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NASA NTRS2010

Modeling and Simulation of an UAS Collision Avoidance Systems

This paper describes a Modeling and Simulation of an Unmanned Aircraft Systems (UAS) Collision Avoidance System, capable of representing different types of scenarios for UAS collision avoidance. Commercial and military piloted aircraft currently utilize various systems for collision avoidance such as Traffic Alert and Collision A voidance System (TCAS), Automatic Dependent Surveillance-Broadcast (ADS-B), Radar and ElectroOptical and Infrared Sensors (EO-IR). The integration of information from these systems is done by the pilot in the aircraft to determine the best course of action. In order to operate optimally in the National Airspace System (NAS) UAS have to work in a similar or equivalent manner to a piloted aircraft by applying the principle of "detect-see and avoid" (DSA) to other air traffic. Hence, we have taken these existing sensor technologies into consideration in order to meet the challenge of researching the modeling and simulation of an approximated DSA system. A Schematic Model for a UAS Collision Avoidance System (CAS) has been developed ina closed loop block diagram for that purpose. We have found that the most suitable software to carry out this task is the Satellite Tool Kit (STK) from Analytical Graphics Inc. (AGI). We have used the Aircraft Mission Modeler (AMM) for modeling and simulation of a scenario where a UAS is placed on a possible collision path with an initial intruder and then with a second intruder, but is able to avoid them by executing a right tum maneuver and then climbing. Radars have also been modeled with specific characteristics for the UAS and both intruders. The software provides analytical, graphical user interfaces and data controlling tools which allow the operator to simulate different conditions. Extensive simulations have been carried out which returned excellent results.

Oliveros, Edgardo V.↗
NASA NTRS2010

Assessing the Relative Performance of Microwave-Based Satellite Rain Rate Retrievals Using TRMM Ground Validation Data

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (Aqua) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25 terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSRE over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm/hr. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products. 3

Wolff, David B.↗
NASA NTRS2011

Assessing the Relative Performance of Microwave-Based Satellite Rain Rate Retrievals Using TRMM Ground Validation Data

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (Aqua) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25deg terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSR-E over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm/hr. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products.

Wolff, David B.↗
NASA NTRS1999

Using Spaceborne Ku-Band Scatterometer for Global Snow Cover Monitoring

We demonstrate for the first time the utility of spaceborne Ku-band scatterometer for global snow cover monitoring. Satellite radar data were collected over the globe by the NASA Scatterometer (NSCAT) operated at 14 GHz on board the Japanese ADEOS spacecraft from September 1996 to June 1997, spanning the 1997 seasonal snow season. First, we present backscatter signature of dry and wet snow to facilitate the interpretation of NSCAT backscatter evolution over snow cover regions. Surface field experiments indicated that dry snow backscatter at Ku band is approximately 40 times stronger than that at C band. Thus, Ku-band scatterometer measurements are sensitive to snow cover, which is typically transparent to C-band scatterometer returns. Furthermore, Ku-band backscatter does not saturate for most of natural snow depths as compared to radar responses at 19 GHz and 37 GHz or higher frequencies which have more limited penetration depths into snow. Ku-band backscatter is also sensitive to wetness in snow, which is appropriate to detect early snow melt conditions. Using the snow backscatter characteristics, we investigate NSCAT backscatter evolution over global snow cover regions throughout the 1997 snow season. The results reveal detail delineations between different regional snow areas. We show the correlation of these delineations with the boundaries of different global snow classes defined by the U.S. Army Cold Regions Research and Engineering Laboratory snow classification system. Using in-situ snow depth data from the U.S. National Climatic Data Center, we show that Ku-band backscatter corresponds very well to the trend of snow melt while snow mapping products (U.S. Climate Prediction Center gridded snow charts) from visible sensors does not reflect the fast snow melt trend. To illustrate the practical application of global snow monitoring with spaceborne Ku-band scatterometer, we present NSCAT backscatter response corresponding to the snow event leading to the 1997 Flood of the Century over the U.S. Northern plains and the Canadian prarie region, which caused loss of lives and several billion dollars in flood related damages and cleanup costs. Finally, we show that the fixed incidence configuration of Seawinds scatterometers, to be launched on QuickSCAT and on ADEOS-2 in the near future, is better for snow monitoring and then discuss the use of future high-resolution scatterometers for global snow mapping.

