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At least 73 records · Page 4

The AIRNOISE-UAM Tool and its Application to ATM-X

The noise disruption caused by Urban Air Mobility (UAM) vehicles will be a major factor in public acceptance of UAM. The noise exposure caused by fixed-wing aircraft and helicopters is predicted using Aviation Environment and Design Tool (AEDT) software at the airport. The base noise is calculated by interpolation or extrapolation of Noise-Power-Distance (NPD) database in the AEDT. In this presentation, a new noise prediction tool "AIRNOISE-UAM" is developed with a new NPD database for eVTOL vehciles in collaboration with a RVLT team. The noise exposure results have been verified with those from AEDT. Lastly, we demonstrate two use cases of AIRNOISE-UAM to the ATM-X project. The first use case is to develop noise exposure maps for FAA UAM compliance planning. The sceond use case is to develop noise-aware flight route planning algorithm.

Urban Air Mobility↗

Managing Multi-Instrument Data Streams in Secure Environments

The capture and curation of all primary instrument data is a potentially valuable source of added insight into experiments or diagnostics in laboratory experiments. The data can, when properly curated, enable analysis beyond the current practice that uses just a subset of the as-measured data. Complete curated data can also be input for machine learning and other data exploration tools. Conveniently storing and accessing instrument data requires that the instruments are connected to databases and users through a networking infrastructure. This infrastructure needs to accommodate a wide array of instruments which can range from single laboratory mounted probes for environment monitoring to computers managing multiple instruments. These resources may also include mobile devices on which researchers record instrument and experiment state related notes. These varied data sources bring with them the challenges of different communications capabilities and protocols as well as the primary data typically being produced in proprietary formats. These challenges are further compounded when the instruments need to operate in secure environments such as required in national laboratories. We will discuss the SmartLab, an ongoing effort to set up a system for instrument and simulation data curation at NASA Langley Research Center. We will outline the challenges faced in managing the data sources required for ongoing research activities and the solutions that are being considered and implemented to address those challenges.

instrument data management↗

Requirement Discovery Using Embedded Knowledge Graph with ChatGPT

The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) concept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze requirements within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.

systems engineering↗

Requirement Discovery Using Embedded Knowledge Graph With ChatGPT

The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) con-cept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze require-ments within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.

systems engineering↗

Requirement Discovery Using Embedded Knowledge Graph With ChatGPT - Poster

The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) con-cept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze require-ments within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.

systems engineering↗

NASA Tech Briefs, March 2014

Topics include: Data Fusion for Global Estimation of Forest Characteristics From Sparse Lidar Data; Debris and Ice Mapping Analysis Tool - Database; Data Acquisition and Processing Software - DAPS; Metal-Assisted Fabrication of Biodegradable Porous Silicon Nanostructures; Post-Growth, In Situ Adhesion of Carbon Nanotubes to a Substrate for Robust CNT Cathodes; Integrated PEMFC Flow Field Design for Gravity-Independent Passive Water Removal; Thermal Mechanical Preparation of Glass Spheres; Mechanistic-Based Multiaxial-Stochastic-Strength Model for Transversely-Isotropic Brittle Materials; Methods for Mitigating Space Radiation Effects, Fault Detection and Correction, and Processing Sensor Data; Compact Ka-Band Antenna Feed with Double Circularly Polarized Capability; Dual-Leadframe Transient Liquid Phase Bonded Power Semiconductor Module Assembly and Bonding Process; Quad First Stage Processor: A Four-Channel Digitizer and Digital Beam-Forming Processor; Protective Sleeve for a Pyrotechnic Reefing Line Cutter; Metabolic Heat Regenerated Temperature Swing Adsorption; CubeSat Deployable Log Periodic Dipole Array; Re-entry Vehicle Shape for Enhanced Performance; NanoRacks-Scale MEMS Gas Chromatograph System; Variable Camber Aerodynamic Control Surfaces and Active Wing Shaping Control; Spacecraft Line-of-Sight Stabilization Using LWIR Earth Signature; Technique for Finding Retro-Reflectors in Flash LIDAR Imagery; Novel Hemispherical Dynamic Camera for EVAs; 360 deg Visual Detection and Object Tracking on an Autonomous Surface Vehicle; Simulation of Charge Carrier Mobility in Conducting Polymers; Observational Data Formatter Using CMOR for CMIP5; Propellant Loading Physics Model for Fault Detection Isolation and Recovery; Probabilistic Guidance for Swarms of Autonomous Agents; Reducing Drift in Stereo Visual Odometry; Future Air-Traffic Management Concepts Evaluation Tool; Examination and A Priori Analysis of a Direct Numerical Simulation Database for High-Pressure Turbulent Flows; and Resource-Constrained Application of Support Vector Machines to Imagery.

