Schedule matters: Understanding the relationship between schedule delays and costs on overruns

Abstract: This paper examines the relationship between schedule delays and cost overruns on complex projects. It is generally accepted by many project practitioners that cost overruns are directly related to schedule delays. But what does “directly related to” actually mean? Some reasons or root causes for schedule delays and associated cost overruns are obvious, if only in hindsight. For example, unrealistic estimates, supply chain difficulties, insufficient schedule margin, technical problems, scope changes, or the occurrence of risk events can negatively impact schedule performance. Other factors driving schedule delays and cost overruns may be less obvious and more difficult to quantify. Examples of these less obvious factors include project complexity, flawed estimating assumptions, over-optimism, political factors, “black swan” events, or even poor leadership and communication. Indeed, is it even possible the schedule itself could be a source of delay and subsequent cost overrun? Through literature review, surveys of project practitioners, and the authors' own experience on NASA programs and projects, the authors will categorize and examine the various factors affecting the relationshi


A Proportional Hazards Model for the Subdistribution of a Competing Risk

Journal of the American Statistical Association

Abstract: Abstract With explanatory covariates, the standard analysis for competing risks data involves modeling the cause-specific hazard functions via a proportional hazards assumption. Unfortunately, the cause-specific hazard function does not have a direct interpretation in terms of survival probabilities for the particular failure type. In recent years many clinicians have begun using the cumulative incidence function, the marginal failure probabilities for a particular cause, which is intuitively appealing and more easily explained to the nonstatistician. The cumulative incidence is especially relevant in cost-effectiveness analyses in which the survival probabilities are needed to determine treatment utility. Previously, authors have considered methods for combining estimates of the cause-specific hazard functions under the proportional hazards formulation. However, these methods do not allow the analyst to directly assess the effect of a covariate on the marginal probability function. In this article we propose a novel semiparametric proportional hazards model for the subdistribution. Using the partial likelihood principle and weighting techniques, we derive estimation and inference


Introduction to the Analysis of Survival Data in the Presence of Competing Risks

Abstract: Competing risks occur frequently in the analysis of survival data. A competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. In a study examining time to death attributable to cardiovascular causes, death attributable to noncardiovascular causes is a competing risk. When estimating the crude incidence of outcomes, analysts should use the cumulative incidence function, rather than the complement of the Kaplan-Meier survival function. The use of the Kaplan-Meier survival function results in estimates of incidence that are biased upward, regardless of whether the competing events are independent of one another. When fitting regression models in the presence of competing risks, researchers can choose from 2 different families of models: modeling the effect of covariates on the cause-specific hazard of the outcome or modeling the effect of covariates on the cumulative incidence function. The former allows one to estimate the effect of the covariates on the rate of occurrence of the outcome in those subjects who are currently event free. The latter allows one to estimate the effect of covariates on the absolute risk of the outcome over time.


Five things you should know about cost overrun

Transportation Research Part A: Policy and Practice

Abstract: This paper gives an overview of good and bad practice for understanding and curbing cost overrun in large capital investment projects, with a critique of Love and Ahiaga-Dagbui (2018) as point of departure. Good practice entails: (a) Consistent definition and measurement of overrun; in contrast to mixing inconsistent baselines, price levels, etc. (b) Data collection that includes all valid and reliable data; as opposed to including idiosyncratically sampled data, data with removed outliers, non-valid data from consultancies, etc. (c) Recognition that cost overrun is systemically fat-tailed; in contrast to understanding overrun in terms of error and randomness. (d) Acknowledgment that the root cause of cost overrun is behavioral bias; in contrast to explanations in terms of scope changes, complexity, etc. (e) De-biasing cost estimates with reference class forecasting or similar methods based in behavioral science; as opposed to conventional methods of estimation, with their century-long track record of inaccuracy and systemic bias. Bad practice is characterized by violating at least one of these five points. Love and Ahiaga-Dagbui violate all five. In so doing, they produce an exceptionally useful and comprehensive catalog of the many pitfalls that exist, and must be avoided, for properly understanding and curbing cost overrun.


Time is Money: A Re-Assessment of the Passenger Social Savings from Victorian British Railways


Difference-in-Differences with multiple time periods

Abstract: Difference-in-Differences (DID) is one of the most important and popular designs for evaluating causal effects of policy changes. In its standard format, there are two time periods and two groups: in the first period no one is treated, and in the second period a "treatment group" becomes treated, whereas a "control group" remains untreated. However, many em- pirical applications of the DID design have more than two periods and variation in treatment timing. In this article, we consider identification and estimation of treatment effect parameters using DID with (i) multiple time periods, (ii) variation in treatment timing, and (iii) when the "parallel trends assumption" holds potentially only after conditioning on observed covariates. We propose a simple two-step estimation strategy, establish the asymptotic prop- erties of the proposed estimators, and prove the validity of a computationally convenient bootstrap procedure. Furthermore we propose a semiparametric data-driven testing procedure to assess the credibility of the DID design in our context. Finally, we analyze the effect of the minimum wage on teen employment from 2001-2007. By using our proposed methods we confront the challenges related to variation in the timing of the state-level minimum wage policy changes. Open-source software is available for implementing the proposed methods.


Social Savings (Leunig 2010)


Difference-in-differences with variation in treatment timing


What's trending in difference-in-differences? A synthesis of the recent econometrics literature


Difference-in-Differences with a Continuous Treatment


Why you should avoid using multiple Fine–Gray models: insights from (attempts at) simulating proportional subdistribution hazards data

Journal of the Royal Statistical Society: Series A (Statistics in Society)

Abstract: Studies considering competing risks will often aim to estimate the cumulative incidence functions conditional on an individual’s baseline characteristics. While the Fine–Gray subdistribution hazard model is tailor-made for analysing only one of the competing events, it may still be used in settings where multiple competing events are of scientific interest, where it is specified for each cause in turn. In this work, we provide an overview of data-generating mechanisms where proportional subdistribution hazards hold for at least one cause. We use these to motivate why the use of multiple Fine–Gray models should be avoided in favour of better alternatives such as cause-specific hazard models.


Cumulative Incidence Function in Competing Risks: A Case Study of Primary Biliary Cirrhosis in Liver Disease

Rasheed K. Lamidi
Bello Ishola Sanni
Saheed Kunle Ajibade
B. I. Doroh
Aishat Olaosebikan

International Journal of Science for Global Sustainability

Abstract: Competing risks have a potential to cause biased estimates in the context of survival analysis using both traditional tools like the Kaplan-Meier estimator and Cox proportional hazards model. This is especially applicable in Primary Biliary Cirrhosis (PBC) which is a chronic liver disease where the patients can die or undergo liver transplantation as a mutually exclusive outcome. This study used the cumulative incidence function (CIF) and Fine-Gray sub-distribution hazard model to measure competing risks in patients with PBC using data in the Mayo Clinic randomised trial of 312 patients. The likelihoods of death and liver transplantation as time progressed were estimated using CIFs which adequately considered competing events, and Fine-Gray was also used to determine the impact of D-penicillmain therapy versus placebo and the important prognostic factors. The findings revealed that, the cumulative death rates were always higher than the cumulative transplantation rates of the liver during the follow-up time. Even though the patients who were treated with D-penicillmain reported a slightly low mortality and slightly higher rate of transplantation compared to the patients provided with placebo; the difference was considered insignificant. Competing outcomes were found to be significantly predicted by age, ascites, disease stage, and platelet count and not by sex, bilirubin, and albumin. Altogether, the research proves that the competing risks approach is better in terms of its accuracy and clinical significance of assessment outcomes in PBC and the significance of CIF-based methodology in the assessment of treatment effects and prognosis in chronic liver disease.


Engineering Visual Presentation E08: Tackling the 65% megaproject failure epidemic: six key levers amplified by digital and AI

Abstract: Engineering Visual Presentation E08 Capital megaprojects (>US$1 billion) face a 65% failure rate, defined as cost overruns ≥25%, schedule delays ≥5%, or significant operational problems. Our research reveals six critical factors driving failures. (1) People and organisation (25–26% of issues): 70% of projects understaffed; only 15% of organisations report high agility. (2) Technical challenges (21–23%): operators often unprepared, especially in complex environments. (3) Governance (18%): 58% of projects misaligned with strategy; short-term focus impacts lifecycle value. (4) External stakeholders (14%): early engagement failures lead to disruptions. (5) Contracting and procurement (12–13%): inefficient strategies impact schedules, costs, and quality. (6) Project management processes (6–9%): poor front-end loading (FEL) implementation; projects average 26 months late. Our work has identified key mitigations for each. (1) Secure experienced teams: hire proven leaders and their established teams; implement talent management programs. (2) Early technology screening: engage vendors early, opt for proven technologies with ‘frozen experience’. (3) Prioritise lifecycle value: use reference class forecasting for realistic predictions; integrate risk assessment into decision-making. (4) Proactive stakeholder engagement: identify and engage stakeholders early, tailor communication strategies. (5) Strategic procurement: leverage sourcing power, establish vendor relationships, pre-order critical items. (6) Thorough FEL: allocate sufficient budget and time for detailed scope definition and risk assessment; use rigorous peer reviews. We bring our case study where targeted interventions orchestrated through a digital asset in front-end engineering and design (FEED) mitigated >A$1.5 B in capital expenditure (CAPEX) costs for a megaproject. By addressing these six areas systematically, the industry can significantly improve megaproject success rates and achieve the coming energy transformation capital delivery wave. To access the Visual Presentation click on the link on the right. To read the full paper click here


Tackling the 65% megaproject failure epidemic: six key levers amplified by digital and AI

Abstract: Capital megaprojects (>US$1 billion) face a 65% failure rate, defined as cost overruns ≥25%, schedule delays ≥5%, or significant operational problems. Our research reveals six critical factors driving failures. (1) People and organisation (25–26% of issues): 70% of projects understaffed; only 15% of organisations report high agility. (2) Technical challenges (21–23%): operators often unprepared, especially in complex environments. (3) Governance (18%): 58% of projects misaligned with strategy; short-term focus impacts lifecycle value. (4) External stakeholders (14%): early engagement failures lead to disruptions. (5) Contracting and procurement (12–13%): inefficient strategies impact schedules, costs, and quality. (6) Project management processes (6–9%): poor front-end loading (FEL) implementation; projects average 26 months late. Our work has identified key mitigations for each. (1) Secure experienced teams: hire proven leaders and their established teams; implement talent management programs. (2) Early technology screening: engage vendors early, opt for proven technologies with ‘frozen experience’. (3) Prioritise lifecycle value: use reference class forecasting for realistic predictions; integrate risk assessment into decision-making. (4) Proactive stakeholder engagement: identify and engage stakeholders early, tailor communication strategies. (5) Strategic procurement: leverage sourcing power, establish vendor relationships, pre-order critical items. (6) Thorough FEL: allocate sufficient budget and time for detailed scope definition and risk assessment; use rigorous peer reviews. We bring our case study where targeted interventions orchestrated through a digital asset in front-end engineering and design (FEED) mitigated >A$1.5 B in capital expenditure (CAPEX) costs for a megaproject. By addressing these six areas systematically, the industry can significantly improve megaproject success rates and achieve the coming energy transformation capital delivery wave.


