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

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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Sea ice motions in the central arctic ice central arctic pack ice as inferred from AVHRR imagery

Synoptic observations of ice motion in the Arctic Basin are currently limited to those acquired by drifting buoys and, more recently, radar data from ERS-1. Buoys are not uniformly distributed throughout the Arctic, and SAR coverage is limited regionally and temporally due to the data volume, swath width, processing requirements, and power needs of the SAR. Additional ice-motion observations that can map ice responses simultaneously over large portions of the Arctic on daily to weekly time intervals are thus needed to augment the SAR and buoy data and to provide an intermediate-scale measure of ice drift suitable for climatological analyses and ice modeling. Merging the remotely-sensed ice motions with SAR, AVHRR, OLS, and SSM/I imagery permits studies of ice processes over a range of space and time scales. A medium-resolution passive microwave sensor might be ideal for this purpose, but no such satellite will be available in the near future. Combinations of existing data types are thus likely to be our best bet for generating motion fields with a coverage and resolution best suited to climate studies. Principal objectives of this project are to: (1) demonstrate whether sufficient ice features and ice motion existed within the consolidated ice pack to permit motion tracking using AVHRR imagery; (2) determine the limits imposed on AVHRR mapping by cloud cover; and (3) test the applicability of AVHRR-derived motions in studies of ice-atmosphere interactions.

Emery, William↗
NASA NTRS2012

SweepSAR: Beam-forming on Receive Using a Reflector-Phased Array Feed Combination for Spaceborne SAR

In this paper, an alternative approach is described that is suited for longer wavelength SARs in particular, employing a large, deployable reflector antenna and a much simpler phased array feed. To illuminate a wide swath, a substantial fraction of the phased array feed is excited on transmit to sub-illuminate the reflector. Shorter transmit pulses are required than for conventional SAR. On receive, a much smaller portion of the phased array feed is used to collect the return echo, so that a greater portion of the reflector antenna area is used. The locus of the portion of the phased array used on receive is adjusted using an analog beam steering network, to 'sweep' the receive beam(s) across the illuminated swath, tracking the return echo. This is similar in some respects to the whiskbroom approach to optical sensors, hence the name: SweepSAR.SweepSAR has advantages over conventional SAR in that it requires less transmit power, and if the receive beam is narrow enough, it is relatively immune to range ambiguities. Compared to direct radiating arrays with digital beam- forming, it is much simpler to implement, uses currently available technologies, is better suited for longer wavelength systems, and does not require extremely high data rates or onboard processing.

synthetic aperture radars (SARs)↗
NASA NTRS1991

Rainfall estimation over oceans from scanning multichannel microwave radiometer and special sensor microwave/imager microwave data

The brightness temperature (T sub b) measured at 37 GHz shows fairly strong emission from rain, and only slight effects caused by scattering by ice above the rain clouds. At frequencies below 37 GHz, were the fov is larger and the volume extinction coefficient is weaker, it is found that the observations do not yield appreciable additional information about rain. At 85 GHz (fov = 15 km), where the volume extinction is considerably larger, direct information about rain below the clouds is usually masked. Based on the above ideas, 37 GHz observations with a 30 km fov from SMMR and SSM/I are selected to develop an empirical method for the estimation of rain rate. In this method, the statistics of the observed T sub b's at 37 GHz in a rain storm are related to the rain rate statistics in that storm. The underestimation of rain rate, arising from the inability of the radiometer to respond sensitively to rain rate above a given threshold, is rectified in this technique with the aid of two parameters that depend on the total water vapor content in the atmosphere. The retrieved rain rates compare favorably with radar observations and monthly mean global maps of rain derived from this technique over the oceans.

