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NASA NTRS2020

MODIS and VIIRS Calibration and Characterization in Support of Producing Long-Term High-Quality Data Products

Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) have successfully operated since their launches in 1999 and 2002, respectively, and generated various data products to support the Earth remote sensing disciplines and users worldwide for their research activities and applications, including studies of the Earth system, and its changes over time and geographic regions. The MODIS data have also significantly contributed to the continuity of multi-decadal satellite data records and led to major advances in the Earth remote sensing field. The long-term data records from MODIS observations have been and will continue to be extended by the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments, currently operated aboard the Suomi-National Polar-Orbiting Partnership (NPP) and NOAA-20 satellites. The data quality of satellite instruments strongly depends on their calibration accuracy and stability. In order to help scientists and users gain a better understanding of MODIS and VIIRS data quality, this paper provides an overview of their on-orbit calibration methodologies, approaches, and results derived from instrument on-board calibrators and lunar observations, as well as select Earth view targets. What is also discussed is the calibration consistency between MODIS and VIIRS and its potential impact on producing multi-sensor long-term data records. As illustrated, the overall performance of both MODIS and VIIRS continues to meet their design requirements.

MODIS↗
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 NTRS2019

A Web of Data Analytics Services

Cloud Computing has become the ubiquitous approach to our Big Data challenge. However, one will quickly discover that moving (a.k.a. forklifting) existing on-premise data analytics solutions to the Cloud doesn’t always translate to costing saving and performance boost. The Cloud’s elasticity, its availability, and its wide selection of computing options and selections of costing models making Cloud an attractive environment to tackle our Big Data challenge. The fact is Cloud, on its own, is not the silver bullet to our daunting challenge need for analyze and derive scientific inferences through vast collections of multi-sensor measurements. We would like to have all scientific data in one easy to access environment, but getting the world of scientific data in one analytic system is immensely difficult to achieve. This paper describes the data analytics web architecture NASA is developing by infusing instances of Integrated Data Analytics systems next to the data. The goal is to minimize unnecessary data movement through collection of data access and analytics webservices for researchers to interact with and analyze measurements without have to download data to their local computer. These services are RESTful and provisioned by the data centers with the help from subject matter and science experts. These services encapsulate the physical computing infrastructure, which could local computing cluster, on-premise or public Cloud environment.

Huang, Thomas↗
NASA NTRS2019

Integrated support of NASA satellite and in situ oceanographic data via the PO.DAAC

The NASA Physical Oceanography DAAC (PO.DAAC) serves as one of the premier repositories for oceanographic satellite data. More recently, however, it is also increasingly archiving and distributing complementary in situ datasets from NASA-sponsored field campaigns. Here we present an overview of these projects, the complex multivariate data they produce, and some of the data interoperability challenges faced when dealing with such a heterogeneous suite of observations. We summarize the range of online tools and services currently available via the PODAAC, including data discovery services, web-services for subsetting/extraction, compliance checking and visualization. We also preview some new capabilities under development that may feature in future. These efforts are indicative of an evolution of DAAC services in pursuit of our broader vision: a more integrated approach to multi-sensor oceanographic data access and delivery spanning NASA satellite missions and field campaigns in support of science and applications for societal benefit.

Vannan, Suresh↗
NASA NTRS2020

Integrated Multi-satellite Evaluation for the Global Precipitation Measurement: Impact of Precipitation Types on Spaceborne Precipitation Estimation

An integrated multi-sensor assessment is proposed as a novel approach to advance satellite precipitation validation in order to provide users and algorithm developers with an assessment adequately coping with the varying performances of merged satellite precipitation estimates. Gridded precipitation rates retrieved from space sensors with quasi-global coverage feed numerous applications ranging from water budget studies to forecasting natural hazards caused by extreme events. Characterizing the error structure of satellite precipitation products is recognized as a major issue for the usefulness of these estimates. The Global Precipitation Measurement (GPM) mission aims at unifying precipitation measurements from a constellation of low-earth orbiting (LEO) sensors with various capabilities to detect, classify and quantify precipitation. They are used in combination with geostationary observations to provide gridded precipitation accumulations. The GPM Core Obser­vatory satellite serves as a calibration reference for consistent precipitation retrieval algorithms across the constellation. The propagation of QPE uncertainty from LEO active/passive microwave (PMW) precipitation estimates to gridded QPE is addressed in this study, by focusing on the impact of precipitation typology on QPE from the Level-2 GPM Core Observatory Dual-frequency Precipitation Radar (DPR) to the Microwave Imager (GMI) to Level-3 IMERG precipitation over the Conterminous U.S. A high-resolution surface precipitation used as a consistent reference across scales is derived from the ground radar-based Multi-Radar/Multi­Sensor. While the error structure of the DPR, GMI and subsequent IMERG is complex because of the interaction of various error factors, systematic biases related to precipitation typology are consistently quantified across products. These biases display similar features across Level-2 and Level-3, highlighting the need to better resolve precipitation typology from space and the room for improvement in global­scale precipitation estimates. The integrated analysis and framework proposed herein applies more generally to precipitation estimates from sensors and error sources affecting low-earth orbiting satellites and derived gridded products.