Nghiem, S. V.↗
NASA NTRS2017

Validation of SMAP Surface Soil Moisture Products with Core Validation Sites

The NASA Soil Moisture Active Passive (SMAP) mission has utilized a set of core validation sites as the primary methodology in assessing the soil moisture retrieval algorithm performance. Those sites provide well calibrated in situ soil moisture measurements within SMAP product grid pixels for diverse conditions and locations.The estimation of the average soil moisture within the SMAP product grid pixels based on in situ measurements is more reliable when location specific calibration of the sensors has been performed and there is adequate replication over the spatial domain, with an up-scaling function based on analysis using independent estimates of the soil moisture distribution. SMAP fulfilled these requirements through a collaborative CalVal Partner program.This paper presents the results from 34 candidate core validation sites for the first eleven months of the SMAP mission. As a result of the screening of the sites prior to the availability of SMAP data, out of the 34 candidate sites 18 sites fulfilled all the requirements at one of the resolution scales (at least). The rest of the sites are used as secondary information in algorithm evaluation. The results indicate that the SMAP radiometer-based soil moisture data product meets its expected performance of 0.04 cu m/cu m volumetric soil moisture (unbiased root mean square error); the combined radar-radiometer product is close to its expected performance of 0.04 cu m/cu m, and the radar-based product meets its target accuracy of 0.06 cu m/cu m (the lengths of the combined and radar-based products are truncated to about 10 weeks because of the SMAP radar failure). Upon completing the intensive CalVal phase of the mission the SMAP project will continue to enhance the products in the primary and extended geographic domains, in co-operation with the CalVal Partners, by continuing the comparisons over the existing core validation sites and inclusion of candidate sites that can address shortcomings.

SMAP↗
NASA NTRS2010

Australian Soil Moisture Field Experiments in Support of Soil Moisture Satellite Observations

Large-scale field campaigns provide the critical fink between our understanding retrieval algorithms developed at the point scale, and algorithms suitable for satellite applications at vastly larger pixel scales. Retrievals of land parameters must deal with the substantial sub-pixel heterogeneity that is present in most regions. This is particularly the case for soil moisture remote sensing, because of the long microwave wavelengths (L-band) that are optimal. Yet, airborne L-band imagers have generally been large, heavy, and required heavy-lift aircraft resources that are expensive and difficult to schedule. Indeed, US soil moisture campaigns, have been constrained by these factors, and European campaigns have used non-imagers due to instrument and aircraft size constraints. Despite these factors, these campaigns established that large-scale soil moisture remote sensing was possible, laying the groundwork for satellite missions. Starting in 2005, a series of airborne field campaigns have been conducted in Australia: to improve our understanding of soil moisture remote sensing at large scales over heterogeneous areas. These field data have been used to test and refine retrieval algorithms for soil moisture satellite missions, and most recently with the launch of the European Space Agency's Soil Moisture Ocean Salinity (SMOS) mission, to provide validation measurements over a multi-pixel area. The campaigns to date have included a preparatory campaign in 2005, two National Airborne Field Experiments (NAFE), (2005 and 2006), two campaigns to the Simpson Desert (2008 and 2009), and one Australian Airborne Cal/val Experiment for SMOS (AACES), just concluded in the austral spring of 2010. The primary airborne sensor for each campaign has been the Polarimetric L-band Microwave Radiometer (PLMR), a 6-beam pushbroom imager that is small enough to be compatible with light aircraft, greatly facilitating the execution of the series of campaigns, and a key to their success. An L-band imaging radar is being added to the complement to provide simultaneous active-passive L-band observations, for algorithm development activities in support of NASA's upcoming Soil Moisture Active Passive (.S"M) mission. This paper will describe the campaigns, their objectives, their datasets, and some of the unique advantages of working with small/light sensors and aircraft. We will also review the main scientific findings, including improvements to the SMOS retrieval algorithm enabled by NAFE observations and the evaluation of the Simpson Desert as a calibration target for L-band satellite missions. Plans for upcoming campaigns will also be discussed.