Prabhakara, C.↗
NASA NTRS2019

LEAVES: Lofted Environmental and Atmospheric Venus Sensors

LEAVES (Lofted Environmental Atmospheric Venus Sensors) is a design exercise with the goal of dramatically decreasing the cost of obtaining prioritized chemical and physical data in planetary atmospheres. Through the application of a swarm approach this concept parallelizes atmospheric exploration, with geographic coverage far exceeding what is possible with conventional monolithic platforms or sondes. Each unit in the swarm is exceptionally compact, with a powered payload mass of only a few tens of grams and a high-drag, semi-rigid structure that acts to slow each probe as it descends through the atmosphere. This structural design can collapse into a planar form to allow for efficient stowage prior to arrival at the target body. With a total per-unit mass of only 120 g, a fleet of 100 (or more) units can be very reasonably accommodated on a carrier spacecraft.Science operations, which begin when the LEAVES probes reach an altitude of 100 km, are targeted for the cloud-bearing region of Venus' atmosphere. During the roughly 9 hour, terminal velocity descent through the atmosphere, LEAVES collects data of the state and composition of the atmosphere in parallel across multiple units. These data would represent an unprecedented constraint on the distribution and concentration of targeted chemical species, and the detection of local and regional variations in both chemistry and physical properties.A novel and compelling result of this exercise was that the same optimization that produced a structure with an exceptionally low areal mass density (0.126 kg/m2) also resulted in a probe that can be deployed directly from an aerobraking orbit (~140 km at 5 km/s) without the need for aeroshell protection. This translates to a tremendous mass savings and gives LEAVES the flexibility to be carried as a secondary payload aboard either a descending surface probe or an orbital radar mapper. Because such missions are under active development or have already been proposed (but not flown), we infer that LEAVES is well positioned as a technology

Swarm↗
NASA NTRS1981

Using radar image simulation to assess relative geometric distortions inherent in radar imagery

A unique method for observing the relative contributions of backscatter and propagation effects is afforded by radar image simulation. Digital terrain data are used in modeling radar image formation. Backscatter and propagation effects are modeled separately. These are incorporated serially and the image expression of each is noted. Sequences of images are presented illustrating these effects over a range of slopes and angles of incidence. The conclusions reached are that at angles of incidence that are smaller than the average slope of the terrain in a region, propagation phenomena predominate. As the angle of incidence increases beyond this, the radar image portrays an increasingly faithful representation of the backscatter from the ground. It is also demonstrated that digital simulation affords an important tool for evaluating complex interactions between the ground and radar, for training users in radar image interpretation, and for selecting optimum sensor parameters for particular applications.

Kaupp, V. H.↗
NASA NTRSDate not supplied

Sensing Small Uncrewed Aerial Vehicles with Distributed Radars for Advanced Air Mobility Surveillance

In the context of Advanced and Urban Air Mobility major attention is being reserved to the development of sensing strategies for small Uncrewed Aerial Vehicles (sUAVs) to enable their safe operations in and around urban areas. Such strategies should rely on non-cooperative distributed sensors to strengthen the surveillance solution towards the unreliability of Global Navigation Satellite System (GNSS) positioning information, which is typical for low-altitude-flying platforms in urban regions, and increase the monitored airspace volume. To this aim, this paper proposes a fusion solution for a network of distributed ground-based radars, which can be exploited to not only increase the coverage over large airspace volumes but also improve the overall detectability and traceability of sUAVs by leveraging on multiple views over the same area. The solution exploits a centralized fusion scheme in which measurements collected by each radar are shared with a Fusion Center where Kalman Filtering is exploited to build a unique, fused track. Tests conducted on experimental data collected using two sUAVs as flying targets and three distributed radars showed that the proposed solution can produce an increase in coverage from about 20 % (single radar configuration) to about 80 % of the targets’ flight path, as well as a finer accuracy yielding meter and meter-per-second root mean square error values on position and velocity components.

Federica Vitiello↗
DOE OSTI2019

Arctic Sea Ice Classification Using Microwave Scatterometer and Radiometer Data During 2002-2017

Temporal and spatial variation of sea ice type in the Arctic is an indicator of regional and global change. Arctic sea ice can be classified into two major categories: multiyear ice (MYI) and first-year ice. In this paper, classification method based on machine learning is established and applied to produce daily sea ice classification data set during the winter (November–April) from 2002 to 2017 using active microwave data from QuikSCAT and Advanced Scatterometer as well as passive microwave data from Advanced Microwave Scanning Radiometer for EOS, Special Sensor Microwave Imager/Sounder, and Advanced Microwave Scanning Radiometer 2 radiometer. First, the open water area is flagged out using brightness temperature (Tb) from the passive microwave sensor. Then, K-means algorithm is applied to identify the clusters of the two ice types in the Tb/backscatter parameter space and finally assign pixels to each class. Two optimization methods based on the movement of MYI and marginal ice zone are used to correct the misclassification of MYI. The results have shown a decrease of MYI in winter from 2002 to 2017, especially in 2008 and 2013 with a remarkable recovery in 2014. The classifications are consistent with results by visual interpretation from synthetic aperture radar images in the Canadian Arctic Archipelago with overall classification accuracy over 93%. Comparison with classifications from previous studies and products shows that our method could reflect more differences in MYI declining trend interannually and less anomalous fluctuations in certain years.

