assimilative model benefits from incorporation of regularization techniques to prevent non-physical altitudinal variation in density estimates due to the limited-angle observational geometry. This work focuses on the plasmapause location, plasmasphere erosion time scales and refilling rates during the main and recovery phases of geomagnetic storms as estimated from the PDA 3-dimensional global maps of electron density in the ionosphere/plasmasphere. The comparison between the PDA results with in-situ density measurements from THEMIS and Van Allen Probes, and the RCM-E first-principle model will be also presented.

Role of subsurface ocean in decadal climate predictability over the South Atlantic.

PubMed

Morioka, Yushi; Doi, Takeshi; Storto, Andrea; Masina, Simona; Behera, Swadhin K

2018-06-04

Decadal climate predictability in the South Atlantic is explored by performing reforecast experiments using a coupled general circulation model with two initialization schemes; one is assimilated with observed sea surface temperature (SST) only, and the other is additionally assimilated with observed subsurface ocean temperature and salinity. The South Atlantic is known to undergo decadal variability exhibiting a meridional dipole of SST anomalies through variations in the subtropical high and ocean heat transport. Decadal reforecast experiments in which only the model SST is initialized with the observation do not predict well the observed decadal SST variability in the South Atlantic, while the other experiments in which the model SST and subsurface ocean are initialized with the observation skillfully predict the observed decadal SST variability, particularly in the Southeast Atlantic. In-depth analysis of upper-ocean heat content reveals that a significant improvement of zonal heat transport in the Southeast Atlantic leads to skillful prediction of decadal SST variability there. These results demonstrate potential roles of subsurface ocean assimilation in the skillful prediction of decadal climate variability over the South Atlantic.

Lidar data assimilation for improved analyses of volcanic aerosol events

NASA Astrophysics Data System (ADS)

Lange, Anne Caroline; Elbern, Hendrik

2014-05-01

Observations of hazardous events with release of aerosols are hardly analyzable by today's data assimilation algorithms, without producing an attenuating bias. Skillful forecasts of unexpected aerosol events are essential for human health and to prevent an exposure of infirm persons and aircraft with possibly catastrophic outcome. Typical cases include mineral dust outbreaks, mostly from large desert regions, wild fires, and sea salt uplifts, while the focus aims for volcanic eruptions. In general, numerical chemistry and aerosol transport models cannot simulate such events without manual adjustments. The concept of data assimilation is able to correct the analysis, as long it is operationally implemented in the model system. Though, the tangent-linear approximation, which describes a substantial precondition for today's cutting edge data assimilation algorithms, is not valid during unexpected aerosol events. As part of the European COPERNICUS (earth observation) project MACC II and the national ESKP (Earth System Knowledge Platform) initiative, we developed a module that enables the assimilation of aerosol lidar observations, even during unforeseeable incidences of extreme emissions of particulate matter. Thereby, the influence of the background information has to be reduced adequately. Advanced lidar instruments comprise on the one hand the aspect of radiative transfer within the atmosphere and on the other hand they can deliver a detailed quantification of the detected aerosols. For the assimilation of maximal exploited lidar data, an appropriate lidar observation operator is constructed, compatible with the EURAD-IM (European Air Pollution and Dispersion - Inverse Model) system. The observation operator is able to map the modeled chemical and physical state on lidar attenuated backscatter, transmission, aerosol optical depth, as well as on the extinction and backscatter coefficients. Further, it has the ability to process the observed discrepancies with lidar data in a variational data assimilation algorithm. The implemented method is tested by the assimilation of CALIPSO attenuated backscatter data that were taken during the eruption of the Eyjafjallajökull volcano in April 2010. It turned out that the implemented module is fully capable to integrate unexpected aerosol events in an automatic way into reasonable analyses. The estimations of the aerosol mass concentrations showed promising properties for the application of observations that are taken by lidar systems with both, higher and lower sophistication than CALIOP.

Cheating is evolutionarily assimilated with cooperation in the continuous snowdrift game

PubMed Central

Sasaki, Tatsuya; Okada, Isamu

2015-01-01

It is well known that in contrast to the Prisoner’s Dilemma, the snowdrift game can lead to a stable coexistence of cooperators and cheaters. Recent theoretical evidence on the snowdrift game suggests that gradual evolution for individuals choosing to contribute in continuous degrees can result in the social diversification to a 100% contribution and 0% contribution through so-called evolutionary branching. Until now, however, game-theoretical studies have shed little light on the evolutionary dynamics and consequences of the loss of diversity in strategy. Here, we analyze continuous snowdrift games with quadratic payoff functions in dimorphic populations. Subsequently, conditions are clarified under which gradual evolution can lead a population consisting of those with 100% contribution and those with 0% contribution to merge into one species with an intermediate contribution level. The key finding is that the continuous snowdrift game is more likely to lead to assimilation of different cooperation levels rather than maintenance of diversity. Importantly, this implies that allowing the gradual evolution of cooperative behavior can facilitate social inequity aversion in joint ventures that otherwise could cause conflicts that are based on commonly accepted notions of fairness. PMID:25868940

Cheating is evolutionarily assimilated with cooperation in the continuous snowdrift game.

PubMed

Sasaki, Tatsuya; Okada, Isamu

2015-05-01

It is well known that in contrast to the Prisoner's Dilemma, the snowdrift game can lead to a stable coexistence of cooperators and cheaters. Recent theoretical evidence on the snowdrift game suggests that gradual evolution for individuals choosing to contribute in continuous degrees can result in the social diversification to a 100% contribution and 0% contribution through so-called evolutionary branching. Until now, however, game-theoretical studies have shed little light on the evolutionary dynamics and consequences of the loss of diversity in strategy. Here, we analyze continuous snowdrift games with quadratic payoff functions in dimorphic populations. Subsequently, conditions are clarified under which gradual evolution can lead a population consisting of those with 100% contribution and those with 0% contribution to merge into one species with an intermediate contribution level. The key finding is that the continuous snowdrift game is more likely to lead to assimilation of different cooperation levels rather than maintenance of diversity. Importantly, this implies that allowing the gradual evolution of cooperative behavior can facilitate social inequity aversion in joint ventures that otherwise could cause conflicts that are based on commonly accepted notions of fairness. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Adjoint assimilation of altimetric, surface drifter, and hydrographic data in a quasi-geostrophic model of the Azores Current

NASA Astrophysics Data System (ADS)

Morrow, Rosemary; de Mey, Pierre

1995-12-01

The flow characteristics in the region of the Azores Current are investigated by assimilating TOPEX/POSEIDON and ERS 1 altimeter data into the multilevel Harvard quasigeostrophic (QG) model with open boundaries (Miller et al., 1983) using an adjoint variational scheme (Moore, 1991). The study site lies in the path of the Azores Current, where a branch retroflects to the south in the vicinity of the Madeira Rise. The region was the site of an intensive field program in 1993, SEMAPHORE. We had two main aims in this adjoint assimilation project. The first was to see whether the adjoint method could be applied locally to optimize an initial guess field, derived from the continous assimilation of altimetry data using optimal interpolation (OI). The second aim was to assimilate a variety of different data sets and evaluate their importance in constraining our QG model. The adjoint assimilation of surface data was effective in optimizing the initial conditions from OI. After 20 iterations the cost function was generally reduced by 50-80%, depending on the chosen data constraints. The primary adjustment process was via the barotropic mode. Altimetry proved to be a good constraint on the variable flow field, in particular, for constraining the barotropic field. The excellent data quality of the TOPEX/POSEIDON (T/P) altimeter data provided smooth and reliable forcing; but for our mesoscale study in a region of long decorrelation times O(30 days), the spatial coverage from the combined T/P and ERS 1 data sets was more important for constraining the solution and providing stable flow at all levels. Surface drifters provided an excellent constraint on both the barotropic and baroclinic model fields. More importantly, the drifters provided a reliable measure of the mean field. Hydrographic data were also applied as a constraint; in general, hydrography provided a weak but effective constraint on the vertical Rossby modes in the model. Finally, forecasts run over a 2-month period indicate that the initial conditions optimized by the 20-day adjoint assimilation provide more stable, longer-term forecasts.

Streamflow forecasting and data assimilation: bias in precipitation, soil moisture states, and groundwater fluxes.

NASA Astrophysics Data System (ADS)

McCreight, J. L.; Gochis, D. J.; Hoar, T.; Dugger, A. L.; Yu, W.

2014-12-01

Uncertainty in precipitation forcing, soil moisture states, and model groundwater fluxes are first-order sources of error in streamflow forecasting. While near-surface estimates of soil moisture are now available from satellite, very few soil moisture observations below 5 cm depth or groundwater discharge estimates are available for operational forecasting. Radar precipitation estimates are subject to large biases, particularly during extreme events (e.g. Steiner et al., 2010) and their correction is not typically available in real-time. Streamflow data, however, are readily available in near-real-time and can be assimilated operationally to help constrain uncertainty in these uncertain states and improve streamflow forecasts. We examine the ability of streamflow observations to diagnose bias in the three most uncertain variables: precipitation forcing, soil moisture states, and groundwater fluxes. We investigate strategies for their subsequent bias correction. These include spinup and calibration strategies with and without the use of data assimilation and the determination of the proper spinup timescales. Global and spatially distributed multipliers on the uncertain states included in the assimilation state vector (e.g. Seo et al., 2003) will also be evaluated. We examine real cases and observing system simulation experiments for both normal and extreme rainfall events. One of our test cases considers the Colorado Front Range flood of September 2013 where the range of disagreement amongst five precipitation estimates spanned a factor of five with only one exhibiting appreciable positive bias (Gochis et al, submitted). Our experiments are conducted using the WRF-Hydro model with the NoahMP land surface component and the data assimilation research testbed (DART). A variety of ensemble data assimilation approaches (filters) are considered. ReferencesGochis, DJ, et al. "The Great Colorado Flood of September 2013" BAMS (Submitted 4-7-14). Seo, DJ, V Koren, and N Cajina. "Real-time variational assimilation of hydrologic and hydrometeorological data into operational hydrologic forecasting." J Hydromet (2003). Steiner, Matthias, JA Smith, SJ Burges, CV Alonso, and RW Darden. "Effect of bias adjustment and rain gauge data quality control on radar rainfall estimation." WRR (1999).

Development of a multi-data assimilation scheme to integrate Bio-Argo floats data with ocean colour satellite data into the CMEMS MFC-Biogeochemistry

NASA Astrophysics Data System (ADS)

Cossarini, Gianpiero; D'Ortenzio, Fabrizio; Mariotti, Laura; Mignot, Alexandre; Salon, Stefano

2017-04-01

The Mediterranean Sea is a very promising site to develop and test the assimilation of Bio-Argo data since 1) the Bio-Argo network is one of the densest of the global ocean, and 2) a consolidate data assimilation framework of biogeochemical variables (3DVAR-BIO, presently based on assimilation of satellite-estimated surface chlorophyll data) already exists within the CMEMS biogeochemical model system for Mediterranean Sea. The MASSIMILI project, granted by the CMEMS Service Evolution initiative, is aimed to develop the assimilation of Bio-Argo Floats data into the CMEMS biogeochemical model system of the Mediterranean Sea, by means of an upgrade of the 3DVAR-BIO scheme. Specific developments of the 3DVAR-BIO scheme focus on the estimate of new operators of the variational decomposition of the background error covariance matrix and on the implementation of the new observation operator specifically for the Bio-Argo float vertical profile data. In particular, a new horizontal covariance operator for chlorophyll, nitrate and oxygen is based on 3D fields of horizontal correlation radius calculated from a long-term reanalysis simulation. A new vertical covariance operator is built on monthly and spatial varying EOF decomposition to account for the spatiotemporal variability of vertical structure of the three variables error covariance. Further, the observation error covariance is a key factor for an effective assimilation of the Bio-Argo data into the model dynamics. The sensitivities of assimilation to the different factors are estimated. First results of the implementation of the new 3DVAR-BIO scheme show the impact of Bio-Argo data on the 3D fields of chlorophyll, nitrate and oxygen. Tuning the length scale factors of horizontal covariance, analysing the sensitivity of the observation error covariance, introducing non-diagonal biogeochemical covariance operator and non-diagonal multi-platform operator (i.e. Bio-Argo and satellite) are crucial future steps for the success of the MASSIMILI project. In our contribute, we will discuss the recent and promising advancements this strategic project has been having in the past year and its potential for the whole operational biogeochemical modelling community.

Integrating Enhanced Grace Terrestrial Water Storage Data Into the U.S. and North American Drought Monitors

NASA Technical Reports Server (NTRS)

Housborg, Rasmus; Rodell, Matthew

2010-01-01

NASA's Gravity Recovery and Climate Experiment (GRACE) satellites measure time variations nf the Earth's gravity field enabling reliable detection of spatio-temporal variations in total terrestrial water storage (TWS), including ground water. The U.S. and North American Drought Monitors are two of the premier drought monitoring products available to decision-makers for assessing and minimizing drought impacts, but they rely heavily on precipitation indices and do not currently incorporate systematic observations of deep soil moisture and groundwater storage conditions. Thus GRACE has great potential to improve the Drought Monitors hy filling this observational gap. Horizontal, vertical and temporal disaggregation of the coarse-resolution GRACE TWS data has been accomplished by assimilating GRACE TWS anomalies into the Catchment Land Surface Model using ensemble Kalman smoother. The Drought Monitors combine several short-term and long-term drought indices and indicators expressed in percentiles as a reference to their historical frequency of occurrence for the location and time of year in question. To be consistent, we are in the process of generating a climatology of estimated soil moisture and ground water based on m 60-year Catchment model simulation which will subsequently be used to convert seven years of GRACE assimilated fields into soil moisture and groundwater percentiles. for systematic incorporation into the objective blends that constitute Drought Monitor baselines. At this stage we provide a preliminary evaluation of GRACE assimilated Catchment model output against independent datasets including soil moisture observations from Aqua AMSR-E and groundwater level observations from the U.S. Geological Survey's Groundwater Climate Response Network.

Assimilation of repeated woody biomass observations constrains decadal ecosystem carbon cycle uncertainty in aggrading forests

NASA Astrophysics Data System (ADS)

Smallman, T. L.; Exbrayat, J.-F.; Mencuccini, M.; Bloom, A. A.; Williams, M.

2017-03-01

Forest carbon sink strengths are governed by plant growth, mineralization of dead organic matter, and disturbance. Across landscapes, remote sensing can provide information about aboveground states of forests and this information can be linked to models to estimate carbon cycling in forests close to steady state. For aggrading forests this approach is more challenging and has not been demonstrated. Here we apply a Bayesian approach, linking a simple model to a range of data, to evaluate their information content, for two aggrading forests. We compare high information content analyses using local observations with retrievals using progressively sparser remotely sensed information (repeated, single, and no woody biomass observations). The net biome productivity of both forests is constrained to be a net sink with <2 Mg C ha-1 yr-1 variation across the range of inputs. However, the sequestration of particular carbon pool(s) varies with assimilated biomass information. Assimilation of repeated biomass observations reduces uncertainty and/or bias in all ecosystem C pools not just wood, compared to analyses using single or no stock information. As verification, our repeated biomass analysis explains 78-86% of variation in litter dynamics at one forest, while at the second forest total dead organic matter estimates are within observational uncertainty. The uncertainty of retrieved ecosystem traits in the repeated biomass analysis is reduced by up to 50% compared to analyses with less biomass information. This study quantifies the importance of repeated woody observations in constraining the dynamics of both wood and dead organic matter, highlighting the benefit of proposed remote sensing missions.

Exploration of relationships between low-Ti and high-Ti pristine lunar glasses using an armalcolite assimilation model

NASA Technical Reports Server (NTRS)

Jones, John H.; Delano, John W.

1991-01-01

The pristine glasses of Delano are the most primitive lunar basaltic magma compositions discovered to date. They are grouped into two (and possibly three) arrays: a low-alumina array and a high alumina array. These glasses are very olivine normative and are multiply saturated at pressures of approximately 20 kbar, implying a depth of origin of 400 to 500 km in the Moon. Thus, these glasses appear to be the best candidates for primitive partial melts of the upper lunar mantle. One of the most perplexing characteristics of the pristine glasses is a positive correlation between Ni and SiO2 within each array. This is contrary to the terrestrial experience, where Ni is observed to positively correlate with MgO and negatively correlate with SiO2. These systematics are believed to be due to the depletion of Ni by olivine fractionation. The difference between the lunar and terrestrial Ni vs. SiO2 trends may be partially ascribed to the Ti-rich component. In the case of the pristine glasses, SiO2 increases not because of olivine fractionation, but because they contain less of the high-Ti component. An attempt was made to model this variation in Ni and SiO2 with a simple assimilation-fractional crystallization (AFC) model. Silica and Ni both decreased dramatically as the AFC process proceeded. Only 15 to 20 percent AFC was necessary to produce the observed variation, and the SiO2 vs. Ni variation was modeled quite well. The D(Ni) for olivine/liquid in this model was taken to be 10 and the olivine was assumed to be Fe sub 80. However, the results of this model for Ti and Mg were less than satisfactory. It seemed difficult to achieve the high TiO2 contents of some glasses (16 to 17 wt. percent) by this method. Continual addition of ilmenite by AFC could indeed raise the titania concentrations to the necessary levels, but only by enriching the magma in FeO and greatly depleting the magma in MgO. An attempt was made to circumvent this problem by using armalcolite, (Fe, Mg)Ti2O5, in the AFC model, and the results are presented.

Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review

PubMed Central

Montzka, Carsten; Pauwels, Valentijn R. N.; Franssen, Harrie-Jan Hendricks; Han, Xujun; Vereecken, Harry

2012-01-01

More and more terrestrial observational networks are being established to monitor climatic, hydrological and land-use changes in different regions of the World. In these networks, time series of states and fluxes are recorded in an automated manner, often with a high temporal resolution. These data are important for the understanding of water, energy, and/or matter fluxes, as well as their biological and physical drivers and interactions with and within the terrestrial system. Similarly, the number and accuracy of variables, which can be observed by spaceborne sensors, are increasing. Data assimilation (DA) methods utilize these observations in terrestrial models in order to increase process knowledge as well as to improve forecasts for the system being studied. The widely implemented automation in observing environmental states and fluxes makes an operational computation more and more feasible, and it opens the perspective of short-time forecasts of the state of terrestrial systems. In this paper, we review the state of the art with respect to DA focusing on the joint assimilation of observational data precedents from different spatial scales and different data types. An introduction is given to different DA methods, such as the Ensemble Kalman Filter (EnKF), Particle Filter (PF) and variational methods (3/4D-VAR). In this review, we distinguish between four major DA approaches: (1) univariate single-scale DA (UVSS), which is the approach used in the majority of published DA applications, (2) univariate multiscale DA (UVMS) referring to a methodology which acknowledges that at least some of the assimilated data are measured at a different scale than the computational grid scale, (3) multivariate single-scale DA (MVSS) dealing with the assimilation of at least two different data types, and (4) combined multivariate multiscale DA (MVMS). Finally, we conclude with a discussion on the advantages and disadvantages of the assimilation of multiple data types in a simulation model. Existing approaches can be used to simultaneously update several model states and model parameters if applicable. In other words, the basic principles for multivariate data assimilation are already available. We argue that a better understanding of the measurement errors for different observation types, improved estimates of observation bias and improved multiscale assimilation methods for data which scale nonlinearly is important to properly weight them in multiscale multivariate data assimilation. In this context, improved cross-validation of different data types, and increased ground truth verification of remote sensing products are required. PMID:23443380

Multivariate and multiscale data assimilation in terrestrial systems: a review.

