Methods of processing satellite data.
Processing satellite data at Goddard Center noting Tiros IR data, cloud pictures and Imp measurements
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Processing satellite data at Goddard Center noting Tiros IR data, cloud pictures and Imp measurements
Vector winds computed from GMD-1 and FPS-16 radar tracking data indicate accurate wind macro- and mesoscale resolution from GMD data
Image processing method is capable of contrast enhancement, noise filtering, and photometric distortion removal in near real time. System uses digital image integration and digital video recorder as image buffer. Each frame of data is entered into memory; registers provide readout of stored TV frame.
Correction to: Naturehttps://doi.org/10.1038/s41586-024-07117-5 Published online 13 March 2024 In the version of the article initially published, several errors were present and have been corrected in the HTML and PDF versions of the article and Supplementary Information. The main results, conclusions, and our interpretations of the data remain unchanged. See the new Supplementary Information Section S15 for a more detailed description of the errors corrected and the resulting effects on the analysis. Data processing and methods corrections Overflight count correction: We previously used pre-computed source coverage data for some Carbon Mapper campaigns that was computed differently than was required for our analysis. We have re-computed Carbon Mapper source coverage based on flightline polygons and source coordinates. Transition point computation, well sites: The updated version now correctly compares the cumulative emissions distribution of simulated well site emissions with that of aerially detected sources (rather than plumes) when computing the transition point. Transition point computation, midstream: Additionally, the transition point calculation has been corrected to exclude aerially detected midstream emissions below the transition point, which was previously leading to double counting of these emissions. This error was not present for upstream (well site) emissions. Calculation errors Unit error: We corrected a specific unit conversion error affecting well site emissions in the Kairos Fort Worth dataset. Across all datasets, we also correct the conversion factor for converting from standard volume to mass for midstream emissions. Sorting error: We correct code that was applying incorrect sorting when computing correction factors to account for partial detection at well sites. Small typographical corrections were made in Fig. 1b and SI Section S4.1. Data processing and methods corrections Overflight count correction: We previously used pre-computed source coverage data for some Carbon Mapper campaigns that was computed differently than was required for our analysis. We have re-computed Carbon Mapper source coverage based on flightline polygons and source coordinates. Transition point computation, well sites: The updated version now correctly compares the cumulative emissions distribution of simulated well site emissions with that of aerially detected sources (rather than plumes) when computing the transition point. Transition point computation, midstream: Additionally, the transition point calculation has been corrected to exclude aerially detected midstream emissions below the transition point, which was previously leading to double counting of these emissions. This error was not present for upstream (well site) emissions. Calculation errors Unit error: We corrected a specific unit conversion error affecting well site emissions in the Kairos Fort Worth dataset. Across all datasets, we also correct the conversion factor for converting from standard volume to mass for midstream emissions. Sorting error: We correct code that was applying incorrect sorting when computing correction factors to account for partial detection at well sites. Small typographical corrections were made in Fig. 1b and SI Section S4.1. The following practices may help researchers conducting similar analyses avoid making similar errors: 1, Clear, accessible documentation explaining the interpretation of all columns in data input tables and all internal variables within the model, 2, Simple cross-check calculations computed before and after unit conversions.
Forest allocation of net primary productivity (NPP) to reproduction is poorly quantified globally, despite its critical role in forest regeneration and a well-supported trade-off with allocation to growth. Although field measurements of total NPP are rare, our work finds that a proxy for reproductive carbon allocation constructed from leaf (L) and reproductive (R) litterfall fluxes, R/(R+L), is strongly correlated with R/NPP, facilitating analysis across a wide range of sites where biometric estimates of NPP are not available (R² = 0.85; Hanbury-Brown et al., 2022, Ward et al., in prep). To investigate relationships between ecosystem-scale reproductive allocation (RA) and climate, soil fertility, and stand age gradients, we conducted a literature search and synthesized 824 observations of annual average leaf and reproductive litterfall fluxes across forest sites globally. The zip file includes 1) a folder Data/ containing the litterfall data ("GlobalForestRA_data.csv") and metadata ("GlobalForestRA_metadata.doc") files. The data file includes geographic coordinates, long-term mean annual temperature and precipitation (1970-2000, extracted from WorldClim2.1), leaf and reproductive litterfall fluxes, sampling interval and protocols, forest characteristics (dominant leaf morphology, information pertaining to forest age and successional stage, and disturbance history) and soil properties (% sand, %silt, %clay, total phosphorus (P), nitrogen (N), cation exchange capacity (CEC) and pH) extracted from SoilGrids250 and from on-site measurements, where available. The metadata file contains information about each variable reported in the data file, including data sources, processing methods, and all references. The Data folder contains two additional files used to create Figure 1; these are described in greater detail in the README.2) R scripts GloalForestRA_analysis.r and GlobalForestRA_SI.r and a folder /Functions used to produce results, figures, and tables in the manuscript Ward et al. (in press)3) a README file describing how the data and R scripts can be used to reproduce statistical results, figures, and tables found in the manuscript. Ward et al. (in press)This repository can also be found at: https://github.com/r-ward/Global_Analysis_ForestRA.Ward, R.E., Zhang-Zheng, H. Aernethy, K., Adu-Bredu, S., Arroyo, L., Bailey, A. et al. (in press). Forest age rivals climate to explain reproductive allocation patterns in forest ecosystems globally. Ecology Letters. Hanbury-Brown, A.R., Ward, R.E. & Kueppers, L.M. (2022). Forest regeneration within Earth system models: current process representations and ways forward. New Phytol., 235, 20–40.Ward et al. (2025), Forest age rivals climate to explain reproductive allocation patterns in forest ecosystems globally, in prep.