Source record↗

Expedition Memory: Towards Agent-based Web Services for Creating and Using Mars Exploration Data.

Explorers ranging over kilometers of rugged, sometimes "feature-less" terrain for over a year could be overwhelmed by tracking and sharing what they have done and learned. An automated system based on the existing Mobile Agents design [ I ] and Mars Exploration Rover experience [2], could serve as an "expedition memory" that would be indexed by voice as wel1 as a web interface, linking people, places, activities, records (voice notes, photographs, samples). and a descriptive scientific ontology. This database would be accessible during EVAs by astronauts, annotated by the remote science team, linked to EVA plans, and allow cross indexing between sites and expeditions. We consider the basic problem, our philosophical approach, technical methods, and uses of the expedition memory for facilitating long-term collaboration between Mars crews and Earth support teams. We emphasize that a "memory" does not mean a database per se, but an interactive service that combines different resources, and ultimately could be like a helpful librarian.

Clancey, William J.↗

The Application of Remotely Sensed Data and Models to Benefit Conservation and Restoration Along the Northern Gulf of Mexico Coast

New data, tools, and capabilities for decision making are significant needs in the northern Gulf of Mexico and other coastal areas. The goal of this project is to support NASA s Earth Science Mission Directorate and its Applied Science Program and the Gulf of Mexico Alliance by producing and providing NASA data and products that will benefit decision making by coastal resource managers and other end users in the Gulf region. Data and research products are being developed to assist coastal resource managers adapt and plan for changing conditions by evaluating how climate changes and urban expansion will impact land cover/land use (LCLU), hydrodynamics, water properties, and shallow water habitats; to identify priority areas for conservation and restoration; and to distribute datasets to end-users and facilitating user interaction with models. The proposed host sites for data products are NOAA s National Coastal Data Development Center Regional Ecosystem Data Management, and Mississippi-Alabama Habitat Database. Tools will be available on the Gulf of Mexico Regional Collaborative website with links to data portals to enable end users to employ models and datasets to develop and evaluate LCLU and climate scenarios of particular interest. These data will benefit the Mobile Bay National Estuary Program in ongoing efforts to protect and restore the Fish River watershed and around Weeks Bay National Estuarine Research Reserve. The usefulness of data products and tools will be demonstrated at an end-user workshop.

Quattrochi, Dale↗

Developing Urban Air Mobility Vehicle Models to Support Air Traffic Management Concept Development

To support Urban Air Mobility (UAM) research efforts at NASA, the Airspace Target Generator (ATG) software used in the FutureFlight Central (FFC) air traffic control tower simulator is undergoing updates to support physics-based UAM vehicle models. A process was developed to integrate UAM vertical takeoff and landing (VTOL) aircraft into the fixed-wing ATG modeling environment without significant change to the underlying equations of motion and vehicle model database. The VTOL aircraft models were converted from a six degrees-of-freedom (6-DOF) representation into a four degrees-of-freedom (4-DOF) representation for integration within ATG. Three vehicle designs from the NASA Revolutionary Vertical-Lift Technologies (RVLT) project were selected: a lift-plus-cruise (LPC) aircraft model and quadrotor, electric-powered (QEP) 1-seater and 6-seater models. With the LPC model comprised of a nonlinear force and moment build-up, and the QEP models comprised of linearized stability derivatives, two separate processes were developed to convert the lift, drag, and propulsion characteristics of each model into the ATG model database. Key aircraft performance characteristics including climb, cruise, and descent performance were preserved during the conversion process. Because ATG simulates fixed-wing aircraft through ground taxi and takeoff to approach and landing, acceleration command algorithms were developed to model the vertical takeoff and vertical landing phase of UAM operations. A strategy was then developed to transition the aircraft model to- and from- the new control mode.

