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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Life Cycle of Midlatitude Deep Convective Systems in a Lagrangian Framework

Deep Convective Systems (DCSs) consist of intense convective cores (CC), large stratiform rain (SR) regions, and extensive non-precipitating anvil clouds (AC). This study focuses on the evolution of these three components and the factors that affect convective AC production. An automated satellite tracking method is used in conjunction with a recently developed multi-sensor hybrid classification to analyze the evolution of DCS structure in a Lagrangian framework over the central United States. Composite analysis from 4221 tracked DCSs during two warm seasons (May-August, 2010-2011) shows that maximum system size correlates with lifetime, and longer-lived DCSs have more extensive SR and AC. Maximum SR and AC area lag behind peak convective intensity and the lag increases linearly from approximately 1-hour for short-lived systems to more than 3-hours for long-lived ones. The increased lag, which depends on the convective environment, suggests that changes in the overall diabatic heating structure associated with the transition from CC to SR and AC could prolong the system lifetime by sustaining stratiform cloud development. Longer-lasting systems are associated with up to 60% higher mid-tropospheric relative humidity and up to 40% stronger middle to upper tropospheric wind shear. Regression analysis shows that the areal coverage of thick AC is strongly correlated with the size of CC, updraft strength, and SR area. Ambient upper tropospheric wind speed and wind shear also play an important role for convective AC production where for systems with large AC (radius greater than 120-km) they are 24% and 20% higher, respectively, than those with small AC (radius=20 km).

Feng, Zhe↗
NASA NTRS1993

(abstract) Reddening Measurements using CCD uvby (Stromgren) Photometry

Reddening measurements for interstellar clouds have usually relied on one of two techniques: Star counts or spectro-photometric measurements. We present preliminary results for a technique to determine the extinction using CCD uvby (Stromgren) photometry, without the need to perform special classification of the stars. The method relies on the fact that the uvby system allows two independent measurements of the reddening through the m(sub 1)and c(sub 1) indices to select the true intrensic colors for each star. Using observations from the Burrell Schmidt on Kitt Peak, we find that the technique allows reliable reddening measurements to be made down to at least V=15&supm;.

measurements↗
NASA NTRS2008

NASA Tech Briefs, December 2008

Topics covered include: Crew Activity Analyzer; Distributing Data to Hand-Held Devices in a Wireless Network; Reducing Surface Clutter in Cloud Profiling Radar Data; MODIS Atmospheric Data Handler; Multibeam Altimeter Navigation Update Using Faceted Shape Model; Spaceborne Hybrid-FPGA System for Processing FTIR Data; FPGA Coprocessor for Accelerated Classification of Images; SiC JFET Transistor Circuit Model for Extreme Temperature Range; TDR Using Autocorrelation and Varying-Duration Pulses; Update on Development of SiC Multi-Chip Power Modules; Radio Ranging System for Guidance of Approaching Spacecraft; Electromagnetically Clean Solar Arrays; Improved Short-Circuit Protection for Power Cells in Series; Electromagnetically Clean Solar Arrays; Logic Gates Made of N-Channel JFETs and Epitaxial Resistors; Improved Short-Circuit Protection for Power Cells in Series; Communication Limits Due to Photon-Detector Jitter; System for Removing Pollutants from Incinerator Exhaust; Sealing and External Sterilization of a Sample Container; Converting EOS Data from HDF-EOS to netCDF; HDF-EOS 2 and HDF-EOS 5 Compatibility Library; HDF-EOS Web Server; HDF-EOS 5 Validator; XML DTD and Schemas for HDF-EOS; Converting from XML to HDF-EOS; Simulating Attitudes and Trajectories of Multiple Spacecraft; Specialized Color Function for Display of Signed Data; Delivering Alert Messages to Members of a Work Force; Delivering Images for Mars Rover Science Planning; Oxide Fiber Cathode Materials for Rechargeable Lithium Cells; Electrocatalytic Reduction of Carbon Dioxide to Methane; Heterogeneous Superconducting Low-Noise Sensing Coils; Progress toward Making Epoxy/Carbon-Nanotube Composites; Predicting Properties of Unidirectional-Nanofiber Composites; Deployable Crew Quarters; Nonventing, Regenerable, Lightweight Heat Absorber; Miniature High-Force, Long-Stroke SMA Linear Actuators; "Bootstrap" Configuration for Multistage Pulse-Tube Coolers; Reducing Liquid Loss during Ullage Venting in Microgravity; Ka-Band Transponder for Deep-Space Radio Science; Replication of Space-Shuttle Computers in FPGAs and ASICs; Demisable Reaction-Wheel Assembly; Spatial and Temporal Low-Dimensional Models for Fluid Flow; Advanced Land Imager Assessment System; Range Imaging without Moving Parts.

