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NASA NTRS2011

Satellite-Derived Distributions, Inventories and Fluxes of Dissolved and Particulate Organic Matter Along the Northeastern U.S. Continental Margin

Estuaries and the coastal ocean experience a high degree of variability in the composition and concentration of particulate and dissolved organic matter (DOM) as a consequence of riverine and estuarine fluxes of terrigenous DOM, sediments, detritus and nutrients into coastal waters and associated phytoplankton blooms. Our approach integrates biogeochemical measurements, optical properties and remote sensing to examine the distributions and inventories of organic carbon in the U.S. Middle Atlantic Bight and Gulf of Maine. Algorithms developed to retrieve colored DOM (CDOM), Dissolved (DOC) and Particulate Organic Carbon (POC) from NASA's MODIS-Aqua and SeaWiFS satellite sensors are applied to quantify the distributions and inventories of DOC and POC. Horizontal fluxes of DOC and POC from the continental margin to the open ocean are estimated from SeaWiFS and MODIS-Aqua distributions of DOC and POC and horizontal divergence fluxes obtained from the Northeastern North Atlantic ROMS model. SeaWiFS and MODIS imagery reveal the importance of estuarine outflow to the export of CDOM and DOC to the coastal ocean and a net community production of DOC on the shelf.

Mannino, A.↗
NASA NTRS2006

Processing TES Level-1B Data

TES L1B Subsystem is a computer program that performs several functions for the Tropospheric Emission Spectrometer (TES). The term "L1B" (an abbreviation of "level 1B"), refers to data, specific to the TES, on radiometric calibrated spectral radiances and their corresponding noise equivalent spectral radiances (NESRs), plus ancillary geolocation, quality, and engineering data. The functions performed by TES L1B Subsystem include shear analysis, monitoring of signal levels, detection of ice build-up, and phase correction and radiometric and spectral calibration of TES target data. Also, the program computes NESRs for target spectra, writes scientific TES level-1B data to hierarchical- data-format (HDF) files for public distribution, computes brightness temperatures, and quantifies interpixel signal variability for the purpose of first-order cloud and heterogeneous land screening by the level-2 software summarized in the immediately following article. This program uses an in-house-developed algorithm, called "NUSRT," to correct instrument line-shape factors.

DeBaca, Richard C.↗
NASA NTRS2011

Asia from Space: New Ideas for Exploration

Megafans, also known (incorrectly) as inland deltas, are partial cones of fluvial sediment with radii >~100 km. Each is generated by a formative river avulsing across a lowland. The alluvial tract (channel, levee, overbank, etc.) is the building block for megafans. Planform and sectional analyses, based on SRTM data, reveal their conical, low-angle morphology. Megafans are not geologically rare as often assumed but a normal feature in the hierarchy of fluvial features that is slowly beginning to be understood. Our global survey contains a total of >150 examples worldwide, in all tectonic basin types, with a majority of smaller, more easily distinguished megafans occupying classic foreland basins (54%) which may explain the general view that megafans only form in Himalaya-like foreland basins, especially since the Kosi Megafan in the Indogangetic Plain is one of the best known in the geological literature. Recent research has shown that all actively alluviating basins are occupied by fanlike sediment masses, from the well known smaller and steeper alluvial fan (level 8 in Miall s hierarchy of fluvial forms), to the much larger megafan feature (level 9). A close relationship exists between upland basin size and fan size and slope. Larger upland drainage basins give rise to low-slope megafan sedimentation, which can cover very large areas where the receptacle basin exists individual megafan areas are 10(sup 3-5) square kilometers, and collectively cover 1.2 million square kilometers in South America, for example. The habitat of megafans is now sufficiently well understood that prediction of some modern cryptic megafan locations has been successfully achieved. Underground prediction therefore seems possible, where sufficient data exists. It seems necessary to distinguish megafans from (i) steep, coarse-grained mountain-front alluvial fans which are overwhelmingly coarse-grained, (ii) deltas, since megafans lack distal shoreline processes), and (iii) confined floodplains which lack radial drainage. Numerous other differences can be identified. As a normal component of the modern fluvial environment, megafans must exist in the subsurface. Megafan size, predictable channel patterns of the formative river, and the gradation from coarser to finer sediments from apex to toe of megafans are characteristics that ought to assist in understanding subsurface patterns of hydrocarbon host rocks, and possibly source rocks as well. We show examples from various producing basins. A roughness map of Asia, based on an algorithm developed for Mars, shows megafan landscapes to be dominated by short baseline roughness and low slopes, consistent with megafan-dominated plains worldwide. Interestingly, this a unique signature for a larger continental landform.