Image processing methods for mapping standing biomass for a forest in Maine, using NASA/JPL airborne synthetic aperture radar (AIRSAR) polarimeter data, are presented. By examining the dependence of backscattering on standing biomass, it is determined that the ratio of HV backscattering from a longer wavelength (P- or L-band) to a shorter wavelength (C) is a good combination for mapping total biomass. This ratio enhances the correlation of the image signature to the standing biomass and compensates for a major part of the variations in backscattering attributed to radar incidence angle. The image processing methods used include image calibration, ratioing, filtering, and segmentation. The image segmentation algorithm uses both means and variances of the image, and it is combined with the image filtering process. Preliminary assessment of the resultant biomass maps suggests that this is a promising method.
The present invention is an improved method of processing conglomerate data. The method employs a Triband Wavelet Transform that decomposes and decimates the conglomerate signal to obtain a final result. The invention may be employed to improve performance of Optical Frequency Domain Reflectometry systems.
We present a method for computing uncertainties in spectral models, i.e., level populations, line emissivities, and emission line ratios, based upon the propagation of uncertainties originating from atomic data.We provide analytic expressions, in the form of linear sets of algebraic equations, for the coupled uncertainties among all levels. These equations can be solved efficiently for any set of physical conditions and uncertainties in the atomic data. We illustrate our method applied to spectral models of Oiii and Fe ii and discuss the impact of the uncertainties on atomic systems under different physical conditions. As to intrinsic uncertainties in theoretical atomic data, we propose that these uncertainties can be estimated from the dispersion in the results from various independent calculations. This technique provides excellent results for the uncertainties in A-values of forbidden transitions in [Fe ii]. Key words: atomic data - atomic processes - line: formation - methods: data analysis - molecular data - molecular processes - techniques: spectroscopic
A brief review of the development over the last decade of earth resource information systems is presented. Machine data preprocessing and analysis methods are surveyed and illustrated. These include preprocessing steps intended to modify geometric and radiometric aspects of earth observational image data to enhance the ability of either human interpreters or machine algorithms to extract information from the data. Illustrations of processed and analyzed images from spaceborne sensors including the Earth Resources Technology Satellite are discussed.
The atmospheric trace molecule spectroscopy (ATMOS) instrument, a high-speed Fourier transform spectrometer operating in the middle IR (2.2-16 microns), recorded more than 1500 solar spectra at about 0.0105/cm resolution during its first mission onboard the shuttle Challenger in the spring of 1985. These spectra were acquired during high-sun conditions for studies of the solar atmosphere and during low-sun conditions for studies of the earth's upper atmosphere. This paper describes the steps by which the telemetry data were converted into spectra suitable for analysis, the analysis software and methods developed for the atmospheric and solar studies, and the ATMOS data analysis facility.
Method for processing images of rural uplands produced by airborne multispectral scanner (MSS), semiautomatically classifies types of land cover, and involves selection of wavelength bands, radiometric calibration, correction for effects of scan angle and atmosphere, training, and assessment of accuracy. Basic version involves classification of each picture element according to spectrum. Augmented with five refinements to increase accuracy: per-field sampling; low-pass filtering; image texture; prior probabilities; and imagery from two dates.
