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NASA NTRSDate not supplied

TPSAS-NF1676L-21193-DND

Researchers at NASA Langley Research Center have been developing an automated pattern recognition algorithm to identify overshooting convective cloud tops (OTs) in support of the GOES-R satellite program. This algorithm identify regions of overshooting at the individual 1-4 km geostationary satellite pixel scale using visible (during daytime only) and infrared channel imagery and numerical weather analysis data. The algorithm has been developed based upon analysis of 0.25-1 km spatial resolution Aqua MODIS imagery, using a database of over 2000 manually identified OT features throughout the world in storms with varying intensity and morphology. The OT database includes storms ranging from small, warm topped cells in Alaska and Mongolia, tornadic supercells over the U.S. Central Plains and Europe, large tropical mesoscale convective systems, and overshooting in the eyewalls and spiral bands of category 5 tropical cyclones. This database is available for use by the research community. The algorithm is designed to operate on data from any current and historical satellite imager, allowing for development of a highly accurate global OT detection climatology that extends back into the 1990's at up to a 15-30 min temporal resolution throughout the diurnal cycle. As members of the McIDAS Users Group, NASA LaRC has immediate access to the full global archive of geostationary imager data which would allows rapid development of OT climatologies and short-term databases. This type of capability has never been available within the weather and climate research community. Regional geostationary OT databases have been already developed over CONUS during SEAC4RS, for 18-years over the Eastern U.S., and for 5-10 years over Australia, Europe, Southeast Asia, and East Africa among many other regions. Some of these datasets are being used by climate researchers and private industry to examine UTLS-penetrating storm spatial distributions and their temporal variability, in addition to weather hazards associated with these storms at unprecedented spatial detail. This presentation will describe the OT pattern recognition algorithm and highlight recent product applications.

Kristopher Bedka↗
NASA NTRS2021

Earth system modeling: can we stay ahead of what's actually happening?

Over the last forty years, Earth System Models have become more and more comprehensive and detailed. They have assimilated previously independent modeling efforts in ocean circulation, atmospheric composition, the stratosphere, land surface, and the cryosphere, and are today demonstrably more skillful than earlier efforts. Yet the structural uncertainty in the latest model projections is actually increasing. Meanwhile, impacts of climate change on, for instance, wildfires, tropical cyclones and sea level, appear to be increasing faster than we have been able to predict. I will address some of the current issues at the bleeding edges of model development and the continuing importance of model evaluation.

Gavin A Schmidt↗
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 NTRS2021

The Inland Maintenance and Reintensification of Tropical Storm Bill (2015). Part II: Precipitation Microphysics

Tropical Storm Bill produced over 400 mmof rainfall to portions of southern Oklahoma from 16-20 June 2015, adding to the catastrophic urban and river flooding that occurred throughout the region in the month prior to landfall. The unprecedented excessive precipitation event that occurred across Oklahoma and Texas during May and June 2015 resulted in anomalously high soil moisture and latent heat fluxes over the region, acting to increase the available boundary layer moisture. Tropical Storm Bill progressed inland over the region of anomalous soil moisture and latent heat fluxes which helped maintain polarimetric radar signatures associated with tropical, warm rain events. Vertical profiles of polarimetric radar variables such as Z(H), Z(DR), K(DP), and ρ(hv) were analyzed in time and space over Texas and Oklahoma. The profiles suggest that Tropical Storm Bill maintained warm rain signatures and collision-coalescence processes as it tracked hundreds of kilometers inland away from the landfall point consistent with tropical cyclone precipitation characteristics. Dual-frequency precipitation radar observations from the NASA GPM DPR were also analyzed post-landfall and showed similar signatures of collision-coalescence while Bill moved over north Texas, southern Oklahoma, eastern Missouri, and western Kentucky.

