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NASA NTRS2005

The Huygens Atmospheric Structure Instrument (HASI): Expected Results at Titan and Performance Verification in Terrestrial Atmosphere

The Huygens ASI is a multi-sensor package resulting from an international cooperation, it has been designed to measure the physical quantities characterizing Titan's atmosphere during the Huygens probe mission. On 14th January, 2005, HASI will measure acceleration, pressure, temperature and electrical properties all along the Huygens probe descent on Titan in order to study Titan s atmospheric structure, dynamics and electric properties. Monitoring axial and normal accelerations and providing direct pressure and temperature measurements during the descent, HASI will mainly contribute to the Huygens probe entry and trajectory reconstruction. In order to simulate the Huygens probe descent and verify HASI sensors performance in terrestrial environment, stratospheric balloon flight experiment campaigns have been performed, in collaboration with the Italian Space Agency (ASI). The results of flight experiments have allowed to determine the atmospheric vertical profiles and to obtain a set of data for the analysis of probe trajectory and attitude reconstruction.

Ferri, F.↗
NASA NTRS2007

Hydra Rendezvous and Docking Sensor

The U.S. technology to support a CEV AR&D activity is mature and was developed by NASA and supporting industry during an extensive research and development program conducted during the 1990's and early 2000 time frame at the Marshall Space Flight Center. Development and demonstration of a rendezvous/docking sensor was identified early in the AR&D Program as the critical enabling technology that allows automated proxin~ity operations and docking. A first generation rendezvous/docking sensor, the Video Guidance Sensor (VGS) was developed and successfully flown on STS 87 and again on STS 95, proving the concept of a video-based sensor. Advances in both video and signal processing technologies and the lessons learned from the two successful flight experiments provided a baseline for the development of a new generation of video based rendezvous/docking sensor. The Advanced Video Guidance Sensor (AVGS) has greatly increased performance and additional capability for longer-range operation. A Demonstration Automatic Rendezvous Technology (DART) flight experiment was flown in April 2005 using AVGS as the primary proximity operations sensor. Because of the absence of a docking mechanism on the target satellite, this mission did not demonstrate the ability of the sensor to co~ltrold ocking. Mission results indicate that the rendezvous sensor operated successfully in "spot mode" (2 km acquisition of the target, bearing data only) but was never commanded to "acquire and track" the docking target. Parts obsolescence issues prevent the construction of current design AVGS units to support the NASA Exploration initiative. This flight proven AR&D technology is being modularized and upgraded with additional capabilities through the Hydra project at the Marshall Space Flight Center. Hydra brings a unique engineering approach and sensor architecture to the table, to solve the continuing issues of parts obsolescence and multiple sensor integration. This paper presents an approach to sensor hardware trades, to address the needs of future vehicles that may rendezvous and dock with the International Space Station (ISS). It will also discuss approaches for upgrading AVGS to address parts obsolescence, and concepts for modularizing the sensor to provide configuration flexibility for multiple vehicle applications. Options for complementary sensors to be integrated into the multi-head Hydra system will also be presented. Complementary sensor options include ULTOR, a digital image correlator system that could provide relative six-degree-of-freedom information independently from AVGS, and time-of-flight sensors, which determine the range between vehicles by timing pulses that travel from the sensor to the target and back. Common targets and integrated targets, suitable for use with the multi-sensor options in Hydra, will also be addressed.

Roe, Fred↗
NASA NTRS2007

Multi-Sensor Triangulation of Multi-Source Spatial Data

The introduced methodologies are successful in: a) Ising LIDAR features for photogrammetric geo-refererncing; b) Delivering a geo-referenced imagery of the same quality as point-based geo-referencing procedures; c) Taking advantage of the synergistic characteristics of spatial data acquisition systems. The triangulation output can be used for the generation of 3-D perspective views.