58 GEOSCIENCES↗
NASA NTRS1999

An Overview of Future NASA Missions, Concepts, and Technologies Related to Imaging of the World's Land Areas

In the near term NASA is entering into the peak activity period of the Earth Observing System (EOS). The EOS AM-1 /"Terra" spacecraft is nearing launch and operation to be followed soon by the New Millennium Program (NMP) Earth Observing (EO-1) mission. Other missions related to land imaging and studies include EOS PM-1 mission, the Earth System Sciences Program (ESSP) Vegetation Canopy Lidar (VCL) mission, the EOS/IceSat mission. These missions involve clear advances in technologies and observational capability including improvements in multispectral imaging and other observing strategies, for example, "formation flying". Plans are underway to define the next era of EOS missions, commonly called "EOS Follow-on" or EOS II. The programmatic planning includes concepts that represent advances over the present Landsat-7 mission that concomitantly recognize the advances being made in land imaging within the private sector. The National Polar Orbiting Environmental Satellite Series (NPOESS) Preparatory Project (NPP) is an effort that will help to transition EOS medium resolution (herein meaning spatial resolutions near 500 meters), multispectral measurement capabilities such as represented by the EOS Moderate Resolution Imaging Spectroradiometer (MODIS) into the NPOESS operational series of satellites. Developments in Synthetic Aperture Radar (SAR) and passive microwave land observing capabilities are also proceeding. Beyond these efforts the Earth Science Enterprise Technology Strategy is embarking efforts to advance technologies in several basic areas: instruments, flight systems and operational capability, and information systems. In the case of instruments architectures will be examined that offer significant reductions in mass, volume, power and observational flexibility. For flight systems and operational capability, formation flying including calibration and data fusion, systems operation autonomy, and mechanical and electronic innovations that can reduce spacecraft and subsystem resource requirements. The efforts in information systems will include better approaches for linking multiple data sets, extracting and visualizing information, and improvements in collecting, compressing, transmitting, processing, distributing and archiving data from multiple platforms. Overall concepts such as sensor webs, constellations of observing systems, and rapid and tailored data availability and delivery to multiple users comprise and notions Earth Science Vision for the future.

Salomonson, Vincent V.↗
NASA NTRS2014

The New NASA Orbital Debris Engineering Model ORDEM 3.0

The NASA Orbital Debris Program Office (ODPO) has released its latest Orbital Debris Engineering Model, ORDEM 3.0. It supersedes ORDEM 2.0. This newer model encompasses the Earth satellite and debris flux environment from altitudes of low Earth orbit (LEO) through geosynchronous orbit (GEO). Debris sizes of 10 microns through 1 m in non-GEO and 10 cm through 1 m in GEO are modeled. The inclusive years are 2010 through 2035. The ORDEM model series has always been data driven. ORDEM 3.0 has the benefit of many more hours from existing data sources and from new sources that weren't available to past versions. Returned surfaces, ground tests, and remote sensors all contribute data. The returned surface and ground test data reveal material characteristics of small particles. Densities of fragmentation debris particles smaller than 10 cm are grouped in ORDEM 3.0 in terms of high-, medium-, and lowdensities, along with RORSAT sodium-potassium droplets. Supporting models have advanced significantly. The LEO-to-GEO ENvironment Debris model (LEGEND) includes an historical and a future projection component with yearly populations that include launched and maneuvered intacts, mission related debris (MRD), and explosion and collision fragments. LEGEND propagates objects with ephemerides and physical characteristics down to 1 mm in size. The full LEGEND yearly population acts as an a priori condition for a Bayesian statistical model. Specific, well defined populations are added like the Radar Ocean Reconnaissance Satellite (RORSAT) sodium-potassium (NaK) droplets, recent major accidental and deliberate collision fragments, and known anomalous debris event fragments. For microdebris of sizes 10 microns to 1 mm the ODPO uses an in-house Degradation/Ejecta model in which a MLE technique is used with returned surface data to estimate populations. This paper elaborates on the upgrades of this model over previous versions highlighting the material density splits and consequences of that to the penetration risk to spacecraft.