Kim, Edward↗
NASA NTRS1986

Shuttle imaging radar views the Earth from Challenger: The SIR-B experiment

In October 1984, SIR-B obtained digital image data of about 6.5 million km2 of the Earth's surface. The coverage is mostly of selected experimental test sites located between latitudes 60 deg north and 60 deg south. Programmed adjustments made to the look angle of the steerable radar antenna and to the flight attitude of the shuttle during the mission permitted collection of multiple-incidence-angle coverage or extended mapping coverage as required for the experiments. The SIR-B images included here are representative of the coverage obtained for scientific studies in geology, cartography, hydrology, vegetation cover, and oceanography. The relations between radar backscatter and incidence angle for discriminating various types of surfaces, and the use of multiple-incidence-angle SIR-B images for stereo measurement and viewing, are illustrated with examples. Interpretation of the images is facilitated by corresponding images or photographs obtained by different sensors or by sketch maps or diagrams.

Ford, J. P.↗
NASA NTRS2011

Lidar Systems for Precision Navigation and Safe Landing on Planetary Bodies

The ability of lidar technology to provide three-dimensional elevation maps of the terrain, high precision distance to the ground, and approach velocity can enable safe landing of robotic and manned vehicles with a high degree of precision. Currently, NASA is developing novel lidar sensors aimed at needs of future planetary landing missions. These lidar sensors are a 3-Dimensional Imaging Flash Lidar, a Doppler Lidar, and a Laser Altimeter. The Flash Lidar is capable of generating elevation maps of the terrain that indicate hazardous features such as rocks, craters, and steep slopes. The elevation maps collected during the approach phase of a landing vehicle, at about 1 km above the ground, can be used to determine the most suitable safe landing site. The Doppler Lidar provides highly accurate ground relative velocity and distance data allowing for precision navigation to the landing site. Our Doppler lidar utilizes three laser beams pointed to different directions to measure line of sight velocities and ranges to the ground from altitudes of over 2 km. Throughout the landing trajectory starting at altitudes of about 20 km, the Laser Altimeter can provide very accurate ground relative altitude measurements that are used to improve the vehicle position knowledge obtained from the vehicle navigation system. At altitudes from approximately 15 km to 10 km, either the Laser Altimeter or the Flash Lidar can be used to generate contour maps of the terrain, identifying known surface features such as craters, to perform Terrain relative Navigation thus further reducing the vehicle s relative position error. This paper describes the operational capabilities of each lidar sensor and provides a status of their development. Keywords: Laser Remote Sensing, Laser Radar, Doppler Lidar, Flash Lidar, 3-D Imaging, Laser Altimeter, Precession Landing, Hazard Detection

Amzajerdian, Farzin↗
DOE OSTI2024

Deriving cloud droplet number concentration from surface-based remote sensors with an emphasis on lidar measurements