Wilkinson, M. Justin↗
NASA NTRS2004

Fast Query-Optimized Kernel-Machine Classification

A recently developed algorithm performs kernel-machine classification via incremental approximate nearest support vectors. The algorithm implements support-vector machines (SVMs) at speeds 10 to 100 times those attainable by use of conventional SVM algorithms. The algorithm offers potential benefits for classification of images, recognition of speech, recognition of handwriting, and diverse other applications in which there are requirements to discern patterns in large sets of data. SVMs constitute a subset of kernel machines (KMs), which have become popular as models for machine learning and, more specifically, for automated classification of input data on the basis of labeled training data. While similar in many ways to k-nearest-neighbors (k-NN) models and artificial neural networks (ANNs), SVMs tend to be more accurate. Using representations that scale only linearly in the numbers of training examples, while exploring nonlinear (kernelized) feature spaces that are exponentially larger than the original input dimensionality, KMs elegantly and practically overcome the classic curse of dimensionality. However, the price that one must pay for the power of KMs is that query-time complexity scales linearly with the number of training examples, making KMs often orders of magnitude more computationally expensive than are ANNs, decision trees, and other popular machine learning alternatives. The present algorithm treats an SVM classifier as a special form of a k-NN. The algorithm is based partly on an empirical observation that one can often achieve the same classification as that of an exact KM by using only small fraction of the nearest support vectors (SVs) of a query. The exact KM output is a weighted sum over the kernel values between the query and the SVs. In this algorithm, the KM output is approximated with a k-NN classifier, the output of which is a weighted sum only over the kernel values involving k selected SVs. Before query time, there are gathered statistics about how misleading the output of the k-NN model can be, relative to the outputs of the exact KM for a representative set of examples, for each possible k from 1 to the total number of SVs. From these statistics, there are derived upper and lower thresholds for each step k. These thresholds identify output levels for which the particular variant of the k-NN model already leans so strongly positively or negatively that a reversal in sign is unlikely, given the weaker SV neighbors still remaining. At query time, the partial output of each query is incrementally updated, stopping as soon as it exceeds the predetermined statistical thresholds of the current step. For an easy query, stopping can occur as early as step k = 1. For more difficult queries, stopping might not occur until nearly all SVs are touched. A key empirical observation is that this approach can tolerate very approximate nearest-neighbor orderings. In experiments, SVs and queries were projected to a subspace comprising the top few principal- component dimensions and neighbor orderings were computed in that subspace. This approach ensured that the overhead of the nearest-neighbor computations was insignificant, relative to that of the exact KM computation.

Mazzoni, Dominic↗
NASA NTRS2011

Evaluating the Application of Multi-Satellite Observations in Hydrologic Modeling

When monitoring local or regional hydrosphere dynamics for applications such as agricultural productivity or drought and flooding events, it is necessary to have accurate, high-resolution estimates of terrestrial water and energy storages. Though in-situ observations provide reliable estimates of hydrologic states and fluxes, they are only capable of accurately capturing the dynamics at relatively discrete points in space and time, which makes them inadequate for characterizing the variability of the water budget across scales. In contrast, satellite-based remote sensing is ideal for providing observations of hydrological states and fluxes because it provides spatially-distributed observations at spatial and temporal scales required for regional land surface process modeling. Due to the continued progress in algorithm development and emerging satellite technology, we now have near-real time monitoring of several components of the water cycle including precipitation, soil moisture, lake and river height, terrestrial water storage, snow cover, and evapotranspiration. As these data become more readily available, their application to hydrologic modeling is becoming more common, however there remains little consensus on the most appropriate method for optimal integration and evaluation in regard to hydrological applications. Here we present two case studies operationally applying several remotely sensed products from AMSR-E, GRACE, and MODIS and discuss assimilation strategies, ease of integration and interpretation, and methods for quantifying the success of the application methodology.