Noah S. Brauer↗
NASA NTRS2008

QuikSCAT follow - on concept study

Global, real-time observations of the speed and direction of winds over the oceans ocean surface vector winds [OSVW]) are high priority measurements for National Oceanic and Atmospheric Administration’s (NOAA’s) weather forecasting, prediction, and hazard warning communities. At present, these data are provided by the experimental National Aeronautics and Space Administration (NASA) QuikSCAT satellite sensor, which is operating well beyond its design lifetime. To continue to meet the Nation’s need for operational OSVW observations beyond QuikSCAT, NOAA tasked the Jet Propulsion Laboratory (JPL) to design and provide costs for a set of QuikSCAT Follow-On mission options. Three scenarios were examined: 1) a QuikSCAT Replacement mission with capabilities commensurate to QuikSCAT, 2) a next-generation Extended Ocean Vector Winds Mission (XOVWM), as recommended in the National Research Council’s decadal survey to provide significantly improved all-weather, allwind, high spatial resolution measurements, and 3) an XOVWM Constellation consisting of two XOVWM observatories to provide improved temporal resolution. In parallel, NOAA asked its users to provide a quantitative assessment of each option’s benefit to NOAA. This report presents the JPL design, risk assessment, and cost for each of three options, together with a summary of the NOAA users’ benefit assessment. The report concludes that though all options are technically feasible for immediate implementation and have a risk posture consistent with a NOAA operational mission, the XOVWM options provide significant observational benefits. While a QuikSCAT Replacement option would continue current operational measurement capabilities, there is a strong and clearly defined operational need for improved capabilities in high winds (e.g., hurricanes or extra-tropical cyclones), heavy precipitation, and near coasts to enable significantly improved severe storm and coastal hazard forecasts, which are provided only by the XOVWM options.

Rodriquez, Ernesto↗
NASA NTRS1996

SIR-C/X-SAR Observations of Rain Storms

Teh SIR-C/X-sar radar observations of rain storms are the first multi-polarization and multi-frequency observations of percipitation from space. In addition to numerous, often dramatic images of severe weather systems obtained by forming a synthetic aperture in the usual side-looking attitude, several data takes were performed while the radar antennas were parallel to the ground and the radar beams were pointing at nadir. These oppoprtunities coincided with the passage of the Shuttle over Tropical Cyclone Odille in the southern Indian Ocean during the first flight, and over Typhoon Seth in the Western Pacific during the second flight. The resulting observations, or more apropriately, the resulting measurements, demonstrate for the first time the capability of a spaceborne multi-frequency multi-polarization microwave radar system to quantify precipitation rates, to detect hydrometer phase, and to classify rain type.

SIR-C↗
NASA NTRS2018

Observing Fast Mesoscale Atmospheric Processes with a Geostationary Microwave Sounder

A geostationary microwave sounder, GeoSTAR, capable of providing continuous monitoring every 15 minutes of atmospheric temperature, water vapor, clouds, precipitation, and wind in the presence of clouds and precipitation, which will add tremendously to our ability to observe rapidly evolving dynamic atmospheric phenomena, such as hurricanes and severe storms, monsoonal moisture flow, and atmospheric rivers, has been developed at the Jet Propulsion Laboratory. GeoSTAR uses aperture synthesis to overcome the difficulty of attaining adequate spatial resolution from geostationary orbit. It is made possible with new technology that has now been developed and fully tested. Low-risk mission development can start as soon as funding becomes available. The sensor can be hosted on a commercial communications satellite, which could reduce the cost substantially. Plans have been developed at JPL for such a mission, called “GeoStorm”, focused on observing severe convective storms – tropical cyclones, mesoscale convective systems, and extratropical cyclones – with a goal of improving our understanding, modeling and prediction of these destructive phenomena. It can equally well be configured as an operational mission, where the goal is to collect data for immediate assimilation into regional forecast systems, provide “now-casting” as the storms unfold, and support post-disaster relief and recovery efforts. With key observables including vertical profiles of temperature, water vapor, wind and precipitation over a wide area, many focused applications are possible, particularly pertaining to aviation, transportation and marine operations, in both the civilian and defense domains.

