scholarly journals Carbon Monitoring Satellite (CarbonSat): assessment of atmospheric CO<sub>2</sub> and CH<sub>4</sub> retrieval errors by error parameterization

2013 ◽  
Vol 6 (12) ◽  
pp. 3477-3500 ◽  
Author(s):  
M. Buchwitz ◽  
M. Reuter ◽  
H. Bovensmann ◽  
D. Pillai ◽  
J. Heymann ◽  
...  
Keyword(s):  
Power Plants ◽  
Measurement Precision ◽  
Anthropogenic Sources ◽  
Retrieval Algorithm ◽  
Terrestrial Vegetation ◽  
Single Measurement ◽  
Data Set ◽  
Sources And Sinks ◽  
Spatial Coverage ◽  
One Year

Abstract. Carbon Monitoring Satellite (CarbonSat) is one of two candidate missions for ESA's Earth Explorer 8 (EE8) satellite to be launched around the end of this decade. The overarching objective of the CarbonSat mission is to improve our understanding of natural and anthropogenic sources and sinks of the two most important anthropogenic greenhouse gases (GHGs) carbon dioxide (CO2) and methane (CH4). The unique feature of CarbonSat is its "GHG imaging capability", which is achieved via a combination of high spatial resolution (2 km × 2 km) and good spatial coverage (wide swath and gap-free across- and along-track ground sampling). This capability enables global imaging of localized strong emission source, such as cities, power plants, methane seeps, landfills and volcanos, and likely enables better disentangling of natural and anthropogenic GHG sources and sinks. Source–sink information can be derived from the retrieved atmospheric column-averaged mole fractions of CO2 and CH4, i.e. XCO2 and XCH4, by inverse modelling. Using the most recent instrument and mission specification, an error analysis has been performed using the Bremen optimal EStimation DOAS (BESD/C) retrieval algorithm. We assess the retrieval performance for atmospheres containing aerosols and thin cirrus clouds, assuming that the retrieval forward model is able to describe adequately all relevant scattering properties of the atmosphere. To compute the errors for each single CarbonSat observation in a one-year period, we have developed an error parameterization scheme comprising six relevant input parameters: solar zenith angle, surface albedo in two bands, aerosol and cirrus optical depth, and cirrus altitude variations. Other errors, e.g. errors resulting from aerosol type variations, are partially quantified but not yet accounted for in the error parameterization. Using this approach, we have generated and analysed one year of simulated CarbonSat observations. Using this data set we estimate that systematic errors are for the overwhelming majority of cases (≈ 85%) below 0.3 ppm for XCO2 (below 0.5 ppm for 99.5%) and below 2 ppb for XCH4 (below 4 ppb for 99.3%). We also show that the single-measurement precision is typically around 1.2 ppm for XCO2 and 7 ppb for XCH4 (1σ). The number of quality-filtered observations over cloud- and ice-free land surfaces is in the range of 33 to 47 million per month depending on season. Recently it has been shown that terrestrial vegetation chlorophyll fluorescence (VCF) emission needs to be considered for accurate XCO2 retrieval. We therefore retrieve VCF from clear Fraunhofer lines located around 755 nm and show that CarbonSat will provide valuable information on VCF. We estimate that the VCF single-measurement precision is approximately 0.3 mW m−2 nm−1 sr−1 (1σ).

scholarly journals Carbon Monitoring Satellite (CarbonSat): assessment of scattering related atmospheric CO<sub>2</sub> and CH<sub>4</sub> retrieval errors and first results on implications for inferring city CO<sub>2</sub> emissions

2013 ◽  
Vol 6 (3) ◽  
pp. 4769-4850 ◽  
Author(s):  
M. Buchwitz ◽  
M. Reuter ◽  
H. Bovensmann ◽  
D. Pillai ◽  
J. Heymann ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Systematic Errors ◽  
Measurement Precision ◽  
Anthropogenic Sources ◽  
Retrieval Algorithm ◽  
Single Measurement ◽  
Data Set ◽  
Sources And Sinks ◽  
Spatial Coverage ◽  
One Year

