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Simulated multispectral data sets used in the Thematic Mapper design parameter investigation are analyzed. The sets were produced by processing high-resolution digital multispectral data through models of the various Thematic Mapper configurations and then through three resampling techniques as a simulation of ground processing. Emphasis is placed on classification processing and analysis of results.
A prototype expert system was developed to demonstrate the feasibility of classifying multispectral remotely sensed data on the basis of spectral knowledge. The spectral expert was developed and tested with Thematic Mapper Simulator (TMS) data having eight spectral bands and a spatial resolution of 5 m. A knowledge base was developed that describes the target categories in terms of characteristic spectral relationships. The knowledge base was developed under the following assumptions: the data are calibrated to ground reflectance, the area is well illuminated, the pixels are dominated by a single category, and the target categories can be recognized without the use of spatial knowledge. Classification decisions are made on the basis of convergent evidence as derived from applying the spectral rules to a multiple spatial resolution representation of the image. The spectral expert achieved an accuracy of 80-percent correct or higher in recognizing 11 spectral categories in TMS data for the washington, DC, area. Classification performance can be expected to decrease for data that do not satisfy the above assumptions as illustrated by the 63-percent accuracy for 30-m resolution Thematic Mapper data.
Namibia is considered as one of the countries that are most vulnerable to climate change due to its generally dry climate and the percentage of its population that rely on subsistence agriculture for their livelihoods. Early warning systems are an important aspect of adapting to climate change. Weather forecasting relies on the use of numerical weather prediction models and these need to be configured properly. In this study we investigate the effects of using multi-nests and a convection scheme on the simulation of a heavy rainfall event over the Northwestern region of Kunene, Namibia. The event which was associated with a cut-off low system, was short-lived and resulted in over 45 mm of rainfall in one hour. For the multi-nest, a 9 km grid length parent domain is nested within the Global Forecast System (GFS) simulations, which in turn forces a 3 km grid spacing child domain. A different set of simulations are produced using a single nest of 3 km grid spacing, nested directly inside the GFS data. The simulations are produced with the convection scheme switched on and off. The impact of a single versus multi nest is found to be small in general, with slight differences in the location of high rainfall intensity. Switching off the convection schemes results in high rainfall intensity and increased detail in the simulations, including when a grid spacing of 9 km mis used. Using a grid spacing of 3 km with the convection scheme on, results in a loss of detail in the simulations as well as lower rainfall amounts. The study shows a need for different configurations to be tested before an optimum configuration can be selected for operational forecasting. We recommend further tests with different synoptic forcing to be conducted to identify a suitable configuration for Namibia.
To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT worldwide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures greater than 24 C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively.
Ninety day manned test of regenerative life support system in space station simulator, presenting operational and maintenance data
The Onboard Navigation rendezvous expert system is designed to aid the ground flight controller in monitoring the shuttle onboard navigation system. The system is designed to keep track of the navigation sensors and relative state vectors. In addition, the system also keeps an event log and fills out forms usually handled by the flight controller. This expert system is one of the few rendezvous specific systems being developed for the Mission Control Center. The expert system has been in development for six years. Through these years the system has seen hardware, software, and personnel changes. Initial development was done by the Information Systems Directorate (ISD) and Mission Operations Directorate (MOD) at Johnson Space Center. As of October 1, 1991 the system has been turned over to MOD. The system is completely developed except for some minor adjustments to the user interface. The rule base is in the verification stage with total certification of the system due to be completed by May 1992. Test cases for verification are obtained by saving data used for flight controller integrated simulations. The actual data comes from both the shuttle mission simulator and the Mission Control Center Computer. So far no actual flight data has been available. This paper covers all aspects of the system from the development history to the current hardware, software, and use of the system.
