Introduction to Bose Chaudhuri Hocquenghem codes
Bose-Chaudhuri-Hocquenghem codes for correcting random errors
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Bose-Chaudhuri-Hocquenghem codes for correcting random errors
Global sets of surface radiation budget (SRB) have been obtained from satellite programs. These satellite-based estimates need validation with ground-truth observations. This study validates the estimates of monthly mean surface insolation contained in two satellite-based SRB datasets with the surface measurements made at worldwide radiation stations from the Global Energy Balance Archive (GEBA). One dataset was developed from the Earth Radiation Budget Experiment (ERBE) using the algorithm of Li et al. (ERBE/SRB), and the other from the International Satellite Cloud Climatology Project (ISCCP) using the algorithm of Pinker and Laszlo and that of Staylor (GEWEX/SRB). Since the ERBE/SRB data contain the surface net solar radiation only, the values of surface insolation were derived by making use of the surface albedo data contained GEWEX/SRB product. The resulting surface insolation has a bias error near zero and a root-mean-square error (RMSE) between 8 and 28 W/sq m. The RMSE is mainly associated with poor representation of surface observations within a grid cell. When the number of surface observations are sufficient, the random error is estimated to be about 5 W/sq m with present satellite-based estimates. In addition to demonstrating the strength of the retrieving method, the small random error demonstrates how well the ERBE derives from the monthly mean fluxes at the top of the atmosphere (TOA). A larger scatter is found for the comparison of transmissivity than for that of insolation. Month to month comparison of insolation reveals a weak seasonal trend in bias error with an amplitude of about 3 W/sq m. As for the insolation data from the GEWEX/SRB, larger bias errors of 5-10 W/sq m are evident with stronger seasonal trends and almost identical RMSEs.
To better understand and characterize current uncertainties in the important observational constraint of climate models of aerosol optical depth (AOD), we evaluate and intercompare 14 satellite products, representing nine different retrieval algorithm families using observations from five different sensors on six different platforms. The satellite products (super-observations consisting of 1°×1° daily aggregated retrievals drawn from the years 2006, 2008 and 2010) are evaluated with AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) data. Results show that different products exhibit different regionally varying biases (both under- and overestimates) that may reach ±50 %, although a typical bias would be 15 %–25 % (depending on the product). In addition to these biases, the products exhibit random errors that can be 1.6 to 3 times as large. Most products show similar performance, although there are a few exceptions with either larger biases or larger random errors. The intercomparison of satellite products extends this analysis and provides spatial context to it. In particular, we show that aggregated satellite AOD agrees much better than the spatial coverage (often driven by cloud masks) within the 1°×1° grid cells. Up to ∼50 % of the difference between satellite AOD is attributed to cloud contamination. The diversity in AOD products shows clear spatial patterns and varies from 10 % (parts of the ocean) to 100 % (central Asia and Australia). More importantly, we show that the diversity may be used as an indication of AOD uncertainty, at least for the better performing products. This provides modellers with a global map of expected AOD uncertainty in satellite products, allows assessment of products away from AERONET sites, can provide guidance for future AERONET locations and offers suggestions for product improvements. We account for statistical and sampling noise in our analyses. Sampling noise, variations due to the evaluation of different subsets of the data, causes important changes in error metrics. The consequences of this noise term for product evaluation are discussed.
This paper documents a check standard wind tunnel test conducted in the Langley 0.3-Meter Transonic Cryogenic Tunnel (0.3M TCT) that was designed and analyzed using the Modern Design of Experiments (MDOE). The test designed to partition the unexplained variance of typical wind tunnel data samples into two constituent components, one attributable to ordinary random error, and one attributable to systematic error induced by covariate effects. Covariate effects in wind tunnel testing are discussed, with examples. The impact of systematic (non-random) unexplained variance on the statistical independence of sequential measurements is reviewed. The corresponding correlation among experimental errors is discussed, as is the impact of such correlation on experimental results generally. The specific experiment documented herein was organized as a formal test for the presence of unexplained variance in representative samples of wind tunnel data, in order to quantify the frequency with which such systematic error was detected, and its magnitude relative to ordinary random error. Levels of systematic and random error reported here are representative of those quantified in other facilities, as cited in the references.