Subdistribution hazard models for competing risks in discrete time.

M. Berger
M. Schmid
T. Welchowski
S. Schmitz-Valckenberg
J. Beyersmann

Abstract: A popular modeling approach for competing risks analysis in longitudinal studies is the proportional subdistribution hazards model by Fine and Gray (1999. A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association94, 496-509). This model is widely used for the analysis of continuous event times in clinical and epidemiological studies. However, it does not apply when event times are measured on a discrete time scale, which is a likely scenario when events occur between pairs of consecutive points in time (e.g., between two follow-up visits of an epidemiological study) and when the exact lengths of the continuous time spans are not known. To adapt the Fine and Gray approach to this situation, we propose a technique for modeling subdistribution hazards in discrete time. Our method, which results in consistent and asymptotically normal estimators of the model parameters, is based on a weighted ML estimation scheme for binary regression. We illustrate the modeling approach by an analysis of nosocomial pneumonia in patients treated in hospitals.


Competing risks analyses: objectives and approaches

Marcel Wolbers
Michael Koller
Vianda S Stel
Beat Schaer
Kitty J. Jager
Karen Leffondré
Georg Heinze

Abstract: Studies in cardiology often record the time to multiple disease events such as death, myocardial infarction, or hospitalization. Competing risks methods allow for the analysis of the time to the first observed event and the type of the first event. They are also relevant if the time to a specific event is of primary interest but competing events may preclude its occurrence or greatly alter the chances to observe it. We give a non-technical overview of competing risks concepts for descriptive and regression analyses. For descriptive statistics, the cumulative incidence function is the most important tool. For regression modelling, we introduce regression models for the cumulative incidence function and the cause-specific hazard function, respectively. We stress the importance of choosing statistical methods that are appropriate if competing risks are present. We also clarify the role of competing risks for the analysis of composite endpoints.


Reducing risks in megaprojects: The potential of reference class forecasting

Abstract: Large infrastructure projects often suffer from cost and schedule overruns, mainly due to optimism bias and strategic misrepresentation. Reference class forecasting (RCF) offers a potential remedy. This study presents a comprehensive analysis of the RCF literature with the aim of providing practitioners with key insights and identifying areas for future research. Through a review of 41 selected papers, the paper shows that the effectiveness of RCF is mainly applicable to large-scale projects and depends on the definition of the reference class. The paper calls for the development of an empirically based framework for reference class formation and urges the exploration of RCF's adaptability across industries, challenging the current one-size-fits-all approach. Theoretically, the paper critically assesses the current applications of RCF, while practically it outlines directions for future research and improvements. Overall, the study emphasises the need for detailed, data-driven methodologies and highlights their potential for risk management in projects worldwide.


A Class of $K$-Sample Tests for Comparing the Cumulative Incidence of a Competing Risk

Abstract: In this paper, for right censored competing risks data, a class of tests developed for comparing the cumulative incidence of a particular type of failure among different groups. The tests are based on comparing weighted averages of the hazards of the subdistribution for the failure type of interest. Asymptotic results are derived by expressing the statistics in terms of counting processes and using martingale central limit theory. It is proposed that weight functions very similar to those for the $G^p$ tests from ordinary survival analysis be used. Simulation results indicate that the asymptotic distributions provide adequate approximations in moderate sized samples.


Competing Risk Regression Models for Epidemiologic Data

Bonnie Lau
Stephen R. Cole
Stephen J. Gange

Abstract: Competing events can preclude the event of interest from occurring in epidemiologic data and can be analyzed by using extensions of survival analysis methods. In this paper, the authors outline 3 regression approaches for estimating 2 key quantities in competing risks analysis: the cause-specific relative hazard ((cs)RH) and the subdistribution relative hazard ((sd)RH). They compare and contrast the structure of the risk sets and the interpretation of parameters obtained with these methods. They also demonstrate the use of these methods with data from the Women's Interagency HIV Study established in 1993, treating time to initiation of highly active antiretroviral therapy or to clinical disease progression as competing events. In our example, women with an injection drug use history were less likely than those without a history of injection drug use to initiate therapy prior to progression to acquired immunodeficiency syndrome or death by both measures of association ((cs)RH = 0.67, 95% confidence interval: 0.57, 0.80 and (sd)RH = 0.60, 95% confidence interval: 0.50, 0.71). Moreover, the relative hazards for disease progression prior to treatment were elevated ((cs)RH = 1.71, 95% c


In-hospital mortality from healthcare-associated infection by multidrug-resistant Pseudomonas aeruginosa: a competing risks analysis of a 4-year propensity-matched cohort study in southern China

Mouqing Zhou
Evangelos I. Kritsotakis
Baohua Xu
Zhusheng Guo
Yongfeng Zeng
Bin Zhou
R. Brinks
Jiancong Wang

Abstract: Background: Healthcare-associated infections (HAIs) caused by multidrug-resistant Pseudomonas aeruginosa (MDRPa) pose enormous challenges in healthcare. We examined the incidence and relative mortality rates of patients with MDRPa HAI compared to non-MDRPa HAI in southern China. Methods: A hospital-wide longitudinal cohort study was conducted using prospectively collected surveillance data from 2018 to 2021. Poisson regression was applied to estimate incidence rate ratios (IRRs). Propensity-score matching and competing risks regression analysis (Fine-Gray model) were employed to estimate subdistribution hazard ratios (sHRs) for in-hospital mortality comparing MDRPa to non-MDRPa infections. Results: Among 562 patients studied (mean age 58 years, 74% male, in-hospital mortality 13.7%), 278 (49%) had an MDRPa HAI and 284 (51%) a non-MDRPa HAI. The incidence rate of MDRPa HAIs increased over time (mean monthly IRR: 1.016, 95% CI: 1.007–1.024). No significant difference in 14-day in-hospital mortality between MDRPa and non-MDRPa HAIs were detected in the propensity-matched doubly-robust analysis (adjusted sHR: 1.07, 95% CI: 0.52–2.19). However, MDRPa HAI was associated with a lower probability of 14-day discharge alive (adjusted sHR: 0.44, 95% CI: 0.31–0.63), resulting in longer hospital stays. Conclusions: The study provided real-world evidence of the clinical burden of MDRPa HAIs in China, highlighting their rising incidence and direct effect on prolonging hospitalisation. The findings underscore the need for antimicrobial stewardship interventions to ensure timely de-escalation and optimised antibiotic therapy.


Quantifying time-varying cause-specific hazard and subdistribution hazard ratios with competing risks data

Abstract: <jats:p>Various non-proportional hazard models have been developed in the literature for competing risks data. The regression coefficients under these models, however, typically cannot be compared directly. We propose new methods to quantify the average of the time-varying cause-specific hazard ratios and subdistribution hazard ratios through two general classes of transformations and weight functions that are chosen to reflect the relative importance of the hazard ratios in different time periods. We further propose an [Formula: see text] -norm type of test statistic that incorporates the test statistics for all possible pairs of the transformation function and weight function under consideration. Extensive simulations are conducted under various settings of the hazards and demonstrate that the proposed test performs well under all settings. An application to a clinical trial in follicular lymphoma is examined in detail.</jats:p>


Should We Build More Large Dams? The Actual Costs of Hydropower Megaproject Development

A. Ansar
B. Flyvbjerg
Alexander Budzier
Daniel Lunn

Abstract: A brisk building boom of hydropower mega-dams is underway from China to Brazil. Whether benefits of new dams will outweigh costs remains unresolved despite contentious debates. We investigate this question with the “outside view” or “reference class forecasting” based on literature on decision-making under uncertainty in psychology. We find overwhelming evidence that budgets are systematically biased below actual costs of large hydropower dams—excluding inflation, substantial debt servicing, environmental, and social costs. Using the largest and most reliable reference data of its kind and multilevel statistical techniques applied to large dams for the first time, we were successful in fitting parsimonious models to predict cost and schedule overruns. The outside view suggests that in most countries large hydropower dams will be too costly in absolute terms and take too long to build to deliver a positive risk-adjusted return unless suitable risk management measures outlined in this paper can be affordably provided. Policymakers, particularly in developing countries, are advised to prefer agile energy alternatives that can be built over shorter time horizons to energy megaprojects.