Source record↗
NASA NTRS1977

Application of LANDSAT system for improving methodology for inventory and classification of wetlands

The author has identified the following significant results. Processing of LANDSAT MSS data for detection of prairie ponds and lakes was completed. Data coverage included a 36,876 sq km area in southeastern North Dakota during May and July. Cloud coverage limited the May coverage to 87% of the total area. Data analysis was accomplished using three software programs. Wetland identification by MSS sensors were compared to visual counts obtained by observers in low flying aircraft. Pond numbers identified by LANDSAT averaged about 20% of those counted visually. The discrepancy was attributed to the fact that approximately 75% of the ponds in the glaciated prairie region are less than 0.4 ha in size. It is significant, however, that LANDSAT counts accurately reflect trends.

Gilmer, D. S.↗
NASA NTRS2023

Evaluating MODIS Snow Products Using an Extensive Wildlife Camera Network

Snow covers a maximum of 47 million km 2 of Earth's northern hemisphere each winter and is an important component of the planet's energy balance, hydrology cycles, and ecosystems. Monitoring regional and global snow cover has increased in urgency in recent years due to warming temperatures and declines in snow cover extent. Optical satellite instruments provide large-scale observations of snow cover, but cloud cover and dense forest canopy can reduce accuracy in mapping snow cover. Remote camera networks deployed for wildlife monitoring operate below cloud cover and in forests, representing a virtually untapped source of snow cover observations to supplement satellite observations. Using images from 1,181 wildlife cameras deployed by the Norwegian Institute for Nature Research (NINA), we compared snow cover extracted from camera images to Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products during winter months of 2018–2020. Ordinal snow classifications (scale = 0–4) from cameras were closely related to normalized difference snow index (NDSI) values from the MODIS Terra Snow Cover Daily L3 Global 500m (MOD10A1) Collection 6 product (R 2 = 0.70). Tree canopy cover, the normalized difference vegetation index (NDVI), and image color mode influenced agreement between camera images and MOD10A1 NDSI values. For MOD10A1F, MOD10A1's corresponding cloud-gap filled product, agreement with cloud-gap filled values decreased from 78.5% to 56.4% in the first three days of cloudy periods and stabilized thereafter. Using our camera data as validation, we derived a threshold to create daily binary maps of snow cover from the MOD10A1 product. The threshold corresponding to snow presence was an NDSI value of 40.50, which closely matched a previously defined global binary threshold of 40 using the MOD10A2 8-day product. These analyses demonstrate the utility of camera trap networks for validation of snow cover products from satellite remote sensing, as well as their potential to identify sources of inaccuracy.