We describe results from a new technique for the prediction of complete, self-consistent X-ray spectra from three-dimensional general relativistic magnetohydrodynamic (GRMHD) simulations of black hole accretion flows. Density and cooling rate data from a harm3d GRMHD simulation are post-processed by an improved version of the Monte Carlo radiation transport code pandurata (in the corona) and the Feautrier solver PTRANSX (in the disk), with xstar subroutines. The codes are run in a sequential, iterative fashion to achieve globally energy-conserving and self-consistent radiation fields, temperature maps, and photoionization equilibria. The output is the X-ray spectrum as seen by a distant observer, including features, such as the Fe Kα emission line and corresponding K-edge absorption trough, due to disk reprocessing of coronal power. For the example cases we consider—a non-rotating 10 M(sub ⊙) black hole with solar abundances, accreting at 0.01, 0.03, 0.1, or 0.3 Eddington—we find spectra resembling actual observations of stellar-mass black holes in the soft or steep power-law state: broad thermal peaks (at 1–3 keV), steep power laws extending to high energy (Γ = 2.7–4.5), and prominent, asymmetric Fe Kα emission lines with equivalent widths in the range 40–400 eV (larger EW at lower accretion rates). By starting with simulation data, we obviate the need for parameterized descriptions of the accretion flow geometry—no a priori specification of the corona's shape or flux, or the disk temperature or density, etc., is needed. Instead, we apply the relevant physical principles to simulation output using appropriate numerical techniques; this procedure allows us to calculate inclination-dependent spectra after choosing only a small number of physically meaningful parameters: black hole mass and spin, accretion rate, and elemental abundances.
This paper presents an encounter-based simulation architecture developed at NASA to facilitate flexible and efficient Detect and Avoid modeling in parametric or tradespace studies on large data sets. The basic premise of this tool is that large-scale input data can be reduced to a set of `canonical encounters' and that using the reduced data in simulations does not lead to loss of fidelity. A canonical encounter is specified as ownship and intruder flight portions potentially resulting in a loss of well clear along with a set of properties that characterize the encounter. The advantages of using canonical encounters include faster simulations, reduced memory footprint, ability to select encounters based on user-specified criteria, shared encounters across multiple teams, peer-reviewed encounters, and a better understanding of the input data set, to name a few.
In March 2006, the Tracking and Data Relay Satellite (TDRS)-3 experienced an unexpected thrusting event, which caused significant changes to its orbit. Recovery from this anomaly was protracted, raising concerns during the Independent Review Team (IRT) investigation of the anomaly regarding the contingency response readiness. The simulations and readiness exercises discussed in this paper were part of the response to the IRT concerns. This paper explains the various levels of simulation needed to enhance the proficiency of the Flight Dynamics Facility (FDF) and supporting elements in recovery from a TDRS contingency situation. The main emergency to address is when a TDRS has experienced uncommanded, unreported, or misreported thrusting, causing a ground station to lose the ability to acquire the spacecraft, as happened in 2006. The following levels of simulation are proposed: 1) Tests that would be performed by the individual support sites to verify that internal procedures and tools are in place and up to date; 2) Tabletop simulations that would involve all of the key support sites talking through their respective operating procedures to ensure that proper notifications are made and communications links are established; and 3) Comprehensive simulations that would be infrequent, but realistic, involving data exchanges between ground sites and voice and electronic communications among the supporting elements.
A possible method for assimilating large quantities of asynoptic data, such as satellite observations, into numerical weather forecasts, is investigated. The method is based on a transformation of the time coordinate to coincide with satellite paths, and a well-posed problem is solved in order to obtain forecasts beyond the time of data acquisition. Simulated barotropic forecasts based on the proposed technique are compared with forecasts derived from the direct insertion of data. Results indicate that only in ideal situations, when asynoptic data are obtained both continuosly and accurately, will the method function well as compared with direct insertion techniques.
A series of field experiments was carried out to extend the data base available for verifying agricultural row effect models of emissivity. The row effects model was used to simulate a data base from which an algorithm could be developed to account for row effects when the scene dielectric constant and small-scale roughness are unknown. One objective of the study was to quantify the significance of row structure and to develop a practical procedure for removing the effects of periodic row structure on the microwave emissivity of a soil in order to use the emissivity values to estimate the soil moisture. A second objective was to expand the data set available for model verification through field observations using a truck-mounted 1.4-GHz microwave radiometer.
When using the J. V. Dave dataset, two channels of simulated METSAT advanced very high resolution radiometer (AVHRR) data compare favorably with actual data. Simulated NOAA6 and NOAA7 AVHRR data are presented as radiance profiles of reflected solar energy through atmosphere with three different aerosol levels. Effects of the atmosphere on the data are presented as functions of satellite view angle or pixel position on scanline. Vegetative index simultations are also profiled.
