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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

The Relationship of Forest Fires Detected by MODIS and SRTM Derived Topographic Features in Central Siberia

Fires are a common occurrence in the Siberian boreal forest. The MOD14 Thermal anomalies product of the Terra MODIS Moderate Resolution Spectroradiometer) product set is designed to detect thermal anomalies (i.e. hotspots or fires) on the Earth's surface. Recent field studies showed a dependence of fire occurrence on topography. In this study MODIS thermal anomaly data and SRTM topography data were merged and analyzed to evaluate if forest fires are more likely to occur at certain combinations of elevation, slope and aspect. Using the satellite data over a large area can lead to better understanding how topography and forest fires are related. The study area covers a 2.5 Million krn(exp 2) portion of the Central Siberian southern taiga from 72 deg to 110 deg East and from 50 deg to 60 deg North. About 57% of the study area is forested and 80% of the forest grows between 200 and 1000 m. Forests with pine (Pinus sylvestris), larch (Larix sibirica, L. gmelinii), Siberian pine (Pinus sibirica), spruce (Picea obovata.) and fir (Abies sibirica) cover most of the landscape. Deciduous stands with birch (Betula pendula, B. pubescens) and aspen (Populus tremula) cover the areas of lower elevation in this region. The climate of this area is distinctly continental with long, cold winters and short hot summers. The tree line in this part of the world is around 1500 m in elevation with alpine tundra, snow and ice fields and rock outcrops extending up to over 3800 m. A 500 m resolution landcover map was developed using 2001 MODIS MOD13 Normalized Vegetation Index (NDVI) and Middle Infrared (MIR) products for seven 16-day periods. The classification accuracy was over 87%. The SRTM version 2 data, which is distributed in 1 degree by 1 degree tiles were mosaiced using the ENVI software. In this study, only those MODIS pixels were used that were flagged as "nominal or high confidence fire" by the MODIS fire product team. Using MODIS data from the years 2000 to 2005 along with the improved Shuttle Radar Topographic Mission (SRTM) version 2 data at 100 m resolution, the distribution of hot spots was examined by elevation, slope and aspect as well as by forest type. The results show that more forest area burns at lower elevations but a larger percentage of the available forest area burns at higher elevations. This is probably because steep slopes occur at higher elevations. Fires are only more common on slopes with a southern exposure if the slope is steeper than 15 degrees. The next step in this study will be to monitor areas where the risk of fire is high (steep slopes with a southern exposure) and to refine this method by incorporating anthropogenic features for more accurate fire disturbance monitoring.

Ranson, Jon K.↗
NASA NTRS2009

Improved Temperature Sounding and Quality Control Methodology Using AIRS/AMSU Data: The AIRS Science Team Version 5 Retrieval Algorithm

This paper describes the AIRS Science Team Version 5 retrieval algorithm in terms of its three most significant improvements over the methodology used in the AIRS Science Team Version 4 retrieval algorithm. Improved physics in Version 5 allows for use of AIRS clear column radiances in the entire 4.3 micron CO2 absorption band in the retrieval of temperature profiles T(p) during both day and night. Tropospheric sounding 15 micron CO2 observations are now used primarily in the generation of clear column radiances .R(sub i) for all channels. This new approach allows for the generation of more accurate values of .R(sub i) and T(p) under most cloud conditions. Secondly, Version 5 contains a new methodology to provide accurate case-by-case error estimates for retrieved geophysical parameters and for channel-by-channel clear column radiances. Thresholds of these error estimates are used in a new approach for Quality Control. Finally, Version 5 also contains for the first time an approach to provide AIRS soundings in partially cloudy conditions that does not require use of any microwave data. This new AIRS Only sounding methodology, referred to as AIRS Version 5 AO, was developed as a backup to AIRS Version 5 should the AMSU-A instrument fail. Results are shown comparing the relative performance of the AIRS Version 4, Version 5, and Version 5 AO for the single day, January 25, 2003. The Goddard DISC is now generating and distributing products derived using the AIRS Science Team Version 5 retrieval algorithm. This paper also described the Quality Control flags contained in the DISC AIRS/AMSU retrieval products and their intended use for scientific research purposes.