Habib, Ayman↗
NASA NTRS2007

2004 Space Report: Environment and Strategy for Space Research at NATO's Research and Technology Organisation (RTO)

This report describes the motivation for and a strategy to enhance the NATO Research and Technology Organisation's (RTO) current space research effort to reflect NATO's growing military dependence on space systems. Such systems and services provided by these systems are critical elements of military operations. NATO uses space systems for operational planning and support, communication, radio navigation, multi-sensor and multi-domain demonstrations. Such systems are also used to promote regional stability. A quantitative analysis of work related to space in the NATO RTO showed that during the period of 1998 - 2004, 5% of the research pursued in the NATO RTO has been clearly focused on space applications. Challenging environmental and organizational barriers for increasing RTO space research were identified. In part, these include lack of sufficient space expertise representation on panels, the military sensitivity of space, current panel work loads and the need for specific technical recommendations from peers. A strategy for enhancing space research in the RTO is to create a limited-life Space Advisory Group (SAG) composed of Space Expert Consultants who are panel members with appropriate expertise and additional expertise from the nations. The SAG will recommend and find support in the nations for specific technical activities related to space in the areas of Space Science, Remote Sensing Data Analysis, Spacecraft Systems, Surveillance and Early Warning, Training and Simulation and Policy. An RTO Space Advisory Group will provide an organizational mechanism to gain recognition of RTO as a forum for trans-Atlantic defence space research and to enhance space research activities.

Woods-Vedeler, Jessica A.↗
NASA NTRS2008

Multi-sensor Testing for Automated Rendezvous and Docking

During the past two years, many sensors have been tested in an open-loop fashion in the Marshall Space Flight Center (MSFC) Flight Robotics Laboratory (FRL) to both determine their suitability for use in Automated Rendezvous and Docking (AR&D) systems and to ensure the test facility is prepared for future multi-sensor testing. The primary focus of this work was in support of the CEV AR&D system, because the AR&D sensor technology area was identified as one of the top risks in the program. In 2006, four different sensors were tested individually or in a pair in the MSFC FRL. In 2007, four sensors, two each of two different types, were tested simultaneously. In each set of tests, the target was moved through a series of pre-planned trajectories while the sensor tracked it. In addition, a laser tracker "truth" sensor also measured the target motion. The tests demonstrated the functionality of testing four sensors simultaneously as well as the capabilities (both good and bad) of all of the different sensors tested. This paper outlines the test setup and conditions, briefly describes the facility, summarizes the earlier results of the individual sensor tests, and describes in some detail the results of the four-sensor testing. Post-test analysis includes data fusion by minimum variance estimation and sequential Kalman filtering. This Sensor Technology Project work was funded by NASA's Exploration Technology Development Program.

Howard, Richard T.↗
NASA NTRS2010

Multi-Sensor Mud Detection

Robust mud detection is a critical perception requirement for Unmanned Ground Vehicle (UGV) autonomous offroad navigation. A military UGV stuck in a mud body during a mission may have to be sacrificed or rescued, both of which are unattractive options. There are several characteristics of mud that may be detectable with appropriate UGV-mounted sensors. For example, mud only occurs on the ground surface, is cooler than surrounding dry soil during the daytime under nominal weather conditions, is generally darker than surrounding dry soil in visible imagery, and is highly polarized. However, none of these cues are definitive on their own. Dry soil also occurs on the ground surface, shadows, snow, ice, and water can also be cooler than surrounding dry soil, shadows are also darker than surrounding dry soil in visible imagery, and cars, water, and some vegetation are also highly polarized. Shadows, snow, ice, water, cars, and vegetation can all be disambiguated from mud by using a suite of sensors that span multiple bands in the electromagnetic spectrum. Because there are military operations when it is imperative for UGV's to operate without emitting strong, detectable electromagnetic signals, passive sensors are desirable. JPL has developed a daytime mud detection capability using multiple passive imaging sensors. Cues for mud from multiple passive imaging sensors are fused into a single mud detection image using a rule base, and the resultant mud detection is localized in a terrain map using range data generated from a stereo pair of color cameras.

