Engineering topics

Bloom, S. C.

Publications and source records attributed to Bloom, S. C..

17 records

NASA NTRS2001

The Impact of QuikScat on Weather Analysis and Forecasting

Scatterometer observations of the ocean surface wind speed and direction improve the depiction and prediction of storms at sea. These data are especially valuable where observations are otherwise sparse, mostly in the Southern Hemisphere and tropics, but also on occasion in the North Atlantic and North Pacific The SeaWinds scatterometer on the QuikScat satellite was launched in June 1999 and it represents a dramatic departure in design from the other scatterometer instruments launched during the past decade (ERS-1,2 and NSCAT). More details on the SeaWinds instrument can be found in Atlas et al. (2001) and Bloom et al. (1999). This presentation shows the influence of QuikScat data in data assimilation systems both from the NASA Data Assimilation Office (GEOS-3) and from NCEP (GDAS).

Atlas, Robert↗

NASA NTRS2001

A Two-Dimensional Variational Analysis Method for NSCAT Ambiguity Removal: Methodology, Sensitivity, and Tuning

In this study, we apply a two-dimensional variational analysis method (2d-VAR) to select a wind solution from NASA Scatterometer (NSCAT) ambiguous winds. 2d-VAR determines a "best" gridded surface wind analysis by minimizing a cost function. The cost function measures the misfit to the observations, the background, and the filtering and dynamical constraints. The ambiguity closest in direction to the minimizing analysis is selected. 2d-VAR method, sensitivity and numerical behavior are described. 2d-VAR is compared to statistical interpolation (OI) by examining the response of both systems to a single ship observation and to a swath of unique scatterometer winds. 2d-VAR is used with both NSCAT ambiguities and NSCAT backscatter values. Results are roughly comparable. When the background field is poor, 2d-VAR ambiguity removal often selects low probability ambiguities. To avoid this behavior, an initial 2d-VAR analysis, using only the two most likely ambiguities, provides the first guess for an analysis using all the ambiguities or the backscatter data. 2d-VAR and median filter selected ambiguities usually agree. Both methods require horizontal consistency, so disagreements occur in clumps, or as linear features. In these cases, 2d-VAR ambiguities are often more meteorologically reasonable and more consistent with satellite imagery.

Hoffman, R. N.↗

NASA NTRS1999

Assimilation of SeaWinds Scatterometer Data in the GEOS Data Assimilation System

The first SeaWinds scatterometer was launched in to space aboard the Quikscat satellite on June 19, 1999 at 7:15 p.m. PDT. Flying in a near polar orbit 800 km above the earth's surface, SeaWinds uses an advanced scatterometer design to measure surface wind velocity over 90 percent of the ice free oceans ever 24 hours. This first SeaWinds mission is designed to replace the NASA Scatterometer (NSCAT) which ceased providing wind velocity data when the ADEOS I satellite failed. A second SeaWinds is scheduled to be launched late in 2000 aboard ADEOS II. Previous scatterometer assimilation experiments conducted by the NASA Data Assimilation Office, using both ERS and NSCAT wind observations, have demonstrated considerable potential for this type of data to improve both atmospheric analyses and forecasts, however much of the smaller scale information content of the scatterometer data could not be taken into account in the early coarse resolution versions of the Goddard (GEOS) Data Assimilation System (DAS) or in operational data assimilation systems. In this paper, we will describe data assimilation experiments in which the new higher resolution versions of the GOES DAS are used to assimilate SeaWinds scatterometer winds. Following a brief discussion of the SeaWinds design and the methodology used to assimilate scatterometer data in the GOES DAS, the quality of the SeaWinds data and the impact of SeaWinds on GOES analyses and forecasts at different resolutions will be presented.

Atlas, Robert↗

NASA NTRS1998

The Impact of NASA Scatterometer Data on GEOS-2 Analyses and Forecasts

Experiments with the evolving GEOS-2 data assimilation system (DAS) delineate the impact of NASA scatterometer (NSCAT) data on ocean surface analysis and numerical weather prediction (NWP). Extensions and refinements of the DAS to account for the characteristics of NSCAT data produced better results than were obtained with the GEOS-1 DAS. The two key extensions are to increase the vertical influence of the surface wind data and to take proper account of the time difference between the observation and the analysis. The results of these experiments show that surface wind analyses are improved by NSCAT data. This is seen in subjective evaluation of synoptic cases and in forecast impacts. NSCAT data have a very significant positive impact on the GEOS-2 forecasts in the southern hemisphere (SH). In the northern hemisphere (NH) the overall statistics show a modest positive impact. However, on a case by case basis, in the NH, the impact is generally neutral or significantly positive. The GEOS-2 results are compared to results obtained using the GEOS-1 DAS and using the 1995 National Center for Environmental Prediction (NCEP95) DAS. The GEOS-2 control forecasts are more accurate than those of GEOS-1. All three impact experiments show a large positive impact in the SH. In the NH, both the GEOS-2 and the NCEP95 NSCAT impacts are positive while the GEOS-1 impact is neutral.

