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NASA NTRS2025

Rolling DICE to Advance Knowledge of Land-Atmosphere Interactions

The Diurnal Land-Atmosphere Coupling Experiment (DICE) aims to explore the complex interactions between the land surface and atmospheric boundary layer, which are generally not well understood and difficult to isolate in models. The project involves over 10 different models, combining expertise from both land surface and atmospheric boundary-layer modelling groups. A simple three-stage methodology is designed to assess land-atmosphere feedbacks. Stage 1: the individual components are assessed in isolation, driven and evaluated against observational data; stage 2: the impact of coupling is investigated; stage 3: the sensitivity of the stand-alone models to variations in driving data is explored. For this initial study, a three-day clear-sky period in the mid-west USA over, an assumed simple, predominantly grass surface was simulated using data from the CASES-99 field campaign. Key conclusions from the study include: (1) the memory of vegetation state within Land Surface Models (LSMs) needs attention; (2) the height of atmospheric forcing for LSMs is important, particularly for the nocturnal boundary layer, and this has implications for both observations and vertical resolution for atmospheric models; (3) land-atmosphere feedbacks reduce errors in simulated surface fluxes at the expense of the accuracy of the variables that the models are designed to simulate (e.g., temperature, humidity and wind speed); (4) problems remain in representing the stable boundary layer in atmospheric models; (5) the mixing of temperature and humidity within the boundary layer may need to be represented separately; (6) differences in daytime profiles of heat, moisture and momentum between models are mainly due to the way the models erode the inversion at the top of the boundary layer, rather than differences in the surface fluxes. Resultant variations in modelled boundary layer heights have a substantial impact on relative humidity and could partially explain variations in coupling strength between models in the GLACE experiment.

Martin Best↗

NASA NTRS2010

Flow, Aurora and PI2 Associations Observed by Themis

It has been known for decades that auroral substorm onset occurs on (or at least near) the most equatorward auroral arc, which is thought to map to the near geosynchronous region. The lack of auroral signatures poleward of this arc prior to onset has been a major criticism of flow-burst driven models of sub storm onset. The combined THEMIS 5 spacecraft in-situ and ground array measurements provide an unprecedented opportunity to examine the causal relationship between midtail plasma flows, aurora, and ground magnetic signatures. I first present an event from 2008 using multi-spectral all sky imager data from Gillam and in-situ data from THEMIS. The multispectral data indicate an equatorward moving auroral form prior to sub storm onset. When this forms reaches the most equatorward arc, the arc brightens and an auroral substorm begins. The THEMIS data show fast Earthward flows prior to onset as well. I suggest that the results strongly support flow-burst driven models of magnetospheric activity. I discuss further the association of flow bursts and Pi2 pulsations, and discuss the possibility of using Pi2 waveforms to infer midtail reconnection dynamics

Kepko, E. I.↗

NASA NTRS1999

Reinventing The Design Process: Teams and Models

The future of space mission designing will be dramatically different from the past. Formerly, performance-driven paradigms emphasized data return with cost and schedule being secondary issues. Now and in the future, costs are capped and schedules fixed-these two variables must be treated as independent in the design process. Accordingly, JPL has redesigned its design process. At the conceptual level, design times have been reduced by properly defining the required design depth, improving the linkages between tools, and managing team dynamics. In implementation-phase design, system requirements will be held in crosscutting models, linked to subsystem design tools through a central database that captures the design and supplies needed configuration management and control. Mission goals will then be captured in timelining software that drives the models, testing their capability to execute the goals. Metrics are used to measure and control both processes and to ensure that design parameters converge through the design process within schedule constraints. This methodology manages margins controlled by acceptable risk levels. Thus, teams can evolve risk tolerance (and cost) as they would any engineering parameter. This new approach allows more design freedom for a longer time, which tends to encourage revolutionary and unexpected improvements in design.