Rankin, Arturo L.↗
NASA NTRS2011

Miniaturized Airborne Imaging Central Server System

In recent years, some remote-sensing applications require advanced airborne multi-sensor systems to provide high performance reflective and emissive spectral imaging measurement rapidly over large areas. The key or unique problem of characteristics is associated with a black box back-end system that operates a suite of cutting-edge imaging sensors to collect simultaneously the high throughput reflective and emissive spectral imaging data with precision georeference. This back-end system needs to be portable, easy-to-use, and reliable with advanced onboard processing. The innovation of the black box backend is a miniaturized airborne imaging central server system (MAICSS). MAICSS integrates a complex embedded system of systems with dedicated power and signal electronic circuits inside to serve a suite of configurable cutting-edge electro- optical (EO), long-wave infrared (LWIR), and medium-wave infrared (MWIR) cameras, a hyperspectral imaging scanner, and a GPS and inertial measurement unit (IMU) for atmospheric and surface remote sensing. Its compatible sensor packages include NASA s 1,024 1,024 pixel LWIR quantum well infrared photodetector (QWIP) imager; a 60.5 megapixel BuckEye EO camera; and a fast (e.g. 200+ scanlines/s) and wide swath-width (e.g., 1,920+ pixels) CCD/InGaAs imager-based visible/near infrared reflectance (VNIR) and shortwave infrared (SWIR) imaging spectrometer. MAICSS records continuous precision georeferenced and time-tagged multisensor throughputs to mass storage devices at a high aggregate rate, typically 60 MB/s for its LWIR/EO payload. MAICSS is a complete stand-alone imaging server instrument with an easy-to-use software package for either autonomous data collection or interactive airborne operation. Advanced multisensor data acquisition and onboard processing software features have been implemented for MAICSS. With the onboard processing for real time image development, correction, histogram-equalization, compression, georeference, and data organization, fast aerial imaging applications, including the real time LWIR image mosaic for Google Earth, have been realized for NASA fs LWIR QWIP instrument. MAICSS is a significant improvement and miniaturization of current multisensor technologies. Structurally, it has a complete modular and solid-state design. Without rotating hard drives and other moving parts, it is operational at high altitudes and survivable in high-vibration environments. It is assembled from a suite of miniaturized, precision-machined, standardized, and stackable interchangeable embedded instrument modules. These stackable modules can be bolted together with the interconnection wires inside for the maximal simplicity and portability. Multiple modules are electronically interconnected as stacked. Alternatively, these dedicated modules can be flexibly distributed to fit the space constraints of a flying vehicle. As a flexibly configurable system, MAICSS can be tailored to interface a variety of multisensor packages. For example, with a 1,024x1,024 pixel LWIR and a 8,984x6,732 pixel EO payload, the complete MAICSS volume is approximately 7x9x11 in. (=18x23x28 cm), with a weight of 25 lb (=11.4 kg).

Sun, Xiuhong↗
NASA NTRS2011

An Accuracy Assessment of the CALIOP/CALIPSO Version 2/Version 3 Daytime Aerosol Extinction Product Based on a Detailed Multi-Sensor, Multi-Platform Case Study

The Cloud Aerosol LIdar with Orthogonal Polarization (CALIOP), on board the CALIPSO platform, has measured profiles of total attenuated backscatter coefficient (level 1 products) since June 2006. CALIOP s level 2 products, such as the aerosol backscatter and extinction coefficient profiles, are retrieved using a complex succession of automated algorithms. The goal of this study is to help identify potential shortcomings in the CALIOP version 2 level 2 aerosol extinction product and to illustrate some of the motivation for the changes that have been introduced in the next version of CALIOP data (version 3, released in June 2010). To help illustrate the potential factors contributing to the uncertainty of the CALIOP aerosol extinction retrieval, we focus on a one-day, multi-instrument, multiplatform comparison study during the CALIPSO and Twilight Zone (CATZ) validation campaign on 4 August 2007. On that day, we observe a consistency in the Aerosol Optical Depth (AOD) values recorded by four different instruments (i.e. spaceborne MODerate Imaging Spectroradiometer, MODIS: 0.67 and POLarization and Directionality of Earth s Reflectances, POLDER: 0.58, airborne High Spectral Resolution Lidar, HSRL: 0.52 and ground-based AErosol RObotic NETwork, AERONET: 0.48 to 0.73) while CALIOP AOD is a factor of two lower (0.32 at 532 nm). This case study illustrates the following potential sources of uncertainty in the CALIOP AOD: (i) CALIOP s low signal-to-noise ratio (SNR) leading to the misclassification and/or lack of aerosol layer identification, especially close to the Earth s surface; (ii) the cloud contamination of CALIOP version 2 aerosol backscatter and extinction profiles; (iii) potentially erroneous assumptions of the aerosol extinction-to-backscatter ratio (Sa) used in CALIOP s extinction retrievals; and (iv) calibration coefficient biases in the CALIOP daytime attenuated backscatter coefficient profiles. The use of version 3 CALIOP extinction retrieval for our case study seems to partially fix factor (i) although the aerosol retrieved by CALIOP is still somewhat lower than the profile measured by HSRL; the cloud contamination (ii) appears to be corrected; no particular change is apparent in the observation-based CALIOP Sa value (iii). Our case study also showed very little difference in version 2 and version 3 CALIOP attenuated backscatter coefficient profiles, illustrating a minor change in the calibration scheme (iv).