Atlas, R.↗

NASA NTRS1991

Space-based surface wind vectors to aid understanding of air-sea interactions

A novel and unique ocean-surface wind data-set has been derived by combining the Defense Meteorological Satellite Program Special Sensor Microwave Imager data with additional conventional data. The variational analysis used generates a gridded surface wind analysis that minimizes an objective function measuring the misfit of the analysis to the background, the data, and certain a priori constraints. In the present case, the European Center for Medium-Range Weather Forecasts surface-wind analysis is used as the background.

Atlas, R.↗

NASA NTRS1988

Assimilation of satellite surface wind speed data using the GLA analysis/forecast system

Research at the Goddard Laboratory for Atmospheres to evaluate methods to assign directions to both real and simulated surface wind speed data is discussed. Surface wind speed measurements are obtained from satellites, including Seasat, Nimbus-7, and Geosat. The methods include the interpolation of modeled forecast winds to the wind speed datum location, the use of surface pressures with a balance relation, and a variational analysis method. It is found that the best estimates may be obtained using a multipass approach to perform a surface analysis incorporating all conventional surface data.

Bloom, S. C.↗

NASA NTRS1986

Analysis/forecast experiments with a flow-dependent correlation function using FGGE data

The use of a flow-dependent correlation function to improve the accuracy of an optimum interpolation (OI) scheme is examined. The development of the correlation function for the OI analysis scheme used for numerical weather prediction is described. The scheme uses a multivariate surface analysis over the oceans to model the pressure-wind error cross-correlation and it has the ability to use an error correlation function that is flow- and geographically-dependent. A series of four-day data assimilation experiments, conducted from January 5-9, 1979, were used to investigate the effect of the different features of the OI scheme (error correlation) on forecast skill for the barotropic lows and highs. The skill of the OI was compared with that of a successive correlation method (SCM) of analysis. It is observed that the largest difference in the correlation statistics occurred in barotropic and baroclinic lows and highs. The comparison reveals that the OI forecasts were more accurate than the SCM forecasts.

Baker, W. E.↗

NASA NTRS1985

Comparison of Optimum Interpolation and Cressman Analyses

The development of a state of the art optimum interpolation (O/I) objective analysis procedure for use in numerical weather prediction studies was investigated. A three dimensional multivariate O/I analysis scheme was developed. Some characteristics of the GLAS O/I compared with those of the NMC and ECMWF systems are summarized. Some recent enhancements of the GLAS scheme include a univariate analysis of water vapor mixing ratio, a geographically dependent model prediction error correlation function and a multivariate oceanic surface analysis.

Baker, W. E.↗

NASA NTRS1985

Recent Developments in Nonlinear Normal Mode Initialization

The importance of a balanced initial condition upon GLAS GCM forecasts and assimilation cycles was assessed. An effort to combine previous work on normal mode initialization at GLA is underway to develop an initialization process for the production version of the GLAS 4th order GCM. The major aspects of this work fall into two parts: vectorization of the linear projector code and the insertion of the mode projector and Machenhauer iteration algorithm into the full GLAS GCM. Memory and paging constraints place restrictions on the number of horizontal modes stored for initialization purposes, and on the manner in which they are stored. Only the first five vertical structures of the gravity modes are used. Differing phase and normalization conventions provided many elusive coding errors. A Machenhauer nonlinear normal mode initialization technique is used. This method entails the insertion of a modified version of the mode projector into the full GCM, and the modification of the GCM to allow for iterative calls to the projector.

Bloom, S. C.↗

NASA NTRS1985

Analysis/forecast experiments with a multivariate statistical analysis scheme using FGGE data

A three-dimensional, multivariate, statistical analysis method, optimal interpolation (OI) is described for modeling meteorological data from widely dispersed sites. The model was developed to analyze FGGE data at the NASA-Goddard Laboratory of Atmospherics. The model features a multivariate surface analysis over the oceans, including maintenance of the Ekman balance and a geographically dependent correlation function. Preliminary comparisons are made between the OI model and similar schemes employed at the European Center for Medium Range Weather Forecasts and the National Meteorological Center. The OI scheme is used to provide input to a GCM, and model error correlations are calculated for forecasts of 500 mb vertical water mixing ratios and the wind profiles. Comparisons are made between the predictions and measured data. The model is shown to be as accurate as a successive corrections model out to 4.5 days.