Wall, Stephen D.↗

NASA NTRSDate not supplied

Prediction of Aircraft Estimated Time of Arrival Using A Supervised Learning Approach

We present a novel data-driven approach for prediction of the estimated time of arrival (ETA) of aircraft in the terminal area via the implementation of a Random Forest regression model. The model uses data fused from a number of sources (flight track, weather, flight plan information, etc.) and provides predictions for the remaining flight time for aircraft landing at Dallas/Fort Worth (DFW) International Airport. The predictions are made when the aircraft is at a distance of 200-miles from the airport. The results show that the model is able to predict estimated time of arrival to within ± 5 min for 90% of the flights in the test data with the mean absolute error being lower at 145 seconds. This paper covers the entire pipeline of data collection, preprocessing, setup and training of the ML model, and the results obtained for DFW.

Machine learning↗

NASA NTRS1994

Graph-Based Dynamic Assignment Of Multiple Processors

Algorithm-to-architecture mapping model (ATAMM) is strategy minimizing time needed to periodically execute graphically described, data-driven application algorithm on multiple data processors. Implemented as operating system managing flow of data and dynamically assigns nodes of graph to processors. Predicts throughput versus number of processors available to execute given application algorithm. Includes rules ensuring application algorithm represented by graph executed periodically without deadlock and in shortest possible repetition time. ATAMM proves useful in maximizing effectiveness of parallel computing systems.

Hayes, Paul J.↗

NASA NTRS2015

The Global Structure of UTLS Ozone in GEOS-5: A Multi-Year Assimilation of EOS Aura Data

Eight years of ozone measurements retrieved from the Ozone Monitoring Instrument (OMI) and the Microwave Limb Sounder, both on the EOS Aura satellite, have been assimilated into the Goddard Earth Observing System version 5 (GEOS-5) data assimilation system. This study thoroughly evaluates this assimilated product, highlighting its potential for science. The impact of observations on the GEOS-5 system is explored by examining the spatial distribution of the observation-minus-forecast statistics. Independent data are used for product validation. The correlation coefficient of the lower-stratospheric ozone column with ozonesondes is 0.99 and the bias is 0.5%, indicating the success of the assimilation in reproducing the ozone variability in that layer. The upper-tropospheric assimilated ozone column is about 10% lower than the ozonesonde column but the correlation is still high (0.87). The assimilation is shown to realistically capture the sharp cross-tropopause gradient in ozone mixing ratio. Occurrence of transport-driven low ozone laminae in the assimilation system is similar to that obtained from the High Resolution Dynamics Limb Sounder (HIRDLS) above the 400 K potential temperature surface but the assimilation produces fewer laminae than seen by HIRDLS below that surface. Although the assimilation produces 5 - 8 fewer occurrences per day (up to approximately 20%) during the three years of HIRDLS data, the interannual variability is captured correctly. This data-driven assimilated product is complementary to ozone fields generated from chemistry and transport models. Applications include study of the radiative forcing by ozone and tracer transport near the tropopause.

GEOS-5↗

NASA NTRSDate not supplied

Lessons Learned in the Application of Machine Learning Techniques to Air Traffic Management

There is an increasing interest in applying methods based on Machine Learning Techniques (MLT) to problems in Air Traffic Management (ATM). The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large databases. This paper reviews the current-state-of-the art in applying MLT to aviation operations, its promises and challenges. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for analysis using data-driven methods. The promises and challenges in applying MLT to ATM is traced through three examples based on the authors’ experience, each separated by a decade, to show the influence of data and feature selection in the successful application of MLT to ATM. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.

Machine Learning Techniques↗

NASA NTRSDate not supplied

Natural Language Processing Analysis of Notices to Airmen for Air Traffic Management Optimization