Krisko, P. H.↗
NASA NTRS2009

Vibro-Acoustic Analysis of NASA's Space Shuttle Launch Pad 39A Flame Trench Wall

A vital element to NASA's manned space flight launch operations is the Kennedy Space Center Launch Complex 39's launch pads A and B. Originally designed and constructed In the 1960s for the Saturn V rockets used for the Apollo missions, these pads were modified above grade to support Space Shuttle missions. But below grade, each of the pad's original walls (including a 42 feet deep, 58 feet wide, and 450 feet long tunnel designed to deflect flames and exhaust gases, the flame trench) remained unchanged. On May 31, 2008 during the launch of STS-124, over 3500 of the. 22000 interlocking refractory bricks that lined east wall of the flame trench, protecting the pad structure were liberated from pad 39A. The STS-124 launch anomaly spawned an agency-wide initiative to determine the failure root cause, to assess the impact of debris on vehicle and ground support equipment safety, and to prescribe corrective action. The investigation encompassed radar imaging, infrared video review, debris transport mechanism analysis using computational fluid dynamics, destructive testing, and non-destructive evaluation, including vibroacoustic analysis, in order to validate the corrective action. The primary focus of this paper is on the analytic approach, including static, modal, and vibro-acoustic analysis, required to certify the corrective action, and ensure Integrity and operational reliability for future launches. Due to the absence of instrumentation (including pressure transducers, acoustic pressure sensors, and accelerometers) in the flame trench, defining an accurate acoustic signature of the launch environment during shuttle main engine/solid rocket booster Ignition and vehicle ascent posed a significant challenge. Details of the analysis, including the derivation of launch environments, the finite element approach taken, and analysistest/ launch data correlation are discussed. Data obtained from the recent launch of STS-126 from Pad 39A was instrumental in validating the design analysis philosophies outlined in this paper.

Margasahayam, Ravi N.↗
DOE OSTI2020

Online Parameter Estimation Methods for Adaptive Cruise Control Systems

Modeling Adaptive Cruise Control (ACC) vehicles enables the understanding of the impact of these vehicles on traffic flow. In this work, two online methods are used to provide real time system identification of ACC enabled vehicles. The first technique is a recursive least squares (RLS) approach, while the second method solves a nonlinear joint state and parameter estimation problem via particle filtering (PF). We provide a parameter identifiability analysis for both methods to analytically show that the model parameters are not identifiable using equilibrium driving. The accuracy and computational runtime of the online methods are compared to a commonly used offline simulation-based optimization (i.e., batch optimization) approach. The methods are tested on synthetic data as well as on empirical data collected directly from a 2019 model year ACC vehicle using data from sensors that are part of the stock ACC system. The online methods are scalable and provide comparable accuracy to the batch method. RLS runs in real time and is two orders of magnitude faster than the batch method for modest sized (e.g., 15 min) datasets. The particle filter also runs in real- time, and is also suitable in streaming applications in which the datasets can grow arbitrarily large.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1984

Comparison of winds, waves, and turbulence as observed by airborne lidar, ground-based radars, and instrumented tower

On June 29, 1981, two ground-based Doppler radars, an airborne Doppler optical radar (lidar), an instrumented tower, and a rawinsonde were employed to collect wind data in the planetary boundary layer (PBL) in central Oklahoma. The main objectives of this experiment were related to a comparison of wind estimates and the visualization of the three-dimensional eddy structure in the convective atmospheric boundary layer. Discrepancies in the mean wind and wind profile detected by the different sensing systems were explained as being caused by a Schuler resonance of the aircraft's inertial navigation system, which caused an erroneous component of the aircraft's ground-relative velocity vector to be subtracted from the lidar-measured radial velocities. It is concluded that NASA's airborne Doppler optical radar system is capable of measuring wind fields in clear air on a smaller scale than was previously available with fixed remote sensors.

Eilts, M. D.↗
NASA NTRS2014

Transition Marshall Space Flight Center Wind Profiler Splicing Algorithm to Launch Services Program Upper Winds Tool