urban air mobility↗

NASA Tech Briefs, January 2013

Topics include: Single-Photon-Sensitive HgCdTe Avalanche Photodiode Detector; Surface-Enhanced Raman Scattering Using Silica Whispering-Gallery Mode Resonators; 3D Hail Size Distribution Interpolation/Extrapolation Algorithm; Color-Changing Sensors for Detecting the Presence of Hypergolic Fuels; Artificial Intelligence Software for Assessing Postural Stability; Transformers: Shape-Changing Space Systems Built with Robotic Textiles; Fibrillar Adhesive for Climbing Robots; Using Pre-Melted Phase Change Material to Keep Payloads in Space Warm for Hours without Power; Development of a Centrifugal Technique for the Microbial Bioburden Analysis of Freon (CFC-11); Microwave Sinterator Freeform Additive Construction System (MS-FACS); DSP/FPGA Design for a High-Speed Programmable S-Band Space Transceiver; On-Chip Power-Combining for High-Power Schottky Diode-Based Frequency Multipliers; FPGA Vision Data Architecture; Memory Circuit Fault Simulator; Ultra-Compact Transputer-Based Controller for High-Level, Multi-Axis Coordination; Regolith Advanced Surface Systems Operations Robot Excavator; Magnetically Actuated Seal; Hybrid Electrostatic/Flextensional Mirror for Lightweight, Large-Aperture, and Cryogenic Space Telescopes; System for Contributing and Discovering Derived Mission and Science Data; Remote Viewer for Maritime Robotics Software; Stackfile Database; Reachability Maps for In Situ Operations; JPL Space Telecommunications Radio System Operating Environment; RFI-SIM: RFI Simulation Package; ION Configuration Editor; Dtest Testing Software; IMPaCT - Integration of Missions, Programs, and Core Technologies; Integrated Systems Health Management (ISHM) Toolkit; Wind-Driven Wireless Networked System of Mobile Sensors for Mars Exploration; In Situ Solid Particle Generator; Analysis of the Effects of Streamwise Lift Distribution on Sonic Boom Signature; Rad-Tolerant, Thermally Stable, High-Speed Fiber-Optic Network for Harsh Environments; Towed Subsurface Optical Communications Buoy; High-Collection-Efficiency Fluorescence Detection Cell; Ultra-Compact, Superconducting Spectrometer-on-a-Chip at Submillimeter Wavelengths; UV Resonant Raman Spectrometer with Multi-Line Laser Excitation; Medicine Delivery Device with Integrated Sterilization and Detection; Ionospheric Simulation System for Satellite Observations and Global Assimilative Model Experiments - ISOGAME; Airborne Tomographic Swath Ice Sounding Processing System; flexplan: Mission Planning System for the Lunar Reconnaissance Orbiter; Estimating Torque Imparted on Spacecraft Using Telemetry; PowderSim: Lagrangian Discrete and Mesh-Free Continuum Simulation Code for Cohesive Soils; Multiple-Frame Detection of Subpixel Targets in Thermal Image Sequences; Metric Learning to Enhance Hyperspectral Image Segmentation; Basic Operational Robotics Instructional System; Sheet Membrane Spacesuit Water Membrane Evaporator; Advanced Materials and Manufacturing for Low-Cost, High-Performance Liquid Rocket Combustion Chambers; Motor Qualification for Long-Duration Mars Missions.