Dealing with construction cost overruns using data mining

Abstract:
One of the main aims of any construction client is to procure a project within the limits of a predefined budget.
\nHowever, most construction projects routinely overrun their cost estimates. Existing theories on construction
\ncost overrun suggest a number of causes ranging from technical difficulties, optimism bias, managerial incompetence
\nand strategic misrepresentation. However, much of the budgetary decision-making process in the early
\nstages of a project is carried out in an environment of high uncertainty with little available information for accurate
\nestimation. Using non-parametric bootstrapping and ensemble modelling in artificial neural networks, final
\nproject cost-forecasting models were developed with 1600 completed projects. This helped to extract information
\nembedded in data on completed construction projects, in an attempt to address the problem of the dearth of
\ninformation in the early stages of a project. It was found that 92% of the 100 validation predictions were within
\n±10% of the actual final cost of the project while 77% were within ±5% of actual final cost. This indicates the
\nmodel’s ability to generali


Reducing Cost Overrun in Public Housing Projects: A Simplified Reference Class Forecast for Small Island Developing States

Aaron Chadee
Hector Martin
Sihara Gallage
Upaka Rathnayake

Abstract: <jats:p>Inaccuracies in cost estimation on construction projects is a contested topic in praxis. Among the leading explanations for cost overrun (CO), factors accounting for large variances in actual cost are shown to have psychological or political roots. The context of public sector social housing projects (PSSHPs) in Small Island Developing States (SIDS) is positioned with similar CO challenges. This study is the fifth phase of a series of research projects on the vulnerability of PSSHPs to COs, and the need to de-risk cost estimates. The aim of this study is to present a simple and practical application of Reference Class Forecasting (RCF), a promising solution utilizing an “outside view” approach, as an effective control to reduce the variance of forecasted cost inaccuracies. Using a sample set of 82 housing projects, a reference class of 23 projects was selected based on properties such as design-build procurement type and local contractor involvement. A probability distribution was then established for this reference class, and required cost uplifts to be applied were based on the level of risk a housing agency is willing to accept for PSSHPs. Finally, the accuracy of the reference class was tested using a recently completed project. The results showed that the RCF method, based on a 50th percentile risk acceptance of CO, provides a closer estimate to the actual costs of the project as compared to the contracted costs. This empirical study is the first to undertake and implement RCF in the 52 SIDS and presents the first instance of practical RCF in public housing projects worldwide, thus providing a platform for improvement in future PSSHPs’ budget forecasting. The research can be applied to lessen societal and economic welfare losses as well as significant financial risks for governments. The implementation of practical safeguards, such as RCF, together with contemporary standard project controls, provides immediate advantages for enhancing accuracy in present forecasting approaches against financial risks. It allows for improved value derived from social infrastructure projects, improved supply of public housing, and consequently progress for these nations towards achieving their sustainable development goals.</jats:p>


Managing the cost overrun risks of hydroelectric dams: An application of reference class forecasting techniques

Renewable and Sustainable Energy Reviews


Estimation of the Absolute Risk of Cardiovascular Disease and Other Events: Issues With the Use of Multiple Fine-Gray Subdistribution Hazard Models

P. Austin
H. Putter
Douglas S. Lee
E. Steyerberg

Circulation. Cardiovascular Quality and Outcomes

Abstract: Supplemental Digital Content is available in the text. Background: The Fine-Gray subdistribution hazard model is frequently used in the cardiovascular literature to estimate subject-specific probabilities of the occurrence of an event of interest over time in the presence of competing risks. A little-known limitation of this approach is that, for some subjects and for some time points, the sum of the subject-specific probabilities for the different event types (eg, cardiovascular and noncardiovascular death) can exceed one. Methods: We used data on 8238 patients hospitalized with congestive heart failure in Ontario, Canada. We fit 2 Fine-Gray subdistribution hazards models, one for cardiovascular death and one for noncardiovascular death and estimated the probability of death due to each cause within 5 years of hospital admission. We also fit 2 cause-specific hazard models for the 2 event types and combined the estimated cause-specific hazard functions to obtain subject-specific estimates of the probabilities of each of the 2 event types occurring within 5 years. Results: When adding the probabilities of 5-year cardiovascular death and 5-year noncardiovascular death obtained from the Fine-Gray subdistribution hazard models, 8.6% of subjects had an estimated probability of 5-year all-cause mortality that exceeded 1 (100%). This problem was avoided by fitting 2 cause-specific hazard models, one for each outcome type, and combining the estimated cause-specific hazard functions to obtain subject-specific estimates of the risk of cardiovascular and noncardiovascular death. Conclusions: The Fine-Gray subdistribution hazard model may be problematic to use for a comprehensive assessment of absolute risks of multiple outcomes, while the combination of 2 cause-specific hazard models shows better statistical behaviour. Cause-specific modeling should not be discarded in competing risk situations.


An Additive-Multiplicative Cox-Aalen Subdistribution Hazard Model for Competing Risks Data

Journal of Systems Science and Complexity


Governing Large Projects: A Three-Stage Process to Get It Right

Dan Lovallo
Matteo Cristofaro
Bent Flyvbjerg

Abstract: Private and public megaprojects, whether new plant facilities, IT systems, skyscrapers, airports, railways, roads, or the Olympics, are frequently associated with dramatic cost and schedule overruns. The root causes are behavioral biases – such as optimism and deliberate deception – accompanied by principal-agent issues and a lack of project-related skills. Through a three-stage process – i.e., Forecasting, Organizing, and Executing (FOX) – we organize and offer solutions to mitigate the cognitive biases and agency issues planners and policymakers face in large projects. Following the three-stage FOX process and building on Behavioral Decision Theory (BDT), we first review evidence for the accuracy of Reference Class Forecasting (RCF), which considers comparable past projects to forecast a current, planned project. We provide evidence for RCF performance and recent methodological extensions such as Similarity Based Forecasting (SBF). Second, considering the principal-agent and project governance literature, we offer organizational solutions to reduce unfounded optimism and deception, including debiasing techniques and specific measures to curb principal-agent issues. Third, we sugg


Reference Class Forecasting and Machine Learning for Improved Offshore Oil and Gas Megaproject Planning: Methods and Application

Abstract: <jats:p> This article develops and describes rigorous oil and gas project forecasting methods. First, it builds a theoretical foundation by mapping megaproject performance literature to these projects. Second, it draws on heuristics and biases literature, using a questionnaire to demonstrate forecasting-related biases and principal-agent issues among industry project professionals. Third, it uses methodically collected project performance data to demonstrate that overrun distributions are non-normal and fat-tailed. Fourth, reference-class forecasting is demonstrated for cost and schedule uplifts. Finally, a predictive approach using machine learning (ML) considers project-specific factors to forecast the most likely cost and schedule overruns in a project. </jats:p>


The Influence of Optimism Bias on Time and Cost on Construction Projects

Aaron Anil Chadee
Salisha R. Hernandez
Héctor Martín

Abstract: The unresolved scholarly debate to curtail cost and time performances in projects has led to alternate solutions, departing from the dominant technical school of thought to include concepts from behavioural sciences. In this paper, we consider the psychological effect, namely optimism bias, as one of the root causes for delays in cost overruns on projects. The research objectives were to determine the level of bias among project participants, rank time and cost overrun causes according to the participants’ bias score and establish a mitigation strategy to curb potential delays and cost overrun impacts based on the bias scores obtained. A literature survey was conducted to determine causal factors contributing to delays and cost overruns linked to optimism bias. Through a pilot survey of three semi-structured interviews, eighty factors obtained from the literature survey were reduced to 24 critical delay and cost overrun factors relevant to Trinidad and Tobago. A questionnaire was subsequently developed seeking construction professionals to rate their bias scores based on an 11-point Likert scale. The research confirms that project planners and decision-makers exhibit moderate level


Toward a Deeper Understanding of Optimism Bias and Transport Project Cost Overrun

Yizi Chen
Dominic D. Ahiaga-Dagbui
Muhammad Jamaluddin Thaheem
Asheem Shrestha

Abstract: <jats:p> There is a growing face-value acceptance of optimism bias as the primary cause of transport cost overruns. This article provides a timely review of literature on optimism bias and transport infrastructure project cost overruns. The article identifies significant gaps and unanswered questions about the relationship between optimism bias in project cost appraisal and cases of transport infrastructure cost overruns. The presence and nature of optimism bias in the complex institutional environment of project cost appraisal are largely understudied and not well understood. Consequently, this has significant implications for the development of effective mitigation strategies for improving transport project cost performance. </jats:p>


SAS macros for estimation of direct adjusted cumulative incidence curves under proportional subdistribution hazards models

Computer Methods and Programs in Biomedicine


An additive subdistribution hazard model for competing risks data

Communications in Statistics - Theory and Methods


A competing risks model for correlated data based on the subdistribution hazard

Stephanie N. Dixon
Gerarda A. Darlington
Anthony F. Desmond


Fine–Gray proportional subdistribution hazards model for competing risks data under length-biased sampling


Two-Way Fixed Effects and Difference-in-Differences Estimators with Heterogeneous Treatment Effects and Imperfect Parallel Trends

Clément de Chaisemartin
Xavier d'Haultfoeuille


The choice of time scales in survival analysis has implications: calendar time versus patients’ time-to-event

J. Vilsmeier
Gisela Büchele
M. Rehm
Theresa Unseld
D. Rothenbacher
J. Beyersmann

Abstract: In the EvaCoM project, a retrospective cohort study of Germany-wide health insurance data of 29607 geriatric patients with fractures, the use of different time scales led to contradicting estimates in the Cox proportional hazards model. Here, the impact of quality audit, resulting in certification, of healthcare providers was of interest, with possible time scales being calendar time in which the audit acts or a patient’s time-to-event. For the analysis the patients were divided into groups depending on whether the certification occurred before or after the patient’s hospitalization, which adds a temporal component to the intervention (treatment in a certified hospital or not). A Cox model in calendar time found a harmful association of the audit with mortality (hazard ratio (HR) 1.13; 95% CI [1.04-1.23]), but in “time since hospital admission” no significant association was found (HR 1.04 [0.99-1.10]). To investigate these contradictions, the hazards in “time since hospital admission” are parametrically approximated with piecewise linear functions and transformed to calendar time by deriving patient-specific hazards based on their hospital admission date. A Poisson regression with both time scales simultaneously is used to investigate the contradictory results further. Matching is used to analyse the association in calendar time without using an additional time scale. The analysis of the approximated and transformed hazards leads to the conclusion that using calendar time causes a structural bias, because, in calendar time, patients of the pre-certification group enter the study earlier than those in the post-certification group. In combination with time-dependent and decreasing hazards, this leads to an artificial increase of the hazard ratio. A Poisson regression using both time scales (HR 1.00 [0.92-1.08]) and a Cox regression on matched data (HR 0.99 [0.90-1.09]) confirmed this result, finding no significant association between audits and mortality. In EvaCoM, the estimation of significant association in calendar time is a consequence of a structural bias caused by patients being shifted in calendar time and not a true harmful effect. The choice of time scale may severely impact the results. We present methods to disentangle the effects of different time scales.


