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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS1981

The relative contributions of increased resolution in the data assimilation and in the forecast model to satellite data impact

Assimilation cycles were carried out with two versions of the GLAS second order GCM: a coarse version with 4 deg latitude by 5 deg longitude resolution, called the C model, and a fine version with 2.5 deg latitude by 3 deg longitude resolution called the F model. For the two DST-6 cases where the combined influence of satellite data and model resolution are at a maximum at sea level, the relative contributions of increased resolution in the data assimilation and in the forecast models were evaluated. F model forecasts were generated from the C model SAT assimilation interpolated by the F grid, and C model forecasts were generated from the F model SAT assimilation interpolated to the C grid. These forecasts were then compared with the corresponding forecasts which had utilized the same grid resolution in the data assimilation and forecast models, CS and FS.

Atlas, R.↗
NASA NTRS1994

A fixed-lag Kalman smoother for retrospective data assimilation

Analyses incorporating future data, as well as current and past data, are termed retrospective analyses. In this paper, the fixed-lag Kalman smoother (FLKS) is proposed as a means of providing retrospective analysis capability in data assimilation. The FLKS is a direct generalization of the Kalman filter. It incorporates all data observed up to and including some fixed amount of time past each analysis time. A computationally efficient form of the FLKS is derived. A simple scalar examination of the FLKS demonstrates that incorporating future data improves analyses the most in the presence of dynamical instabilities, for accurate models and for accurate observations. An implementation of the FLKS for two-dimensional linear shallow-water model corroborates the scalar analysis. The numerical experiments also demonstrate the ability of the FLKS to propagate information upstream as well as downstream, thus improving analysis quality substantially in data voids.

Cohn, Stephen E.↗
NASA NTRS1985

Variational mesoscale satellite data assimilation and initialization

The problems of mesoscale satellite data assimilation were examined. Assimilation of satellite data to improve the forecasts made by mesoscale forecast models was undertaken. Assimilation of high resolution satellite derived temperature data into a mesoscale model with horizontal resolution of 50 to 60 km is reported. Unlike global assimilation, in which a small portion of the forecast model domain is subject to data insertion at virtually every time step, the mesoscale assimilation virtually all of the forecast model domain is subject to data insertion at one time step. The mesoscale problem lends itself naturally to intermittent data assimilation and the forecast model is reinitialized whenever a new satellite pass covers its domain with data. The satellite data assimilation as an initialization problem are discussed.

Sasaki, Y. K.↗
NASA NTRS1993

Examples of data assimilation in mesoscale models

The keynote address was the problem of physical initialization of mesoscale models. The classic purpose of physical or diabatic initialization is to reduce or eliminate the spin-up error caused by the lack, at the initial time, of the fully developed vertical circulations required to support regions of large rainfall rates. However, even if a model has no spin-up problem, imposition of observed moisture and heating rate information during assimilation can improve quantitative precipitation forecasts, especially early in the forecast. The two key issues in physical initialization are the choice of assimilating technique and sources of hydrologic/hydrometeor data. Another example of data assimilation in mesoscale models was presented in a series of meso-beta scale model experiments with and 11 km version of the MASS model designed to investigate the sensitivity of convective initiation forced by thermally direct circulations resulting from differential surface heating to four dimensional assimilation of surface and radar data. The results of these simulations underscore the need to accurately initialize and simulate grid and sub-grid scale clouds in meso- beta scale models. The status of the application of the CSU-RAMS mesoscale model by the NOAA Forecast Systems Lab for producing real-time forecasts with 10-60 km mesh resolutions over (4000 km)(exp 2) domains for use by the aviation community was reported. Either MAPS or LAPS model data are used to initialize the RAMS model on a 12-h cycle. The use of MAPS (Mesoscale Analysis and Prediction System) model was discussed. Also discussed was the mesobeta-scale data assimilation using a triply-nested nonhydrostatic version of the MM5 model.

