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NASA NTRS2017

PHM Enabled Autonomous Propellant Loading Operations

The utility of Prognostics and Health Management (PHM) software capability applied to Autonomous Operations (AO) remains an active research area within aerospace applications. The ability to gain insight into which assets and subsystems are functioning properly, along with the derivation of confident predictions concerning future ability, reliability, and availability, are important enablers for making sound mission planning decisions. When coupled with software that fully supports mission planning and execution, an integrated solution can be developed that leverages state assessment and estimation for the purposes of delivering autonomous operations. The authors have been applying this integrated, model-based approach to the autonomous loading of cryogenic spacecraft propellants at Kennedy Space Center.

Walker, Mark↗

NASA NTRS2010

Optical Structural Health Monitoring Device

This non-destructive, optical fatigue detection and monitoring system relies on a small and unobtrusive light-scattering sensor that is installed on a component at the beginning of its life in order to periodically scan the component in situ. The method involves using a laser beam to scan the surface of the monitored component. The device scans a laser spot over a metal surface to which it is attached. As the laser beam scans the surface, disruptions in the surface cause increases in scattered light intensity. As the disruptions in the surface grow, they will cause the light to scatter more. Over time, the scattering intensities over the scanned line can be compared to detect changes in the metal surface to find cracks, crack precursors, or corrosion. This periodic monitoring of the surface can be used to indicate the degree of fatigue damage on a component and allow one to predict the remaining life and/or incipient mechanical failure of the monitored component. This wireless, compact device can operate for long periods under its own battery power and could one day use harvested power. The prototype device uses the popular open-source TinyOS operating system on an off-the-shelf Mica2 sensor mote, which allows wireless command and control through dynamically reconfigurable multi-node sensor networks. The small size and long life of this device could make it possible for the nodes to be installed and left in place over the course of years, and with wireless communication, data can be extracted from the nodes by operators without physical access to the devices. While a prototype has been demonstrated at the time of this reporting, further work is required in the system s development to take this technology into the field, especially to improve its power management and ruggedness. It should be possible to reduce the size and sensitivity as well. Establishment of better prognostic methods based on these data is also needed. The increase of surface roughness with fatigue is closely connected to the microstructure of the metal, and ongoing research is seeking to connect this observed evidence of the fatigue state with microstructural theories of fatigue evolution to allow more accurate prognosis of remaining component life. Plans are also being discussed for flight testing, perhaps on NASA s SOFIA platform.

Buckner, Benjamin D.↗

DOE OSTI2020

Cyber Secure Sensor Network for Fossil Fuel Power Generation Assets Monitoring

The energy sector is undergoing digital transformation which is to say more and more power generation assets have become automated and connected to the internet. The connected sensors can tap into plants to monitor the health of assets and manage fleets remotely. These are just some of the benefits digitalization is bringing to the power industry. Within a power plant, control systems are no longer concerned with one system or one piece of equipment, but rather whole fleet of assets inter-connected with smart sensors which have the function of continuously monitoring and transmitting real-time operational data to operators. These connected systems will form a part of the industrial internet of things (IIoT). The big data scenarios provide benefits of performing system prognostics and optimization which is a key selling point of power plant digitalization.

20 FOSSIL-FUELED POWER PLANTS↗

NASA NTRSDate not supplied

In-Time Safety Assessment & Risk Prediction for Unmanned Aerial Systems

One of the critical challenges in emerging autonomous systems is timely mitigation of hazards encountered during operation which may not be known or accounted for at the time of design. Efficient execution of unmanned systems therefore demands a paradigm shift from scheduled periodic maintenance to predictive risk analysis that includes condition-based-monitoring, real-time reliability assessment and hazard mitigation. Particularly, the state-of-health parameters needs to be computed at the component level, unit level as well as the integrated system level. While in the former two levels, the physics of health propagation may be based on underlying electro-mechanical properties, system level prognostics often relies on data-driven models. Further, uncertainty from model, measurements and input sources should be accurately quantified to generate meaningful prediction results that can be fed into reliable decision making processes. Finally, the expected risk and time to failure has to be computed based on the current state-of-health of the overall system. This talk presents a conceptual design of such an in-time safety assurance approach for unmanned aerial vehicles (UAV) operating at low altitudes near and over populated areas. Typical in-flight hazard incidents include unplanned detour, proximity to obstacles, mid-flight component faults, limited battery life and poor quality of GPS measurements. Safety assessment therefore comprises trajectory generation and re-plan, battery RUL computation, distributed fault diagnostics and uncertainty management of predicted trajectory based on GPS measurement noise. The entire monitoring framework will be demonstrated on simulated as well as real UAV flight experiments conducted at the NASA Langley Research Center. This tutorial will therefore guide the audience through a step-by-step tracking of an autonomous system with focus on in-time risk prediction in the presence of unforeseen hazards and uncertain environment.