A competing risks analysis should report results on all cause-specific hazards and cumulative incidence functions

Aurélien Latouche
Arthur Allignol
Jan Beyersmann
Myriam Labopin
Jason P. Fine


Curbing Optimism Bias and Strategic Misrepresentation in Planning: Reference Class Forecasting in Practice

Abstract: Absract The American Planning Association recently endorsed a new forecasting method called reference class forecasting, which is based on theories of planning and decision-making that won the 2002 Nobel prize in economics. This paper details the method and describes the first instance of reference class forecasting in planning practice. First, the paper documents that inaccurate projections of costs, demand, and other impacts of plans are a major problem in planning. Second, the paper explains inaccuracy in terms of optimism bias and strategic misrepresentation. Third, the theoretical basis is presented for reference class forecasting, which achieves accuracy in projections by basing them on actual performance in a reference class of comparable actions and thereby bypassing both optimism bias and strategic misrepresentation. Fourth, the paper presents the first case of practical reference class forecasting, which concerns cost projections for planning of large transportation infrastructure investments in the UK, including the Edinburgh Tram and London's £15 billion Crossrail project. Finally, potentials for and barriers to reference class forecasting are assessed.


Procedures for Dealing with Optimism Bias in Transport Planning: Guidance Document

Bent Flyvbjerg
Carsten Glenting
Arne Rønnest

VBN Forskningsportal (Aalborg Universitet)

Abstract: The UK Treasury (HM Treasury) has noted there is a demonstrated, systematic, tendency for project appraisers to be overly optimistic and that to redress this tendency appraisers should make explicit, empirically based adjustments to the estimates of a project's costs, benefits, and duration. HM Treasury recommends that these adjustments be based on data from past projects or similar projects elsewhere, and adjusted for the unique characteristics of the project in hand. In response to this, the UK Department for Transport (DfT), has produced the present Guidance Document. The main aims of the document are to provide empirically based optimism bias up-lifts for selected reference classes of transport infrastructure projects and provide guidance on using the established uplifts to produce more realistic forecasts for the individual project's capital expenditures. Furthermore, the underlying causes and institutional context for optimism bias in British transport projects are discussed and some possibilities for reducing optimism bias in project preparation and decision-making are identified.


Reference class forecasting for Hong Kong’s major roadworks projects

Bent Flyvbjerg
Chi-keung Hon
Wing Huen Fok

Proceedings of the Institution of Civil Engineers - Civil Engineering

Abstract: Reference class forecasting is a method to remove optimism bias and strategic misrepresentation in infrastructure projects and programmes. In 2012 the Hong Kong government’s Development Bureau commissioned a feasibility study on reference class forecasting in Hong Kong – a first for the Asia-Pacific region. This study involved 25 roadwork projects, for which forecast costs and durations were compared with actual outcomes. The analysis established and verified the statistical distribution of the forecast accuracy at various stages of project development, and benchmarked the projects against a sample of 863 similar projects. The study contributed to the understanding of how to improve forecasts by de-biasing early estimates, explicitly considering the risk appetite of decision makers, and safeguarding public funding allocation by balancing exceedance and under-use of project budgets.


Technology Readiness Level, Schedule Risk, and Slippage in Spacecraft Design


Technology Readiness Level, Schedule Risk and Slippage in Spacecraft Design: Data Analysis and Modeling (conference antecedent)


Complexity based cost estimating relationships for space systems

Abstract: As cost estimating methods evolve and grow increasingly important in the lifecycle of complex engineering feats, effective cost analysis methods are finding their way into the early system level design phases. What follows outlines and how to develop a parametrically determined cost estimating relationship (CER) that can be used to support the system design tradeoffs that take place throughout the study and development phases of space systems project. There are three steps in building a CCER. First, the key technical elements that determine the complexity of the subsystem are hypothesized; secondly, the elements are ranked with respect to the characteristics of the same class in our historical database; finally, all individual ranks are combined to quantify the overall subsystem complexity. At this point the systems engineer must make use of his or her experience and expertise to verify the results given by the CCER by answering the following questions, among others: was subsystem A more complex than subsystem B? If so did it affect costs accordingly? What about the schedule or time lapse to complete the product? How did these three parameters interact? Once the CCER answers the ab


How (In)accurate Are Demand Forecasts in Public Works Projects? The Case of Transportation

B. Flyvbjerg
M. K. Skamris Holm
S. L. Buhl

Journal of the American Planning Association

Abstract: This article presents results from the first statistically significant study of traffic forecasts in transportation infrastructure projects. The sample used is the largest of its kind, covering 210 projects in 14 nations worth US$59 billion. The study shows with very high statistical significance that forecasters generally do a poor job of estimating the demand for transportation infrastructure projects. The result is substantial downside financial and economic risks. Such risks are typically ignored or downplayed by planners and decision makers, to the detriment of social and economic welfare. For nine out of ten rail projects passenger forecasts are overestimated; average overestimation is 106 percent. This results in large benefit shortfalls for rail projects. For half of all road projects the difference between actual and forecasted traffic is more than 20 percent. Forecasts have not become more accurate over the 30-year period studied. If techniques and skills for arriving at accurate demand forecasts have improved over time, as often claimed by forecasters, this does not show in the data. The causes of inaccuracy in forecasts are different for rail and road projects, with pol


Railroads and American Economic Growth: Essays in Econometric History (Fogel 1964)


Railroads and American Economic Growth: A Market Access Approach (Donaldson & Hornbeck 2013)


Railroads, Reallocation, and the Rise of American Manufacturing (2019)


Railroad Impact in Backward Economies: Spain, 1850-1913 (Herranz-Loncan 2006)


Economic Benefits of the Global Positioning System (GPS) to the U.S. Private Sector (RTI International for NIST, 2019)


An evaluation of dependencies of critical infrastructure timing systems on the Global Positioning System (GPS) (NIST 2021)


Orbital debris and the market for satellites (Rao 2023)


OPUS: An Integrated Assessment Model for Satellites and Orbital Debris (2023)


Space, the Final Economic Frontier (Weinzierl 2018)


Satellite launch traffic forecast based on the global Gross Domestic Product and constellation plans (Retagne & Colombo 2026)


Space sustainability rating: Designing a composite indicator to incentivize satellite operators to pursue long-term sustainability of the space environment (Rathnasabapathy et al. 2025)


Pretest with Caution: Event-Study Estimates after Testing for Parallel Trends


Evaluating the impact of space activities in low earth orbit


Evaluating the environmental impact of space missions


Tipping Points of Space Debris in Low Earth Orbit


Impact of Rocket Launch and Space Debris Air Pollutant Emissions on Stratospheric Ozone and Global Climate


The environmental impact of emissions from space launches: A comprehensive review


A cost effective methodology for building flight spares for robotic life extension

Abstract: Cost-effective flight-spares methodology, relevant to heritage build and development cost-time trends.


Technology roadmapping methodology for future transportation systems

Abstract: Technology readiness and roadmapping methodology relevant to TRL-driven schedule risk for first-of-kind systems.


CARDINAL WTRL: TECHNOLOGY MATURITY, SCHEDULE SLIPPAGE AND TREND FORECASTING.

Abstract: Maturity assessments of technology is a crucial process to identify and acquire compatible technologies for a system’s development. However, being a complex and highly subjective process, the Government Accountability Office (GAO) has reported cost overruns and schedule slippages through the years. This study provides a unique Weighted Technology Readiness Level (WTRL) framework which utilizes cardinal factors to ascertain the maturity, schedule and trend of NASA’s 7 Technologies based on their maturity time. The framework utilizes MCDM methods to determine the cardinal complexity of each TRL. It allows the assimilation of other cardinal factors using a simple, open structure to track the overall technology maturity and readiness. Furthermore, this study highlights the importance of tailored TRL frameworks to determine the accurate cardinal coefficient of the said technology and the inferences derived otherwise. It eliminates several limitations of previous frameworks and compares against their performance using a verified statistical representation of processed data.


The JPL Advanced Projects Design Team's Spacecraft Instrument Cost Model: an Objective, Multivariate Approach

Abstract: This paper describes the development of a new satellite instrument cost model currently in use with the Jet Propulsion Laboratory's Advanced Projects Design Team.


