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A method for compensating for the effect of the varying travel time of a transmitted laser pulse to a satellite is described. The 'observed minus predicted' range differences then appear to be linear, which makes data screening or use in range gating more effective.
The on-line range prediction system is designed for providing a prediction of the target range in the case of a laser data dropout. It consists of real time implementation of a Kalman filter on an IBM PC/AT equipped with necessary hardware. The system was set up and tested at Crows Landing in the Fall of 1987. The improvements made on the on-line range prediction system during 1988 are examined. Solutions are proposed and discussed to the several problems encountered during system tests. Then, the improvements made on the filter software are explained, namely, accounting for the time lag and providing data continously. Finally, the ideas are mentioned that can be considered in the future.
Use of geosynchronous satellites to improve the short range prediction of convective and mesoscale phenomena by measuring cloud parameters; surface temperature; and temperature and moisture profiles and gradients (in clear and partly cloudy areas) with temporal resolutions of minutes, and spatial scales ranging from 1 km (for clouds) to tens to kilometers (temperature and moisture profiles) is discussed. Improvements can be made with microwave sounding (temperature and moisture profiles in cloudy areas) and imaging (precipitation). Higher spectral resolution infrared sounding yields a 50% better vertical resolution for temperature and moisture profiles. Very high resolution imaging (infrared resolution of 1 km, visible resolution of 200 m) is advocated for convection monitoring.
NASA LaRC personnel have conducted a strudy of the predicted acoustic detection ranges associated with reduced helicopter main rotor speeds. This was accomplished by providing identical input information to both the aural detection program ICHIN 6, (I Can Hear It Now, version 6) and the electronic acoustic detection program ARCAS (Assessment of Rotorcraft Detection by Acoustics Sensing). In this study, it was concluded that reducing the main rotor speed of the helicopter by 27 percent reduced both the predicted aural and electronic detection ranges by approximately 50 percent. Additionally, ARCAS was observed to function better with narrowband spectral input than with one-third octave band spectral inputs and the predicted electronic range of acoustic detection is greater than the predicted aural detection range.
A framework and methodology for long term projection of demand for aviation fuels is presented. The approach taken includes two basic components. The first was a new technique for establishing the socio-economic environment within which the future aviation industry is embedded. The concept utilized was a definition of an overall societal objective for the very long run future. Within a framework so defined, a set of scenarios by which the future will unfold are then written. These scenarios provide the determinants of the air transport industry operations and accordingly provide an assessment of future fuel requirements. The second part was the modeling of the industry in terms of an abstracted set of variables to represent the overall industry performance on a macro scale. The model was validated by testing the desired output variables from the model with historical data over the past decades.
The paper describes the methodology for long-time projection of aircraft fuel requirements. A new concept of social and economic factors for future aviation industry which provides an estimate of predicted fuel usage is presented; it includes air traffic forecasts and lead times for producing new engines and aircraft types. An air transportation model is then developed in terms of an abstracted set of variables which represent the entire aircraft industry on a macroscale. This model was evaluated by testing the required output variables from a model based on historical data over the past decades.
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This paper describes the results of extended range predictability experiments using an efficient two-level spherical quasi-geostrophic model. The experiments have an initial rms doubling time of about two days. This growth rate, along with an initial error of about one-half the initial error of present operational models, produces an rms error equal to the climatological rms error and a correlation of 0.5 on about day 12 of the forecast. On the largest scales, this limiting point is reached shortly thereafter. The error continues to grow at a decreasing rate until at about 30 days the forecast skill is extremely small and comparable to the skill of a persistence forecast. Various time averages at various lags are examined for skill in the extended range. Filters that weighted most strongly in the initial forecast days provide increased skill.
This paper presents a computerized technique for medium-range (12-48h) prediction of both the location and severity of thunderstorms utilizing atmospheric predictions from the National Meteorological Center's limited-area fine-mesh model (LFM). A regional-scale analysis scheme is first used to examine the spatial and temporal distributions of forecasted variables associated with the structure and dynamics of mesoscale systems over an area of approximately 10 to the 6th sq km. The final prediction of thunderstorm location and severity is based upon an objective combination of these regionally analyzed variables. Medium-range thunderstorm predictions are presented for the late afternoon period of April 10, 1979, the day of the Wichita Falls, Texas tornado. Conventional medium-range thunderstorm forecasts, made from observed data, are presented with the case study to demonstrate the possible application of this objective technique in improving 12-48 h thunderstorm forecasts for aviation.