We report on airborne lidar measurements of atmospheric CO2 column density for an approach being developed as a candidate for NASA's ASCENDS mission. It uses a pulsed dual-wavelength lidar measurement based on the integrated path differential absorption (IPDA) technique. We demonstrated the approach using the CO2 measurement from aircraft in July and August 2009 over four locations. The results show clear CO2 line shape and absorption signals, which follow the expected changes with aircraft altitude from 3 to 13 km. The 2009 measurements have been analyzed in detail and the results show approx.1 ppm random errors for 8-10 km altitudes and approx.30 sec averaging times. Airborne measurements were also made in 2010 with stronger signals and initial analysis shows approx. 0.3 ppm random errors for 80 sec averaging times for measurements at altitudes> 6 km.
This study focuses on the optimization of beam chamber geometry designs for future direct-drive laser facilities. It provides a review of leading target chamber geometries, with a particular emphasis on random errors. Through comprehensive solid-sphere illuminations and analysis, we identify an optimized beam geometry design, highlighting its robustness and performance under realistic experimental conditions. Three major sources of random errors are evaluated, closely linked to experimental evaluations at OMEGA. The findings underscore the importance of optimizing the irradiation system alongside beam pattern considerations to enhance the efficiency and reliability of inertial confinement fusion experiments. We conclude that for a desired illumination uniformity of 1% in the presence of system errors, the split icosahedron design is the most robust. However, for a 0.3% uniformity goal, the charged-particle, icosahedron, and t-sphere methods exhibit similar performance.
A propagation model method for extracting the normal incidence impedance of an acoustic material installed as a finite length segment in a wall of a duct carrying a nonprogressive wave field is presented. The method recasts the determination of the unknown impedance as the minimization of the normalized wall pressure error function. A finite element propagation model is combined with a coarse/fine grid impedance plane search technique to extract the impedance of the material. Results are presented for three different materials for which the impedance is known. For each material, the input data required for the prediction scheme was computed from modal theory and then contaminated by random error. The finite element method reproduces the known impedance of each material almost exactly for random errors typical of those found in many measurement environments. Thus, the method developed here provides a means for determining the impedance of materials in a nonprogressirve wave environment such as that usually encountered in a commercial aircraft engine and most laboratory settings.
We characterize the magnitude of seasonally and spatially varying biases in the National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2) Version 8 (v8) and the Atmospheric CO2 Observations from Space (ACOS) Greenhouse Gas Observing SATellite (GOSAT) version 7.3 (v7.3) satellite CO2 retrievals by comparisons to measurements collected by the Total Carbon Column Observing Network (TCCON), Atmospheric Tomography (ATom) experiment, and National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) and U. S. Department of Energy (DOE) aircraft, and surface stations. Although the ACOS-GOSAT estimates of the column averaged carbon dioxide (CO2) dry air mole fraction (XCO2) have larger random errors than the OCO-2 XCO2 estimates, and the space-based estimates over land have larger random errors than those over ocean, the systematic errors are similar across both satellites and surface types, 0.6 ± 0.1 ppm. We find similar estimates of systematic error whether dynamic versus geometric coincidences or ESRL/DOE aircraft versus TCCON are used for validation (over land), once validation and co-location errors are accounted for. We also find that areas with sparse throughput of good quality data (due to quality flags and preprocessor selection) over land have ~double the error of regions of high-throughput of good quality data. We characterize both raw and bias-corrected results, finding that bias correction improves systematic errors by a factor of 2 for land observations and improves errors by ~0.2 ppm for ocean. We validate the lowermost tropospheric (LMT) product for OCO-2 and ACOS-GOSAT by comparison to aircraft and surface sites, finding systematic errors of ~1.1 ppm, while having 2-3 times the variability of XCO2. We characterize the time and distance scales of correlations for OCO-2 XCO2 errors, and find error correlations on scales of 0.3 degrees, 5-10 degrees, and 60 days. We find comparable scale lengths for the bias correction term. Assimilation of the OCO-2 bias correction term is used to estimate flux errors resulting from OCO-2 seasonal biases, finding annual flux errors on the order of 0.3 and 0.4 PgC/yr for Transcom-3 ocean and land regions, respectively.
An analysis of the expected performance for the Multiple Access (MA) system is provided. The analysis covers the expected bit error rate performance, the effects of synchronization loss, the problem of self-interference, and the problem of phase ambiguity. The problem of false acceptance of a command word due to data inversion is discussed. A mathematical determination of the probability of accepting an erroneous command word due to a data inversion is presented. The problem is examined for three cases: (1) a data inversion only, (2) a data inversion and a random error within the same command word, and a block (up to 256 48-bit words) containing both a data inversion and a random error.