Lambrigsten, Bjorn↗
NASA NTRS2021

Second Time-Resolved Observations of Precipitation Structure and Storm Intensity With a Constellation of Smallsats (TROPICS) Mission Applications Workshop

The NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission is a constellation of state-of-the-science observing platforms that will measure temperature and humidity soundings and precipitation with spatial resolution comparable to current operational passive microwave (PMW) sounders but with unprecedented temporal resolution (Mission Website).1 TROPICS is a cost-capped ($30.2 million), Venture-class mission funded by the NASA Earth Science Division (ESD) and led by Principal Investigator Dr. William Blackwell from the Massachusetts Institute of Technology Lincoln Laboratory (MIT LL). The mission consists of a constellation of six, three-unit (3U) CubeSats (approximately 10×10×34 cm), each hosting a 12-channel PMW spectrometer based on the Micro-sized Microwave Atmospheric Satellite-2 (MicroMAS-2) developed at MIT LL, but with a substantially improved design. TROPICS will provide imagery near 91 and 205 GHz, temperature sounding near 118 GHz, and moisture sounding near 183 GHz. Spatial resolution at nadir will be around 27 km for temperature and 17 km for moisture and precipitation with a swath width of approximately 2,000 km from a 550-km orbit altitude. Both the spatial resolution and swath width are similar to the Advanced Technology Microwave Sounder (ATMS) that is being flown as part of the Suomi National Polar-orbiting Partnership (SNPP) and National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS). In addition, TROPICS meets many of the requirements outlined in the 2007 Decadal Survey for the Precision and All-Weather Tempera-ture and Humidity (PATH) mission, which was originally envisioned as a microwave instrument in geostationary orbit. TROPICS enables temporal resolution approaching that of geostationary orbit but at a much lower cost, demonstrating a technology that could impact the design of future Earth-observing missions. The satellites for the TROPICS mission were delivered to NASA in 2019 for launches planned in the 2021–2022 timeframe. The primary mission objective of TROPICS is to relate temperature, humidity, and precipitation structure to the evolution of tropical cyclone (TC) intensity.

remote sensing↗
NASA NTRS2019

Assimilating microwave cloudy observations into NASA GEOS model using a novel Bayesian Monte Carlo technique

Despite the importance of clouds and their influence on atmospheric water and energy balance, Numerical Weather Prediction (NWP) centers systematically exclude cloud information from the assimilation process and only assimilate clear-sky radiances (Janiskov´a et al. 2012). In order to ensure that only clear sky radiances are assimilated, strict cloud detection thresholds are applied before radiances are fed into data assimilation (DA) systems. This process not only excludes a large portion of satellite radiances, but causes loss of information in the regions that are of high interest to meteorologists and are most challenging for weather forecasts (Errico et al. 2007; Haddad et al. 2015). Although, in recent years there has been great advances in the operational weather forecasting, the prediction of tropical cyclones (TC), especially the intensity of TCs, remains challenging. According to Aksoy et al. (2013), in addition to the model deficiencies, another important factor that contributes to this challenge includes lack of observations in the peripheral environment (rain- bands) of TCs mainly because of the selective assimilation of existing observations. Satellite observations provide more than 90 % of the input data for the initialization of NWP models but more than 75 % of satellite observations are discarded due to the cloud contamination as well as land, snow, and ice emissivity issues (Bauer et al. 2010).

Rainband↗
NASA NTRS2020

Object-Based Comparison of Data-Driven and Physics-Driven Satellite Estimates of Extreme Rainfall

The Global Precipitation Measurement (GPM) constellation of spaceborne sensors provides a variety of direct and indirect measurements of precipitation processes. Such observations can be employed to derive spatially and temporally consistent gridded precipitation estimates either via data-driven retrieval algorithms or by assimilation into physically based numerical weather models. We compare the data-driven Integrated Multisatellite Retrievals for GPM (IMERG) and the assimilation-enabled NASA-Unified Weather Research and Forecasting (NU-WRF) model against Stage IV reference precipitation for four major extreme rainfall events in the southeastern United States using an object-based analysis framework that decomposes gridded precipitation fields into storm objects. As an alternative to conventional ‘‘grid-by-grid analysis,’’ the object-based approach provides a promising way to diagnose spatial properties of storms, trace them through space and time, and connect their accuracy to storm types and input data sources. The evolution of two tropical cyclones are generally captured by IMERG and NU-WRF, while the less organized spatial patterns of two mesoscale convective systems pose challenges for both. NU-WRF rain rates are generally more accurate, while IMERG better captures storm location and shape. Both show higher skill in detecting large, intense storms compared to smaller, weaker storms. IMERG’s accuracy depends on the input microwave and infrared data sources; NU-WRF does not appear to exhibit this dependence. Findings highlight that an object-oriented view can provide deeper insights into satellite precipitation performance and that the satellite precipitation community should further explore the potential for ‘‘hybrid’’ data-driven and physics-driven estimates in order to make optimal usage of satellite observations.