An earlier study established the existence of globally coherent interannual fluctuations in atmospheric angular momentum (AAM), associated with the El Nino-Southern Oscillation (ENSO) cycle. In this paper, we pursue the origin and the structure of these fluctuations using an ensemble of experiments generated by the National Centers for Environmental Prediction, medium range forecast model version 9. In the control experiments, where the observed sea surface temperatures (SSTs) were used as the lower boundary conditions, the model captures the characteristic V-like structure in time-latitude plots of zonally averaged AAM, while experiments with climatological SSTs and those with either perpetual warm or cold ENSO conditions superimposed on the climatological SSTs failed to reproduce this structure. The numerical results indicate that these AAM structures are related to SST variations associated with transitions between different phases of the ENSO cycle and have both propagating and standing components. The largest zonal wind contribution from the levels studied (850, 500, and 200 hPa) is at 200 hPa, where the tropical convective outflow is the strongest. Composites of zonal wind and geopotential height show a clear relationship between the stages of the global AAM oscillation and the ENSO cycle. The strong similarity between the simulated and observed AAM series attests to the model's ability to realistically simulate the interannual response of the atmosphere to ENSO SST anomalies.
One function of the Deep Space Network is validation of the range data that they receive. In this paper we present an automated online sequential range predictor which shows promise of significantly reducing computational and manpower expenditures. The proposed algorithm, a U-D covariance factored Kalman filter, is demonstrated by processing a four-month record of Viking spacecraft data taken enroute to Mars.
Long range solar flare forecasting noting correlation between planetary conjunctions and proton events
Report and supplementary materials present method for estimating maximum distance which Spartan free flyer seen by star trackers on Space Shuttle. Brightness, and visible range calculated by using simplified model of object as reflector, taking into consideration orientation of object and predicted relative positions of Shuttle and Sun.
Reusable surface insulation materials, which were developed as heat shields for the space shuttle, were tested over a range of conditions including heat-transfer rates between 160 and 620 kW/sq m. The lowest of these heating rates was in a range predicted for the space shuttle during reentry, and the highest was more than twice the predicted entry heating on shuttle areas where reusable surface insulation would be used. Individual specimens were tested repeatedly at increasingly severe conditions to determine the maximum heating rate and temperature capability. A silica-base material experienced only minimal degradation during repeated tests which included conditions twice as severe as predicted shuttle entry and withstood cumulative exposures three times longer than the best mullite material. Mullite-base materials cracked and experienced incipient melting at conditions within the range predicted for shuttle entry. Neither silica nor mullite materials consistently survived the test series with unbroken waterproof surfaces. Surface temperatures for a silica and a mullite material followed a trend expected for noncatalytic surfaces, whereas surface temperatures for a second mullite material appeared to follow a trend expected for a catalytic surface.
Near-real-time validation of ranging system data is currently restricted to multiple range acquisitions during single passes (Pseudo-DRVID). A technique (Differential Range Validation) which utilizes predicted range and doppler pseudo-residuals to validate two-station, contiguous-pass range acquisitions down to the 10-meter level is described.
Until recently, all operational meteorological data has been made available to forecasters in a variety of different forms. Predictions based upon these different data formats have been complicated by the inability of forecasters to easily assimilate, in real-time, all data to provide an optimum decision regarding future weather occurrences. By March 1980, a joint NASA/NOAA effort had been initiated to develop the Centralized Storm Information System (CSIS). The primary objectives of this joint project are related to an improvement of the overall severe storm forecast and warning procedure and to a demonstration of the operational utility of techniques developed within the applied research community. CSIS is to utilize the Man Computer Interactive Data Access System (McIDAS). The present investigation is concerned with one of the first attempts to employ the CSIS system for the evaluation of a new research technique involving the prediction of thunderstorms over a forecast period of 12-48 hours.
Techniques for remote sensing of particles, from cloud droplet to hailstone size, using optical and microwave frequencies are reviewed. The inherent variability of atmospheric particulates is examined to delineate conditions when the signal can give information to be effectively utilized in a forecasting context. The physical limitations resulting from the phase, size, orientation and concentration variability of the particulates are assessed.
Range pseudo-residuals may be improved to the level required for data validation by a measurement updating process which utilizes Bierman's adaptation of the Kalman filter measurement updating algorithms together with process noise compensation to account for model errors. This algorithm involves combining the currently available range predictions and measurements to produce an updated range residual measurement whose accuracy is constrained by the range data quality and by the estimated error in the prediction. The algorithm is compact and fast, and is thus suitable for on-line applications in network control or at the station.