A three part experiment was conducted to develop and compare time transfer techniques. The experiment consisted of (1) a very long baseline interferometer (VLBI), (2) a high precision portable clock time transfer system between the two sites, and (3) a television time transfer. A comparison of the VLBI and traveling clock shows each technique can perform satisfactorily at the five nsec level. There was a systematic offset of 59 nsec between the two methods, which we attributed to a difference in epochs between VLBI formatter and station clock. The VLBI method had an internal random error of one nsec at the three sigma level for a two day period. Thus, the Mark II system performed well, and VLBI shows promise of being an accurate method of time transfer. The TV system, which had technical problems during the experiment, transferred time with a random error of about 50 nsec.
We use a spectral-based approach that employs principal component analysis along with a relatively shallow artificial neural network (NN) to substantially reduce noise and other artifacts in terrestrial chlorophyll solar-induced fluorescence (SIF) retrievals. SIF is a very small emission at red and far-red wavelengths that is difficult to measure and is highly sensitive to random errors and systematic artifacts. Our approach relies upon an assumption that a trained NN can effectively reconstruct the total SIF signal from a relatively small number of leading principal components of the satellite-observed far-red radiance spectra without using information from the trailing modes that contain most of the random errors. We test the approach with simulated reflectance spectra produced with a full atmospheric and surface radiative transfer model using different observing and geophysical parameters and various noise levels. Resulting noisy and noise-reduced retrieved SIF values are compared with true values to assess performance. We then apply our noise reduction approach to SIF derived from two different satellite spectrometers. For evaluation, since the truth in this case is unknown, we compare SIF retrievals from two independent sensors with each other. We also compare the noise-reduced SIF temporal variations with those from an independent gross primary product (GPP) product that should display similar variations. Results show that our noise reduction approach improves capture of SIF seasonal and inter-annual variability. Our approach should be applicable to many noisy data products derived from spectral measurements. Our methodology does not replace the original retrieval algorithms; rather the original noisy retrievals are needed as the target for the NN training process.
The majority of people in East Africa rely on the agro-pastoral system for their livelihood, which is highly vulnerable to droughts and flooding. Agro-pastoral droughts are endemic to the region and are considered the main natural hazard that contributes to food insecurity. Drought begins with rainfall deficit, gradually leading to soil moisture deficit, higher land surface temperature, and finally impacts to vegetation growth. Therefore, monitoring vegetation conditions is essential in understanding the progression of drought, potential effects on food security, and providing early warning information needed for drought mitigation decisions. Because vegetation processes couple the land and atmosphere, monitoring of vegetation conditions requires consideration of both water provision and demand. While there is consensus in using either the Normalized Difference Vegetation Index (NDVI) or evapotranspiration(ET) for vegetation monitoring, a comprehensive assessment optimizing the use of both has not yet been done. Moreover, the evaluation methods for understanding the relationships between NDVI and ET for vegetation monitoring are also limited. Taking these gaps into account we have developed a framework to optimize vegetation monitoring using both NDVI and ET by identifying where they perform the best by using triple collocation and cross-correlation methods. We estimated the random error structure in Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI; ET from the Operational Simplified Surface Energy Balance (SSEBop) model; and ET from land surface models (LSMs). LSM ET and SSEBop ET have been found to be better indicators for vegetation monitoring during extreme drought events, while NDVI could provide better information on vegetation condition during wetter than normal conditions. The random error structures of these variables suggest that LSM ET is most likely to provide important information for vegetation monitoring over low and high ends of the vegetation fraction areas. Over moderate vegetative areas, any of these variables could provide important vegetation information for drought characterization and food security assessments. While this study provides a framework for optimizing vegetation monitoring for drought and food security assessments over East Africa, the framework can be adopted to optimize vegetation monitoring over any other drought and food insecure region of the world.