PubMed

Montzka, Carsten; Pauwels, Valentijn R N; Franssen, Harrie-Jan Hendricks; Han, Xujun; Vereecken, Harry

2012-11-26

More and more terrestrial observational networks are being established to monitor climatic, hydrological and land-use changes in different regions of the World. In these networks, time series of states and fluxes are recorded in an automated manner, often with a high temporal resolution. These data are important for the understanding of water, energy, and/or matter fluxes, as well as their biological and physical drivers and interactions with and within the terrestrial system. Similarly, the number and accuracy of variables, which can be observed by spaceborne sensors, are increasing. Data assimilation (DA) methods utilize these observations in terrestrial models in order to increase process knowledge as well as to improve forecasts for the system being studied. The widely implemented automation in observing environmental states and fluxes makes an operational computation more and more feasible, and it opens the perspective of short-time forecasts of the state of terrestrial systems. In this paper, we review the state of the art with respect to DA focusing on the joint assimilation of observational data precedents from different spatial scales and different data types. An introduction is given to different DA methods, such as the Ensemble Kalman Filter (EnKF), Particle Filter (PF) and variational methods (3/4D-VAR). In this review, we distinguish between four major DA approaches: (1) univariate single-scale DA (UVSS), which is the approach used in the majority of published DA applications, (2) univariate multiscale DA (UVMS) referring to a methodology which acknowledges that at least some of the assimilated data are measured at a different scale than the computational grid scale, (3) multivariate single-scale DA (MVSS) dealing with the assimilation of at least two different data types, and (4) combined multivariate multiscale DA (MVMS). Finally, we conclude with a discussion on the advantages and disadvantages of the assimilation of multiple data types in a simulation model. Existing approaches can be used to simultaneously update several model states and model parameters if applicable. In other words, the basic principles for multivariate data assimilation are already available. We argue that a better understanding of the measurement errors for different observation types, improved estimates of observation bias and improved multiscale assimilation methods for data which scale nonlinearly is important to properly weight them in multiscale multivariate data assimilation. In this context, improved cross-validation of different data types, and increased ground truth verification of remote sensing products are required.

Continuous Data Assimilation for a 2D Bénard Convection System Through Horizontal Velocity Measurements Alone

NASA Astrophysics Data System (ADS)

Farhat, Aseel; Lunasin, Evelyn; Titi, Edriss S.

2017-06-01

In this paper we propose a continuous data assimilation (downscaling) algorithm for a two-dimensional Bénard convection problem. Specifically we consider the two-dimensional Boussinesq system of a layer of incompressible fluid between two solid horizontal walls, with no-normal flow and stress-free boundary conditions on the walls, and the fluid is heated from the bottom and cooled from the top. In this algorithm, we incorporate the observables as a feedback (nudging) term in the evolution equation of the horizontal velocity. We show that under an appropriate choice of the nudging parameter and the size of the spatial coarse mesh observables, and under the assumption that the observed data are error free, the solution of the proposed algorithm converges at an exponential rate, asymptotically in time, to the unique exact unknown reference solution of the original system, associated with the observed data on the horizontal component of the velocity.

Verification and Validation of a Navy ESPC Hindcast with Loosely Coupled Data Assimilation

NASA Astrophysics Data System (ADS)

Metzger, E. J.; Barton, N. P.; Smedstad, O. M.; Ruston, B. C.; Wallcraft, A. J.; Whitcomb, T. R.; Ridout, J. A.; Franklin, D. S.; Zamudio, L.; Posey, P. G.; Reynolds, C. A.; Phelps, M.

2016-12-01

The US Navy is developing an Earth System Prediction Capability (ESPC) to provide global environmental information to meet Navy and Department of Defense (DoD) operations and planning needs from the upper atmosphere to under the sea. It will be a fully coupled global atmosphere/ocean/ice/wave/land prediction system providing daily deterministic forecasts out to 16 days at high horizontal and vertical resolution, and daily probabilistic forecasts out to 45 days at lower resolution. The system will run at the Navy DoD Supercomputing Resource Center with an initial operational capability scheduled for the end of FY18 and the final operational capability scheduled for FY22. The individual model and data assimilation components include: atmosphere - NAVy Global Environmental Model (NAVGEM) and Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System - Accelerated Representer (NAVDAS-AR); ocean - HYbrid Coordinate Ocean Model (HYCOM) and Navy Coupled Ocean Data Assimilation (NCODA); ice - Community Ice CodE (CICE) and NCODA; WAVEWATCH III™ and NCODA; and land - NAVGEM Land Surface Model (LSM). Currently, NAVGEM/HYCOM/CICE are three-way coupled and each model component is cycling with its respective assimilation scheme. The assimilation systems do not communicate with each other, but future plans call for these to be coupled as well. NAVGEM runs with a 6-hour update cycle while HYCOM/CICE run with a 24-hour update cycle. The T359L50 NAVGEM/0.08° HYCOM/0.08° CICE system has been integrated in hindcast mode and verification/validation metrics have been computed against unassimilated observations and against stand-alone versions of NAVGEM and HYCOM/CICE. This presentation will focus on typical operational diagnostics for atmosphere, ocean, and ice analyses including 500 hPa atmospheric height anomalies, low-level winds, temperature/salinity ocean depth profiles, ocean acoustical proxies, sea ice edge, and sea ice drift. Overall, the global coupled ESPC system is performing with comparable skill to the stand-alone systems at the nowcast time.

Assimilation and contrast are on the same scale of food anticipated-experienced pleasure divergence.

PubMed

Davidenko, Olga; Delarue, Julien; Marsset-Baglieri, Agnès; Fromentin, Gilles; Tomé, Daniel; Nadkarni, Nachiket; Darcel, Nicolas

2015-07-01

Consumption of a product is preceded by an anticipation of its qualities by the consumer, which can itself modify the consumption experience. Improved knowledge of anticipation would allow better manipulation of it, for example to enhance the acceptance of healthier foods. According to the Assimilation-Contrast theory, the size of anticipation-reality divergence determines how anticipation influences consumers' satisfaction. For small divergences, experienced pleasure is the same as the anticipated pleasure (Assimilation); for large ones, the effect of surprise provokes an even larger discordance with that which was anticipated (Contrast). Few studies have attempted to observe both effects simultaneously, or to consider the anticipation-reality divergence quantitatively rather than qualitatively; these were the study's objectives. A range of 10 flavored drinks was developed to vary progressively in intensity. Ninety healthy young men consumed samples during two separate sessions. In session 1, hedonic and sensory scores of all drinks were recorded during blind tasting. In session 2, three drinks were chosen as references for taste intensity, and associated with neutral symbols that served as labels. Subjects then consumed 36 drink samples, each one bearing a label. For half of the samples drinks did not correspond to labels, creating a range of anticipation-reality divergence. By predicting session 2 scores using linear modeling with session 1 blind ratings as input, it was confirmed that both Assimilation and Contrast effects on hedonic ratings were present (Assimilation (t(89) = 5.645, p < 0.0001) and Contrast (t(89) = 3.186, p = 0.002 or t(89) = 2.494, p = 0.015, depending on the drink-label combination)). This study was the first to position Assimilation and Contrast within a quantitative context using controlled divergence variation rather than products from distinct categories. Copyright © 2015 Elsevier Ltd. All rights reserved.

Acculturation and well-being among Arab-European mixed-ethnic adolescents in Israel.

PubMed

Abu-Rayya, Hisham Motkal

2006-11-01

To examine the relationship between two ethnic dimensions (Arab and European), and between a modified version of Berry's four acculturation styles (integration, assimilation into the Arab heritage, assimilation into the European heritage, and marginalization) and measures of psychological well-being among adolescents born to European mothers and Israeli Arab fathers. A total of 127 Arab-European adolescents (aged 13 to 18 years; 64 males and 63 females) in Israel completed ethnic identification and well-being measures. Arab and European ethnic identifications emerged as being uncorrelated among the participants, providing a basis to use four acculturation styles to describe participants' variations in ethnic identification. The study found that integration and assimilation into the Arab heritage were connected with higher levels of desirable well-being correlates (self-esteem and positive relations with others) and with lower levels of undesirable correlates (depression and anxiety). The study also found that although assimilation into the European heritage was linked with high levels of self-esteem and low levels of depression, this style was linked with high levels of anxiety and low levels of positive relations with others. The marginalization style was consistently positively associated with high levels of poor mental health. The underlying assumption of Berry's four-fold model, notably the independence of ethnic identifications, tends to be borne out among mixed-ethnic individuals. On the basis of this independence the study revealed that a modified version of Berry's four acculturation styles could prevail among Arab-European individuals over the period of adolescence and that these styles play a predictive role in well-being measures of the individuals. Specifically, integration and assimilation into the Arab heritage emerged to be the best options for individuals' well-being; individuals' assimilation into their European heritage seemed to be simultaneously connected with high and low well-being outcomes; and ethnic marginalization of individuals was consistently correlated with poor well-being.

On the Direct Assimilation of Along-track Sea Surface Height Observations into a Free-surface Ocean Model Using a Weak Constraints Four Dimensional Variational (4dvar) Method

NASA Astrophysics Data System (ADS)

Ngodock, H.; Carrier, M.; Smith, S. R.; Souopgui, I.; Martin, P.; Jacobs, G. A.

2016-02-01

The representer method is adopted for solving a weak constraints 4dvar problem for the assimilation of ocean observations including along-track SSH, using a free surface ocean model. Direct 4dvar assimilation of SSH observations along the satellite tracks requires that the adjoint model be integrated with Dirac impulses on the right hand side of the adjoint equations for the surface elevation equation. The solution of this adjoint model will inevitably include surface gravity waves, and it constitutes the forcing for the tangent linear model (TLM) according to the representer method. This yields an analysis that is contaminated by gravity waves. A method for avoiding the generation of the surface gravity waves in the analysis is proposed in this study; it consists of removing the adjoint of the free surface from the right hand side (rhs) of the free surface mode in the TLM. The information from the SSH observations will still propagate to all other variables via the adjoint of the balance relationship between the barotropic and baroclinic modes, resulting in the correction to the surface elevation. Two assimilation experiments are carried out in the Gulf of Mexico: one with adjoint forcing included on the rhs of the TLM free surface equation, and the other without. Both analyses are evaluated against the assimilated SSH observations, SSH maps from Aviso and independent surface drifters, showing that the analysis that did not include adjoint forcing in the free surface is more accurate. This study shows that when a weak constraint 4dvar approach is considered for the assimilation of along-track SSH observations using a free surface model, with the aim of correcting the mesoscale circulation, an independent model error should not be assigned to the free surface.

The Impact of Ensemble Kalman Filter Assimilation of Near-Surface Observations on the Predictability of Atmospheric Conditions over Complex Terrain: Results from Recent MATERHORN Field Program

NASA Astrophysics Data System (ADS)

Pu, Z.; Zhang, H.

2013-12-01

Near-surface atmospheric observations are the main conventional observations for weather forecasts. However, in modern numerical weather prediction, the use of surface observations, especially those data over complex terrain, remains a unique challenge. There are fundamental difficulties in assimilating surface observations with three-dimensional variational data assimilation (3DVAR). In our early study[1] (Pu et al. 2013), a series of observing system simulation experiments was performed with the ensemble Kalman filter (EnKF) and compared with 3DVAR for its ability to assimilate surface observations with 3DVAR. Using the advanced research version of the Weather Research and Forecasting (WRF) model, results demonstrate that the EnKF can overcome some fundamental limitations that 3DVAR has in assimilating surface observations over complex terrain. Specifically, through its flow-dependent background error term, the EnKF produces more realistic analysis increments over complex terrain in general. Over complex terrain, the EnKF clearly performs better than 3DVAR, because it is more capable of handling surface data in the presence of terrain misrepresentation. With this presentation, we further examine the impact of EnKF data assimilation on the predictability of atmospheric conditions over complex terrain with the WRF model and the observations obtained from the most recent field experiments of the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program. The MATERHORN program provides comprehensive observations over mountainous regions, allowing the opportunity to study the predictability of atmospheric conditions over complex terrain in great details. Specifically, during fall 2012 and spring 2013, comprehensive observations were collected of soil states, surface energy budgets, near-surface atmospheric conditions, and profiling measurements from multiple platforms (e.g., balloon, lidar, radiosondes, etc.) over Dugway Proving Ground (DPG), Utah. With the near-surface observations and sounding data obtained during the MATERHORN fall 2012 field experiment, a month-long cycled EnKF analysis and forecast was produced with the WRF model and an advanced EnKF data assimilation system. Results are compared with the WRF near real-time forecasting during the same month and a set of analysis with 3DVAR data assimilation. Overall evaluation suggests some useful insights on the impacts of different data assimilation methods, surface and soil states, terrain representation on the predictability of atmospheric conditions over mountainous terrain. Details will be presented. References [1] Pu, Z., H. Zhang, and J. A. Anderson,. 'Ensemble Kalman filter assimilation of near-surface observations over complex terrain: Comparison with 3DVAR for short-range forecasts.' Tellus A, vol. 65,19620. 2013. http://dx.doi.org/10.3402/tellusa.v65i0. 19620.

Proceedings of the 23rd Annual Penn Linguistics Colloquium. University of Pennsylvania Working Papers in Linguistics, Volume 6, Number 1.

ERIC Educational Resources Information Center

Alexander, Jim, Ed.; Han, Na-Rae, Ed.; Fox, Michelle Minnick, Ed.

This issue includes the following articles: "Assimilation to the Unmarked" (Eric Bakovic); "On the Non-Universality of Functional Projections and the Effects on Parametrized Variation: Evidence from Creoles" (Marlyse Baptista); "What Turkish Acquisition Tells Us about Underlying Word Order and Scrambling" (Natalie Batman-Ratyosyan, Karin…

Modifications to a LATE MERISTEM IDENTITY-1 gene are responsible for the major leaf shapes of Upland cotton (Gossypium hirsutum L.)

USDA-ARS?s Scientific Manuscript database

Leaf shape varies spectacularly among plants. Leaves are the primary source of photo-assimilate in crop plants and understanding the genetic basis of variation in leaf morphology is critical to improving agricultural productivity. Leaf shape played a unique role in cotton improvement, as breeders ha...

Genotypic variation in sulfur assimilation and metabolism of onion (Allium cepa L.) III. Characterization of sulfite reductase

USDA-ARS?s Scientific Manuscript database

Genomic and cDNA sequences corresponding to a ferredoxin-sulfite reductase (SiR) have been cloned from bulb onion (Allium cepa L.) and the expression of the gene and activity of the enzyme characterised with respect to sulfur (S) supply. Cloning, mapping and expression studies revealed that onion ha...

Parameterization of Middle Atmospheric Water Vapor Photochemistry for High-Altitude NWP and Data Assimilation

DTIC Science & Technology

2008-01-01

Hood, L. L.: Solar- QBO in- teraction and its impact on stratospheric ozone in a zonally aver- aged photochemical transport model of the middle...Mon. Weather Rev., 132, 1254–1268, 2004. Randel, W. J., Wu, F., Russel, J. M., Roche, A., and Waters, J. W.: Seasonal cycles and QBO variations in

Estimation of soil thermal properties using in-situ temperature measurements in the active layer and permafrost.

Treesearch

D.J. Nicolsky; V.E. Romanovsky; G.G. Panteleev

2008-01-01

A variational data assimilation algorithm is developed to reconstruct thermal properties, porosity, and parametrization of the unfrozen water content for fully saturated soils. The algorithm is tested with simulated synthetic temperatures. The simulations are performed to determine the robustness and sensitivity of algorithm to estimate soil properties from in-situ...

Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: Functional relations and potential climate feedbacks.

PubMed

Ollinger, S V; Richardson, A D; Martin, M E; Hollinger, D Y; Frolking, S E; Reich, P B; Plourde, L C; Katul, G G; Munger, J W; Oren, R; Smith, M-L; Paw U, K T; Bolstad, P V; Cook, B D; Day, M C; Martin, T A; Monson, R K; Schmid, H P

2008-12-09

The availability of nitrogen represents a key constraint on carbon cycling in terrestrial ecosystems, and it is largely in this capacity that the role of N in the Earth's climate system has been considered. Despite this, few studies have included continuous variation in plant N status as a driver of broad-scale carbon cycle analyses. This is partly because of uncertainties in how leaf-level physiological relationships scale to whole ecosystems and because methods for regional to continental detection of plant N concentrations have yet to be developed. Here, we show that ecosystem CO(2) uptake capacity in temperate and boreal forests scales directly with whole-canopy N concentrations, mirroring a leaf-level trend that has been observed for woody plants worldwide. We further show that both CO(2) uptake capacity and canopy N concentration are strongly and positively correlated with shortwave surface albedo. These results suggest that N plays an additional, and overlooked, role in the climate system via its influence on vegetation reflectivity and shortwave surface energy exchange. We also demonstrate that much of the spatial variation in canopy N can be detected by using broad-band satellite sensors, offering a means through which these findings can be applied toward improved application of coupled carbon cycle-climate models.

Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: Functional relations and potential climate feedbacks

PubMed Central

Ollinger, S. V.; Richardson, A. D.; Martin, M. E.; Hollinger, D. Y.; Frolking, S. E.; Reich, P. B.; Plourde, L. C.; Katul, G. G.; Munger, J. W.; Oren, R.; Smith, M.-L.; Paw U, K. T.; Bolstad, P. V.; Cook, B. D.; Day, M. C.; Martin, T. A.; Monson, R. K.; Schmid, H. P.