Abstract. Carbon Monitoring Satellite (CarbonSat) is one of two candidate missions for ESA's Earth Explorer 8 (EE8) satellite – the selected one to be launched around the end of this decade. The objective of the CarbonSat mission is to improve our understanding of natural and anthropogenic sources and sinks of the two most important anthropogenic greenhouse gases (GHG) carbon dioxide (CO2) and methane (CH4). The unique feature of CarbonSat is its "GHG imaging capability", which is achieved via a combination of high spatial resolution (2 km × 2 km) and good spatial coverage (wide swath and gap-free across- and along-track ground sampling). This capability enables global imaging of localized strong emission source such as cities, power plants, methane seeps, landfills and volcanos and better disentangling of natural and anthropogenic GHG sources and sinks. Source/sink information can be derived from the retrieved atmospheric column-averaged mole fractions of CO2 and CH4, i.e. XCO2 and XCH4, via inverse modeling. Using the most recent instrument and mission specification, an error analysis has been performed using the BESD/C retrieval algorithm. We focus on systematic errors due to aerosols and thin cirrus clouds, as this is the dominating error source especially with respect to XCO2 systematic errors. To compute the errors for each single CarbonSat observation in a one year time period, we have developed an error parameterization scheme based on six relevant input parameters: we consider solar zenith angle, surface albedo in two bands, aerosol and cirrus optical depth, and cirrus altitude variations but neglect, for example, aerosol type variations. Using this method we have generated and analyzed one year of simulated CarbonSat observations. Using this data set we estimate that scattering related systematic errors are mostly (approx. 85%) below 0.3 ppm for XCO2 (<0.5 ppm: 99.5%) and below 2 ppb for XCH4 (<4 ppb: 99.3%). We also show that the single measurement precision is typically around 1.2 ppm for XCO2 and 7 ppb for XCH4 (1-sigma). The number of quality filtered observations over cloud and ice free land surfaces is in the range 33–47 million per month depending on month. Recently it has been shown that terrestrial Vegetation Chlorophyll Fluorescence (VCF) emission needs to be considered for accurate XCO2 retrieval. We therefore retrieve VCF from clear Fraunhofer lines located at 755 nm and show that CarbonSat will provide valuable information on VCF. The VCF single measurement precision is approximately 0.3 mW m−2 nm−1 sr−1 (1-sigma). As a first application of the one year data set we assess the capability of CarbonSat to quantify the CO2 emissions of large cities using Berlin, the capital of Germany, as an example. We show that the precision of the inferred Berlin CO2 emissions as obtained from single CarbonSat overpasses is in the range 5–10 Mt CO2 yr−1 (10–20%). We found that systematic errors could be on the same order depending on which assumptions are used with respect to observational and biogenic XCO2 modeling errors.

scholarly journals Trends of tropical tropospheric ozone from twenty years of European satellite measurements and perspectives for Sentinel-5 Precursor

2016 ◽  
Author(s):  
Klaus-Peter Heue ◽  
Melanie Coldewey-Egbers ◽  
Andy Delcloo ◽  
Christophe Lerot ◽  
Diego Loyola ◽  
...  
Keyword(s):  
Tropospheric Ozone ◽  
Total Ozone ◽  
Atlantic Coast ◽  
Convective Cloud ◽  
Differential Method ◽  
Retrieval Algorithm ◽  
Data Set ◽  
Cloud Data ◽  
One Year ◽  
The Individual