Pierre-Emmanuel Kirstette↗
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 NTRSDate not supplied

Open-source Techniques for Automated Landslide Inventory Generation for Rapid Response

Manual mapping is the most used method for generating landslide inventories. For rapid response scenario this method becomes tedious and time consuming. The Landslide team at NASA Goddard Space Flight Center has been developing open-source landslide mapping systems for rapid generation of landslide inventories. We have developed a Python-based landslide mapping framework known as the Semi-Automatic Landslide Detection (SALaD) system that uses Object-based Image Analysis and machine learning. For production of event-based inventories, SALaD was modified to include a change detection module (SALaD-CD). Utilizing high-resolution imagery form from Planet and Maxar, we have generated multiple rapid response landslide inventories that have been used by emergency responders on the ground, the NASA Disasters program, and academia. Currently, we are exploiting deep learning frameworks for landslide mapping. We are interested to learn about efficient way to harmonize multi-sensor data for creating a long-term record of landslides, training strategies and ongoing deep learning-based efforts for natural hazard characterization within NASA and UMD.

Pukar Amatya↗
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 NTRS2022

Spatial Grid-Based Object Localization from A Single Passive Sensor: A Deep Learning-Integrated Approach

Ongoing efforts at NASA’s Langley Research Center have produced a single passive sensor system for detecting ground objects and pinpointing their real-world location to a desired level of precision. The Langley center serves as a test range for unmanned aerial systems (UAS) and real-time knowledge about the location of people on campus is needed to inform least-risk UAS flight operations. The proposed system provides this knowledge through a camera combined with a convolutional neural network and an algorithm that projects an imaginary grid of square cells from the ground plane onto the perspective view of the camera. The position of detected objects on the camera’s projected grid determines their location in the real-world. The imaginary grid is easily mapped to a universal coordinate system, such as longitude and latitude, to provide both relative and absolute positional information of the detected objects. This simple system is shown to be accurate and effective, with decisive advantages over alternative multi-sensor and active sensor approaches. Extensions to the system are described to allow adaptation to a variety of other use cases.

object localization↗
NASA NTRSDate not supplied

NASA SPoRT Land Information System Products for Soil Moisture Analysis

The NASA Short-term Prediction Research and Transition (SPoRT) Program has been producing a near real-time instance of NASA’s Land Information System (hereafter known as SPoRT-LIS) since 2010, which contains output of soil moisture and temperature at layered depths. The unique configuration of the SPoRT-LIS enables decision-making on operational timescales since it assimilates near real-time observations such as Green Vegetation Fraction from the Visible Infrared Imaging Radiometer Suite and Quantitative Precipitation Estimates from the Multi-Radar Multi-Sensor System. The SPoRT-LIS was developed initially to provide land surface initialization variables for local numerical weather prediction models, such as the Weather Research and Forecasting (WRF) model. The earliest documented uses of the SPoRT-LIS as a tool for local drought analysis came from the National Weather Service Office in Huntsville, AL in 2011, in which data were used to provide recommendations for drought category changes to the U.S. Drought Monitor. Through engagement with SPoRT collaborative partners, use of the SPoRT-LIS has gradually expanded in recent years as a component of drought analysis and feedback to the U.S. Drought Monitor. This effort was aided by feedback from end users who expressed needs for specific soil moisture variables, which led to increased applicability for analysis by others in the drought community. This presentation will provide information on the SPoRT-LIS for drought analysis and the collaborative process that has led to changes in product output to meet user needs, with focus in the Southern Appalachian region. However, applications for hydrology and fire weather will also be discussed.