Bolten, John↗
NASA NTRS2011

Effects of Forest Disturbances on Forest Structural Parameters Retrieval from Lidar Waveform Data

The effect of forest disturbance on the lidar waveform and the forest biomass estimation was demonstrated by model simulation. The results show that the correlation between stand biomass and the lidar waveform indices changes when the stand spatial structure changes due to disturbances rather than the natural succession. This has to be considered in developing algorithms for regional or global mapping of biomass from lidar waveform data.

Ranson, K, Lon↗
NASA NTRS2011

4 Vesta in Color: High Resolution Mapping from Dawn Framing Camera Images

Rotational surface variations on asteroid 4 Vesta have been known from ground-based and HST observations, and they have been interpreted as evidence of compositional diversity. NASA s Dawn mission entered orbit around Vesta on July 16, 2011 for a year-long global characterization. The framing cameras (FC) onboard the Dawn spacecraft will image the asteroid in one clear (broad) and seven narrow band filters covering the wavelength range between 0.4-1.0 microns. We present color mapping results from the Dawn FC observations of Vesta obtained during Survey orbit (approx.3000 km) and High-Altitude Mapping Orbit (HAMO) (approx.950 km). Our aim is to create global color maps of Vesta using multi spectral FC images to identify the spatial extent of compositional units and link them with other available data sets to extract the basic mineralogy. While the VIR spectrometer onboard Dawn has higher spectral resolution (864 channels) allowing precise mineralogical assessment of Vesta s surface, the FC has three times higher spatial resolution in any given orbital phase. In an effort to extract maximum information from FC data we have developed algorithms using laboratory spectra of pyroxenes and HED meteorites to derive parameters associated with the 1-micron absorption band wing. These parameters will help map the global distribution of compositionally related units on Vesta s surface. Interpretation of these units will involve the integration of FC and VIR data.

Reddy, V.↗
NASA NTRS2005

A Portable Electronic Nose For Toxic Vapor Detection, Identification, and Quantification

A new prototype instrument based on electronic nose (e-nose) technology has demonstrated the ability to identify and quantify many vapors of interest to the Space Program at their minimum required concentrations for both single vapors and two-component vapor mixtures, and may easily be adapted to detect many other toxic vapors. To do this, it was necessary to develop algorithms to classify unknown vapors, recognize when a vapor is not any of the vapors of interest, and estimate the concentrations of the contaminants. This paper describes the design of the portable e-nose instrument, test equipment setup, test protocols, pattern recognition algorithms, concentration estimation methods, and laboratory test results.

Linnell, B. R.↗
NASA NTRS2005

A Portable Electronic Nose for Toxic Vapor Detection, Identification, and Quantification

The Space Program and military use large quantities of hydrazine and monomethyl hydrazine as rocket propellant, which are very toxic and suspected human carcinogens. Current off-the-shelf portable instruments require 10 to 20 minutes of exposure to detect these compounds at the minimum required concentrations and are prone to false positives, making them unacceptable for many operations. In addition, post-mission analyses of grab bag air samples from the Shuttle have confirmed the occasional presence of on-board volatile organic contaminants, which also need to be monitored to ensure crew safety. A new prototype instrument based on electronic nose (e-nose) technology has demonstrated the ability to qualify (identify) and quantify many of these vapors at their minimum required concentrations, and may easily be adapted to detect many other toxic vapors. To do this, it was necessary to develop algorithms to classify unknown vapors, recognize when a vapor is not any of the vapors of interest, and estimate the concentrations of the contaminants. This paper describes the design of the portable e-nose instrument, test equipment setup, test protocols, pattern recognition algorithms, concentration estimation methods, and laboratory test results.