diagnostics↗

NASA NTRSDate not supplied

A Predictive GNSS Performance Monitor for Autonomous Air Vehicles in Urban Environments

The emergence and development of advanced technologies and vehicle types has created a growing demand for the introduction of new forms of flight operations. These new and increasingly complex operational paradigms such as Advanced and Urban Air Mobility (AAM/UAM) present regulatory authorities and the aviation community with several design and implementation challenges – particularly for highly autonomous vehicles. An overarching and daunting task is finding methods to integrate these emerging operations without compromising safety or disrupting traditional airspace operations. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. This paper focus on the development and testing of a prognostic service aimed at estimating the quality of Global Navigation Satellite System (GNSS) performance for an autonomous aircraft in complex environments. The intent of this function is to proactively reduce a flight operations risk of exposure to states that may induce poor or unacceptable navigation system performance by factoring in estimates of GNSS quality into pre-flight and/or in-flight route planning. Methodologies for producing quality estimates are specified and results are provided for selected simulation and flight test cases.

GNSS↗

NASA NTRS2011

An Integrated Hydrological and Water Management Study of the Entire Nile River System - Lake Victoria to Nile Delta

The Nile basin River system spans 3 million km(exp 2) distributed over ten nations. The eight upstream riparian nations, Ethiopia, Eretria, Uganda, Rwanda, Burundi, Congo, Tanzania and Kenya are the source of approximately 86% of the water inputs to the Nile, while the two downstream riparian countries Sudan and Egypt, presently rely on the river's flow for most of the their needs. Both climate and agriculture contribute to the complicated nature of Nile River management: precipitation in the headwaters regions of Ethiopia and Lake Victoria is variable on a seasonal and inter-annual basis, while demand for irrigation water in the arid downstream region is consistently high. The Nile is, perhaps, one of the most difficult trans-boundary water issue in the world, and this study would be the first initiative to combine NASA satellite observations with the hydrologic models study the overall water balance in a to comprehensive manner. The cornerstone application of NASA's Earth Science Research Results under this project are the NASA Land Data Assimilation System (LDAS) and the USDA Atmosphere-land Exchange Inverse (ALEXI) model. These two complementary research results are methodologically independent methods for using NASA observations to support water resource analysis in data poor regions. Where an LDAS uses multiple sources of satellite data to inform prognostic simulations of hydrological process, ALEXI diagnoses evapotranspiration and water stress on the basis of thermal infrared satellite imagery. Specifically, this work integrates NASA Land Data Assimilation systems into the water management decision support systems that member countries of the Nile Basin Initiative (NBI) and Regional Center for Mapping of Resources for Development (RCMRD, located in Nairobi, Kenya) use in water resource analysis, agricultural planning, and acute drought response to support sustainable development of Nile Basin water resources. The project is motivated by the recognition that accurate, frequent, and spatially distributed estimates of the water balance are necessary for effective water management. This creates a challenge for watersheds that are large, include data poor regions, and/or span multiple nations. All of these descriptors apply to the Nile River basin, yet successful management of the Nile is critical for development and political stability in the region. For this reason, improved hydrological data to support cooperative water management in the Nile basin is a priority for USAID, the US State Department, the World Bank and other international organizations. In this project, the U.S. based research team is working with partners at RCMRD, Nile Basin Initiative (NBI), and their member national-level agencies to develop satellite-based land cover maps, satellite-derived evapotranspiration estimates (using the ALEXI algorithm), and NASA's Land Data Assimilation System (LDAS) customized to match identified information needs. The cornerstone applied sciences product of the project is the development of a customized "Nile LDAS" that will produce optimal estimates of hydrological states and fluxes, as vetted against the in situ observations of NBI and RCMRD member organizations and independent satellite-derived hydrological estimates. Nile LDAS will be applied to improve the reliability of emerging Decision Support Systems in applications that include drought monitoring, reservoir management, and irrigation planning. The end-users such as RCMRD, NBI, Ethiopian and Kenya Meteorological and Famine Early Warning System Network (FEWSNet) will be the eventual benefactors of this work. There will be a capacity building process involving the above end-user organizations and transfer the models and the results for these organizations to execute for future use. The team has already initiated this study and the early results of first years' work are shown. The plan is to complete this work by late 2013.