extreme events↗
NASA NTRS2021

Investigating the potential of a global precipitation forecast to inform landslide prediction

Extreme rainfall events within landslide-prone areas can be catastrophic, resulting in loss of property, infrastructure, and life. A global Landslide Hazard Assessment for Situational Awareness (LHASA) model provides routine near-real time estimates of landslide hazard using Integrated Multi-Satellite Precipitation Retrievals for the Global Precipitation Mission (IMERG). However, it does not provide information on potential landslide hazard in the future. Forecasting potential landslide events at a global scale presents an area of open research. This study compares a global precipitation forecast provided by NASA's Goddard Earth Observing System (GEOS) with near-real time satellite precipitation estimates. The Multi-Radar Multi-Sensor gauge corrected (MRMS-GC) reference is used to assess the performance of both satellite and model-based precipitation products over the contiguous United States (CONUS). The forecast lead time of 24hrs is considered, with a focus on extreme precipitation events. The performance of IMERG and GEOS-Forecast products is assessed in terms of the probability of detection, success ratio, critical success index and hit bias as well as continuous statistics. The results show that seasonality influences the performance of both satellite and model-based precipitation products. Comparison of IMERG and GEOS-Forecast globally as well as in several event case studies (Colombia, southeast Asia, and Tajikistan) reveals that GEOS-Forecast detects extreme rainfall more frequently relative to IMERG for these specific analyses. For recent landslide points across the globe, the 24hr accumulated precipitation forecast >100 mm corresponds well with near-real time daily accumulated IMERG precipitation estimates. GEOS-Forecast and IMERG precipitation match more closely for tropical cyclones than for other types of storms. The main intention of this study is to assess the viability of using a global forecast for landslide predictions and understand the extent of the variability between these products to inform where we would expect the landslide modeling results to most prominently diverge. Results of this study will be used to inform how forecasted precipitation estimates can be incorporated into the LHASA model to provide the first global predictive view of landslide hazards.

S. Khan↗
NASA NTRS2021

Satellites Support Disaster Response to Storm-Driven Landslides

High winds and flooding storm surges driven by tropical cyclones cause some of the deadliest and most damaging weather-related conditions around the world. The rainfall that cyclones bring compounds these conditions and, in hilly or mountainous areas, can trigger landslides that cause even more widespread and devastating impacts. When extreme precipitation occurs over short time frames, hillslopes may become saturated and critically unstable. The most intense storms can trigger thousands of landslides in mountainous areas, as was dramatically illustrated in Puerto Rico in September 2017, when Hurricane Maria’s rains left the landscape scarred by roughly 40,000 landslides. Before and during a major cyclone, disaster responders need information about where landslides are likely to occur. In the aftermath, locating landslides quickly helps authorities direct resources to where they are most needed to save people and critical infrastructure. However, this information is often unavailable during an event response or is presented only for small regions, constraining the effectiveness of response efforts.

Robert Emberson↗
NASA NTRS2022

Advances in the prediction of MJO-Teleconnections in the S2S forecast systems

This study evaluates the ability of state-of-the-art subseasonal to seasonal (S2S) forecasting systems to represent and predict the teleconnections of the Madden Julian Oscillations and their effects on weather in terms of midlatitude weather patterns and North Atlantic tropical cyclones. This evaluation of forecast systems applies novel diagnostics developed to track teleconnections along their preferred pathways in the troposphere and stratosphere, and to measure the global and regional responses induced by teleconnections across both the Northern and Southern Hemispheres. Results of this study will help the modeling community understand to what extent the potential to predict the weather on S2S time scales is achieved by the current generation of forecasting systems, while informing where to focus further development efforts. The findings of this study will also provide impact modelers and decision makers with a better understanding of the potential of S2S predictions related to MJO teleconnections.

