Operational earth observation systems and resources management - A global program

A number of concurrent computers have been designed, such as [Ametek 1988] and [NCUBE 1986]. Taking advantage of concurrency on these computers will require new languages, operating systems, and environments to support the programming, monitoring and control of applications. The Rosette project at MCC is developing a system architecture for general-purpose concurrent computation based on the Actor model [Agha 1986]. The aim of the Rosette architecture is to support the use of concurrent computers in solving computationally intensive problems whose structure is not statically determined. The architecture will support variable grain size, dynamic resource management, reflection, and heterogeneity. A number of concurrent language proposals have employed computational models that are limited in their applicability because of assumptions such as granularity of concurrency, static process topology, or inherent communications cost. Others, for example, functional programming, cannot be used to model shared, modifiable objects. The Actor model addresses these issues directly. However, the difficulties of using concurrent computers cannot be addressed solely through programming languages. Traditional operating systems treat each process as an independent entity for the purposes of resource management (i.e., for the run-time allocation of processor, storage, and communication resources.) In the context of concurrent applications, effective operation on a concurrent computer requires that communities of processes be managed together, taking into account the ways in which they interact, and providing for the application itself to guide resource management. For example, consider that programming systems often provide only limited support for the control of resource consumption in search problems in the face of combinatorial explosion; further, it is generally recognized that to optimize search performance, it is useful to vary the resources provided depending on how promising intermediate results of a given path are judged to be. The Rosette architecture will allow the programmer to integrate high-level algorithms within the application for dividing resources dynamically among the sub-computations that are spawned. These algorithms will provide an assessment of the progress of the sub-computations, and will be used to make the large number of low-level resource management decisions that are necessary in carrying out the computation. Thus, operating system functionality will be folded into the application. The Rosette architecture has two major components: an interface layer and a system environment . The interface layer provides mechanisms for the monitoring and control of concurrent applications. It supports a uniform view of the system resources and applications through the use of actors and message-passing semantics. The interface layer includes a set of actors that represent the processing, storage and communication resources across the hardware base. The system environment contains actor communities which implement resource management policies , and support for programming concurrent applications. The environment consists of an extensible set of actors that are organized via a class-based object model. These actors provide monitoring, debugging, resource management system simulation, and compilation/transformation facilities. To demonstrate the effectiveness of the Rosette architecture, as well as to drive its development, two classes of applications are being used. The first class is the Rosette system itself: all of the system components necessary for resource management, monitoring, debugging, and program development are being constructed in the Rosette language and will take full advantage of both concurrency and object-oriented programming support. The second category of applications is chosen to be somewhat independent of the Rosette system so as to ensure adequate feed-back concerning applicability to domains other than software engineering. These applications are in the general area of knowledge-based signal interpretation. The Rosette architecture is implemented via a concurrent object-oriented programming language that provides the following features: Inherent concurrency : A declarative approach is needed which allows fine-grained concurrency to arise naturally from the program. If the programmer is forced to express all of the details of the concurrent execution of an application, it becomes difficult to re-map a given program to different concurrent computers which have varying concurrency characteristics. Dynamic creation/modification of objects : This allows the system to be extensible and reconfigurable. Sharing : The language should support mechanisms such as inheritance so that code, structure, and values need not be replicated. Classes : Objects can be organized into classes to permit more effective management of resources, as for example in a very large database. Classes should be first-class objects and hence it should be possible to create classes dynamically. The Rosette language is a high-level language that is translated into a base language that directly expresses concurrent computations in terms of the semantics of the Actor model. The Rosette language extends the base language with data abstraction facilities and a library of classes, and is used to implement the non-built-in actors of the interface layer, components of the system environment, and applications. Using data abstraction extensions to the base language, the Rosette language organizes actors into classes according to common sets of behaviors and local state. Inheritance is used as a means of sharing code and structure that is common among classes. There is considerable evidence, for example [LaLonde 1986] and [Snyder 1987], that a robust class system should support separate hierarchies that represent what is modeled by a class and how some class of objects are actually implemented. Our approach is to use three different kinds of classes to organize the various kinds of actors in a system and information about these actors: abstract classes specify observable requests, responses, and actions; representation classes specify resource management characteristics of implementations; and behavior classes contain the actual implementations. Associated with an abstract class is the set of representations that are available to implement the particular abstraction. Each representation is associated with the behavior class that contains the corresponding implementation. The power of this separation is that each of the three kinds of class may be related to others in a class hierarchy. This means that the relationships between abstract classes are not forced to have the same structure as the relationships among implementations. Sharing via inheritance among behavior classes is related to engineering concerns; relations among abstract classes are dictated by external/observable behavior. As an example, an abstract class might specify the operations available over real-matrices . There are several ways of representing a matrix as a community of actors, and each of these would be specified with appropriate representation and behavior classes. There is a well-defined abstract hierarchy of matrices such as invertible, positive , etc. These abstract kinds of matrices are independent of the various implementations, which may be chosen according to application and hardware resource constraints. The Rosette language embodies a reflective model of the interface layer that is similar to that used in ABCL/R, [Watanabe and Yonezawa 1988]. This model is t