Kacenelenbogen, M.↗
NASA NTRS2011

Multi-Sensor Aerosol Products Sampling System

Global and local properties of atmospheric aerosols have been extensively observed and measured using both spaceborne and ground-based instruments, especially during the last decade. Unique properties retrieved by the different instruments contribute to an unprecedented availability of the most complete set of complimentary aerosol measurements ever acquired. However, some of these measurements remain underutilized, largely due to the complexities involved in analyzing them synergistically. To characterize the inconsistencies and bridge the gap that exists between the sensors, we have established a Multi-sensor Aerosol Products Sampling System (MAPSS), which consistently samples and generates the spatial statistics (mean, standard deviation, direction and rate of spatial variation, and spatial correlation coefficient) of aerosol products from multiple spacebome sensors, including MODIS (on Terra and Aqua), MISR, OMI, POLDER, CALIOP, and SeaWiFS. Samples of satellite aerosol products are extracted over Aerosol Robotic Network (AERONET) locations as well as over other locations of interest such as those with available ground-based aerosol observations. In this way, MAPSS enables a direct cross-characterization and data integration between Level-2 aerosol observations from multiple sensors. In addition, the available well-characterized co-located ground-based data provides the basis for the integrated validation of these products. This paper explains the sampling methodology and concepts used in MAPSS, and demonstrates specific examples of using MAPSS for an integrated analysis of multiple aerosol products.

Petrenko, M.↗
NASA NTRS2012

Spatial Distribution of Accuracy of Aerosol Retrievals from Multiple Satellite Sensors

Remote sensing of aerosols from space has been a subject of extensive research, with multiple sensors retrieving aerosol properties globally on a daily or weekly basis. The diverse algorithms used for these retrievals operate on different types of reflected signals based on different assumptions about the underlying physical phenomena. Depending on the actual retrieval conditions and especially on the geographical location of the sensed aerosol parcels, the combination of these factors might be advantageous for one or more of the sensors and unfavorable for others, resulting in disagreements between similar aerosol parameters retrieved from different sensors. In this presentation, we will demonstrate the use of the Multi-sensor Aerosol Products Sampling System (MAPSS) to analyze and intercompare aerosol retrievals from multiple spaceborne sensors, including MODIS (on Terra and Aqua), MISR, OMI, POLDER, CALIOP, and SeaWiFS. Based on this intercomparison, we are determining geographical locations where these products provide the greatest accuracy of the retrievals and identifying the products that are the most suitable for retrieval at these locations. The analyses are performed by comparing quality-screened satellite aerosol products to available collocated ground-based aerosol observations from the Aerosol Robotic Network (AERONET) stations, during the period of 2006-2010 when all the satellite sensors were operating concurrently. Furthermore, we will discuss results of a statistical approach that is applied to the collocated data to detect and remove potential data outliers that can bias the results of the analysis.