Land surface modeling↗
NASA NTRS2022

Analysis of the MODIS Above-Cloud Aerosol Retrieval Algorithm Using MCARS

The Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS) presently produces synthetic radiance data from Goddard Earth Observing System version 5 (GEOS-5) model output as if the Moderate Resolution Imaging Spectroradiometer (MODIS) was viewing a combination of atmospheric column inclusive of clouds, aerosols and a variety of gases and land/ocean surface at a specific location. In this paper we use MCARS to study the MODIS Above-Cloud AEROsol retrieval algorithm (MOD06ACAERO). MOD06ACAERO is presently a regional research algorithm able to retrieve aerosol optical thickness over clouds, in particular absorbing biomass burning aerosols overlying marine boundary layer clouds in the Southeastern Atlantic Ocean. The algorithm’s ability to provide aerosol information in cloudy conditions makes it a valuable source of information for modeling and climate studies n an area where current clear sky-only operational MODIS aerosol retrievals effectively have a data gap between the months of June and October. We use MCARS for a verification and closure study of the MOD06ACAERO algorithm. The purpose of this study is to develop a set of constraints a model developer might use during assimilation of MOD06ACAERO data. Our simulations indicate that the MOD06ACAERO algorithm performs well for marine boundary layer clouds in the SE Atlantic provided some specific screening rules are observed. For the present study, a combination of five simulated MODIS data granules was used for a dataset of 13.5 million samples with known input conditions. When pixel retrieval uncertainty was less than 30%, optical thickness of the underlying cloud layer was greater than 4 and scattering angle range within the cloud bow was excluded, MOD06ACAERO retrievals agreed with the underlying ground truth (GEOS-5 cloud and aerosol profiles used to generate the synthetic radiances) with a slope of 0.913, offset of 0.06, and RMSE=0.107. When only near-nadir pixels were considered (view zenith angle within +/-20 degrees) the agreement with source data further improved (0.977, 0.051 and 0.096 respectively). Algorithm closure was examined using a single case out of the five 38 used for verification. For closure, the MOD06ACAERO code was modified to use GEOS-5 temperature and moisture profiles as ancillary. Agreement of MOD06ACAERO retrievals with source data for the closure study had a slope of 0.996 with offset -0.007 and RMSE of 0.097 at pixel uncertainty level of less than 40%, illustrating the benefits of high-quality ancillary atmospheric data for such retrievals.

MODIS↗
NASA NTRSDate not supplied

TROPOMI Geometry-dependent Lambertian-Equivalent surface Reflectivity (GLER) Product for Improved Trace-Gas Retrieval

Accurate information about the reflectivity of the Earth's surface is required for most satellite retrievals of atmospheric composition, and this information is generally taken from monthly surface reflectivity climatology that neglects angular dependence. Previously we introduced Geometry-dependent Lambertian-equivalent surface reflectivity (GLER) which captures solar and satellite viewing angle dependence as well as daily and seasonal changes. GLER is calculated from simulations of Rayleigh-only top-of-atmosphere (TOA) radiances over non-Lambertian surfaces. We use NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) product over land and the wind-dependent Cox–Munk wave-facet slope distribution including water-leaving radiance over water to accounts for surface BRDF. We have developed global GLER product, previously for the Ozone Monitoring Instrument (OMI) and recently for Sentinel-5 Precursor (S5P) TROPOspheric Monitoring Instrument (TROPOMI) with several new improvements and updates. We have implemented the near real time daily V006 MODIS MCD43C1 BRDF data and gap-filled with a daily BRDF coefficient climatology created from 2002-2017 V006 MCD43GF data. The NASA’s Global Modelling Initiative hourly 0.25 x 0.25 deg Replay simulations are used for more accurate determination of pixel specific terrain pressure. To improve detection of seasonal snow/ice scenes, we use the 4-km snow cover product from the Interactive Multi-sensor Snow and Ice Mapping System (IMS). Finally, we use an improved version of the vector linearized discrete ordinate radiative transfer (VLIDORT) for update of the top-of-atmosphere (TOA) radiance look-up-tables (LUTs). We demonstrate how the use of GLER is beneficial to TROPOMI’s high spatial resolution (up to 3.5 km x 3.5 km) measurements to monitor atmospheric trace gas pollutants down to the sub-city scale.