Cronus [1-7] is an object oriented distributed system which operates in a heterogeneous computer environment of interconnected local area networks. As a distributed system architecture. Cronus faces a number of resource management issues not present in non-distributed architectures. Strategies for effectively controlling the redundancy and reconfigurability inherent in Cronus are needed to take advantage of the distributed system environment. These strategies for resource management are often conveniently separated into policies and mechanisms. A policy is a goal or guideline set by a system administrator or component designer constraining the decisions made by a resource allocator. A mechanism is an internal system structure designed to implement a class of policies.In the Cronus distributed system model, there are currently two general aspects of resource allocation mechanisms which are particular to the network environment and must be effectively managed. One of these is the binding of a client request to a particular resource manager for those resources which are available redundantly. Redundancy comes in two forms: replicated objects, (e.g., a multi-copy file) and replicated managers (any manager for a type can create a new instance of that object type). For both forms the selection of an object manager to provide the given service is an important resource management policy decision. The other important aspect of resource management mechanism is the ability to reconfigure parts of the system by dynamically migrating objects. Object migration is a powerful tool for matching system resources to tasks in a manner that attempts to maximize some measure of system performance, reliability, or survivability. Both static reconfiguration (e.g., choosing or amending the placement of object managers and their associated objects), and dynamic reconfiguration (e.g., moving an individual object in direct response to demand for its use possible in the Cronus architecture and design.The general approach to resource management in Cronus is to individually control the management of the classes of objects which make up the system. This approach extends Cronus resource management concepts beyond system resources to the abstract resources developed by applications. Resource management for an individual abstract resource (type) and for groups of related resource types (services) in Cronus is based on combining a number of carefully planned mechanisms which are part of the system architecture. In addition to resource management by resource type, a client specific policy which considers collections of object types used in a specific context can be constructed.In Cronus we achieve system wide and easily controllable resource management by requiring the object managers to cooperate in enforcing a resource management policy for their resource type. An object manager can redirect operations to a peer manager on another processor on the basis of current resource status. In the case for creation of a file to an alternate file manager which may have more storage available. Part of the basis for decisions to redirect requests are parameters, settable dynamically by system administrators through monitoring and control functions, which control the resource management strategy. The creation of objects and resource management in general thus becomes a responsibility that is decentralized among object managers on each processor based on a global allocation policy, both in terms of sharing current status and possible redirection of operations between managers.The Cronus resource management model is based on the integration of the following set of primitive mechanisms: the kernel routes requests for a given type to any available manager of that type, using the Locate mechanismCronus managers redirect requests to a more appropriate peer to accomplish resource management objectivesmanagers periodically collect current status of their peers, via a standard mechanism (ReportStatus), to be used as a basis for selecting a site for redirecting an operationusers and applications optionally indicate specific location preferences with requestsan operator can monitor and regulate the functioning of the resource management policyapplications can use the basic system mechanisms (e.g. broadcast or multicast, ReportStatus operations) to build special-purpose resource management strategies tailored to their needThere is a hierarchy of desirable locations for implementing resource management policies: object managers, shared libraries, and finally client programs or users themselves. Managers are the most desirable because they are readily identifiable with a limited set of object types which are likely to have similar policy goals. Also, managers are addressable as a group for administrative control to regulate the policy parameters, and for peer manager cooperation to implement the policy. Many decisions are negotiated between managers based upon information periodically obtained via the ReportStatus operation. The Cronus Monitoring and Control System uses the same ReportStatus operation to present the operator with a global view of how well the resource management policy is proceeding.In this paper we elaborate on these approaches to distributed resource management in Cronus, including appropriate background, detailed system design, and experiences with incorporating these ideas into three different resource managers.