Linnell, B. R.↗
NASA NTRS2011

Evaluation of Long-Range Lightning Detection Networks Using TRMM/LIS Observations

Recent advances in long-range lightning detection technologies have improved our understanding of thunderstorm evolution in the data sparse oceanic regions. Although the expansion and improvement of long-range lightning datasets have increased their applicability, these applications (e.g., data assimilation, atmospheric chemistry, and aviation weather hazards) require knowledge of the network detection capabilities. Toward this end, the present study evaluates data from the World Wide Lightning Location Network (WWLLN) using observations from the Lightning Imaging Sensor (LIS) aboard the Tropical Rainfall Measurement Mission (TRMM) satellite. The study documents the WWLLN detection efficiency and location accuracy relative to LIS observations, describes the spatial variability in these performance metrics, and documents the characteristics of LIS flashes that are detected by WWLLN. Improved knowledge of the WWLLN detection capabilities will allow researchers, algorithm developers, and operational users to better prepare for the spatial and temporal coverage of the upcoming GOES-R Geostationary Lightning Mapper (GLM).

Rudlosky, Scott D.↗
NASA NTRS2012

Gimbal Control Algorithms for the Global Precipitation Measurement Core Observatory

There are two gimbaled systems on the Global Precipitation Measurement Core Observatory: two single-degree-of-freedom solar arrays (SAs) and one two-degree-of-freedom high gain antenna (HGA). The guidance, navigation, and control analysis team was presented with the following challenges regarding SA orientation control during periods of normal mission science: (1) maximize solar flux on the SAs during orbit day, subject to battery charging limits, (2) minimize atmospheric drag during orbit night to reduce frequency of orbit maintenance thruster usage, (3) minimize atmospheric drag during orbits for which solar flux is nearly independent of SA orientation, and (4) keep array-induced spacecraft attitude disturbances within allocated tolerances. The team was presented with the following challenges regarding HGA control during mission science periods: (1) while tracking a ground-selected Tracking Data and Relay Satellite (TDRS), keep HGA control error below about 4', (2) keep array-induced spacecraft attitude disturbances small, and (3) minimize transition time between TDRSs subject to constraints imposed by item 2. This paper describes the control algorithms developed to achieve these goals and certain analysis done as part of that work.

Welter, Gary L.↗
NASA NTRS2012

Detection Thresholds of Falling Snow from Satellite-Borne Active and Passive Sensors

Precipitation, including rain and snow, is a critical part of the Earth's energy and hydrology cycles. Precipitation impacts latent heating profiles locally while global circulation patterns distribute precipitation and energy from the equator to the poles. For the hydrological cycle, falling snow is a primary contributor in northern latitudes during the winter seasons. Falling snow is the source of snow pack accumulations that provide fresh water resources for many communities in the world. Furthermore, falling snow impacts society by causing transportation disruptions during severe snow events. In order to collect information on the complete global precipitation cycle, both liquid and frozen precipitation must be collected. The challenges of estimating falling snow from space still exist though progress is being made. These challenges include weak falling snow signatures with respect to background (surface, water vapor) signatures for passive sensors over land surfaces, unknowns about the spherical and non-spherical shapes of the snowflakes, their particle size distributions (PSDs) and how the assumptions about the unknowns impact observed brightness temperatures or radar reflectivities, differences in near surface snowfall and total column snow amounts, and limited ground truth to validate against. While these challenges remain, knowledge of their impact on expected retrieval results is an important key for understanding falling snow retrieval estimations. Since falling snow from space is the next precipitation measurement challenge from space, information must be determined in order to guide retrieval algorithm development for these current and future missions. This information includes thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types. For example, can a lake effect snow system with low (approx 2.5 km) cloud tops having an ice water content (IWC) at the surface of 0.25 g / cubic m and dendrite snowflakes be detected? If this information is known, we can focus retrieval efforts on detectable storms and concentrate advances on achievable results. Here, the focus is to determine thresholds of detection for falling snow for various snow conditions over land and lake surfaces. The results rely on simulated Weather Research Forecasting (WRF) simulations of falling snow cases since simulations provide all the information to determine the measurements from space and the ground truth. Sensitivity analyses were performed to better ascertain the relationships between multifrequency microwave and millimeter-wave sensor observations and the falling snow/underlying field of view. In addition, thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types were studied. Results will be presented for active radar at Ku, Ka, and W-band and for passive radiometer channels from 10 to 183 GHz.