Source record↗

Automating CapCom Using Mobile Agents and Robotic Assistants

We have developed and tested an advanced EVA communications and computing system to increase astronaut self-reliance and safety, reducing dependence on continuous monitoring and advising from mission control on Earth. This system, called Mobile Agents (MA), is voice controlled and provides information verbally to the astronauts through programs called personal agents. The system partly automates the role of CapCom in Apollo-including monitoring and managing EVA navigation, scheduling, equipment deployment, telemetry, health tracking, and scientific data collection. EVA data are stored automatically in a shared database in the habitat/vehicle and mirrored to a site accessible by a remote science team. The program has been developed iteratively in the context of use, including six years of ethnographic observation of field geology. Our approach is to develop automation that supports the human work practices, allowing people to do what they do well, and to work in ways they are most familiar. Field experiments in Utah have enabled empirically discovering requirements and testing alternative technologies and protocols. This paper reports on the 2004 system configuration, experiments, and results, in which an EVA robotic assistant (ERA) followed geologists approximately 150 m through a winding, narrow canyon. On voice command, the ERA took photographs and panoramas and was directed to move and wait in various locations to serve as a relay on the wireless network. The MA system is applicable to many space work situations that involve creating and navigating from maps (including configuring equipment for local topology), interacting with piloted and unpiloted rovers, adapting to environmental conditions, and remote team collaboration involving people and robots.

Clancey, William J.↗

Liftoff and Transition Aerodynamics of the Ares I (A106) Launch Vehicle

An investigation has been conducted in the NASA Langley Research Center 14- by 22- Foot Subsonic Wind Tunnel to obtain the liftoff and transition aerodynamics of the Ares I (A106) Crew Launch Vehicle. Data were obtained in free-air at angles of attack from 10 to 90 at various roll angles and at roll angles of 0 to 360 at various angles of attack. In addition, tower effects were assessed by testing with and without a mobile launcher/tower at all wind azimuth angles and at various model heights to simulate the rise of the vehicle as it clears the tower on launch. The free-air data will be used for low speed high angle of attack flight simulation and as a bridge to the low angle of attack ascent database (0.5 < Mach < 5.0) being developed with data from the Langley Unitary Plan Wind Tunnel and Boeing Polysonic Wind Tunnel. The Ares I Database Development Team will add incremental tower effects data to the free-air data to develop the database for tower clearance.

Capone, Francis J.↗

Qualitative Data Coding of User Experience with an Urban Air Mobility Fleet Manager Interface

The NASA Aeronautics Research Mission Directorate created the High Density Vertiplex (HDV) project to integrate and evaluate a prototype Urban Air Mobility (UAM) ecosystem. Part of the HDV testing environment included a prototype operator user interface called the Fleet Manager Interface (FMI). In 2023, HDV conducted flight testing with the FMI during which a user experience (UX) study was performed to assess the quality of UX and elicit design recommendations. As a result, a large database of open-ended, qualitative responses was generated and then coded using a new qualitative data coding technique called Directive String Coding, which used a blended coding approach to generate actionable heuristics that stakeholders (e.g., researchers, UI designers, software developers) could use to answer specific research questions and make future design decisions. The key themes that arose from the coded responses showed that the UX was pleasant, system notifications should be more salient, and information across multiple screens should be integrated into a central display. As the Fleet Manger operational role is still being defined, it is vital to increase our understanding of the tools and capabilities needed for such a role. The results from HDV work will eventually feed into standards for vertiport operations that will enable safe and scalable UAM operations in the United States.

high density vertiplex↗

Qualitative Data Coding of User Experience with an Urban Air Mobility Fleet Manager Interface

The NASA Aeronautics Research Mission Directorate created the High Density Vertiplex (HDV) project to integrate and evaluate a prototype Urban Air Mobility (UAM) ecosystem. Part of the HDV testing environment included a prototype operator user interface called the Fleet Manager Interface (FMI). In 2023, HDV conducted flight testing with the FMI during which a user experience (UX) study was performed to assess the quality of UX and elicit design recommendations. As a result, a large database of open-ended, qualitative responses was generated and then coded using a new qualitative data coding technique called Directive String Coding, which used a blended coding approach to generate actionable heuristics that stakeholders (e.g., researchers, UI designers, software developers) could use to answer specific research questions and make future design decisions. The key themes that arose from the coded responses showed that the UX was pleasant, system notifications should be more salient, and information across multiple screens should be integrated into a central display. As the Fleet Manger operational role is still being defined, it is vital to increase our understanding of the tools and capabilities needed for such a role. The results from HDV work will eventually feed into standards for vertiport operations that will enable safe and scalable UAM operations in the United States.