Carr, Fred↗
NASA NTRS1985

Systematic estimation of forecast and observation error covariances in four-dimensional data assimilation

A two-part algorithm is presented for reliably computing weather forecast model and observational error covariances during data assimilation. Data errors arise from instrumental inaccuracies and sub-grid scale variability, whereas forecast errors occur because of modeling errors and the propagation of previous analysis errors. A Kalman filter is defined as the primary algorithm for estimating the forecast and analysis error convariance matrices. A second algorithm is described for quantifying the noise covariance matrices of any degree to obtain accurate values for the observational error covariances. Numerical results are provided from a linearized one-dimensional shallow-water model. The results cover observational noise covariances, initial instrumental errors and erroneous model values.

Dee, D. P.↗
NASA NTRS1985

Future possibilities in objective analysis and data assimilation for atmospheric dynamics

The role that dynamics plays in estimating the state of the atmosphere from incomplete and noisy data is reviewed. Objective analysis represents an attempt at relying mostly on the data and minimizing the role of dynamics in the estimation. Data assimilation tries to balance properly the roles of dynamical and observational information. Sequential estimation is presented as the proper framework for understanding this balance, and the Kalman filter as the ideal, optimal procedure for data assimilation. The optimal filter computes forecast error covariances of a given atmospheric model exactly, and hence data assimilation should be closely connected with predictability studies. This connection is described, and consequences drawn for currently active areas of the atmospheric and related sciences, namely, mesoscale meteorology, long range forecasting, and upper ocean dynamics. Possibilities offered by judicious data assimilation in understanding barotropic adjustment, a phenomenon that appears to play a crucial role in atmospheric behavior on the scale of weeks to months, and hence in long range forecasting are addressed.

Ghil, M.↗
NASA NTRS1994

Data assimilation and model evaluation experiment datasets

The Institute for Naval Oceanography, in cooperation with Naval Research Laboratories and universities, executed the Data Assimilation and Model Evaluation Experiment (DAMEE) for the Gulf Stream region during fiscal years 1991-1993. Enormous effort has gone into the preparation of several high-quality and consistent datasets for model initialization and verification. This paper describes the preparation process, the temporal and spatial scopes, the contents, the structure, etc., of these datasets. The goal of DAMEE and the need of data for the four phases of experiment are briefly stated. The preparation of DAMEE datasets consisted of a series of processes: (1) collection of observational data; (2) analysis and interpretation; (3) interpolation using the Optimum Thermal Interpolation System package; (4) quality control and re-analysis; and (5) data archiving and software documentation. The data products from these processes included a time series of 3D fields of temperature and salinity, 2D fields of surface dynamic height and mixed-layer depth, analysis of the Gulf Stream and rings system, and bathythermograph profiles. To date, these are the most detailed and high-quality data for mesoscale ocean modeling, data assimilation, and forecasting research. Feedback from ocean modeling groups who tested this data was incorporated into its refinement. Suggestions for DAMEE data usages include (1) ocean modeling and data assimilation studies, (2) diagnosis and theoretical studies, and (3) comparisons with locally detailed observations.

Lai, Chung-Cheng A.↗
NASA NTRS1993

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

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.

Li, Y.↗
NASA NTRS1984

VAS data assimilation into a mesoscale model

In numerical weather prediction, one of the arising problems has always been related to insufficient data, especially in cases involving scales smaller than the synoptic rawinsonde network. The Visible Infrared Spin-Scan Radiometer (VISSR) Atmospheric Sounder (VAS) instrument on the Geostationary Operational Environmental Satellite (GOES) can provide mesoscale vertical profiles of temperature and moisture several times over a twelve hour period. However, as this data is only available over a relatively small area at any one time, the VAS data is more suited to data assimilation. A variational method has been developed to assimilate VAS data into a mesoscale model. In the present paper, the assimilation method and VAS data from one case are discussed. It is found that the greatest improvements to the forecast resulted when the VAS data resolved the mesoscale structure of the temperature and relative humidity fields.