2008-01-01

The availability of nitrogen represents a key constraint on carbon cycling in terrestrial ecosystems, and it is largely in this capacity that the role of N in the Earth's climate system has been considered. Despite this, few studies have included continuous variation in plant N status as a driver of broad-scale carbon cycle analyses. This is partly because of uncertainties in how leaf-level physiological relationships scale to whole ecosystems and because methods for regional to continental detection of plant N concentrations have yet to be developed. Here, we show that ecosystem CO2 uptake capacity in temperate and boreal forests scales directly with whole-canopy N concentrations, mirroring a leaf-level trend that has been observed for woody plants worldwide. We further show that both CO2 uptake capacity and canopy N concentration are strongly and positively correlated with shortwave surface albedo. These results suggest that N plays an additional, and overlooked, role in the climate system via its influence on vegetation reflectivity and shortwave surface energy exchange. We also demonstrate that much of the spatial variation in canopy N can be detected by using broad-band satellite sensors, offering a means through which these findings can be applied toward improved application of coupled carbon cycle–climate models. PMID:19052233

Modeling Chinese ionospheric layer parameters based on EOF analysis

NASA Astrophysics Data System (ADS)

Yu, You; Wan, Weixing; Xiong, Bo; Ren, Zhipeng; Zhao, Biqiang; Zhang, Yun; Ning, Baiqi; Liu, Libo

2015-05-01

Using 24-ionosonde observations in and around China during the 20th solar cycle, an assimilative model is constructed to map the ionospheric layer parameters (foF2, hmF2, M(3000)F2, and foE) over China based on empirical orthogonal function (EOF) analysis. First, we decompose the background maps from the International Reference Ionosphere model 2007 (IRI-07) into different EOF modes. The obtained EOF modes consist of two factors: the EOF patterns and the corresponding EOF amplitudes. These two factors individually reflect the spatial distributions (e.g., the latitudinal dependence such as the equatorial ionization anomaly structure and the longitude structure with east-west difference) and temporal variations on different time scales (e.g., solar cycle, annual, semiannual, and diurnal variations) of the layer parameters. Then, the EOF patterns and long-term observations of ionosondes are assimilated to get the observed EOF amplitudes, which are further used to construct the Chinese Ionospheric Maps (CIMs) of the layer parameters. In contrast with the IRI-07 model, the mapped CIMs successfully capture the inherent temporal and spatial variations of the ionospheric layer parameters. Finally, comparison of the modeled (EOF and IRI-07 model) and observed values reveals that the EOF model reproduces the observation with smaller root-mean-square errors and higher linear correlation coefficients. In addition, IRI discrepancy at the low latitude especially for foF2 is effectively removed by EOF model.

Modeling Chinese ionospheric layer parameters based on EOF analysis

NASA Astrophysics Data System (ADS)

Yu, You; Wan, Weixing

2016-04-01

Using 24-ionosonde observations in and around China during the 20th solar cycle, an assimilative model is constructed to map the ionospheric layer parameters (foF2, hmF2, M(3000)F2, and foE) over China based on empirical orthogonal function (EOF) analysis. First, we decompose the background maps from the International Reference Ionosphere model 2007 (IRI-07) into different EOF modes. The obtained EOF modes consist of two factors: the EOF patterns and the corresponding EOF amplitudes. These two factors individually reflect the spatial distributions (e.g., the latitudinal dependence such as the equatorial ionization anomaly structure and the longitude structure with east-west difference) and temporal variations on different time scales (e.g., solar cycle, annual, semiannual, and diurnal variations) of the layer parameters. Then, the EOF patterns and long-term observations of ionosondes are assimilated to get the observed EOF amplitudes, which are further used to construct the Chinese Ionospheric Maps (CIMs) of the layer parameters. In contrast with the IRI-07 model, the mapped CIMs successfully capture the inherent temporal and spatial variations of the ionospheric layer parameters. Finally, comparison of the modeled (EOF and IRI-07 model) and observed values reveals that the EOF model reproduces the observation with smaller root-mean-square errors and higher linear correlation co- efficients. In addition, IRI discrepancy at the low latitude especially for foF2 is effectively removed by EOF model.

Potential risks from UV/H2O2 oxidation and UV photocatalysis: A review of toxic, assimilable, and sensory-unpleasant transformation products.

PubMed

Wang, Wen-Long; Wu, Qian-Yuan; Huang, Nan; Xu, Zi-Bin; Lee, Min-Yong; Hu, Hong-Ying

2018-05-15

UV based advanced oxidation processes (UV-AOPs) that efficiently eliminate organic pollutants during water treatment have been the subject of numerous investigations. Most organic pollutants are not completely mineralized during UV-AOPs but are partially oxidized into transformation products (TPs), thereby adding complexity to the treated water and posing risks to humans, ecological systems, and the environment. While the degradation kinetics and mechanisms of pollutants have been widely documented, there is little information about the risks associated with TPs. In this review, we have collated recent knowledge about the harmful TPs that are generated in UV/H 2 O 2 and UV photocatalysis, two UV-AOPs that have been studied extensively. Toxic and assimilable TPs were ubiquitously observed in more than 80% of UV-AOPs of organic pollutants, of which the toxicity and assimilability levels changed with variations in the reaction conditions, such as the UV fluence and oxidant dosage. Previous studies and modeling assessments showed that toxic and assimilable TPs may be generated during hydroxylation, dealkylation, decarboxylation, and deamination. Among various reactions, TPs generated from dealkylation and decarboxylation were generally less and more toxic than the parent pollutants, respectively; TPs generated from decarboxylation and deamination were generally less and more assimilable than the parent pollutants, respectively. There is also potential concern about the sensory-unpleasant TPs generated by oxidations and subsequent metabolism of microorganisms. In this overview, we stress the need to include both the concentrations of organic pollutants and the evaluations of the risks from TPs for the quality assessments of the water treated by UV-AOPs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Benthic Food Webs of the Chukchi and Beaufort Seas: Relative Importance of Ultimate Carbon Sources in a Changing Climate

NASA Astrophysics Data System (ADS)

Dunton, K. H.; Schonberg, S. V.; Mctigue, N.; Bucolo, P. A.; Connelly, T. L.; McClelland, J. W.

2014-12-01

Changes in sea-ice cover, coastal erosion, and freshwater run-off have the potential to greatly influence carbon assimilation pathways and affect trophic structure in benthic communities across the western Arctic. In the Chukchi Sea, variations in the duration and timing of ice cover affect the delivery of ice algae to a relatively shallow (40-50 m) shelf benthos. Although ice algae are known as an important spring carbon subsidy for marine benthic fauna, ice algal contributions may also help initiate productivity of an active microphytobenthos. Recent studies provide clear evidence that the microphytobenthos are photosynthetically active, and have sufficient light and nutrients for in situ growth. The assimilation of benthic diatoms from both sources may explain the 13C enrichment observed in benthic primary consumers throughout the northern Chukchi. On the eastern Beaufort Sea coast, shallow (2-4 m) estuarine lagoon systems receive massive subsidies of terrestrial carbon that is assimilated by a benthic fauna of significant importance to upper trophic level species, but again, distinct 13C enrichment in benthic primary consumers suggests the existence of an uncharacterized food source. Since ice algae are absent, we believe the 13C enrichment in benthic fauna is caused by the assimilation of benthic microalgae, as reflected in seasonally high benthic chlorophyll in spring under replete light and nutrient conditions. Our observations suggest that changes in ice cover, on both temporal and spatial scales, are likely to have significant effects on the magnitude and timing of organic matter delivery to both shelf and nearshore systems, and that locally produced organic matter may become an increasingly important carbon subsidy that affects trophic assimilation and secondary ecosystem productivity.

Impact of Atmospheric Infrared Sounder (AIRS) Thermodynamic Profiles on Regional Weather Forecasting

NASA Technical Reports Server (NTRS)

Chou, Shih-Hung; Zavodsky, Bradley T.; Jedlovee, Gary J.

2010-01-01

In data sparse regions, remotely-sensed observations can be used to improve analyses and lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with accuracy comparable to that of radiosondes. The purpose of this paper is to describe a procedure to assimilate AIRS thermodynamic profile data into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimension variational (3DVAR) analysis component (WRF-Var). Quality indicators are used to select only the highest quality temperature and moisture profiles for assimilation in both clear and partly cloudy regions. Separate error characteristics for land and water profiles are also used in the assimilation process. Assimilation results indicate that AIRS profiles produce an analysis closer to in situ observations than the background field. Forecasts from a 37-day case study period in the winter of 2007 show that AIRS profile data can lead to improvements in 6-h cumulative precipitation forecasts due to instability added in the forecast soundings by the AIRS profiles. Additionally, in a convective heavy rainfall event from February 2007, assimilation of AIRS profiles produces a more unstable boundary layer resulting in enhanced updrafts in the model. These updrafts produce a squall line and precipitation totals that more closely reflect ground-based observations than a no AIRS control forecast. The location of available high-quality AIRS profiles ahead of approaching storm systems is found to be of paramount importance to the amount of impact the observations will have on the resulting forecasts.

Impact of multiple radar reflectivity data assimilation on the numerical simulation of a flash flood event during the HyMeX campaign

NASA Astrophysics Data System (ADS)

Maiello, Ida; Gentile, Sabrina; Ferretti, Rossella; Baldini, Luca; Roberto, Nicoletta; Picciotti, Errico; Alberoni, Pier Paolo; Silvio Marzano, Frank

2017-11-01

An analysis to evaluate the impact of multiple radar reflectivity data with a three-dimensional variational (3-D-Var) assimilation system on a heavy precipitation event is presented. The main goal is to build a regionally tuned numerical prediction model and a decision-support system for environmental civil protection services and demonstrate it in the central Italian regions, distinguishing which type of observations, conventional and not (or a combination of them), is more effective in improving the accuracy of the forecasted rainfall. In that respect, during the first special observation period (SOP1) of HyMeX (Hydrological cycle in the Mediterranean Experiment) campaign several intensive observing periods (IOPs) were launched and nine of which occurred in Italy. Among them, IOP4 is chosen for this study because of its low predictability regarding the exact location and amount of precipitation. This event hit central Italy on 14 September 2012 producing heavy precipitation and causing several cases of damage to buildings, infrastructure, and roads. Reflectivity data taken from three C-band Doppler radars running operationally during the event are assimilated using the 3-D-Var technique to improve high-resolution initial conditions. In order to evaluate the impact of the assimilation procedure at different horizontal resolutions and to assess the impact of assimilating reflectivity data from multiple radars, several experiments using the Weather Research and Forecasting (WRF) model are performed. Finally, traditional verification scores such as accuracy, equitable threat score, false alarm ratio, and frequency bias - interpreted by analysing their uncertainty through bootstrap confidence intervals (CIs) - are used to objectively compare the experiments, using rain gauge data as a benchmark.

Sensitivity analysis of a data assimilation technique for hindcasting and forecasting hydrodynamics of a complex coastal water body

NASA Astrophysics Data System (ADS)

Ren, Lei; Hartnett, Michael

2017-02-01

Accurate forecasting of coastal surface currents is of great economic importance due to marine activities such as marine renewable energy and fish farms in coastal regions in recent twenty years. Advanced oceanographic observation systems such as satellites and radars can provide many parameters of interest, such as surface currents and waves, with fine spatial resolution in near real time. To enhance modelling capability, data assimilation (DA) techniques which combine the available measurements with the hydrodynamic models have been used since the 1990s in oceanography. Assimilating measurements into hydrodynamic models makes the original model background states follow the observation trajectory, then uses it to provide more accurate forecasting information. Galway Bay is an open, wind dominated water body on which two coastal radars are deployed. An efficient and easy to implement sequential DA algorithm named Optimal Interpolation (OI) was used to blend radar surface current data into a three-dimensional Environmental Fluid Dynamics Code (EFDC) model. Two empirical parameters, horizontal correlation length and DA cycle length (CL), are inherent within OI. No guidance has previously been published regarding selection of appropriate values of these parameters or how sensitive OI DA is to variations in their values. Detailed sensitivity analysis has been performed on both of these parameters and results presented. Appropriate value of DA CL was examined and determined on producing the minimum Root-Mean-Square-Error (RMSE) between radar data and model background states. Analysis was performed to evaluate assimilation index (AI) of using an OI DA algorithm in the model. AI of the half-day forecasting mean vectors' directions was over 50% in the best assimilation model. The ability of using OI to improve model forecasts was also assessed and is reported upon.

Data Assimilation using observed streamflow and remotely-sensed soil moisture for improving sub-seasonal-to-seasonal forecasting

NASA Astrophysics Data System (ADS)

Arumugam, S.; Mazrooei, A.; Lakshmi, V.; Wood, A.

2017-12-01

Subseasonal-to-seasonal (S2S) forecasts of soil moisture and streamflow provides critical information for water and agricultural systems to support short-term planning and mangement. This study evaluates the role of observed streamflow and remotely-sensed soil moisture from SMAP (Soil Moisture Active Passive) mission in improving S2S streamflow and soil moisture forecasting using data assimilation (DA). We first show the ability to forecast soil moisture at monthly-to-seaasonal time scale by forcing climate forecasts with NASA's Land Information System and then compares the developed soil moisture forecast with the SMAP data over the Southeast US. Our analyses show significant skill in forecasting real-time soil moisture over 1-3 months using climate information. We also show that the developed soil moisture forecasts capture the observed severe drought conditions (2007-2008) over the Southeast US. Following that, we consider both SMAP data and observed streamflow for improving S2S streamflow and soil moisture forecasts for a pilot study area, Tar River basin, in NC. Towards this, we consider variational assimilation (VAR) of gauge-measured daily streamflow data in improving initial hydrologic conditions of Variable Infiltration Capacity (VIC) model. The utility of data assimilation is then assessed in improving S2S forecasts of streamflow and soil moisture through a retrospective analyses. Furthermore, the optimal frequency of data assimilation and optimal analysis window (number of past observations to use) are also assessed in order to achieve the maximum improvement in S2S forecasts of streamflow and soil moisture. Potential utility of updating initial conditions using DA and providing skillful forcings are also discussed.

Improving Snow Modeling by Assimilating Observational Data Collected by Citizen Scientists

NASA Astrophysics Data System (ADS)

Crumley, R. L.; Hill, D. F.; Arendt, A. A.; Wikstrom Jones, K.; Wolken, G. J.; Setiawan, L.

2017-12-01

Modeling seasonal snow pack in alpine environments includes a multiplicity of challenges caused by a lack of spatially extensive and temporally continuous observational datasets. This is partially due to the difficulty of collecting measurements in harsh, remote environments where extreme gradients in topography exist, accompanied by large model domains and inclement weather. Engaging snow enthusiasts, snow professionals, and community members to participate in the process of data collection may address some of these challenges. In this study, we use SnowModel to estimate seasonal snow water equivalence (SWE) in the Thompson Pass region of Alaska while incorporating snow depth measurements collected by citizen scientists. We develop a modeling approach to assimilate hundreds of snow depth measurements from participants in the Community Snow Observations (CSO) project (www.communitysnowobs.org). The CSO project includes a mobile application where participants record and submit geo-located snow depth measurements while working and recreating in the study area. These snow depth measurements are randomly located within the model grid at irregular time intervals over the span of four months in the 2017 water year. This snow depth observation dataset is converted into a SWE dataset by employing an empirically-based, bulk density and SWE estimation method. We then assimilate this data using SnowAssim, a sub-model within SnowModel, to constrain the SWE output by the observed data. Multiple model runs are designed to represent an array of output scenarios during the assimilation process. An effort to present model output uncertainties is included, as well as quantification of the pre- and post-assimilation divergence in modeled SWE. Early results reveal pre-assimilation SWE estimations are consistently greater than the post-assimilation estimations, and the magnitude of divergence increases throughout the snow pack evolution period. This research has implications beyond the Alaskan context because it increases our ability to constrain snow modeling outputs by making use of snow measurements collected by non-expert, citizen scientists.

The Rewiring of Ubiquitination Targets in a Pathogenic Yeast Promotes Metabolic Flexibility, Host Colonization and Virulence

PubMed Central

Childers, Delma S.; Raziunaite, Ingrida; Mol Avelar, Gabriela; Mackie, Joanna; Budge, Susan; Stead, David; Gow, Neil A. R.; Lenardon, Megan D.; Ballou, Elizabeth R.; MacCallum, Donna M.; Brown, Alistair J. P.

2016-01-01

Efficient carbon assimilation is critical for microbial growth and pathogenesis. The environmental yeast Saccharomyces cerevisiae is “Crabtree positive”, displaying a rapid metabolic switch from the assimilation of alternative carbon sources to sugars. Following exposure to sugars, this switch is mediated by the transcriptional repression of genes (carbon catabolite repression) and the turnover (catabolite inactivation) of enzymes involved in the assimilation of alternative carbon sources. The pathogenic yeast Candida albicans is Crabtree negative. It has retained carbon catabolite repression mechanisms, but has undergone posttranscriptional rewiring such that gluconeogenic and glyoxylate cycle enzymes are not subject to ubiquitin-mediated catabolite inactivation. Consequently, when glucose becomes available, C. albicans can continue to assimilate alternative carbon sources alongside the glucose. We show that this metabolic flexibility promotes host colonization and virulence. The glyoxylate cycle enzyme isocitrate lyase (CaIcl1) was rendered sensitive to ubiquitin-mediated catabolite inactivation in C. albicans by addition of a ubiquitination site. This mutation, which inhibits lactate assimilation in the presence of glucose, reduces the ability of C. albicans cells to withstand macrophage killing, colonize the gastrointestinal tract and cause systemic infections in mice. Interestingly, most S. cerevisiae clinical isolates we examined (67%) have acquired the ability to assimilate lactate in the presence of glucose (i.e. they have become Crabtree negative). These S. cerevisiae strains are more resistant to macrophage killing than Crabtree positive clinical isolates. Moreover, Crabtree negative S. cerevisiae mutants that lack Gid8, a key component of the Glucose-Induced Degradation complex, are more resistant to macrophage killing and display increased virulence in immunocompromised mice. Thus, while Crabtree positivity might impart a fitness advantage for yeasts in environmental niches, the more flexible carbon assimilation strategies offered by Crabtree negativity enhance the ability of yeasts to colonize and infect the mammalian host. PMID:27073846

Establishing an IERS Sub-Center for Ocean Angular Momentum

NASA Technical Reports Server (NTRS)

Ponte, Rui M.

2001-01-01

With the objective of establishing the Special Bureau for the Oceans (SBO), a new archival center for ocean angular momentum (OAM) products, we have computed and analyzed a number of OAM products from several ocean models, with and without data assimilation. All three components of OAM (axial term related to length of day variations and equatorial terms related to polar motion) have been examined in detail, in comparison to the respective Earth rotation parameters. An 11+ year time series of OAM given at 5-day intervals has been made publicly available. Other OAM products spanning longer periods and with higher temporal resolution, as well as products calculated from ocean model/data assimilation systems, have been prepared and should become part of the SBO archives in the near future.

The suitability of remotely sensed soil moisture for improving operational flood forecasting

NASA Astrophysics Data System (ADS)

Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S. M.; Bierkens, M. F. P.