This document is intended as an introduction to a set of common signal processing learning methods that may be used in the software portion of a functional crew state monitoring system. This includes overviews of both the theory of the methods involved, as well as examples of implementation. Practical considerations are discussed for implementing modular, flexible, and scalable processing and classification software for a multi-modal, multi-channel monitoring system. Example source code is also given for all of the discussed processing and classification methods.
The United States Space Force (USSF) is responsible for space vehicle launches at its Eastern Range (ER), which includes the Cape Canaveral Space Force Station (CCSFS). Multiple systems are used to measure the atmosphere at the ER, including suites of Doppler Radar Wind Profilers (DRWPs) operating at 915MHz that measure winds within the lowest few kilometers of the atmosphere. Observations of boundary layer winds can be used for multiple applications, including serving as input to toxic dispersion models and characterizing winds for low-level aborts. In fall 2020 the USSF requested NASA’s Marshall Space Flight Center (MSFC) Natural Environments Branch (NE) to evaluate wind output from the False Cape(FC) DRWP system (MSFC NE 2021). In that analysis, it was found that data availability was low in the lowest few hundred meters of a profile, shown in Figure 1. To increase the available data, the USSF modified the quality control (QC) algorithm for the FCDRWP, which collected data from October 2020 to January 2021. In Fall 2021, the USSF requested NASA’s MSFC NE to evaluate the modified QC algorithm by comparing the data processed by the modified method (referred to as beam data) to data processed with the original QC method (referred to as original data). This report describes the system and the analyses that MSFC NE conducted to compare the availability and quality of data from the two QC methods
The CAD/CAM interactive computer graphics system was described; uses to which it has been put were shown, and current developments of the system were outlined. The system supports batch, time sharing, and fully interactive graphic processing. Engineers using the system may switch between these methods of data processing and problem solving to make the best use of the available resources. It is concluded that the introduction of on-line computing in the form of teletypes, storage tubes, and fully interactive graphics has resulted in large increases in productivity and reduced timescales in the geometric computing, numerical lofting and part programming areas, together with a greater utilization of the system in the technical departments.
Accurate, direct measurement of thrust or impulse is one of the most critical elements of electric thruster characterization, and one of the most difficult measurements to make. The American Institute of Aeronautics and Astronautics has started an initiative to develop standards for many important measurement processes in electric propulsion, including thrust measurements. This paper summarizes recommended practices for the design, calibration, and operation of pendulum thrust stands, which are widely recognized as the best approach for measuring micro N- to mN-level thrust and micro Ns-level impulse bits. The fundamentals of pendulum thrust stand operation are reviewed, along with its implementation in hanging pendulum, inverted pendulum, and torsional balance configurations. Methods of calibration and recommendations for calibration processes are presented. Sources of error are identified and methods for data processing and uncertainty analysis are discussed. This review is intended to be the first step toward a recommended practices document to help the community produce high quality thrust measurements.
Data from record tracks of all accelerometers is injected simultaneously into electronic circuits which convert inputs into single, composite graphical representation. Three adequate methods of processing data: peak acceleration at a frequency, average of all channels, and quad-mean of all channels.
Validation of a wall-interference assessment/correction (WIAC) procedure for a given facility requires its application to several sets of test data. It is necessary therefore to create a somewhat automated method for processing data through the various steps in the procedure. An automated procedure is also welcomed by the test engineer or eventual user in order to reduce the required effort and opportunity for error. Such a procedure has been developed for the Langley 0.3-m Transonic Cryogenic Tunnel using the TWINTN4 WIAC code. This code provides a four-wall, 2-D, transonic correction; that is, it accounts for sidewall boundary-layer effects, as well as for top and bottom wall effects on the airfoil tests. The TWINTN4 code utilizes measured pressure data at the tunnel walls and on the airfoil model; thus, classical homogeneous-wall boundary conditions are not used in the correction procedure.
Described here is the development and implementation of on-line, near real time controller performance evaluation (CPE) methods capability. Briefly discussed are the structure of data flow, the signal processing methods used to process the data, and the software developed to generate the transfer functions. This methodology is generic in nature and can be used in any type of multi-input/multi-output (MIMO) digital controller application, including digital flight control systems, digitally controlled spacecraft structures, and actively controlled wind tunnel models. Results of applying the CPE methodology to evaluate (in near real time) MIMO digital flutter suppression systems being tested on the Rockwell Active Flexible Wing (AFW) wind tunnel model are presented to demonstrate the CPE capability.