Validation↗
NASA NTRSDate not supplied

Flood Detection with Synthetic Aperture Radar: A Case Study of Houston, Texas Following Hurricane Harvey (2017) using C- and X-Band Observations

Hurricane Harvey produced record-breaking rainfall of up to 60 inches resulting in extensive flooding in Houston, Texas, in late August and early September of 2017. The slow forward motion of the storm following landfall left much of the area unobservable to optical remote sensing instruments for several days due to cloud cover. The active nature of Synthetic Aperture Radar (SAR) instruments allows for observations through clouds, which can supplement efforts to estimate hurricane-induced flood impacts. A growing fleet of SAR constellations has helped lower the latency of imagery following a hurricane, allowing for more timely detections of flooding to help support emergency response efforts. In this study, we leverage publicly available C-band SAR observations from the European Space Agency’s Sentinel-1B (S1B) satellite, collected on 30 August, and X-band SAR imagery collected on 1 September by the Airbus TanDEM-X (TDX) satellite made available through the NASA Commercial Smallsat Data Acquisition (CSDA) program. For each dataset, one co-polarized, StripMap, Radiometric Terrain Corrected (RTC) image was used to create a binary water/no water classification map by referencing permanent water in the Cropland Data Layer (CDL) dataset to determine thresholds. A validation dataset of randomly distributed “ground truth” points was generated using optical imagery from PlanetScope on 31 August, where the domain had relatively little cloud cover. We found that the S1B- and TDX-derived open water maps achieved overall accuracies of 94.17% and 91.57%, respectively. The variation in performance is attributed to both the penetrative abilities of C- and X-band SAR wavelengths in vegetated areas and the increased spatial resolution of the commercial SAR (~3 m) over Sentinel-1 (30 m). While publicly available Sentinel-1 observations are commonly relied upon in response and recovery efforts, these results suggest that including commercial X-band SAR imagery could be beneficial by providing both increased spatial resolution and more frequent revisits when deriving post-event flood mapping products.

Alexander M Melancon↗
NASA NTRS2012

Comparison of Aerosol Classification Results from Airborne High Spectral Resolution Lidar (HSRL) Measurements and the Calipso Vertical Feature Mask

Knowledge of the vertical profile, composition, concentration, and size of aerosols is required for assessing the direct impact of aerosols on radiation, the indirect effects of aerosols on clouds and precipitation, and attributing these effects to natural and anthropogenic aerosols. Because anthropogenic aerosols are predominantly submicrometer, fine mode fraction (FMF) retrievals from satellite have been used as a tool for deriving anthropogenic aerosols. Although column and profile satellite retrievals of FMF have been performed over the ocean, such retrievals have not yet been been done over land. Consequently, uncertainty in satellite estimates of the anthropogenic component of the aerosol direct radiative forcing is greatest over land, due in large part to uncertainties in the FMF. Satellite measurements have been used to detect and evaluate aerosol impacts on clouds; however, such efforts have been hampered by the difficulty in retrieving vertically-resolved cloud condensation nuclei (CCN) concentration, which is the most direct parameter linking aerosol and clouds. Recent studies have shown correlations between average satellite derived column aerosol optical thickness (AOT) and in situ measured CCN. However, these same studies, as well as others that use detailed airborne in situ measurements have noted that vertical variability of the aerosol distribution, impacts of relative humidity, and the presence of coarse mode aerosols such as dust introduce large uncertainties in such relations.

Burton, S. P.↗
NASA NTRS2004

Seasonal Surface Spectral Emissivity Derived from Terra MODIS Data

The CERES (Clouds and the Earth's Radiant Energy System) Project is measuring broadband shortwave and longwave radiances and deriving cloud properties form various images to produce a combined global radiation and cloud property data set. In this paper, simultaneous data from Terra MODIS (Moderate Resolution Imaging Spectroradiometer) taken at 3.7, 8.5, 11.0, and 12.0 m are used to derive the skin temperature and the surface emissivities at the same wavelengths. The methodology uses separate measurements of clear sky temperature in each channel determined by scene classification during the daytime and at night. The relationships between the various channels at night are used during the day when solar reflectance affects the 3.7- m radiances. A set of simultaneous equations is then solved to derive the emissivities. Global monthly emissivity maps are derived from Terra MODIS data while numerical weather analyses provide soundings for correcting the observed radiances for atmospheric absorption. These maps are used by CERES and other cloud retrieval algorithms.