Abstract. In preparation of the TROPOMI/S5P launch in autumn 2016 a tropospheric ozone retrieval based on the convective cloud differential method was developed. For intensive tests we applied the algorithm to the total ozone columns and cloud data of the satellites GOME, SCIAMACHY, OMI, GOME-2A and GOME-2B. Thereby a time series of 20 years (1995–2015) of tropospheric ozone columns was retrieved. To have a consistent total ozone data set for all sensors one common retrieval algorithm, namely GODFITv3, has been applied to all sensors and the L1 reflectances have also been soft calibrated. These data were input into the tropospheric ozone retrieval. However, the Tropical Tropospheric Ozone Columns (TTOC) for the individual instruments still showed small differences and therefore we harmonised the data set. For this purpose a multi-variant function was fitted to the averaged difference between SCIAMACHY's TTOC and those from the other sensors. The original TTOC was corrected by the fitted offset. GOME-2B data were corrected relative to the harmonised data from OMI and GOME-2A. The harmonisation leads to a better agreement between the different instruments. Also a direct comparison of the TTOCs in the overlapping periods proves that GOME-2A agrees much better with SCIAMACHY after the harmonisation. The improvements for OMI were small. The GOME and SCIAMACHY data overlap for one year for the complete tropics, this turned out to be insufficient to extrapolate back until 1995. Based on the harmonised observations, we created a merged data product, containing the TTOC from July 1995 to Dec. 2015. A first application of this 20 years record is a trend analysis. The global tropical trend is 0.75 &amp;pm; 0.12 DU decade−1. Regionally the trends reaches up to 1.8 DU decade−1 like on the African Atlantic coast, over the Western Pacific the tropospheric ozone declined over the last 20 years with up to 0.8 DU decade−1. The tropical tropospheric data record will be extended in the future with the TROPOMI/S5P data, where the TTOC is part of the operational products.

scholarly journals The greenhouse gas project of ESA’s climate change initiative (GHG-CCI): overview, achievements and future plans

2015 ◽  
Vol XL-7/W3 ◽  
pp. 165-172 ◽  
Author(s):  
M. Buchwitz ◽  
M. Reuter ◽  
O. Schneising ◽  
H. Boesch ◽  
I. Aben ◽  
...  
Keyword(s):  
Climate Change ◽  
Greenhouse Gases ◽  
Near Infrared ◽  
Global Climate ◽  
Anthropogenic Sources ◽  
Change Initiative ◽  
Data Set ◽  
Near Surface ◽  
Sources And Sinks ◽  
Future Plans

The GHG-CCI project (<a href="http://www.esa-ghg-cci.org/"target="_blank">http://www.esa-ghg-cci.org/</a>) is one of several projects of the European Space Agency’s (ESA) Climate Change Initiative (CCI). The goal of the CCI is to generate and deliver data sets of various satellite-derived Essential Climate Variables (ECVs) in line with GCOS (Global Climate Observing System) requirements. The “ECV Greenhouse Gases” (ECV GHG) is the global distribution of important climate relevant gases – namely atmospheric CO2 and CH4 - with a quality sufficient to obtain information on regional CO2 and CH4 sources and sinks. The main goal of GHG-CCI is to generate long-term highly accurate and precise time series of global near-surface-sensitive satellite observations of CO2 and CH4, i.e., XCO2 and XCH4, starting with the launch of ESA’s ENVISAT satellite. These products are currently retrieved from SCIAMACHY/ENVISAT (2002-2012) and TANSO-FTS/GOSAT (2009-today) nadir mode observations in the near-infrared/shortwave-infrared spectral region. In addition, other sensors (e.g., IASI and MIPAS) and viewing modes (e.g., SCIAMACHY solar occultation) are also considered and in the future also data from other satellites. The GHG-CCI data products and related documentation are freely available via the GHG-CCI website and yearly updates are foreseen. Here we present an overview about the latest data set (Climate Research Data Package No. 2 (CRDP#2)) and summarize key findings from using satellite CO2 and CH4 retrievals to improve our understanding of the natural and anthropogenic sources and sinks of these important atmospheric greenhouse gases. We also shortly mention ongoing activities related to validation and initial user assessment of CRDP#2 and future plans.

scholarly journals Variations in global methane sources and sinks during 1910–2010