Abstract. Given the importance of constraining cloud droplet number concentrations (Nd) in low-level clouds, we explore two methods for retrieving Nd from surface-based remote sensing that emphasize the information content in lidar measurements. Because Nd is the zeroth moment of the droplet size distribution (DSD), and all remote sensing approaches respond to DSD moments that are at least 2 orders of magnitude greater than the zeroth moment, deriving Nd from remote sensing measurements has significant uncertainty. At minimum, such algorithms require the extrapolation of information from two other measurements that respond to different moments of the DSD. Lidar, for instance, is sensitive to the second moment (cross-sectional area) of the DSD, while other measures from microwave sensors respond to higher-order moments. We develop methods using a simple lidar forward model that demonstrates that the depth to the maximum in lidar-attenuated backscatter (Rmax⁡) is strongly sensitive to Nd when some measure of the liquid water content vertical profile is given or assumed. Knowledge of Rmax⁡ to within 5 m can constrain Nd to within several tens of percent. However, operational lidar networks provide vertical resolutions of > 15 m, making a direct calculation of Nd from Rmax⁡ very uncertain. Therefore, we develop a Bayesian optimal estimation algorithm that brings additional information to the inversion such as lidar-derived extinction and radar reflectivity near the cloud top. This statistical approach provides reasonable characterizations of Nd and effective radius (re) to within approximately a factor of 2 and 30 %, respectively. By comparing surface-derived cloud properties with MODIS satellite and aircraft data collected during the MARCUS and CAPRICORN II campaigns, we demonstrate the utility of the methodology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Optimal Traffic Signal Control Using Priority Metric Based on Real-Time Measured Traffic Information

Optimizing traffic control systems at traffic intersections can reduce network-wide fuel consumption as well as improve traffic flow. While traffic signals have conventionally been controlled based on predetermined schedules, various adaptive control systems have been developed recently using advanced sensors such as cameras, radars, and LiDARs. By utilizing rich traffic information enabled by the advanced sensors, more efficient or optimal traffic signal control is possible in response to varying traffic conditions. This paper proposes an optimal traffic signal control method to minimize network-wide fuel consumption utilizing real-time traffic information provided by advanced sensors. This new method employs a priority metric calculated by a weighted sum of various factors, including the total number of vehicles, total vehicle speed, vehicle waiting time, and road preference. Genetic Algorithm (GA) is used as a global optimization method to determine the optimal weights in the priority metric. In order to evaluate the effectiveness of the proposed method, a traffic simulation model is developed in a high-fidelity traffic simulation environment called SUMO, based on a real-world traffic network. The traffic flow within this model is simulated using actual measured traffic data from the traffic network, enabling a comprehensive assessment of the novel optimal traffic signal control method in realistic conditions. The simulation results show that the proposed priority metric-based real-time traffic signal control algorithm can significantly reduce network-wide fuel consumption compared to the conventional fixed-time control and coordinated actuated control methods that are currently used in the modeled network. Additionally, incorporating truck priority in the priority metric leads to further improvements in fuel consumption reduction.

47 OTHER INSTRUMENTATION↗
NASA NTRS2012

Estimating Sea Surface Salinity and Wind Using Combined Passive and Active L-Band Microwave Observations

Several L-band microwave radiometer and radar missions have been, or will be, operating in space for land and ocean observations. These include the NASA Aquarius mission and the Soil Moisture Active Passive (SMAP) mission, both of which use combined passive/ active L-band instruments. Aquarius s passive/active L-band microwave sensor has been designed to map the salinity field at the surface of the ocean from space. SMAP s primary objectives are for soil moisture and freeze/thaw detection, but it will operate continuously over the ocean, and hence will have significant potential for ocean surface research. In this innovation, an algorithm has been developed to retrieve simultaneously ocean surface salinity and wind from combined passive/active L-band microwave observations of sea surfaces. The algorithm takes advantage of the differing response of brightness temperatures and radar backscatter to salinity, wind speed, and direction, thus minimizing the least squares error (LSE) measure, which signifies the difference between measurements and model functions of brightness temperatures and radar backscatter. The algorithm uses the conjugate gradient method to search for the local minima of the LSE. Three LSE measures with different measurement combinations have been tested. The first LSE measure uses passive microwave data only with retrieval errors reaching 1 to 2 psu (practical salinity units) for salinity, and 1 to 2 m/s for wind speed. The second LSE measure uses both passive and active microwave data for vertical and horizontal polarizations. The addition of active microwave data significantly improves the retrieval accuracy by about a factor of five. To mitigate the impact of Faraday rotation on satellite observations, the third LSE measure uses measurement combinations invariant under the Faraday rotation. For Aquarius, the expected RMS SSS (sea surface salinity) error will be less than about 0.2 psu for low winds, and increases to 0.3 psu at 25 m/s wind speed for warm waters (25 C). To achieve the required 0.2 psu accuracy, the impact of sea surface roughness (e.g. wind-generated ripples) on the observed brightness temperature has to be corrected to better than one tenth of a degree Kelvin. With this algorithm, the accuracy of retrieved wind speed will be high, varying from a few tenths to 0.6 m/s. The expected direction accuracy is also excellent (less than 10 ) for mid to high winds, but degrades for lower speeds (less than 7 m/s).