Sun-Mack, Sunny↗
NASA NTRS1973

The use of ERTS-1 MSS data for mapping strip mines and acid mine drainage in Pennsyvania

Digital processing of ERTS-I MSS data for areas around the west branch of the Susquehanna River permits identification of stripped areas including ones that are not discernible from visual analysis of ERTS imagery. Underflight data and ground-based observations are used for ground-truth and as a basis for designing more refined operators to make sub-classifications of stripped areas, particularly with regard to manifestations of acid mine drainage; because of associated diagnostic effects on vegetation, seasonal changes in classifiction criteria are being documented as repeated, cloud-free ERTS-I coverage of the same area becomes available. Preliminary results indicate that ERTS data can be used to moniter not only the total extent of stripping in given areas but also the effectiveness of reclamation and pollution abatement procedures.

Alexander, S. S.↗
NASA NTRS1983

Continental land cover classification using meteorological satellite data

The use of the National Oceanic and Atmospheric Administration's advanced very high resolution radiometer satellite data for classifying land cover and monitoring of vegetation dynamics over an extremely large area is demonstrated for the continent of Africa. Data from 17 imaging periods of 21 consecutive days each were composited by a technique sensitive to the in situ green-leaf biomass to provide cloud-free imagery for the whole continent. Virtually cloud-free images were obtainable even for equatorial areas. Seasonal variation in the density and extent of green leaf vegetation corresponded to the patterns of rainfall associated with the inter-tropical convergence zone. Regional variations, such as the 1982 drought in east Africa, were also observed. Integration of the weekly satellite data with respect to time produced a remotely sensed assessment of biological activity based upon density and duration of green-leaf biomass. Two of the 21-day composited data sets were used to produce a general land cover classification. The resultant land cover distributions correspond well to those of existing maps.

Tucker, C. J.↗
NASA NTRS1982

Study of sensor spectral responses and data processing algorithms and architectures for onboard feature identification

A computational model of the deterministic and stochastic processes involved in remote sensing is used to study spectral feature identification techniques for real-time onboard processing of data acquired with advanced earth-resources sensors. Preliminary results indicate that: Narrow spectral responses are advantageous; signal normalization improves mean-square distance (MSD) classification accuracy but tends to degrade maximum-likelihood (MLH) classification accuracy; and MSD classification of normalized signals performs better than the computationally more complex MLH classification when imaging conditions change appreciably from those conditions during which reference data were acquired. The results also indicate that autonomous categorization of TM signals into vegetation, bare land, water, snow and clouds can be accomplished with adequate reliability for many applications over a reasonably wide range of imaging conditions. However, further analysis is required to develop computationally efficient boundary approximation algorithms for such categorization.

Huck, F. O.↗
NASA NTRS2019

Deriving High-Resolution Reservoir Bathymetry from ICESat-2 Prototype Photon-Counting Lidar and Landsat Imagery

Knowledge of reservoir bathymetry is essential for many studies on terrestrial hydrological and biogeochemical processes. However, there are currently no cost-effective approaches to derive reservoir bathymetry at the global scale. This study explores the potential of generating high-resolution global bathymetry using elevation data collected by the 532-nm Advanced Topographic Laser Altimeter System (ATLAS) onboard the Ice, Cloud, and Land Elevation Satellite (ICESat-2). The novel algorithm was developed and tested using the ICESat-2 airborne prototype, the Multiple Altimeter Beam Experimental Lidar (MABEL), with Landsat-based water classifications (from 1982 to 2017). MABEL photon elevations were paired with Landsat water occurrence percentiles to establish the elevation-area (E-A) relationship, which in turn was applied to the percentile image to obtain partial bathymetry over the historic dynamic range of reservoir area. The bathymetry for the central area was projected to achieve the full bathymetry. The bathymetry image was then embedded onto the digital elevation model (DEM). Results were validated over Lake Mead against survey data. Results over four transects show coefficient of determination (R²) values from 0.82 to 0.99 and root-mean-square error (RMSE) values from 1.18 to 2.36 m. In addition, the E-A and elevation-storage (E-S) curves have RMSEs of 1.56 m and 0.08 km³, respectively. Over the entire dynamic reservoir area, the derived bathymetry agrees very well with independent survey data, except for within the highest and lowest percentile bands. With abundant overpassing tracks and high spatial resolution, the newly launched ICESat-2 should enable the derivation of bathymetry over an unprecedented number of reservoirs.