Susskind, Joel↗
NASA NTRS1947

The Effectiveness at High Speeds of a 20-Percent-chord Plain Trailing-edge Flap on the NACA 65-210 Airfoil Section

An analysis has been made of the lift-control effectiveness of a 20-percent-chord plain trailing-edge flap on the NACA 65-210 airfoil section from section lift-coefficient data obtained at Mach numbers from 0.3 to 0.875. In addition, the effectiveness of the plain flap as a lift-control device has been compared with the corresponding effectiveness of both a spoiler and a dive-recovery flag on the INCA 65-210 airfoil section.

Stivers, Louis S., Jr.↗
NASA NTRS2009

Modular Battery Charge Controller

A new approach to masterless, distributed, digital-charge control for batteries requiring charge control has been developed and implemented. This approach is required in battery chemistries that need cell-level charge control for safety and is characterized by the use of one controller per cell, resulting in redundant sensors for critical components, such as voltage, temperature, and current. The charge controllers in a given battery interact in a masterless fashion for the purpose of cell balancing, charge control, and state-of-charge estimation. This makes the battery system invariably fault-tolerant. The solution to the single-fault failure, due to the use of a single charge controller (CC), was solved by implementing one CC per cell and linking them via an isolated communication bus [e.g., controller area network (CAN)] in a masterless fashion so that the failure of one or more CCs will not impact the remaining functional CCs. Each micro-controller-based CC digitizes the cell voltage (V(sub cell)), two cell temperatures, and the voltage across the switch (V); the latter variable is used in conjunction with V(sub cell) to estimate the bypass current for a given bypass resistor. Furthermore, CC1 digitizes the battery current (I1) and battery voltage (V(sub batt) and CC5 digitizes a second battery current (I2). As a result, redundant readings are taken for temperature, battery current, and battery voltage through the summation of the individual cell voltages given that each CC knows the voltage of the other cells. For the purpose of cell balancing, each CC periodically and independently transmits its cell voltage and stores the received cell voltage of the other cells in an array. The position in the array depends on the identifier (ID) of the transmitting CC. After eight cell voltage receptions, the array is checked to see if one or more cells did not transmit. If one or more transmissions are missing, the missing cell(s) is (are) eliminated from cell-balancing calculations. The cell-balancing algorithm is based on the error between the cell s voltage and the other cells and is categorized into four zones of operation. The algorithm is executed every second and, if cell balancing is activated, the error variable is set to a negative low value. The largest error between the cell and the other cells is found and the zone of operation determined. If the error is zero or negative, then the cell is at the lowest voltage and no balancing action is needed. If the error is less than a predetermined negative value, a Cell Bad Flag is set. If the error is positive, then cell balancing is needed, but a hysteretic zone is added to prevent the bypass circuit from triggering repeatedly near zero error. This approach keeps the cells within a predetermined voltage range.

Button, Robert↗
NASA NTRS2010

Reporting Differences Between Spacecraft Sequence Files

A suite of computer programs, called seq diff suite, reports differences between the products of other computer programs involved in the generation of sequences of commands for spacecraft. These products consist of files of several types: replacement sequence of events (RSOE), DSN keyword file [DKF (wherein DSN signifies Deep Space Network)], spacecraft activities sequence file (SASF), spacecraft sequence file (SSF), and station allocation file (SAF). These products can include line numbers, request identifications, and other pieces of information that are not relevant when generating command sequence products, though these fields can result in the appearance of many changes to the files, particularly when using the UNIX diff command to inspect file differences. The outputs of prior software tools for reporting differences between such products include differences in these non-relevant pieces of information. In contrast, seq diff suite removes the fields containing the irrelevant pieces of information before processing to extract differences, so that only relevant differences are reported. Thus, seq diff suite is especially useful for reporting changes between successive versions of the various products and in particular flagging difference in fields relevant to the sequence command generation and review process.