Habib, Shahid↗

DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (4th Annual Report)

Nuclear plant sites collect and store large volumes of data collected from various equipment and systems. These datasets typically include plant process parameters, maintenance records, technical logs, online monitoring data, and equipment failure data. The collection of such data affords an opportunity to leverage data-driven machine learning and artificial intelligence technologies to provide diagnostic and prognostic capabilities within the nuclear power industry to reduce operating and maintenance costs. In this way, nuclear energy can become more economically competitive with other energy sources, and premature closures can be avoided. From a maintenance standpoint, savings can be achieved by leveraging machine learning and artificial intelligence technologies to develop data-driven algorithms to better diagnose and predict potential faults within the system. Improved model accuracy can lead to reductions in unnecessary maintenance and more efficient planning of future maintenance, thus lowering the costs associated with parts, labor, and unnecessary planned, forced, or extended outages. From an operations perspective, cost savings can be generated by shifting from route-based monitoring to wireless technologies for online monitoring, and by transitioning from onsite- to cloud-based computing and storage services. Wireless monitoring would reduce the operator manhours required for taking routine measurements, while cloud computing services would generate cost savings by reducing the amount of hardware needing to be purchased and maintained—all while scaling to both computational and storage demands. This report summarizes this project’s effort to shift from costly, labor-intensive preventative maintenance to cheaper predictive maintenance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Methodology for Good Machine Learning with Multi‐Omics Data

In 2020, Novartis Pharmaceuticals Corporation and the U.S. Food and Drug Administration (FDA) started a 4‐year scientific collaboration to approach complex new data modalities and advanced analytics. The scientific question was to find novel radio‐genomics‐based prognostic and predictive factors for HR+/HER− metastatic breast cancer under a Research Collaboration Agreement. This collaboration has been providing valuable insights to help successfully implement future scientific projects, particularly using artificial intelligence and machine learning. This tutorial aims to provide tangible guidelines for a multi‐omics project that includes multidisciplinary expert teams, spanning across different institutions. We cover key ideas, such as “maintaining effective communication” and “following good data science practices,” followed by the four steps of exploratory projects, namely (1) plan, (2) design, (3) develop, and (4) disseminate. We break each step into smaller concepts with strategies for implementation and provide illustrations from our collaboration to further give the readers actionable guidance.

Pharmacology & Pharmacy↗

NASA NTRS2005

V and V of ISHM Software for Space Exploration

NASA has established a far-reaching and long-term program for robotic and manned exploration of the solar system, beginning with missions to the moon and Mars. The Crew Transportation System (CTS), a key system for space exploration, imposes four requirements' that ISHM addresses. These requirements have a wide range of implications for V&V and certification of ISHM. There is a range of time-criticality for ISHM actions, from prognostication, which is often (but not always) non-time-critical, to time-critical state estimation and system management under off-nominal emergency conditions. These are externally imposed requirements on ISHM that are subject to V&V. - In addition, a range of techniques are needed to implement an ISHM. The approaches to ISHM are described elsewhere. These approaches range from well-understood algorithms for low-level data analysis, validation and reporting, to AI techniques for state estimation and planning. The range of techniques, and specifically the use of AI techniques such as reasoning under uncertainty and mission planning (and re-planning), implies that several V&V approaches may be required. Depending on the ISHM architecture, traditional testing approaches may be adequate for some ISHM functionality. The AI-based approaches to reasoning under uncertainty, model-based reasoning, and planning share characteristics typical of other complex software systems, but they also have characteristics that set them apart and challenge standard V&V techniques. The range of possible solutions to the overall ISHM problem impose internal challenges to V&V. The V&V challenges increase when hard real-time constraints are imposed for time-critical functionality. For example, there is an external requirement that impending catastrophic failure of the Launch Vehicle (LV) at launch time be detected and life-saving action be taken within two seconds. In this paper we outline the challenges for ISHM V&V, existing approaches and analogs in other software application areas, and possible new approaches to the V&V challenges for space exploration ISHM.