With new emerging technologies in the field of NLP, we explore their applications to digitize and analyze heritage Air Traffic Management (ATM) documents for planning and optimizing airspace operations. Specifically, this research focuses on harvesting semi-structured or un-structured information contained in Notices to Airmen (NOTAMs). Using NLP and other advanced data analytics, we will construct a data-driven framework which facilitates finding language patterns and the use of pretrained language models for classification and extraction of useful airspace constraints and restrictions. These may lead to tools that assist airspace users in understanding the constraints more efficiently, contributing to better route planning and safer execution. This paper explores three workflows entailing different NLP tasks. First, unsupervised techniques like word embedding and topic modeling are used for pattern finding and document classification. Second, a dataset is created by extracting information from the semi-structured NOTAM format as metadata for categorizing, visualizing, and extracting key entities driving NOTAM content. Third, modern pre-built deep learning based transformer models such as BERT, RoBERTa, and XLNet are evaluated on the question answering task, an even more robust approach to information extraction, as well as their respective fine-tuning tasks. In this work we include various performance metrics for the trained models to evaluate both accuracy and precision and we show that the models can be generalized for their respective tasks. The research work developed shows promise in uncovering trends in digital NOTAMs in the NAS and also offers a new framework for digitizing and inferring insights from free-form legacy NOTAMs, that are yet to be digitized.

Natural Language Processing↗

NASA NTRSDate not supplied

Natural Language Processing (NLP) Analysis of NOTAMs for Air Traffic Management Optimization

Natural Language Processing↗

NASA NTRS2009

General Purpose Data-Driven System Monitoring for Space Operations

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, satellite vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed, or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery.

Satellites↗

NASA NTRSDate not supplied

Understanding and Utilizing PBL Height Data from Multiple Observing Systems in the GEOS System

The accuracy of PBL height simulation is a key issue in many applications including forecasting near surface meteorology and air quality, however, it is a very challenging problem due to the lack of not only comprehensive, global Planetary Boundary Layer (PBL) observations but also a strategy and infrastructure to utilize PBL height data from a variety of sensors. Following the designation of PBL as an incubation class observable in the 2017 Decadal Survey, the PBL Incubation Study Team Report [14] made clear that “a future global PBL observing system requires modeling and data assimilation as essential components.” There is an urgent need for global modeling development in order to utilize Program of Record (POR) observations, assess their impacts, and identify gaps to be filled by future PBL missions. Our overall objective is to develop PBL data assimilation capabilities in the NASA Global Earth Observing System (GEOS), focusing on PBL height from multiple observing systems, to support the assessment and use of future PBL observations. The NASA GEOS system is composed of the GEOS global atmospheric general circulation model (AGCM) and the atmospheric data assimilation system (ADAS). The PBL parameterizations include the “Lock” K-profile scheme driven by surface and cloud-top buoyancy fluxes ([4]), and the “Louis” local scheme for stable conditions based on the Richardson number ([5]). Above the mixed layer defined by the Lock surface plume, shallow cumulus convection is represented by the mass flux scheme of [9]. Additional parameterizations are summarized in [1]. The ADAS employs the hybrid 4D Ensemble- Variational (EnVar) configuration ([15]), with the ensemble providing flow-dependent background error covariance information. The resultant analysis increments are fed back to the forecast model through the 4D incremental analysis update (IAU) approach ([11]). In this study, PBL height data are being or have been generated from radiosondes, GNSS RO, satellite (CATS, CALIPSO and ICESat-2) and ground-based (MPLNET) lidars, and wind profiler. Investigations have been conducted to specify quality marks for PBL height retrievals for the data assimilation purpose. These PBL height data have different strengths and weaknesses ([2], [3], [6], [7], [8], [10]), and the satellite PBL height data provide better global coverage and complement in-situ PBL height data. Radiosondes offer high accuracy and in situ measurement of temperature and humidity profiles, but with poor spatio-temporal sampling. The in-situ observing systems like MPLNET and wind profiler provide long history of PBL height records at each station. The GNSS RO based PBL height is retrieved based on the sharp gradients in refractivity profile that represent the fine vertical structure of temperature and moisture changes above the PBL. However, not all RO refractivity profiles reach the surface depending on location and regime, and RO refractivity retrievals can be negatively biased below 2km. The PBL height data from satellite lidars provide high resolution along track PBL height retrievals, but over land they are affected by previous day convective PBL aerosol and strongly associated with mixing layer and retrievals cannot be made below thick, attenuating clouds. A successful assimilation of PBL height data requires a thorough understanding of the observing method and the retrieval algorithm for each observing system in order to use the PBL height data from multiple observing systems properly. Due to the sensitivity of PBL height data to the observing method and choice of algorithm, it is important to use a model definition appropriate for each observation type to compute differences between PBL height data and model PBL height (OmFs). The GEOS model currently includes two PBL height definitions suitable for direct comparison with observed PBL height, and additional definitions are being added in this study. Evaluation of different model PBL height definitions is underway. Meanwhile, efforts have been made in the GEOS data assimilation system to develop PBL height data assimilation capability. PBL height data can be assimilated using two different approaches. The traditional approach is to construct an observation operator and its tangent linear and adjoint, which link control variables to PBL height data from each observing system. This observation operator can be very complicated, e.g., the lidar-based PBL height observation operator includes the backscatter lidar forward observation operator, the algorithm to derive PBL height from attenuated total backscatter, interpolation, and calculations handling the mismatch between observed and model scales. The other approach is to augment PBL height to the control variable vector, and it is adopted in this study. The latter approach was also used in previous studies, e.g., the assimilation of PBL height data from radiosonde and aircraft in the Real Time Mesoscale Analysis (RTMA) system for a dispersion modelling study ([13]); the PBL height assimilation study using lidar PBL height data at Greensburg, Kansas for a field campaign ([12]). The PBL height assimilation from multiple observing systems in this study allows us to take advantage of the diverse PBL height data that provide much better global coverage collectively under different meteorological conditions and with different temporal and spatial scales. As all the PBL heights are tightly coupled with the PBL thermodynamic variables, the strong correlations, which are provided by the 4D ensemble forecast, enable PBL height data from various sources to interact and combine coherently and provide additional information for PBL temperature and moisture fields. The results of comparisons among PBL height data from different sources and the evaluation of the model PBL height definitions with the PBL height data will be presented, and the PBL height data synergy strategies and preliminary results will also be discussed at the conference.