Yao Li↗
NASA NTRS2017

The Earth Observing System (EOS)

The Earth Observing System is a US National Aeronautics and Space Administration (NASA) program consisting of a science segment, a data system, and a space segment made up of a series of polar-orbiting and mid-inclination satellites for long-term monitoring of the Earth as an integrated system, including observations of the land surface, biosphere, atmosphere, cryosphere, and oceans. Initially conceived in the mid-late 1980s, it was implemented as a series of large "flagship" missions and smaller focused satellites, often in partnership with instruments and sometimes spacecraft from other nations. In 1984 NASA formed several instrument panels to develop concepts for “facility instruments” that would provide comprehensive support to the scientific and applications’ communities, each with a supporting science team competitively selected via peer-review. An Announcement of Opportunity was released in 1988 and proposals were solicited for three types of proposals: (i) instrument investigations that would include the provision of instrumentation for flight on a polar platform, including non-Earth science payloads requiring polar orbits (PI instruments), (ii) research facility instrument team member and team leader investigations for facility instruments, and (iii) interdisciplinary science investigations to provide data analysis and modeling, preparing to use data acquired from the space platforms. The initial selections made in 1989 included support for 24 instrument investigations, 6 research facility instrument investigations, and 28 interdisciplinary investigations. Although considerable changes were made over the years in the structure and orbital configuration of the various instruments and platforms, a total of 10 different missions were eventually flown, as shown in Fig. 1. These included three multiinstrument flagship platforms known as Terra (launched in 1999), Aqua (launched in 2002), and Aura (launched in 2004), where the Terra mission emphasis was primarily on land processes, Aqua on the hydrological cycle, and Aura on atmospheric chemistry. In addition to these flagship missions the EOS missions included Landsat 7 (land cover classification and change), QuikScat (wind speed and direction over the oceans), Jason-1 (ocean surface topography and sea level), ACRIMSAT (total solar irradiance (TSI)), SAGE III (stratospheric aerosol and ozone levels), ICESat (ice, cloud, and land elevation), and SORCE (Solar Radiation and Climate Experiment; total and spectral solar irradiance). In addition EOS supported CERES (an Earth radiation budget sensor) and LIS (a lightning sensor), that flew as part of the NASA/JAXA Tropical Rainfall Measuring Mission (1997), and SeaWinds (wind speed and direction), that flew as part of the short-lived ADEOS II (Midori II, 2002) mission of JAXA (Japan Aerospace Exploration Agency). The EOS contributions are too numerous to present in detail in this article, but can be viewed for convenience as a means of obtaining global measurements (with appropriate characteristics) of the 24 variables and variable groups listed in Table 1. This table was created by the EOS community in the late 1990s as a way to succinctly indicate which instruments on the various EOS platforms were expected to contribute to each of the specified variables in primary (bold), secondary (bold italics), or contributing (roman) roles. All of the science data products produced by these space missions are available without charge throughout the world, and are thus highly valued for their many contributions to applications such as air quality, fires, land cover change, and the state of the oceans, as well as for science and monitoring of the state of the Earth system and its changes over time. These and other international satellites have played a crucial role in understanding and documenting global change, including (i) global surface temperature and atmospheric temperature and water vapor profiles, (ii) glacial retreat, (iii) sea ice extent and change, (iv) solar radiation into and out of the Earth-atmosphere system, (v) atmospheric aerosol and cloud properties, (vi) sources and sinks of

ICESat↗
NASA NTRS2020

Leveraging Google Earth Engine User Interface for Semiautomated Wetland Classification in the Great Lakes Basin at 10 m With Optical and Radar Geospatial Datasets