The United States space program is in the throes of a major shift in emphasis from exploration of the moon and nearby planets to the application of remote sensing technology toward increased scientific understanding and economic exploitation of the earth itself. Over one hundred potential applications have already been identified. Since data from the unmanned Earth Resources Technology Satellites and the manned Earth Resources Observation Satellites are not yet available, the experimentation required to realize the ambitious goals of these projects is carried out through approximation of the expected characteristics of the data by means of images derived from weather satellite vidicon and spin-scan cameras, Gemini and Apollo photographs, and the comprehensive sensor complement of the NASA earth resources observation aircraft. The extensive and varied work currently underway is reviewed in terms of the special purpose scan and display equipment and efficient data manipulation routines required for high-resolution images; the essential role of interactive processing; the application of supervised classification methods to crop and timber forecasts, geological exploration, and hydrological surveys; the need for nonsupervised classification techniques for video compaction and for more efficient utilization of ground-control samples; and the outstanding problem of mapping accurately the collected data on a standard coordinate system. An attempt is made to identify among the welter of "promising" results areas of tangible achievement as well as likely bottlenecks, and to assess the contribution to be expected of digital image-processing methods in both operational and experimental utilization of the forthcoming torrent of data.

Contributors

The scanning and telescope field of view requirements for making Earth Resources (ER) and Meteorological (MET) phe-nomena observations from a Synchronous Earth Observation Satellite (SEOS) have been defined. The equations relating spacecraft scan capability to user scan field of view requirements are given and evaluated using four different telescope fields of view. The tradeoffs between telescope field-of-view and user scan fields of view requirements are shown, and it was determined that a 0.6 degree by 1.2 degree telescope FOV is optimum for the SEOS telescope. It was shown that an internal scan as well as external scan is required to satisfy both ER and MET users.