Y. Zhu↗

NASA NTRS2023

Towards an Indian Land Data Assimilation System (ILDAS): A Coupled Hydrologic-Hydraulic System for Water Balance Assessments

Effective management of water resources requires reliable estimates of land surface states and fluxes, including water balance components. But most land surface models run in uncoupled mode and do not produce river discharge at catchment scales to be useful for water resources management applications. Such integrated systems are also rare over India where hydrometeorological extremes have wreaked havoc on the economy and people. So, an Indian Land Data Assimilation System (ILDAS) with a coupled land surface and a hydrodynamic model has been developed and driven by multiple meteorological forcings (0.1°, daily) to estimate land surface states, channel discharge, and floodplain inundation. ILDAS benefits from an integrated framework as well as the largest suite of observation records collected over India and has been used to produce a reanalysis product for 1981–2021 using four forcing datasets, namely, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), ECMWF’s ERA-5, and Indian Meteorological Department (IMD) gridded precipitation. We assessed the uncertainty and bias in these precipitation datasets and validated all major components of the terrestrial water balance, i.e., surface runoff, soil moisture, terrestrial water storage anomalies, evapotranspiration, and streamflow, against a combination of satellite and in situ observation datasets. Our assessment shows that ILDAS can represent the hydrological processes reasonably well over the Indian landmass with IMD precipitation showing the best relative performance. Evaluation against ESA-CCI soil moisture shows that MERRA-2 based estimates outperform the others, whereas ERA-5 performs best in simulating evapotranspiration when evaluated against MODIS ET. Evaluations against observed records show that CHIRPS-based estimates have the highest performance in reconstructing surface runoff and streamflow. Once operational, this system will be useful for supporting transboundary water management decision making in the region.