2015 ◽  
Vol 15 (5) ◽  
pp. 2595-2612 ◽  
Author(s):  
A. Ghosh ◽  
P. K. Patra ◽  
K. Ishijima ◽  
T. Umezawa ◽  
A. Ito ◽  
...  
Keyword(s):  
Air Temperature ◽  
General Circulation ◽  
Ice Cores ◽  
Growth Period ◽  
Rate Of Change ◽  
Anthropogenic Sources ◽  
Biogeochemical Model ◽  
Data Set ◽  
Bottom Up ◽  
Sources And Sinks

Abstract. Atmospheric methane (CH4) increased from ~900 ppb (parts per billion, or nanomoles per mole of dry air) in 1900 to ~1800 ppb in 2010 at a rate unprecedented in any observational records. However, the contributions of the various methane sources and sinks to the CH4 increase are poorly understood. Here we use initial emissions from bottom-up inventories for anthropogenic sources, emissions from wetlands and rice paddies simulated by a~terrestrial biogeochemical model, and an atmospheric general circulation model (AGCM)-based chemistry-transport model (i.e. ACTM) to simulate atmospheric CH4 concentrations for 1910–2010. The ACTM simulations are compared with the CH4 concentration records reconstructed from Antarctic and Arctic ice cores and firn air samples, and from direct measurements since the 1980s at multiple sites around the globe. The differences between ACTM simulations and observed CH4 concentrations are minimized to optimize the global total emissions using a mass balance calculation. During 1910–2010, the global total CH4 emission doubled from ~290 to ~580 Tg yr−1. Compared to optimized emission, the bottom-up emission data set underestimates the rate of change of global total CH4 emissions by ~30% during the high growth period of 1940–1990, while it overestimates by ~380% during the low growth period of 1990–2010. Further, using the CH4 stable carbon isotopic data (δ13C), we attribute the emission increase during 1940–1990 primarily to enhancement of biomass burning. The total lifetime of CH4 shortened from 9.4 yr during 1910–1919 to 9 yr during 2000–2009 by the combined effect of the increasing abundance of atomic chlorine radicals (Cl) and increases in average air temperature. We show that changes of CH4 loss rate due to increased tropospheric air temperature and CH4 loss due to Cl in the stratosphere are important sources of uncertainty to more accurately estimate the global CH4 budget from δ13C observations.

scholarly journals Total column water vapour measurements from GOME-2 MetOp-A and MetOp-B

2014 ◽  
Vol 7 (3) ◽  
pp. 3021-3073 ◽  
Author(s):  
M. Grossi ◽  
P. Valks ◽  
D. Loyola ◽  
B. Aberle ◽  
S. Slijkhuis ◽  
...  
Keyword(s):  
Water Vapour ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Vertical Column ◽  
Data Set ◽  
Vertical Column Density ◽  
Data Processor ◽  
Climate Analysis ◽  
Total Column

Abstract. The knowledge of the total column water vapour (TCWV) global distribution is fundamental for climate analysis and weather monitoring. In this work, we present the retrieval algorithm used to derive the operational TCWV from the GOME-2 sensors and perform an extensive inter-comparison and validation in order to estimate their absolute accuracy and long-term stability. We use the recently reprocessed data sets retrieved by the GOME-2 instruments aboard EUMETSAT's MetOp-A and MetOp-B satellites and generated by DLR in the framework of the O3M-SAF using the GOME Data Processor (GDP) version 4.7. The retrieval algorithm is based on a classical Differential Optical Absorption Spectroscopy (DOAS) method and combines H2O/O2 retrieval for the computation of the trace gas vertical column density. We introduce a further enhancement in the quality of the H2O column by optimizing the cloud screening and developing an empirical correction in order to eliminate the instrument scan angle dependencies. We evaluate the overall consistency between about 8 months measurements from the newer GOME-2 instrument on the MetOp-B platform with the GOME-2/MetOp-A data in the overlap period. Furthermore, we compare GOME-2 results with independent TCWV data from ECMWF and with SSMIS satellite measurements during the full period January 2007–August 2013 and we perform a validation against the combined SSM/I + MERIS satellite data set developed in the framework of the ESA DUE GlobVapour project. We find global mean biases as small as ± 0.03 g cm−2 between GOME-2A and all other data sets. The combined SSM/I-MERIS sample is typically drier than the GOME-2 retrievals (−0.005 g cm−2), while on average GOME-2 data overestimate the SSMIS measurements by only 0.028 g cm−2. However, the size of some of these biases are seasonally dependent. Monthly average differences can be as large as 0.1 g cm−2, based on the analysis against SSMIS measurements, but are not as evident in the validation with the ECMWF and the SSM/I + MERIS data. Studying two exemplary months, we estimate regional differences and identify a very good agreement between GOME-2 total columns and all three independent data sets, especially for land areas, although some discrepancies over ocean and over land areas with high humidity and a relatively large surface albedo are also present.