Cram, J. M.↗
NASA NTRS1986

Sequential estimation and satellite data assimilation in meteorology and oceanography

The central theme of this review article is the role that dynamics plays in estimating the state of the atmosphere and of the ocean from incomplete and noisy data. Objective analysis and inverse methods represent an attempt at relying mostly on the data and minimizing the role of dynamics in the estimation. Four-dimensional data assimilation tries to balance properly the roles of dynamical and observational information. Sequential estimation is presented as the proper framework for understanding this balance, and the Kalman filter as the ideal, optimal procedure for data assimilation. The optimal filter computes forecast error covariances of a given atmospheric or oceanic model exactly, and hence data assimilation should be closely connected with predictability studies. This connection is described, and consequences drawn for currently active areas of the atmospheric and oceanic sciences, namely, mesoscale meteorology, medium and long-range forecasting, and upper-ocean dynamics.

Ghil, M.↗
NASA NTRS1992

The minor stratospheric warming of January 1989 - Results from STRATAN, a stratospheric-tropospheric data assimilation system

Using a stratospheric-tropospheric data assimilation system, referred to as STRATAN, a minor sudden stratospheric warming that occurred in January 1989 is investigated. The event had a maximum influence on the stratospheric circulation near 2 hPa. The zonal mean circulation reversed briefly in the polar region as the temperature increased 34 K in 3 days. The cause of the warming is shown to be the rapid development and subsequent movement of a warm anomaly, which initially developed in the midlatitudes. The development of the warm anomaly is caused by adiabatic descent, and the dissipation by radiative cooling. A brief comparison with the NMC analysis and temperature sounding data is also presented.

Steenrod, Stephen D.↗
NASA NTRS1994

Advanced data assimilation in strongly nonlinear dynamical systems

Advanced data assimilation methods are applied to simple but highly nonlinear problems. The dynamical systems studied here are the stochastically forced double well and the Lorenz model. In both systems, linear approximation of the dynamics about the critical points near which regime transitions occur is not always sufficient to track their occurrence or nonoccurrence. Straightforward application of the extended Kalman filter yields mixed results. The ability of the extended Kalman filter to track transitions of the double-well system from one stable critical point to the other depends on the frequency and accuracy of the observations relative to the mean-square amplitude of the stochastic forcing. The ability of the filter to track the chaotic trajectories of the Lorenz model is limited to short times, as is the ability of strong-constraint variational methods. Examples are given to illustrate the difficulties involved, and qualitative explanations for these difficulties are provided. Three generalizations of the extended Kalman filter are described. The first is based on inspection of the innovation sequence, that is, the successive differences between observations and forecasts; it works very well for the double-well problem. The second, an extension to fourth-order moments, yields excellent results for the Lorenz model but will be unwieldy when applied to models with high-dimensional state spaces. A third, more practical method--based on an empirical statistical model derived from a Monte Carlo simulation--is formulated, and shown to work very well. Weak-constraint methods can be made to perform satisfactorily in the context of these simple models, but such methods do not seem to generalize easily to practical models of the atmosphere and ocean. In particular, it is shown that the equations derived in the weak variational formulation are difficult to solve conveniently for large systems.

Miller, Robert N.↗
NASA NTRS1993

Thermodynamic balance of three-dimensional stratospheric winds derived from a data assimilation procedure

The NASA/Goddard three-dimensional chemistry and transport model is driven by winds from a stratospheric data assimilation system. Synoptic- and planetary-scale patterns, apparent in satellite observations of trace constituents, are successfully reproduced for seasonal integrations. As model integrations proceed, however, the quality of simulations decreases, and systematic differences between calculation and measurement appear. The differences are explained by examining the zonal-mean residual circulation. The vertical velocity w-bar (sup star) is calculated two ways: (1) from the diabatic heating rates and temperature tendency and (2) from the Eulerian vertical velocity and the horizontal eddy heat flux convergence. The results from these calculations differ substantially. Periodic insertion of observational data during the assimilation process continually shocks the general circulation model and produces these differences, which leads to an overestimate of the mean vertical heat and cconstituent transport. Such differences are expected to be general to all data assimilation products. This interpretation is corroborated by two-dimensional (2D) model calculations. When w-bar(sup star) is calculated from (2), the 2D ozone evolution is unrealistic and qualitatively similar to the 3D model simulation. The 2D ozone evolution is reasonable when w-bar (sup star) from (1).