2013-11-01

We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model for flood predictions with lead times up to 10 days. For this study, satellite-derived soil moisture from ASCAT, AMSR-E and SMOS is assimilated into the EFAS system for the Upper Danube basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into EFAS, an Ensemble Kalman Filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure optimal performance of the EnKF. For the validation, additional discharge observations not used in the EnKF, are used as an independent validation dataset. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the Mean Absolute Error (MAE) of the ensemble mean is reduced by 65%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of base flows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the Continuous Ranked Probability Score (CRPS) shows a performance increase of 5-10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more data is assimilated into the system and the best performance is achieved with the assimilation of both discharge and satellite observations. The additional gain is highest when discharge observations from both upstream and downstream areas are used in combination with the soil moisture data. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

The suitability of remotely sensed soil moisture for improving operational flood forecasting

NASA Astrophysics Data System (ADS)

Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S. M.; Bierkens, M. F. P.

2014-06-01

We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model (LISFLOOD) for flood predictions with lead times of up to 10 days. For this study, satellite-derived soil moisture from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer - Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is assimilated into the LISFLOOD model for the Upper Danube Basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into the hydrological model, an ensemble Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure increased performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation data set. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the mean absolute error (MAE) of the ensemble mean is reduced by 35%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of baseflows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the continuous ranked probability score (CRPS) shows a performance increase of 5-10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more observational data is assimilated into the system. The added values of the satellite data is largest when these observations are assimilated in combination with distributed discharge observations. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

The CAMS interim Reanalysis of Carbon Monoxide, Ozone and Aerosol for 2003-2015

NASA Astrophysics Data System (ADS)

Flemming, Johannes; Benedetti, Angela; Inness, Antje; Engelen, Richard J.; Jones, Luke; Huijnen, Vincent; Remy, Samuel; Parrington, Mark; Suttie, Martin; Bozzo, Alessio; Peuch, Vincent-Henri; Akritidis, Dimitris; Katragkou, Eleni

2017-02-01

A new global reanalysis data set of atmospheric composition (AC) for the period 2003-2015 has been produced by the Copernicus Atmosphere Monitoring Service (CAMS). Satellite observations of total column (TC) carbon monoxide (CO) and aerosol optical depth (AOD), as well as several TC and profile observations of ozone, have been assimilated with the Integrated Forecasting System for Composition (C-IFS) of the European Centre for Medium-Range Weather Forecasting. Compared to the previous Monitoring Atmospheric Composition and Climate (MACC) reanalysis (MACCRA), the new CAMS interim reanalysis (CAMSiRA) is of a coarser horizontal resolution of about 110 km, compared to 80 km, but covers a longer period with the intent to be continued to present day. This paper compares CAMSiRA with MACCRA and a control run experiment (CR) without assimilation of AC retrievals. CAMSiRA has smaller biases than the CR with respect to independent observations of CO, AOD and stratospheric ozone. However, ozone at the surface could not be improved by the assimilation because of the strong impact of surface processes such as dry deposition and titration with nitrogen monoxide (NO), which were both unchanged by the assimilation. The assimilation of AOD led to a global reduction of sea salt and desert dust as well as an exaggerated increase in sulfate. Compared to MACCRA, CAMSiRA had smaller biases for AOD, surface CO and TC ozone as well as for upper stratospheric and tropospheric ozone. Finally, the temporal consistency of CAMSiRA was better than the one of MACCRA. This was achieved by using a revised emission data set as well as by applying careful selection and bias correction to the assimilated retrievals. CAMSiRA is therefore better suited than MACCRA for the study of interannual variability, as demonstrated for trends in surface CO.

Carbon dioxide exchange of buds and developing shoots of boreal Norway spruce exposed to elevated or ambient CO2 concentration and temperature in whole-tree chambers.

PubMed

Hall, Marianne; Räntfors, Mats; Slaney, Michelle; Linder, Sune; Wallin, Göran

2009-04-01

Effects of ambient and elevated temperature and atmospheric carbon dioxide concentration ([CO2]) on CO2 assimilation rate and the structural and phenological development of shoots during their first growing season were studied in 45-year-old Norway spruce trees (Picea abies (L.) Karst.) enclosed in whole-tree chambers. Continuous measurements of net assimilation rate (NAR) in individual buds and shoots were made from early bud development to late August in two consecutive years. The largest effect of elevated temperature (TE) was manifest early in the season as an earlier start and completion of shoot length development, and a 1-3-week earlier shift from negative to positive NAR compared with the ambient temperature (TA) treatments. The largest effect of elevated [CO2] (CE) was found later in the season, with a 30% increase in maximum NAR compared with trees in the ambient [CO2] treatments (CA), and shoots assimilating their own mass in terms of carbon earlier in the CE treatments than in the CA treatments. Once the net carbon assimilation compensation point (NACP) had been reached, TE had little or no effect on the development of NAR performance, whereas CE had little effect before the NACP. No interactive effects of TE and CE on NAR were found. We conclude that in a climate predicted for northern Sweden in 2100, current-year shoots of P. abies will assimilate their own mass in terms of carbon 20-30 days earlier compared with the current climate, and thereby significantly contribute to canopy assimilation during their first year.

Ensemble assimilation of ARGO temperature profile, sea surface temperature and Altimetric satellite data into an eddy permitting primitive equation model of the North Atlantic ocean

NASA Astrophysics Data System (ADS)

Yan, Yajing; Barth, Alexander; Beckers, Jean-Marie; Candille, Guillem; Brankart, Jean-Michel; Brasseur, Pierre

2015-04-01

Sea surface height, sea surface temperature and temperature profiles at depth collected between January and December 2005 are assimilated into a realistic eddy permitting primitive equation model of the North Atlantic Ocean using the Ensemble Kalman Filter. 60 ensemble members are generated by adding realistic noise to the forcing parameters related to the temperature. The ensemble is diagnosed and validated by comparison between the ensemble spread and the model/observation difference, as well as by rank histogram before the assimilation experiments. Incremental analysis update scheme is applied in order to reduce spurious oscillations due to the model state correction. The results of the assimilation are assessed according to both deterministic and probabilistic metrics with observations used in the assimilation experiments and independent observations, which goes further than most previous studies and constitutes one of the original points of this paper. Regarding the deterministic validation, the ensemble means, together with the ensemble spreads are compared to the observations in order to diagnose the ensemble distribution properties in a deterministic way. Regarding the probabilistic validation, the continuous ranked probability score (CRPS) is used to evaluate the ensemble forecast system according to reliability and resolution. The reliability is further decomposed into bias and dispersion by the reduced centred random variable (RCRV) score in order to investigate the reliability properties of the ensemble forecast system. The improvement of the assimilation is demonstrated using these validation metrics. Finally, the deterministic validation and the probabilistic validation are analysed jointly. The consistency and complementarity between both validations are highlighted. High reliable situations, in which the RMS error and the CRPS give the same information, are identified for the first time in this paper.

South Atlantic Ocean circulation: Simulation experiments with a quasi-geostrophic model and assimilation of TOPEX/POSEIDON and ERS 1 altimeter data

NASA Astrophysics Data System (ADS)

Florenchie, P.; Verron, J.

1998-10-01

Simulation experiments of South Atlantic Ocean circulations are conducted with a 1/6°, four-layered, quasi-geostrophic model. By means of a simple nudging data assimilation procedure along satellite tracks, TOPEX/POSEIDON and ERS 1 altimeter measurements are introduced into the model to control the simulation of the basin-scale circulation for the period from October 1992 to September 1994. The model circulation appears to be strongly influenced by the introduction of altimeter data, offering a consistent picture of South Atlantic Ocean circulations. Comparisons with observations show that the assimilating model successfully simulates the kinematic behavior of a large number of surface circulation components. The assimilation procedure enables us to produce schematic diagrams of South Atlantic circulation in which patterns ranging from basin-scale currents to mesoscale eddies are portrayed in a realistic way, with respect to their complexity. The major features of the South Atlantic circulation are described and analyzed, with special emphasis on the Brazil-Malvinas Confluence region, the Subtropical Gyre with the formation of frontal structures, and the Agulhas Retroflection. The Agulhas eddy-shedding process has been studied extensively. Fourteen eddies appear to be shed during the 2-year experiment. Because of their strong surface topographic signature, Agulhas eddies have been tracked continuously during the assimilation experiment as they cross the South Atlantic basin westward. Other effects of the assimilation procedure are shown, such as the intensification of the Subtropical Gyre, the appearance of a strong seasonal cycle in the Brazil Current transport, and the increase of the mean Brazil Current transport. This last result, combined with the westward oriention of the Agulhas eddies' trajectories, leads to a southward transport of mean eddy kinetic energy across 30°S.

Understanding the influence of assimilating satellite-derived observations on mesoscale analyses and forecasts of tropical cyclone track and structure

NASA Astrophysics Data System (ADS)

Wu, Ting-Chi

This dissertation research explores the influence of assimilating satellite-derived observations on mesoscale numerical analyses and forecasts of tropical cyclones (TC). The ultimate goal is to provide more accurate mesoscale analyses of TC and its surrounding environment for superior TC track and intensity forecasts. High spatial and temporal resolution satellite-derived observations are prepared for two TC cases, Typhoon Sinlaku and Hurricane Ike (both 2008). The Advanced Research version of the Weather and Research Forecasting Model (ARW-WRF) is employed and data is assimilated using the Ensemble Adjustment Kalman Filter (EAKF) implemented in the Data Assimilation Research Testbed. In the first part of this research, the influence of assimilating enhanced atmospheric motion vectors (AMVs) derived from geostationary satellites is examined by comparing three parallel WRF/EnKF experiments. The control experiment assimilates the same AMV dataset assimilated in NCEP operational analysis along with conventional observations from radiosondes, aircraft, and advisory TC position data. During Sinlaku and Ike, the Cooperative Institute for Meteorological Satellite Studies (CIMSS) generates hourly AMVs along with Rapid-Scan (RS) AMVs when the satellite RS mode is activated. With an order of magnitude more AMV data assimilated, the assimilation of hourly CIMSS AMV dataset exhibit superior initial TC position, intensity and structure estimates to the control analyses and the subsequent short-range forecasts. When RS AMVs are processed and assimilated, the addition of RS AMVs offers additional modification to the TC and its environment and leads to Sinlaku's recurvature toward Japan, albeit prematurely. The results demonstrate the promise of assimilating enhanced AMV data into regional TC models. The second part of this research continues the work in the first part and further explores the influence of assimilating enhanced AMV datasets by conducting parallel data-denial WRF/EnKF experiments that assimilate AMVs subsetted horizontally by their distances to the TC center (interior and exterior) and vertically by their assigned heights (upper, middle, and lower layers). For both Sinlaku and Ike, it is found: 1) interior AMVs are important for accurate TC intensity, 2) excluding upper-layer AMVs generally results in larger track errors and ensemble spread, 3) exclusion of interior AMVs has the largest impact on the forecast of TC size than exclusively removing AMVs in particular tropospheric layers, 4) the largest ensemble spreads are found in track, intensity, and size forecasts when interior and upper-layer AMVs are not included, 5) withholding the middle-layer AMVs can improve the track forecasts. Findings from this study could influence future scenarios that involve the targeted acquisition and assimilation of high-density AMV observations in TC events. The last part of the research focuses on the assimilation of hyperspectral temperature and moisture soundings and microwave based vertically-integrated total precipitable water (TPW) products derived from polar-orbiting satellites. A comparison is made between the assimilation of soundings retrieved from the combined use of Advanced Microwave Scanning Radiometer and Atmospheric Infrared Sounder (AMSU-AIRS) and sounding products provided by CIMSS (CIMSS-AIRS). AMSU-AIRS soundings provide broad spatial coverage albeit coarse resolution, whilst CIMSS-AIRS is geared towards mesoscale applications and thus provide higher spatial resolution but restricted coverage due to the use of radiance in clear sky. The assimilation of bias-corrected CIMSS-AIRS soundings provides slightly more accurate TC structure than the control case. The assimilation of AMSU-AIRS improves the track forecasts but produces weaker and smaller storm. Preliminary results of assimilating TPW product derived from the Advanced Microwave Scanning Radiometer-EOS indicate improved TC structure over the control case. However, the short-range forecasts exhibit the largest TC track errors. In all, this study demonstrates the influence of assimilating high-resolution satellite data on mesoscale analyses and forecasts of TC track and structure. The results suggest the inclusion and assimilation of observations with high temporal resolution, broad spatial coverage, and greater proximity to TCs does indeed improve TC track and structure forecasts. Such findings are beneficial for future decisions on data collecting and retrievals that are essential for TC forecasts.

Variational data assimilation system "INM RAS - Black Sea"

NASA Astrophysics Data System (ADS)

Parmuzin, Eugene; Agoshkov, Valery; Assovskiy, Maksim; Giniatulin, Sergey; Zakharova, Natalia; Kuimov, Grigory; Fomin, Vladimir

2013-04-01

Development of Informational-Computational Systems (ICS) for Data Assimilation Procedures is one of multidisciplinary problems. To study and solve these problems one needs to apply modern results from different disciplines and recent developments in: mathematical modeling; theory of adjoint equations and optimal control; inverse problems; numerical methods theory; numerical algebra and scientific computing. The problems discussed above are studied in the Institute of Numerical Mathematics of the Russian Academy of Science (INM RAS) in ICS for Personal Computers (PC). Special problems and questions arise while effective ICS versions for PC are being developed. These problems and questions can be solved with applying modern methods of numerical mathematics and by solving "parallelism problem" using OpenMP technology and special linear algebra packages. In this work the results on the ICS development for PC-ICS "INM RAS - Black Sea" are presented. In the work the following problems and questions are discussed: practical problems that can be studied by ICS; parallelism problems and their solutions with applying of OpenMP technology and the linear algebra packages used in ICS "INM - Black Sea"; Interface of ICS. The results of ICS "INM RAS - Black Sea" testing are presented. Efficiency of technologies and methods applied are discussed. The work was supported by RFBR, grants No. 13-01-00753, 13-05-00715 and by The Ministry of education and science of Russian Federation, project 8291, project 11.519.11.1005 References: [1] V.I. Agoshkov, M.V. Assovskii, S.A. Lebedev, Numerical simulation of Black Sea hydrothermodynamics taking into account tide-forming forces. Russ. J. Numer. Anal. Math. Modelling (2012) 27, No.1, 5-31 [2] E.I. Parmuzin, V.I. Agoshkov, Numerical solution of the variational assimilation problem for sea surface temperature in the model of the Black Sea dynamics. Russ. J. Numer. Anal. Math. Modelling (2012) 27, No.1, 69-94 [3] V.B. Zalesny, N.A. Diansky, V.V. Fomin, S.N. Moshonkin, S.G. Demyshev, Numerical model of the circulation of Black Sea and Sea of Azov. Russ. J. Numer. Anal. Math. Modelling (2012) 27, No.1, 95-111 [4] V.I. Agoshkov, S.V. Giniatulin, G.V. Kuimov. OpenMP technology and linear algebra packages in the variation data assimilation systems. - Abstracts of the 1-st China-Russia Conference on Numerical Algebra with Applications in Radiactive Hydrodynamics, Beijing, China, October 16-18, 2012. [5] Zakharova N.B., Agoshkov V.I., Parmuzin E.I., The new method of ARGO buoys system observation data interpolation. Russian Journal of Numerical Analysis and Mathematical Modelling. Vol. 28, Issue 1, 2013.

Ensemble assimilation of ARGO temperature profile, sea surface temperature, and altimetric satellite data into an eddy permitting primitive equation model of the North Atlantic Ocean

NASA Astrophysics Data System (ADS)

Yan, Y.; Barth, A.; Beckers, J. M.; Candille, G.; Brankart, J. M.; Brasseur, P.

2015-07-01

Sea surface height, sea surface temperature, and temperature profiles at depth collected between January and December 2005 are assimilated into a realistic eddy permitting primitive equation model of the North Atlantic Ocean using the Ensemble Kalman Filter. Sixty ensemble members are generated by adding realistic noise to the forcing parameters related to the temperature. The ensemble is diagnosed and validated by comparison between the ensemble spread and the model/observation difference, as well as by rank histogram before the assimilation experiments. An incremental analysis update scheme is applied in order to reduce spurious oscillations due to the model state correction. The results of the assimilation are assessed according to both deterministic and probabilistic metrics with independent/semiindependent observations. For deterministic validation, the ensemble means, together with the ensemble spreads are compared to the observations, in order to diagnose the ensemble distribution properties in a deterministic way. For probabilistic validation, the continuous ranked probability score (CRPS) is used to evaluate the ensemble forecast system according to reliability and resolution. The reliability is further decomposed into bias and dispersion by the reduced centered random variable (RCRV) score in order to investigate the reliability properties of the ensemble forecast system. The improvement of the assimilation is demonstrated using these validation metrics. Finally, the deterministic validation and the probabilistic validation are analyzed jointly. The consistency and complementarity between both validations are highlighted.

Algal Nitrate Assimilation and Productivity in an Urban, Concrete-Lined Stream Dominated by Tertiary Treated Municipal Waste-Water

NASA Astrophysics Data System (ADS)

Kent, R. H.; Burton, C. A.

2001-12-01

This study examined the extent and variabiltity of nitrate loss in a 2.85 km reach of Cucamonga Creek, which is concrete-lined and dominated by treated municipal waste-water. Primary production was measured to determine if the loss could be attributed to algal assimilation. Samples for nitrite plus nitrate analysis were collected at the top and bottom of the study reach every hour throughout the 24-hour sampling period; samples for analyses of other parameters were collected less frequently. Water temperature, dissolved oxygen (DO), pH and specific conductance were monitored continuously throughout the sampling period using in-stream probes. During the two weeks prior to the study, periphyton samples were collected periodically at four stations along the reach for standing crop measurements and a growth rate time-series using Chlorophyll A and ash-free-dry mass. Water samples from the upstream station were compared to those taken an hour later (the approximate travel time) at the downstream station. Nitrate concentrations were lower at the downstream station in 21 of 25 of the paired samples, indicating nearly continuous loss in the reach. The total loss of NO3 for the day was about 0.71 g as N/m2. Most of the loss occurred during daylight hours, with the peak occurring at midday. During the night, CO2 concentrations were relatively constant at about 25 mg/L. Concentrations began to decline at sunrise, and declined to 0 mg/L at the lower site after midday. Peak nitrate loss occurred at about the same time as the CO2 concentration was at its minimum. DO declined slightly during the night, began to rise at sunrise, reached a peak during midday, and declined in late afternoon through evening; pH followed a similar pattern. Net primary productivity, as measured by the differences in DO between the two sites was 13 g O2/m2 for the day. Using the Redfield ratio, the predicted nitrate assimilation is about 0.66 g NO3 as N/m2. The continuous loss of nitrate between the two sites; the comparability between the observed loss in nitrate and that predicted using the Redfield ratio; and the timing of changes in nitrate loss, DO, pH and CO2 indicate that nitrate loss in this concrete-lined channel was primarily due to algal assimilation. The timing of the peak nitrate loss relative to the depletion of CO2 suggests that CO2 may be limiting photosynthesis, and therefore assimilation of nitrate by algae.