The relationship among surface rainfall, its intensity, and its associated stratiform amount is established by examining observed precipitation data from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). The results show that for moderate-high stratiform fractions, rain probabilities are strongly skewed toward light rain intensities. For convective-type rain, the peak probability of occurrence shifts to higher intensities but is still significantly skewed toward weaker rain rates. The main differences between the distributions for oceanic and continental rain are for heavily convective rain. The peak occurrence, as well as the tail of the distribution containing the extreme events, is shifted to higher intensities for continental rain. For rainy areas sampled at 0.58 horizontal resolution, the occurrence of conditional rain rates over 100 mm/day is significantly higher over land. Distributions of rain intensity versus stratiform fraction for simulated precipitation data obtained from cloud-resolving model (CRM) simulations are quite similar to those from the satellite, providing a basis for mapping simulated cloud quantities to the satellite observations. An improved convective-stratiform heating (CSH) algorithm is developed based on two sources of information: gridded rainfall quantities (i.e., the conditional intensity and the stratiform fraction) observed from the TRMM PR and synthetic cloud process data (i.e., latent heating, eddy heat flux convergence, and radiative heating/cooling) obtained from CRM simulations of convective cloud systems. The new CSH algorithm-derived heating has a noticeably different heating structure over both ocean and land regions compared to the previous CSH algorithm. Major differences between the new and old algorithms include a significant increase in the amount of low- and midlevel heating, a downward emphasis in the level of maximum cloud heating by about 1 km, and a larger variance between land and ocean in the new CSH algorithm.
NASA's Atmospheric Effects of Aviation Project (AEAP) is developing a scientific basis for assessment of the atmospheric impact of subsonic and supersonic aviation. A primary goal is to assist assessments of United Nations scientific organizations and hence, consideration of emissions standards by the International Civil Aviation Organization (ICAO). Engine tests have been conducted at AEDC to fulfill the need of AEAP. The purpose of these tests is to obtain a comprehensive database to be used for supplying critical information to the atmospheric research community. It includes: (1) simulated sea-level-static test data as well as simulated altitude data; and (2) intrusive (extractive probe) data as well as non-intrusive (optical techniques) data. A commercial-type bypass engine with aviation fuel was used in this test series. The test matrix was set by parametrically selecting the temperature, pressure, and flow rate at sea-level-static and different altitudes to obtain a parametric set of data.
We present results from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI) Phase I, which aligned 14 global gridded crop models (GGCMs) and 11 climatic forcing datasets (CFDs) in order to understand how the selection of climate data affects simulated historical crop productivity of maize, wheat, rice and soybean. Results show that CFDs demonstrate mean biases and differences in the probability of extreme events, with larger uncertainty around extreme precipitation and in regions where observational data for climate and crop systems are scarce. Countries where simulations correlate highly with reported FAO national production anomalies tend to have high correlations across most CFDs, whose influence we isolate using multi-GGCM ensembles for each CFD. Correlations compare favorably with the climate signal detected in other studies, although production in many countries is not primarily climate-limited (particularly for rice). Bias-adjusted CFDs most often were among the highest model-observation correlations, although all CFDs produced the highest correlation in at least one top-producing country. Analysis of larger multi-CFD-multi-GGCM ensembles (up to 91 members) shows benefits over the use of smaller subset of models in some regions and farming systems, although bigger is not always better. Our analysis suggests that global assessments should prioritize ensembles based on multiple crop models over multiple CFDs as long as a top-performing CFD is utilized for the focus region.
The author has identified the following significant results. Methods of manual, semi-automatic, and automatic (computer) data processing were evaluated, as were the requirements for spatial physiographic and limnological information. The coupling of specially processed ERTS data with simulation models of the watershed precipitation/runoff process provides potential for water resources management. Optimal and full use of the data requires a mix of data processing and analysis techniques, including single band editing, two band ratios, and multiband combinations. A combination of maximum likelihood ratio and near-IR/red band ratio processing was found to be particularly useful.
Procedures for dealiasing Seasat data and developing global surface wind and latent and sensible heat flux fields are discussed. Seasat data from September 20, 1978 was dealiased using the Goddard Laboratory for Atmospheres (GLA) analysis/forecast system. The wind data obtained with the objective GLA forecast model are compared to the data subjectively dealiased by Peteherych et al. (1984) and Hoffman (1982, 1984). The GLA procedure is also verified using simulated Seasat data. The areas of high and low heat fluxes and cyclonic and anticyclonic wind stresses detected in the generated fields are analyzed and compared to climatological fields. It is observed that there is good correlation between the time-averaged analyses of wind stress obtained subjectively and objectively, and the monthly mean wind stress and latent fluxes agree with climatological fields and atmospheric and oceanic features.
The physics of stationary processes in space plasmas is examined theoretically in an introduction intended for graduate students. The approach involves the extensive use of numerical simulations. Chapters are devoted to fundamental principles, small-amplitude waves, and the stationary solar plasma system; typical measurement data and simulation results are presented graphically.