We have developed a pulsed lidar technique for measuring the tropospheric CO2 concentrations as a candidate for NASA's ASCENDS mission and have demonstrated the CO2 and O2 measurements from aircraft. Our technique uses two pulsed lasers allowing simultaneous measurement of a single CO2 absorption line near 1572 nm, O2 extinction in the Oxygen A-band, surface height and backscatter profile. The lasers are stepped in wavelength across the CO2 line and an O2 line doublet during the measurement. The column densities for the CO2 and O2 are estimated from the differential optical depths (DOD) of the scanned absorption lines via the IPDA technique. For the 2009 ASCENDS campaign we flew the CO2 lidar on a Lear-25 aircraft, and measured the absorption line shapes of the CO2 line using 20 wavelength samples per scan. Measurements were made at stepped altitudes from 3 to 12.6 km over the Lamont OK, central Illinois, North Carolina, and over the Virginia Eastern Shore. Although the received signal energies were weaker than expected for ASCENDS, clear CO2 line shapes were observed at all altitudes. Most flights had 5-6 altitude steps with 200-300 seconds of recorded measurements per step. We averaged every 10 seconds of measurements and used a cross-correlation approach to estimate the range to the scattering surface and the echo pulse energy at each wavelength. We then solved for the best-fit CO2 absorption line shape, and calculated the DOD of the fitted CO2 line, and computed its statistics at the various altitude steps. We compared them to CO2 optical depths calculated from spectroscopy based on HITRAN 2008 and the column number densities calculated from the airborne in-situ readings. The 2009 measurements have been analyzed and they were similar on all flights. The results show clear CO2 line shape and absorption signals, which follow the expected changes with aircraft altitude from 3 to 13 km. They showed the expected nearly the linear dependence of DOD vs altitude. The measurements showed ~ 1 ppm random errors for 8-10 km altitudes and ~30 sec averaging times. For the 2010 ASCENDS campaigns we flew the CO2lidar on the NASA DC-8 and added an 02lidar channel. During July 2010 we made measurements of CO2 and O2 column absorption during longer flights over Railroad Valley NV, the Pacific Ocean and over Lamont OK. CO2 measurements were made with 30 steps/scan, 300 scans/sec and improved line resolution and receiver sensitivity. Analysis of the 2010 CO2 measurements shows the expected ~linear change of DOD with altitude. For measurements at altitudes> 6 km the random errors were 0.3 ppm for 80 sec averaging times. For the summer 2011 ASCENDS campaigns we made further improvements to the lidar's CO2 line scan and receiver sensitivity. We demonstrated measurements over the California Central Valley, to stratus cloud tops over the Pacific Ocean, over mountain regions with snow, and over several areas with broken clouds. Details of the lidar measurements and their analysis will be described in the presentation.
We have developed a pulsed lidar technique for measuring the tropospheric CO2 concentrations as a candidate for NASA's ASCENDS mission and have demonstrated the CO2 and O2 measurements from aircraft. Our technique uses two pulsed lasers allowing simultaneous measurement of a single CO2 absorption line near 1572 nm, O2 extinction in the Oxygen A-band, surface height and backscatter profile. The lasers are stepped in wavelength across the CO2 line and an O2 line doublet during the measurement. The column densities for the CO2 and O2 are estimated from the differential optical depths (DOD) of the scanned absorption lines via the IPDA technique. For the 2009 ASCENDS campaign we flew the CO2 lidar only on a Lear-25 aircraft, and measured the absorption line shapes of the CO2 line using 20 wavelength samples per scan. Measurements were made at stepped altitudes from 3 to 12.6 km over the Lamont OK, central Illinois, North Carolina, and over the Virginia Eastern Shore. Although the received signal energies were weaker than expected for ASCENDS, clear C02 line shapes were observed at all altitudes. Most flights had 5-6 altitude steps with 200-300 seconds of recorded measurements per step. We averaged every 10 seconds of measurements and used a cross-correlation approach to estimate the range to the scattering surface and the echo pulse energy at each wavelength. We then solved for the best-fit CO2 absorption line shape, and calculated the DOD of the fitted CO2 line, and computed its statistics at the various altitude steps. We compared them to CO2 optical depths calculated from spectroscopy based on HITRAN 2008 and the column number densities calculated from the airborne in-situ readings. The 2009 measurements have been analyzed in detail and they were similar on all flights. The results show clear CO2 line shape and absorption signals, which follow the expected changes with aircraft altitude from 3 to 13 km. They showed the expected nearly the linear dependence of DOD vs altitude. The measurements showed -1 ppm random errors for 8-10 km altitudes and -30 sec averaging times. For the 2010 ASCENDS campaigns we flew the CO2 lidar on the NASA DC-8 and added an O2 lidar channel. During July 2010 we made measurements of CO2 and O2 column absorption during longer flights over Railroad Valley NV, the Pacific Ocean and over Lamont OK. CO2 measurements were made with 30 steps/scan, 300 scans/sec and improved line resolution and receiver sensitivity. Analysis of the 2010 CO2 measurements shows the expected -linear change of DOD with altitude. For measurements at altitudes> 6 km the random errors were 0.3 ppm for 80 sec averaging times. For the summer 2011 ASCENDS campaigns we made further improvements to the lidar's CO2 line scan and receiver sensitivity. The seven flights in the 2011 Ascends campaign were flown over a wide variety of surface and cloud conditions in the US, which produced a wide variety of lidar signal conditions. Details of the lidar measurements and their analysis will be described in the presentation.