As one of the world’s largest freshwater ecosystems,the Great Lakes Basin houses hundreds of thousands of acres of wetlands that support a variety of crucial ecological and environmental functions at the local, regional, and global levels.Monitoring these wetlands is critical to conservation and restoration efforts, however current methods that rely on field monitoring are labor-intensive, costly, and often outdated. In this study, we present a graphical user interface constructed in Google Earth Engine called the Wetland Extent Tool (WET),which allows semi-automatic wetland classification according to a user-input area of interest and date range. WET composites datasets and conducts multi source, moderate resolution processing utilizing Landsat 8 OLI, Sentinel-2 MSI, Sentinel-1 C-SAR, and Shuttle Radar Topography Mission (SRTM) datasets to classify wetlands in the entire Great Lakes Basin. We evaluated classification results of wetlands, uplands, and open water from May-September 2019, and tested whether SRTM elevation, slope,or the Dynamic Surface Water Extent produced the most accurate results in each Great Lake Basin in conjunction with optical indices and radar composites. We found that elevation produced the most accurate classification in Lake Erie, Michigan,and Ontario, while slope performed best in Lake Huron and Superior. Lake Erie, Michigan, Ontario, and Huron achieved high overall accuracy and identification of wetlands. WET leverages cloud-computing for multi source processing of moderate resolution remote sensing data, and employs a user interface in Google Earth Engine that wetland managers and conservationists can use to monitor wetland extent in the Great Lakes Basin in near real-time.

Vanessa L Valenti↗
NASA NTRS2012

Submillimetric Spectroscopic Observations of Volatiles in Comet C-2004 Q2 (Machholz)

Submillimeter spectroscopic observations of comets provide an important tool for understanding their chemical composition and enable a taxonomic classification. Aims. We aim to determine the production rates of several parent- and product volatiles and the C-12/C-13 isotopic carbon ratio in the long-period comet C/2004 Q2 (Machholz), which is likely to originate from the Oort Cloud. Methods. The line emission from several molecules in the coma was measured with high signal-to-noise ratio in January 2005 at heliocentric distance of 1.2 AU by means of high-resolution spectroscopic observations using the Submillimeter Telescope (SMT) at the Arizona Radio Observatory (ARO). Results. We have obtained production rates of several volatiles (CH3OH, HCN, H(sup 13)CN, HNC, H2CO, CO, and CS) by comparing the observed and simulated line-integrated intensities. We calculated the synthetic profiles using a radiative transfer code that includes collisions between neutrals and electrons, and the effects of radiative pumping of the fundamental vibrational levels by solar infrared radiation. Furthermore, multiline observations of the CH3OH J = 7-6 series allow us to estimate the rotational temperature using the rotation diagram technique. We find that the CH3OH population distribution of the levels sampled by these lines can be described by a rotational temperature of 40 +/- 3 K. Derived mixing ratios relative to hydrogen cyanide are CO/CH3OH/H2CO/CS/HNC/HC-13N/HCN= 30.9/24.6/4.8/0.57/0.031/0.013/1 assuming a pointing offset of 8" due to the uncertain ephemeris at the time of the observations and the telescope pointing error. Conclusions. The measured relative molecular abundances in C/2004 Q2 (Machholz) are between low- to typical values of those obtained in Oort Cloud comets, suggesting that it has visited the inner solar system previously and undergone thermal processing. The HNC/HCN abundance ratio of approx 3.1% is comparable to that found in other comets, accounting for the dependence on the heliocentric distance, and could possibly be explained by ion-molecule chemical processes in the low-temperature atmosphere. From a tentative HC-13N detection, the measured value of 97 +/- 30 for the HC-12N/HC-13N isotopologue pair is consistent with a telluric value. The outgassing variability observed in the HCN production rates over a period of two hours is consistent with the rotation of the nucleus derived using different observational techniques.

chemical composition↗
NASA NTRS1997

Surface-Based Observations of Contrail Occurrence Over the US, Apr. 1993 to Apr. 1994