Yueh, Simon H.↗
NASA NTRS2014

Hurricane Imaging Radiometer Wind Speed and Rain Rate Retrievals during the 2010 GRIP Flight Experiment

Microwave remote sensing observations of hurricanes, from NOAA and USAF hurricane surveillance aircraft, provide vital data for hurricane research and operations, for forecasting the intensity and track of tropical storms. The current operational standard for hurricane wind speed and rain rate measurements is the Stepped Frequency Microwave Radiometer (SFMR), which is a nadir viewing passive microwave airborne remote sensor. The Hurricane Imaging Radiometer, HIRAD, will extend the nadir viewing SFMR capability to provide wide swath images of wind speed and rain rate, while flying on a high altitude aircraft. HIRAD was first flown in the Genesis and Rapid Intensification Processes, GRIP, NASA hurricane field experiment in 2010. This paper reports on geophysical retrieval results and provides hurricane images from GRIP flights. An overview of the HIRAD instrument and the radiative transfer theory based, wind speed/rain rate retrieval algorithm is included. Results are presented for hurricane wind speed and rain rate for Earl and Karl, with comparison to collocated SFMR retrievals and WP3D Fuselage Radar images for validation purposes.

Sahawneh, Saleem↗
NASA NTRS2012

The NASA Soil Moisture Active Passive (SMAP) Mission - Science and Data Product Development Status

The Soil Moisture Active Passive (SMAP) mission, planned for launch in late 2014, has the objective of frequent, global mapping of near-surface soil moisture and its freeze-thaw state. The SMAP measurement system utilizes an L-band radar and radiometer sharing a rotating 6-meter mesh reflector antenna. The instruments will operate on a spacecraft in a 685 km polar orbit with 6am/6pm nodal crossings, viewing the surface at a constant 40-degree incidence angle with a 1000-km swath width, providing 3-day global coverage. Data from the instruments will yield global maps of soil moisture and freeze/thaw state at 10 km and 3 km resolutions, respectively, every two to three days. The 10-km soil moisture product will be generated using a combined radar and radiometer retrieval algorithm. SMAP will also provide a radiometer-only soil moisture product at 40-km spatial resolution and a radar-only soil moisture product at 3-km resolution. The relative accuracies of these products will vary regionally and will depend on surface characteristics such as vegetation water content, vegetation type, surface roughness, and landscape heterogeneity. The SMAP soil moisture and freeze/thaw measurements will enable significantly improved estimates of the fluxes of water, energy and carbon between the land and atmosphere. Soil moisture and freeze/thaw controls of these fluxes are key factors in the performance of models used for weather and climate predictions and for quantifYing the global carbon balance. Soil moisture measurements are also of importance in modeling and predicting extreme events such as floods and droughts. The algorithms and data products for SMAP are being developed in the SMAP Science Data System (SDS) Testbed. In the Testbed algorithms are developed and evaluated using simulated SMAP observations as well as observational data from current airborne and spaceborne L-band sensors including data from the SMOS and Aquarius missions. We report here on the development status of the SMAP data products. The Testbed simulations are designed to capture various sources of errors in the products including environment effects, instrument effects (nonideal aspects of the measurement system), and retrieval algorithm errors. The SMAP project has developed a Calibration and Validation (Cal/Val) Plan that is designed to support algorithm development (pre-launch) and data product validation (post-launch). A key component of the Cal/Val Plan is the identification, characterization, and instrumentation of sites that can be used to calibrate and validate the sensor data (Level l) and derived geophysical products (Level 2 and higher).