Khanampompan, Teerapat↗
NASA NTRS2010

Performance Bounds on Two Concatenated, Interleaved Codes

A method has been developed of computing bounds on the performance of a code comprised of two linear binary codes generated by two encoders serially concatenated through an interleaver. Originally intended for use in evaluating the performances of some codes proposed for deep-space communication links, the method can also be used in evaluating the performances of short-block-length codes in other applications. The method applies, more specifically, to a communication system in which following processes take place: At the transmitter, the original binary information that one seeks to transmit is first processed by an encoder into an outer code (Co) characterized by, among other things, a pair of numbers (n,k), where n (n > k)is the total number of code bits associated with k information bits and n k bits are used for correcting or at least detecting errors. Next, the outer code is processed through either a block or a convolutional interleaver. In the block interleaver, the words of the outer code are processed in blocks of I words. In the convolutional interleaver, the interleaving operation is performed bit-wise in N rows with delays that are multiples of B bits. The output of the interleaver is processed through a second encoder to obtain an inner code (Ci) characterized by (ni,ki). The output of the inner code is transmitted over an additive-white-Gaussian- noise channel characterized by a symbol signal-to-noise ratio (SNR) Es/No and a bit SNR Eb/No. At the receiver, an inner decoder generates estimates of bits. Depending on whether a block or a convolutional interleaver is used at the transmitter, the sequence of estimated bits is processed through a block or a convolutional de-interleaver, respectively, to obtain estimates of code words. Then the estimates of the code words are processed through an outer decoder, which generates estimates of the original information along with flags indicating which estimates are presumed to be correct and which are found to be erroneous. From the perspective of the present method, the topic of major interest is the performance of the communication system as quantified in the word-error rate and the undetected-error rate as functions of the SNRs and the total latency of the interleaver and inner code. The method is embodied in equations that describe bounds on these functions. Throughout the derivation of the equations that embody the method, it is assumed that the decoder for the outer code corrects any error pattern of t or fewer errors, detects any error pattern of s or fewer errors, may detect some error patterns of more than s errors, and does not correct any patterns of more than t errors. Because a mathematically complete description of the equations that embody the method and of the derivation of the equations would greatly exceed the space available for this article, it must suffice to summarize by reporting that the derivation includes consideration of several complex issues, including relationships between latency and memory requirements for block and convolutional codes, burst error statistics, enumeration of error-event intersections, and effects of different interleaving depths. In a demonstration, the method was used to calculate bounds on the performances of several communication systems, each based on serial concatenation of a (63,56) expurgated Hamming code with a convolutional inner code through a convolutional interleaver. The bounds calculated by use of the method were compared with results of numerical simulations of performances of the systems to show the regions where the bounds are tight (see figure).

Moision, Bruce↗
NASA NTRS2010

Ten Years of Cloud Optical and Microphysical Retrievals from MODIS

The MODIS cloud optical properties algorithm (MOD06/MYD06 for Terra and Aqua MODIS, respectively) has undergone extensive improvements and enhancements since the launch of Terra. These changes have included: improvements in the cloud thermodynamic phase algorithm; substantial changes in the ice cloud light scattering look up tables (LUTs); a clear-sky restoral algorithm for flagging heavy aerosol and sunglint; greatly improved spectral surface albedo maps, including the spectral albedo of snow by ecosystem; inclusion of pixel-level uncertainty estimates for cloud optical thickness, effective radius, and water path derived for three error sources that includes the sensitivity of the retrievals to solar and viewing geometries. To improve overall retrieval quality, we have also implemented cloud edge removal and partly cloudy detection (using MOD35 cloud mask 250m tests), added a supplementary cloud optical thickness and effective radius algorithm over snow and sea ice surfaces and over the ocean, which enables comparison with the "standard" 2.1 11m effective radius retrieval, and added a multi-layer cloud detection algorithm. We will discuss the status of the MOD06 algorithm and show examples of pixellevel (Level-2) cloud retrievals for selected data granules, as well as gridded (Level-3) statistics, notably monthly means and histograms (lD and 2D, with the latter giving correlations between cloud optical thickness and effective radius, and other cloud product pairs).