Surface observers stationed at 19 U.S. Air Force Bases and Army Air Stations recorded the daytime occurrence of contrails and cloud fraction on an hourly basis for the period April 1993 through April 1994. Each observation uses one of four main categories to report contrails as unobserved, non-persistent, persistent, and indeterminate. Additional classification includes the co-occurrence of cirrus with each report. The data cover much of the continental U.S. including locations near major commercial air routes. The mean annual frequency of occurrence in unobstructed viewing conditions is 13 percent for these sites. Contrail occurrence varied substantially with location and season. Most contrails occurred during the winter months and least during the summer with a pronounced minimum during July. Although nocturnal observations are not available, it appears that the contrails have a diurnal variation that peaks during mid morning over most areas. Contrails were most often observed in areas near major commercial air corridors and least often over areas far removed from the heaviest air traffic. A significant correlation exists between mean contrail frequency and aircraft fuel usage above 7 km suggesting predictive potential for assessing future contrail effects on climate.

Minnis, Patrick↗
NASA NTRS2016

How We Can Constrain Aerosol Type Globally

In addition to aerosol number concentration, aerosol size and composition are essential attributes needed to adequately represent aerosol-cloud interactions (ACI) in models. As the nature of ACI varies enormously with environmental conditions, global-scale constraints on particle properties are indicated. And although advanced satellite remote-sensing instruments can provide categorical aerosol-type classification globally, detailed particle microphysical properties are unobtainable from space with currently available or planned technologies. For the foreseeable future, only in situ measurements can constrain particle properties at the level-of-detail required for ACI, as well as to reduce uncertainties in regional-to-global-scale direct aerosol radiative forcing (DARF). The limitation of in situ measurements for this application is sampling. However, there is a simplifying factor: for a given aerosol source, in a given season, particle microphysical properties tend to be repeatable, even if the amount varies from day-to-day and year-to-year, because the physical nature of the particles is determined primarily by the regional environment. So, if the PDFs of particle properties from major aerosol sources can be adequately characterized, they can be used to add the missing microphysical detail the better sampled satellite aerosol-type maps. This calls for Systematic Aircraft Measurements to Characterize Aerosol Air Masses (SAM-CAAM). We are defining a relatively modest and readily deployable, operational aircraft payload capable of measuring key aerosol absorption, scattering, and chemical properties in situ, and a program for characterizing statistically these properties for the major aerosol air mass types, at a level-of-detail unobtainable from space. It is aimed at: (1) enhancing satellite aerosol-type retrieval products with better aerosol climatology assumptions, and (2) improving the translation between satellite-retrieved aerosol optical properties and species-specific aerosol mass and size that are tracked in aerosol transport and climate models used to assess aerosol forcing and its anthropogenic components. This presentation will summarize where we stand on the development of the SAM-CAAM concept.

DARF↗
NASA NTRS2009

Small Negative Cloud-to-Ground Lightning Reports at the KSC-ER

'1he NASA Kennedy Space Center (KSC) and Air Force Eastern Range (ER) use data from two cloud-to-ground (CG) lightning detection networks, the CGLSS and the NLDN, and a volumetric lightning mapping array, LDAR, to monitor and characterize lightning that is potentially hazardous to ground or launch operations. Data obtained from these systems during June-August 2006 have been examined to check the classification of small, negative CGLSS reports that have an estimated peak current, [I(sup p)] less than 7 kA, and to determine the smallest values of I(sup p), that are produced by first strokes, by subsequent strokes that create a new ground contact (NGC), and by subsequent strokes that remain in a pre-existing channel (PEC). The results show that within 20 km of the KSC-ER, 21% of the low-amplitude negative CGLSS reports were produced by first strokes, with a minimum I(sup p) of-2.9 kA; 31% were by NGCs, with a minimum I(sup p) of-2.0 kA; and 14% were by PECs, with a minimum I(sup p) of -2.2 kA. The remaining 34% were produced by cloud pulses or lightning events that we were not able to classify.

Wilson, Jennifer G.↗