Markosian, Lawrence↗

NASA NTRS2017

Assessment of the State-of-the-Art of System-Wide Safety and Assurance Technologies

Since its initiation, the System-wide Safety Assurance Technologies (SSAT) Project has been focused on developing multidisciplinary tools and techniques that are verified and validated to ensure prevention of loss of property and life in NextGen and enable proactive risk management through predictive methods. To this end, four technical challenges have been listed to help realize the goals of SSAT, namely (i) assurance of flight critical systems, (ii) discovery of precursors to safety incidents, (iii) assuring safe human-systems integration, and (iv) prognostic algorithm design for safety assurance. The objective of this report is to provide an extensive survey of SSAT-related research accomplishments by researchers within and outside NASA to get an understanding of what the state-of-the-art is for technologies enabling each of the four technical challenges. We hope that this report will serve as a good resource for anyone interested in gaining an understanding of the SSAT technical challenges, and also be useful in the future for project planning and resource allocation for related research.

Roychoudhury, Indranil↗

DOE OSTI2021

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants: 2nd Annual Report

For economic reasons, the nuclear industry is witnessing premature closure of nuclear power plants, despite excellent safety records. Operations and Maintenance (O&M) activities are some of the largest costs in operating legacy light-water plants. By reducing O&M costs, nuclear energy can become more economically competitive with other energy sources. This can be achieved by leveraging machine-learning and artificial intelligence technologies to develop data-driven algorithms to better diagnose potential faults within the system. Improved accuracy of the models can lead to a reduction in unnecessary maintenance, thus reducing costs associated with parts, labor, and unnecessary planned, forced, or extended outages. To address these challenges, the goal of this project is to perform research and development in the area of digital monitoring, i.e., the application of advanced sensor technologies (particularly wireless sensor technologies) and data science based analytic capabilities, to advance online monitoring and predictive maintenance in nuclear plants and improve plant performance (efficiency gain and economic competitiveness). This report summarizes the fiscal year 2020 research progress encompassing (1) different wireless vibration sensor and data indicators used to assess the health of a plant asset; (2) development of diagnostic models for fault detection; and (3) development of prognostic models for estimating the health of the system up to 7 days ahead.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2017

Prospective Architectures for Onboard vs Cloud-Based Decision Making for Unmanned Aerial Systems

This paper investigates propsective architectures for decision-making in unmanned aerial systems. When these unmanned vehicles operate in urban environments, there are several sources of uncertainty that affect their behavior, and decision-making algorithms need to be robust to account for these different sources of uncertainty. It is important to account for several risk-factors that affect the flight of these unmanned systems, and facilitate decision-making by taking into consideration these various risk-factors. In addition, there are several technical challenges related to autonomous flight of unmanned aerial systems; these challenges include sensing, obstacle detection, path planning and navigation, trajectory generation and selection, etc. Many of these activities require significant computational power and in many situations, all of these activities need to be performed in real-time. In order to efficiently integrate these activities, it is important to develop a systematic architecture that can facilitate real-time decision-making. Four prospective architectures are discussed in this paper; on one end of the spectrum, the first architecture considers all activities/computations being performed onboard the vehicle whereas on the other end of the spectrum, the fourth and final architecture considers all activities/computations being performed in the cloud, using a new service known as Prognostics as a Service that is being developed at NASA Ames Research Center. The four different architectures are compared, their advantages and disadvantages are explained and conclusions are presented.