Platnick, Steven↗
NASA NTRS2009

Distribution and Radiative Forcing of Tropical Thin Cirrus Clouds

To understand the radiative impact of tropical thin cirrus clouds, the frequency of occurrence and optical depths of these clouds have been derived. Thin cirrus clouds are defined here as being those that are not detected by the operational Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask, corresponding to an optical depth value of approximately 0.3 or smaller, but that are detectable in terms of the cirrus reflectance product based on the MODIS 1.375-micron channel. With such a definition, thin cirrus clouds were present in more than 40% of the pixels flagged as clear sky by the operational MODIS cloud mask algorithm. It is shown that these thin cirrus clouds are frequently observed in deep convective regions in the western Pacific. Thin cirrus optical depths were derived from the cirrus reflectance product. Regions of significant cloud fraction and large optical depths were observed in the Northern Hemisphere during the boreal spring and summer and moved southward during the boreal autumn and winter. The radiative effects of tropical thin cirrus clouds were studied on the basis of the retrieved cirrus optical depths, the atmospheric profiles derived from the Atmospheric Infrared Sounder (AIRS) observations, and a radiative transfer model in conjunction with a parameterization of ice cloud spectral optical properties. To understand how these clouds regulate the radiation field in the atmosphere, the instantaneous net fluxes at the top of the atmosphere (TOA) and at the surface were calculated. The present study shows positive and negative net forcings at the TOA and at the surface, respectively. The positive (negative) net forcing at the TOA (surface) is due to the dominance of longwave (shortwave) forcing. Both the TOA and surface forcings are in a range of 0-20 W/sq m, depending on the optical depths of thin cirrus clouds.

THIN CIRRUS CLOUDS↗
NASA NTRS2006

Robust Control for the Mercury Laser Altimeter

Mercury Laser Altimeter Science Algorithms is a software system for controlling the laser altimeter aboard the Messenger spacecraft, which is to enter into orbit about Mercury in 2011. The software will control the altimeter by dynamically modifying hardware inputs for gain, threshold, channel-disable flags, range-window start location, and range-window width, by using ranging information provided by the spacecraft and noise counts from instrument hardware. In addition, because of severe bandwidth restrictions, the software also selects returns for downlink.

Rosenberg, Jacob S.↗
NASA NTRS2010

The MODIS Aerosol Algorithm: Critical Evaluation and Plans for Collection 6

For ten years the MODIS aerosol algorithm has been applied to measured MODIS radiances to produce a continuous set of aerosol products, over land and ocean. The MODIS aerosol products are widely used by the scientific and applied science communities for variety of purposes that span operational air quality forecasting in estimates o[ clear-sky direct radiative effects over ocean and aerosol-cloud interactions. The products undergo continual evaluation, including self-consistency checks and comparisons with highly accurate ground-based instruments. The result of these evaluation exercises is a quantitative understanding of the strengths and weaknesses of the retrieval, where and when the products are accurate and the situations where and when accuracy degrades. We intend 10 present results of the most recent critical evaluations including the first comparison of the over ocean products against the shipboard aerosol optical depth measurements of the Marine Aerosol Network (MAN), the demonstration of the lack of sensitivity to size parameter in the over land products and identification of residual problems and regional issues. While the current data set is undergoing evaluation, we are preparing for the next data processing, labeled Collection 6. Collection 6 will include transparent Quality Flags, a 3 km aerosol product and the 500m resolution cloud mask used within the aerosol n:bicvu|. These new products and adjustments to algorithm assumptions should provide users with more options and greater control, as they adapt the product for their own purposes.