Abstract. Forests are an important component in the framework of nature-based solutions for mitigating climate change. However, there are still uncertainties about the biogeophysical effects of forest cover changes affecting heat and water fluxes as captured by Earth System Models (ESMs) simulations and observations. In this study, we investigate the differences in the surface temperature response to idealized, complete deforestation and the temperature sensitivity to percentage change in forest cover in ESMs and observations. In this comparison, the separation between local (at the place of deforestation) and non-local (nearby or distant locations) effects is crucial as observations capture only the former. Here, we propose a modified methodology to separate local and non-local effects in climate models suitable for simulations with linear rate of deforestation. The local sensitivity of a climate variable per unit deforested area is represented by the slope of the linear regression, where tree cover is an explanatory variable. The non-local effect is defined as the difference between the overall change in the respective climate variable and the local effect. Our analysis of eleven ESMs of the Coupled Model Intercomparison Project Phase 6 (CMIP6) that participated in the idealized global deforestation experiment deforest-glob, reveals a coherent local temperature response among climate models characterized by warming in the tropics and cooling in the northern higher latitudes. The temperature response however varies in magnitude, space and time with ESMs showing distinctive seasonal and spatial patterns. A closer look at the albedo response to deforestation across northern latitudes shows an overestimation in the ESMs in comparison to observations that translates via an emergent constraint (i.e. resulting from the linear relationships between local albedo and surface temperature within the model ensemble) into an overestimation of the overall simulated cooling effect. The overestimation of the local albedo sensitivity cannot be explained solely by the higher percentage of snow cover in ESMs. In terms of local latent heat flux sensitivity, the ESMs ensemble mean is overestimated for the boreal region, but it is in good agreement with the observational constraint in the temperate forests and the tropics. However, the inter-model spread and the internal model variation in these regions are considerable. ESMs having higher local albedo and latent heat flux sensitivities than the current observational constraints can still exhibit a realistic temperature response due to compensatory effects between the two sensitivities. Non-local effects contribute to consistent cooling throughout the globe, which persists also during the summer when the influence of the overestimated albedo sensitivity over snow is weaker. Having a deeper understanding of how local and non-local biogeophysical effects are represented in ESMs can give us insights into the net climate impact of deforestation and help us improve next generation ESMs.

Experimental applications of data from multispectral and other advanced sensors have demonstrated that remote sensing can make a valuable contribution to the monitoring and management of Canada's land resources. More frequent coverage and additional spectral bands on satellites planned for the mid-1980s and beyond will increase the opportunities for regular use of remotely sensed data. To effectively utilize these data in resource management, the remote sensing input must be matched with the resource management systems existing at that time. Thus, it is essential to anticipate the needs of resource management systems of the late 1980s and 1990s, to determine the appropriate role for remotely sensed data and to develop and implement a plan which will yield the remote sensing systems and methodologies necessary to meet the operational resource management requirements Previous studies of resource information requirements indicate that there will be a need for geocoded remotely sensed data, improved image analysis techniques and better information integration concepts for future resource management systems. To develop a plan for meeting the anticipated requirements, the flow from the recording of the remotely sensed data to the end use of the derived information is considered first. The timeliness and accuracy requirements of different users, the diverse data types and forms for individual applications, the analysis methods/decision models needed and the implications of these factors for the configuration(s) of remote sensing input into the future resource management systems are examined. From this analysis, areas requiring further work (research, development, demonstration, transfer) are identified, and a plan of action is suggested.

Abstract: Cloud service providers increasingly rely on automated control policies to manage capacity, performance, and energy usage, yet much managerial research still assumes rule-based or static optimisation approaches. This study investigates the managerial implications of adopting deep reinforcement learning (DRL) for resource management in cloud environments. PyTorch-based DRL agents are embedded in a joint CloudSim and NS-3 simulation framework to model dynamic decisions on server activation, virtual machine placement, and routing under stochastic demand and network variability. DRL-derived policies are systematically compared with traditional threshold rules and optimisation-based baselines in terms of energy cost, service-level agreement (SLA) violations, utilisation, and congestion. The proposed research design incorporates scenarios inspired by diverse operational contexts, including hyperscale providers and regional cloud operators in both mature and emerging markets. Scenario analysis is used to characterise behavioural properties of learned policies, such as aggressiveness, risk sensitivity, and responsiveness to shocks and demand bursts. The study aims to clarify when DRL-based automation creates managerial value, which governance mechanisms and oversight structures are required, and how AI controllers reshape the roles of operations managers and site reliability engineering (SRE) teams. The work contributes to technology and operations management by linking AI control policies with strategic concerns around reliability, efficiency, sustainability, and organisational control, thereby offering a structured basis for decision-making by researchers, practitioners, and policymakers. Keywords: deep reinforcement learning, cloud services, resource management, operations management, automation, AI governance, simulation, PyTorch