Software engineering↗

NASA NTRS2004

Validation of Mars-GRAM and Planned New Features

For altitudes below 80 km, Mars Global Reference Atmospheric Model (Mars-GRAM 2001) is based on output climatology from NASA Ames Mars General Circulation Model (MGCM). At COSPAR 2002, results were presented of validation tests of Mars-GRAM versus data from Mars Global Surveyor Thermal Emission Spectrometer (TES) and Radio Science (RS) experiment. Further validation tests are presented comparing Mars- GRAM densities with those from the European Mars Climate Database (MCD), and comparing densities from both Mars-GRAM and MCD against TES observations. Throughout most of the height and latitude range of TES data (040 km and 70s to 70N), good agreement is found between atmospheric densities from Mars-GRAM and MCD. However, at the season and latitude zone for Mars Phoenix arrival and landing (Ls = 65 to 80 degrees and latitude 65 to 75N), Mars-GRAM densities are about 30 to 45 percent higher than MCD densities near 40 km altitude. Further evaluation is warranted concerning potential impact of these model differences on planning for Phoenix entry and descent. Three planned features for Mars-GRAM update are also discussed: (1) new MGCM and Thermospheric General Circulation Model data sets to be used as a revised basis for Mars-GRAM mean atmosphere, (2) a new feature to represent planetary-scale traveling waves for upper altitude density variations (such as found during Mars Odyssey aerobraking), and (3) a new model for effects of high resolution topographic slope on winds near the surface (0 to 4.5 km above MOLA topography level). Mars-GRAM slope winds will be computed from a diagnostic (algebraic) relationship based on Ye, Segal, and Pielke (1990). This approach differs from mesoscale models (such as MRAMS and Mars MM5), which use prognostic, full-physics solutions of the time- and space-dependent differential equations of motion. As such, slope winds in Mars-GRAM will be consistent with its "engineering-level" approach, and will be extremely fast and easy to evaluate, compared with mesoscale model solutions. Mars-GRAM slope winds are not being suggested as a replacement for sophisticated, full-physics Mars mesoscale models, but may have value, particularly for preliminary screening of large numbers of candidate landing sites for future Mars missions, such as Phoenix and Mars Science Laboratory. Test output is presented from Mars-GRAM slope winds in the area of Gusev Crater and Valles Marineris.

Justus, C. G.↗

NASA NTRS2010

Monitoring System for Storm Readiness and Recovery of Test Facilities: Integrated System Health Management (ISHM) Approach

Severe weather events are likely occurrences on the Mississippi Gulf Coast. It is important to rapidly diagnose and mitigate the effects of storms on Stennis Space Center's rocket engine test complex to avoid delays to critical test article programs, reduce costs, and maintain safety. An Integrated Systems Health Management (ISHM) approach and technologies are employed to integrate environmental (weather) monitoring, structural modeling, and the suite of available facility instrumentation to provide information for readiness before storms, rapid initial damage assessment to guide mitigation planning, and then support on-going assurance as repairs are effected and finally support recertification. The system is denominated Katrina Storm Monitoring System (KStorMS). Integrated Systems Health Management (ISHM) describes a comprehensive set of capabilities that provide insight into the behavior the health of a system. Knowing the status of a system allows decision makers to effectively plan and execute their mission. For example, early insight into component degradation and impending failures provides more time to develop work around strategies and more effectively plan for maintenance. Failures of system elements generally occur over time. Information extracted from sensor data, combined with system-wide knowledge bases and methods for information extraction and fusion, inference, and decision making, can be used to detect incipient failures. If failures do occur, it is critical to detect and isolate them, and suggest an appropriate course of action. ISHM enables determining the condition (health) of every element in a complex system-of-systems or SoS (detect anomalies, diagnose causes, predict future anomalies), and provide data, information, and knowledge (DIaK) to control systems for safe and effective operation. ISHM capability is achieved by using a wide range of technologies that enable anomaly detection, diagnostics, prognostics, and advise for control: (1) anomaly detection algorithms and strategies, (2) fusion of DIaK for anomaly detection (model-based, numerical, statistical, empirical, expert-based, qualitative, etc.), (3) diagnostics/prognostics strategies and methods, (4) user interface, (5) advanced control strategies, (6) integration architectures/frameworks, (7) embedding of intelligence. Many of these technologies are mature, and they are being used in the KStorMS. The paper will describe the design, implementation, and operation of the KStorMS; and discuss further evolution to support other needs such as condition-based maintenance (CBM).

Figueroa, Fernando↗

DOE OSTI2023

Predictive Analytics for Hydropower Fleet Intelligence

A primary challenge in hydropower industry is the ability to maintain cost-competitiveness, reliability, and security of hydropower assets through evolving power system contexts and aging of the fleet. Maintaining cost-effective and reliable operations under these conditions is expected to require new modernization and maintenance paradigms for changing contexts. Changes in existing practices for O&M will require an understanding of the current state and health of hydropower assets, and the impact of changing paradigms on asset health and reliability. The Hydropower Fleet Intelligence project is developing and evaluating standardized methodologies and analysis tools for data-driven asset reliability and management technologies for hydropower, leading to eventual predictive maintenance planning, repair/replacement decision making, and asset-reliability and cost-optimized operations. A key question is the feasibility of using existing data sets at hydropower facilities to perform assessments of asset reliability. This document uses data from hydropower facilities to assess the potential for using available analytics methods for asset reliability estimates. In addition to reliability assessments, the feasibility of using existing analytics techniques for several other potential applications is discussed. Finally, a case study that a data-driven model is trained to learn nominal operations via vibration data from an asset of a certain plant, and then utilized to identify anomalies on a similar asset from a different plant, highlighting the generic use of proposed Prognostics and Health Management (PHM) approaches.