TROPOMI↗
NASA NTRSDate not supplied

Expanding the Time Dimension of Hyperspectral Infrared Sounding Observations: Designing the NUCAPS-Forecast System

Low Earth orbit (LEO) hyper-spectral infrared (IR) sounders have significant yet untapped potential for characterizing thermodynamic environments of convective initiation and ongoing convection. While LEO soundings are of enormous value to weather forecasters, they cannot provide the temporal resolution needed to resolve the rapidly evolving thermodynamics of the convective environment. A novel methodology was developed to extend snapshots of LEO soundings forward in time up to six hours into the future and has been made available within the NOAA National Weather Service Advanced Weather Interactive Processing System (AWIPS) for testing in a quasi-operational setting. This methodology is based on parcel forward-trajectory calculations from the satellite observing time to recreate future soundings of temperature and moisture at regularly gridded intervals in space and time. The soundings are based on NOAA-Unique Combined Atmospheric Processing System (NUCAPS) retrievals from the Suomi NPP and NOAA-20 satellite platforms to allow for rapid near-real time product processing. Science code was transitioned to operational production in 2019 and the product, NUCAPS-Forecast, was tested at the NOAA Hazardous Weather Testbed Spring Experiment in 2019 and 2021. Forecaster feedback has led to improving the product and processing system to address product limitations and improve its applicability for pre-convective forecasting. This presentation highlights the design of the NUCAPS-Forecast system. Solutions were tested and implemented to remove excess moisture and conjoin forward-trajected parcels into vertical profiles for calculating grids of stability indices. Additionally, use cases that explore the tendencies of derived convective available potential energy (CAPE) and convective inhibition (CIN) were investigated. When evaluated against gridded, hourly accumulated rainfall obtained from the Multi-Radar Multi-Sensor (MRMS) observational data set areas with elevated CAPE (reduced CIN) are shown to be associated with areas of precipitation and increase confidence the LEO soundings advected forward in time physically represent atmospheric processes. These results suggest that adiabatic parcel advection of LEO satellite sounding snapshots forward in time exhibits skill in discriminating locations and times that are more likely to lead to convective initiation. Leveraging trajectory modeling to increase the spatial and temporal resolution of LEO IR sounding observations today demonstrates the value of these observations from the future constellation of JPSS satellites and GeoXO mission.

weather↗
NASA NTRS2022

Using Desis and EO-1 Hyperion Reflectance Time Series for the Assessment of Vegetation Traits and Gross Primary Production (GPP)

This study evaluates the potential of the DLR Earth Sensing Imaging Spectrometer (DESIS) visible through near-infrared (VNIR) surface reflectance to augment the EO-1 Hyperion full spectrum (400-2400 nm) reflectance collection over vegetated flux sites to extend the reflectance time series up to the present. We compared DESIS and Hyperion surface reflectance magnitude and variability at a pseudo-invariant site (PICS) and a vegetated flux site (VFS). VNIR reflectance magnitudes between the two sensors did not significantly differ at the PICS. However, DESIS variability was higher, likely due to differences in the data acquisition time and observation geometry. Using empirical and biophysical models, both DESIS and Hyperion datasets captured the seasonal variations in gross primary production (GPP) and canopy bio-physical parameters such as chlorophyll content, leaf area index (LAI), and senescent material at the VFS. Differences in the magnitudes of the bio-physical parameters were observed, likely due to the differences in the sensors spectral range and resolution. Using VNIR reflectance from EO-1 Hyperion with DESIS convolved to Hyperion spectral resolution to estimate canopy chlorophyll and GPP, we demonstrate that combining historic and current space-based reflectance data in a common multi-sensor approach is feasible. This is of importance for extending the reflectance record established with EO-1 Hyperion to provide continuity with the current orbital instruments (e.g., DESIS/ISS, PRISMA/ASI) and the forthcoming NASA Surface Biology and Geology (SBG), ESA CHIME and DLR EnMAP satellite missions, which is of key importance for comparisons of current and past trends in the seasonal dynamics of vegetation traits and photosynthetic function.