Remer, Lorraine↗
NASA NTRS2009

MISR Aerosol Product Attributes and Statistical Comparisons with MODIS

In this paper, Multi-angle Imaging SpectroRadiometer (MISR) aerosol product attributes are described, including geometry and algorithm performance flags. Actual retrieval coverage is mapped and explained in detail using representative global monthly data. Statistical comparisons are made with coincident aerosol optical depth (AOD) and Angstrom exponent (ANG) retrieval results from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. The relationship between these results and the ones previously obtained for MISR and MODIS individually, based on comparisons with coincident ground-truth observations, is established. For the data examined, MISR and MODIS each obtain successful aerosol retrievals about 15% of the time, and coincident MISR-MODIS aerosol retrievals are obtained for about 6%-7% of the total overlap region. Cloud avoidance, glint and oblique-Sun exclusions, and other algorithm physical limitations account for these results. For both MISR and MODIS, successful retrievals are obtained for over 75% of locations where attempts are made. Where coincident AOD retrievals are obtained over ocean, the MISR-MODIS correlation coefficient is about 0.9; over land, the correlation coefficient is about 0.7. Differences are traced to specific known algorithm issues or conditions. Over-ocean ANG comparisons yield a correlation of 0.67, showing consistency in distinguishing aerosol air masses dominated by coarse-mode versus fine-mode particles. Sampling considerations imply that care must be taken when assessing monthly global aerosol direct radiative forcing and AOD trends with these products, but they can be used directly for many other applications, such as regional AOD gradient and aerosol air mass type mapping and aerosol transport model validation. Users are urged to take seriously the published product data-quality statements.

Kahn, Ralph A.↗
NASA NTRS2010

Rapid Corner Detection Using FPGAs

In order to perform precision landings for space missions, a control system must be accurate to within ten meters. Feature detection applied against images taken during descent and correlated against the provided base image is computationally expensive and requires tens of seconds of processing time to do just one image while the goal is to process multiple images per second. To solve this problem, this algorithm takes that processing load from the central processing unit (CPU) and gives it to a reconfigurable field programmable gate array (FPGA), which is able to compute data in parallel at very high clock speeds. The workload of the processor then becomes simpler; to read an image from a camera, it is transferred into the FPGA, and the results are read back from the FPGA. The Harris Corner Detector uses the determinant and trace to find a corner score, with each step of the computation occurring on independent clock cycles. Essentially, the image is converted into an x and y derivative map. Once three lines of pixel information have been queued up, valid pixel derivatives are clocked into the product and averaging phase of the pipeline. Each x and y derivative is squared against itself, as well as the product of the ix and iy derivative, and each value is stored in a WxN size buffer, where W represents the size of the integration window and N is the width of the image. In this particular case, a window size of 5 was chosen, and the image is 640 480. Over a WxN size window, an equidistance Gaussian is applied (to bring out the stronger corners), and then each value in the entire window is summed and stored. The required components of the equation are in place, and it is just a matter of taking the determinant and trace. It should be noted that the trace is being weighted by a constant k, a value that is found empirically to be within 0.04 to 0.15 (and in this implementation is 0.05). The constant k determines the number of corners available to be compared against a threshold sigma to mark a valid corner. After a fixed delay from when the first pixel is clocked in (to fill the pipeline), a score is achieved after each successive clock. This score corresponds with an (x,y) location within the image. If the score is higher than the predetermined threshold sigma, then a flag is set high and the location is recorded.