Abstract. Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model – ESM – simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling and retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multilayer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5∘ resolution (available at https://doi.org/10.6084/m9.figshare.13661312.v1; Wang and Mao, 2021) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations (mean bias from −0.044 to 0.033 m3 m−3, root mean square errors from 0.076 to 0.104 m3 m−3, Pearson correlations from 0.35 to 0.67) and multiple gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Three of the new SM products, which were produced by applying any of the three merging methods to the source datasets excluding the ESMs, had lower bias and root mean square errors and higher correlations than the ESM-dependent merged products. The ESM-independent products also showed a better ability to capture historical large-scale drought events than the ESM-dependent products. The merged products generally showed reasonable temporal homogeneity and physically plausible global sensitivities to observed meteorological factors, except that the ESM-dependent products underestimated the low-frequency temporal variability in SM and overestimated the high-frequency variability for the 50–100 cm depth. Based on these evaluation results, the three ESM-independent products were finally recommended for future applications because of their better performances than the ESM-dependent ones. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.

Integrating stakeholder perspectives is increasingly important in environmental science as a growing number of research projects are justified with a “solutions” orientation prioritizing societal relevance. In earth systems modeling, there is potential for model developers to engage with stakeholders who may use modeling results to inform decisions about resource management and policy. Challenges associated with stakeholder engagement relate to how researchers perceive the role of stakeholders and how they view the utility of integrating knowledge and perspectives from outside academia in model development. This study analyzes researchers’ perceptions of stakeholder engagement within BioEarth, a large collaborative regional earth systems modeling project designed to integrate input from agriculture and forestry sector decision-makers. The project addresses the impact of climate change on water, nitrogen and carbon cycling in the US Pacific Northwest. Surveys and semi-structured interviews were conducted to assess perceptions of stakeholder engagement among the 18principal investigators (PIs). Results reveal that PIs have varying perceptions of the role of stakeholders in earth systems modeling and diverse assessments of the optimal type and timing of stakeholder engagement. As funding agencies and research institutions promote increased collaboration with stakeholders from outside academia, these findings demonstrate fundamental differences of opinion among environmental scientists regarding the value of stakeholder engagement. This research has implications for transdisciplinary research projects that seek to address sustainability challenges by involving stakeholders in technical academic modeling. Facilitating learning opportunities for researchers who are new to stakeholder engagement is essential, as is close collaboration among researchers with different levels of prior stakeholder engagement experiences.

To address the challenge of rapidly selecting optimal numerical model products for weather forecasting in critical applications such as aviation route planning, this study proposes an enhanced object-based methodology comprising individual object scoring matching and a regional overall forecast selection scheme, building upon previous research. The method focuses on radar reflectivity forecasts within critical areas along air routes. Individual thunderstorm cells are evaluated using weighted scores for multiple parameters, including the Threat Score (TS), center-of-mass position, maximum radar reflectivity intensity, and shape forecasting accuracy. The regional overall score is then calculated by applying different weights to each convective cell within the area. After examining case studies of various convection types and bulk tests from June to September of 2024 and 2025, the results demonstrate that this method effectively selects the optimal convective forecasts from among the numerical models initiated at different times. The methodology shows promising applications in aviation weather forecasting. Different optimal selection schemes yield varying results: for large-scale convective weather, various test schemes generally align with TS score selection; for small-scale convective weather, schemes emphasizing radar reflectivity intensity show better performance; for scattered convection, schemes prioritizing center-of-mass position forecasting demonstrate superior results. These findings provide valuable insights for precision weather forecasting in both aviation and the agricultural–ecological sectors, in which accurate convective weather prediction is crucial for operational safety and resource management.

Implementing In Situ Simulation of High-Risk/Low-Volume Emergency Situations for Pediatric Transport Teams.