NASAs LSP customers and the future SLS program rely on observations of upper-level winds for steering, loads, and trajectory calculations for the launch vehicles flight. On the day of launch, the 45th Weather Squadron (45 WS) Launch Weather Officers (LWOs) monitor the upper-level winds and provide forecasts to the launch team via the AMU-developed LSP Upper Winds tool for launches at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station. This tool displays wind speed and direction profiles from rawinsondes released during launch operations, the 45th Space Wing 915-MHz Doppler Radar Wind Profilers (DRWPs) and KSC 50-MHz DRWP, and output from numerical weather prediction models.The goal of this task was to splice the wind speed and direction profiles from the 45th Space Wing (45 SW) 915-MHz Doppler radar Wind Profilers (DRWPs) and KSC 50-MHz DRWP at altitudes where the wind profiles overlap to create a smooth profile. In the first version of the LSP Upper Winds tool, the top of the 915-MHz DRWP wind profile and the bottom of the 50-MHz DRWP were not spliced, sometimes creating a discontinuity in the profile. The Marshall Space Flight Center (MSFC) Natural Environments Branch (NE) created algorithms to splice the wind profiles from the two sensors to generate an archive of vertically complete wind profiles for the SLS program. The AMU worked with MSFC NE personnel to implement these algorithms in the LSP Upper Winds tool to provide a continuous spliced wind profile.The AMU transitioned the MSFC NE algorithms to interpolate and fill data gaps in the data, implement a Gaussian weighting function to produce 50-m altitude intervals in each sensor, and splice the data together from both DRWPs. They did so by porting the MSFC NE code written with MATLAB software into Microsoft Excel Visual Basic for Applications (VBA). After testing the new algorithms in stand-alone VBA modules, the AMU replaced the existing VBA code in the LSP Upper Winds tool with the new algorithms. They then tested the code in the LSP Upper Winds tool with archived data. The tool will be delivered to the 45 WS after the 50-MHz DRWP upgrade is complete and the tool is tested with real-time data. The 50-MHz DRWP upgrade is expected to be finished in October 2014.

Space launch↗
NASA NTRS2019

HYDRAFloods Near Real-Time Mapping of Flood Events Using Multiple Satellite Sensors

Information about inundated areas is critical for distributing aid and resources in flood emergency response operations. Conventional methods of monitoring floods, like gauge based observations and reports from local authorities, provide very detailed and accurate information about flood depth and location. However, the geographic coverage of these point-based observations is limited and delays are common. Satellite-based images can help address these challenges, providing near real-time flood extent information over large areas of coverage. The Hydrological Remote Sensing Analysis of Floods (HYDRAFloods) tool, currently being developed by SERVIR-Mekong in collaboration with the Myanmar Department of Disaster Management, is one such example. Generating flood maps, even from satellite imagery, is challenging, given the many disparate sources of information. HYDRAFloods leverages the most recently available remotely sensed data acquired by multiple satellite platforms to automate the creation of daily flood maps. Through combining multiple satellite sources, including optical, microwave, and synthetic aperture radar datasets, near real-time flood maps with reduced cloud impact and increased satellite observations can be generated for use by disaster managers.

Nauman, Claire↗
NASA NTRS1981

Potential application of satellite radar to monitor soil moisture

The microwave backscattering characteristics of soils as a function of moisture content are reviewed as a basis for the evaluation of the applicability of satellite radar to soil moisture determinations. Results of experiments showing the dependence of the complex dielectric constant, power reflection coefficient and backscattering coefficient of soil on its volumetric moisture content are presented. Results of a research program using the truck-mounted University of Kansas microwave active spectrometer to determine if, by the proper choice of sensor frequency, polarization and incidence, the sensor dynamic range in response to moisture variations may be greater than its response to other variations are considered in detail, and the optimum conditions of frequency (between 4 and 5 GHz), angular incidence (between 7 and 20 deg from nadir) and polarization (HH) obtained are indicated. An empirical model for the backscattering coefficient as a function of gravimetric moisture content derived on the basis of the experimental data is presented, and it is noted that available airborne and spaceborne data confirm the results of the ground-based sensors.