Nloku, E.↗
DOE OSTI2019

Lidar Mapping and Surveillance of Nuclear Infrastructure

Wear is unavoidable, whether it occurs in a nuclear facility or abridge on the highway its detection is crucial to preventing the failure of a system. While tools capable of detecting localized damage exist, industry lacks methods to evaluate the health of larger infrastructure. Routine inspection of nuclear facilities must be undertaken to ensure the safety of personnel and proper containment of nuclear materials. Most systems in a nuclear facility are designated as radioactive and confined spaces. To constantly monitor such systems in person would be hazardous but with a remote inspection tool it can be done in a safer and easier way. Lidar: Operates by emitting a laser pulse and looking for the reflected light. Allows user to gain 3D information of the environment. Project Objective: To develop an inspection tool for evaluating the health of large scale infrastructure using Lidar and machine learning. Autonomous Platform + Lidar + Data Fusion + Big Data. Use SLAM to build a 2D map of the environment. Point cloud created by transforming the points between reference frames. Point cloud to Surface: Inherent representation of surface geometry; Difficult to process with conventional techniques. Point cloud to Image: Easily processed by conventional machine learning algorithms. The quantity of data generated will necessitate a big data framework to manage the data for processing. Additional Sensors: Detecting the temperature (a) chemical makeup (b) and radiation emission (c) of a surface could assist in detecting problems. Human inspection can detect irregularities in both physical objects and their point-cloud representations. This is a good indication that mapping nuclear sites using Lidar will provide qualitative insight into the health of the facility. If deep learning can be used to automatically identify problem areas then the completed tool could become an invaluable asset.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2009

Combining Satellite Microwave Radiometer and Radar Observations to Estimate Atmospheric Latent Heating Profiles

In this study, satellite passive microwave sensor observations from the TRMM Microwave Imager (TMI) are utilized to make estimates of latent + eddy sensible heating rates (Q1-QR) in regions of precipitation. The TMI heating algorithm (TRAIN) is calibrated, or "trained" using relatively accurate estimates of heating based upon spaceborne Precipitation Radar (PR) observations collocated with the TMI observations over a one-month period. The heating estimation technique is based upon a previously described Bayesian methodology, but with improvements in supporting cloud-resolving model simulations, an adjustment of precipitation echo tops to compensate for model biases, and a separate scaling of convective and stratiform heating components that leads to an approximate balance between estimated vertically-integrated condensation and surface precipitation. Estimates of Q1-QR from TMI compare favorably with the PR training estimates and show only modest sensitivity to the cloud-resolving model simulations of heating used to construct the training data. Moreover, the net condensation in the corresponding annual mean satellite latent heating profile is within a few percent of the annual mean surface precipitation rate over the tropical and subtropical oceans where the algorithm is applied. Comparisons of Q1 produced by combining TMI Q1-QR with independently derived estimates of QR show reasonable agreement with rawinsonde-based analyses of Q1 from two field campaigns, although the satellite estimates exhibit heating profile structure with sharper and more intense heating peaks than the rawinsonde estimates. 2

Grecu, Mircea↗
NASA NTRS2016

Analysis of 2015 Winter In-Flight Icing Case Studies with Ground-Based Remote Sensing Systems Compared to In-Situ SLW Sondes