The development of Map Assessments, especially in analog space missions, plays a fundamental role in identifying, assessing, and mitigating potential threats and challenges that may arise during space operations in extraterrestrial environments. These missions are designed to simulate real space flight conditions, allowing space agencies and exploration teams to test and refine procedures, technologies, and protocols before implementing them in actual space missions. During the risk analysis, various factors are considered, including: i) Environmental Risks: the context in which the mission will occur, as extreme weather conditions; ii) Health and Safety Risks: health and safety of the team members, as exposure to radiation; iii) Operational Risks: associated with operational procedures, communications, resource management, team coordination, and decision-making during the mission; iv) Psychosocial Risks: interpersonal conflicts, and emotion management along with the crew. Based on the analysis of these risks, strategies and mitigation measures are developed to minimize the likelihood of adverse events and their potential consequences. This study aims to analyze occupation risks, divided into physical, chemical, ergonomic, biological, and mechanical, at the analog space station Habitat Marte, located in Rio Grande do Norte, Brazil. As a result, various sources of danger and vulnerabilities in analog space operations were identified, which include: a. identification of the biological risk group, which includes symptoms associated with transmitted diseases or infections, the possibilities of contamination and spread of transmissible diseases; b. identification of the chemical risk group, which includes the presence of harmful gases, such as carbon dioxide and methane, symptoms of exposure, such as breathing difficulties; c. Identification of the mechanical risk group, where insect bites, possible fires, structural collapses, d. Identification of the physical risk group, which includes exposure to extreme temperatures, dehydration, fatigue and falls, e. Identification of the ergonomic risk group, strongly present in issues such as work in confined spaces and repetitive movements, f. Identification of the psychological risk group, which ultimately encompasses stress due to confinement, lack of rest, and fear of incidents during the journey. By conducting this comprehensive and multidisciplinary risk analysis, space agencies and exploration teams can significantly increase the safety, efficacy, and success of their future missions in space. These results provide a solid foundation for the continuous development of risk management strategies and safety measures for long-duration space missions, contributing to the viability and success of future human expeditions to Mars and beyond.

This research introduces a methodology employing Mini-Fall Off injection tests to enhance reservoir characterization and inform Drill Stem Testing (DST) decision-making in deepwater conventional reservoirs. The methodology begins with log-driven permeability estimation, which is corrected using mobility data from Wire Formation Tester (WFT) point results. Despite these corrections, uncertainties persisted regarding the dominant permeability, which is crucial for ensuring the minimum required deliverability for DST. To address these uncertainties and flow assurance challenges, the Mini-Fall Off injection test was employed to estimate permeability within the target flow zone unit of the conventional reservoir. This approach allowed for precise permeability assessment in specific zones, thereby enhancing the overall understanding of reservoir quality and well deliverability. The Mini-Fall Off test provided critical Value of Information (VOI) by revealing that the well deliverability did not meet the minimum required flow rates for DST, leading to the cancellation of DST and substantial cost savings. The application of Mini-Fall Off tests demonstrates a significant advancement in reservoir assessment strategies, offering a cost-effective and efficient alternative to traditional methods. By integrating log-driven permeability data with WFT-derived mobility data and employing the Mini-Fall Off test, the methodology provides a comprehensive framework for reducing uncertainties and optimizing resource allocation in the oil and gas industry. Furthermore, this technique underscores the importance of VOI in data acquisition and decision-making processes throughout the asset lifecycle, from exploration to development. The results of this study highlight the potential for Mini-Fall Off tests to become a standard practice in reservoir characterization, particularly in deepwater conventional reservoirs where cost and efficiency are paramount. This work presents a novel, cost-effective approach to obtaining accurate reservoir characterization, thereby demonstrating its potential to optimize operational efficiency and resource management in the oil and gas sector.

This paper develops techniques used to manage tacit knowledge within enterprises and build ways to achieve effective decisions. A particular example addresses the work and interfaces that a security Incidents Response Team (IRT) has to conduct to protect enterprise assets, and provides a proactive system capable of adapting operational procedures, resource management and corporate strategy to the evolution of digital risk. Finally, we propose a knowledge management system based on a set of processes for the creation, storage and dissemination of knowledge required for information security, decision-making and IRT tacit knowledge management.