Indian Land Data Assimilation System (ILDAS)↗

NASA NTRSDate not supplied

NASA’s Safety, Reliability, and Mission Assurance Digital Future

The evolution from “document-centric” to “data-centric” and “model-centric” information leveraging structured data and model-based approaches is at the heart of digital engineering transformational efforts underway across industry and government. It is these approaches that pave the way for data lakes, Authoritative Sources of Truth (ASOTs), and systems- of-systems interoperability and the corresponding transformational benefits thereof. Such benefits include increased data availability, data access equity, data traceability, real-time analytics, batch analytics, and (most importantly) acceleration of the time-to-value and time-to-insights associated with engineering products and analyses. The longer-term benefits of reusability, customization and traceability are even more promising. For Safety and Mission Assurance (SMA), and Mission Success (SMS) activities; realization of such benefits is essential to provide engineers and analysts alike vital information when needed to support critical decision making throughout the entire life cycle. The SMA community often operate in parallel with engineering activities, for which information exchange with relevant context is paramount. Far too often, such information lags key decision points and/or is absent of the robust, integrated, knowledge needed, given inherent barriers associated with traditional document-centric means to data sharing, analysis, and reporting. This paper provides an overview of how NASA’s Office of Safety and Mission Assurance (OSMA) is evolving its policies, standards, guidance, and training to transform to eliminate such barriers, thus realizing the benefits emerging in this new digital era. A roadmap for achieving this digital future is presented along with key building blocks involving use and implementation of concepts such as: Objectives-Hierarchies, Objective-Driven Requirements, Accepted Standards, Safety and Assurance Cases, data digitization (i.e., ontologies, structured data, and model-centric data), FAIR (Findable, Accessible, Interoperable, & Reusable) and/or FAIRUST (Findable, Accessible, Interoperable, Reusable, Understandable, Secure, and Trusted) principles [1]. This paper also describes how OSMA, leveraging the Agency’s overall commitment to Digital Transformation (DT), is using the power of Policy, “Digital” Domain representation, Product Evolution, and Community Outreach and Engagement as part of a strategic vision and roadmap to evolve and transform its SMA organizations to become better able to serve its stakeholders and customers. Future publications will elaborate on these building blocks and deeper concepts.

Authoritative Source of Truth (ASOT),↗

NASA NTRS1999

On the Relative Importance of Convection and Temperature on the Behavior of the Ionosphere in North American during January 6-12, 1997

Measurements from a network of digisondes and an incoherent scatter radar In Eastern North American For January 6-12, 1997 have been compared with the Field Line Interhemispheric Plasma (FLIP) model which now includes the effects of electric field convective. With the exception of Bermuda, the model reproduces the daytime electron density very well most of the time. As is typical behavior for winter solar minimum on magnetically undisturbed nights, the measurements at Millstone Hill show high electron temperatures before midnight followed by a rapid decay, which is accompanied by a pronounced density enhancement in the early morning hours. The FLIP model reproduces the nighttime density enhancement well, provided the model is constrained to follow the topside electron temperature and the flux tube is full. Similar density enhancements are seen at Goose Bay, Wallops Island and Bermuda. However, the peak height variation and auroral images indicate the density enhancements at Goose Bay are most likely due to particle precipitation. Contrary to previously published work we find that the nighttime density variation at Millstone Hill is driven by the temperature behavior and not the other way around. Thus, in both the data and model, the overall nighttime density is lowered and the enhancement does not occur if the temperature remains high all night. Our calculations show that convections of plasma from higher magnetic latitudes does not cause the observed density maximum but it may enhance the density maximum if over-full flux tubes are convected over the station. On the other had, convection of flux tubes with high temperatures and depleted densities may prevent the density maximum from occurring. Despite the success in modeling the nighttime density enhancements, there remain two unresolved problems. First, the measured density decays much faster than the modeled density near sunset at Millstone Hill and Goose Bay though not at lower latitude stations. Second, we cannot fully explain the large temperatures before midnight nor the sudden decay near midnight.