Melt inclusion evidence for the relative timing of assimilation and crystallisation in high MgO lavas, Mull, Scotland

NASA Astrophysics Data System (ADS)

Peate, D. W.; Ukstins Peate, I.; Rowe, M. C.; Thompson, J. M.; Kerr, A. C.

2010-12-01

Whole rock data on the Mull Plateau Group lavas (Scotland) show that the most primitive lavas (MgO >8 wt%) are the most crustally contaminated. One model is that hot, high-MgO magmas flow turbulently during ascent allowing more assimilation to occur than in the laminar flow regime expected for cooler, more viscous, lower-MgO magmas. We present data on rehomogenized olivine-hosted melt inclusions from four representative high-MgO flows to investigate the nature of the assimilation process in more detail. One complication on Mull is the potential effect of pervasive hydrothermal metamorphism on whole rock compositions. Melt inclusions are more protected against alteration effects within their host olivine crystal, and potentially allow more robust estimates of magmatic liquid compositions. Low sulphur contents were used to screen for degassed / breached inclusions, and the compositions of unbreached inclusions were corrected for post-entrapment crystallisation and Fe-loss. The four whole rock samples show a limited variation in Na2O (2.4-2.8 wt%) and K2O (0.23-0.29 wt%) despite a wide range in immobile element contents (e.g. Zr 62-126 ppm, Nb 2.4-4.6 ppm). In contrast, the melt inclusions show a far greater variability in Na2O (1.8-4.0 wt%) and K2O (0.02-0.35 wt%) and coherent positive correlations between K and Na. Melt inclusions from different samples show systematic correlations between alkalis (K+Na) and incompatible element ratios such as Zr/Y and La/Sm, indicating that the melt inclusions are recording magmatic values for fluid mobile elements such as K and Na. For the two most incompatible element enriched samples, the whole rock analysis is similar to the melt inclusions except for lower Na and higher Ba that are related to alteration. Therefore, any crustal assimilation in these magmas must have take place prior to the growth of the olivines in the samples. For the two more depleted samples, the melt inclusions have less contaminated compositions than the whole rocks, and also show broad trends of increasing K/Ti (extent of assimilation) with decreasing Fo% of the host olivine (extent of differentiation). For these samples, significant crustal assimilation must have taken place both during and after growth of the olivines in the samples. Melt inclusions from individual samples show limited variability in Zr/Y compared with K/Ti, indicating that aggregation of melts from different parts of the melting column must have occurred at deeper levels prior to growth of the olivines in the samples. Reconnaissance H2O and CO2 analyses by SIMS allow estimates to be made of minimum inclusion entrapment depths of at least 3 to 7 km. Although it is apparent that whole rock compositional variations still capture the broad details of crustal assimilation and melting histories for Mull lavas despite the variable effects of hydrothermal alteration, we demonstrate that melt inclusion data can more clearly resolve details of these magmatic processes.

Species-specific intrinsic water use efficiency and its mediation of carbon assimilation during the drought

NASA Astrophysics Data System (ADS)

Yi, K.; Wenzel, M. K.; Maxwell, J. T.; Novick, K. A.; Gray, A.; Roman, D. T.

2015-12-01

Drought is expected to occur more frequently and intensely in the future, and many studies have suggested frequent and intense droughts can significantly alter carbon and water cycling in forest ecosystems, consequently decreasing the ability of forests to assimilate carbon. Predicting the impact of drought on forest ecosystem processes requires an understanding of species-specific responses to drought, especially in eastern US where species composition is highly dynamic. An emerging approach for describing species-specific drought response is to classify the plant water use strategy into isohydric and anisohydric behaviors. Trees utilizing isohydric behavior regulate water potential by closing stomata to reduce water loss during drought conditions, while anisohydric trees allow water potential to drop by sustaining stomatal conductance, but with the risk of hydraulic failure caused by cavitation of xylem tissues. Since catastrophic cavitation occurs infrequently in the relatively wet eastern U.S., we hypothesize that 1) tree growth of isohydric trees will be more limited during the drought than the anisohydric trees due to decreased stomatal conductance, but 2) variation in intrinsic water use efficient (iWUE) during drought in isohydric trees will mediate the effects of drought on carbon assimilation. We will test these hypotheses by 1) analyzing tree-ring chronologies and dendrometer data on productivity, and 2) estimating intrinsic water use efficiency (iWUE) at multiple scales by analyzing gas exchange data for the leaf-level, inter-annual variability of d13C in tree stem cores for the tree-level, and eddy covariance technique for the stand-level. Our study site is the Morgan-Monroe State Forest (Indiana, USA). A 46 m flux tower has been continuously recording the carbon, water and energy fluxes, and tree diameter has been measured every 2 weeks using dendrometers, since 1998. Additional research, including gas exchange measurements performed during the growing seasons of 2011-2013 and tree-ring chronologies collected in 2014 and 2015, enable us to assess the long-term impact of climate on the ecosystem processes at multiple scales. Finally, the severe drought experienced in this region in 2012 will help us evaluate how productivity and iWUE respond to an especially severe drought event.

Linking habitat quality with trophic performance of steelhead along forest gradients in the South Fork Trinity River watershed, California

Treesearch

Sarah G. McCarthy; Jeffrey J. Duda; John M. Emlen; Garth R. Hodgson; David A. Beauchamp

2009-01-01

We examined invertebrate prey, fish diet, and energy assimilation in relation to habitat variation for steelhead Oncorhynchus mykiss (anadromous rainbow trout) and rainbow trout in nine low-order tributaries of the South Fork Trinity River, northern California. These streams spanned a range of environmental conditions, which allowed us to use...

Parental Practices and Achievement of Mexican and Chinese Immigrant Children in the USA: Assimilation Patterns?

ERIC Educational Resources Information Center

Bodovski, Katerina; Durham, Rachel E.

2010-01-01

The authors used the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 (ECLS-K) data to examine the mathematics and science achievement of two immigrant groups in the United States--Chinese and Mexican students. The authors also assessed variation in parental practices and fifth-grade achievement according to ethnicity and the age…

Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts

NASA Astrophysics Data System (ADS)

Ma, Chaoqun; Wang, Tijian; Zang, Zengliang; Li, Zhijin

2018-07-01

Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation (DA) and model output statistics (MOS). However, the relative importance and combined effects of the two techniques have not been clarified. Here, a one-month air quality forecast with the Weather Research and Forecasting-Chemistry (WRF-Chem) model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational (3DVar) DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3DVar DA in improving the operational forecasting ability of WRF-Chem.

Four-dimensional variational Ocean ReAnalysis for the Western North Pacific over 30 years (FORA-WNP30)

NASA Astrophysics Data System (ADS)

Hirose, N.; Takatsuki, Y.; Usui, N.; Wakamatsu, T.; Tanaka, Y.; Toyoda, T.; Nishikawa, S.; Fujii, Y.; Igarashi, H.; Nishikawa, H.; Ishikawa, Y.; Kuragano, T.; Kamachi, M.

2016-12-01

An ocean reanalysis, FORA-WNP30, was produced by the collaborative work of Meteorological Research Institute, Japan Meteorological Agency (JMA/MRI) and Japan Agency for Marine-Earth Science and Technology (JAMSTEC). A state-of-the-art 4-dimensional variational ocean data assimilation system, MOVE-4DVAR (Usui et al., 2015) was used. The calculation for the reanalysis, with the horizontal resolution of 0.1 degree (about 10 km) and the period between 1 January 1982 and 31 December 2014, was carried out on the Earth Simulator with the support of JAMSTEC. The model forcing is derived from the JRA-55 atmospheric reanalysis product. In-situ temperature and salinity profiles above 1500m-depth, satellite-based sea surface temperature (SST) and sea surface height (SSH) data are assimilated in FORA-WNP30.Using the current observations obtained by the Acoustic Doppler Current Profiler (ADCP) installed in two JMA research vessels, we validate the current (velocity) field in FORA-WNP30 and MOVE-3DVAR system, the latter of which is an operational ocean data assimilation system in JMA. The ADCP current data are independent because they are not assimilated in both systems. The current fields at 100-m depth during 2001-2012, in both of FORA-WNP30 and MOVE-3DVAR show high correlation with ADCP observation in the south of Japan, the East China Sea and the Kuroshio extension region, and relatively low correlation in the Japan Sea and the Oyashio region. The correlation coefficients of current speed for FORA-WNP30 are higher than those for MOVE-3DVAR in all regions.FORA-WNP30 successfully reproduces not only the major ocean current such as the Kuroshio and Oyashio, but also the associated meso-scale phenomena such as eddies, fronts, and meanders. In addition, it replicates the Kuroshio large meander events and the strong intrusion event of the Oyashio in 1980s, in spite of no satellite altimeter data for this period. Therefore, FORA-WNP30 is a valuable dataset for use in a variety of oceanographic process study and related fields such as climate study, meteorology, and fisheries.

Assimilation and High Resolution Forecasts of Surface and Near Surface Conditions for the 2010 Vancouver Winter Olympic and Paralympic Games

NASA Astrophysics Data System (ADS)

Bernier, Natacha B.; Bélair, Stéphane; Bilodeau, Bernard; Tong, Linying

2014-01-01

A dynamical model was experimentally implemented to provide high resolution forecasts at points of interests in the 2010 Vancouver Olympics and Paralympics Region. In a first experiment, GEM-Surf, the near surface and land surface modeling system, is driven by operational atmospheric forecasts and used to refine the surface forecasts according to local surface conditions such as elevation and vegetation type. In this simple form, temperature and snow depth forecasts are improved mainly as a result of the better representation of real elevation. In a second experiment, screen level observations and operational atmospheric forecasts are blended to drive a continuous cycle of near surface and land surface hindcasts. Hindcasts of the previous day conditions are then regarded as today's optimized initial conditions. Hence, in this experiment, given observations are available, observation driven hindcasts continuously ensure that daily forecasts are issued from improved initial conditions. GEM-Surf forecasts obtained from improved short-range hindcasts produced using these better conditions result in improved snow depth forecasts. In a third experiment, assimilation of snow depth data is applied to further optimize GEM-Surf's initial conditions, in addition to the use of blended observations and forecasts for forcing. Results show that snow depth and summer temperature forecasts are further improved by the addition of snow depth data assimilation.

Cognitive group therapy for depressive students: The case study

PubMed Central

Tiuraniemi, Juhani; Korhola, Jarno

2009-01-01

The aims of this study were to assess whether a course of cognitive group therapy could help depressed students and to assess whether assimilation analysis offers a useful way of analysing students' progress through therapy. “Johanna” was a patient in a group that was designed for depressive students who had difficulties with their studies. The assimilation of Johanna's problematic experience progressed as the meetings continued from level one (unpleasant thoughts) to level six (solving the problem). Johanna's problematic experience manifested itself as severe and excessive criticism towards herself and her study performance. As the group meetings progressed, Johanna found a new kind of tolerance that increased her determination and assertiveness regarding the studies. The dialogical structure of Johanna's problematic experience changed: she found hope and she was more assertive after the process. The results indicated that this kind of psycho-educational group therapy was an effective method for treating depression. The assimilation analysis offered a useful way of analysing the therapy process. PMID:20523883

Colonial context and age group relations among plains Indians.

PubMed

Fowler, L

1990-04-01

Federal policy toward American Indians has taken two approaches: 1)protection and close supervision or "paternalism," or 2) quick assimilation of Indians into mainstream society. Both approaches involved undermining the influence and prestige of elderly Indians. The northern division of the Arapaho was subjected to protection and close supervision and the southern division to abrupt assimilation. This paper compares how federal policy toward Wyoming and Oklahoma Arapaho from the 1870s to the present affected the elderly's status and role in Arapaho society. The comparison shows that in Oklahoma the elderly, who no longer direct native religious rituals, have less authority and prestige than in Wyoming and that their circumstances are largely the product of the execution of the assimilation policy in Oklahoma. The research also shows that while the economic contribution of the elderly to family subsistence was undermined to a greater extent in Oklahoma than in Wyoming, of equal importance in the Wyoming Arapaho elders' retension of authority was their continued ability to direct religious ritual.

Towards Verifying National CO2 Emissions

NASA Astrophysics Data System (ADS)

Fung, I. Y.; Wuerth, S. M.; Anderson, J. L.

2017-12-01

With the Paris Agreement, nations around the world have pledged their voluntary reductions in future CO2 emissions. Satellite observations of atmospheric CO2 have the potential to verify self-reported emission statistics around the globe. We present a carbon-weather data assimilation system, wherein raw weather observations together with satellite observations of the mixing ratio of column CO2 from the Orbiting Carbon Observatory-2 are assimilated every 6 hours into the NCAR carbon-climate model CAM5 coupled to the Ensemble Kalman Filter of DART. In an OSSE, we reduced the fossil fuel emissions from a country, and estimated the emissions innovations demanded by the atmospheric CO2 observations. The uncertainties in the innovation are analyzed with respect to the uncertainties in the meteorology to determine the significance of the result. The work follows from "On the use of incomplete historical data to infer the present state of the atmosphere" (Charney et al. 1969), which maps the path for continuous data assimilation for weather forecasting and the five decades of progress since.

Assessing the Impact of Observations on the Prediction of Effective Atmospheric Angular Momentum from NAVGEM

NASA Astrophysics Data System (ADS)

Baker, N. L.; Langland, R.

2016-12-01

Variations in Earth rotation are measured by comparing a time based on Earth's variable rotation rate about its axis to a time standard based on an internationally coordinated ensemble of atomic clocks that provide a uniform time scale. The variability of Earth's rotation is partly due to the changes in angular momentum that occur in the atmosphere and ocean as weather patterns and ocean features develop, propagate, and dissipate. The NAVGEM Effective Atmospheric Angular Momentum Functions (EAAMF) and their predictions are computed following Barnes et al. (1983), and provided to the U.S. Naval Observatory daily. These along with similar data from the NOAA GFS model are used to calculate and predict the Earth orientation parameters (Stamatakos et al., 2016). The Navy's high-resolution global weather prediction system consists of the Navy Global Environmental Model (NAVGEM; Hogan et al., 2014) and a hybrid four-dimensional variational data assimilation system (4DVar) (Kuhl et al., 2013). An important component of NAVGEM is the Forecast Sensitivity Observation Impact (FSOI). FSOI is a mathematical method to quantify the contribution of individual observations or sets of observations to the reduction in the 24-hr forecast error (Langland and Baker, 2004). The FSOI allows for dynamic monitoring of the relative quality and value of the observations assimilated by NAVGEM, and the relative ability of the data assimilation system to effectively use the observation information to generate an improved forecast. For this study, along with the FSOI based on the global moist energy error norm, we computed the FSOI using an error norm based on the Effective Angular Momentum Functions. This modification allowed us to assess which observations were most beneficial in reducing the 24-hr forecast error for the atmospheric angular momentum.

Dynamics of leaf gas exchange, xylem and phloem transport, water potential and carbohydrate concentration in a realistic 3-D model tree crown.

PubMed

Nikinmaa, Eero; Sievänen, Risto; Hölttä, Teemu

2014-09-01

Tree models simulate productivity using general gas exchange responses and structural relationships, but they rarely check whether leaf gas exchange and resulting water and assimilate transport and driving pressure gradients remain within acceptable physical boundaries. This study presents an implementation of the cohesion-tension theory of xylem transport and the Münch hypothesis of phloem transport in a realistic 3-D tree structure and assesses the gas exchange and transport dynamics. A mechanistic model of xylem and phloem transport was used, together with a tested leaf assimilation and transpiration model in a realistic tree architecture to simulate leaf gas exchange and water and carbohydrate transport within an 8-year-old Scots pine tree. The model solved the dynamics of the amounts of water and sucrose solute in the xylem, cambium and phloem using a fine-grained mesh with a system of coupled ordinary differential equations. The simulations predicted the observed patterns of pressure gradients and sugar concentration. Diurnal variation of environmental conditions influenced tree-level gradients in turgor pressure and sugar concentration, which are important drivers of carbon allocation. The results and between-shoot variation were sensitive to structural and functional parameters such as tree-level scaling of conduit size and phloem unloading. Linking whole-tree-level water and assimilate transport, gas exchange and sink activity opens a new avenue for plant studies, as features that are difficult to measure can be studied dynamically with the model. Tree-level responses to local and external conditions can be tested, thus making the approach described here a good test-bench for studies of whole-tree physiology.

Global CO emission estimates inferred from assimilation of MOPITT and IASI CO data, together with observations of O3, NO2, HNO3, and HCHO.

NASA Astrophysics Data System (ADS)

Zhang, X.; Jones, D. B. A.; Keller, M.; Jiang, Z.; Bourassa, A. E.; Degenstein, D. A.; Clerbaux, C.; Pierre-Francois, C.

2017-12-01

Atmospheric carbon monoxide (CO) emissions estimated from inverse modeling analyses exhibit large uncertainties, due, in part, to discrepancies in the tropospheric chemistry in atmospheric models. We attempt to reduce the uncertainties in CO emission estimates by constraining the modeled abundance of ozone (O3), nitrogen dioxide (NO2), nitric acid (HNO3), and formaldehyde (HCHO), which are constituents that play a key role in tropospheric chemistry. Using the GEOS-Chem four-dimensional variational (4D-Var) data assimilation system, we estimate CO emissions by assimilating observations of CO from the Measurement of Pollution In the Troposphere (MOPITT) and the Infrared Atmospheric Sounding Interferometer (IASI), together with observations of O3 from the Optical Spectrograph and InfraRed Imager System (OSIRIS) and IASI, NO2 and HCHO from the Ozone Monitoring Instrument (OMI), and HNO3 from the Microwave Limb Sounder (MLS). Our experiments evaluate the inferred CO emission estimates from major anthropogenic, biomass burning and biogenic sources. Moreover, we also infer surface emissions of nitrogen oxides (NOx = NO + NO2) and isoprene. Our results reveal that this multiple species chemical data assimilation produces a chemical consistent state that effectively adjusts the CO-O3-OH coupling in the model. The O3-induced changes in OH are particularly large in the tropics. Overall, our analysis results in a better constrained tropospheric chemical state.

Seasonal Variation in Food Consumption, Assimilation, and Conversion Efficiency of Indian Bivoltine Hybrid Silkworm, Bombyx mori

PubMed Central

Rahmathulla, V. K.; Suresh, H. M.