Petrenko, Maksym↗
NASA NTRS2012

Quantifying Above-Cloud Aerosols through Integrating Multi-Sensor Measurements from A-Train Satellites

Quantifying above-cloud aerosols can help improve the assessment of aerosol intercontinental transport and climate impacts. Large-scale measurements of aerosol above low-level clouds had been generally unexplored until very recently when CALIPSO lidar started to acquire aerosol and cloud profiles in June 2006. Despite CALIPSO s unique capability of measuring above-cloud aerosol optical depth (AOD), such observations are substantially limited in spatial coverage because of the lidar s near-zero swath. We developed an approach that integrates measurements from A-Train satellite sensors (including CALIPSO lidar, OMI, and MODIS) to extend CALIPSO above-cloud AOD observations to substantially larger areas. We first examine relationships between collocated CALIPSO above-cloud AOD and OMI absorbing aerosol index (AI, a qualitative measure of AOD for elevated dust and smoke aerosol) as a function of MODIS cloud optical depth (COD) by using 8-month data in the Saharan dust outflow and southwest African smoke outflow regions. The analysis shows that for a given cloud albedo, above-cloud AOD correlates positively with AI in a linear manner. We then apply the derived relationships with MODIS COD and OMI AI measurements to derive above-cloud AOD over the whole outflow regions. In this talk, we will present spatial and day-to-day variations of the above-cloud AOD and the estimated direct radiative forcing by the above-cloud aerosols.

Zhang, Yan↗
NASA NTRS2013

Multi-sensor Cloud Retrieval Simulator and Remote Sensing from Model Parameters : Synthetic Sensor Radiance Formulation - Pt. 1

In this paper we describe a general procedure for calculating synthetic sensor radiances from variable output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint, the algorithm takes explicit account of the model subgrid variability, in particular its description of the probability density function of total water (vapor and cloud condensate.) The simulated sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies.We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products). We focus on clouds because they are very important to model development and improvement.

Simulations↗
NASA NTRS2014

A New Study of Sea Spray Optical Properties from Multi-Sensor Spaceborne Observations

Retrievals of aerosol optical depth (AOD) from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite sensor require the assumption of an extinction-to-backscatter ratio, also known as the lidar ratio. This paper evaluates a new method to calculate lidar ratio of sea spray aerosol using two independent sources: the AOD from Synergized Optical Depth of Aerosols (SODA) and the integrated attenuated backscatter from CALIOP. The method applied in this study allows particulate lidar ratio to be calculated for individual CALIOP retrievals of single aerosol layer columns over the ocean. Analyses are carried out using CALIOP level 2, 5km sea spray aerosol layer products and collocated SODA nighttime data from December 2007 to December 2009. The global mean lidar ratio for sea spray aerosols was found to be 26 sr, roughly 30 higher than the one prescribed by CALIOP. Data analysis also showed considerable spatiotemporal variability in calculated lidar ratio over different parts of the remote oceans. The calculated aerosol lidar ratios are shown to be inversely related to the mean ocean surface wind speed: increase in ocean surface wind speed (U10) from 0 to 15 ms-1 reduces the mean lidar ratios for sea spray particles from 32 sr (for 0 U10 4 ms-1) to 22 sr (for U10 15 ms-1). Such changes in the lidar ratio are expected to have a corresponding effect on sea spray AOD. The outcomes of this study are relevant for future improvements of the SODA and CALIOP operational product and could lead to more accurate retrievals of sea spray AOD.

aerosol optical depth↗
NASA NTRSDate not supplied

Adaptive Multi-Sensor Localization Information Fusion for Autonomous Urban Air Mobility Operations

An adaptive method is developed to iteratively fuse the information provided by multiple sensors to enable autonomous urban air mobility type operations. First, noisy and bias corrupted IMU readings are processed as soon as they arrive using kinematic equations represented in the vehicle's body frame. To correct the systems drift resulting from the integration, an information content measure is introduced to decide on the environment. For the cluttered environment the information provided by environmental sensors is counted as reliable and the drift correction as accurate. For the open space, the GPS data is counted as reliable, and the drift correction is done based on the GPS readings. The measurement noise effects are minimize using Iterated Extended Kalman Filter framework. The algorithm is implemented in the in-house developed FlightDeckz simulation environment using an IMU model, simulated video recorded from a camera mounted on the vehicle (for the purpose of this study, outside scenery was generated with XPlane), which flies in an urban environment, and GPS data generated from the environment's digital map.