Baker, W. E.↗

NASA NTRS1984

Comparison of Optimum Interpolation and Cressman Analyses

The objective of this investigation is to develop a state-of-the-art optimum interpolation (O/I) objective analysis procedure for use in numerical weather prediction studies. A three-dimensional multivariate O/I analysis scheme has been developed. Some characteristics of the GLAS O/I compared with those of the NMC and ECMWF systems are summarized. Some recent enhancements of the GLAS scheme include a univariate analysis of water vapor mixing ratio, a geographically dependent model prediction error correlation function and a multivariate oceanic surface analysis.

Baker, W. E.↗

NASA NTRS1984

Normal Mode Initialization

The objective of this research is the development and implementation of normal mode procedures for use with the GLAS analysis/forecast system. Specific tasks of this work include: (1) high latitude filtering of model fields to preserve the GLAS GCM's linear stability during integration; (2) development of nonlinear normal mode initialization (NLNMI) processes, both adiabatic and diabatic. Using NLNMI to initialize GLAS analyses; investigation of the impact of normal mode initialization on the GLAS analysis/forecast system, especially in regard to data assimilation; and (3) diagnosis of the 1-3 day systemic forecast errors of the GLAS GCM.

Bloom, S. C.↗

NASA NTRS1984

Multivariate optimum interpolation of surface pressure and winds over oceans

The observations of surface pressure are quite sparse over oceanic areas. An effort to improve the analysis of surface pressure over oceans through the development of a multivariate surface analysis scheme which makes use of surface pressure and wind data is discussed. Although the present research used ship winds, future versions of this analysis scheme could utilize winds from additional sources, such as satellite scatterometer data.

Bloom, S. C.↗

NASA NTRS1984

Design of a linear projector for use with the normal modes of the GLAS 4th order GCM

The design of a linear projector for use with the normal modes of a model of atmospheric circulation is discussed. A central element in any normal mode initialization scheme is the process by which a set of data fields - winds, temperatures or geopotentials, and surface pressures - are expressed ("projected') in terms of the coefficients of a model's normal modes. This process is completely analogous to the Fourier decomposition of a single field (indeed a FFT applied in the zonal direction is a part of the process). Complete separability in all three spatial dimensions is assumed. The basis functions for the modal expansion are given. An important feature of the normal modes is their coupling of the structures of different fields, thus a coefficient in a normal mode expansion would contain both mass and momentum information.

Bloom, S. C.↗

NASA NTRS1984

Computational aspects of the nonlinear normal mode initialization of the GLAS 4th order GCM

Using the normal modes of the GLAS 4th Order Model, a Machenhauer nonlinear normal mode initialization (NLNMI) was carried out for the external vertical mode using the GLAS 4th Order shallow water equations model for an equivalent depth corresponding to that associated with the external vertical mode. A simple procedure was devised which was directed at identifying computational modes by following the rate of increase of BAL sub M, the partial (with respect to the zonal wavenumber m) sum of squares of the time change of the normal mode coefficients (for fixed vertical mode index) varying over the latitude index L of symmetric or antisymmetric gravity waves. A working algorithm is presented which speeds up the convergence of the iterative Machenhauer NLNMI. A 24 h integration using the NLNMI state was carried out using both Matsuno and leap-frog time-integration schemes; these runs were then compared to a 24 h integration starting from a non-initialized state. The maximal impact of the nonlinear normal mode initialization was found to occur 6-10 hours after the initial time.

Navon, I. M.↗

NASA NTRS1984

Multivariate optimum interpolation of surface pressure and surface wind over oceans

The present multivariate analysis method for surface pressure and winds incorporates ship wind observations into the analysis of surface pressure. For the specific case of 0000 GMT, on February 3, 1979, the additional data resulted in a global rms difference of 0.6 mb; individual maxima as larse as 5 mb occurred over the North Atlantic and East Pacific Oceans. These differences are noted to be smaller than the analysis increments to the first-guess fields.

Bloom, S. C.↗

NASA NTRS1983

The use of dynamical constraints in the analysis of mesoscale rawinsonde data

A process for data interpolation which seeks to maintain dynamical consistency is applied to the analysis of mesoscale rawinsonde data obtained from the National Severe Storms Laboratory. The analysis procedure is in two steps: a successive-corrections interpolation to gridpoints, followed by a variational adjustment which uses a set of forecast equations of horizontal momentum as dynamical constraints. The objective analysis is applied in case studies of conditions on May 22, 1966 and June 8, 1966, and it is shown that the adjusted winds and geopotentials have greatly reduced residuals compared to the initial fields. Adjustments to the wind and geopotential fields have root mean square values of 1-2 m/sec and 30-50 sq m/sec per sec, respectively. In addition, the vertical velocity fields computed from the adjusted winds are found to be in closer agreement with actual weather data.

Bloom, S. C.↗