2012-01-01

Food consumption and utilization is influenced by various biotic and abiotic factors. Under different environmental, feeding, and nutritional conditions, and with ingestion of the same amount of mulberry leaves, the silkworm shows significant difference in its ability to digest, absorb, and convert food to body matter. Here, influences of season, temperature, and humidity on food intake, assimilation, and conversion efficiency of the Indian bivoltine hybrid (CSR2 × CSR4) Bombyx mori L. (Lepidoptera: Bombycidae) were studied. The results indicated that food ingestion and assimilation were significantly higher among silkworm batches where optimum temperature and humidity were maintained compared with silkworm batches exposed to natural climatic conditions of the respective season. However, during summer the nutritional efficiency parameters were significantly higher among silkworms reared under natural temperature and humidity conditions when compared with the control. During the winter and rainy season, the nutritional efficiency parameters were significantly higher in control batches, where optimum temperature and humidity were maintained. Ingesta and digesta required to produce one gram of cocoon/shell were also lower in control batches for all seasons except summer. This may be due to the physiological adaptation of silkworms to overcome stress during the summer season. PMID:23414194

Climate seasonality limits leaf carbon assimilation and wood productivity in tropical forests

DOE PAGES

Wagner, Fabien H.; Hérault, Bruno; Bonal, Damien; ...

2016-04-28

Here, the seasonal climate drivers of the carbon cycle in tropical forests remain poorly known, although these forests account for more carbon assimilation and storage than any other terrestrial ecosystem. Based on a unique combination of seasonal pan-tropical data sets from 89 experimental sites (68 include aboveground wood productivity measurements and 35 litter productivity measurements), their associated canopy photosynthetic capacity (enhanced vegetation index, EVI) and climate, we ask how carbon assimilation and aboveground allocation are related to climate seasonality in tropical forests and how they interact in the seasonal carbon cycle. We found that canopy photosynthetic capacity seasonality responds positivelymore » to precipitation when rainfall is < 2000 mm yr -1 (water-limited forests) and to radiation otherwise (light-limited forests). On the other hand, independent of climate limitations, wood productivity and litterfall are driven by seasonal variation in precipitation and evapotranspiration, respectively. Consequently, light-limited forests present an asynchronism between canopy photosynthetic capacity and wood productivity. First-order control by precipitation likely indicates a decrease in tropical forest productivity in a drier climate in water-limited forest, and in current light-limited forest with future rainfall < 2000 mm yr -1.« less

Climate seasonality limits leaf carbon assimilation and wood productivity in tropical forests

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wagner, Fabien H.; Hérault, Bruno; Bonal, Damien

Here, the seasonal climate drivers of the carbon cycle in tropical forests remain poorly known, although these forests account for more carbon assimilation and storage than any other terrestrial ecosystem. Based on a unique combination of seasonal pan-tropical data sets from 89 experimental sites (68 include aboveground wood productivity measurements and 35 litter productivity measurements), their associated canopy photosynthetic capacity (enhanced vegetation index, EVI) and climate, we ask how carbon assimilation and aboveground allocation are related to climate seasonality in tropical forests and how they interact in the seasonal carbon cycle. We found that canopy photosynthetic capacity seasonality responds positivelymore » to precipitation when rainfall is < 2000 mm yr -1 (water-limited forests) and to radiation otherwise (light-limited forests). On the other hand, independent of climate limitations, wood productivity and litterfall are driven by seasonal variation in precipitation and evapotranspiration, respectively. Consequently, light-limited forests present an asynchronism between canopy photosynthetic capacity and wood productivity. First-order control by precipitation likely indicates a decrease in tropical forest productivity in a drier climate in water-limited forest, and in current light-limited forest with future rainfall < 2000 mm yr -1.« less

Decorrelation scales for Arctic Ocean hydrography - Part I: Amerasian Basin

NASA Astrophysics Data System (ADS)

Sumata, Hiroshi; Kauker, Frank; Karcher, Michael; Rabe, Benjamin; Timmermans, Mary-Louise; Behrendt, Axel; Gerdes, Rüdiger; Schauer, Ursula; Shimada, Koji; Cho, Kyoung-Ho; Kikuchi, Takashi

2018-03-01

Any use of observational data for data assimilation requires adequate information of their representativeness in space and time. This is particularly important for sparse, non-synoptic data, which comprise the bulk of oceanic in situ observations in the Arctic. To quantify spatial and temporal scales of temperature and salinity variations, we estimate the autocorrelation function and associated decorrelation scales for the Amerasian Basin of the Arctic Ocean. For this purpose, we compile historical measurements from 1980 to 2015. Assuming spatial and temporal homogeneity of the decorrelation scale in the basin interior (abyssal plain area), we calculate autocorrelations as a function of spatial distance and temporal lag. The examination of the functional form of autocorrelation in each depth range reveals that the autocorrelation is well described by a Gaussian function in space and time. We derive decorrelation scales of 150-200 km in space and 100-300 days in time. These scales are directly applicable to quantify the representation error, which is essential for use of ocean in situ measurements in data assimilation. We also describe how the estimated autocorrelation function and decorrelation scale should be applied for cost function calculation in a data assimilation system.

Comparative phenomics and targeted use of genomics reveals variation in carbon and nitrogen assimilation among different Brettanomyces bruxellensis strains.

PubMed

Crauwels, S; Van Assche, A; de Jonge, R; Borneman, A R; Verreth, C; Troels, P; De Samblanx, G; Marchal, K; Van de Peer, Y; Willems, K A; Verstrepen, K J; Curtin, C D; Lievens, B

2015-11-01

Recent studies have suggested a correlation between genotype groups of Brettanomyces bruxellensis and their source of isolation. To further explore this relationship, the objective of this study was to assess metabolic differences in carbon and nitrogen assimilation between different B. bruxellensis strains from three beverages, including beer, wine, and soft drink, using Biolog Phenotype Microarrays. While some similarities of physiology were noted, many traits were variable among strains. Interestingly, some phenotypes were found that could be linked to strain origin, especially for the assimilation of particular α- and β-glycosides as well as α- and β-substituted monosaccharides. Based upon gene presence or absence, an α-glucosidase and β-glucosidase were found explaining the observed phenotypes. Further, using a PCR screen on a large number of isolates, we have been able to specifically link a genomic deletion to the beer strains, suggesting that this region may have a fitness cost for B. bruxellensis in certain fermentation systems such as brewing. More specifically, none of the beer strains were found to contain a β-glucosidase, which may have direct impacts on the ability for these strains to compete with other microbes or on flavor production.

Inferring Land Surface Model Parameters for the Assimilation of Satellite-Based L-Band Brightness Temperature Observations into a Soil Moisture Analysis System

NASA Technical Reports Server (NTRS)

Reichle, Rolf H.; De Lannoy, Gabrielle J. M.

2012-01-01

The Soil Moisture and Ocean Salinity (SMOS) satellite mission provides global measurements of L-band brightness temperatures at horizontal and vertical polarization and a variety of incidence angles that are sensitive to moisture and temperature conditions in the top few centimeters of the soil. These L-band observations can therefore be assimilated into a land surface model to obtain surface and root zone soil moisture estimates. As part of the observation operator, such an assimilation system requires a radiative transfer model (RTM) that converts geophysical fields (including soil moisture and soil temperature) into modeled L-band brightness temperatures. At the global scale, the RTM parameters and the climatological soil moisture conditions are still poorly known. Using look-up tables from the literature to estimate the RTM parameters usually results in modeled L-band brightness temperatures that are strongly biased against the SMOS observations, with biases varying regionally and seasonally. Such biases must be addressed within the land data assimilation system. In this presentation, the estimation of the RTM parameters is discussed for the NASA GEOS-5 land data assimilation system, which is based on the ensemble Kalman filter (EnKF) and the Catchment land surface model. In the GEOS-5 land data assimilation system, soil moisture and brightness temperature biases are addressed in three stages. First, the global soil properties and soil hydraulic parameters that are used in the Catchment model were revised to minimize the bias in the modeled soil moisture, as verified against available in situ soil moisture measurements. Second, key parameters of the "tau-omega" RTM were calibrated prior to data assimilation using an objective function that minimizes the climatological differences between the modeled L-band brightness temperatures and the corresponding SMOS observations. Calibrated parameters include soil roughness parameters, vegetation structure parameters, and the single scattering albedo. After this climatological calibration, the modeling system can provide L-band brightness temperatures with a global mean absolute bias of less than 10K against SMOS observations, across multiple incidence angles and for horizontal and vertical polarization. Third, seasonal and regional variations in the residual biases are addressed by estimating the vegetation optical depth through state augmentation during the assimilation of the L-band brightness temperatures. This strategy, tested here with SMOS data, is part of the baseline approach for the Level 4 Surface and Root Zone Soil Moisture data product from the planned Soil Moisture Active Passive (SMAP) satellite mission.

Reanalysis of biogeochemical properties in the Mediterranean Sea

NASA Astrophysics Data System (ADS)

Cossarini, Gianpiero; Teruzzi, Anna; Salon, Stefano; Solidoro, Cosimo

2014-05-01

In the 3D variational (3DVAR) assimilation approach the error covariance matrix can be decomposed in a series of operators. The decomposition makes the 3DVAR particularly suitable for marine biogeochemistry data assimilation, because of the reduced computational costs of the method and its modularity, which allows to define the covariance among the biogeochemical variables in a specific operator. In the present work, the results of 3DVAR assimilation of surface chlorophyll concentration in a multi-annual simulation of the Mediterranean Sea biogeochemistry are presented. The assimilated chlorophyll concentrations are obtained from satellite observations (Volpe et al. 2012). The multi-annual simulation is carried out using the OPATM-BFM model (Lazzari et al. 2012), which describes the low trophic web dynamics and is offline coupled with the MFS physical model (Oddo et al. 2009). In the OPATM-BFM four types of phytoplankton are simulated in terms of their content in carbon, nitrogen, phosphorous, silicon and chlorophyll. In the 3DVAR the error covariance matrix has been decomposed in three different operators, which account for the vertical, the horizontal and the biogeochemical covariance (Teruzzi et al. 2014). The biogeochemical operator propagates the result of the assimilation to the OPATM-BFM variables, providing innovation for the components of the four phytoplankton types. The biogeochemical covariance has been designed supposing that the assimilation preserves the physiological status and the relative abundances of phytoplankton types. Practically, the assimilation preserves the internal quotas of the components for each phytoplankton as long as the optimal growth rate condition are maintained. The quotas preservation is not applied when the phytoplankton is in severe declining growth phase, and the correction provided by the assimilation is set equal to zero. Moreover, the relative abundances among the phytoplankton functional types are preserved. The 3DVAR has been applied to the Mediterranean Sea for the period 1999-2010 with weekly assimilation. The results of the multi-annual run show that the assimilation improves the model skill in terms of a better representation of the mean chlorophyll concentrations over the Mediterranean Sea sub-regions and also in terms of spatial and temporal definition of local bloom events. Furthermore, the comparison with nutrients climatology based on in situ measurements show that the non assimilated variables are consistent with observations. The application of the 3DVAR revealed that in specific cases the correction introduced by the assimilation is not maintained by the model dynamics. In these cases, the satellite observations are characterized by local patchy bloom events, which are not well captured by the model. It has been observed that, since the bloom events are strongly affected by the vertical mixing dynamics, which support nutrients to the surface layer, a possible source of error are the mixing conditions provided by the physical model. Oddo et al. 2009. Ocean Science, 5(4), 461-473, doi:10.5194/os-5-461-2009. Lazzari et al. 2012. Biogeosciences, 9(1), 217-233, doi:10.5194/bg-9-217-2012. Teruzzi et al. 2014. Journal of Geophysical Research, 119, 1-18, doi:10.1002/2013JC009277. Volpe et al. 2012. Ocean Science Discussions, 9(2), 1349-1385, doi:10.5194/osd-9-1349-2012.

Moisture rivals temperature in limiting photosynthesis by trees establishing beyond their cold-edge range limit under ambient and warmed conditions.

PubMed

Moyes, Andrew B; Germino, Matthew J; Kueppers, Lara M

2015-09-01

Climate change is altering plant species distributions globally, and warming is expected to promote uphill shifts in mountain trees. However, at many cold-edge range limits, such as alpine treelines in the western United States, tree establishment may be colimited by low temperature and low moisture, making recruitment patterns with warming difficult to predict. We measured response functions linking carbon (C) assimilation and temperature- and moisture-related microclimatic factors for limber pine (Pinus flexilis) seedlings growing in a heating × watering experiment within and above the alpine treeline. We then extrapolated these response functions using observed microclimate conditions to estimate the net effects of warming and associated soil drying on C assimilation across an entire growing season. Moisture and temperature limitations were each estimated to reduce potential growing season C gain from a theoretical upper limit by 15-30% (c. 50% combined). Warming above current treeline conditions provided relatively little benefit to modeled net assimilation, whereas assimilation was sensitive to either wetter or drier conditions. Summer precipitation may be at least as important as temperature in constraining C gain by establishing subalpine trees at and above current alpine treelines as seasonally dry subalpine and alpine ecosystems continue to warm. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

A Maximum Likelihood Ensemble Data Assimilation Method Tailored to the Inner Radiation Belt

NASA Astrophysics Data System (ADS)

Guild, T. B.; O'Brien, T. P., III; Mazur, J. E.

2014-12-01

The Earth's radiation belts are composed of energetic protons and electrons whose fluxes span many orders of magnitude, whose distributions are log-normal, and where data-model differences can be large and also log-normal. This physical system thus challenges standard data assimilation methods relying on underlying assumptions of Gaussian distributions of measurements and data-model differences, where innovations to the model are small. We have therefore developed a data assimilation method tailored to these properties of the inner radiation belt, analogous to the ensemble Kalman filter but for the unique cases of non-Gaussian model and measurement errors, and non-linear model and measurement distributions. We apply this method to the inner radiation belt proton populations, using the SIZM inner belt model [Selesnick et al., 2007] and SAMPEX/PET and HEO proton observations to select the most likely ensemble members contributing to the state of the inner belt. We will describe the algorithm, the method of generating ensemble members, our choice of minimizing the difference between instrument counts not phase space densities, and demonstrate the method with our reanalysis of the inner radiation belt throughout solar cycle 23. We will report on progress to continue our assimilation into solar cycle 24 using the Van Allen Probes/RPS observations.

Cross-language categorization of French and German vowels by naive American listeners.

PubMed

Strange, Winifred; Levy, Erika S; Law, Franzo F

2009-09-01

American English (AE) speakers' perceptual assimilation of 14 North German (NG) and 9 Parisian French (PF) vowels was examined in two studies using citation-form disyllables (study 1) and sentences with vowels surrounded by labial and alveolar consonants in multisyllabic nonsense words (study 2). Listeners categorized multiple tokens of each NG and PF vowel as most similar to selected AE vowels and rated their category "goodness" on a nine-point Likert scale. Front, rounded vowels were assimilated primarily to back AE vowels, despite their acoustic similarity to front AE vowels. In study 1, they were considered poorer exemplars of AE vowels than were NG and PF back, rounded vowels; in study 2, front and back, rounded vowels were perceived as similar to each other. Assimilation of some front, unrounded and back, rounded NG and PF vowels varied with language, speaking style, and consonantal context. Differences in perceived similarity often could not be predicted from context-specific cross-language spectral similarities. Results suggest that listeners can access context-specific, phonetic details when listening to citation-form materials, but assimilate non-native vowels on the basis of context-independent phonological equivalence categories when processing continuous speech. Results are interpreted within the Automatic Selective Perception model of speech perception.

Cross-language categorization of French and German vowels by naïve American listeners

PubMed Central

Strange, Winifred; Levy, Erika S.; Law, Franzo F.

2009-01-01

American English (AE) speakers’ perceptual assimilation of 14 North German (NG) and 9 Parisian French (PF) vowels was examined in two studies using citation-form disyllables (study 1) and sentences with vowels surrounded by labial and alveolar consonants in multisyllabic nonsense words (study 2). Listeners categorized multiple tokens of each NG and PF vowel as most similar to selected AE vowels and rated their category “goodness” on a nine-point Likert scale. Front, rounded vowels were assimilated primarily to back AE vowels, despite their acoustic similarity to front AE vowels. In study 1, they were considered poorer exemplars of AE vowels than were NG and PF back, rounded vowels; in study 2, front and back, rounded vowels were perceived as similar to each other. Assimilation of some front, unrounded and back, rounded NG and PF vowels varied with language, speaking style, and consonantal context. Differences in perceived similarity often could not be predicted from context-specific cross-language spectral similarities. Results suggest that listeners can access context-specific, phonetic details when listening to citation-form materials, but assimilate non-native vowels on the basis of context-independent phonological equivalence categories when processing continuous speech. Results are interpreted within the Automatic Selective Perception model of speech perception. PMID:19739759

Biomass and productivity of three phytoplankton size classes in San Francisco Bay.

USGS Publications Warehouse

Cole, B.E.; Cloern, J.E.; Alpine, A.E.

1986-01-01

The 5-22 mu m size accounted for 40-50% of annual production in each embayment, but production by phytoplanton >22 mu m ranged from 26% in the S reach to 54% of total phytoplankton production in the landward embayment of the N reach. A productivity index is derived that predicts daily productivity for each size class as a function of ambient irradiance and integrated chlorophyll a in the photic zone. For the whole phytoplankton community and for each size class, this index was constant at approx= 0.76 g C m-2 (g chlorophyll a Einstein)-1. The annual means of maximum carbon assimilation numbers were usually similar for the three size classes. Spatial and temporal variations in size-fractionated productivity are primarily due to differences in biomass rather than size-dependent carbon assimilation rates. -from Authors

Improving volcanic sulfur dioxide cloud dispersal forecasts by progressive assimilation of satellite observations

NASA Astrophysics Data System (ADS)

Boichu, Marie; Clarisse, Lieven; Khvorostyanov, Dmitry; Clerbaux, Cathy

2014-04-01

Forecasting the dispersal of volcanic clouds during an eruption is of primary importance, especially for ensuring aviation safety. As volcanic emissions are characterized by rapid variations of emission rate and height, the (generally) high level of uncertainty in the emission parameters represents a critical issue that limits the robustness of volcanic cloud dispersal forecasts. An inverse modeling scheme, combining satellite observations of the volcanic cloud with a regional chemistry-transport model, allows reconstructing this source term at high temporal resolution. We demonstrate here how a progressive assimilation of freshly acquired satellite observations, via such an inverse modeling procedure, allows for delivering robust sulfur dioxide (SO2) cloud dispersal forecasts during the eruption. This approach provides a computationally cheap estimate of the expected location and mass loading of volcanic clouds, including the identification of SO2-rich parts.

Implementing of lognormal humidity and cloud-related control variables for the NCEP GSI hybrid EnVAR Assimilation scheme.