Probabilistic Cost-Schedule Risk Analysis in Offshore Construction Projects Using Monte Carlo Simulation and Quantitative Assessment

International Journal of Business and Technology Management

Abstract: <jats:p>Offshore construction projects are frequently subject to considerable schedule delays and cost overruns, driven by technical complexity, environmental unpredictability, and market fluctuations. Traditional deterministic approaches often fail to capture these risks, offering limited value for decision-making in high-risk project settings. This study applies an integrated probabilistic approach by combining Schedule Risk Analysis (SRA) and Cost Risk Analysis (CRA) across three offshore case studies: (i) wellhead platform developments, (ii) a Very Low Abandonment Pressure (VLAP) project, and (iii) a brownfield Teja–Pepulut (TePu) development. Monte Carlo simulations were utilized to account for both aleatory and epistemic uncertainties, generate probabilistic estimates (P10, P50, P90), and identify key risk contributors. The findings reveal consistent discrepancies between deterministic plans and probabilistic results. Schedule outcomes at both the P50 and P90 levels were regularly exceeded, showing a systematic underestimation of schedule risks. Cost analysis also indicated increased contingency requirements, with hook-up and commissioning (HUC) activities identified as the most significant contributors. Additional cost exposure was linked to contractor performance limitations and volatile market conditions. While risk profiles varied across projects, deterministic models consistently underestimated the proper level of uncertainty. The study highlights the significance of probabilistic techniques in offshore project planning, offering practical insights into contingency allocation, contractor evaluation, and resource optimization in complex marine environments.</jats:p>


Reference Class Forecasting and Its Application to Fusion Power Plant Cost Estimates

Abstract: Developments in fusion energy technology and the aspiration to build and run commercial fusion energy power plants have seen the commencement of numerous publicly and privately funded projects in recent years. Megaprojects, like fusion power plants, by their very nature are inherently complex and risky, therefore, providing a robust cost estimate in the early stages is challenging. In the fusion sector, this is amplified by the fact that very little data exist on which to base an estimate due to the novel nature of the materials and technologies involved. A common phenomenon during the estimating phase of a new project is the concept of optimism bias (OB), where underestimation of time, cost, and risk can cause impacts on the baseline cost and schedule, leading to significant over-runs during the project lifetime. Reference class forecasting (RCF) is a method used to mitigate against these factors, based on using actual performance data in a reference class of comparable projects to improve forecasting accuracy. This article will discuss RCF, how it has been used in recent megaprojects, and how it is intended to be used in the Spherical Tokamak for Energy Production (STEP) program to provide a full program cost model for a prototype fusion energy plant.


Cost Estimation of Electronic Parts in NASA Space Missions

Abstract: This paper provides a cost model to develop early estimates for electronics, electrical, and electromechanical (EEE) parts costs during early design phases for instruments and spacecraft. Often, to support a certified proposal, the EEE parts group is required to generate a detailed cost estimate for instruments and spacecraft during preliminary design phases. This requires a premature development of a detailed bill of materials (BOM) far in advance of the final design and fabrication phases; it is therefore no surprise that EEE parts costs grow as a given design is refined and finalized and the required BOM is established. The aerospace cost estimating community lacks sufficient data and understanding of what contributes to EEE parts costs, which is becoming increasingly critical as many advanced instrument and spacecraft designs are relying more heavily on advanced electronics and field-programmable gate array (FPGA) development. This analysis seeks to identify several high-level parameters that can be identified early in the design phase (pre-Phase A through Phase C) to generate a realistic range of EEE parts. The resulting cost model can be used to inform the EEE parts estimating process, and provide program management with the potential cost range for appropriate reserve allocation. Actual costs of APL missions, instruments, and EEE parts are used. Technical leads provide the potential cost drivers as well as the technical data. These cost drivers (such as parts class, radiation, board area, and complexity of design and requirements) are investigated through multiple linear regression analysis. The regression results are used to develop cost estimating relationships (CERs) by determining the best predictors of EEE parts cost. It benefits the NASA and DoD cost communities to obtain a rough estimate of EEE cost during the design phase to more properly budget and provide a reasonable cost risk assessment.


Effectiveness of Firm-Fixed Price Spacecraft Contracts to Curb Cost Growth

Abstract: There are many types of contracts used by NASA for science missions to procure spacecraft, this paper focuses on two main categories: Cost-Plus (CP) contracts and Firm-Fixed Price (FFP) contracts. When CP contracts are used, the government agrees to cover the actual expenses of the project which include the planned original cost and any additional labor or material costs incurred to complete the work. The CP contract mechanism is ideally implemented when government requirements are not well-defined, and the likelihood of a modification to the scope of the project is high. When FFP contracts are used, the government agrees to cover a non-variable cost and will not pay for any additional labor or material costs incurred to complete the work. The FFP contract mechanism is ideally implemented when the government requirements are well-defined, costs can be predicted, and the contractor has experience in manufacturing a product that fulfills the requirements. This paper investigates historical cost growth of spacecraft for a variety of NASA science missions launched over the last 20 years, by comparing historical cost growth of CP and FFP spacecraft from contract start to delivery. Additionally, this paper will also examine the potential causes of cost growth on FFP contracts, including schedule delays, requirement changes, the addition of new scope after the contract was signed, and mistakenly formulating a basis of estimate by assuming high heritage to a previous spacecraft. The results of this study will provide guidelines and lessons learned to help NASA and other government agencies determine when an FFP contract can be used effectively and efficiently.


Math is EZIE: How Contracts Help Control Cost

Abstract: The aerospace cost estimating community relies on risk analyses to estimate confidence in a project's budget. At a NASA mission's preliminary design review (PDR), convention requires that baseline cost confidence plus project-held reserves should be around the 50th percentile and cost plus project-held reserves and unallocated future expenses (UFE) should be around the 70th percentile of the joint distribution of total cost and schedule. But how can we test whether our approach to determining 50th and 70th percentiles for missions going into PDR is reliable? An analysis of historical costs from past NASA missions shows that there is an 84 % chance that a mission will experience cost growth from PDR to Launch. At the empirical 50th and 70th percentiles, NASA missions are spending their full budgets plus 16% and 27 %, respectively. But does this change when we compare in-house spacecraft builds to contracted builds? As more commercial hardware options become available, NASA missions have relied on various cost contracts, particularly cost-plus (CP) contracts and firm-fixed-price contracts (FFP). In May 2022, the Electrojet Zeeman Imaging Explorer (EZIE), passed its PDR. EZIE is just one of many recent examples of NASA using commercially available hardware and FFP contract vehicles to control costs. With carefully designed science techniques that take advantage of commercial off the shelf (COTS) hardware from Blue Canyon Technologies, EZIE will be able to collect groundbreaking science for a lifecycle cost of $57.5M (FY22). To ensure the success of the EZIE mission, NASA must allocate and budget adequate funding. In this analysis, actual costs of NASA missions with contracted spacecraft are compared with actual costs of NASA missions with in-house builds. Comparisons are made between contract start estimate and final delivery costs. This paper will allow us to examine the NASA cost community's approach to reserve postures, particularly when contracts are employed. Using the empirical dataset as our guide, how can projects approaching PDR provide cost and schedule analysis that supports the goal of achieving 70% confidence in the budget at the portfolio level?


Final Report of the NASA Technology Readiness Assessment (TRA) Study Team

NASA STI Repository (National Aeronautics and Space Administration)

Abstract: The material in this report covers the results on the NASA-wide TRA team, who are responsible for ascertaining the full extent of issues and ambiguities pertaining to TRATRL and to provide recommendations for mitigation. The team worked for approximately 6 months to become knowledgeable on the current TRATRL process and guidance and to derive recommendations for improvement.The team reviewed the TRA processes of other government agencies (OGA), including international agencies, and found that while the high-level processes are similar, the NASA process has a greater level of detail. Finally, NASAs HQ OCT continues to monitor the GAOs efforts to produce a TRA Best Practices Guide, a draft of which was received in February 2016. This Guide could impact the recommendations of this report.


Examining the Effects of Implementing Data-Driven Uncertainty in Cost Estimating Models

Abstract: When conducting probabilistic cost analysis, correlation assumptions are key assumptions and often a driver for the total output or point estimate of a cost model. Although the National Aeronautics and Space Administration (NASA) has an entire community dedicated to the development of statistical cost estimating tools and techniques to manage program and project performance, the application of accurate and data-driven correlation coefficients within these models is often overlooked. Due to the uncertain nature of correlation between random variables, NASA has had difficulty quantifying the relationships between spacecraft subsystems with specific, data-driven correlation matrices. Previously, the NASA cost analysis community has addressed this challenge by either selecting a blanket correlation value to address uncertainty within the model or opting out of using any correlation value altogether. One hypothesized method of improving NASA cost estimates involves deriving subsystem correlation coefficients from the residuals of the regression equations for the cost estimating relationships (CERs) of various spacecraft subsystems and support functions. This paper investigates the feasibility of this methodology using the CERs from NASA's Project Cost Estimating Capability (PCEC) model. The correlation coefficients for each subsystem of the NASA Work Breakdown Structure were determined by correlating the residuals of PCEC's subsystem CERs. These correlation coefficients were then compiled into a 20x20 correlation matrix and were implemented into PCEC as an uncertainty factor influencing the model's pre-existing cost distributions. Once this correlation matrix was implemented into the cost distributions of PCEC, the Latin Hypercube Sampling function of the Microsoft Excel add-in Argo was used to simulate PCEC results for 40 missions within the PCEC database. These steps were repeated three additional times using the following correlation matrices: (1) a correlation matrix assuming the correlation between each subsystem is zero, (2) a correlation matrix assuming the correlation between each subsystem is 1, and (3) a correlation matrix using a blanket value of 0.3. The results of these simulations showed that the correlation matrix derived from the residuals of the subsystem CERs significantly reduced bias and error within PCEC's estimating capability. The results also indicated that the probability density function and cumulative distribution function of each mission in the PCEC database were altered significantly by the correlation matrices that were implemented into the model. This research produced (1) a standard subsystem correlation matrix that has been proven to improve estimating accuracy within PCEC and (2) a replicable methodology for creating this correlation matrix that can be used in future cost estimating models. This information can help the NASA cost analysis community understand the effects of applying uncertainty within cost models and perform sensitivity analyses on project cost estimates. This is significant because NASA has been frequently critiqued for underestimating project costs and this methodology has shown promise in improving NASA's future cost estimates and painting a more realistic picture of the total possible range of spacecraft development costs.