scholarly journals Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm

2015 ◽  
Vol 8 (2) ◽  
pp. 1787-1832 ◽  
Author(s):  
J. Heymann ◽  
M. Reuter ◽  
M. Hilker ◽  
M. Buchwitz ◽  
O. Schneising ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Detailed Comparison ◽  
Mean Difference ◽  
Total Carbon ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Time Period ◽  
Comparison Results ◽  
The Future

Abstract. Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY onboard ENVISAT (March 2002–April 2012) and TANSO-FTS onboard GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System (IFS). We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between the SCIAMACHY and the GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT-SCIAMACHY (linear correlation coefficient r = 0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT-TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY-TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (±2 h, 10° × 10° around TCCON sites), i.e., the observed air masses are not exactly identical, but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by using also data from other missions (e.g., OCO-2, GOSAT-2, CarbonSat) in the future.

Retrievals of Atmospheric Carbonyl Sulfide from IASI

2021 ◽  
Author(s):  
Michael P. Cartwright ◽  
Jeremy J. Harrison ◽  
David P. Moore
Keyword(s):  
Gross Primary Production ◽  
Carbonyl Sulfide ◽  
Optimal Estimation ◽  
Stratospheric Aerosol ◽  
Data Set ◽  
Spatial Coverage ◽  
Retrieval Scheme ◽  
The University ◽  
Fresh Insight

&lt;p&gt;Carbonyl sulfide (OCS) is the most abundant sulfur containing gas in the atmosphere and is an important source of stratospheric aerosol. Furthermore, it has been shown that OCS can be used as a proxy for photosynthesis, which is a powerful tool in quantifying global gross primary production. While considerable improvements have been made in our understanding of the location and magnitude of OCS fluxes over the past few decades, recent studies highlight the need for a new satellite dataset to help reduce the uncertainties in current estimations. The Infrared Atmospheric Sounding Interferometer (IASI) instruments on-board the MetOp satellites offer over 14 years of nadir viewing radiance measurements with excellent spatial coverage. Given that there are currently three IASI instruments in operation, there is the potential for a significantly larger OCS dataset than is currently available elsewhere. Retrievals of OCS from these IASI radiances have been made using an adapted version of the University of Leicester IASI Retrieval Scheme (ULIRS). OCS total column amounts are calculated from profiles retrieved on a 31-layer equidistant pressure grid, using an optimal estimation approach for microwindows in the range 2000 &amp;#8211; 2100 cm&lt;sup&gt;-1&lt;/sup&gt; wavenumbers. Sensitivity of the measurements peak in the mid-troposphere, between 5 &amp;#8211; 10 km.&lt;/p&gt;&lt;p&gt;The outlook of this work is to produce a long-term OCS satellite observational data set that provides fresh insight to the spatial distribution and trend of atmospheric OCS. Here, we present subsets of data in the form of case studies for different geographic regions and time periods.&lt;/p&gt;

Determinants of spatial variation in fire return period in a semiarid African savanna

2011 ◽  
Vol 20 (4) ◽  
pp. 540 ◽  
Author(s):  
T. G. O'Connor ◽  
C. M. Mulqueeny ◽  
P. S. Goodman
Keyword(s):  
Spatial Variation ◽  
Return Period ◽  
Vegetation Type ◽  
Fire Frequency ◽  
Fuel Load ◽  
Annual Rainfall ◽  
Vegetation Types ◽  
Terrestrial Vegetation ◽  
African Savanna ◽  
Data Set