Morfopoulos, Arin C.↗
NASA NTRS2010

Data Access Services that Make Remote Sensing Data Easier to Use

This slide presentation reviews some of the processes that NASA uses to make the remote sensing data easy to use over the World Wide Web. This work involves much research into data formats, geolocation structures and quality indicators, often to be followed by coding a preprocessing program. Only then are the data usable within the analysis tool of choice. The Goddard Earth Sciences Data and Information Services Center is deploying a variety of data access services that are designed to dramatically shorten the time consumed in the data preparation step. On-the-fly conversion to the standard network Common Data Form (netCDF) format with Climate-Forecast (CF) conventions imposes a standard coordinate system framework that makes data instantly readable through several tools, such as the Integrated Data Viewer, Gridded Analysis and Display System, Panoply and Ferret. A similar benefit is achieved by serving data through the Open Source Project for a Network Data Access Protocol (OPeNDAP), which also provides subsetting. The Data Quality Screening Service goes a step further in filtering out data points based on quality control flags, based on science team recommendations or user-specified criteria. Further still is the Giovanni online analysis system which goes beyond handling formatting and quality to provide visualization and basic statistics of the data. This general approach of automating the preparation steps has the important added benefit of enabling use of the data by non-human users (i.e., computer programs), which often make sub-optimal use of the available data due to the need to hard-code data preparation on the client side.

Lynnes, Christopher↗
NASA NTRS2010

Ambiguity of Quality in Remote Sensing Data

This slide presentation reviews some of the issues in quality of remote sensing data. Data "quality" is used in several different contexts in remote sensing data, with quite different meanings. At the pixel level, quality typically refers to a quality control process exercised by the processing algorithm, not an explicit declaration of accuracy or precision. File level quality is usually a statistical summary of the pixel-level quality but is of doubtful use for scenes covering large areal extents. Quality at the dataset or product level, on the other hand, usually refers to how accurately the dataset is believed to represent the physical quantities it purports to measure. This assessment often bears but an indirect relationship at best to pixel level quality. In addition to ambiguity at different levels of granularity, ambiguity is endemic within levels. Pixel-level quality terms vary widely, as do recommendations for use of these flags. At the dataset/product level, quality for low-resolution gridded products is often extrapolated from validation campaigns using high spatial resolution swath data, a suspect practice at best. Making use of quality at all levels is complicated by the dependence on application needs. We will present examples of the various meanings of quality in remote sensing data and possible ways forward toward a more unified and usable quality framework.

Lynnes, Christopher↗
NASA NTRS2011

EOS MLS Level 1B Data Processing Software

This software is an improvement on Version 2, which was described in EOS MLS Level 1B Data Processing, Version 2.2, NASA Tech Briefs, Vol. 33, No. 5 (May 2009), p. 34. It accepts the EOS MLS Level 0 science/engineering data, and the EOS Aura spacecraft ephemeris/attitude data, and produces calibrated instrument radiances and associated engineering and diagnostic data. This version makes the code more robust, improves calibration, provides more diagnostics outputs, defines the Galactic core more finely, and fixes the equator crossing. The Level 1 processing software manages several different tasks. It qualifies each data quantity using instrument configuration and checksum data, as well as data transmission quality flags. Statistical tests are applied for data quality and reasonableness. The instrument engineering data (e.g., voltages, currents, temperatures, and encoder angles) is calibrated by the software, and the filter channel space reference measurements are interpolated onto the times of each limb measurement with the interpolates being differenced from the measurements. Filter channel calibration target measurements are interpolated onto the times of each limb measurement, and are used to compute radiometric gain. The total signal power is determined and analyzed by each digital autocorrelator spectrometer (DACS) during each data integration. The software converts each DACS data integration from an autocorrelation measurement in the time domain into a spectral measurement in the frequency domain, and estimates separately the spectrally, smoothly varying and spectrally averaged components of the limb port signal arising from antenna emission and scattering effects. Limb radiances are also calibrated.