Estimating the Cost to Transition a Space System from Expendable to Reusable

Abstract: In recent years, the benefit of hardware reuse for space systems has been demonstrated in the launch market as reusable launch vehicles have enabled companies, primarily SpaceX with their Falcon 9 vehicle, to drive down launch costs prompting competitors to include reusability in their future designs to stay competitive. Although several reusable systems are currently being developed, the high cost and technical challenges of developing such systems remain an issue. To reduce these barriers, some organizations have chosen to first develop an expendable version of a system then transition it to a reusable version once the design has been proven. SpaceX used this approach when developing their Falcon 9 and Dragon vehicles with both starting as expendable systems which were transitioned to reusable after several successful missions. This approach allows for the benefits of creating an expendable design, relative simplicity and faster time to market, to be realized while still working towards a reusable vehicle. Although such an approach provides benefit, a significant drawback is the financial risk associated with developing two distinct versions of a system. Thus, there is a need for a method to optimize the design of an expendable space system, which will be transitioned to reusable, to minimize overall development cost. To realize this need, we propose a method to estimate the program development cost by estimating the cost to develop the initial expendable vehicle and the cost to transition it to reusable. This paper first discusses the primary considerations for modifying an expendable system for reuse, focusing on the hardware changes required. The methods used to estimate the initial expendable system cost and the transition cost are then discussed. The expendable system cost estimation uses cost estimating relationships and analogous cost estimating, which are modified by a change factor and technology readiness level growth factor depending on the design of each entity. A discount rate factor is also established for each reusable entity that is included in the expendable design to account for the time value of money and an inflation factor is used to convert to constant-year dollars. The transition cost is estimated by calculating the cost of each entity which is added to or modified from the expendable design. For reusable entities that are modified versions of entities used in the original expendable system, a reliability growth factor is applied to account for the increased reliability necessary for the reusable version. However, if the entity only exists in the reusable design, its cost is estimated using similar methods as for estimating the cost of the expendable system. An Integration & Test (I&T) factor is calculated for each entity added or modified when creating the reusable design to reflect the cost of integrating changes into the expendable design to make it reusable along with associated testing. In future work, this cost estimation methodology will be used in a multidisciplinary design optimization framework to calculate program life-cycle cost objective values for each space system evaluated.


How Common Is Cost Overrun and Cost Growth?


Cumulative incidence functions should replace integrated cause-specific hazard functions to describe probabilities of competing events over time


Impact of instrument schedule growth on mission cost and schedule growth for recent NASA missions

K. A. Kipp
S. C. Ringler
E. L. Chapman
C. W. Freaner


The effect of policy changes on NASA science mission cost & schedule growth

Robert Bitten
Charles Hunt
Debra Emmons
Robert Kellogg
Eric Mahr
Sarah Lang


NASA Instrument Cost Model for Explorer-Like Mission Instruments (NICM-E)


NASA Space Flight Instruments: Cost-Time Trends


The next Landsat satellite: The Landsat Data Continuity Mission

Abstract: The Landsat program is one of the longest running satellite programs for Earth observations from space. The program was initiated by the launch of Landsat 1 in 1972. Since then a series of six more Landsat satellites were launched and at least one of those satellites has been in operations at all times to continuously collect images of the global land surface. The Department of Interior (DOI) U.S. Geological Survey (USGS) preserves data collected by all of the Landsat satellites at their Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota. This 40-year data archive provides an unmatched record of the Earth's land surface that has undergone dramatic changes in recent decades due to the increasing pressure of a growing population and advancing technologies. EROS provides the ability for anyone to search the archive and order digital Landsat images over the internet for free. The Landsat data are a public resource for observing, characterizing, monitoring, trending, and predicting land use change over time providing an invaluable tool for those addressing the profound consequences of those changes to society. The most recent launch of a Landsat satellite occurred in 1999 when Landsat 7 was placed in orbit. While Landsat 7 remains in operation, the National Aeronautics and Space Administration (NASA) and the DOI/ USGS are building its successor satellite system currently called the Landsat Data Continuity Mission (LDCM). NASA has the lead for building and launching the satellite that will carry two Earth-viewing instruments, the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). The OLI will take images that measure the amount of sunlight reflected by the land surface at nine wavelengths of light with three of those wavelengths beyond the range of human vision. T1RS will collect coincident images that measure light emitted by the land surface as a function of surface temperature at two longer wavelengths well beyond the range of human vision. The DOI/USGS is developing the ground system that will command and control the LDCM satellite in orbit and manage the OLI and TIRS data transmitted by the satellite. DOI/USGS will thus operate the satellite and collect, archive, and distribute the image data as part of the EROS archive. DOI/USGS has committed to renaming LDCM as Landsat 8 following launch. By either name the satellite and its sensors will extend the 40-year archive with images sufficiently consistent with data from earlier Landsat satellites to allow multi-decadal, broad-area studies of our dynamic landscapes. The next Landsat satellite and ground system are on schedule for a January, 2013 launch.


What is value in the New Space Economy? The end-users' perspective on satellite data and solutions (Paravano, Locatelli & Trucco 2023)


The View from Above: Applications of Satellite Data in Economics

Abstract: The past decade or so has seen a dramatic change in the way that economists can learn by watching our planet from above. A revolution has taken place in remote sensing and allied fields such as computer science, engineering, and geography. Petabytes of satellite imagery have become publicly accessible at increasing resolution, many algorithms for extracting meaningful social science information from these images are now routine, and modern cloud-based processing power allows these algorithms to be run at global scale. This paper seeks to introduce economists to the science of remotely sensed data, and to give a flavor of how this new source of data has been used by economists so far and what might be done in the future.


Spacecraft and optics design considerations for a spaceborne lidar mission

Abstract: Design study of a spaceborne active lidar Earth-observation instrument, illustrating the first-of-kind active-sensor archetype whose technology maturity drives development schedule.


Dual-telescope multi-channel thermal-infrared radiometer for outer planet fly-by

Abstract: Thermal-infrared radiometer instrument design, illustrating the passive-radiometer archetype with heritage-driven development.


Utilization of Ancillary Data Sets for Conceptual SMAP Mission Algorithm Development and Product Generation

Abstract: The planned Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council's Decadal Survey, Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond [1]. Scheduled to launch late in 2014, the proposed SMAP mission would provide high resolution and frequent revisit global mapping of soil moisture and freeze/thaw state, utilizing enhanced Radio Frequency Interference (RFI) mitigation approaches to collect new measurements of the hydrological condition of the Earth's surface. The SMAP instrument design incorporates an L-band radar (3 km) and an L band radiometer (40 km) sharing a single 6-meter rotating mesh antenna to provide measurements of soil moisture and landscape freeze/thaw state [2]. These observations would (1) improve our understanding of linkages between the Earth's water, energy, and carbon cycles, (2) benefit many application areas including numerical weather and climate prediction, flood and drought monitoring, agricultural productivity, human health, and national security, (3) help to address priority questions on climate change, and (4) potentially provide continuity with brightness temperature and soil moisture measurements from ESA's SMOS (Soil Moisture Ocean Salinity) and NASA's Aquarius missions. In the planned SMAP mission prelaunch time frame, baseline algorithms are being developed for generating (1) soil moisture products both from radiometer measurements on a 36 km grid and from combined radar/radiometer measurements on a 9 km grid, and (2) freeze/thaw products from radar measurements on a 3 km grid. These retrieval algorithms need a variety of global ancillary data, both static and dynamic, to run the retrieval models, constrain the retrievals, and provide flags for indicating retrieval quality. The choice of which ancillary dataset to use for a particular SMAP product would be based on a number of factors, including its availability and ease of use, its inherent error and resulting impact on the overall soil moisture or freeze/thaw retrieval accuracy, and its compatibility with similar choices made by the SMOS mission. All decisions regarding SMAP ancillary data sources would be fully documented by the SMAP Project and made available to the user community.


Parts quality management: Direct part marking of data matrix symbol for mission assurance

Chantrice Moss
Suman Chakrabarti
David W. Scott

Abstract: A United States Government Accountability Office (GAO) review of twelve NASA programs found widespread parts quality problems contributing to significant cost overruns, schedule delays, and reduced system reliability. Direct part marking with Data Matrix symbols could significantly improve the quality of inventory control and parts lifecycle management. This paper examines the feasibility of using direct part marking technologies for use in future NASA programs. A structural analysis is based on marked material type, operational environment (e.g., ground, suborbital, Low Earth Orbit), durability of marks, ease of operation, reliability, and affordability. A cost-benefits analysis considers marking technology (label printing, data plates, and direct part marking) and marking types (two-dimensional machine-readable, human-readable). Previous NASA parts marking efforts and historical cost data are accounted for, including in-house vs. outsourced marking. Some marking methods are still under development. While this paper focuses on NASA programs, results may be applicable to a variety of industrial environments.