Gail Skofronick-Jackson↗
NASA NTRS2012

Relationships Between Long-Range Lightning Networks and TRMM/LIS Observations

Recent advances in long-range lightning detection technologies have improved our understanding of thunderstorm evolution in the data sparse oceanic regions. Although the expansion and improvement of long-range lightning datasets have increased their applicability, these applications (e.g., data assimilation, atmospheric chemistry, and aviation weather hazards) require knowledge of the network detection capabilities. The present study intercompares long-range lightning data with observations from the Lightning Imaging Sensor (LIS) aboard the Tropical Rainfall Measurement Mission (TRMM) satellite. The study examines network detection efficiency and location accuracy relative to LIS observations, describes spatial variability in these performance metrics, and documents the characteristics of LIS flashes that are detected by the long-range networks. Improved knowledge of relationships between these datasets will allow researchers, algorithm developers, and operational users to better prepare for the spatial and temporal coverage of the upcoming GOES-R Geostationary Lightning Mapper (GLM).

Rudlosky, Scott D.↗
NASA NTRS2012

An Affordability Comparison Tool (ACT) for Space Transportation

NASA bas recently emphasized the importance of affordability for Commercial Crew Development Program (CCDP), Space Launch Systems (SLS) and Multi-Purpose Crew Vehicle (MPCV). System architects and designers are challenged to come up with architectures and designs that do not bust the budget. This paper describes the Affordability Comparison Tool (ACT) analyzes different systems or architecture configurations for affordability that allows for a comparison of: total life cycle cost; annual recurring costs, affordability figures-of-merit, such as cost per pound, cost per seat, and cost per flight, as well as productivity measures, such as payload throughput. Although ACT is not a deterministic model, the paper develops algorithms and parametric factors that use characteristics of the architectures or systems being compared to produce important system outcomes (figures-of-merit). Example applications of outcome figures-of-merit are also documented to provide the designer with information on the relative affordability and productivity of different space transportation applications.

McCleskey, C. M.↗
NASA NTRS2012

Polarimetric Signatures of Initiating Convection During MC3E

One of the goals of the Mid-latitude Continental Convective Clouds Experiment (MC3E) field campaign was to provide constraints for space-based rainfall retrieval algorithms over land. This study used datasets collected during the 2011 field campaign to combine radiometer and ground-based radar polarimetric retrievals in order to better understand hydrometeor type, habit and distribution for initiating continental convection. Cross-track and conically scanning nadir views from the Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR) were compared with ground-based polarimetric radar retrievals along the ER-2 flight track. Polarimetric signatures for both airborne radiometers and ground-based radars were well co-located with deep convection to relate radiometric signatures with low-level polarimetric radar data for hydrometeor identification and diameter estimation. For the time period of study, Z(sub DR) values indicated no presence of hail at the surface. However, the Z(sub DR) column extended well above the melting level into the mixed phase region, suggesting a possible source of frozen drop embryos for the future formation of hail. The results shown from this study contribute ground truth datasets for GPM PR algorithm development for convective events, which is an improvement upon previous stratiform precipitation centered framework.