Perun, Vincent S.↗
NASA NTRS2006

Predicting Lifetime of a Thermomechanically Loaded Component

NASALIFE is a computer program for predicting the lifetime, as affected by low cycle fatigue (LCF) and creep rupture, of a structural component subject to temporally varying, multiaxial thermomechanical loads. The component could be, for example, part of an aircraft turbine engine. Empirical data from LCF tests, creep rupture tests, and static tensile tests are used as references for predicting the number of missions the component can withstand under a given thermomechanical loading condition. The user prepares an input file containing the creep-rupture and cyclic-fatigue information, temperature-dependent material properties, and mission loading and control flags. The creep rupture information can be entered in tabular form as stress versus life or by means of parameters of the Larson-Miller equation. The program uses the Walker mean-stress model to adjust predicted life for ranges of the ratio between the maximum and minimum stresses. Data representing complex load cycles are reduced by the rainflow counting method. Miner's rule is utilized to combine the damage at different load levels. Finally, the program determines the total damage due to creep and combines it with the fatigue damage due to the cyclic loading and predicts the approximate number of missions a component can endure before failing.

Murthy, Pappu L. N.↗
NASA NTRS2010

Detection of Multi-Layer and Vertically-Extended Clouds Using A-Train Sensors

The detection of mUltiple cloud layers using satellite observations is important for retrieval algorithms as well as climate applications. In this paper, we describe a relatively simple algorithm to detect multiple cloud layers and distinguish them from vertically-extended clouds. The algorithm can be applied to coincident passive sensors that derive both cloud-top pressure from the thermal infrared observations and an estimate of solar photon pathlength from UV, visible, or near-IR measurements. Here, we use data from the A-train afternoon constellation of satellites: cloud-top pressure, cloud optical thickness, the multi-layer flag from the Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) and the optical centroid cloud pressure from the Aura Ozone Monitoring Instrument (OMI). For the first time, we use data from the CloudSat radar to evaluate the results of a multi-layer cloud detection scheme. The cloud classification algorithms applied with different passive sensor configurations compare well with each other as well as with data from CloudSat. We compute monthly mean fractions of pixels containing multi-layer and vertically-extended clouds for January and July 2007 at the OMI spatial resolution (l2kmx24km at nadir) and at the 5kmx5km MODIS resolution used for infrared cloud retrievals. There are seasonal variations in the spatial distribution of the different cloud types. The fraction of cloudy pixels containing distinct multi-layer cloud is a strong function of the pixel size. Globally averaged, these fractions are approximately 20% and 10% for OMI and MODIS, respectively. These fractions may be significantly higher or lower depending upon location. There is a much smaller resolution dependence for fractions of pixels containing vertically-extended clouds (approx.20% for OMI and slightly less for MODIS globally), suggesting larger spatial scales for these clouds. We also find higher fractions of vertically-extended clouds over land as compared with ocean, particularly in the tropics and summer hemisphere.

Joiner, J.↗
NASA NTRS2005

Automated Counting of Particles To Quantify Cleanliness

A machine vision system, similar to systems used in microbiological laboratories to count cultured microbes, has been proposed for quantifying the cleanliness of nominally precisely cleaned hardware by counting residual contaminant particles. The system would include a microscope equipped with an electronic camera and circuitry to digitize the camera output, a personal computer programmed with machine-vision and interface software, and digital storage media. A filter pad, through which had been aspirated solvent from rinsing the hardware in question, would be placed on the microscope stage. A high-resolution image of the filter pad would be recorded. The computer would analyze the image and present a histogram of sizes of particles on the filter. On the basis of the histogram and a measure of the desired level of cleanliness, the hardware would be accepted or rejected. If the hardware were accepted, the image would be saved, along with other information, as a quality record. If the hardware were rejected, the histogram and ancillary information would be recorded for analysis of trends. The software would perceive particles that are too large or too numerous to meet a specified particle-distribution profile. Anomalous particles or fibrous material would be flagged for inspection.

Rhode, James↗