Curbing cost overruns in infrastructure investment

European journal of transport and infrastructure research

Abstract: Infrastructure projects around the world have long been notorious for exceeding their budgets. To address persistent cost overruns, the American Planning Association urged planners to adopt reference class forecasting alongside traditional methods but the practice has not caught on in the U.S. Conversely, the U.K. adopted Kahneman’s Nobel Prize-winning theory to challenge biases in human judgment and mandated reference class forecasting for major projects in 2003. Has reference class forecasting, originally developed to rectify honest mistakes, brought the promised success in the public sector wherein political pressure is significant? Through before-and-after and with-and-without comparisons of 107 major projects, this empirical study examines the practical relevance of reference class forecasting for infrastructure investments. A before-and-after comparison reveals that the average cost overrun declined from 38% to 5% following the introduction of reference class forecasting. A with-and-without comparison also demonstrates that the U.K. surpassed its targeted probability of completing projects within budget by 12% using reference class forecasting, whereas the U.S. underperformed


Current status of Landsat program, science, and applications

Michael A. Wulder
Thomas R. Loveland
David P. Roy
Christopher J. Crawford
Jeffrey G. Masek
Curtis E. Woodcock
Richard G. Allen
Martha C. Anderson

Abstract: Formal planning and development of what became the first Landsat satellite commenced over 50 years ago in 1967. Now, having collected earth observation data for well over four decades since the 1972 launch of Landsat-1, the Landsat program is increasingly complex and vibrant. Critical programmatic elements are ensuring the continuity of high quality measurements for scientific and operational investigations, including ground systems, acquisition planning, data archiving and management, and provision of analysis ready data products. Free and open access to archival and new imagery has resulted in a myriad of innovative applications and novel scientific insights. The planning of future compatible satellites in the Landsat series, which maintain continuity while


1.02  NASA Earth Observations

Angela M Erb
Peter B Boucher
Miguel O Roman
Ian L Paynter

Abstract:
The NASA Earth Science Division (ESD) is part of the Science Mission Directorate and was established to advance understanding of Earth as an integrated system through space-based observations of Earth’s atmosphere, land, oceans, and biosphere. In the mid-late 1980s, the Earth Observing System (EOS) was conceived and implemented as a series of large “flagship” missions and smaller focused satellites, often in partnership with instruments and spacecraft from other nations and agencies. The EOS launched is first mission in 1999 and subsequently 9 additional missions highlighted in the first
edition of this chapter. In the years since, NASA ESD has expanded its mission scope, science, and partnerships to include national, international, commercial, and academic partners in the quest to better understand our Home Planet and deliver decision-ready science-quality data that strengthens resilience, supports economic security, and guides effective Earth system management. These efforts safeguard the continuity of critical Earth System Data Records, which guide decisions related to long-term variability, extreme events, wildfire behavior, and global agricultural productivity among many other societal benefit areas.


Feature Detection Systems Enhance Satellite Imagery

Abstract: In 1963, during the ninth orbit of the Faith 7 capsule, astronaut Gordon Cooper skipped his nap and took some photos of the Earth below using a Hasselblad camera. The sole flier on the Mercury-Atlas 9 mission, Cooper took 24 photos - never-before-seen images including the Tibetan plateau, the crinkled heights of the Himalayas, and the jagged coast of Burma. From his lofty perch over 100 miles above the Earth, Cooper noted villages, roads, rivers, and even, on occasion, individual houses. In 1965, encouraged by the effectiveness of NASA s orbital photography experiments during the Mercury and subsequent Gemini manned space flight missions, U.S. Geological Survey (USGS) director William Pecora put forward a plan for a remote sensing satellite program that would collect information about the planet never before attainable. By 1972, NASA had built and launched Landsat 1, the first in a series of Landsat sensors that have combined to provide the longest continuous collection of space-based Earth imagery. The archived Landsat data - 37 years worth and counting - has provided a vast library of information allowing not only the extensive mapping of Earth s surface but also the study of its environmental changes, from receding glaciers and tropical deforestation to urban growth and crop harvests. Developed and launched by NASA with data collection operated at various times by the Agency, the National Oceanic and Atmospheric Administration (NOAA), Earth Observation Satellite Company (EOSAT, a private sector partnership that became Space Imaging Corporation in 1996), and USGS, Landsat sensors have recorded flooding from Hurricane Katrina, the building boom in Dubai, and the extinction of the Aral Sea, offering scientists invaluable insights into the natural and manmade changes that shape the world. Of the seven Landsat sensors launched since 1972, Landsat 5 and Landsat 7 are still operational. Though both are in use well beyond their intended lifespans, the mid-resolution satellites, which provide the benefit of images detailed enough to reveal large features like highways while still broad enough for global coverage, continue to scan the entirety of the Earth s surface. In 2012, NASA plans to launch the Landsat Data Continuity Mission (LDCM), or Landsat 8, to extend the Landsat program s contributions to cartography, water management, natural disaster relief planning, and more.


The NASA Earth Science Program and Small Satellites

Abstract: Earth's changing environment impacts every aspect of life on our planet and climate change has profound implications on society. Studying Earth as a single complex system is essential to understanding the causes and consequences of climate change and other global environmental concerns. NASA's Earth Science Division (ESD) shapes an interdisciplinary view of Earth, exploring interactions among the atmosphere, oceans, ice sheets, land surface interior, and life itself. This enables scientists to measure global and climate changes and to inform decisions by Government, other organizations, and people in the United States and around the world. The data collected and results generated are accessible to other agencies and organizations to improve the products and services they provide, including air quality indices, disaster prediction and response, agricultural yield projections, and aviation safety. ESD's Flight Program provides the spacebased observing systems and supporting infrastructure for mission operations and scientific data processing and distribution that support NASA's Earth science research and modeling activities. The Flight Program currently has 21 operating Earth observing space missions, including the recently launched Global Precipitation Measurement (GPM) mission, the Orbiting Carbon Observatory-2 (OCO-2), the Soil Moisture Active Passive (SMAP) mission, and the International Space Station (ISS) RapidSCAT and Cloud-Aerosol Transport System (CATS) instruments. The ESD has 22 more missions and instruments planned for launch over the next decade. These include first and second tier missions from the 2007 Earth Science Decadal Survey, Climate Continuity missions to assure availability of key climate data sets, and small-sized competitively selected orbital missions and instrument missions of opportunity belonging to the Earth Venture (EV) Program. Small satellites (~500 kg or less) are critical contributors to these current and future satellite missions. Some examples are the aforementioned Orbiting Carbon Observatory-2 (OCO-2), the Gravity Recovery and Climate Experiment Follow On (GRACE FO), and the Cyclone Global Navigation Satellite System (CYGNSS) microsatellite constellation. Small satellites also support ESD in space validation and risk reduction of enabling technologies (components and systems). The status of the ESD Flight Program and the role of small satellites will be discussed.


The Stratospheric Aerosol and Gas Experiment (SAGE) IV Pathfinder

Robert Damadeo
Charles Hill
Michael Obland
John Leckey

Abstract:
Atmospheric aerosols and ozone are designated as observing system priorities in the 2017–2027 Decadal Survey report [1]. Accurate records of stratospheric aerosols are a vital piece of the puzzle regarding climate change. Stratospheric ozone has been the subject of observation and research for decades. Its importance is exhibited in the United States Clean Air Act [2], which mandates that NASA monitor atmospheric ozone. Measurements from satellites provided data on the initial decline of ozone in the late 1970s and early 1980s that supported the adoption of the Montreal Protocol, and current observations hint at a potential recovery. Adequate determination of that recovery requires continuous and, in the case of multiple instruments, overlapping data records. However, most current satellite systems are well beyond their expected lifetimes, and so we look towards the future of satellite observations of stratospheric ozone and aerosols to develop the Stratospheric Aerosol and Gas Experiment (SAGE) IV Pathfinder. Enabled by the NASA Earth Science Technology Office (ESTO)’s Instrument Incubator Program (IIP), the SAGE IV Pathfinder project has developed and validated a prototype demonstration that paves the way for a future SAGE IV spaceflight mission. Utilizing solar occultation imaging, SAGE IV will be capable of measuring ozone, aerosol, and other trace gas species with the same quality as previous SAGE instruments but with greatly improved pointing knowledge. Furthermore, current technological advancements allow SAGE IV to fit within a CubeSat framework and make use of commercial hardware, significantly reducing the size and cost when compared with traditional missions and enabling sustainability of future measurements. SAGE IV will meet the definition of the newly-recommended Venture-Continuity missions by “bringing forward innovative approaches to sustain measurements at lower costs”. The latest results from development of the SAGE IV Pathfinder are presented.

[1] “Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space,” National Academies of Sciences, Engineering, and Medicine, 2018.
[2] “United State Clean Air Act,” 42 U.S.C. 767 1b(d)(2).


On-Going and Planned Mission Concept Studies for the Preparation of Future ESA Earth Observation Satellites

P. Martimort
B. C. Domínguez
Arnaud Hélière
Josep Roselló
Martin Suess
J. Angevain
Adrien Bardou
F. Bräuer

IEEE International Geoscience and Remote Sensing Symposium

Abstract: This paper will present the status of the on-going and planned mission concept studies in the period 2023 to 2025 in the frame of the ESA’s Future Earth Observation Programme (FutureEO).The European Space Agency (ESA) is conducting preparatory activities on innovative Earth Observation (EO) missions to foster better scientific understanding of the Earth system through research missions, respond to the requirements of the operational users via operational missions and assess the potential of "Newspace" approach with smallsat Earth Observation concepts to boost innovation through the Φsat missions.ESA’s program of EO research missions comprises Earth Explorers, Scouts and Mission of Opportunity. Earth Explorer missions objective is to advance Earth science by providing answers to key scientific questions on the Earth system. Up to date, five Earth Explorers have been launched and another five are in different stages of development. ESA’s Earth Explorer 11 mission candidates (CAIRT, Nitrosat, SEASTAR and WIVERN) are undergoing Phase 0 activities while preparatory activities to study mission concepts for the Earth Explorer 12 are expected to start in 2024.Missions of Opportunity are research missions implemented under cooperation frameworks with Space Agencies outside Europe and Canada. Next Generation Gravity Mission (NGGM) is a candidate Mission of Opportunity for ESA-NASA cooperation in the framework of MAGIC (MAss change and Geosciences International Constellation).Scouts are small budget research mission based on small satellites or CubeSats, implemented in three years and complementing Earth Explorers. Two mission concepts, TANGO and NanoMagSat, are currently undergoing risk retirement activities.Preparatory activities for operational mission are underway for the next generation of Sentinels in the frame of the Copernicus programme (e,g. Sentinel-2 Next Generation and Sentinel-3 Next Generation Optical, which are currently in phase 0) in close coordination with the European Commission). Preparatory activities are planned in the coming years for the next generation of meteorological missions (Meteosat 4th Generation and EPS/MetOp 3rd Generation) in close coordination with EUMETSAT.Φsat missions are CubeSat missions aimed to demonstrate radical and new technologies or remote sensing concepts in space, such as Φsat -2, which is currently under development and is focused on onboard Artificial Intelligence applications.