Richards, P. G.↗

NASA NTRS2019

Cyber Physical Security (CPS) Extension to Air Traffic Management (ATM) Testbed

The Air Traffic Management (ATM) Testbed is being developed at NASA to enable benefit, impact, safety and cost assessments for accelerating the deployment of Concept and Technologies (C&T) in the National Airspace System (NAS). Today, C&T introduction into the NAS takes decades. The primary reason for this is an inability to assess the operational impact of the interaction between the proposed C&T and operationally deployed systems (Realistic Technologies) in terms of NAS-wide safety, traffic flow efficiency, roles and workload of controllers and traffic managers, and impact on airline fleet operations. Transition of C&T to operations requires mathematical modeling and simulation, Human-in-the-Loop (HITL) testing and shadow-mode evaluation driven by operational data. Whereas interaction with the operational system during testing and stages of deployment is not permissible due to safety concerns, it is certainly possible to create a simulation environment that closely mimics the NAS using the same operational systems/hardware for enabling such assessments. This presentation focuses on a proposed Cyber Physical Security extension to the ATM Testbed for creating a modeling and simulation architecture to study how well the Air Traffic Management system will perform and analyze effectiveness of mitigating security measures against particular cyber-attack scenarios.

Datta, Koushik↗

NASA NTRS2010

Space Weather Products at the Community Coordinated Modeling Center

In addition to supporting space research in the international community, the Community Coordinated Modeling Center (CCMC) has as its second objective to bring to apply the power of modern research models toward space weather specification and forecasting. Initially motivated by the objective to test models and to ease the transition of research models to space weather forecasting organization, the CCMC has developed a number of real-time modeling systems, as well as large number of modeling and data products for space weather forecasting. Over time, these activities have evolved into tailored products for partners, as well as into a direct support of the space weather needs within NASA robotic mission community. Accessible through a customizable interface, users within the US or at partnering institutions internationally have access to space weather tools driven by the most advanced space research models. Through partnering with agencies and institutions in the US and abroad, the CCMC strives to set up further data sharing agreements to the benefit of all participating institutions. In this presentation, we provide an overview of existing CCMC space weather services and products, and we will explore additional avenues for international collaborations.

Hesse, Michael↗

NASA NTRS1989

A user-friendly, menu-driven, language-free laser characteristics curves graphing program for desk-top IBM PC compatible computers

A facility was established that uses collected data and feeds it into mathematical models that generate improved data arrays by correcting for various losses, base line drift, and conversion to unity scaling. These developed data arrays have headers and other identifying information affixed and are subsequently stored in a Laser Materials and Characteristics data base which is accessible to various users. The two part data base: absorption - emission spectra and tabulated data, is developed around twelve laser models. The tabulated section of the data base is divided into several parts: crystalline, optical, mechanical, and thermal properties; aborption and emission spectra information; chemical name and formulas; and miscellaneous. A menu-driven, language-free graphing program will reduce and/or remove the requirement that users become competent FORTRAN programmers and the concomitant requirement that they also spend several days to a few weeks becoming conversant with the GEOGRAF library and sequence of calls and the continual refreshers of both. The work included becoming thoroughly conversant with or at least very familiar with GEOGRAF by GEOCOMP Corp. The development of the graphing program involved trial runs of the various callable library routines on dummy data in order to become familiar with actual implementation and sequencing. This was followed by trial runs with actual data base files and some additional data from current research that was not in the data base but currently needed graphs. After successful runs, with dummy and real data, using actual FORTRAN instructions steps were undertaken to develop the menu-driven language-free implementation of a program which would require the user only know how to use microcomputers. The user would simply be responding to items displayed on the video screen. To assist the user in arriving at the optimum values needed for a specific graph, a paper, and pencil check list was made available to use on the trial runs.

Klutz, Glenn↗

NASA NTRS1995

Software For Computing Reliability Of Other Software

Computer Aided Software Reliability Estimation (CASRE) computer program developed for use in measuring reliability of other software. Easier for non-specialists in reliability to use than many other currently available programs developed for same purpose. CASRE incorporates mathematical modeling capabilities of public-domain Statistical Modeling and Estimation of Reliability Functions for Software (SMERFS) computer program and runs in Windows software environment. Provides menu-driven command interface; enabling and disabling of menu options guides user through (1) selection of set of failure data, (2) execution of mathematical model, and (3) analysis of results from model. Written in C language.

Nikora, Allen↗