high density veriplex↗

From the Knowledge-based Digital Platform (KbDP) Concept for Advanced Air Mobility Research to a Preliminary Prototype

Advanced Air Mobility (AAM) encompasses a range of innovative operational and technological changes to aviation (electric aircraft, increasingly automated aircraft, increasingly automated airspace operations, etc.) that are transforming aviation’s role in everyday movement of people and goods. There are multiple associated concepts and use cases for AAM, all interrelated, including small Unmanned Aircraft System (UAS) Traffic Management (UTM), Upper-Class E Traffic Management (ETM), Extensible Traffic Management (xTM), Regional Air Mobility (RAM), and Urban Air Mobility (UAM). These AAM operations must integrate with traditional Air Traffic Management (ATM) operations, as well as non-aviation modes of transportation and logistics. National Aeronautics and Space Administration (NASA) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers together across organizational boundaries. It does so through the management of an information database defined by mathematical, data science, and system engineering principles. Machine Learning (ML) algorithms play a key role in this concept by extracting meaningful knowledge from the information database, which the human user leverages to greatly improve the efficiency and effectiveness of their research. Expected benefits of this concept include improved technology transfers from research to production, improved research portfolio investments, and research outcomes that are more integrated with all aspects of the multi-modal transportation problem. The preliminary KbDP prototype has been realized using UAM as a pathfinder use case and developed by a team of system engineer, software developer, data scientist, and interns.

Systems Engineering↗

High Performance EVA Glove Collaboration: Glove Injury Data Mining Effort

Human hands play a significant role during Extravehicular Activity (EVA) missions and Neutral Buoyancy Lab (NBL) training events, as they are needed for translating and performing tasks in the weightless environment. Because of this high frequency usage, hand and arm related injuries are known to occur during EVA and EVA training in the NBL. The primary objectives of this investigation were to: 1) document all known EVA glove related injuries and circumstances of these incidents, 2) determine likely risk factors, and 3) recommend interventions where possible that could be implemented in the current and future glove designs. METHODS: The investigation focused on the discomforts and injuries of U.S. crewmembers who had worn the pressurized Extravehicular Mobility Unit (EMU) spacesuit and experienced 4000 Series or Phase VI glove related incidents during 1981 to 2010 for either EVA ground training or in-orbit flight. We conducted an observational retrospective case-control investigation using 1) a literature review of known injuries, 2) data mining of crew injury, glove sizing, and hand anthropometry databases, 3) descriptive statistical analyses, and finally 4) statistical risk correlation and predictor analyses to better understand injury prevalence and potential causation. Specific predictor statistical analyses included use of principal component analyses (PCA), multiple logistic regression, and survival analyses (Cox proportional hazards regression). Results of these analyses were computed risk variables in the forms of odds ratios (likelihood of an injury occurring given the magnitude of a risk variable) and hazard ratios (likelihood of time to injury occurrence). Due to the exploratory nature of this investigation, we selected predictor variables significant at p≤0.15. RESULTS: Through 2010, there have been a total of 330 NASA crewmembers, from which 96 crewmembers performed 322 EVAs during 1981-2010, resulting in 50 crewmembers being injured inflight and 44 injured during 11,704 ground EVA training events. Of the 196 glove related injury incidents, 106 related to EVA and 90 to EVA training. Over these 196 incidents, 277 total injuries (126 flight; 151 training) were reported and were then grouped into 23 types of injuries. Of EVA flight injuries, 65% were commonly reported to the hand (in general), metacarpophalangeal (MCP) joint, and finger (not including thumb) with fatigue, abrasion, and paresthesia being the most common injury types (44% of total flight injuries). Training injuries totaled to more than 70% being distributed to the fingernail, MCP joint, and finger crotch with 88% of the specific injuries listed as pain, erythema, and onycholysis. Of these training injuries, when reporting pain or erythema, the most common location was the index finger, but when reporting onycholysis, it was the middle finger. Predictor variables specific to increased risk of onycholysis included: female sex (OR=2.622), older age (OR=1.065), increased duration in hours of the flight or training event (OR=1.570), middle finger length differences in inches between the finger and the EVA glove (OR=7.709), and use of the Phase VI glove (OR=8.535). Differentiation between training and flight and injury reporting during 2002-2004 were significant control variables. For likelihood of time to first onycholysis injury, there was a 24% reduction in rate of reporting for each year increase in age. Also, more experienced crewmembers, based on number of EVA flight or training events completed, were less likely to report an onycholysis injury (3% less for every event). Longer duration events also found reporting rates to occur 2.37 times faster for every hour of length. Crewmembers with larger hand size reported onycholysis 23% faster than those with smaller hand size. Finally, for every 1/10th of an inch increase in difference between the middle finger length and the glove, the rate of reporting increased by 60%. DISCUSSION: One key finding was that the Series 4000 glove had a lower injury risk than the Phase VI, which provides a platform for further evaluation. General interventions that reduce hand overexertion and repetitive use exposure through tool development, procedural changes and shorter exposures may be one mitigation path, but due to the way the training event times were reported, we cannot provide a guideline for a specific event duration change. When the finger length was different from the glove length, the risk of injury increased indicating that the use of larger finger take-ups could be contributing to injury and therefore may not be recommended. Prior to this investigation, there was one previous investigation indicating hand anthropometry may be related to onycholysis. We found different hand anthropometry variables indicated by this investigation as compared to the prior, specifically differences in middle finger length compared to glove finger length, which point more towards a sizing issue than a specific anthropometry issue. Additionally, although this investigation has identified sizing as an issue, the force and environmental-related variables of the EVA glove that could also cause injury were not accounted for.