National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) have developed an icing remote sensing technology that has demonstrated skill at detecting and classifying icing hazards in a vertical column above an instrumented ground station. This technology has recently been extended to provide volumetric coverage surrounding an airport. Building on the existing vertical pointing system, the new method for providing volumetric coverage utilizes a vertical pointing cloud radar, a multi-frequency microwave radiometer with azimuth and elevation pointing, and a NEXRAD radar. The new terminal area icing remote sensing system processes the data streams from these instruments to derive temperature, liquid water content, and cloud droplet size for each examined point in space. These data are then combined to ultimately provide icing hazard classification along defined approach paths into an airport. To date, statistical comparisons of the vertical profiling technology have been made to Pilot Reports and Icing Forecast Products. With the extension into relatively large area coverage and the output of microphysical properties in addition to icing severity, the use of these comparators is not appropriate and a more rigorous assessment is required. NASA conducted a field campaign during the early months of 2015 to develop a database to enable the assessment of the new terminal area icing remote sensing system and further refinement of terminal area icing weather information technologies in general. In addition to the ground-based remote sensors listed earlier, in-situ icing environment measurements by weather balloons were performed to produce a comprehensive comparison database. Balloon data gathered consisted of temperature, humidity, pressure, super-cooled liquid water content, and 3-D position with time. Comparison data plots of weather balloon and remote measurements, weather balloon flight paths, bulk comparisons of integrated liquid water content and icing cloud extent agreement, and terminal-area hazard displays are presented. Discussions of agreement quality and paths for future development are also included.

aviation meteorology↗
NASA NTRSDate not supplied

Dissemination of Global Flood Severity and Surface Water Mapping using Remote Sensing Data to Global Stakeholders

Flooding is a natural event that occurs frequently with high severity worldwide, responsible for significant societal and economic impacts. Disaster managers face significant challenges managing essential information for preparedness, response, and recovery efforts. The development of an open access, global flood alerting system for effective identification of flood impacted areas, classification of potential impacts, and the formulation of effective emergency response measures requires the incorporation of a wide variety of flood models and remote sensing data sources from multiple platforms. NASA is currently funding projects focused on flood forecasting, post-event flood mapping, flood depth estimation and pre-event flood severity estimation using Earth observation (EO) datasets and derived flood products. A new initiative in the Disasters Program is underway to disseminate flood products from different hydrologic models and sensors to global stakeholders via Pacific Disaster Center’s DisasterAWARE®, NASA’s Disasters Mapping Portal and potentially other mechanisms. This initiative focuses on improving response capacity and use of EO products in near real-time by a broader community for resource planning in case of extreme events. As part of this initiative, we have deployed Model of Models (MoM) – an open-source ensemble approach, that integrates outputs from hydrologic models and EO data from optical imagery to assess flood severity daily at sub-watershed level globally. The MoM output is integrated with the incident event system of DisasterAWARE to generate flood severity risk and flood impact boundaries, which are disseminated via the DisasterAWARE platform to different stakeholders globally for decision-making and response efforts. The next step will focus on using MoM outputs to estimate flood depth and extent mapping using high-resolution Synthetic Aperture Radar imagery, impact assessment using optical imagery and population datasets, and damage estimation using critical infrastructure datasets, which would be disseminated via DisasterAWARE to decision-makers, emergency managers and first responders around the world.

flood↗
NASA NTRS1990

An automated system for mosaicking spaceborne SAR imagery

An automated system has been developed for mosaicking spaceborne synthetic aperture radar (SAR) imagery. The system is capable of producing multiframe mosaics for large-scale mapping by combining images in both the along-track direction and adjacent cross-track swaths from ascending and descending passes. The system requires no operator interaction and is capable of achieving high registration accuracy. The output product is a geocoded mosaic on a standard map grid such as UTM or polar stereographic. The procedure described in detail in this paper consists essentially of remapping the individual image frames into these standard grids, frame-to-frame image registration and radiometric smoothing of the seams. These procedures are directly applicable to both the Magellan Venus Mapper and a scanning SAR design such as Radarsat, Eos SAR in addition to merging image frames from traditional SAR systems such as SEASAT and SIR-B. With minor modifications, it may also be applied to spaceborne optical sensor data to generate large-scale mosaics efficiently and with a high degree of accuracy. The system has been tested with SEASAT, SIR-B and Landsat TM data. Examples presented in this paper include a 38-frame mosaic of the Yukon River basin in central Alaska, a 33-frame mosaic of southern California and a three-frame terrain-corrected geocoded mosaic of the Wind River basin in Wyoming.

Kwok, Ronald↗