Forecasting↗
NASA NTRS2021

Investigating the Potential of A Global Precipitation Forecast to Inform Landslide Prediction

Extreme rainfall events within landslide-prone areas can be catastrophic, resulting in loss of property, infra-structure, and life. A global Landslide Hazard Assessment for Situational Awareness (LHASA) model provides routine near-real time estimates of landslide hazard using Integrated Multi-Satellite Precipitation Retrievals for the Global Precipitation Mission (IMERG). However, it does not provide information on potential landslide hazard in the future. Forecasting potential landslide events at a global scale presents an area of open research. This study compares a global precipitation forecast provided by NASA’s Goddard Earth Observing System (GEOS) with near-real time satellite precipitation estimates. The Multi-Radar Multi-Sensor gauge corrected (MRMS-GC) reference is used to assess the performance of both satellite and model-based precipitation products over the contiguous United States (CONUS). The forecast lead time of 24hrs is considered, with a focus on extreme precipitation events. The performance of IMERG and GEOS-Forecast products is assessed in terms of the probability of detection, success ratio, critical success index and hit bias as well as continuous statistics. The results show that seasonality influences the performance of both satellite and model-based precipitation products. Comparison of IMERG and GEOS-Forecast globally as well as in several event case studies (Colombia, southeast Asia, and Tajikistan) reveals that GEOS-Forecast detects extreme rainfall more frequently relative to IMERG for these specific analyses. For recent landslide points across the globe, the 24hr accumulated precipitation forecast >100 mm corresponds well with near-real time daily accumulated IMERG precipitation estimates. GEOS-Forecast and IMERG precipitation match more closely for tropical cyclones than for other types of storms. The main intention of this study is to assess the viability of using a global forecast for landslide predictions and understand the extent of the variability between these products to inform where we would expect the landslide modeling results to most prominently diverge. Results of this study will be used to inform how forecasted precipitation estimates can be incorporated into the LHASA model to provide the first global predictive view of landslide hazards.

S. Khan↗
NASA NTRSDate not supplied

NASA DEVELOP’s Approach to Building Capacity to Apply Earth Observations for Coastal and Marine Decision Making

Part of NASA’s Applied Sciences, the DEVELOP Program builds capacity through 10-week interdisciplinary feasibility studies that identify how NASA Earth observations can be integrated into decision making processes. The program is a dual capacity building effort that fosters programmatic participants (early and transitioning career professionals, students, and recent graduates) to apply Earth observations to partner organizations’ decision making needs. The program conducts 50-60 projects annually, with about 10-15% of those focused on coastal and marine community concerns. These projects explore the use of satellite data and model outputs for measuring and mapping shoreline delineation, barrier island transgression, mangrove health and extent, sedimentation and water quality, and tropical cyclone impacts to coastal regions. This presentation will introduce DEVELOP and a series of project case studies, an overview of the organizations engaged and Earth observations utilized, and lessons learned.

Capacity Building↗
NASA NTRSDate not supplied

Using NASA LANCE Near Real-Time Earth Observations for Disaster Risk Reduction

The NASA Earth Science Disasters Program handles requests from stakeholders and provides rapid response for Disaster Risk Reduction using Near Real-Time (NRT) products from NASA’s Land, Atmosphere NRT Capability for Earth Observing System (EOS) (LANCE). The combination of all available LANCE NRT satellite products provides global coverage at multiple times per day, which makes it possible to help users in different phases of the disaster’s life cycle. For wildfires and volcano eruption disasters, LANCE NRT fire and atmosphere products have been used to locate fires and high-temperature heat sources, and to assess the extent of air pollutions. Knowledge of the geographical position and direction of smokes, fires, lava flows provided critical information for disaster prediction and prevention. For hurricanes, tropical cyclones and earthquakes, LANCE global flood products and NASA’s Black Marble night-time light products have been used for monitoring land cover and land use change over time in disaster impacted areas. Users can use pre- and post-disaster maps to assess the extent of damage, and to make decisions for activities in reconstruction and recovery in infrastructure and health on the ground. LANCE NRT data are made available through the Earth data website and have been archived and visualized in NASA Disasters Mapping Portal, NASA LANCE Fire Information for Resource Management System (FIRMS) and NASA Worldview for the use of stakeholders.