Fire pattern is predicted to vary across an African savanna in accordance with spatial variation in rainfall through its effects on fuel production, vegetation type (on account of differences in fuel load and in flammability), and distribution of herbivores (because of their effects on fuel load). These predictions were examined for the 23 651-ha Mkuzi Game Reserve, KwaZulu-Natal, based on a 37-year data set. Fire return period varied from no occurrence to a fire every 1.76 years. Approximately 75% of the reserve experienced a fire approximately every 5 years, 25% every 4.1–2.2 years and less than 1% every 2 years on average. Fire return period decreased in relation to an increase in mean annual rainfall. For terrestrial vegetation types, median fire return periods decreased with increasing herbaceous biomass, from forest that did not burn to grasslands that burnt every 2.64 years. Fire was absent from some permanent wetlands but seasonal wetlands burnt every 5.29 years. Grazer biomass above 0.5 animal units ha–1 had a limiting influence on the maximum fire frequency of fire-prone vegetation types. The primary determinant of long-term spatial fire patterns is thus fuel load as determined by mean rainfall, vegetation type, and the effects of grazing herbivores.

Digital I&C Operating Experience in the US

2008 ◽  
Author(s):  
Bruce Geddes ◽  
Ray Torok
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Failure Modes ◽  
Operating Experience ◽  
Data Set ◽  
Common Cause ◽  
Hardware Failure ◽  
Common Cause Failure ◽  
Defensive Measures

The Electric Power Research Institute (EPRI) is conducting research in cooperation with the Nuclear Energy Institute (NEI) regarding Operating Experience of digital Instrumentation and Control (I&C) systems in US nuclear power plants. The primary objective of this work is to extract insights from US nuclear power plant Operating Experience (OE) reports that can be applied to improve Diversity and Defense in Depth (D3) evaluations and methods for protecting nuclear plants against I&C related Common Cause Failures (CCF) that could disable safety functions and thereby degrade plant safety. Between 1987 and 2007, over 500 OE events involving digital equipment in US nuclear power plants were reported through various channels. OE reports for 324 of these events were found in databases maintained by the Nuclear Regulatory Commission (NRC) and the Institute of Nuclear Power Operations (INPO). A database was prepared for capturing the characteristics of each of the 324 events in terms of when, where, how, and why the event occurred, what steps were taken to correct the deficiency that caused the event, and what defensive measures could have been employed to prevent recurrence of these events. The database also captures the plant system type, its safety classification, and whether or not the event involved a common cause failure. This work has revealed the following results and insights: - 82 of the 324 “digital” events did not actually involve a digital failure. Of these 82 non-digital events, 34 might have been prevented by making full use of digital system fault tolerance features. - 242 of the 324 events did involve failures in digital systems. The leading contributors to the 242 digital failures were hardware failure modes. Software change appears as a corrective action twice as often as it appears as an event root cause. This suggests that software features are being added to avoid recurrence of hardware failures, and that adequately designed software is a strong defensive measure against hardware failure modes, preventing them from propagating into system failures and ultimately plant events. 54 of the 242 digital failures involved a Common Cause Failure (CCF). - 13 of the 54 CCF events affected safety (1E) systems, and only 2 of those were due to Inadequate Software Design. This finding suggests that software related CCFs on 1E systems are no more prevalent than other CCF mechanisms for which adherence to various regulations and standards is considered to provide adequate protection against CCF. This research provides an extensive data set that is being used to investigate many different questions related to failure modes, causes, corrective actions, and other event attributes that can be compared and contrasted to reveal useful insights. Specific considerations in this study included comparison of 1E vs. non-1E systems, active vs. potential CCFs, and possible defensive measures to prevent these events. This paper documents the dominant attributes of the evaluated events and the associated insights that can be used to improve methods for protecting against digital I&C related CCFs, applying a test of reasonable assurance.

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