Fifty years of Landsat science and impacts

Michael A. Wulder
David P. Roy
Volker C. Radeloff
Thomas R. Loveland
Martha C. Anderson
David M. Johnson
Sean P. Healey
Zhe Zhu

Abstract: Since 1972, the Landsat program has been continually monitoring the Earth, to now provide 50 years of digital, multispectral, medium spatial resolution observations. Over this time, Landsat data were crucial for many scientific and technical advances. Prior to the Landsat program, detailed, synoptic depictions of the Earth's surface were rare, and the ability to acquire and work with large datasets was limited. The early years of the Landsat program delivered a series of technological breakthroughs, pioneering new methods, and demonstrating the ability and capacity of digital satellite imagery, creating a template for other global Earth observation missions and programs. Innovations driven by the Landsat program have paved the way for subsequent science, application, and policy support activities. The economic and scientific value of the knowledge gained through the Landsat program has been long recognized, and despite periods of funding uncertainty, has resulted in the program's 50 years of continuity, as well as substantive and ongoing improvements to payload and mission performance. Free and open access to Landsat data, enacted in 2008, was unprecedented for medium spatial resol


Benefits of the free and open Landsat data policy

Zhe Zhu
Michael A. Wulder
David P. Roy
Curtis E. Woodcock
Matthew C. Hansen
Volker C. Radeloff
Sean P. Healey
Crystal Schaaf

Abstract: The United States (U.S.) federal government provides imagery obtained by federally funded Earth Observation satellites typically at no cost. For many years Landsat was an exception to this trend, until 2008 when the United States Geological Survey (USGS) made Landsat data accessible via the internet for free. Substantial increases in downloads of Landsat imagery ensued and led to a rapid


A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring

Abstract: Combination of different satellite data will provide increased opportunities for more frequent cloud-free surface observations due to variable cloud cover at the different satellite overpass times and dates. Satellite data from the polar-orbiting Landsat-8 (launched 2013), Sentinel-2A (launched 2015) and Sentinel-2B (launched 2017) sensors offer 10 m to 30 m multi-spectral global coverage. Together, they advance the virtual constellation paradigm for mid-resolution land imaging. In this study, a global analysis of Landsat-8, Sentinel-2A and Sentinel-2B metadata obtained from the committee on Earth Observation Satellite (CEOS) Visualization Environment (COVE) tool for 2016 is presented. A global equal area projection grid defined every 0.05° is used considering each sensor and combined together. Histograms, maps and global summary statistics of the temporal revisit intervals (minimum, mean, and maximum) and the number of observations are reported. The temporal observation frequency improvements afforded by sensor combination are shown to be significant. In particular, considering Landsat-8, Sentinel-2A, and Sentinel-2B together will provide a global median average revisit interval o


Developments in Landsat Land Cover Classification Methods: A Review

Abstract: Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize analysis of Landsat images in order to attain the desired results. This review suggests that the development of land cover classification methods grew alongside the launches of a new series of Landsat sensors and advancements in computer science. Most classification methods were initially developed in the 1970s and 1980s; however, many advancements in specific classifiers and algorithms have occurred in the last decade. The first methods of land cover classification to be applied to Landsat images were visual analyses in the early 1970s, followed by unsupervised and supervised pixel-based classification methods using maximum likelihood, K-means and Iterative Self-Organizing Data Analysis Technique (ISODAT) classifiers. After 1980, other methods such as sub-pixel, knowledge-based, contextual-


Landsat 9: Empowering Open Science and Applications Through Continuity

Jeffrey G. Masek
Michael A. Wulder
Brian Markham
Joel Mccorkel
Christopher J. Crawford
James Storey
Del T Jenstrom

Abstract: The history of Earth observation from space is well reflected through the Landsat program. With data collection beginning with Landsat-1 in 1972, the program has evolved technical capabilities while maintaining continuity of land observations. In so doing, Landsat has provided a critical reference for assessing long-term changes to Earth's land environment due to both natural and human forcing. Poised for launch in mid-2021, the joint NASA-USGS Landsat 9 mission will continue this important data record. In many respects Landsat 9 is a clone of Landsat-8. The Operational Land Imager-2 (OLI-2) is largely identical to Landsat 8 OLI, providing calibrated imagery covering the solar reflected wavelengths. The Thermal Infrared Sensor-2 (TIRS-2) improves upon Landsat 8 TIRS, addressing known issues including stray light incursion and a malfunction of the instrument scene select mirror. In addition, Landsat 9 adds redundancy to TIRS-2, thus upgrading the instrument to a 5-year design life commensurate with other elements of the mission. Initial performance testing of OLI-2 and TIRS-2 indicate that the instruments are of excellent quality and expected to match or improve on Landsat 8 data quality. Landsat-9 will maintain the current data acquisition rate of up to 740 scenes per day, with these scenes available from the Landsat archive at no cost to users. In this communication, we provide background and rationale for the Landsat 9 mission, describe the instrument payloads and ground system, and discuss data products available from the Landsat 9 mission through USGS.


PHILIPPINE EARTH OBSERVATION SATELLITE MISSIONS AND APPLICATIONS: A DECADAL SURVEY

International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences ISPRS Archives


Satellite Systems Design/Simulation Environment: A Systems Approach to Pre-Phase A Design

Ferebee, Melvin J., Jr.
Troutman, Patrick A.
Monell, Donald W.

Abstract: A toolset for the rapid development of small satellite systems has been created. The objective of this tool is to support the definition of spacecraft mission concepts to satisfy a given set of mission and instrument requirements. The objective of this report is to provide an introduction to understanding and using the SMALLSAT Model. SMALLSAT is a computer-aided Phase A design and technology evaluation tool for small satellites. SMALLSAT enables satellite designers, mission planners, and technology program managers to observe the likely consequences of their decisions in terms of satellite configuration, non-recurring and recurring cost, and mission life cycle costs and availability statistics. It was developed by Princeton Synergetic, Inc. and User Systems, Inc. as a revision of the previous TECHSAT Phase A design tool, which modeled medium-sized Earth observation satellites. Both TECHSAT and SMALLSAT were developed for NASA.


Monitored background radiometer

Abstract: The infrared (IR) testing of the Olympus thermal model has provided a capability to perform cost effective thermal balance testing of satellites and satellite components. A high-accuracy monitored background radiometer was developed for the measurement of absorbed radiation heat flux encountered during IR thermal vacuum testing of spacecraft. The design, development, and calibration of this radiometer is described.


Progress Towards a 2012 Landsat Launch

Irons, Jim
Sabelhaus, Phil
Masek, Jeff
Cook, Bruce
Dabney, Phil
Loveland, Tom

Abstract: The Landsat Data Continuity Mission (LDCM) is on schedule for a December 2012 launch date. The mission is being managed by an interagency partnership between NASA and the U.S. Geological Survey (USGS). NASA leads the development and launch of the satellite observatory while leads ground system development. USGS will assume responsibility for operating the satellite and for collecting, archiving, and distributing the LDCM data following launch. When launched the satellite will carry two sensors into orbit. The Operational Land Imager (OLI) will collect data for nine shortwave spectral bands with a spatial resolution of 30 m (with a 15 m panchromatic band). The Thermal Infrared Sensor (TIRS) will coincidently collect data for two thermal infrared bands with a spatial resolution of 100 m. The OLI is fully assembled and tested and has been shipped by it?s manufacturer, Ball Aerospace and Technology Corporation, to the Orbital Sciences Corporation (Orbital) facility where it is being integrated onto the LDCM spacecraft. Pre-launch testing indicates that OLI will meet all performance specification with margin. TIRS is in development at the NASA Goddard Space Flight Center (GSFC) and is in final testing before shipping to the Orbital facility in January, 2012. The ground data processing system is in development at the USGS Earth Resources Observation and Science (EROS) Center. The presentation will describe the LDCM satellite system, provide the status of system development, and present prelaunch performance data for OLI and TIRS. The USGS has committed to renaming the satellite as Landsat 8 following launch.


A Decadal Survey Without Analogs: Earth Observation Needs for a Warming World

K. R. Miner
R. K. Braghiere
C. E. Miller
N. Schlegel
D. Schimel

Abstract: <jats:title>Abstract</jats:title><jats:p>Since 2007, the National Academy for Sciences Engineering and Medicine (NASEM) has recommended Earth Science research and investment priorities every 10 years. The Decadal Survey balances the continuation of essential climate variable time series against unmet measurement needs and new Earth Observations made possible by technological breakthroughs. The next survey (2027–2028, DS28) will be framed by a rapidly changing world, and it will be critical to anticipate the observational needs of the 2030s–2040s, a world increasingly dominated by climate extremes and a rapidly changing Earth system. Here, we highlight some of the changes that factor into a framework for the DS28.</jats:p>


The future of Earth observation in hydrology

Matthew F. McCabe
Matthew Rodell
Douglas Alsdorf
Diego G. Miralles
R. Uijlenhoet
Wolfgang Wagner
Arko Lucieer
Rasmus Houborg

Abstract: In just the past five years, the field of Earth observation has progressed beyond the offerings of conventional space agency based platforms to include a plethora of sensing opportunities afforded by CubeSats, Unmanned Aerial Vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically on the order of one billion dollars per satellite and with concept-to-launch timelines on the order of two decades (for new missions). More recently, the proliferation of smartphones has helped to miniaturise sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist five years ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of the cost of traditional satellite missions. With these advances