Tian Yao↗
NASA NTRSDate not supplied

Changes in Lightning Flash Size and Energy during the Intensification of Hurricanes Florence (2018) and Laura (2020)

Lightning is a useful tool in forecasting and understanding the behavior of tropical cyclones (TCs). The Geostationary Lightning Mapper (GLM) aboard the GOES-16 geostationary satellite measures a multitude of lightning parameters, such as flash extent density (FED), average flash area (AFA) and optical energy for most of North America, South America, and the Pacific Ocean. GLM has brought about an era of spaceborne measurements to track storms many miles off the coasts and has allowed for the study of lightning throughout a TCs entire lifespan. FED has been found to be indicative of strength and intensification in TCs, but optical energy is a relatively new parameter that has not been studied intensively. This presentation analyzes the relationship between rapid intensification (RI) and GLM optical energy, AFA, and FED in Hurricanes Florence (2018) and Laura (2020). Using a 24-hour time interval in which each storm underwent RI, optical energy was analyzed from the innermost 100 kilometers of the eye and then was averaged for each minute to determine if there was a relationship between optical energy, AFA, FED, and intensification. Optical energy, FED, and AFA spiked just before RI began and then fell as the storms intensified more rapidly. These parameters increased again as intensification slowed slightly, while the optical energy peaks much lower in Hurricane Laura and much higher in Hurricane Florence. While these results show a pattern in lightning in these two TCs, further investigation of parameters like updraft strength and charge separation from microwave imagery during these secondary peaks in which optical energy behaves differently than the first peak may provide further insight as to the behavior of these TCs during RI.

Kiahna Mollette↗
NASA NTRS2022

Global Landslide Forecasting System for Hazard Assessment and Situational Awareness

Landslides triggered by extreme rainfall can be devastating, resulting in loss of life, property, and infrastructure. Landslide forecasting systems provide an opportunity to build awareness of potential hazards and ultimately take preemptive measures. There is currently a dearth of forecasting systems that provide regional or global coverage, but these systems can offer important situational awareness in data-sparse, ungauged, or large-scale catchments. A near global, primarily satellite-based system calledthe Landslide Hazard Assessment for Situational Awareness (LHASA) provides near real-time estimates of potential landslide hazard and exposure around the world. In this work, a precipitation forecast module is introduced into LHASA to complement the existing LHASA framework and provide an estimate of landslide hazard up to three days in advanceat 1km resolution. The model-based Goddard Earth Observing System-Forward Processing (GEOS-FP) precipitation forecast product is used as the forcing input for the model in place of the satellite-based Integrated Multi-satellitE Retrievals for Global Precipitation Mission product. Soil moisture and snow depth from the GEOS-FP assimilated product are also incorporated. The study period January 2020–January 2021 is usedto test the model performance against the LHASA near real-time estimates at multiple spatiotemporal scales. Validation of the model is carried out using a collection of rainfall-triggered landslide inventories from around the world as case studies to demonstrate the potential utility and limitations of this system.The rescaling of the GEOS-FP precipitation product is a critical step in incorporating the forecasted precipitation data within LHASA-Forecast (LHASA-F). Combining different streams of forecasted data within the LHASA-F framework shows promise, particularly for larger events at the 1-and 2-day lead time for events. Results indicate that for the case studies evaluated, the LHASA-F is generally able to resolve major landslide events triggered by extreme rainfall, such as from tropical cyclones. The analysis shows that landslide forecast outputs may be represented differently depending on the user’s needs. This framework serves as a first milestone in providing a global predictive view of landslide hazard.

Landslide forecasting system↗