NASA Astrophysics Data System (ADS)

Fletcher, S. J.; Kleist, D.; Ide, K.

2017-12-01

As the resolution of operational global numerical weather prediction system approach the meso-scale, then the assumption of Gaussianity for the errors at these scales may not valid. However, it is also true that synoptic variables that are positive definite in behavior, for example humidity, cannot be optimally analyzed with a Gaussian error structure, where the increment could force the full field to go negative. In this presentation we present the initial work of implementing a mixed Gaussian-lognormal approximation for the temperature and moisture variable in both the ensemble and variational component of the NCEP GSI hybrid EnVAR. We shall also lay the foundation for the implementation of the lognormal approximation to cloud related control variables to allow for a possible more consistent assimilation of cloudy radiances.

Assimilating AOD retrievals from GOCI and VIIRS to forecast surface PM2.5 episodes over Eastern China

NASA Astrophysics Data System (ADS)

Pang, Jiongming; Liu, Zhiquan; Wang, Xuemei; Bresch, Jamie; Ban, Junmei; Chen, Dan; Kim, Jhoon

2018-04-01

In this study, Geostationary Ocean Color Imager (GOCI) AOD and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD data were assimilated to forecast surface PM2.5 concentrations over Eastern China, by using the three-dimensional variational (3DAVR) data assimilation (DA) system, to compare DA impacts by assimilating AOD retrievals from these two types of satellites. Three experiments were conducted, including a CONTROL without the AOD assimilation, and GOCIDA and VIIRSDA with the assimilation of AOD retrievals from GOCI and VIIRS, respectively. By utilizing the Weather Research and Forecasting with Chemistry (WRF/Chem) model, 48-h forecasts were initialized at each 06 UTC from 19 November to 06 December 2013. These forecasts were evaluated with 248 ground-based measurements from the air quality monitoring network across 67 China cities. The results show that overall the CONTROL underestimated surface PM2.5 concentrations, especially over Jing-Jin-Ji (JJJ) region and Yangtze River Delta (YRD) region. Both the GOCIDA and VIIRSDA produced higher surface PM2.5 concentrations mainly over Eastern China, which fits well with the PM2.5 measurements at these eastern sites, with more than 8% error reductions (ER). Moreover, compared to CONTROL, GOCIDA reduced 14.0% and 6.4% error on JJJ region and YRD region, respectively, while VIIRSDA reduced respectively 2.0% and 13.4% error over the corresponding areas. During the heavy polluted period, VIIRSDA improved all sites within YRD region, and GOCIDA enhanced 84% sites. Meanwhile, GOCIDA improved 84% sites on JJJ region, while VIIRSDA did not affect that region. These geographic distinctions might result from spatial dissimilarity between GOCI AOD and VIIRS AOD at time intervals. Moreover, the larger increment produced by AOD DA under stable meteorological conditions could lead to a longer duration (e.g., 1-2 days, > 2 days) of AOD DA impacts. Even though with AOD DA, surface PM2.5 concentrations were still underestimated clearly over heavy polluted periods. And 3% sites performed worse, where low PM2.5 values were observed and CONTROL performed well. With this study, the results indicate that AOD DA can partially improve the accuracy of PM2.5 forecasts. And the obvious geographic differences on forecasts emphasize the potential and importance of combining AOD retrievals from GOCI and VIIRS into data assimilation.

A Prototype Regional GSI-based EnKF-Variational Hybrid Data Assimilation System for the Rapid Refresh Forecasting System: Dual-Resolution Implementation and Testing Results

NASA Astrophysics Data System (ADS)

Pan, Yujie; Xue, Ming; Zhu, Kefeng; Wang, Mingjun

2018-05-01

A dual-resolution (DR) version of a regional ensemble Kalman filter (EnKF)-3D ensemble variational (3DEnVar) coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution (HR) deterministic background forecast with lower-resolution (LR) EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/˜13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation (GSI) 3D variational (3DVar) analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar. Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds.

Evaluation of the impact of observations on blended sea surface winds in a two-dimensional variational scheme using degrees of freedom

NASA Astrophysics Data System (ADS)

Wang, Ting; Xiang, Jie; Fei, Jianfang; Wang, Yi; Liu, Chunxia; Li, Yuanxiang

2017-12-01

This paper presents an evaluation of the observational impacts on blended sea surface winds from a two-dimensional variational data assimilation (2D-Var) scheme. We begin by briefly introducing the analysis sensitivity with respect to observations in variational data assimilation systems and its relationship with the degrees of freedom for signal (DFS), and then the DFS concept is applied to the 2D-Var sea surface wind blending scheme. Two methods, a priori and a posteriori, are used to estimate the DFS of the zonal ( u) and meridional ( v) components of winds in the 2D-Var blending scheme. The a posteriori method can obtain almost the same results as the a priori method. Because only by-products of the blending scheme are used for the a posteriori method, the computation time is reduced significantly. The magnitude of the DFS is critically related to the observational and background error statistics. Changing the observational and background error variances can affect the DFS value. Because the observation error variances are assumed to be uniform, the observational influence at each observational location is related to the background error variance, and the observations located at the place where there are larger background error variances have larger influences. The average observational influence of u and v with respect to the analysis is about 40%, implying that the background influence with respect to the analysis is about 60%.

Relation of Chlorophyll Fluorescence Sensitive Reflectance Ratios to Carbon Flux Measurements of Montanne Grassland and Norway Spruce Forest Ecosystems in the Temperate Zone

PubMed Central

Ač, Alexander; Malenovský, Zbyněk; Urban, Otmar; Hanuš, Jan; Zitová, Martina; Navrátil, Martin; Vráblová, Martina; Olejníčková, Julie; Špunda, Vladimír; Marek, Michal

2012-01-01

We explored ability of reflectance vegetation indexes (VIs) related to chlorophyll fluorescence emission (R 686/R 630, R 740/R 800) and de-epoxidation state of xanthophyll cycle pigments (PRI, calculated as (R 531 − R 570)/(R 531 − R 570)) to track changes in the CO2 assimilation rate and Light Use Efficiency (LUE) in montane grassland and Norway spruce forest ecosystems, both at leaf and also canopy level. VIs were measured at two research plots using a ground-based high spatial/spectral resolution imaging spectroscopy technique. No significant relationship between VIs and leaf light-saturated CO2 assimilation (A MAX) was detected in instantaneous measurements of grassland under steady-state irradiance conditions. Once the temporal dimension and daily irradiance variation were included into the experimental setup, statistically significant changes in VIs related to tested physiological parameters were revealed. ΔPRI and Δ(R 686/R 630) of grassland plant leaves under dark-to-full sunlight transition in the scale of minutes were significantly related to A MAX (R 2 = 0.51). In the daily course, the variation of VIs measured in one-hour intervals correlated well with the variation of Gross Primary Production (GPP), Net Ecosystem Exchange (NEE), and LUE estimated via the eddy-covariance flux tower. Statistical results were weaker in the case of the grassland ecosystem, with the strongest statistical relation of the index R 686/R 630 with NEE and GPP. PMID:22701368

Evaluation of NASA satellite- and assimilation model-derived long-term daily temperature date over the continental US

USDA-ARS?s Scientific Manuscript database

Agricultural research increasingly is expected to provide precise, quantitative information with an explicit geographic coverage. Limited availability of continuous daily meteorological records often constrains efforts to provide such information through integrated use of simulation models, spatial ...

Autotrophic and heterotrophic soil respiration determined with trenching, soil CO2 fluxes and 13CO2/12CO2 concentration gradients in a boreal forest ecosystem

NASA Astrophysics Data System (ADS)

Pumpanen, Jukka; Shurpali, Narasinha; Kulmala, Liisa; Kolari, Pasi; Heinonsalo, Jussi

2017-04-01

Soil CO2 efflux forms a substantial part of the ecosystem carbon balance, and it can contribute more than half of the annual ecosystem respiration. Recently assimilated carbon which has been fixed in photosynthesis during the previous days plays an important role in soil CO2 efflux, and its contribution is seasonally variable. Moreover, the recently assimilated C has been shown to stimulate the decomposition of recalcitrant C in soil and increase the mineralization of nitrogen, the most important macronutrient limiting gross primary productivity (GPP) in boreal ecosystems. Podzolic soils, typical in boreal zone, have distinctive layers with different biological and chemical properties. The biological activity in different soil layers has large seasonal variation due to vertical gradient in temperature, soil organic matter and root biomass. Thus, the source of CO2 and its components have a vertical gradient which is seasonally variable. The contribution of recently assimilated C and its seasonal as well as spatial variation in soil are difficult to assess without disturbing the system. The most common method of partitioning soil respiration into its components is trenching which entails the roots being cut or girdling where the flow of carbohydrates from the canopy to roots has been isolated by cutting of the phloem. Other methods for determining the contribution of autotrophic (Ra) and heterotrophic (Rh) respiration components in soil CO2 efflux are pulse labelling with 13CO2 or 14CO2 or the natural abundance of 13C and/or 14C isotopes. Also differences in seasonal and short-term temperature response of soil respiration have been used to separate Ra and Rh. We compared the seasonal variation in Ra and Rh using the trenching method and differences between seasonal and short-term temperature responses of soil respiration. I addition, we estimated the vertical variation in soil biological activity using soil CO2 concentration and the natural abundance of 13C and 12C in CO2 in different soil layers in a boreal forest in Southern Finland and compared them to seasonal variation in GPP. Our results show that Ra followed a seasonal variation in GPP with a time lag of about 2 weeks. The contribution of Ra on soil CO2 efflux was largest in July and August. There was also a distinct seasonal pattern in the vertical distribution of soil CO2 concentration and the abundances of natural isotopes 13C/12C in soil CO2 which reflected the changes in biological activity in the soil profile. Our results indicate that all methods were able to distinguish seasonal variability in Ra and Rh. The soil CO2 gradient method was able to reproduce the temporal variation in soil CO2 effluxes relatively well when compared to those measured with chambers. However, variation in soil moisture also causes significant variation in soil air CO2 concentrations which interferes with the variation resulted from soil temperatures and belowground allocation of carbon from recent photosynthate. Also, the assumptions used in gradient method calculations, such as soil porosity and transport distances have to be taken into account when interpreting the results.

Water-use efficiency of willow: Variation with season, humidity and biomass allocation

NASA Astrophysics Data System (ADS)

Lindroth, Anders; Verwijst, Theo; Halldin, Sven

1994-04-01

Information on the water-use efficiency (WUE) of a vegetation cover improves understanding of the interrelationship between the water and carbon cycles, and enables hydrological practices to be related to agricultural and silvicultural planning and management. This study determined seasonal and climatic variations of the WUE of a fertilized and irrigated short-rotation stand of Salix viminalis L. on a clay soil. The WUE was determined as the ratio of above-ground production to transpiration or, alternatively, to transpiration divided by the saturation vapour pressure deficit. Growth was estimated from a combination of destructive and non-destructive measurements for 10 day periods during the growing seasons of 1986 and 1988. Daily transpiration was estimated using a physically based evaporation model, tuned against energy-balance/Bowen-ratio measurements of total stand evaporation. Nutrients were adequate and climate conditions were similar in both years. In spite of irrigation soil-water deficits developed during midsummer and affected growth rates in different ways: in 1986, both stem and leaf growth decreased, while in 1988 only stem growth decreased. Exceptionally high stem growth rates, twice the total potential growth rates, were recorded after the drought of 1988. They were probably caused by root-allocated assimilates that were sent above-ground after the drought. In both years, stem growth ceased 2-3 weeks after the leaf area had reached its maximum. Since light and temperature were still sufficient to maintain assimilation, all growth presumably took place below ground towards the end of the season. Changes in root-shoot allocation caused large variations in the WUE in 1988. The WUE, weighted by the saturation vapour pressure deficit, was fairly constant in 1986. In both years, the WUE was correlated with the vapour pressure deficit. Towards the end of both growing seasons, when all assimilates were sent below ground, the WUE decreased rapidly to zero. The total WUE, estimated as the seasonally accumulated above-ground production divided by accumulated transpiration, was 4.1 g kg -1 in 1986 and 5.5 g kg -1 in 1988, which is relatively high in comparison with other species.

Variation in Sulfur and Selenium Accumulation Is Controlled by Naturally Occurring Isoforms of the Key Sulfur Assimilation Enzyme ADENOSINE 5′-PHOSPHOSULFATE REDUCTASE2 across the Arabidopsis Species Range1[W][OPEN

PubMed Central

Chao, Dai-Yin; Baraniecka, Patrycja; Danku, John; Koprivova, Anna; Lahner, Brett; Luo, Hongbing; Yakubova, Elena; Dilkes, Brian; Kopriva, Stanislav; Salt, David E.

2014-01-01

Natural variation allows the investigation of both the fundamental functions of genes and their role in local adaptation. As one of the essential macronutrients, sulfur is vital for plant growth and development and also for crop yield and quality. Selenium and sulfur are assimilated by the same process, and although plants do not require selenium, plant-based selenium is an important source of this essential element for animals. Here, we report the use of linkage mapping in synthetic F2 populations and complementation to investigate the genetic architecture of variation in total leaf sulfur and selenium concentrations in a diverse set of Arabidopsis (Arabidopsis thaliana) accessions. We identify in accessions collected from Sweden and the Czech Republic two variants of the enzyme ADENOSINE 5′-PHOSPHOSULFATE REDUCTASE2 (APR2) with strongly diminished catalytic capacity. APR2 is a key enzyme in both sulfate and selenate reduction, and its reduced activity in the loss-of-function allele apr2-1 and the two Arabidopsis accessions Hodonín and Shahdara leads to a lowering of sulfur flux from sulfate into the reduced sulfur compounds, cysteine and glutathione, and into proteins, concomitant with an increase in the accumulation of sulfate in leaves. We conclude from our observation, and the previously identified weak allele of APR2 from the Shahdara accession collected in Tadjikistan, that the catalytic capacity of APR2 varies by 4 orders of magnitude across the Arabidopsis species range, driving significant differences in sulfur and selenium metabolism. The selective benefit, if any, of this large variation remains to be explored. PMID:25245030

Meteorological Research Institute multivariate ocean variational estimation (MOVE) system: Some early results

NASA Astrophysics Data System (ADS)

Usui, Norihisa; Ishizaki, Shiro; Fujii, Yosuke; Tsujino, Hiroyuki; Yasuda, Tamaki; Kamachi, Masafumi

The Meteorological Research Institute multivariate ocean variational estimation (MOVE) System has been developed as the next-generation ocean data assimilation system in Japan Meteorological Agency. A multivariate three-dimensional variational (3DVAR) analysis scheme with vertical coupled temperature salinity empirical orthogonal function modes is adopted. The MOVE system has two varieties, the global (MOVE-G) and North Pacific (MOVE-NP) systems. The equatorial Pacific and western North Pacific are analyzed with assimilation experiments using MOVE-G and -NP, respectively. In each system, the salinity and velocity fields are well reproduced, even in cases without salinity data. Changes in surface and subsurface zonal currents during the 1997/98 El Niño event are captured well, and their transports are reasonably consistent with in situ observations. For example, the eastward transport in the upper layer around the equator has 70 Sv in spring 1997 and weakens in spring 1998. With MOVE-NP, the Kuroshio transport has 25 Sv in the East China Sea, and 40 Sv crossing the ASUKA (Affiliated Surveys of the Kuroshio off Cape Ashizuri) line south of Japan. The variations in the Kuroshio transports crossing the ASUKA line agree well with observations. The Ryukyu Current System has a transport ranging from 6 Sv east of Taiwan to 17 Sv east of Amami. The Oyashio transport crossing the OICE (Oyashio Intensive observation line off Cape Erimo) line south of Hokkaido has 14 Sv southwestward (near shore) and 11 Sv northeastward (offshore). In the Kuroshio Oyashio transition area east of Japan, the eastward transport has 41 Sv (32 36°N) and 12 Sv (36 39°N) crossing the 145°E line.

The potential impact of scatterometry on oceanography - A wave forecasting case

NASA Technical Reports Server (NTRS)

Cane, M. A.; Cardone, V. J.

1981-01-01

A series of observing system simulation experiments have been performed in order to assess the potential impact of marine surface wind data on numerical weather prediction. In addition to conventional data, the experiments simulated the time-continuous assimilation of remotely sensed marine surface wind or temperature sounding data. The wind data were fabricated directly for model grid points intercepted by a Seasat-1 scatterometer swath and were assimilated into the lowest active level (945 mb) of the model using a localized successive correction method. It is shown that Seasat wind data can greatly improve numerical weather forecasts due to better definition of specific features. The case of the QE II storm is examined.

Bioconversion of Xylan to triglycerides by oil-rich yeasts. [Cryptococcus albidus; Cryptococcus terricoluus; Trichosporon

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fall, R.; Phelps, P.; Spindler, D.

A series of lipid-accumulating yeasts was examined for their potential to saccharify xylan and accumulate triglyceride. Of the genera tested, including Candida, Cryptococcus, Lipomyces, Rhodosporidium, Rhodotorula, and Trichosporon, only Crytococcus and Trichosporon isolates saccharified xylan. All of the strains could assimilate xylose and accumuate triglyceride under nitrogen-limiting conditions. Strains of Cryptococcus albidus were found to be especially useful for a one-step saccharification of xylan coupled to triglyceride synthesis. Crytococcus terricolus, a strain constitutive for lipid accumulation, lacked extracellular xylanase, but did assimilate xylose and xylobiose and was able to continuously convert xylan to triglyceride if the culture medium was supplementedmore » with xylanase. 22 references.« less

Incorporating Satellite Time-Series Data into Modeling

NASA Technical Reports Server (NTRS)

Gregg, Watson

2008-01-01

In situ time series observations have provided a multi-decadal view of long-term changes in ocean biology. These observations are sufficiently reliable to enable discernment of even relatively small changes, and provide continuous information on a host of variables. Their key drawback is their limited domain. Satellite observations from ocean color sensors do not suffer the drawback of domain, and simultaneously view the global oceans. This attribute lends credence to their use in global and regional model validation and data assimilation. We focus on these applications using the NASA Ocean Biogeochemical Model. The enhancement of the satellite data using data assimilation is featured and the limitation of tongterm satellite data sets is also discussed.

Impact of the Assimilation of Hyperspectral Infrared Profiles on Advanced Weather and Research Model Simulations of a Non-Convective Wind Event

NASA Technical Reports Server (NTRS)

Berndt, Emily B.; Zavodsky, Bradley T; Jedlovec, Gary J.; Elmer, Nicholas J.