Ulaby, F. T.↗
NASA NTRS2015

Extremely Low Passive Microwave Brightness Temperatures Due to Thunderstorms

Extreme events by their nature fall outside the bounds of routine experience. With imperfect or ambiguous measuring systems, it is appropriate to question whether an unusual measurement represents an extreme event or is the result of instrument errors or other sources of noise. About three weeks after the Tropical Rainfall Measuring Mission (TRMM) satellite began collecting data in Dec 1997, a thunderstorm was observed over northern Argentina with 85 GHz brightness temperatures below 50 K and 37 GHz brightness temperatures below 70 K (Zipser et al. 2006). These values are well below what had previously been observed from satellite sensors with lower resolution. The 37 GHz brightness temperatures are also well below those measured by TRMM for any other storm in the subsequent 16 years. Without corroborating evidence, it would be natural to suspect a problem with the instrument, or perhaps an irregularity with the platform during the first weeks of the satellite mission. Automated quality control flags or other procedures in retrieval algorithms could treat these measurements as errors, because they fall outside the expected bounds. But the TRMM satellite also carries a radar and a lightning sensor, both confirming the presence of an intense thunderstorm. The radar recorded 40+ dBZ reflectivity up to about 19 km altitude. More than 200 lightning flashes per minute were recorded. That same storm's 19 GHz brightness temperatures below 150 K would normally be interpreted as the result of a low-emissivity water surface (e.g., a lake, or flood waters) if not for the simultaneous measurements of such intense convection. This paper will examine records from TRMM and related satellite sensors including SSMI, AMSR-E, and the new GMI to find the strongest signatures resulting from thunderstorms, and distinguishing those from sources of noise. The lowest brightness temperatures resulting from thunderstorms as seen by TRMM have been in Argentina in November and December. For SSMI sensors carried on five DMSP satellites examined so far, the lowest thunderstorm-related brightness temperatures have been from Argentina in November - December and from Minnesota in June-July. The Minnesota cases were associated with spotter reports of large hail, significant severe wind, and tornadoes. Those locations have the record-holders for each satellite. The lowest AMSR-E 36.5 GHz brightness temperatures associated with deep convection have been in Argentina; the lowest 89.0 GHz brightness temperatures were from Typhoon Bolaven in the Philippine Sea. This paper will show examples of cases with the lowest brightness temperatures, and map the locations of these and other storms with brightness temperatures nearly as low. The study is largely motivated by the new GMI sensor on the Global Precipitation Mission core satellite, launched in February 2014, with its high resolution expected to reveal unprecedented low brightness temperatures when extreme events are encountered.

Cecil, Daniel J.↗
NASA NTRS2012

The InSAR Scientific Computing Environment

We have developed a flexible and extensible Interferometric SAR (InSAR) Scientific Computing Environment (ISCE) for geodetic image processing. ISCE was designed from the ground up as a geophysics community tool for generating stacks of interferograms that lend themselves to various forms of time-series analysis, with attention paid to accuracy, extensibility, and modularity. The framework is python-based, with code elements rigorously componentized by separating input/output operations from the processing engines. This allows greater flexibility and extensibility in the data models, and creates algorithmic code that is less susceptible to unnecessary modification when new data types and sensors are available. In addition, the components support provenance and checkpointing to facilitate reprocessing and algorithm exploration. The algorithms, based on legacy processing codes, have been adapted to assume a common reference track approach for all images acquired from nearby orbits, simplifying and systematizing the geometry for time-series analysis. The framework is designed to easily allow user contributions, and is distributed for free use by researchers. ISCE can process data from the ALOS, ERS, EnviSAT, Cosmo-SkyMed, RadarSAT-1, RadarSAT-2, and TerraSAR-X platforms, starting from Level-0 or Level 1 as provided from the data source, and going as far as Level 3 geocoded deformation products. With its flexible design, it can be extended with raw/meta data parsers to enable it to work with radar data from other platforms

geodetic imaging↗
NASA NTRS2022

A Multi-sensor Evaluation of Precipitation Uncertainty for Landslide-triggering Storm Events

Extreme precipitation can have profound consequences for communities, resulting in natural hazards such as rainfall-triggered landslides that cause casualties and extensive property damage. A key challenge to understanding and predicting rainfall triggered landslides comes from observational uncertainties in the depth and intensity of precipitation preceding the event. Practitioners and researchers must select among a wide range of precipitation products, often with little guidance. Here we evaluate the degree of precipitation uncertainty across multiple precipitation products for a large set of landslide triggering storm events and investigate the impact of these uncertainties on predicted landslide probability using published intensity-duration thresholds. The average intensity, peak intensity, duration, and NOAA-Atlas return periods are compared ahead of reported landslides across the continental US and Canada. Precipitation data are taken from four products that cover disparate measurement methods: near real-time and post-processed satellite (IMERG), radar (MRMS), and gauge-based (NLDAS-2). Landslide-triggering precipitation was found to vary widely across precipitation products with the depth of individual storm events diverging by as much as 296mm with an average range of 51mm. Peak intensity measurements, which are typically influential in triggering landslides, were also highly variable with an average range of 7.8262745mm/hr and as much as 57mm/hr. The two products more reliant upon ground-based observations (MRMS and NLDAS-2) performed better at identifying landslides according to published intensity duration storm thresholds, but all products exhibited hit-ratios of greater than 0.56. A greater proportion of landslides were predicted when including only manually-verified landslide locations. We recommend practitioners consider low-latency products like MRMS for investigating landslides, given their near-real time data availability and good performance in detecting landslides. Practitioners would be well-served considering more than one product as a way to confirm intense storm signals and minimize the influence of noise and false alarms.

Precipitation inter-comparison↗