Weaver, Clark J.↗
NASA NTRS1991

A Pacific Ocean general circulation model for satellite data assimilation

A tropical Pacific Ocean General Circulation Model (OGCM) to be used in satellite data assimilation studies is described. The transfer of the OGCM from a CYBER-205 at NOAA's Geophysical Fluid Dynamics Laboratory to a CRAY-2 at NASA's Ames Research Center is documented. Two 3-year model integrations from identical initial conditions but performed on those two computers are compared. The model simulations are very similar to each other, as expected, but the simulations performed with the higher-precision CRAY-2 is smoother than that with the lower-precision CYBER-205. The CYBER-205 and CRAY-2 use 32 and 64-bit mantissa arithmetic, respectively. The major features of the oceanic circulation in the tropical Pacific, namely the North Equatorial Current, the North Equatorial Countercurrent, the South Equatorial Current, and the Equatorial Undercurrent, are realistically produced and their seasonal cycles are described. The OGCM provides a powerful tool for study of tropical oceans and for the assimilation of satellite altimetry data.

Chao, Y.↗
NASA NTRS1991

Simplification of the Kalman filter for meteorological data assimilation

The paper proposes a new statistical method of data assimilation that is based on a simplification of the Kalman filter equations. The forecast error covariance evolution is approximated simply by advecting the mass-error covariance field, deriving the remaining covariances geostrophically, and accounting for external model-error forcing only at the end of each forecast cycle. This greatly reduces the cost of computation of the forecast error covariance. In simulations with a linear, one-dimensional shallow-water model and data generated artificially, the performance of the simplified filter is compared with that of the Kalman filter and the optimal interpolation (OI) method. The simplified filter produces analyses that are nearly optimal, and represents a significant improvement over OI.

Dee, Dick P.↗
NASA NTRS1980

The compatible balancing approach to initialization, and four-dimensional data assimilation

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.

Ghil, M.↗
NASA NTRS1987

Forecast experiments with the NASA/GLA stratospheric/tropospheric data assimilation system

For the first time, a four-dimensional stratospheric/tropospheric data assimilation system with a top analysis level at 0.4 mb has been developed and used to produce physically consistent gridded analyses for the stratosphere as well as the troposphere for a period during the First GARP Global Experiment (FGGE) and Limb Infrared Monitor of the Stratosphere (LIMS) (November 1978-May 1979). The system consists of a two-dimensional optimum interpolation analysis with 18 mandatory pressure levels and a 19-level fourth order stratospheric/tropospheric general circulation model with a horizontal resolution of 4 (latitude) by 5 deg (longitude) and a top at 0.3 mb. The system allows the utilization of stratospheric data including LIMS, Tiros-N retrievals, rocketsondes and vertical temperature profile radiometer soundings in addition to the other FGGE level 2b data. These data are analyzed every six hours. In order to examine the quality of the analyzed data, forecast experiments starting from different analyses are performed for the period of the stratospheric sudden warming of late February 1979. The results indicate that by employing the present four-dimensional assimilation approach, the medium-range forecast skill for this event is improved.

Takano, Kenji↗
NASA NTRS1994

A comparison of winds from the STRATAN data assimilation system to balanced wind estimates

Winds derived from a stratospheric and tropospheric data assimilation system (STRATAN) are compared with balance winds derived from National Meteorological Center/Climate Analysis Center (NMC/CAC) heights. At middle latitudes in the lower stratosphere, the results show that STRATAN winds are comparable to the balance winds. In addition STRATAN winds provide useful horizontal divergence analyses, and hence, vertical velocity fields. More generally, the STRATAN winds are useful in a more extended domain than the balanced winds. In particular, they are useful in the Tropics and the upper stratosphere where the balanced winds fail. The assimilation also captures the quasi-biennial oscillation, but does not do a good job of representing tropical waves.

Coy, Lawrence↗