2013-01-01

Non-convective wind events commonly occur with passing extratropical cyclones and have significant societal and economic impacts. Since non-convective winds often occur in the absence of specific phenomena such as a thunderstorm, tornado, or hurricane, the public are less likely to heed high wind warnings and continue daily activities. Thus non-convective wind events result in as many fatalities as straight line thunderstorm winds. One physical explanation for non-convective winds includes tropopause folds. Improved model representation of stratospheric air and associated non-convective wind events could improve non-convective wind forecasts and associated warnings. In recent years, satellite data assimilation has improved skill in forecasting extratropical cyclones; however errors still remain in forecasting the position and strength of extratropical cyclones as well as the tropopause folding process. The goal of this study is to determine the impact of assimilating satellite temperature and moisture retrieved profiles from hyperspectral infrared (IR) sounders (i.e. Atmospheric Infrared Sounder (AIRS), Cross-track Infrared and Microwave Sounding Suite (CrIMSS), and Infrared Atmospheric Sounding Interferometer (IASI)) on the model representation of the tropopause fold and an associated high wind event that impacted the Northeast United States on 09 February 2013. Model simulations using the Advanced Research Weather Research and Forecasting Model (ARW) were conducted on a 12-km grid with cycled data assimilation mimicking the operational North American Model (NAM). The results from the satellite assimilation run are compared to a control experiment (without hyperspectral IR retrievals), North American Regional Reanalysis (NARR) reanalysis, and Rapid Refresh analyses.

Utah State University Global Assimilation of Ionospheric Measurements Gauss-Markov Kalman filter model of the ionosphere: Model description and validation

NASA Astrophysics Data System (ADS)

Scherliess, L.; Schunk, R. W.; Sojka, J. J.; Thompson, D. C.; Zhu, L.

2006-11-01

The Utah State University Gauss-Markov Kalman Filter (GMKF) was developed as part of the Global Assimilation of Ionospheric Measurements (GAIM) program. The GMKF uses a physics-based model of the ionosphere and a Gauss-Markov Kalman filter as a basis for assimilating a diverse set of real-time (or near real-time) observations. The physics-based model is the Ionospheric Forecast Model (IFM), which accounts for five ion species and covers the E region, F region, and the topside from 90 to 1400 km altitude. Within the GMKF, the IFM derived ionospheric densities constitute a background density field on which perturbations are superimposed based on the available data and their errors. In the current configuration, the GMKF assimilates slant total electron content (TEC) from a variable number of global positioning satellite (GPS) ground sites, bottomside electron density (Ne) profiles from a variable number of ionosondes, in situ Ne from four Defense Meteorological Satellite Program (DMSP) satellites, and nighttime line-of-sight ultraviolet (UV) radiances measured by satellites. To test the GMKF for real-time operations and to validate its ionospheric density specifications, we have tested the model performance for a variety of geophysical conditions. During these model runs various combination of data types and data quantities were assimilated. To simulate real-time operations, the model ran continuously and automatically and produced three-dimensional global electron density distributions in 15 min increments. In this paper we will describe the Gauss-Markov Kalman filter model and present results of our validation study, with an emphasis on comparisons with independent observations.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kent, A.J.R.; Clague, D.A.; Honda, M.

Many tholeiitic and transitional pillow-rim and fragmental glasses from Loihi seamount, Hawaii, have high Cl contents and Cl/K{sub 2}O ratios (and ratios of Cl to other incompatible components, such as P{sub 2}O{sub 5}, H{sub 2}O, etc.) relative to other Hawaiian subaerial volcanoes (e.g., Mauna Loa, Mauna Kea, and Kilauea). The authors suggest that this results from widespread contamination of Loihi magmas by a Cl-rich, seawater-derived component. Assimilation of high-Cl phases such as saline brine or Cl-rich minerals (halite or iron-hydroxychlorides) with high Cl/H{sub 2}O ratios can explain the range and magnitude of Cl contents in Loihi glasses, as well asmore » the variations in the ratios of Cl to other incompatible elements. Brines and Cl-rich minerals are thought to form from seawater within the hydrothermal systems associated with submarine volcanoes, and Loihi magmas could plausibly have assimilated such materials from the hydrothermal envelope adjacent to the magma chamber. Their model can also explain semiquantitatively the observed contamination of Loihi glasses with atmospheric-derived noble gases, provided the assimilant has concentrations of Ne and Ar comparable to or slightly less than seawater. This is more likely for brines than for Cl-rich minerals, leading the authors to favor brines as the major assimilant. Cl/Br ratios for a limited number of Loihi samples are also seawater-like, and show no indication of the higher values expected to be associated with the assimilation of Cl-rich hydrothermal minerals. Although Cl enrichment is a common feature of lavas from Loihi, submarine glasses from other Hawaiian volcanoes show little (Kilauea) or no (Mauna Loa, Mauna Kea) evidence of this process, suggesting that assimilation of seawater-derived components is more likely to occur in the early stages of growth of oceanic volcanoes. Summit collapse events such as the one observed at Loihi in October 1996 provide a ready mechanism for depositing brine-bearing rocks from the volcanic edifice into the top of a submarine summit magma chamber.« less

Sampling strategies based on singular vectors for assimilated models in ocean forecasting systems

NASA Astrophysics Data System (ADS)

Fattorini, Maria; Brandini, Carlo; Ortolani, Alberto

2016-04-01

Meteorological and oceanographic models do need observations, not only as a ground truth element to verify the quality of the models, but also to keep model forecast error acceptable: through data assimilation techniques which merge measured and modelled data, natural divergence of numerical solutions from reality can be reduced / controlled and a more reliable solution - called analysis - is computed. Although this concept is valid in general, its application, especially in oceanography, raises many problems due to three main reasons: the difficulties that have ocean models in reaching an acceptable state of equilibrium, the high measurements cost and the difficulties in realizing them. The performances of the data assimilation procedures depend on the particular observation networks in use, well beyond the background quality and the used assimilation method. In this study we will present some results concerning the great impact of the dataset configuration, in particular measurements position, on the evaluation of the overall forecasting reliability of an ocean model. The aim consists in identifying operational criteria to support the design of marine observation networks at regional scale. In order to identify the observation network able to minimize the forecast error, a methodology based on Singular Vectors Decomposition of the tangent linear model is proposed. Such a method can give strong indications on the local error dynamics. In addition, for the purpose of avoiding redundancy of information contained in the data, a minimal distance among data positions has been chosen on the base of a spatial correlation analysis of the hydrodynamic fields under investigation. This methodology has been applied for the choice of data positions starting from simplified models, like an ideal double-gyre model and a quasi-geostrophic one. Model configurations and data assimilation are based on available ROMS routines, where a variational assimilation algorithm (4D-var) is included as part of the code These first applications have provided encouraging results in terms of increased predictability time and reduced forecast error, also improving the quality of the analysis used to recover the real circulation patterns from a first guess quite far from the real state.

JRAero: the Japanese Reanalysis for Aerosol v1.0

NASA Astrophysics Data System (ADS)

Yumimoto, Keiya; Tanaka, Taichu Y.; Oshima, Naga; Maki, Takashi

2017-09-01

A global aerosol reanalysis product named the Japanese Reanalysis for Aerosol (JRAero) was constructed by the Meteorological Research Institute (MRI) of the Japan Meteorological Agency. The reanalysis employs a global aerosol transport model developed by MRI and a two-dimensional variational data assimilation method. It assimilates maps of aerosol optical depth (AOD) from MODIS onboard the Terra and Aqua satellites every 6 h and has a TL159 horizontal resolution (approximately 1.1° × 1.1°). This paper describes the aerosol transport model, the data assimilation system, the observation data, and the setup of the reanalysis and examines its quality with AOD observations. Comparisons with MODIS AODs that were used for the assimilation showed that the reanalysis showed much better agreement than the free run (without assimilation) of the aerosol model and improved under- and overestimation in the free run, thus confirming the accuracy of the data assimilation system. The reanalysis had a root mean square error (RMSE) of 0.05, a correlation coefficient (R) of 0.96, a mean fractional error (MFE) of 23.7 %, a mean fractional bias (MFB) of 2.8 %, and an index of agreement (IOA) of 0.98. The better agreement of the first guess, compared to the free run, indicates that aerosol fields obtained by the reanalysis can improve short-term forecasts. AOD fields from the reanalysis also agreed well with monthly averaged global AODs obtained by the Aerosol Robotic Network (AERONET) (RMSE = 0.08, R = 0. 90, MFE = 28.1 %, MFB = 0.6 %, and IOA = 0.93). Site-by-site comparison showed that the reanalysis was considerably better than the free run; RMSE was less than 0.10 at 86.4 % of the 181 AERONET sites, R was greater than 0.90 at 40.7 % of the sites, and IOA was greater than 0.90 at 43.4 % of the sites. However, the reanalysis tended to have a negative bias at urban sites (in particular, megacities in industrializing countries) and a positive bias at mountain sites, possibly because of insufficient anthropogenic emissions data, the coarse model resolution, and the difference in representativeness between satellite and ground-based observations.

Prediction Activities at NASA's Global Modeling and Assimilation Office

NASA Technical Reports Server (NTRS)

Schubert, Siegfried

2010-01-01

The Global Modeling and Assimilation Office (GMAO) is a core NASA resource for the development and use of satellite observations through the integrating tools of models and assimilation systems. Global ocean, atmosphere and land surface models are developed as components of assimilation and forecast systems that are used for addressing the weather and climate research questions identified in NASA's science mission. In fact, the GMAO is actively engaged in addressing one of NASA's science mission s key questions concerning how well transient climate variations can be understood and predicted. At weather time scales the GMAO is developing ultra-high resolution global climate models capable of resolving high impact weather systems such as hurricanes. The ability to resolve the detailed characteristics of weather systems within a global framework greatly facilitates addressing fundamental questions concerning the link between weather and climate variability. At sub-seasonal time scales, the GMAO is engaged in research and development to improve the use of land information (especially soil moisture), and in the improved representation and initialization of various sub-seasonal atmospheric variability (such as the MJO) that evolves on time scales longer than weather and involves exchanges with both the land and ocean The GMAO has a long history of development for advancing the seasonal-to-interannual (S-I) prediction problem using an older version of the coupled atmosphere-ocean general circulation model (AOGCM). This includes the development of an Ensemble Kalman Filter (EnKF) to facilitate the multivariate assimilation of ocean surface altimetry, and an EnKF developed for the highly inhomogeneous nature of the errors in land surface models, as well as the multivariate assimilation needed to take advantage of surface soil moisture and snow observations. The importance of decadal variability, especially that associated with long-term droughts is well recognized by the climate community. An improved understanding of the nature of decadal variability and its predictability has important implications for efforts to assess the impacts of global change in the coming decades. In fact, the GMAO has taken on the challenge of carrying out experimental decadal predictions in support of the IPCC AR5 effort.

O the Development and Use of Four-Dimensional Data Assimilation in Limited-Area Mesoscale Models Used for Meteorological Analysis.

NASA Astrophysics Data System (ADS)

Stauffer, David R.

1990-01-01

The application of dynamic relationships to the analysis problem for the atmosphere is extended to use a full-physics limited-area mesoscale model as the dynamic constraint. A four-dimensional data assimilation (FDDA) scheme based on Newtonian relaxation or "nudging" is developed and evaluated in the Penn State/National Center for Atmospheric Research (PSU/NCAR) mesoscale model, which is used here as a dynamic-analysis tool. The thesis is to determine what assimilation strategies and what meterological fields (mass, wind or both) have the greatest positive impact on the 72-h numerical simulations (dynamic analyses) of two mid-latitude, real-data cases. The basic FDDA methodology is tested in a 10-layer version of the model with a bulk-aerodynamic (single-layer) representation of the planetary boundary layer (PBL), and refined in a 15-layer version of the model by considering the effects of data assimilation within a multi-layer PBL scheme. As designed, the model solution can be relaxed toward either gridded analyses ("analysis nudging"), or toward the actual observations ("obs nudging"). The data used for assimilation include standard 12-hourly rawinsonde data, and also 3-hourly mesoalpha-scale surface data which are applied within the model's multi-layer PBL. Continuous assimilation of standard-resolution rawinsonde data into the 10-layer model successfully reduced large-scale amplitude and phase errors while the model realistically simulated mesoscale structures poorly defined or absent in the rawinsonde analyses and in the model simulations without FDDA. Nudging the model fields directly toward the rawinsonde observations generally produced results comparable to nudging toward gridded analyses. This obs -nudging technique is especially attractive for the assimilation of high-frequency, asynoptic data. Assimilation of 3-hourly surface wind and moisture data into the 15-layer FDDA system was most effective for improving the simulated precipitation fields because a significant portion of the vertically integrated moisture convergence often occurs in the PBL. Overall, the best dynamic analyses for the PBL, mass, wind and precipitation fields were obtained by nudging toward analyses of rawinsonde wind, temperature and moisture (the latter uses a weaker nudging coefficient) above the model PBL and toward analyses of surface-layer wind and moisture within the model PBL.

Benchmarking a soil moisture data assimilation system for agricultural drought monitoring

USDA-ARS?s Scientific Manuscript database

Agricultural drought is defined as a shortage of moisture in the root zone of plants. Recently available satellite-based remote sensing data have accelerated development of drought early warning system by providing spatially continuous soil moisture information repeatedly at short-term interval. Non...

A 15-year global biogeochemical reanalysis with ocean colour data assimilation

NASA Astrophysics Data System (ADS)

Ford, David; Barciela, Rosa

2013-04-01

A continuous global time-series of remotely sensed ocean colour observations is available from 1997 to the present day. However coverage is incomplete, and limited to the sea surface. Models are therefore required to provide full spatial coverage, and to investigate the relationships between physical and biological variables and the carbon cycle. Data assimilation can then be used to constrain models to fit the observations, thereby combining the advantages of both sources of information. As part of the European Space Agency's Climate Change Initiative (ESA-CCI), we assimilate chlorophyll concentration derived from ocean colour observations into a coupled physical-biogeochemical model. The data assimilation scheme (Hemmings et al., 2008, J. Mar. Res.; Ford et al., 2012, Ocean Sci.) uses the information from the observations to update all biological and carbon cycle state variables within the model. Global daily reanalyses have been produced, with and without assimilation of merged ocean colour data provided by GlobColour, for the period September 1997 to August 2012. The assimilation has been shown to significantly improve the model's representation of chlorophyll concentration, at the surface and at depth. Furthermore, there is evidence of improvement to the representation of pCO2, nutrients and zooplankton concentration compared to in situ observations. We use the results to quantify recent seasonal and inter-annual variability in variables including chlorophyll concentration, air-sea CO2 flux and alkalinity. In particular, we explore the impact of physical drivers such as the El Niño Southern Oscillation (ENSO) on the model's representation of chlorophyll and the carbon cycle, and the pros and cons of the model reanalyses compared to observation-based climatologies. Furthermore, we perform a comparison between the GlobColour product and an initial version of a new merged product being developed as part of the ESA-CCI. Equivalent year-long hindcasts are performed with assimilation of each data set, and compared to a control run. Differences in the products are discussed, along with their impact on model accuracy compared to in situ observations, and the representation of the carbon cycle in each hindcast.

Assimilating satellite soil moisture into rainfall-runoff modelling: towards a systematic study

NASA Astrophysics Data System (ADS)

Massari, Christian; Tarpanelli, Angelica; Brocca, Luca; Moramarco, Tommaso

2015-04-01

Soil moisture is the main factor for the repartition of the mass and energy fluxes between the land surface and the atmosphere thus playing a fundamental role in the hydrological cycle. Indeed, soil moisture represents the initial condition of rainfall-runoff modelling that determines the flood response of a catchment. Different initial soil moisture conditions can discriminate between catastrophic and minor effects of a given rainfall event. Therefore, improving the estimation of initial soil moisture conditions will reduce uncertainties in early warning flood forecasting models addressing the mitigation of flood hazard. In recent years, satellite soil moisture products have become available with fine spatial-temporal resolution and a good accuracy. Therefore, a number of studies have been published in which the impact of the assimilation of satellite soil moisture data into rainfall-runoff modelling is investigated. Unfortunately, data assimilation involves a series of assumptions and choices that significantly affect the final result. Given a satellite soil moisture observation, a rainfall-runoff model and a data assimilation technique, an improvement or a deterioration of discharge predictions can be obtained depending on the choices made in the data assimilation procedure. Consequently, large discrepancies have been obtained in the studies published so far likely due to the differences in the implementation of the data assimilation technique. On this basis, a comprehensive and robust procedure for the assimilation of satellite soil moisture data into rainfall-runoff modelling is developed here and applied to six subcatchment of the Upper Tiber River Basin for which high-quality hydrometeorological hourly observations are available in the period 1989-2013. The satellite soil moisture product used in this study is obtained from the Advanced SCATterometer (ASCAT) onboard Metop-A satellite and it is available since 2007. The MISDc ("Modello Idrologico SemiDistribuito in continuo") continuous hydrological model is used for flood simulation. The Ensemble Kalman Filter (EnKF) is employed as data assimilation technique for its flexibility and good performance in a number of previous applications. Different components are involved in the developed data assimilation procedure. For the correction of the bias between satellite and modelled soil moisture data three different techniques are considered: mean-variance matching, Cumulative Density Function (CDF) matching and least square linear regression. For properly generating the ensembles of model states, required in the application of EnKF technique, an exhaustive search of the model error parameterization and structure is carried out, differentiated for each study catchments. A number of scores and statistics are employed for the evaluation the reliability of the ensemble. Similarly, different configurations for the observation error are investigated. Results show that for four out six catchments the assimilation of the ASCAT soil moisture product improves discharge simulation in the validation period 2010-2013, mainly during flood events. The two catchments in which the assimilation does not improve the results are located in the mountainous part of the region where both MISDc and satellite data perform worse. The analysis on the data assimilation choices highlights that the selection of the observation error seems to have the largest influence on discharge simulation. Finally, the bias correction approaches have a lower effect and the selection of linear techniques is preferable. The assessment of all the components involved in the data assimilation procedure provides a clear understanding of results and it is advised to follow a similar procedure in this kind of studies.

Simulation of Seasonal Snow Microwave TB Using Coupled Multi-Layered Snow Evolution and Microwave Emission Models

NASA Technical Reports Server (NTRS)

Brucker, Ludovic; Royer, Alain; Picard, Ghislain; Langlois, Alex; Fily, Michel

2014-01-01

The accurate quantification of SWE has important societal benefits, including improving domestic and agricultural water planning, flood forecasting and electric power generation. However, passive-microwave SWE algorithms suffer from variations in TB due to snow metamorphism, difficult to distinguish from those due to SWE variations. Coupled snow evolution-emission models are able to predict snow metamorphism, allowing us to account for emissivity changes. They can also be used to identify weaknesses in the snow evolution model. Moreover, thoroughly evaluating coupled models is a contribution toward the assimilation of TB, which leads to a significant increase in the accuracy of SWE estimates.