Emory, Amber↗
NASA NTRS2012

Use NU-WRF and GCE Model to Simulate the Precipitation Processes During MC3E Campaign

One of major CRM approaches to studying precipitation processes is sometimes referred to as "cloud ensemble modeling". This approach allows many clouds of various sizes and stages of their lifecycles to be present at any given simulation time. Large-scale effects derived from observations are imposed into CRMs as forcing, and cyclic lateral boundaries are used. The advantage of this approach is that model results in terms of rainfall and QI and Q2 usually are in good agreement with observations. In addition, the model results provide cloud statistics that represent different types of clouds/cloud systems during their lifetime (life cycle). The large-scale forcing derived from MC3EI will be used to drive GCE model simulations. The model-simulated results will be compared with observations from MC3E. These GCE model-simulated datasets are especially valuable for LH algorithm developers. In addition, the regional scale model with very high-resolution, NASA Unified WRF is also used to real time forecast during the MC3E campaign to ensure that the precipitation and other meteorological forecasts are available to the flight planning team and to interpret the forecast results in terms of proposed flight scenarios. Post Mission simulations are conducted to examine the sensitivity of initial and lateral boundary conditions to cloud and precipitation processes and rainfall. We will compare model results in terms of precipitation and surface rainfall using GCE model and NU-WRF

Tao, Wei-Kuo↗
NASA NTRS2012

First Assessments of Predicted ICESat-2 Performance Using Aircraft Data

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) is a next-generation laser altimeter designed to continue key observations of ice sheet elevation change, sea ice freeboard, vegetation canopy height, earth surface elevation, and sea surface height. Scheduled for launch in mid-2016, ICESat-2 will use a high repetition rate (10 kHz), small footprint (10 m nominal ground diameter) laser, and a single-photon-sensitive detection strategy (photon counting) to measure precise range to the earth's surface. Using green light (532 nm), the six beams of ICESat-2 will provide improved spatial coverage compared with the single beam of ICESat, while the differences in transmit energy among the beams provide a large dynamic range. The six beams are arranged into three pairs of beams which allow slopes to measured on an orbit-by-orbit basis. In order to evaluate models of predicted ICESat-2 performance and provide ICESat-2-like data for algorithm development, an airborne ICESat-2 simulator was developed and first flown in 2010. This simulator, the Multiple Altimeter Beam Experimental Lidar (MABEL) was most recently deployed to Iceland in April 2012 and collected approx 85 hours of science data over land ice, sea ice, and calibration targets. MABEL uses a similar photon-counting measurement strategy to what will be used on ICESat-2. MABEL collects data in 16 green channels and an additional 8 channels in the infrared aligned across the direction of flight. By using NASA's ER-2 aircraft flying at 20km altitude, MABEL flies as close to space as is practical, and collects data through approx 95% of the atmosphere. We present background on the MABEL instrument, and data from the April 2012 deployment to Iceland. Among the 13 MABEL flights, we collected data over the Greenland ice sheet interior and outlet glaciers in the southwest and western Greenland, sea ice data over the Nares Strait and Greenland Sea, and a number of small glaciers and ice caps in Iceland and Svalbard. Several of the flights were coincident in time and space with NASA's Operation IceBridge, which provides an independent data set for validation. MABEL also collected data along CryoSat track 10482 in north central Greenland approximately one month after CryoSat passed overhead.

Neumann, Thomas↗
NASA NTRS2011

Description and Status of the North Alabama Lightning Mapping Array

The North Alabama Lightning Mapping Array (LMA) is a network LMA detectors that detects and maps lightning using VHF radiation (TV Channel 5) in a region centered about Huntsville, Alabama that includes North Alabama, Central Tennessee and parts of Georgia and Mississippi. The North Alabama LMA has been in operation since late 2001, and has been providing real time data to regional National Weather Service (NSF) Weather Forecast Offices (WFOs) since mid 2003 through the NASA Short-term Prediction Research and Transition (SPoRT) center. Data from this network (as well as other from other LMA systems) are now being used to create proxy Geostationary Lightning Mapper (GLM) data sets for GOES-R risk reduction and algorithm development activities. In addition, since spring 2009 data are provided to the Storm Prediction Center in support of Hazardous Weather Testbed and GOES-R Proving Ground activities during the Spring Program. Description, status and plans will be discussed.

Blakeslee, Richard J.↗