DESIS↗
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↗
NASA NTRS2022

Local Scale (3-M) Soil Moisture Mapping Using SMAP and Planet Superdove

A capability for mapping meter-level resolution soil moisture with frequent temporal sampling over large regions is essential for quantifying local-scale environmental heterogeneity and eco-hydrologic behavior. However, available surface soil moisture (SSM) products generally involve much coarser grain sizes ranging from 30 m to several 10s of kilometers. Hence a new method is proposed to estimate 3-m resolution SSM using a combination of multi-sensor fusion, machine- learning (ML) and Cumulative Distribution Function (CDF) matching approaches. This method established favorable SSM correspondence between 3-m pixels and overlying 9-km grid cells from overlapping Planet SuperDove (PSD) observations and NASA Soil Moisture Active-Passive (SMAP) mission products. The resulting 3-m SSM predictions showed improved accuracy by reducing ab- solute bias and RMSE by ~0.01 cm3/cm3 over the original SMAP data in relation to in-situ soil moisture measurements for the Australian Yanco region, while preserving the high sampling frequency (1-3 day global revisit) and sensitivity to surface wetness (R 0.865) from SMAP. Heterogeneous soil moisture distributions varying with vegetation biomass gradients and irrigation regimes were generally captured within a selected study area. Further algorithm refinement and implementation for regional applications will allow for improvement in water resources management, precision agriculture, and disaster forecasts and responses.

soil moisture↗
NASA NTRS2026

Incremental Learning for Passive Microwave Precipitation Retrievals using Advanced Technology Microwave Sounder

Spaceborne passive microwave (PMW) radiometry is central to global precipitation monitoring, yet retrieval uncertainties remain substantial, particularly for cross-track sounders whose variable footprints and channel configurations are optimized for atmospheric temperature and moisture profiling rather than precipitation. Consequently, existing operational products often exhibit angular-dependent biases, limited effective swath utilization, unrealistic rainfall probability distributions, and systematic misclassification of precipitation phase. These limitations are further compounded by the scarcity of globally accurate and representative precipitation observations, as training data from the Dual-frequency Precipitation Radar (DPR) and the Cloud Profiling Radar (CPR) are spatially sparse, lack uniform global coverage, and exhibit heterogeneous error characteristics across precipitation regimes. To address these challenges, this study presents a supervised retrieval algorithm that incrementally trains an ensemble of extreme gradient-boosted decision trees by augmenting base learners with pre-training on reanalysis data and post-training on coincident DPR and CPR observations matched with the Advanced Technology Microwave Sounder (ATMS). By transferring prior information from reanalysis to posterior constraints from radar observations and adopting a sequential detection–estimation strategy for precipitation phase and rate retrieval, the proposed approach yields retrievals across the full ATMS swath that are largely free from persistent deficiencies in current Global Precipitation Measurement (GPM) passive microwave operational products. In particular, the method resolves bimodal artifacts in rainfall retrievals and mitigates systematic high-latitude snowfall biases, including overestimation across the Arctic and underestimation across the Antarctic. Validation against independent Multi-Radar Multi-Sensor (MRMS) data over the Contiguous United States (CONUS) further demonstrates improved performance in precipitation phase detection and rate estimation relative to both reanalysis and current GPM PMW products.

Mahyar Garshasbi↗
DOE OSTI2025

Infrastructure-Based Cooperative Perception at a Traffic Intersection: Overview and Challenges

Traffic intersections are crucial and challenging nodes in transportation networks where multiple lanes of vehicles and pedestrians converge. About one-quarter of traffic fatalities and about one-half of all traffic injuries in the United States happen at traffic intersections . Effective management of these intersections is important to ensure safety and efficiency of all users - vehicles, pedestrians, cyclists, and vulnerable road users (VRUs). With advancements in sensor perception technologies such as radar, light detection and ranging (lidar), and cameras, traffic intersections are developing into dynamic and data-rich environments. By using these data to create a real-time digital twin, we can enable real-time data-driven decision making and a range of applications such as sharing perception information to connected vehicles (CVs) and connected autonomous vehicles (CAVs), safety affirmative signaling, and curb optimizing to improve efficiency and enhance safety.This paper presents an overview of the concept and examines the challenges involved in implementing an infrastructure-based cooperative perception engine at a traffic intersection. In addition to outlining the physical components, this study also addresses important challenges involved in a multi-sensor system. We present results from deploying the National Renewable Energy Laboratory's (NREL's) Infrastructure Perception and Control (IPC) mobile trailer at a traffic intersection in the city of Colorado Springs, Colorado, USA that employed multiple radars and lidars to capture the data. This study provides necessary practical learning for the Cooperative Driving Automation (CDA) and traffic engineering communities for next-generation infrastructure-based cooperative perception that promises improvements in signal control for optimized traffic flow, among other applications, and documents findings for ongoing research and development efforts in other areas.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