We introduce a trilateration scheme that evaluates the 3-dimensional (3-D) relative position between a reference spacecraft and a target spacecraft using raw-range measurements from a distance baseline of known locations, which we call “anchors”. The anchors can be antennas of a ground-based network (e.g., Deep Space Network (DSN) or Near Earth Network (NEN) stations), or satellites of a spacebased network (e.g., global positioning system (GPS) or tracking and data relay satellite (TDRS)). We define raw-range as the range that includes all the systematic errors that occur during range measurements. A unique feature of this approach is that accurate relative position is derived from a “differencing function” of raw-range measurements of the reference spacecraft and target spacecraft, thereby eliminating most of the systematic errors, such as media effects, ephemeris errors, instrument delays, clock bias, etc. There can be an arbitrary number of target spacecraft, and relative positioning of target spacecraft with respect to the reference spacecraft can be done simultaneously. In this paper, we first assume an idealized system in which clocks on the reference and target spacecraft are synchronized, with clocks of the anchors synchronized as well. We develop a novel iterative algorithm that computes the relative position of the target spacecraft with respect to the reference spacecraft. We illustrate the relative positioning method using the scenario of a network of three ground stations (i.e., the anchors) at Goldstone, California, USA, Madrid, Spain, and Marlargue, Argentina tracking two spacecraft at geosynchronous orbit distance. We demonstrate that the algorithm converges to submeter accuracy in estimating the relative position, in the presence of random errors and systematic errors in raw-range measurements, and in the presence of angular errors in estimating the pointing vectors between the anchors and the reference spacecraft. Next, we relax the requirement of perfect time synchronization between spacecraft, and show that by using an additional anchor, one can estimate and remove the clock biases between the reference and target spacecraft. We add a ground station at Kourou to the above example of three ground stations of Goldstone, Madrid, and Marlargue, and demonstrate that the updated algorithm also converges to meter-level accuracy (submeter in some cases) in the presence of clock biases in addition to the random errors, systematic errors, and angular errors as shown in the above case. We compare this scheme with a similar trilateration scheme for relative positioning scheme first proposed by Montenbruck in 2002.
No abstract available
Are we alone in the Universe is probably the most compelling science question of our generation. To answer it requires a large aperture telescope with extreme wavefront stability. To image and characterize Earth-like planets requires the ability to block 10(exp 10) of the host stars light with a 10(exp -11) stability. For an internal coronagraph, this requires correcting wavefront errors and keeping that correction stable to a few picometers rms for the duration of the science observation. This requirement places severe specifications upon the performance of the observatory, telescope and primary mirror. A key task of the AMTD project (initiated in FY12) is to define telescope level specifications traceable to science requirements and flow those specifications to the primary mirror. From a systems perspective, probably the most important question is: What is the telescope wavefront stability specification? Previously, we suggested this specification should be 10 picometers per 10 minutes; considered issues of how this specification relates to architecture, i.e. monolithic or segmented primary mirror; and asked whether it was better to have few or many segmented. This paper reviews the 10 picometers per 10 minutes specification; provides analysis related to the application of this specification to segmented apertures; and suggests that a 3 or 4 ring segmented aperture is more sensitive to segment rigid body motion that an aperture with fewer or more segments.
There are two types of experimental measurement errors of the Doppler data associated with the radio occultation, random and systematic. Random errors are due to thermal noise in the transmission channel, and the phase lock loop, and quantization error in the digital circuitry. These are called noise type errors. The systematic errors are due to geometric uncertainty and equipment phase instability. Considered is the amount of uncertainty, due to random measurement errors, in the refractivity profiles reconstructed by this type of indirect sensing experiment. A class of refractivity profiles is defined which approximately fit the set of measured data. Bounds are placed on the extent of this class of solution profiles. To accomplish this, the sensivity of the reconstructed refractivity profiles to errors in the measured quantity and the statistics of the errors in the measurement are examined.