Onboard Perception↗
NASA NTRSDate not supplied

Application of the Global Precipitation Forecast dataset for Global Landslide Forecasting System

Landslides are extremely damaging, pervasive and cause fatalities and economic impacts. Extreme rainfall events, coupled with inopportune surface conditions, are the primary triggers of landslides around the world. Forecasting landslide events represent an area of open research. A global Landslide Hazard Assessment for Situational Awareness (LHASA) model has been created that provides near real-time dynamic landslide characterization using Integrated Multi-Satellite Retrievals for Global Precipitation Mission (IMERG). However, it does not provide information on prediction of landslides into future. This study considers how global precipitation forecast data compares to satellite rainfall at different spatiotemporal scales and outlines the potential for its use in landslide hazard prediction/forecasting system. NASA Goddard Earth Observing System (GEOS)-Forecast model assimilates new observations every 6 hours, at 00, 06, 12, and 18 UTC. The GEOS-Forecast model is initialized at 00 UTC and is evaluated with IMERG Early, using Multi-Radar Multi-Sensor gauge corrected (MRMS-GC) precipitation product as a reference over contiguous United States (CONUS). Categorical and continuous statistics along with probability density functions and cumulative distribution functions are considered to assess the performance of the precipitation products, with a focus on landslide hotspots. Seasonality appears to influence the performance of both the GEOS-Forecast and IMERG Early product. Moreover, the global comparison between the GEOS-Forecast and IMERG Early is carried out in terms of percentile difference, correlation, and bias maps. For extreme rainfall events in regions such as Mekong, Colombia, and Tajikistan, GEOS-Forecast appears to resolve high rainfall relative to IMERG Early more frequently. Validation over landslide points reveal that for 24hr rainfall accumulations > 100mm, GEOS-Forecast and IMERG coincide. GEOS-Forecast and IMERG Early precipitation matches more closely for tropical cyclones than other types of storms. Overall, the performance varies with respect to location, rainfall intensities, and type of precipitation events.

Sana Khan↗
NASA NTRSDate not supplied

Enhanced Monitoring and Forecasting of South Asian Air Quality Episodes with Multi-Sensor Satellite Products and Dispersion Modeling

Air pollution and particulates represent a serious environmental, public health, and overarching societal concern in the Hindu Kush Himalaya (HKH) region of south-central Asia, especially during the boreal winter and spring months. Frequent contributors to poor air quality of the region include dust and particulate matter transport from the Middle East region and western India, persistent nocturnal fog and smog during the stable dry monsoon months, and biomass burning during the pre-monsoon months of boreal spring. Nocturnal and multi-day persistent stable fog and smog events can occur during the stable conditions of the dry monsoon months that lead to hazardous visibility reductions (e.g., at major airports such as Delhi, India)and poor air quality, particularly in urban corridors.Our project goal is therefore to design and implement a robust air quality and chemistry observation and modeling system using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) and the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) models. This system effectively assimilates aerosol and trace gas retrievals from geostationary and polar-orbiting satellites to advance the monitoring and prediction capabilities of air quality and visibility reductions throughout the HKH region. For this presentation, we highlight several[multi-spectral] geostationary satellite products and dispersion modeling capabilities being developed and implemented for improved situational awareness and forecasting of air quality episodes.Launched in December 2018, the Advanced Meteorological Imager (AMI) aboard the Geostationary-Korean Multi-Purpose Satellite-2A (GEO-KOMPSAT-2A, or GK2A) provides 16 channels of multi-spectral information similar to the U.S. GOES-16/17 satellites. Proven community recipes for generating multi-spectral Red-Green-Blue (RGB) composite products to detect specific meteorological phenomena are applied to the AMI data for the HKH region. We implemented four such RGB products consisting of truecolor with Rayleigh correction to aid in smoke detection; dust RGB for monitoring larger particulate matter; nighttime microphysics for fog/smog detection;and natural color fire RGB and the shortwave 3.8 micron channel for fire hot-spot detection.We additionally configured HYSPLIT for dust/sand dispersion and concentration forecasts over HKH. The HYSPLIT dispersion simulations are initially being conducted with two different methodologies: (1) dust/sand lofting based on initializing plume releases from visual inspection of the dust RGB imagery; and (2) dust/sand release and dispersion using the internal HYSPLIT algorithm that determines lofting from input wind speeds and prescribed land use. The goal is to develop a near real-time HYSPLIT modeling solution for routinely simulating the transport and dispersion of dust plumes across the HKH region. This presentation illustrates these products and capabilities for various use cases from the 2019 to 2021 period, highlighting a particularly unhealthy episode during late March 2021 across Nepal.