Reid, C. R.↗

A Systematic Review of Local to Regional Yield Forecasting Approaches and Frequently Used Data Resources

Forecasting crop yields, or providing an expectation of ex-ante harvest amounts, is highly relevant to the whole agricultural production chain. Farmers can adapt their management, traders or insurers their pricing schemes, suppliers their stocks, logistic companies their routes, national authorities their food balance sheets to guide import or export and, finally, international aid organizations can mobilize reliefs. Evidence has grown in the literature that such forecasts with a meaningful lead time are possible on various geographic scales and for a broad range of crops. Here, we present a systematic review of the methods applied in end-of-season yield forecasting and three frequently used data sources: weather data, satellite data and crop masks. Our literature database comprises 362 studies (2004–2019) which were evaluated regarding methods, crops, regions, data sources, lead time and performance. Moreover, we present 24 sources of real-time and predictive weather data, 21 sources of remote sensing data and 16 crop masks. Yield forecasting in our literature sample has been performed for 44 crops in 71 countries, also including many non-staple crops, but with an apparent bias in regions and crops. Forecasting performance depends on various factors, including crop, region, method, lead time to harvest and input diversity. Our systematic review supports a broader application of locally successful approaches at larger scales by providing a comprehensive, accessible compendium of necessary information for yield forecasting. We discuss improvement potentials with respect to methodological approaches and available data sources. We additionally suggest standardization procedures for future forecasting studies and encourage studying additional crops and geographic regions. Implications of forecasts for different target groups on different scales and the adaptation towards climate change are also discussed.

Seasonal crop yield forecasting↗

Contrail-Cirrus Studies at FARS

The three primary goals have been to: (1) increase our contrail remote sensing database collected from the Facility for Atmospheric Remote Sensing (FARS) by expanding our Project FIRE Extended Time Observations (ETO) program to include contrail persistence studies: (2) continue our retrospective analyses of the statistical, physical, and radiative properties of contrails derived from our 10-year ETO cirrus cloud dataset to examine their potential direct impact for regional climate change: and, (3) prepare our mobile remote sensing systems and participate in the SUCCESS field campaign from the DOE Southern Great Plains ARM CART site.

Sassen, Kenneth↗