Yucesan, Yigit↗

NASA NTRS1989

Assessment of surface turbulent fluxes using geostationary satellite surface skin temperatures and a mixed layer planetary boundary layer scheme

A method is presented for evaluating the fluxes of sensible and latent heating at the land surface, using satellite-measured surface temperature changes in a composite surface layer-mixed layer representation of the planetary boundary layer. The basic prognostic model is tested by comparison with synoptic station information at sites where surface evaporation climatology is well known. The remote sensing version of the model, using satellite-measured surface temperature changes, is then used to quantify the sharp spatial gradient in surface heating/evaporation across the central United States. An error analysis indicates that perhaps five levels of evaporation are recognizable by these methods and that the chief cause of error is the interaction of errors in the measurement of surface temperature change with errors in the assigment of surface roughness character. Finally, two new potential methods for remote sensing of the land-surface energy balance are suggested which will relay on space-borne instrumentation planned for the 1990s.

Diak, George R.↗

NASA NTRS2011

Vehicle Integrated Prognostic Reasoner (VIPR) 2010 Annual Final Report

Honeywell's Central Maintenance Computer Function (CMCF) and Aircraft Condition Monitoring Function (ACMF) represent the state-of-the art in integrated vehicle health management (IVHM). Underlying these technologies is a fault propagation modeling system that provides nose-to-tail coverage and root cause diagnostics. The Vehicle Integrated Prognostic Reasoner (VIPR) extends this technology to interpret evidence generated by advanced diagnostic and prognostic monitors provided by component suppliers to detect, isolate, and predict adverse events that affect flight safety. This report describes year one work that included defining the architecture and communication protocols and establishing the user requirements for such a system. Based on these and a set of ConOps scenarios, we designed and implemented a demonstration of communication pathways and associated three-tiered health management architecture. A series of scripted scenarios showed how VIPR would detect adverse events before they escalate as safety incidents through a combination of advanced reasoning and additional aircraft data collected from an aircraft condition monitoring system. Demonstrating VIPR capability for cases recorded in the ASIAS database and cross linking them with historical aircraft data is planned for year two.

Hadden, George D.↗

NASA NTRS2004

GPM Mission, its Scientific Agenda, and its Ground Validation Program

The GPM mission is currently planned for start in the late 2010 time frame. From the perspective of NASA s Earth Science Enterprise (ESE) and within the framework of ESE's global water and energy cycle (GWEC) research program, its main scientific goal is to help answer pressing scientific problems concerning how global and regional water cycle processes and precipitation fluctuations and trends influence the variability intrinsic to climate, weather, and hydrology. These problems cut across a hierarchy of space-time scales and include improving understanding of climate-water cycle interactions, developing better techniques for incorporating satellite precipitation measurements into weather and climate predictions, and demonstrating that more accurate, more complete, and better sampled observations of precipitation and other water budget variables used as inputs can improve the ability of prognostic hydrometeorological models in the prediction of hazardous flood-producing storms, seasonal flood/draught conditions, and fresh water resource stores. The GPM mission will expand the scope of precipitation measurement through the use of a constellation of some 9 satellites, one of which will be an advanced TRMM-like core satellite carrying a dual-frequency Ku-Ka band precipitation radar (DPR) and an advanced, multifrequency passive microwave radiometer with vertical-horizontal polarization discrimination (GMI). The other constellation members will include a combination of new dedicated satellites and co-existing operational/research satellites carrying similar (but not identical) passive microwave radiometers. The goal of the constellation is to achieve 3-hour sampling at any spot on the globe -- continuously. The constellation s orbit architecture will consist of a mix of sun-synchronous and non-sun-synchronous satellites with the core satellite providing measurements of calibration-quality rainrates, plus cloud-precipitation microphysical processes, to be used in conjunction with more basic rain retrievals from the other constellation satellites to ensure bias-free constellation coverage.

Smith Eric A.↗