Remote Sensing↗
NASA NTRSDate not supplied

Medium-Range River Flood Forecasts Using a Long Short-Term Memory Network

River flooding and the impacts are a concern for decision makers throughout the United States. Accurate medium-range forecasts (~3-7 days) are critical for providing advanced outlooks to emergency management officials. Unfortunately, accurately forecasting rainfall-runoff and the subsequent rise and fall within rivers remain a challenge in hydrological modeling. While complex physical modeling systems are the standard for representing the hydrological processes, they are computationally demanding and can require extensive calibration. Further, uncertainties remain in the model parameters and input data. The use of machine learning can reduce some of the computational demand while maintaining high accuracy. Therefore, this project makes use of a Long Short-Term Memory (LSTM) network which explicitly accounts for the time-dependent nature of rainfall-runoff modeling. The developed LSTM was trained to predict river gauge height, or stage height, based on time-lagged input features which include: gauge height to initialize the model, the NASA Short-term Prediction Research and Transition Center’s instance of the Land Information System (SPoRT-LIS) relative soil moisture to describe the rainfall infiltration rate, and 6-hr Multi-Radar Multi-Sensor quantitative precipitation estimate (MRMS QPE). The developed LSTM based system is then used to produce 7-day forecasts with a 6-hr temporal resolution using three different quantitative precipitation forecasts (QPF) from the NWS’s Weather Prediction Center (WPC), the NCEP Global Forecast System (GFS) model and the National Blend of Models (NBM). This trained modeling system has been implemented as an experimental product at over 100 different rivers in collaboration with at multiple National Weather Service (NWS) Forecast Offices and River Forecast Centers (RFC) across the eastern half of the United States. The developed LSTM model achieved average Nash-Sutcliffe efficiency (NSE) 0.89 higher than the equivalent medium-range National Water Model ensemble member forecast over a 7-day forecast. In addition to the initial development and evaluation, this project has continued to expand. While the initial model was developed for precipitation dominated basins, expansion of the project has taken it to basins effected by snow melt. This presentation will provide an overview of the project with focus on recent developments on incorporating snow melt processes into the model.

Andrew T. White↗
NASA NTRS2022

Reservoir Assessment Tool 2.0: Stakeholder driven improvements to satellite remote sensing based reservoir monitoring

In light of the rapidly increasing regulation of rivers due to planned and constructed reservoirs, monitoring reservoir operations has become very crucial. The Reservoir Assessment Tool (RAT) framework was developed to monitor reservoir operations globally, using hydrological modeling and satellite observations. With feedback from stakeholders, improvements in the RAT framework are demonstrated in this study using the Mekong River Basin as an example. A novel multi-sensor reservoir area mapping technique was developed using complementary strengths of optical and SAR sensors at a 1-5 day temporal resolution, allowing the quantification of sub-weekly reservoir operations. Additionally, the skill of radar altimeters in the RAT framework was tested using the Jason-3 altimeter. Using in-situ data from three Thai reservoirs in the Mekong Basin, consistent improvements were observed as compared to the original RAT framework. A functionality for visualizing forecasted outflow was also added using historically inferred reservoir operations or stakeholder-driven target storage expectations.

Reservoirs↗